Folding and Self-Assembly of Biological Macromolecules: Proceedings of the Deuxiemes Entretiens de Bures, Institut Des Hautes Etudes Scientifiques, Bures-sur-Yvette, France, 27 November-1 December 2001

Proceedings of the Deuxiemes Entretiens de Bures

FOLDING AND SELF-ASSEMBLY OF BIOLOGICAL MACROMOLECULES INSTITUT DES HAUTES ETUDES SCIENTIFIQUES

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E Westhof N Hardy

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A Carbone M Gromov F Kepes E Westhof

World Scientific

Proceedings of the Deuxiemes Entretiens de Bures

FOLDING AND SELF-ASSEMBLY OF BIOLOGICAL MACROMOLECULES INSTITUT DES HAUTES ETUDES SCIENTIFIQUES

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Proceedings of the Deuxiemes Erttretiens de Bures

FOLDING AND SELF-ASSEMBLY OF BIOLOGICAL MACROMOLECULES edited by

E Westhof Institut de Biologie Moleculaire et Cellulaire Universite Louis-Pasteur Strasbourg, France

N Hardy

INSTITUT DES HAUTES &TUDES SCIENTIFIQUES Bures-sur-Yvettef France 27 November - 1 December 2001

\[p World Scientific NEW JERSEY • LONDON • SINGAPORE • SHANGHAI • HONGKONG • TAIPEI • CHENNAI

Published by World Scientific Publishing Co. Pte. Ltd. 5 Toh Tuck Link, Singapore 596224 USA office: Suite 202, 1060 Main Street, River Edge, NJ 07661 UK office: 57 Shelton Street, Covent Garden, London WC2H 9HE

British Library Cataloguing-in-Publication Data A catalogue record for this book is available from the British Library.

Model of the secondary and tertiary structure of the catalytic RNA component of bacterial ribonuclease P. Courtesy of Dr. Fabrice Jossinet (IBMC-CNRS, Universite Louis Pasteur, Strasbourg, France).

FOLDING AND SELF-ASSEMBLY OF BIOLOGICAL MACROMOLECULES Proceedings of the Deuxiemes Entretiens de Bures Copyright © 2004 by World Scientific Publishing Co. Pte. Ltd. All rights reserved. This book, or parts thereof, may not be reproduced in any form or by any means, electronic or mechanical, including photocopying, recording or any information storage and retrieval system now known or to be invented, without written permission from the Publisher.

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ORGANIZERS Alessandra Carbone (IHES, Bures-sur-Yvette, France) Misha Gromov (IHES, Bures-sur-Yvette, France) Francois Kepes (CNRS-Genopole®, Evry, France) Eric Westhof (Universite Louis Pasteur, Strasbourg, France)

SPEAKERS Steven Benner (University of Florida, Gainesville, FL, USA) Antoine Danchin (Institut Pasteur, Paris, France, and Hong-Kong) Marc Delarue (Institut Pasteur, Paris, France) Izrail Gelfand (Rutgers University, Piscataway, NJ, USA) Nobuhiro Go (Kyoto University, Japan) Herve Isambert (Universite Louis Pasteur, Strasbourg, France) Jean-Francois Joanny (Institut Curie, Paris, France) John E. Johnson (Scripps Research Institute, La Jolla, CA, USA) Alexander Kister (Rutgers University, Piscataway, NJ, USA) Tanja Kortemme (University of Washington, Seattle, WA, USA) Olivier Lichtarge (Baylor University, Houston, TX, USA) Francois Michel (CNRS-Centre de Genetique Moleculaire, Gif-sur-Yvette, France) Leonid Mirny (Harvard-MIT, Cambridge, MA, USA) David Sankoff (Universite de Montreal, Canada) Peter Schuster (Universitat Wien, Vienna, Austria) Devarajan Thirumalai (University of Maryland, College Park, MD, USA) Eric Westhof (Universite Louis Pasteur, Strasbourg, France) James R. Williamson (Scripps Research Institute, La Jolla, CA, USA) Sarah Woodson (Johns Hopkins University, Baltimore, MD, USA) Michael Zuker (Rensselaer Polytechnic Institute, Troy, NY, USA)

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EDITOR'S NOTE

Folding and Self-assembly of Biological Macromolecules, the title of these proceedings of the Deuxiemes Entretiens de Bures, is a major focus of contemporary research in structural biology. Between November 27 th and December 1st 2001, some twenty leading researchers met to report their recent results on this subject at the Institut des Hautes Etudes Scientifiques in Bures-sur-Yvette (France). They interacted with an audience of more than 150 specialists from a wide range of scientific disciplines, including bioinformatics, biophysics, chemistry, genomics, mathematics, molecular biology, theoretical physics, and virology. In French, entretiens are interactive scientific conferences characterized by lively exchanges among the participants. The title of the proceedings of the first Entretiens, held at the IHES between December 2 nd and 6th, 1997, is Pattern Formation in Biology, Vision and Dynamics, which was edited by the conference organizers, Alessandra Carbone, Misha Gromov, and Przemyslaw Prusinkiewicz. Participants at the Deuxiemes Entretiens explored the folding pathways and mechanisms by which constituent residues interact to yield native biological macromolecules (catalytic RNA molecules and functional proteins), and how ribosomes and other macromolecular complexes self-assemble. These proceedings are the transcribed harvest of fourteen of the talks delivered at the conference, as well as the corresponding verbal exchanges, all of which was professionally captured on videotape by Francois Tisseyre's Atelier EcoutezVoir. Four native English-speakers, Valerie Lafitte, Carol Madden, David Sindell, and Sean Newhouse, transcribed the talks and audience interaction. The texts were then pre-edited and sent to the speakers for self-review and correction. Roberto Incitti, scientific coordinator of the IHES Mathematics:Molecular Biology program, maintained a dedicated internet website for importing the corrected texts and figures provided by the speakers to illustrate their manuscripts. The IHES graphic artist, Marie-Claude Vergne, then prepared the figures according to the publisher's specifications. Helga Dernois, the IHES scientific secretary responsible for producing the final manuscript of the proceedings, patiently processed several preliminary drafts of the manuscripts before adapting the final versions to the publisher's "style-file." Both editors verified successive versions of the manuscripts after the speakers had made their corrections, prior to obtaining permission to

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publish the final versions. The preface by Eric Westhof based on his opening remarks at the Entretiens, is a synopsis of the talks, as well as speculation on the future of the RNA and protein-folding field. The result of these efforts is contained in these pages, which we hope you will find attains a level of interest commensurate with that of the Entretiens itself N. Hardy

PREFACE ERIC WESTHOF Institut de Biologie Moleculaire et Cellulaire, CNRS and Universite Louis Pasteur, Strasbourg, France

In 1988, while Editor-in-Chief of Nature, John Maddox regularly discussed the evolution of science in his columns and worried about the development of molecular biology. There was already such an accumulation of data that its assimilation would lead to a deadlock in any conceptual framework attempting to rationalize this vast quantity of disparate information. Such a concern is also at the origin of the Deuxiemes Entretiens de Bures, "Folding and self-assembly of biological macromolecules," held the Institut des Hautes Etudes Scientifiques (IHES), in Bures-sur-Yvette (France) between November 27th and December 1st, 2001. The Entretiens are organized by the IHES to promote interaction and exchanges among mathematicians, physicists, and biologists. If one peers at any interesting object in molecular biology, the size and number of atoms to be considered are so large that one is soon quite overwhelmed by the complexity of interactions among the constituent particles. For example, the prokaryote ribosome, responsible for the translation of the genetic code from nucleic acid to protein, is composed of two particles, amounting to a total mass of around 2.5 million daltons. This macromolecular assembly contains around 55 ribosomal proteins and three ribosomal RNAs (totaling roughly 4,600 nucleotides). All these molecular objects interact cooperatively so as to make this machine work very precisely, controllable at the same time by several external factors. Where do we start in order to understand such an assembly? We now agree that different levels of organization exist, possibly hierarchically structured. Broadly speaking, one can distinguish the secondary structure of the 16S rRNA present in the 30S particle, as well as its tertiary structure when it is assembled in the 3 OS particle. RNA architecture, and to some extent protein structure, are now understood on the basis of two central design principles: modularity and hierarchy among organizational levels. Unifying principles are thus seen to emerge from the molecular level to that

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of functional biology, since the notion of the hierarchical organization of modularity has recently been uncovered in metabolic networks. Today, tremendous activity is underway around the world in the effort to establish databases for organizing fragments of biological knowledge. In such endeavors, one analyzes, classifies (base-pairs, motifs, interactions, e t c . ) , and dissects how component parts interact with each other. In the Ninteenth Century, without realizing it, Mendeleev prepared the field for quantum chemistry by classifying the chemical elements and devising the periodic table with surprising precision. Nowadays, when we classify biological objects and try to learn about base-pairs or protein-DNA recognition motifs, are we sure the concepts we use are appropriate for preparing the future? In other words, can we really go beyond organizing databases of sequences, structures, motifs, and genomes? Are all our concepts really relevant and pertinent? At the same time, we know that biological structures are the chemical products of our planet's history, and that while these billions of years of evolutionary history are consistent with physical laws, those laws do not determine them, as Steven Benner beautifully illustrates in the first article of this book. This implies that potentially not a single interaction or atomic contact may be neglected, which leads to the inescapable conclusion that one cannot neglect weak interactions, which control fine-tuning in specific binding and recognition. Integration and cooperation between the strong and weak forces, between water molecules and ions, are responsible for the folding and stability of biologically functional macromolecular objects. At the other extreme, physicist Ken Wilson tells us that even if we knew everything about the quantum chemistry of water molecules, we would still be unable to understand the formation of waves. Nowadays, biology extends between two extremes: Attempting to understand biological catalysis and the movement of a single proton in very high-resolution X-ray structures with millions of atoms involved in interactions of various kinds and strengths, all the way to systems biology and the study of complex networks. Two principles constantly permeate biological systems: self-organization and mechanisms of symmetry-breaking. The book begins with articles that focus on self-assembly (RNA molecules and proteins) and ends with examples of symmetrybreaking in viruses and in the central mechanisms of molecular biology. Strong electrostatic interactions dominate the folding of polyelectrolytes, such as DNA and RNA molecules, as shown experimentally by Steven Benner and theoretically illustrated in the next chapter by Jean-Francois Joanny, who seeks the coarse-grained properties of charged systems, avoiding the specific chemistry of the

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charge-bearing molecules as much as possible. But the influences of electrostatic charges on polymer conformation are so pervasive that the deduced principles and laws extend deep into biology. Michael Zuker follows with rules for RNA-folding, based on Boltzmann statistics. For many years, Michael Zuker's software has made it possible for biologists to routinely compute secondary structures of RNA molecules on the basis of nearest-neighbor energies between base-pairs (experimentally obtained by Doug Turner's group) by minimizing the energy of the structure. In this new approach, the reverse process is envisaged: One can derive the frequencies of dinucleotide pairs from phylogenetically aligned sequences, thus obtaining pseudo-free-energies, which may be compared with experimental values. The next three chapters, by Sarah Woodson, Francois Michel, and Jamie Williamson (respectively), describe experimental approaches to the problem of RNA folding. The themes covered in these chapters overlap and intersect, treating the chemical nature of ions that promote folding, folding kinetics, RNA transcription rates, and sequential binding of proteins to ribosomal RNAs. These three authors used various experimental techniques, including UV melting, chemical probes, hydroxyl radicals generated by synchrotron radiation, fluorescence measurements, and single-molecule studies. The main section on RNA folding concludes with the chapter by Herve Isambert, who describes the kinetics of RNA folding, as seen in computer simulation experiments. Interestingly, throughout these conferences, several participants in the audience raised the question of the prevalence of magnesium ions in RNA folding. A large part of the answer lies in physical chemistry and in the lifetimes of the water molecules that are bound to the ions (e.g., very short lifetimes around potassium and very long ones around magnesium ions). Thus, chemically "hard" magnesium ions bind to RNA molecules mainly via the water molecules of their solvation shells, which buffer the strong electrostatic attractions while simultaneously preventing kinetic folding traps. Coupled to the hierarchy in the architectural folding of RNA molecules is a hierarchy of ion-binding (monovalent ions such as sodium first stabilize the secondary structure, then divalent ions such as magnesium lock the tertiary structure; see the chapters by Sarah Woodson and Francois Michel for these aspects). An analogous explanation lies at the origin of the selectivity of potassium channels (membrane proteins that catalyze ion movements which generate electrical signals in neurons), letting through only (dehydrated) potassium ions, not smaller (dehydrated) sodium ions, because only the former may be properly re-solvated during passage through the ion channel. The next five chapters treat protein structure and folding. Like the chapters by Steven Benner and Francois Michel, they all stress the evolutionary history

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contained in protein sequences. This aspect is especially apparent in Olivier Lichtarge's article, which addresses the fundamental problem of integrating sequence, structure, and functional information. Whereas this problem is usually tackled using mathematics, statistics, and physics, the sole link between sequence, structure, and function is biological evolution, the central and unique property of biology. In a counterpoint approach, Alexander Kister and Izrail Gelfand search for sequence determinants, which are strongly related to the structural stability of a given fold and which allow assigning a query protein to its proper structural class. In the following chapter, Marc Delarue first applies bioinformatics tools, then X-ray crystallography, and finally normal mode analysis to DNA polymerase families. These molecules present a fascinating example of molecular evolution with convergent evolution to an active site that is similar in two such families (each of which reveals divergent evolution). The final sections, which treat normal mode analysis and the application of the Poisson-Boltzmann equation to polymerases, demonstrate how an appropriate coarse-grained physical method can reveal important characteristics of the large-amplitude transitions that polymerases must undergo during their polymerization activity and in the translocation step. The last two chapters on protein structure treat the problem of protein-folding per se. Leonid Mirny describes another example, in which a simple physical model of protein-folding on a lattice leads to increased understanding of the crucial phenomena in real protein-folding. Lattice simulations have taught us, among other things, that fast-folding proteins have a stable folding nucleus that stabilizes the transition state and compensates for the loss of entropy. Using sequence comparisons, Mirny further shows that residues which belong to the folding nucleus are more conserved than would be expected if they only contributed to stabilization of the native structure. In the last chapter on protein-folding, Devarajan Thirumalai leaves the realm of spontaneous folding in the Anfinsen sense and introduces us to the formidable nanomachine that is the E. coli chaperonin particle GroEL, showing how this stochastic machine uses ATP in an iterative annealing mechanism to fold polypetide chains to their native state. In exquisite detail, Jack Johnson later presents the processes involved in viral self-assembly and maturation. Although one may argue about whether viruses are living organisms, it is indisputable that their study has made an enormous contribution to our understanding of living systems. More than 50 viral crystal structures displaying diverse molecular biology have been provided by a variety of sources, in 85% of which the capsid protein adopts the sandwich fold. All spherical viruses have icosahedral symmetry, but only the non-functional satellite viruses contain the minimal set of 60 subunits. In order to package enough genetic

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information, functional viruses contain multiples of 60 subunits, in agreement with the concept of quasi-equivalence discovered by Caspar and Klug in 1962. Jack Johnson describes molecular examples of how quasi-symmetry is achieved in viruses with icosahedral symmetry. Finally, Antoine Danchin takes a new look at genomes, asking at which level the genome is "fluid." First he shows that the genetic program leads to biases that favor transcription in the same direction as the replication fork in several organisms, which leads to a G/T-rich bias in the leading strand and an A/C-rich bias in the lagging strand. As a result, proteins that are coded from the leading strand tend to be valine-rich and those coded from the lagging strand threonine-rich. Danchin ends with two central points, the first of which was present throughout the conference and especially discussed in the chapters by Steven Benner and Olivier Lichtarge, and which is at the core of the present difficulties of functional bioinformatics and automatic genome annotation: How to assign a function to a structure and, ultimately, to a sequence. Even without dwelling on the linguistic ambiguities of the word function, the observations that "function captures preexisting structures," and that folded structures are prerequisites for the evolution of function, have a profound influence on how to organize biological observations and databases. Antoine Danchin's second point concerns cell organization, forcefully advancing the idea that the driving force behind it is translation, and that it is structured around translation and the ribosomal network. This brings us back to our initial interrogation. In his classic article, Evolution and tinkering (Science, 1977), Francois Jacob wrote that nature functions by integration. Although global principles concerning biological systems are becoming clearer (as several chapters of this book illustrate), the modeling of complex biological systems will require the integration of computational biology and highthroughput technologies in a network perspective approach. These proceedings consist of the transcribed oral presentations as well as dialog among the speakers and the audience present at the Entretiens. Although the texts were thoroughly edited, an effort was made to not strip them of the liveliness and candor of the verbal exchanges they elicited. On behalf of the organizers, I thank the authors for accepting these long interactive presentations with patience and humor, as well as the conference participants for their numerous and valuable questions and comments. Regrettably, it was not possible to keep track of the names of the persons who intervened during the talks. Finally, 1 commend the efforts of Noah Hardy for his careful and dedicated editing of the entire proceedings, by no means an easy task. He was assisted by Helga Dernois, the IHES scientific secretary whose steadfast work produced the final draft of the

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manuscript, Marie-Claude Vergne, the IHES graphics specialist who handled the figures, and Roberto Incitti, who managed the web-based system for importing the images and corrected texts. Without them, this volume could not have been produced. Last but not least, I thank Jean-Pierre Bourguignon, Director of the Institut des Hautes-Etudes, without whose constant encouragement and stimulating presence these Deuxiemes Entretiens de Bures would not have taken place. To all of them, along with the three other organizers, Alessandra Carbone, Misha Gromov, and Francois Kepes, I extend my warmest acknowledgments.

CONTENTS Organizers and Speakers

v

Editor's Note Noah Hardy

vii

Preface Eric Westhof

ix

Evolution-Based Genome Analysis: An Alternative to Analyze Folding and Function in Proteins Steven Benner

1

Conformation of Charged Polymers: Polyelectrolytes and Polyampholytes Jean-Francois Joanny

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Statistically Derived Rules for RNA Folding Michael Zuker

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Experimental Approaches to RNA Folding Sarah Woodson

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Some Questions Concerning RNA Folding Francois Michel

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RNA Folding in Ribosome Assembly James R. Williamson

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From RNA Sequences to Folding Pathways and Structures: A Perspective Herve Isambert

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An Evolutionary Perspective on the Determinants of Protein Function and Assembly Olivier Lichtarge

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Some Residues are more Equal than Others: Application to Protein Classification and Structure Prediction Alexander Kister and Izrail Gelfand

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Contents

Structure-Function Relationships in Polymerases Marc Delarue

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The Protein-Folding Nucleus: From Simple Models to Real Proteins Leonid Mirny

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Chaperonin-Mediated Protein Folding Devarajan Thirumalai

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Virus Assembly and Maturation John E. Johnson

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The Animal in the Machine: Is There a Geometric Program in the Genetic Program? Antoine Danchin

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EVOLUTION-BASED GENOME ANALYSIS: AN ALTERNATIVE TO ANALYZE FOLDING AND FUNCTION IN PROTEINS STEVEN BENNER Departments of Chemistry, Anatomy, and Cell Biology, University of Florida, Gainesville, FL, USA

From time to time, it is useful to step back from our professional activities to ask "big" questions. One of the biggest is Why are things the way they are! This question may be asked in any discipline. It is frequently asked in physics. It is especially important in biological chemistry; however, since its answer ultimately determines which research problems are interesting and which are not. In biological systems, the Why question may be approached at many levels. At the highest level, we ask why physiology, the structure of our bones and tissues, is the way it is. The question may be asked of decreasingly smaller biological structures as well. We can ask this question about the structure of cells, the structure of proteins in cells, and the structure of individual molecules involved in biological metabolism, for example. Biology offers two classes of answers to such questions. The first holds the structure of a biological system to be a unique solution to a particular biological problem. We frequently encounter this type of explanation when discussing physiology. When we consider the function of a tooth obtained from a fossil organism, for example, we often conclude that an animal ate grass if its teeth have an optimal structure for chewing grass. This implies that teeth in general have been optimized to macerate the substance that is eaten. Biomolecules also often appear to be unique solutions to a particular biological problem. For example, the enzyme triosephosphate isomerase, which is important for the degradation of sugars in our diets, catalyzes the turnover of a substrate molecule to a product molecule whenever it encounters one. This behavior would seem to be optimal for the survival of an organism that is dependent on the enzyme. More broadly, a similar outlook predicts that if you were to go to Mars and find life there, it would be constructed with the same general chemical features - if it used enzymes at all. This type of explanation drives a research strategy. In part, we want to study the details of the structure of biological systems because we believe that they are

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optimized. In this view, within these structures lies a deeper understanding of chemistry, of biochemistry, and of life itself, all awaiting inspection at the correct level of detail. The second class of answers recognizes that the biostructures of life are the products of four billion years of biological evolution and planetary history. Given this, one can also explain the structures of living matter in terms of their historical, geological, and paleontological records. Explanations take the form of stories about the historical past, certainly consistent with physical law, but not necessarily determined by them. This is the approach of the natural historian. Given this perspective, to engage the Why question brings the scientist to the confluence of the three great traditions in science. The first one is the natural history tradition, which is older than civilization and as young as my three-year-old son. It comes from the human compulsion to collect - sticks and stones at first then plants, minerals, and fossils, and to classify them. Natural history gained its standing as a science after the Enlightenment as classification of natural things, and came to be seen more as a consequence of the history of life on Earth than the consequence of divine intervention. Natural history uses a human-constructed metalanguage to describe the natural world. To the naturalist, explanations are historical. The models used to explain the natural world reconstruct events in the historical past that are contingent in large part on random chance. These events are certainly consistent with physical law there are no violations of the laws of thermodynamics - but they are not predictable by physical law. The second tradition derives from physical science, which began as Enlightenment scientists devised mathematical models to explain the motion of planets in the heavens. The physical science paradigm uses mathematical models for both description and explanation. If one asks a physicist why an atomic bomb explodes, (s)he will say, "Because e = mc 2 ." Physicists generally have little use for the natural historical. "Science is either physics or stamp collecting," said Rutherford, near the turn of the last century. Indeed, physicists may view their descriptions and explanations as better than those of the natural historian because theirs are mathematical, and therefore (we presume) "universal." If one asks a Klingon physicist why an atomic bomb explodes, we would expect (s)he would also say, "Because e = mc 2 ." Even the natural historian is somewhat embarrassed by the "just so" storytelling of tradition. However, natural historians are struggling to convert their field to a physical science by adding mathematics to their descriptions whenever possible. Yet the natural historian often finds a purely mathematical law to be an unsatisfying

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explanation. So do humans, generally. If one explains the bomb by the equation "e = mc2", it is only human to then ask, "But why does e equal mc2?" However, both naturalists, with their human-constructed metalanguage and historical explanations, and physicists, with their universal language and mathematical explanations, are confounded when they encounter the third tradition in science; that of chemistry. Chemistry builds its descriptions in terms of universals. There is little doubt that chemists on the planet Klingon would work with the same carbon, hydrogen, oxygen, and nitrogen atoms that earthling chemists do. At the same time, however, chemists use a human-constructed metalanguage for explanation. To the question, "Why is benzene not very soluble in water?" the answer is "Because benzene is hydrophobic." The concept of hydrophobicity is almost certainly grounded in some combination of the enthalpy and entropy of benzene and water, the mathematical descriptions of which may be universal. But the explanatory metalanguage is definitely of human construction. Physicists and naturalists may both view the explanations of organic chemists that were constructed using human-created metalanguage with suspicion; they feel this metalanguage is so malleable that it may be used to explain anything. Indeed, with embarrassing frequency, organic chemists find themselves in a position of having explained an experimental result in terms of their metalanguage, only to discover that the experimental result is the opposite of what they thought, and that they are able to explain the opposite result just as easily within the context of the same metalanguage! Physicists and naturalists alike may be infuriated by the extent to which organic chemists do not view this as a problem in their science. Chemists may in part be forgiven, because they have a peculiar, but powerful, experimental research strategy for developing their (often intuitive) understanding of the behavior of molecules: synthesis. Synthesis, especially in organic chemistry, involves the rational creation of new matter; different forms of the arrangement of atoms. These differences may be selected in order to test models that explain the behavior of molecules. For this reason, synthesis serves as an experimental method for developing understanding. But synthesis in chemistry serves another role: to validate understanding. One knows that one understands a molecular system when one can design a new molecule within that system, synthesize it, and show that the molecule behaves as predicted. This approach to validating understanding is certainly not available to astrophysics. One cannot (today) synthesize a new star to see whether the model is correct. The power of synthesis has made chemistry arguably the most successful of the three sciences. This is certainly true from a technological perspective. Rational

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synthesis based on organic structure theory has generated plastics, dyes, and materials. Essentially all the advances in contemporary biotechnology have come from a description of living systems, using the universal chemical language. The human genome is, after all, nothing more (and nothing less) than a statement of how carbon, oxygen, nitrogen, hydrogen, and phosphorus atoms are bonded in molecules directly involved in human inheritance. The race to do "structural genomics" is nothing more than associating conformation with these chemical constitutional formulas. But synthesis becomes especially important when asking the "big questions" in biology. We can use synthesis to make new forms of biological matter, to ask why not- and what zj-types of questions. Do the forms of biological matter that we see on Earth in fact perform better than alternative forms? If so, we synthesize an alternative form of matter and see how it behaves. Could life not take some other form and perform as well as the life we know? My goal in this lecture is to show the virtue of connecting the three approaches, tying physical science to natural history and molecular structure. The point of this lecture is to show that the answers to these questions require input, data, language, and ideas from the physical and chemical sciences, as well as from natural history. To really understand the world around us in the new millennium and in the age of the genome chemists must become natural historians and natural historians must become chemists. That is the point I would like you to take away with you today.

Why is DNA the way it is? Let us start by considering the structure of DNA, the molecule at the core of genetics. Nucleic acids such as DNA are built from nucleotide units. These are based on one of two sugars: ribose and 2'-deoxyribose, respectively generating RNA and DNA. A nucleobase (or, or more simply, a base) is appended to the sugars to give a nucleoside. In a nucleic acid strand, the nucleoside units are joined by phosphodiester linkages (the "phosphates.") The resulting strand is an irregular polymer whose backbone is a repeating sugar-phosphate chain with a variable heterocyclic nucleobase attached to the side. Information is contained in the order of the nucleobases in the oligonucleotide chain. For those inspecting the figures without a background in organic chemistry, let me simply state that organic chemists represent molecular structures using geometrical structures. They often place letters denoting the chemical elements at

Evolution-Based

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the vertices of a geometric figure representing the molecular structure, but C and H, denoting carbon and hydrogen, are rarely so placed. Carbon is represented without letters by the vertices of the geometric figures in these structures, every unlettered vertex in the graph representing a carbon atom. Carbon makes four bonds, and the bonds between carbon and all atoms except hydrogen are written explicitly. However, bonds made between carbon atoms and hydrogen atoms arc not. This means that if a vertex has fewer than four lines going to it, the missing bonds arc made to hydrogen.

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A nucleic acid strand recognizes its complementary strand by Watson-Crick base-pairing. In the first generation model for DNA proposed by Watson and Crick a half-century ago, two DNA strands form a duplex structure. The duplexes are

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S. Benner

stabilized by base-stacking; the base-pairs stack on top of each other. Base-stacking brings the hydrophobic bases out of water; it also allows "stacking energy," a term from the human metalanguage describing base-pairing, to be realized. The backbone, according to the Watson-Crick model, is largely incidental to the process, simply acting to hold the bases in the strand together. In the first-generation Watson-Crick model for DNA duplex formation, the specificity of base-pairing arises from both size complementarity and hydrogenbonding complementarity between the bases. Big things, like A and G (also known as purines), pair with little things, like T and C (known as pyrimidines), hydrogenbonding complementarity arises from matching between hydrogen-bond donors and hydrogen-bond acceptors. Guanine presents an acceptor-acceptor-donor pattern of hydrogen-bonding on a large component, which is complementary to the donordonor-acceptor pattern of hydrogen bonding on cytosine, the small complement. Thymidine presents an acceptor-donor-acceptor pairing pattern on the small component of a second base-pair, which anticipates a donor-acceptor-donor pattern on its large complement.

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Evolution-Based

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Looking at natural DNA, we immediately see an interesting feature of the structure that departs from this regular design: Adenine is missing the donor group that would enable it to form a third hydrogen bond to thymidine, its complement in natural DNA. As a consequence, the natural A-T base-pair is joined by only two hydrogen-bonds, whereas the G-C base pair is joined by three. This prompts the question why? Is this a defect in the structure of DNA? Would DNA be better if aminoadenine replaced adenine, providing a nucleobase that can form three hydrogen-bonds to thymine? Or, following the first class of explanation that I mentioned earlier, is DNA better able to contribute to the fitness of the organism if it has a stronger base-pair joined by three hydrogen-bonds and a weaker base-pair joined by only two? Alternatively, we might explain this feature historically: Perhaps the incomplete structure of adenine reflects a frozen historical accident. Perhaps adenine (not aminoadenine) was present in the prebiotic soup, life emerged using it, and has since had no opportunity to replace it, at least not without disrupting the life that was attempting to do the replacing. We can generally ask questions of this class with respect to the structure of DNA. Indeed, as soon as one begins to formulate such questions from a chemical perspective, many peculiarities appear in the DNA structure. If one asks too many questions, DNA begins to appear to be poorly designed. Consider just three features of the molecular structure of DNA from the perspective of a chemist who might want to design a molecular recognition system: First, DNA is a floppy molecule. When two DNA strands come together, they must become more rigid. This would imply, perhaps naively, that the DNA strand is losing conformational entropy when it binds to its complementary strand, which is generally regarded as being "bad" for molecular recognition. If in fact, chemists who design artificial molecular recognition systems generally seek rigid "lock-andkey" pairs, they never try to design two floppy things that bind together. Secondly, in water, DNA uses hydrogen bonding to transfer genetic information. But water presents hydrogen-bonding opportunities everywhere. For this reason, few chemists working in the design of molecules that recognize other molecules exploit hydrogen-bonding as a molecular recognition unit in water. But the most remarkable feature of strand-strand binding in DNA comes from the fact that the two molecules that interact are both polyanions; each of the phosphate groups that form the backbone of a DNA strand bears a negative charge. In general, someone seeking to design a molecule that binds to a polyanion would begin by making a polycation, not another polyanion.

8 S. Benner We were not the only ones who thought that binding a polyanion to another polyanion was a peculiar way to design a molecular recognition system. In the late 1980s and early 1990s, an entire industry, known as the "anti-sense industry," consumed a significant amount of venture capital by seeking to replace the anionic phosphate linkers in the backbone of DNA with an uncharged linker (methyl phosphonate groups, for example.) The uncharged DNA analog was expected to passively enter the cell through membranes. Since the backbone has no role in the molecular recognition event, it was expected that the molecular recognition specificity would be retained. When we moved to the ETH (Eidgenossische Technische Hochschule Zurich), we had the opportunity to address the why questions experimentally with DNA, using synthesis as our paradigm. If the first-generation model for DNA structure were correct in postulating no particular role for the backbone, then we ought to be able to design and synthesize these DNA analogs, which take a small step away from the natural backbone. If the first-generation model for nucleic acid pairing were correct, these analogs should retain the rule-based molecular recognition characteristic of DNA; that A pairs with T, G with C, large with small, and hydrogen-bond donors with hydrogen-bond acceptors. In our first step, we replaced the phosphate linkers with a dimethylenesulfone group. This substitution removes the charge. Several talented synthetic organic chemists, in particular, Clemens Richert (now a professor at the University of Konstanz), Zhen Huang (now a professor at the City University of New York), and Andrew Roughton (now with Pharmacia) moved mountains to make these molecules. Fortunately, their hard work was rewarded.

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The first success came with the synthesis of the sulfone-linked GS02C dimer, the analog of the dinucleotide GpC in which the phosphate linker is replaced by a dimethylenesulfone group. The molecule is self-complementary, because G pairs with C. It should form a duplex of these dimers, in which G from one molecule pairs with C from the other, and C from the second position of the first molecule pairs with G from the first position of the second. In fact, GS02C does form a Watson-Crick duplex of this type in the crystal, as shown in the crystal structure of the substance solved by Martin Egli. The structure is isomorphous with the structure of G-phosphate-C, which is a self-complementary RNA molecule whose duplex was crystallized and structure solved by Alan Rich around thirty years ago. Indeed, it is amazing how similar those structures are. This result suggested that Watson and Crick - and their first-generation model - were right. The backbone really isn't all that important; the charge may be replaced by a neutral linker. Perhaps we should have stopped there, but we took the next step, making DNA analogs with bridging sulfones that were four units long. These no longer behaved in a "Watson-Crick" manner. For sure, we saw molecular aggregation, and selfassembly, but not following Watson-Crick rules. For example, the sequence US02CS02AS02U is not self-complementary. It would normally pair with ATGA. In fact, the sequence aggregates with itself. An NMR structure done by Richert showed that duplexes formed, but not of the Watson-Crick type. Longer sulfones were also unusual in their conformation and aggregation. For example, the sequence A-S02-U-S02-G-S02-G-S02-U-S02-C-S02-A-S02-U was prepared by Richert and Roughton. This molecule folds, and melts only at a transition temperature above 75°C. No evidence was ever found that it was able to pair to its complement in an antiparallel Watson-Crick sense. We then asked whether we could compare this behavior with that of other biomolecules we know. By the time any oligosulfone gets beyond a certain length, it has its own unique properties. Some of them are soluble in water, others are not. The chemical properties of various sulfone sequences vary widely, and largely unpredictably. We asked ourselves when we last heard of a molecule whose distinctive properties based on its sequence, whose properties vary widely when the structure changes modestly. Of course, we do know of biopolymers that display such behaviors; they are called proteins. One cannot help but be struck by the observation that by removing the repeating negative charge from DNA, we made a molecule that behaves like a protein. Indeed, we even encountered cases where sulfones were catalysts; they folded and catalyzed reactions.

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In retrospect, in light of these experimental findings, we conclude that perhaps a polyanionic structure is not as absurd as we thought for a molecule involved in genetics. Having now changed a repeating charge and seen the consequences of the change, we can suggest four reasons why negative charges are important to DNA: First, of course, the negative charges render the DNA molecule water-soluble. This is well known and not trivial. Next, when two DNA strands interact with each other, the repeating negative charges force the inter-strand interaction to a position on each strand that is as far away from the backbone as possible. This is important, because DNA offers many sites of interaction. In particular, interaction is well known on the "back side" of the purine ring, involving nitrogen-7, to form a Hoogsteen interaction. Indeed, in Richert's nuclear magnetic resonance structure of the tetrameric sulfone, this is what is seen without the negative charges. It therefore seems that the phosphates control the molecular interactions between molecules that are rich in functional groups. Without the repeating negative charge, DNA is a richly functional molecule that "wants" to spontaneously self-assemble and aggregate. The phosphates control that tendency. The repeating backbone charge requires the strands to interact on edges that are as far from the backbone as possible. This, of course, is the part of the molecule that forms the hydrogen-bonds in a classical Watson-Crick base-pair. So perhaps it makes sense for nature to use a polyanion to bind to another polyanion in a genetic system. A third way that the polyanionic character of DNA contributes to its behavior may be described by using the statistical mechanics theory of biopolymers. Normally a polymer occupies a volume whose radius scales with the length of the polymer to the one-half power. This is not the case if the polymer is a polyanion. A polyanionic polymer has a larger "excluded volume;" it stretches itself out, which allows the molecule to more readily act as a template. Again, the repeating charge in the backbone appears to be useful, if not required, for Watson-Crick rule-based behavior. Again, different from the first-generation DNA model, the nature of the backbone is quite relevant. Last, and most important, the polyanionic nature of the DNA backbone appears to be important to support Darwinian evolution. As noted above, oligosulfone analogs have very different properties, according to their precise sequence. This variation in physical behavior cannot be tolerated by a molecule expected to support Darwinian evolution. Here, the molecule must be able to replicate - we all need to have children - but in order to evolve, we must also be able to have mutant children. The need to support mutation without losing the ability to replicate is therefore essential for a genetic molecule. We have somewhat whimsically converted

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capable of suffering mutations independent of concern over the loss of properties essential for replication into an acronym: COSMIC-LOPER, which is that property of a molecule that allows it to support Darwinian evolution as a genetic system. This property is conferred by the repeating backbone charge of DNA. Reactivity is dominated by this structural feature, surpassing dipolar and quadripolar interactions - indeed, every higher-order molecular interaction involving electronic distribution within a molecule. As long as the repeating negative charges remain, the dipolar interactions (hydrogen-bonds, for example) may be changed without necessarily dramatically changing the solubility of the molecule, or changing the conformation or position to which the DNA migrates on a gel (for example.) This is not the case with proteins. Consider, for example, the behavior of hemoglobin: The replacement of a single amino-acid in hemoglobin results in sickle-cell hemoglobin, which precipitates. The converse implication is that proteins cannot themselves be genetic molecules because they cannot suffer mutation without changing their properties in a way that permits them to be copied (given a mechanism to do so in the first place.) Comment: You talked about the anti-sense companies as if they were in the past... Response: Most of them are. Question: Yes, most of them are, but I know of a new one in Leipzig that is going ahead. What about phosphorothioates, PNA, and all that stuff? Response: Excellent question. Phosphorothioates are DNA analogs in which one of the oxygen atoms in the phosphate linker is replaced by a sulfur atom. To date, these are the only anti-sense molecules that have shown promise in real biological settings. But phosphorothioates still carry a charge on each of the linking groups. PNA, in contrast, lacks the repeating charge. They were developed by Peter Nielsen and Peter Egholm, in Denmark, at the laboratory of Professor O. Buchardt, now deceased. These scientists replaced the backbone of the DNA molecule with a peptide-like linkage that lacks a charge. PNA is the exception that "proves" (or tests) the rule. If a repeating negative charge is in fact a universal feature of genetic molecules in water, PNA should not work. However, PNA displays Watson-Crick behavior, binding to complementary DNA in the Watson-Crick manner. The catch with PNA is that it does so only up to a point. PNA still generally displays WatsonCrick behavior up to ten nucleotides. However at fifteen, especially if the PNA molecule is rich in G, the Watson-Crick behavior begins to disappear amid

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solubility problems. This is the same behavior that is observed in sulfone molecules, but at somewhat longer lengths. Dimers and tetramers of sulfone-linked DNA analogs still display Watson-Crick base-pairing in some cases; but in most cases, longer sulfone-linked DNA analogs do not. As far as I know, PNA holds the record of being the longest non-ionic analog of DNA to retain Watson-Crick pairing properties. Why it does so well is uncertain. It may be due to an unusual interaction between the PNA backbone and water. Nevertheless, what is clear is that PNA itself could not support Darwinian evolution for long genes. Question: What was the strand orientation in these sulfone complexes? Response: Each one is different, but the strand orientation is only anti-parallel in GS0 2 C (as is observed in Watson-Crick DNA-pairing.) In all the other structures that have been examined there is no strand orientation. The sulfones simply fold or precipitate, like proteins. The key feature of the "second-generation" DNA model is that the backbone matters. But so do the heterocycles, or bases. The bases were at the center of the molecular recognition phenomenon, as discussed by Watson and Crick. However, it turns out that bases are the only structures of the DNA duplex that we can engineer without losing rule-based molecular recognition. Changing the bases simply required that we understand the combinatorial rules of hydrogen-bonding patterns. The C base exploits the donor-acceptor-acceptor hydrogen-bonding pattern on the "small" complement. The T base exploits the acceptor-donor-acceptor hydrogenbonding pattern on the "small" complement. But we still have the opportunity to construct organic molecules that use the donor-donor-acceptor, the acceptor-donordonor, the donor-acceptor-donor, and the acceptor-acceptor-donor hydrogenbonding patterns on the "small" component. This means that four more "small" bases and their four "large" complements which are not found in natural DNA are possible within the geometry of the Watson-Crick pair. Being organic chemists, we set out to synthesize the extra nucleobases, then tried to find out whether we could construct a DNA analog with an expanded genetic alphabet. We found that this synthesis was possible. Also, the extra letters in the genetic alphabet form acceptable Watson-Crick base-pairs with Watson-Crick specificity. The Watson and Crick rules may be expanded to include twelve letters, not just the initial four found in natural DNA.

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We closely examined the role of hydrogen-bonding in developing an artificial genetic alphabet. Ronald Geyer and Thomas Battersby, postdoctoral fellows working in my laboratory, determined an enormous number of melting temperatures with more than a dozen DNA analogs in an attempt to determine the role of hydrogen-bonding. In part, this work was motivated by a statement by Myron Goodman, based on some experimental work done by Eric Kool, implying that only size complementarity is important in base-pairing, not hydrogen-bonding complementarity. The work by Ron, who is now a professor at the University of Saskatchewan, and Tom, now with Bayer Diagnostics in California, suggests that these two features are approximately equal in importance.

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In all cases, the number of hydrogen-bonds is an adequate predictor of base-pair stability, as estimated by its contribution to the melting temperature of a duplex that contains it. Further conclusions may be drawn. First, size complementarity and hydrogen-bonding complementarity are about equally important in forming a stable base-pair. Furthermore, both are more important than "context." Context is the metalanguage term to specify which base-pairs lie above and which lie below the pair in the helix. Another predictive feature that this work uncovered is that a charge in the heterocycle is bad for duplex stability. Also bad is an uncompensated amino group, especially in the minor group. C-glycosides, in which the base is joined to the sugar via a carbon-carbon bond (instead of a carbon-nitrogen bond, as is the case in natural DNA), are modestly bad. From these observations derive semi-quantitative "rules" that design the alternative genetic systems. This enables new technology. Rule-based molecular recognition is desirable throughout industry, and tags built from the artificially expanded genetic information system that we have invented are useful throughout industry. For example, James Prudent and his colleagues at EraGen Biosciences (Madison, WI) has used the expanded genetic alphabet to create 76 tags that can capture 76 different species in one tube, permitting a multiplexed assay for DNA variation in a sample. Question: What is the advantage of non-standard base-pairs? Response: The presence of extra letters in the artificial genetic alphabet means that we can generate tags containing non-standard bases that bind to other tags containing non-standard bases without cross-binding to DNA molecules that contain only standard bases. This means that the extent to which a non-standard tag finds and binds to its complement does not depend on the amount of natural DNA contained in the assay mixture. If we try to use tags made from standard bases, adventitious DNA also built from standard bases can contain sequences that interfere. The first diagnostic product that exploits non-standard bases was developed at Chiron. It is a branched DNA diagnostic assay. The assay captures an analyte DNA molecule in a sandwich assay. In figure 5, we imagine the analyte, the molecule we want to detect, to be the meat between two slices of bread. We first captured the analyte DNA with another DNA molecule that was complementary to a piece of the

Evolution-Based Genome Analysis: An Alternative to Analyze ...

15

analyte sequence, using Watson-Crick base-pairing (the bread in the sandwich.) This is then captured onto a solid support.

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Figure 5. The Chiron-Bayer-EraGen branched DNA diagnostics system based on non-standard bases. The other slice of bread is a DNA molecule that captures another part of the analyte DNA, again with Watson-Crick base-pairing. However, this molecule is branched, with -10,000 DNA branches coming off it. Each of these, through Watson-Crick base-pairing, now captures fluorescent molecules. Thus, only if the analyte were present, would fluorescent species be attached to the support. Nonstandard bases enhance the assay by permitting orthogonality. A typical analyte sample contains a sufficient quantity of DNA built from A-T-G-C to capture enough of the branched and fluorescent molecules onto the support, even in the absence of analyte, if the branched and fluorescent molecules are also built from A-T-G-C. This created background noise. However, by making the branched and fluorescent molecules from non-standard bases, the noise decreased and the sensitivity went down to eight molecules. Orthogonality means that you can do molecular recognition out here using one set of rules, and just use this part, A-T-G-C, where you need to, because the natural system contains it, and that way you don't have cross-reactivity between the molecular recognition that is recognizing the analyte and the molecular recognition system that is doing the signaling. That is the key issue. Is this clear? Question: / don 'I understand what the. actual source of the improved specificity is. What you 're saying is that it is easier to recognize something with a non-natural base than with the natural base - but what is the actual source of the specificity?

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Response: The source of the specificity is that non-standard bases pair only with their complements, as defined by the pattern of hydrogen-bonding and sizecomplementarity, and not with standard bases. Question: I've got lots of questions. First of all, in chips, what if you just have longer oligos, would you not then get better specificity? Response: No, you don't. This depends in part on the temperature at which you run the reaction. With DNA molecules that are very long, at reasonable temperatures, sub-sequences bind non-specifically. You have a melting temperature issue at some point, because a short molecule will bind to a short molecule - this is a bit of a cartoon - at low temperatures, and a long molecule binds to a long molecule at high temperatures. So at some point you are limited. As it turns out, there is an upper limit, and you actually don't have many things that bind at 100°C. Comment: But what you can do is work out a sort of deconvolution software. Response: Yes, that has been tried, in sequencing by hybridization, for example, and it hasn't worked very well. In part, it is difficult to know what DNA sequences are found in the background DNA, therefore it is hard to know what complements to avoid. Consider some very simple molecular biology: Let's say you want to design a primer that would be suitable for a PCR reaction. You have some temperaturescale protocol that you would like to use. Obviously, your next problem is that if you have an A-T-rich primer, the temperature scale you would use is different from what you would optimally use for a G-C-rich primer. With these extra bases, EraGen has developed a "gene code" software package in which their first primer fits into an already existing PCR cycle for parallel PCR 70% of the time. So when anthrax hit America, EraGen was approached by Cepheid to develop the chemistry for an anthrax test kit. Within three weeks, EraGen had a working multiplex anthrax test chemistry, just because of the non-orthogonality. Question: So, you could have your non-standard DNA on the chip, right? when you have a sample that is real DNA, how does it...

But

Response: You're always going to have to capture standard DNA with a complementary sequence written in standard DNA... Question: You just convert it, right?

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Response: No, you don't have to convert it, but you have to do what you did here: divide the DNA detection problem into two parts. The first part is the recognition of natural DNA. This must be done with a strand that has A-T-G-C in it. The second part is responsible for the capture onto a support, or signaling. If you try to put A-TG-C into that part, it will cross-react with standard DNA and give you problems. So you use the non-standard nucleic acids for your signaling output and standard bases for the recognition of the natural DNA analyte. Comment: OK, now let's get to the real point: you might ask why nature did not use this, and I would say you could have an RNA equivalent. Response: Yes, we have it. Question: ...andfor Uand T? Response: Yes, we've made it. Comment: - But then it binds too well, and you don't have this exocyclic amine waiting to react with something; you 're not going to get ribozymes that way; you 're not going to get wobble-pairs, and all these other lousy pairs that make life interesting. Response: But you do! These molecules with extra letters have a rich folding chemistry as well; iso-G, in particular, has a great tertiary structure. It is probably better than G at forming three-dimensional structures. I can go through each one of these and give you a chemical problem that may lead to an explanation for why nature does not use that particular structure. So iso-C deaminates with some degree of facility, for example; but C deaminates as well. Likewise, the C-glycoside pyDDA has an epimerization problem, as said in the metalanguage of organic chemistry. But then again, N-glycosides have depurination and depyrimidinylation problems. At some point, you must marvel that any of these things have the chemical stability needed to serve as genetic molecules. Indeed, if you're talking about chemical stability, RNA is not all that great a molecule (it is cleaved easily in base), so one may be astonished that it's used at all! But these questions all come before we can ask whether the duplex stability is too tight. I worry about the chemicals - the covalent bonds holding together in these systems - long before I worry about whether the non-covalent interactions are adequate to support life. But that is a paradox I don't want to ignore. It is not easy to understand why we use

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DNA in the first place. Once we've agreed to use DNA, I have to wave my hands to explain why these four nucleobases are used - as opposed to the other eight - fully recognizing that on any good day, if we had used the other ones, I could explain that just as well. This is a typical organic chemistry problem, which causes some to question whether organic chemistry is a science... Question: Then you must also be changing the geometry of the structures? Response: Yes and no. Obviously, any structural change changes the geometry at some level, perhaps only slightly. This certainly happens with C-glycosides, to which I casually referred. Joining the heterocycle to the sugar ring by a carboncarbon bond instead of a carbon-nitrogen bond changes the pucker of the sugar ring, which might be responsible for the small difference in association constant. But it actually makes it more RNA-like - it's an interesting problem. But the conformational change is on the order of tenths of Angstroms, not Angstroms. Question: Don't you think that the natural bases are maintained because they are resistant to tautomerism over a wide range ofpH? Response: Did everybody understand that? The question is whether the standard nucleobases are resistant to tautomerism over a wide range of pH. I'm perfectly prepared to reject this base-pair (iso-G) on the grounds of tautomeric issues. Iso-G, which we have looked at in detail, has big tautomeric problems. The rest of them do not. What is a tautomer? Let me just illustrate it with iso-G. Keep in mind that the location of hydrogen atoms in a molecule determines the hydrogen-bonding pattern. Iso-G has a hydrogen-bond donor, another hydrogen-bond donor, and a hydrogenbond acceptor. There is an isomer of iso-G where we move a hydrogen from the ring N onto the oxygen. What was once a hydrogen-bond donor-donor-acceptor pattern is now a donor-acceptor-donor pattern. Therefore, this kind of isomerism changes the hydrogen-bonding pattern. In an aqueous solution, the donor-donoracceptor is about 9 1 % of the total, with the isomer presenting the donor-acceptordonor pattern contributing ~9% of the total. Now, the donor-acceptor-donor pattern on the minor tautomer of iso-G pairs with the acceptor-donor-acceptor pattern of T. So, that's the problem; the speculation was that this is an intrinsic reactivity of this arrangement of atoms that makes it unsuitable for a genetic molecule. G, by the way, also has a minor tautomer formed in the natural base; it contributes about one part in 10,000. But its presence is largely insensitive to

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solvent effects. It is amazing how stably that number remains small, actually. But the other tautomer of G would also pair with T. There was a paper by Topal and Fresco in 1976 in which an argument was made that the minor tautomer of G was important for mutations. Question: Actually, may I go back for just a second? Do me a favor and draw it on the board. In the scheme you illustrated; the single mutation detection, what exactly is on the chip and what exactly is labeled? Response: It's complicated, but let me just briefly describe it.: There is a detection step and a readout step. Let me draw you a cartoon, because the actual reality is more complicated. This is what we call an artificially expanded genetic information system, and this sequence is natural DNA. We're going to call it a primer, and what you have is an oligonucleotide molecule; and let's just say it has a G here and a C here. Now what you're going to do is introduce a polymerase here, and what that is going to do is copy the rest of it and make a complete copy. For the sake of argument, let's just say that you now do a polymerase chain reaction-type of reaction, in which you add the primer that is complementary to the product, and read back in the other direction. Question: It's confusing; you know what you're talking about, but we don't; and what you've said a couple of times is that this funny business increases the sensitivity. Response: Do you understand this system here, because this is relatively easy to explain? Answer: Do this; that would do it well. Response: OK, what you're looking for is the red DNA [in Figure 5], which is the analyte, and the readout is going to be glowing solid supports; so at some point you've got glowing solution - this is all present in solution. At some point you're going to recover the solid support and see if is glowing. The green molecules glow - but in any case they give off light from the support only if they're attached to the solid support. The theory is that the specificity of Watson-Crick base-pairing, A-TG-C, will guarantee that the only way the green molecules will stick to the support is if there is something in between to bind them. Now, it is not direct; these

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molecules are covalently attached to that DNA, that DNA binds to this DNA, and this DNA is covalently attached to that DNA. Keep in mind that you can't detect just one glowing DNA molecule; we need an amplification system, so this is 10,000-to-one onto a support. They have to be made of something that does rule-based molecular recognition, which is a problem, because in the natural world there is no system other than DNA that does rule-based molecular recognition. Another possibility would be to build that entire dendrimer out of covalent structure. You could do that, in principle. It is mostly because of cost that it isn't done; that's the primary reason. Comment: So the way I understand it, which might be wrong, is that the noise is decreased by the sandwich, but you have two recognition... Response: No, the noise arises because you have lots of glowing pieces attached to A-T-G-C; you have a support with A-T-G-C on it, and you have a lot of other DNA in there; that is not the red DNA which contains A-T-G-C. Comment: But there is also this sandwich thing, and because you hybridize with two different portions of your analyte, you obviously decrease the non-specificity. Response: That may or may not be obvious, but it is true. The real reason this sandwich exists is that it allows the signal; a glow on a support, right? This sandwich is a way of attaching light-emitters to the support. Ten thousand times is a 10,000-to-l amplification of that, and the background noise is due to the fact that the molecular recognition used to assemble this consists of the same units, A-T-GC, as the contaminating DNA in a biological sample. Question: Right, so then what I'm confused about - because I kind of understood this - is that you said you can do about the same without the sandwich. Response: His point is: why don't you just synthesize all this into one big covalent thing; just make a big glob; make a polystyrene bead and put glowing things on it; put on one tag. The answer to that is that it is a mess! People have tried this type of thing. The glob is a difficult thing to synthesize; it turns out to be an expensive thing to synthesize, it is difficult to synthesize in a form suitable for FDA approval, and also, there is a signal-to-noise issue there as well, as it turns out, because the non-covalent and reversible assembly of this nanostructure is one of the ways you get cleaner results.

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We can now take the next step. Obviously, we now have extra letters in the genetic alphabet and there is an enormous potential for putting functionality on them. Let's ask how to make DNA into a molecule that has the same catalytic potential as proteins. This goes back to the work of Jack Szostak, Jerry Joyce, Larry Gold, and others. They tried to do test tube evolution with DNA. In this work one makes a library of DNA molecules and sets up a selection system such that only those DNA molecules that have a particular catalytic activity survive. This story shows how synthesis permits one to get full practical manipulative control over the behavior of nucleic acids. Here, the metalanguage is simple. We are analyzing only local interactions. We are also using only very simple rules that require no long-range, higher-order analysis. The level of the theory is really very low. The metalanguage that we use is big, with small hydrogen-bond donors and hydrogen-bond acceptors. Sometimes we talk about C-glycosides, sometimes we talk about negative charges, and sometimes we speak of uncompensated functionality. But we are not using quantum mechanics, molecular dynamics, or explicit solvents. The simplicity of the system arises from some key structural features, particularly the polyelectrolyte nature of the backbone. It is this feature of the DNA structure that we think will prove to be unique or universal. We have also learned that some features of the DNA structure, like the structure of the heterocycles, are flexible, and flexible to a good end. The point I want to make before I move on is that this is different from what you see with proteins, a lot different. With proteins, we are not even close to this kind of practical manipulative control. The metalanguage that we use is quite inadequate to explain proteins. Therefore, if we want to understand the way things are with proteins, we need to rely on a more historical approach, which I'm going to talk about as soon as I have cleared all the questions about the design and synthesis approach. Question: Well, I'm curious whether these non-canonical nucleotides can be used as precursors? Response: Yes. That's a long story, and I did not tell it because it's nowhere near as interesting or as clean. For a polymerase to incorporate non-standard bases requires the interaction of a protein with a nucleic acid. From many experiments, we know that that interaction is idiosyncratic. It turns out that if you just take the standard polymerase and throw it at non-standard nucleobases - we did this in 1990 - you can get some incorporation. But it is idiosyncratic. Tag polymerase works here only if this non-standard base is in the template and its complement is in

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the triphosphate, not the other way around. With Tom Battersby as first co-author, we reported the first in vitro experiments using functionalized nucleotides in the Journal of the American Chemical Society around two years ago. We still have a long way to go. Question: [inaudible] Response: We've tried everything. We've tried HIV reverse transcriptase - which is actually the place you start - but HIV reverse transcriptase has the unfortunate feature of not being thermostable, so you don't have the opportunity to use PCR with it as well as you do with some of the others. [Note added in proof: Our laboratory will report the first example of a PCR with six letters; the report will appear late in 2003 in Nucleic Acids Research.'] Let me just go back and talk about proteins. As I mentioned before, with DNA, we are able to look at local sequence interactions and come up with perfectly good models that are predictive and that provide manipulative support to anything we do. With proteins, however, this has not been possible. In particular, local sequence interactions have not been particularly valuable in predicting and manipulating proteins. This is actually an old observation. Back to 1984... Chris Sander noticed that the pentapeptide valine-alanine-histidine-alanine-leucine was found in both triosephosphate isomerase and proteinase-K. It forms a helix in triosephosphate isomerase; it forms a beta-strand in proteinase K. The helix in triosephosphate isomerase is the continuation of a longer helix, whereas in proteinase K the sequence is found in a beta-turn-beta structure. Obviously, in 1984, the database was very small. A few years later, as the database grew, identical hexapeptides were found in two protein contexts; one was a helix and the other a strand. Today we know of identical octapeptide sequences that, in two contexts, form a helix and a strand. This suggests that the protein conformation is not determined by local sequence. This fact defeated the field until we began using a historical approach in analyzing these particular systems. I'm going to have to digress a bit in order to discuss alignments of protein sequences. These are the key to the history of proteins. Question: When God picked the four nucleobases, why did (s)he pick the four? Response: The short answer is that I don't know. The long answer is in the metalanguage of organic chemistry. For example, adenine is a polymer of hydrogen

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cyanide, and is therefore possibly prebiotic. A lot of HCN exists in the cosmos; if you spark it, heat it, and photolyse it right, you get adenine out. But then 1 must also tell you that adenine hydrolyzes in water to give inosine. This means that up to a point, adenine is prebiotic. The only problem with these explanations (as with other explanations in organic chemistry) is whether we could be just as convincing if it were the other way, using the explanatory metalanguage of organic chemistry. In general, we probably could be. Question: Do you have any data concerning the flexibility of non-natural DNA? Response: Flexibility meaning the conformational flexibility, or persistence length? No, we really don't, but I do not expect it to be any different because of theory. That the persistence length will be dominated by the repeating anion is what we would expect. But since you asked me about experimental data, I should not tell you about my expectations. No, we have no data. Comment: That might be an interesting difference. Response: We are willing to collaborate with anyone who wants to do the measurement. There must be experimentalists who do this in the audience. Question: / was thinking, for instance, about super elasticity and all those kinds of things, that are absolutely crucial for segregation of chromosomes and whatever... Response: Yes, Chris Switzer, now a professor at the University of California at Riverside, a former postdoctoral fellow of mine, who has been carrying the iso-C iso-G story forward, has looked at some of the iso-G structures in recombination forks - this type of thing. But keep in mind that the minute you start looking at a real biological system you look at proteins that have evolved over billions of years to handle A-T-G and C, and that's a different question. Question: You, or someone else might do some single molecule rotation.. Response: Well, I don't know how to do those. I will be happy to collaborate with anybody who is interested. So, let me go into a little theory here to discuss sequence alignments. I only have around half an hour left, so I will go through this quickly. For the alignment of

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two sequences, dynamic programming tools are the gold standard. These tools require a scoring scheme to find the alignment with the highest score. However, these tools assume that each site mutates independently. A variety of public tools are available to construct sequence alignments that include the sequences of many homologous proteins. First one must collect the sequences using these tools. Then one places the sequence into a program that returns a multiple sequence alignment, which generally has gaps. Usually the scientist is not satisfied with the gapping, so (s)he normally shuffles the gaps back and forth. One reason the difficulties arise is that the sequence alignment packages are based on what we have come to call the first-order Markovian sequence alignment model. This is a model that assumes that future mutations in a sequence are independent of past mutations; that mutations occur independently at individual positions; that the probability of substitution reflects a twenty-by-twenty log-odds matrix, and that gaps are scored with a penalty-plus-increment formula. I had a good friend in Zurich named Gaston Gonnet, who was a computer scientist. In 1990, Gaston came to Canada from Waterloo and had a look at the protein sequence database. Gaston knew all the computer science tricks to allow us to do what we call an exhaustive matching, in which we compare every sequence in the database with every other sequence. This enabled us to make historical statements about sequences in the database. Exhaustive matching finds sequences that are similar to each other, and we use it to suggest that the sequences are related by common ancestry; that they have a shared history. There is a caveat to that. But the exhaustive matching generated enough sequence pairs to allow us to test this Markovian model for sequence divergence. When the model is put to the test, it turns out that the penalty-plus-increment score for gaps is not a very good approximation for how real proteins suffer insertions and deletions that lead to gaps. In fact, you can even see that by eye, when you inspect a multiple sequence alignment. It turns out that the probability of a gap in an alignment falls off roughly with its length to the three-halves power. Those of you who are experts in polymer mechanics should notice that exponent; it is an important one. Furthermore, it turns out that adjacent substitutions and adjacent positions are strongly correlated. It also turns out that future and past mutations are also strongly correlated. Some sites are more mutable than others. You might ask why the probability of a gap is inversely proportional to its length raised approximately to the three-halves power. The answer is that I do not know; but if you make the assumptions that segments in a polypeptide chain that can be inserted and deleted are random coils, and that insertions or deletions extract or insert segments that end close in space, and if you assume that the same laws that govern, say, the conformation of free coils also

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govern the conformation of coils in a protein, you would derive this relationship (if you model the peptide as a linear polymer with no excluded volume.) The volume occupied by a one-dimensional polymer scales with its length to the three-halves power. The probability of the ends of the polymer being near each other in space is inverse with respect to the volume of the polymer - you might very well expect that. And by the way, the best fit to the experimental data does not raise the length to the three-halves power; the best power fit is 1.7. That is the actual exponent that fits the curve for gap-length distribution. If you go back and measure the volume occupied by a real polymer, as Paul Flory did in 1965 for polyalanine, the power is almost exactly 1.7. This is a remarkable use of a set of historical relationships, aligned protein sequences, to define a physical law. One can now use this in structure prediction. That is, if you see a segment of a protein with a gap, you predict that the ungapped peptides adopt a random coil structure. Notice how we made this prediction without a force field and without molecular dynamics. No calculation of energies is involved. All we did was say that if two proteins divergently evolve under functional constraints, and one loses a segment of the polypeptide chain, then the segment lost must have been a coil in the folded structure. Of course, this approach requires that the homologous proteins that divergently evolve under functional constraints must have analogous folds. There was a great paper by Cyrus Chothia and Arthur Lesk in 1986 with which you may not be familiar, that says that when two proteins divergently evolve from a common ancestor under functional constraints the conformation (or fold) is more or less conserved, even though large amounts of the sequence are not conserved. So far, the historical view of protein sequence and structure has provided only a small piece of structure prediction. Something else that is evident from the exhaustive mapping is the existence of correlated residues. Remember, in the models in which you do alignments, residues / and / +1 are scored independently. In real proteins, substitution at adjacent positions is correlated. In particular, when residue / is conserved, residue / +1 is generally also conserved, at least more than average. However there are exceptions to this general rule. If residue / is a conserved proline, then the adjacent residue is more likely than average to be variable. The numbers used to describe this are ten times the log of a probability difference; the probability that the next residue would be conserved minus the probability that it would be conserved at the next position. The bottom line is that if residue / contains a conserved methionine or valine or threonine, then the next residue (i +1) is likely to be conserved. This correlation of conservation or mutability of adjacent sites holds. But if residue i is a proline or a glycine, or even a glutamate, the adjacent residue is likely to be variable. You

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might ask why that is. I am not really sure. But bear in mind that typical proteins fold, and when they fold there are turns, and turns are generally on the surface. Surface residues, unlike interior residues, generally can suffer substitution without dramatically changing the packing of the inside of the protein. But prolines that define turns are frequently conserved, as are glycines that define turns. So, if residue / is a conserved glycine defining a turn, and since turns occurring on the surface are therefore likely to be adjacent to a residue on the surface and are therefore likely to be able to suffer change without much dramatic impact on fitness, conserved glycines adjacent to variable residues are more likely to appear on the database when they are in turns. You can use this observation to predict the fold of structures as well, whenever you see a conserved proline or glycine adjacent to a variable residue. This predicts that there is likely to be a turn at that position. This is the same pair of sequences; here is the coil. Because of the gap, there is a conserved glycine adjacent to a variable residue, so you'd put a turn there and you'd put a coil there... This is structure prediction, but we are doing it without the use of a large computer, a force field, or an energy calculation. We are using natural history. Now that we have a coil and a turn placed on the segment, we have "parsed" it. I may not know what the secondary structure is between the coil and the turn; maybe it is an alpha-helix; maybe it is a beta-strand. But whatever it is, we can consider secondary structure independent of what is before the coil and after the strand. We can now explore another non-first-order behavior of proteins undergoing divergent evolution under functional constraints: the fact that future mutations are dependent on previous mutations. This is a more complicated argument to make, because you need to understand the concept of "evolutionary distance," or pointaccepted mutations; the PAM-distance. The PAM is the number of point-accepted mutations per hundred amino acids. So, two proteins that are 5.5 PAM units separated are about 95% identical. If 10 PAM units separate two sequences, they are about 90% identical; 42 PAM units means maybe around 70% or 75% identical; it is not 60% or 58%, because there is a possibility for a second mutation occurring at a site where you've already had a first mutation. It turns out that the probability of tryptophan being paired with arginine is greater than average in two proteins separated by 5.5 PAM units, whereas the possibility of tryptophan being paired with a phenylalanine is less than average in two proteins separated by 5.5 PAM units. In contrast, in distant pairs of proteins, the probability of tryptophan being mutated into an arginine is less likely than average, whereas in distant pairs of proteins the probability of tryptophan being mutated into a phenylalanine is more likely than average. That, in and of itself, is

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peculiar, especially if you know the physical chemical properties of the side-chain. Tryptophan and arginine are about as different as you can get in an amino acid sidechain. Trp is a hydrophobic, big, flat, aromatic, oily thing. Arg is water-soluble, positively charged and hydrophilic. Thus, since Trp and Phe are both hydrophobic, flat, aromatic, oily things, you really would expect them to interchange more than you would expect Trp and Arg to interchange. But that is not true at low-level evolutionary distances; it is true only at high evolutionary distances. That observation is also the consequence of this historical analysis here, of roughly 1.7 million lines of paired amino acid sequences. How do we explain this? Again, I do not know for certain. But you can go back and look at the codon for tryptophan. It is U-G-G in the standard code. Suppose I have only a small amount of time to diverge; suppose I only have time to change one of the three bases in the U-G-G codon for tryptophan. I can make AGG or CGG or GGG, or I can make UAG or UCG or UUG, and so on. It turns out that the way the code is structured, none of the codons that arise from a single nucleotide replacement in the tryptophan codon code for phenylalanine; two of them code for arginine, and two for cysteine. This leads to the obvious explanation that at low PAM distances, the code drives variation in the protein. In contrast, chemistry drives variation at high evolutionary distances. So you ask yourself where in its three-dimensional folded structure will a protein tolerate a code-driven substitution when tryptophan is replaced by an arginine? The answer is only on the surface. So when one sees a tryptophan paired to an arginine at a low PAM distance, one infers that the side-chain is on the surface of the folded structure. That provides a piece of tertiary structural information about the protein fold. It is a statement about the disposition of a side-chain of a residue relative to a bulk structure. By the way, you can go back and do this with other patterns of replacement, and you can come up with many statements. Again, local sequence does not reliably predict secondary structure. But you can use a historical model to extract this kind of tertiary interaction from protein sequences divergently evolving under functional constraints. So between the coil and the turn, making reference to the slide showing the protein kinase alignment, position 130 is a surface residue, 131 is an interior residue, 132 and 133 are both surface, 134 is an interior residue, 135 and 136 are surface residues, and 137 and 138 are interior residues. And now I can ask you what the secondary structure is between the coil and the turn. All those that think it is a beta-strand, raise your hands. All those that think it is an alphahelix, raise their hands. The 3.6-residue periodicity in the pattern of surface and interior residues shows that this is a helix. The Edmundson-Schiffer helical wheel is very useful at this

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point, because if you start mapping those residues not as a line, but project that line onto a three-point, six-residue helix, 131 is on the inside, 132 is on the surface, 133 is on the surface, 134 is on the inside, and 135 is on the surface, and it is quite clear that this forms a helix where the inside of the protein is on one side and the water is on the other. This is a prediction, at a level of theory at which not a single person in this room could get tenure. It involves no number-crunching, it involves no force fields; all it requires is a natural history perspective on sequence and structure in proteins. We've been doing this now for about twelve years. Question: How are the two sequences on the slide related? Response: Common ancestry. Question: Which organisms? Response: This prediction was done with 71 sequences of protein kinases from all sorts of organisms, but mostly higher organism yeasts and mammals. I have put only two of them up there, because the slide with 71 sequences becomes extremely confusing. Question: So these two are homologous proteins, two very different... Response: Well, these two happen to be one from yeast and one from mammal; they happen to be protein kinases. But if you really want to do an evolutionary analysis and have true history, the game here actually is that you do not have two sequences; you do not even have four sequences, but you have a whole bunch of sequences scattered around an evolutionary tree, with some degree of symmetry across the tree. Some information is contained in sequences that are very similar. The differences in two sequences that are overall very similar contain information, and the similarities of two sequences that are overall very different contain information, and if you want to actually build a good structural model, you get all of them, and do as many sequences as you want. There were 71 in this particular prediction. What comes out of this is a prediction that looks something like this. That prediction was published before there was an experimental structure for any protein kinase. We sent this prediction to Sue Taylor, who was at UCSD doing the crystal structure with the crystallographers there. When the structure eventually emerged, we were able to overlay the experimental and predicted structures, and

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you can see the correspondence between the two. That is a level of prediction I do not know how to do with higher-level theory. Question: [inaudible] Response: Let me call your attention to the big mistake, which is over here. There is a long internal helix, and since it is inside, it is actually very difficult to apply simple rules to predict secondary structure. The rest of it is remarkably accurate. Question: [inaudible] Response: That is right; Arthur Lesk recently developed a very nice tool for representing the topology of a protein fold. I do not really like the word, since the representation is not really topological, but it suggests the connectivity or folding of these units. This is a resolution that you get from these predictions. For example, I have just said that these two strands in this structure are anti-parallel and form the core of a beta-sheet. That is an explicit part of this prediction. We made this prediction by looking at, again, a non-linear behavior of the protein. It turns out that the evolutionary history of the protein family at site 108 and the evolutionary history of the protein family at site 87 are correlated. A neutral amino acid became a negatively charged amino acid at position 108. During the same episode of evolutionary history, a hydrophobic amino acid at position 87, L-lysine or proline in this branch, mutated to become a positively charged hydrophilic amino acid, arginine. So what we are seeing is correlated change at distant residues, 20 amino acids separate; which leads us to suspect that these two residues, although distant in the sequence, are near each other in the three-dimensional conformation. This allows us to pack those two beta-strands, predicted from other reasons, into an anti-parallel structure. By the way, when the crystal structure emerged, not only were those two residues found to be close, but they also formed a salt bridge. The prediction of the protein kinase fold was important, in part because threading and homology modeling models had failed. Our prediction of an anti-parallel sheet in protein kinase said that this protein was not a distant homologue of adenylate kinase. In adenylate kinase, the central beta-sheet is parallel, a different fold entirely. Logically, if homologous proteins have analogous folds, then proteins with non-analogous folds cannot be homologous. This therefore is a case in which structural prediction was used to deny distant homology, which is the opposite of what we usually do.

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Let us see why the homology modelers failed. Both kinases have a motif: a Gly-X-Gly-X-X-Gly sequence. In adenylate kinase, that motif lay in a strand-turnhelix pattern. In the predicted structure for protein kinase, the Gly-X-Gly-X-X-Gly motif lay in a strand-turn-strand pattern. From that we concluded that the folds of these two proteins were not analogous, and that these two proteins could not be homologous. That was the thing that I think really impressed the crystallographers. Sue Taylor was one of the people who had used that motif to say that protein kinase and adenylate kinase are themselves related by common ancestry, therefore should have analogous folds. In fact, five groups had built models in which they had placed the sequence for protein kinase on top of the sequence for adenylate kinase. They were all wrong, because that motif convergently evolved in these two cases. I think this is why the crystallographers were so nice to us. This is what the crystallographers said about our predictions: "Remarkably accurate, especially for the small lobe," where we had actually had packed the domains correctly. Janet Thornton pointed out that this is "much better than achieved by standard methods," and Lesk and Boswell wrote: "spectacular achievement;" "major breakthrough!" So, this is the kind of thing we can do with a level of theory that is not very high, but just by bringing natural history together with the physics and chemistry paradigms. Question: Then what is your most spectacular failure? Response: Perhaps I just showed you the most spectacular failure; the failure to detect secondary structural elements that are completely buried, as well as the failure to detect secondary structural elements near the active sites, are generic weaknesses of this approach. Why is that? Because active site patterns of variation and conservation are dominated by things other than confirmation of the secondary structure. We had the same problems in nitrogenase and in isopenicillin synthase. Comment: But that is not a spectacular failure. Response: Well, thank you, but these are the failures that you have. Comment: Well, if you are such a clever organic chemist, and you can make all these new kinds of nucleotides... Response: This question cannot be going in a good direction...

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Question: No, it isn't. So, why are you wasting your time with this kind of prediction stuff, sequence-gazing and whatever? Response: Ah, because it is not just sequence-gazing. Let me go on to the next step. Why are we interested in predicting secondary structures? The answer is very close to what you have just seen here. We are very much interested in knowing whether two proteins are related by common ancestry. The reason for that is that we are interested in knowing function. And function can sometimes be indicated by common ancestry. For example, there was a target from the CASP-2 protein structure prediction contest that was called the heat shock protein 90 (HSP90). Now, that title is completely uninformative about function; it tells you that the gene for the protein is turned on when you shock the organism with heat and that the protein has a mass of 90,000. That is what the name means. That's all they knew about it. So CASP2 put this protein out to the structure prediction contest. We and Fernando Bazan both actually produced accurate predictions that told us about function. Fernando was doing pretty much the same thing we were doing by now. You can judge for yourself how well the predictions corresponded to the experimental structure, which was kept secret until the prediction was announced. From that predicted experimental structure we recognized a long-distance homology between this protein HSP90 and the protein called gyrase. Dietlind Gerloff made this observation by eye. We could then draw a functional inference based on a distant homology. Again, the crystallographers were really very nice to us, because as it turned out, the gyrase structure had been solved, but the coordinates had not been deposited in the database, which is actually a very common problem in this business. Experimental studies had said there was no ATP binding-site in HSP90. Our prediction said that there was, based on this distant homology detection. It turned out that the experimental studies were wrong, for reasons that are too complicated to go into. So, this is the kind of thing that we do with predicted structures. We look for distant homologues as a way of inferring function. I have page after page of these, but let me see if I can give you another example. Question: Did I hear heat shock stabilizes the DNA ?

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Response: No. When you hit something with heat, you do all sorts of things. You turn on chaperones, for example. Gyrase is a DNA manipulation protein, which allows you to untangle DNA, basically. This is part of the response to heat shock: turn on a protein, manipulate DNA, help fold proteins, turn on chaperones. Obviously, structure prediction from our perspective is low-resolution. We do not get atomic resolution out of these structures - not that we would know what to do with it if we did. If I have a protein-to-atomic resolution by crystal structure, 1 still do not know enough about things to design a molecule that will bind to it. So this turns out to be a very powerful tool, in part because it allows you to deny distant homology, like we did with protein kinase, or to confirm distant homology, which is what we did with the prediction for the heat shock protein. I am running out of time; let me just see if I can go to the last point, which is relevant to what you just said. In this analysis, we are obviously using so-called contemporary annotation logic. If heat shock protein and gyrase are inferred to be homologous from a prediction of their folds, we are tempted to assume that they have analogous functions. This is the annotation transfer logic: sequence similarity indicates homology; homology implies analogous folds (which it universally does by empirical analysis); analogous folding implies analogous behaviors; and analogous behaviors implies analogous functions. This logic is widely used; in fact, almost all the new sequence databases are being annotated that way. But the logic is easily defeated by one word: recruitment. This is a great example of it. Here are three proteins, all of which have recognizably similar sequences: GSSIMPGK, GSSIMPAK, and GSSAMPYK. All three proteins are homologous; they all fold to give 8-fold alpha-beta barrels. But one of them works in nucleic acid biosynthesis, one of them works in the citric acid cycle, and one of them works in amino acid degradation. This creates a problem for annotation transfer. Can we tell when function might have changed simply by examining the sequence data? Again, we need a natural history perspective. Let me illustrate this using leptin, the obesity gene protein. When you knock the leptin gene out of a mouse, the mouse becomes plump. There was a Science cover with two svelte mice on one side of a balance and a plump one on the other side missing the leptin gene. That was from the Howard Hughes Medical Center at Rockefeller University. We built an evolutionary tree and a sequence alignment for leptins. We then reconstructed the ancestral sequences for ancient leptins throughout the tree. From that, we could predict a leptin fold. The predicted fold is closely related to the cytokine fold, a well-known set of proteins involved in signal regulation with four helices in the structure. Thus, leptin is a distant homologue of the cytokines.

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Next, we reconstructed the history of the mutation, saying what mutations occurred on which branches. We noticed that in the branch evolving to give the hominoid apes, the leptin protein was evolving very rapidly, faster than would be allowed by any model, except for one which says that the child with mutant leptin is more likely to survive than the parent; the mutant is more fit than the parent. If the mutant is more fit than the parent, it means that the protein "function" is changing. We actually did this in a consultation environment. The people at Sandoz wanted to know whether they should go after leptin as a human obesity gene target; as a therapeutic target. Our comment was that if you are going to do that, you had better do pharmacological studies in a primate model rather than a rodent model, because somewhere after primates diverged from the ancestral stem stock something happened to the role of leptin. This is not surprising, based on physiology. Your feeding behavior is different from that of mice. You're a binge eater; when a mouse goes out to find food, it is just as likely to be food as find food. So, there is an enormously strong selection pressure on feeding behavior in rodents that is not present in primates. We published our prediction in 1998. Last year I was delighted to see this article in Nature: "Whatever happened to Leptin?" Quoting from the article: "It seemed just five years ago that a single protein might reverse the rising tide of obesity, but what works in mice has not yet been translated into people." Now, that is not a surprise to us, or to anyone who looked at the leptin sequence from the natural history perspective. Question: The speed of change is just the number of mutations? Response: It is actually the number of mutations at the DNA level that changes the encoded sequence; the non-synonymous mutations, divided by the number of mutations in the DNA that are silent, that do not change the encoded sequences, normalized for the number of silent and non-silent sites. We can make the mathematical model more sophisticated. Consider the PAM distance metric discussed above. The model behind it assumes that individual sites in an amino acid sequence have the same rate of divergence. That is clearly a poor approximation, as is obvious by simple inspection of any of these alignments. We can advance the model to allow some sites to evolve more rapidly than others. A gamma distribution may be used to model the distribution in mutability. But even this is an approximation, because it assumes that some sites that are mutable in some branches of the evolutionary tree will be the same sites that are mutable in other

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branches of the evolutionary tree. That is called a stationary gamma model for sequence divergence. What if a function is changing in two branches of the tree? Well, then you might very well expect different sites to be more mutable in this branch of the tree than are mutable in other branches of the tree. Eric Gaucher, who is a graduate student in the group, went back and had a look at elongation factor Tu in light of this. This protein is used in translation, and is highly conserved. Everybody agrees that the function is "the same" everywhere. But if you look closely, you will see that in eubacteria, the sites that are more mutable in them are not the same as the sites that are more mutable in the eukaryotic branch of the tree, and vice-versa. This implies that the functions in the two branches are not the same. Next, we will use this to do something that people like Olivier Lichtarge have been working on. You place the sites that display peculiar evolutionary properties on the three-dimensional crystal structure and ask yourselves: "Where are the sites in the three-dimensional crystal structure that are more mutable in the eukaryotes and less mutable in the eubacteria, and where are the sites that are more mutable in the eubacteria and less mutable in the eukaryotes?" They are certainly not randomly distributed around the structure. It turns out that the eukaryotic protein must leave the nucleus, must go to the ribosome in the cytoplasm, and must follow an actin filament as it does so. You can identify the actin binding-site on the eukaryotic elongation factor as some residues on the surface that are not as mutable as the corresponding residues in the eubacterial enzyme, which does not have a nucleus to leave and does not have any actin filaments to follow. This kind of analysis becomes very useful when you can go one last step, to which I have already alluded. In the next step, we date the divergences from the molecular records. Obviously, when you have mice and humans, you know roughly when sequences diverge from fossils. When a gene duplicates within an organism, it is more difficult to date divergences, but we have developed a clock based on silent substitutions that does it well. A protein sequence does not change with a clock-like rate constant. Silent substitutions have been a little bit better. It is possible to change the DNA sequence without changing the encoded sequence; selective pressure will not accept or reject that change so well. However, there are twelve different rate constants; for silent substitution, A can go to G and G can go to A, so it is very complicated. You know, people tried to aggregate everything, and what they ended up seeing was nothing. What we've actually done is just one simple thing: When we look at two-fold redundant codon systems, we are looking only at transitions; that is, C to T and T to C, or A to G, and G to A. It turns out that

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transition rates are remarkably clock-like. Let me just show you how clock-like they are. You take the yeast genome... Question: Why do you do that? Response: Instinct is the answer. A chemist views C and T as being very similar nucleotides. The rate constant for interconversion of C and T is generally much faster - such as a pyrimidine for another pyrimidine - than the interconversion of A and C, for example. [inaudible comment or question from audience] Response: No, the source of mutations in the wild is almost certainly not that. The source of natural mutations is not known, but repair mistakes and polymerase errors are possible. There are many types of silent sites in the standard genetic code. Some offer better clocks than others. Most useful are silent sites in codon systems that are twofold redundant. Here exactly two codons encode the same amino acid. These codons are interconverted by transitions, a pyrimidine replacing another pyrimidine, or a purine replacing another purine. When the amino-acid itself is conserved, the divergence at such sites can be modeled as an "approach to equilibrium" kinetic process, just like radioactive decay, with the end-point being the codon bias, b. Here the fraction of paired codons that is conserved, f2> is equal to b + (l-b)'kl, where again k is the first-order rate constant and t is time. Given an estimate of the rate constant k for these "transition-redundant approach-to-equilibrium" processes, if k and b are time-invariant, one can estimate the time, t, for divergences of the two sequences. Empirical analysis suggests that codon biases and rate constants for transitions have been remarkably stable, at least in vertebrates, for hundreds of millions of years. Therefore, approach-to-equilibrium metrics provides dates for events in molecular records within phyla, especially of higher organisms. These dates are useful to time-correlate events in the molecular record with events in the paleontological and geological records. Of course, simultaneous events need not be causally related, especially when simultaneity is judged using dating measurements with variances of millions of years. But an observation that two events in the molecular record are nearly contemporaneous suggests, as a hypothesis, that they might be causally related. Such hypotheses are testable, often by experiment, and are useful because they

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focus experimental work on a subset of what would otherwise be an extremely large set of testable hypotheses. Consider, for example, the yeast Saccharomyces cerevisiae, whose genome encodes ~6,000 proteins. The yeast proteome has 36 million potentially interacting pairs. Some systems biologists are laboring to experimentally examine all of these in the laboratory, hoping to identify these interactions. Correlating dated events in the molecular record offers a complementary approach. Gene duplications generate paralogs, which are homologous proteins within a single genome. Paralogous sequences may be aligned, their f2 calculated, and their divergence dated. In yeast, paralog generation has occurred throughout the historical past. A prominent episode of gene duplication, however, is found with an f2 near 0.84, corresponding to duplication events that occurred -80 Ma, based on clock estimates that generated divergence dates in fungi. These duplications created several new sugar transporters, new glyceraldehyde-3-phosphate dehydrogenases, the non-oxidative pyruvate decarboxylase that generates acetaldehyde from pyruvate, a transporter for the thiamine vitamin that is used by this enzyme, and two alcohol dehydrogenases that interconvert acetaldehyde and alcohol. This is not a random collection of proteins; rather, these proteins all belong to the pathway that yeast uses to ferment glucose to alcohol. Correlating the times of duplication of genes in the yeast genome has identified a pathway. Approach-to-equilibrium dating tools can be more effective at inferring possible pathways from sequence data than other approaches, especially for recently evolved pathways. By adding the geological and paleontological records to the analysis, however, these pathways assume additional biological meaning. Fossils suggest that fermentable fruits also became prominent ~80 Ma, in the Cretaceous, during the age of the dinosaurs. Indeed, over-grazing by dinosaurs may explain why flowering plants flourished. Other genomes evidently also record episodes of duplication near this time, including those of angiosperms (which create the fruit) and fruit flies (whose larvae eat the yeast growing in fermenting fruit.) Thus, time-correlation between the three records connected by approach-toequilibrium dates generates a planetary hypothesis about functions of individual proteins in yeast, one that goes beyond a statement about a behavior ("this protein oxidizes alcohol...") and a pathway ("...acting with pyruvate decarboxylase...") to a statement about planetary function (" ... allowing yeast to exploit a resource, fruits, that became available - 8 0 Ma.") This level of sophistication in the annotation of a gene sequence is difficult to create in any other way. You can then resurrect the alcohol dehydrogenases, work which was done by Andrew Ellington, Hiroshi Nakano, and Mike Thomson, in which they made the

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protein that is the ancestor to the oxidizing enzyme and the fermenting alcohol dehydrogenase in yeast. The ancient enzyme is not fermenting; the fermentation behavior is the derived trait that arises following the duplication and, I should say, an episode of rapid sequence evolution. Since I'm out of time, let me conclude by pointing out that we started off by asking why things were the way they were, recognizing that in some way, we had to combine intrinsic chemical reactivity with the history that leads to the biological systems. With nucleic acids, this story is dominated by intrinsic chemical reactivity, because local interactions work, and the organic metalanguage is adequate. You can get full practical manipulative control over nucleic acids and you can persuade yourself that some of their features are universal. With proteins it is quite different. Non-local interactions are very important, and theory to handle these is still lacking. However, here the historical analysis, becomes powerful. We can solve the prediction problem for proteins at low resolution right now, at least to its limits. But these limits are still good enough to detect distant homologues, which is to apply predictions. We now have dozens of case studies, such as the ones I mentioned, that assess function, using this combination of geo- and paleobiology. In some cases these studies include the resurrection of ancient forms. Our goal is analysis of the global proteome. With scientists at EraGen Biosciences in Madison Wisconsin, we have assembled what is called the Master Catalog, which is based on the fact that after all the genomes of all the organisms on Earth have been sequenced, there are only going to be about 100,000 families of proteins. Every one of them tells a story that we are working through one at a time. If anyone would like to help, please let me know. With that, let me stop. Thank you for your exciting questions. I will be happy to answer any more if you have them. Question: I just wanted to understand why you have this peak in the recent past on the yeast evolution curve? Response: That is an excellent question. The answer is, of course, I do not really know. But I suspect that this recent spate of duplication in yeast is responsible for yeast adapting to its new interaction with man. All the genes that are duplicated either allow yeast to divide faster or to ferment malt. The latter needs no discussion. The former, one may hypothesize, reflects the fact that yeast in the wild is rarely as well fed as it is in human culture, meaning that it never needs to divide as fast.

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[inaudible question from audience] Response: The major episode of duplication in the human genome in the Jurassic may be associated with the emergence of placental reproduction. It is an interesting question. We go back in history and ask where were the major challenges were and where innovation in gene duplication was required. Question: / have the impression that except for those few mistakes concerning the wrong secondary structure assignments, you can pretty much predict the structure from the sequence, so do we have a rough solution to the protein structure prediction problem? Response: Yes, that is right. Keep in mind that structure prediction requires an input of more than one sequence. Our best case with few sequences was with a hemorrhagic metalloprotease, for which we had just seven; but in this case, we were very fortunate that those seven are widely distributed across the tree. It doesn't do to have a hundred sequences that are all very similar - that's like having a hundred copies of the same sequence. But from a nicely balanced tree with twenty sequences; some that are 10-15 PAM units apart, some 20-30 units apart, some 5070 PAM units apart, and some 100 PAM units apart, the secondary structure prediction will identify maybe 80% of the secondary structural units in a way that is clearly obvious, with about 20% being ambiguous. Secondary structural elements near the active sites are difficult to assign, and in a typically sized protein, one secondary structure segment will be completely buried, and you'll scratch your heads for hours to decide whether it is a internal helix or an internal strand. Question: Is this just the secondary structure? Response: Yes, this is just secondary structure prediction. Tertiary structure prediction is then based on identifying active-site residues, which are brought together in the fold. One can also look for covariation, which is the example I showed you in protein kinase, in residues 108 and 87. Here is just a case in which we put together a tertiary structure based on active-site residue assemblies. It was actually predicted for protein tyrosine phosphatase and published in 1995, but it was clear that it is possible to assemble elements of the secondary structure into tertiary structural elements. In general, the structural model is more or less complete, depending on how big the protein is. A very big protein requires you to spend much time sitting there

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trying to figure out alternative structures. With, for example, some smaller proteins like synaptotagmin, which was a CASP-1 project target, we predicted three alternative conformations. It turned out that one of the three was correct. This level of resolution is entirely adequate to ask whether these proteins are clearly not homologous, since they do not have analogous folds. Question: For both secondary and tertiary... Response: Yes, that's right. Question: Your last line is "when is a problem considered solved? " Response: Yes, I'm sorry; I did not get to it. Question: So is the protein-folding problem solved? Response: Yes and no; the answer depends on what you want to do with the predicted folded structures. We ourselves want to answer certain kinds of biological problems. For example, in this case, we wanted to know whether protein serine phosphatase was homologous to protein tyrosine phosphatase. It was not, and that was based on two predicted structures; one that we did and one that Geoff Barton did. We wanted to know whether a cofactor metal, zinc, was required, as it is in the protein serine phosphatase, because we were interested in the mechanistic features of that enzyme. The predicted structure suggested that it was, and this prediction was correct. I think that the challenge today is to get atomic-level resolution in these threedimensional structures. The temptation is to use force fields and number crunching. To do this, however, requires solving problems that are far from being solved. I am not convinced that I understand the packing of small organic crystals, nor can I predict the solubility of a compound in water. These are two issues that are directly related to the folding problem, which we cannot do very well with small molecules. So, the protein structure prediction problem is solved when the predictions answer the biological questions you want to answer. From our perspective, what we understand about the protein structure prediction problem, starting from an input of homologous sequences, is where it will not work, and frankly, unless there is a very good idea, where it will never work. Secondary structure near active sites is a classic case of that. We have never gotten secondary structures near active sites correct reliably. We understand why we can't get it right; patterns of variation near

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the active site are dominated by issues related to catalysis that are not related to fold. Obviously, someone could come along with a good idea and solve that problem, using multiple sequence line-ups, but there are good reasons to believe that the de novo prediction of secondary structure right at the active site will be a very difficult problem to solve by this approach. It is an interesting sociology; we seem to be in the middle of a Kuhnian paradigm shift in this field, something that I never thought actually happened. Many of the number crunchers who participate in the CASP project do not seem able to accept the fact that we predict these three-dimensional structures of proteins without crunching numbers. One can make successful predictions, publish them before experimental structures are known, have judges declare these structures to be correct, and the reaction from some number crunchers is still disbelief. They cannot believe that a solution to the problem is possible without a large computer, a force field, and number crunching. So any solution that does not involve these does not occupy a stable position in their view of the world. Question: So you would argue - maybe I will argue it for you - why anyone would care about trying to predict three-dimensional structures. It seems like a waste of time, apart from an intellectual exercise. What Olivier Lichtarge said in another context, and what you said in this context, was to look at all these footprints that life has left for you — let's sort this out, and once you have done that, more or less, you know everything that is happening, so why would anyone even worry about... Response: Obviously, if we are doing distant homology detection, prediction based on natural history analysis is a way to do it. If we want to place function together with biomolecular structure, again, prediction based on natural history analysis is a useful tool. From there on, the mission of chemistry is to understand the behavior of matter in terms of the behavior of its constituent molecules. There is a role for computation here. Maybe, over the long-term, computational models for molecules may permit organic chemistry to escape from the "non-scientific" features of its explanations; those derived from its non-computational metalanguage. But, at least for biomolecules, the first step is to understand water. Until you have a model that is predictive and manipulative for water, then for things dissolved in water, and for small organic molecules generally, there seems to be little use to apply computation to biomolecules. The drive to do so, at least in the United States, comes from funding agencies, of course. The National Institutes of Health virtually insists that theoreticians look at large molecules, rather than look at the fundamental interactions that govern the behavior of large molecules. I think that this is a

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mistake. If I were the NIH I would not require theoreticians to handle big molecules. I would put money into studying water, things in water, things packed in crystals, from the bottom up, where you have good manipulative tools, and where the system computationally is not so overwhelming that you have to abandon rigor to do it. I would recognize that this is how, over the long-term, the chemistry of biological molecules will come to be understood, not by a large set of poorly rigorous and highly approximated simulations of biomolecules. Question: [inaudible] Response: Yes, if you want to design drugs, it is actually quite useful to even have approximate models. Obviously, the HIV protease inhibitors were designed based on a homology model for the protease. These models need not help you design, per se, but they do shorten the random walk, which combines random synthesis with design synthesis and provides focus. Models also provide motivation to the chemist doing the synthesis, because nothing encourages chemists more than thinking they are doing something rational, even if they are not. So even a wrong model is useful in the pharmaceutical industry, because it drives chemists in that direction. Question: Did you try S02 analogues with template-directed

synthesis?

Response: No, we didn't; that may be worth doing. At some point, we should ask whether the analogues would at least do something prebiotically, as PNAs have been tried with template-directed synthesis; there has been some progress from Leslie's laboratory on that. We have not tried that; it would be a good thing to try. Question: Just for the record, during the last CASP competition, how many structures did you get right and how many did you get wrong, and how did your results compare to the work of other groups that use more automatic methods, like neural networks? Response: We did not go to the last CASP competition. In CASP 1, there were two ab initio prediction targets: phospho-beta-galactosidase and synaptotagmin. For the first, we predicted an eight-fold alpha-beta barrel structure, which was correct. For the second, we presented three possible topologies for an all beta-barrel (out of ca. 200 possible); one was correct. In CASP 2, we made predictions for heat shock protein 90, ferrocheletase, NK lysin, calponin, and fibrinogen. Heat shock protein 90 was the most interesting case, since we predicted (correctly) that it was a distant

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homologue of gyrase, a homology that the authors of the crystal structure did not see, and predicted an ATP binding-site (again correctly), despite the fact that experimental evidence had been presented that the protein did not bind ATP. The rest of the proteins had known functions, so our predictions did not add to the functional interpretation. For ferrocheletase, we correctly predicted all nine helices and six strands, but mistakenly assigned a short strand (3 residues) as a part of a longer helix. For NK. lysin, we predicted an all-helical protein built from four helices, and these were all correct. Calponin was a largely disordered structure, but we did get both of the helices correct. Fibrinogen was a problematic prediction. The prediction correctly identified ten strands and the two long helices. However, it missed one helix and over-predicted two strands. Furthermore, in two regions, disulfide bonds created ambiguities in the secondary structure assignment. We doubt that this would have been an adequate starting point for building a tertiary structural model. Beyond CASP, the interesting metric right now is function prediction - not structure prediction - for homology detection. I think we have reached the limit of what we can do with prediction based on multiple sequence alignments. Today, the Master Catalog, a product marketed by EraGen, has predictions for every family of proteins in the global proteome. The quality of these predictions depends, of course, on the number of homologous sequences in the family; the more the better. Many of these may undoubtedly be assembled into ribbon structures, such as those Arthur Lesk described. For the next CASP, I will need to figure out into just what families the CASP targets fall. We will print out the prediction that we have and see how well it does.

CONFORMATION OF CHARGED POLYMERS: POLYELECTROLYTES AND POLYAMPHOLYTES JEAN-FRANCOIS JOANNY Physicochimie Curie, Institut Curie, Paris, France

First of all, 1 should warn you that I know almost no biology; 1 am something like a physical chemist or a polymer physicist. The problems with which 1 am familiar are much simpler than the ones about which you have been hearing during this conference. In my community the general point of view is that one should look for universal properties, which is somehow orthogonal to what people in biology do. This means studying properties for which you can ignore the specific chemistry as much as possible. What 1 want to talk about is influences polymer conformations. course, it will be extremely general. theme; first of all I would not be able

how the existence of electrostatic charges That is the main theme of my talk, and of But I will not tell you everything about this to, and it would take an infinitely long time.

Flexible polyelectrolytes Blob model, Annealed polyelectrolytes Rigid polyelectrolytes Persistence length, Adsorption on a small sphere Polyelectrolytes in a poor solvent Rayleigh Instability, Chain stretching, Charge distribution Small ion condensation Manning condensation, Charge regulation by a surface Polyampholytes Chain conformation, Adsorption on a charged surface Figure I. Conformation of charged polymers: polyelectrolytes and polyampholytes.

44 J.-F. Joanny 1 have chosen a few topics. Despite the fact that specific interactions are very important for all RNA and protein problems, I want to insist that polymer physics is also important. That is the other aspect 1 will discuss, which has almost not been mentioned so far in this conference. Let us consider the simplest case, that of a flexible polymer carrying very few charges and displaying no interactions other than electrostatics. That will be the first part of my talk, after which 1 will try to make the discussion more realistic. I will then introduce the concept of polymer rigidity, providing you with an example that we worked on recently, namely charged polymers interacting with oppositely charged spheres. Then I will introduce the concept of polymers in a poor solvent. Then, to make it fancy, I will introduce hydrophobic effects, albeit in a very poor man's way. I will talk about small ion condensation, which was mentioned during the first day of the meeting. In the last part of my talk, 1 will discuss what 1 call poly ampholytes, which are polymers that like proteins, have both positive and negative charges along the same chain. In order to do so, I will carry out a sort of review of the subject, mixing-in extremely well-known things sometimes from long before 1 was born - with recent work that we have done ourselves. I will try to make it as simple as possible. Although 1 am a theorist, 1 will start with just some hand-waving or scaling arguments. In some cases we have done more complex calculations, but sometimes we are unable to carry out sophisticated calculations, so the only thing we are left with is the hand-waving arguments. You have to take my word for it that we have done it more seriously than the way it is presented here.

N monomers of size a fraction f of charged monomers Gaussian radius

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Figure 2. Weakly charged polyelectrolytcs.

Conformation of Charged Polymers: Poly electrolytes and Poly ampholytes

45

So, what is the simplest model you can devise for a polymer? First of all, forget about the fact that there are charges on the polymer. The simplest model is to assume that each monomer is a small rod and that the rods are freely jointed. Each rod is randomly oriented with respect to the previous one. If there are N of these rods, this is a trajectory of a random walk, with a size R0 = Nx'2. Maybe it is important to mention that when I model a polymer this way there is no energy in the problem; only entropy. So the only energy scale for this problem is kT. Whatever I do afterward, if I want to compare it to this random coil, I have to compare the energies to kT. The only other thing you need to know, and that you probably all do know, is that if I pull on both ends of the polymer with a force, the polymer reacts like a spring. The spring constant has an entropic origin, which has been known for forty years. I will call R the distance between the two end-points; and the free energy the elastic energy. It is of entropic origin, therefore proportional to kT. Since this is a spring, it is proportional to R2; therefore the spring constant is 3/2(Na2). Since the energy is proportional to R2, there is a restoring force proportional to ^?. This is my starting point, and what I want to do afterward is introduce charges. In a biological problem, the polymer could be DNA, so I put in negative charges. One of the points I want to make is that if you put charges on the polymers, you must add counterions to the solution. You can forget about the counterions for some problems. First I will consider electrostatic interactions. I want to see how polymer conformation is modified by electrostatic interactions. If I put negative charges on the polymer, I must put positive ions into the solution. If I have only one polymer chain, the counterions gain entropy by going to infinity, so I can forget about them. But at finite concentrations, for some of the properties the polymer is not important, and the counterions dominate all properties. For instance, if you measure the osmotic pressure, you do not measure anything about the polymer; you just count the counterions. These are not the properties that I want to talk about, but you have to remember them. How do I introduce electrostatics? Essentially, what I want to know is how much two charges on this polymer interact. If it is in a vacuum or a dielectric medium, the Coulomb potential decays as Mr. As 1 said, the energy scale is kT, so I use kT as a unit, and since this is an interaction, the remaining pre-factor is a length. People call this the Bjerrum length. It is proportional to the square of the charge divided by the dielectric constant of water. The solvent is water, and kT appears in the denominator, because I artificially introduced it in the numerator. This length measures the strength of the electrostatic interaction, and if I take two charges at a distance equal to the Bjerrum length their interaction is kT. You all know that in water, the electrostatic interaction is screened, and during most of this

4(i J.-F. Joanny talk I will use this Dcbye-Hiickel potential, which means that at large distances the screening is exponential and the screening length depends only on the salt- or smallion density, n. The screening length decays as one over the square root of the salt density. If you want numbers: if the solution is 10 molar, the screening length is 100 Angstroms. Something else I want to ask is "How do you get charges on the polymer?" There are essentially two ways: One is to take charged polymers such as salt ions and copolymerize them with uncharged monomers. The other way is to take a polyacid or a polybase and change the pH. Using the language of physicists, I call the first case quenched polyelectrolytes and the second one annealed polyelectrolytes. Physical chemists call quenched polymers strong polyelectrolytes and annealed polymers weak polyelectrolytes. During most of this talk, I will consider quenched polymers, just mentioning a few results concerning the annealed ones. That is my basic model.

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1 'N AU > GU > AC - is exactly what you would expect when inserting the same base combinations within an extended double-stranded helix. However, in this particular case, it is not one Watson-Crick base-pair within a helix, but rather an isolated interaction, an isolated base-pair that most people would consider to be part of the tertiary structure of the molecule, rather than of the secondary structure. There was no point in testing the secondary structure of Group I introns, which at the time had already been very well established by a number of methods, including comparative sequence analysis.

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T(°C) Figure 3. Fraction of initially inactive molecules as a function of temperature for sunY SYT transcripts with various base substitutions in the P9.0a (176-1028) base-pair (redrawn from Jaeger et al., 1993.)

Suppose that base-pair had been part of an extended helix, say one with ten base-pairs. Rather than resorting to the complex kinetic analysis I just described and to which I will return, we would have generated what is known as an 'optical melting curve' to prove the existence of the base-pair. The experiment is quite simple; it consists in watching the changing UV absorbance of an RNA (or DNA) solution at 260 nanometers as that solution is slowly heated. When the temperature range within which the nucleotide bases begin to unstack is reached, there is a sharp rise in absorbance. In practice, the transition curve between order and disorder is described by its TM, the temperature at which half the bases are unstacked (if the

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unfolding process is all-or-none, this is the temperature at which half the molecules are unfolded), and its 'cooperativity' (maximal slope), from which the enthalpy associated with the process may be estimated.

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Figure 4. Optical melting curves of sunY (SYT) precursor transcript samples in standard (absorbance at 260nm as a junction of temperature) and derivate (dA/dT) representations. Monitoring the effects of a Gto-A mutation in the P9.0a base-pair (redrawn from Jaeger et al., 1993).

Why not do the same thing with a solution of Group I intron RNA? At first, we were reluctant to try it, essentially for two reasons. One was that it was not obvious to us that unfolding tertiary structure, that is, three-dimensional structure, would generate a measurable rise in absorbance (remember that the interactions we wanted to investigate at the time were tertiary interactions.) In fact, we were wrong. Had we looked at phenylalanine tRNA, of which there were crystals at the time, we would have noted that even though the molecule comprises only 22 canonical (Watson-Crick and G:U) base-pairs, 71 out of the 76 nucleotide bases are stacked. In fact, when you melt a tRNA molecule, you do detect a signal that corresponds to the unfolding of the entire three-dimensional structure.

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The second reason was that we initially doubted that we could see a signal corresponding to the melting of a single base-pair in a three-hundred or fourhundred nucleotide molecule. We were wrong again, because as I already told you, the unfolding transition during which the individual base-pair is disrupted has very high enthalpy, which is about equivalent to that of a helix of say, 25 base-pairs. As soon as we realized this, it became obvious that we should try to get optical melting curves. Obtaining optical melting curves was the work of Luc Jaeger. Fig. 4 shows a typical experiment that uses the wild-type molecule on one hand and one of the base-substituted RNAs I just described on the other. In the wild-type G:C melting curve, you clearly see an early transition over a temperature range that coincides with the one over which we had been observing conversion of initially active populations of molecules into initially inactive ones. As for the A:C mutant RNA, it also undergoes an early cooperative transition (this is especially clear in the derivate dA/dT representation), but that transition is shifted towards lower temperatures to precisely the same extent, compared to the wild-type, that we had previously observed in our kinetic analyses. In the RNA molecules whose optical melting curves are shown in Fig. 5, base substitutions have been made in a second base-pair (P7 bp 2) and combined or not with substitutions in the P9.0a pair (see Fig. 6 for a secondary structure diagram of the intron).

Figure 5. Optical melting curves of td intron molecules bearing various base substitutions, shown in lower-case (from the data of Brion etai, 1999).

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Michel

Only the early melting range is affected by base substitutions. The thermal shift resulting from replacement of the original G:C pair by an A:C combination is about the same for P9.0a and P7 bp 2 interactions. At P7 bp 2, A:U is intermediate between G:C and A:C, just as was found for P9.0a. Finally, combining mutations in P9.0a and P7 bp 2 has additive effects. We are clearly dealing with the same unfolding transition.

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Figure 6. Secondary structure diagram of the td intron, a close relative of the sun Y intron. Arrows point to intron-exon junctions. The transcripts used to generate the curves in Fig. 5 were inactive because they lacked both junctions (the first 35 and last 5 intron nucleotides were missing). Bases that were mutated (see Figs. 5, 15, 18 and 19) are boxed.

One question you must have been asking yourself is whether any particular set of interactions could be associated with early melting. I already gave you a few hints, and it certainly makes sense that the three-dimensional fold of the molecules, which we call tertiary structure, should be the first to unwrap, or else that the rest of the melting curve presumably reflects the disruption of individual secondary

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structure elements. However, there is something unsatisfactory in what I am just saying, because there does not seem to be any general agreement in the RNA field about what exactly should be called secondary structure. Some people call secondary structure the set of all canonical base-pairs (G:C, A:U, and G:U) with at least one neighboring canonical pair along the primary sequence. This means that you can have a number of intervening nucleotides on one side of the helix, but the pair must have a neighbor on the other side. That is one definition. Another definition is similar, except that it excludes all pseudoknots. Pseudoknots are interactions that interfere with a tree-like representation of the structure. When I say tree-like, I mean something like a natural tree with branches that do not meet again, once they diverge. I apologize to the mathematicians in the audience for not using the right terms. Any interaction between the loops tends to be called a pseudoknot, whatever that means. One reason for excluding pseudoknots from secondary structure was that until the recent work by Rivas and Sean Eddy (1999), the dynamical programming algorithms of the type Michael Zuker is using in his FOLD program would not be able to take pseudoknots into account. By the way, there exist some other programs for folding RNA; the NinioDumas program for instance (1982), which, although less efficient, was able to predict pseudoknots. Yet another problem in defining secondary versus tertiary structure is whether the loops should be considered part of secondary structure. I will return to this later. There are also some people who consider that tree-like representations of canonical base-pairs do not depict secondary structure; they complain about the use of terms that are derived from the protein world and would suggest that we rather call these representations stem-loop diagrams. Now they are all very respected people, so whom do you believe? My own inclination would be to ask nature, which is exactly what we did. Comment: / wouldjust like to make a comment that the field has now reached maturity and is now in decline when we argue about nomenclature. Response: Thanks; that's how I feel. One thing I want to show you is that even the definition of a pseudoknot is somewhat problematic. Coming back to the structure of Group I introns [Fig. 6], it is clear that when taken together, the P3 and P7 pairings are incompatible with a tree-like representation. But which of these pairings should you regard as part of secondary structure and which as part of tertiary structure? I have no idea, at least not from the definitions just given.

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Comment:

Whichever melts out first.

Response: That is exactly what I will try to show. As I was saying, we attempted to determine what exactly was going on during the early transition. But before I go into that, I need to stress that the situation I described for Group I introns is by no means unique. I'm sorry, but I have to introduce another type of catalytic RNA molecule. All you need to know about Group II introns is that they are the same as Group I introns, except they are totally unrelated to them and about twice as large, somewhere around 600 nucleotides. When you look at the melting profiles of a Group II intron, there is again an early transition followed by some quasi-continuous melting. As you might have expected from the larger size of the molecule, the early transition, with an enthalpy of about 300 kcal/mol, is even sharper than in Group I introns. And when you mutate the molecule, again, the transition usually does not vanish, but has simply shifted to lower temperatures. Thus, the problem was a general one, and to find out what was going on, we took the approach of chemically modifying bases, using dimethyl sulphate (DMS). At first you would think this would not work because any chemical reaction will shift the equilibrium between the folded and unfolded forms. In fact, the DMS molecule is fairly unstable in water, so the reaction time is very short, compared to the rates of folding and unfolding. If you use low concentrations of DMS and modify molecules, as Sarah Woodson describes, such that there is less than one modification per molecule, it looks like you have taken a photograph of the situation at the time you added DMS. I will now show you an experiment with a Group II intron. We chose to compare the accessibility of bases at two temperatures, one (42°C) that was just below the temperature at which melting begins and the other (50°C) slightly above the early melting range. What happens when you treat RNA with dimethyl sulphate? You modify bases, and the reaction we will look at is the modification of the N1 position of adenine, one of the groups on the Watson-Crick face of the base. Question: What kind of change occurs at position Nl ? Response: The Nl is methylated by the dimethyl sulphate, and you get a +1 charge on the Nl. Everyone agrees that if the Nl position of the adenine is involved in pairing with another base or another group in the molecule, it normally will not become

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methylated by dimethyl sulphate, whereas if there is no interaction, it will be accessible and react.

Figure 7. Dimethyl sulfate (DMS) modification of the Pl.LSU/2 group II intron at 50°C. 5' and 3' are intron-exon junctions. Roman numerals are used to designate the six separate domains of secondary structure, as predicted by comparative sequence analyses. Filled and empty arrows point to strongly and weakly reactive adenines, respectively. (Redrawn from Costa et al., 1998.)

The first thing we should look at is the methylation map of adenines at 50°C, which is just above the early transition temperature[Fig. 7]. Upon glancing at the secondary structure model, you will immediately notice in domain I, that essentially none of the adenines in secondary structure helices were affected (there is one exception), whereas all the adenines in so-called single-stranded loops were reactive. Question: How do you know what is methylated and what is not?

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Response: I will show you the data [Fig. 8]. This is a sequencing gel, in which you read the sequence of the molecule. The sequence was determined using an enzyme that will stop if there is methylation, so that you get a band. We are looking at the piece of RNA called IC1, and you see that the two adenines...

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Arrows point to the location of the 8 and e' loops in this autoradiograph of a sequencing gel. Bottom part: At 50°C, all four adenines in the single-stranded loops of subdomain 1C1 are modified by DMS, whereas at 42°C, the two adenines that are part of the terminal GUAA loop (known to interact with a specific receptor in domain II) are protected.

Question: - Was nucleic acid digested up to the methylation point? Response: No; there is a polymerase going along the RNA, using it as a template, and it will stop whenever there is a methylation. This gel shows us that none of the adenines in the helices are methylated. But the four adenines in single-stranded loops give signals at 50°C. On the other hand, at 42°C, two adenines (in the 0' loop) still give a strong signal, whereas the remaining two (in the e' loop) give only a barely detectable signal. Returning to the 50°C map [Fig. 7], it looks like the data provide an instant picture of those bases that are part, or not part, of what some (but not everyone) in the field would call the secondary structure of the molecule. But how do we know that this diagram represents the actual, physical secondary structure of this molecule? The structure shown here was derived by comparative sequence analysis, which is the easy and efficient way of inferring canonical base-pairs from sequence data. The problem, however, with comparative sequence analysis is that it provides you with statistical constraints that reflect selection pressures in nature. It tells you that two nucleotides are paired; that they form a Watson-Crick base-pair at some stage in nature. It does not tell you where this occurs. You might argue, for instance, that the base-pair is formed in a single-stranded DNA molecule that is somehow later converted into intron RNA, or vice versa, although in our particular case, most people would be willing to bet that intron RNA is the source of the vast majority of statistical constraints in intron sequences. Much worse, comparative sequence analysis does not tell you that all base-pairs are formed simultaneously. Constraints could refer to different states of the same molecule. And this happens, for instance, in a particle we call the spliceosome, which is responsible for removing introns from pre-messenger RNA in our own cells. When you analyze spliceosomal RNAs by comparative sequence analysis, you end up with bases forming canonical pairs with several different partners. The interpretation is that they do form those base-pairs, but at different times during the lifetime of the spliceosome particle, which has been shown to undergo a number of rearrangements. Fortunately, with Group II introns, this does not seem to be the case, since according to comparative sequence analysis each base has either a single partner or none. That is why we are confident with this structure.

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By the way, something else we could have done is compare the structure generated by comparative sequence analysis with the predicted minimal free-energy folding. Since Michael Zuker described comparative sequence analysis, I thought it fair enough that 1 discuss some minimal free-energy foldings. So I logged onto Mike Zuker's web site and here's what I got [Fig. 9]: This is a minimal free-energy folding for domains I, II, and III of the molecule that we modified with DMS. Segments in black are incompatible with the structure derived by comparative sequence analysis, and you can see that the number of disagreements is very small: five. The only problem is that this was not actually the absolute minimal free-energy folding. If I had taken the best, number I, it would

Some Questions Concerning RNA Folding

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have been much worse [Fig. 10]. Those foldings are very useful when you think you already know the solution.

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The answer, as shown in Fig. 11, is that only part of the adenines in the loops are modified, in contrast to what was found at 50°C. What we are interested in, of course, is the difference map [Fig. 12], which shows adenines that have changed state between 42°C and 50°C during the initial unfolding transition. This map does not tell much as such, because chemical modification makes it possible to determine

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whether or not a particular base has a partner, but not the identity of that partner. Fortunately, we knew far more about Group II introns than their mere secondary structure. When you superimpose some known interactions that are not part of the tree-like secondary structure on the difference map, it begins to make sense [Fig 12]. For instance, the a sequence in a terminal loop pairs with the oc' sequence in an internal loop; this is a beautiful example of a large pseudoknot with over seven canonical base-pairs. As you can see, this pseudoknot unfolds as it is disrupted during the initial unfolding transition. If we are going to listen to nature, we should regard such pseudoknots as part of the tertiary rather than the secondary structure. Question:

What is the magnesium concentration?

Response: Five millimolar. We knew of other interactions in this molecule which, rather than large pseudoknots, were isolated Watson-Crick base-pairs. There is also another class of interactions shown here that involves GNRA loops and their RNA receptors. A GNRA loop is a four-nucleotide loop in which the first base is a G, the last base an A, the third base a purine (either G or A), and N is any nucleotide. The interactions between GNRA loops and RNA receptors were predicted by Eric Westhof and me at the time we built the model of Group I introns, and they have proven quite common in large self-folding RNAs. They are important for the assembly of the final threedimensional fold. There are several examples of such interactions in this molecule [Fig 12]. The GUAA loop is recognized by a helical receptor and the GAAA loop is recognized by a somewhat larger receptor that is partly non-helical. We already knew this from other sets of data. As you can see, during the early unfolding transition, four of the GNRA loops in this molecule do change state. Some of the known receptors do not, because they consist of consecutive G:C pairs within an extended, continuous helix, but the C, receptor for the GAAA C,' loop definitely undergoes some kind of structural rearrangement. The work on Group II introns was done by Maria Costa. We have the same kind of data for Group I introns. Experiments were done by Luc Jaeger in Gif, and independently in the Turner lab by Aloke Banerjee and John Jaeger. When the data are summarized, we all agree that what is going on in the early unfolding transition is complete unfolding of the entire three-dimensional fold of the molecules. You end up at 50°C with a structure that has kept only those double-stranded helices that are not pseudoknots. This is exactly what most people would call the secondary structure of the molecule, which thus has physical existence.

Some Questions Concerning RNA Folding

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Figure 12. DMS difference modification map. Filled and empty arrows point to adenines that respectively undergo major and minor changes in reactivity between 42 and 50°C. Arrowheads indicate intron-exon junctions. EBS1-IBS1, EBS2-IBS2, and Greek letters designate known tertiary interactions that may or may not consist of canonical base-pairs. Question: Did you try using the program by Sean Eddy that predicts pseudoknots this data? Response: No. Question: At what temperature does catalytic activity Response: That was my initial piece of data.

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148 F. Michel Comment: I'm sorry, I walked in a little late. Response: Catalytic activity disappears at exactly the same temperature as tertiary structure. We initially monitored the transition from renaturation and reaction kinetics, and optically only later, and eventually by DMS accessibility. Question: The folding you describe seems to be a very solitary activity. The molecule is doing things to itself. Is it conceivable that if you were to use a higher concentration, the intramolecular interactions could in fact be replaced by intermolecular reactions, in which case, as one molecule folds it helps its neighbor? Response: Definitely; intermolecular interactions become a problem at high concentrations, which is something we are very careful to avoid. We work at the lowest possible concentrations. Comment: Isn't it possible that in vivo it could be the other way around? Question: Rather than help each other to fold, in vivo it may prevent misfolding. At high concentrations, they probably don't fold properly, right? Response: No, of course not; they do not. Comment: / do not know how close you are to in vivo concentrations, but if there were areas in which the folding take place in vivo, one might imagine very high local concentrations, in which case it might not be an artifact you were avoiding, but reality. Response: This is a very good point that I keep in mind whenever we are dealing with in vivo situations. Comment: We also see that in vitro, high concentrations inhibit folding, rather than promote it. The question is perhaps better posed as: "Is it important to have a high total concentration of molecules but not a high concentration of the same molecule? " It is very possible that general crowding may help, but high concentrations of the same sequence may impede folding. Question: What is a high concentration?

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Response: The lowest concentrations that we used were reasonably about 5 to 10 nanomolar. Question: At what point is it high, where crowding occurs ...at the micromolar range? Response: I cannot say exactly, because we are taking such pains to avoid it. We never investigated this in any systematic way. Comment: We had to make it our business to know. It depends on the length of the molecule. For molecules that are around 650 nucleotides long, it is about 1 micromolar. For smaller RNAs, it is at about 10 micromolar that this kicks in. Question: Has there been enough data collection of exactly which nucleotide subsequences are involved in pseudoknots in order to build a database? Response: Yes, I think so. There are hundreds of sequences of Group II introns, and I think I know just about every canonical base-pair in the molecule. I have spent so much time doing comparative sequence analysis that I am unlikely to have missed things that are present in more than say, 5 or 10 sequences. Comment: So that, in fact, provides the basis for an algorithm for the detection of a secondary structure with pseudoknots that is perhaps more reliable. Response: Possibly, however, there are very few examples of pseudoknots in any individual sequence. If we exclude isolated Watson-Crick pairs, which are not going to be detected anyway, I doubt it would be very meaningful. In Group II, we have two extended pseudoknots and one very short one, and that is all. Tertiary structure is mostly different types of interactions. Question: If you do extensive methylation at 42°C you prevent the formation of pseudoknots. I wonder if you could do the type of methylation interference experiments that are routinely done with DNA; that is, to modify at 42°C with high concentrations of DMS. Would pseudoknots still not be formed? Response: You would eventually methylate every methylatable atom if methylation were done at high concentrations. You would unfold not only the tertiary structure, but also the secondary structure.

150 F. Michel Question: Why? Response: Because you are going to drive the equilibrium to the unfolded form, whether they are tertiary or secondary pairings. Comment: Methylation interference works fine with DNA. With DNA, you can certainly do methylation interference without completely unwinding the structure. I do not see why it is technically impossible to do it with RNA. Response: We are doing interference experiments with RNA. But we use very low concentrations, because we want no more than one modification per molecule. We never tried saturation. Comment: / don't think the suggestion is to methylate the hell out of the molecule, but rather that methylation interference would detect interruption of tertiary interactions with methyl groups. Response: Yes. In that case, you would probably get the same sort of map. Comment: Right, it's a common method. A lot of reagents may be used besides DMS, and there is a large body of literature regarding doing this exact sort of thing to probe the structure. Comment: Is this what you mean? Because for the example shown, only one base at a time is modified; only one molecule is methylated at a time. They are all on a single map, but only one molecule at a time is methylated, and then they are all collected and put on a single map. You never have a single molecule with all of those changes. Comment: But ifyou increase the DMS concentration 20-fold, you still would probably not unwind the double helices. Comment: So your question is "How many interactions must be disrupted before unfolding occurs?" Response: This is something that can be found out or guessed from the melting curves, because we know the extent of destabilization that results from the disruption of a single interaction. It is reasonably large, typically 5-6 degrees Celsius for each individual

Some Questions Concerning RNA Folding

151

interaction. I would say you do not have to disrupt many at room temperature in order to get complete unfolding. A brief comment about the interactions between GNRA loops and their receptors. They were initially spotted by comparative sequence analysis. Some of the rules for loop-receptor interactions are shown here [Fig. 13]. The GUGA loop prefers CU:AG helical receptors, whereas GUAA prefers CC:GG and GAAA, an 11-nucleotide motif, CCUAAG:UAUGG. My point is for Michael Zuker with regard to using mutual information. Had we been using mutual information to look for those contacts we might not have found them.

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A major reason why computing mutual information for all pairs of sites is unlikely to provide us with many tertiary contacts is explained here [Fig. 14]. Let us assume we have aligned a number of sequences that are related by descent. It could be RNA, it could be protein, it does not matter. At site number 1, you have either A or a. At site number 2, you find either B or b. If you have A, you always have B. If you have a, you always have b. This is a case of perfect co-variation. Unfortunately, there are two possible extreme interpretations, and any intermediary combination of these two may also be made plausible. One interpretation is that there have been only two events, one at site one and one at site 2, which happened to occur for no profound reason, by chance, along the same deep branch of the

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F. Michel

particular phylogenetic tree, which describes the historical relationships of the sequences being analyzed. The other, contrasting interpretation is that while the contents of sites 1 and 2 have kept changing, they always did so together, because only the AB and ab combinations are allowed by nature. If the latter is true, it is most likely that sites 1 and 2 interact physically; the information extracted from the dataset was about the structure of the molecules (which, as a rule, evolves much more slowly than their sequences.) On the other hand, if only one event took place at each site, the information gained is about the history of the molecules. Clearly, the only way to know which interpretation is most plausible is to try to infer actual phylogenetic relationships within the dataset by using the entire sequence alignment, as well as any additional information available about the host organisms. In the example I chose in Fig. 14, phylogeny number two implies at least four pairs of The data: Sequence # 1 2 3 4 5 6 7 8 9 10

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Some Questions Concerning RNA Folding

153

possibly simultaneous events at sites 1 and 2, whereas in case the actual phylogeny is number one, the hypothesis with maximum likehood is that there have been only two, possibly separate, events at sites 1 and 2. Now, all of this is essentially no problem if you are dealing with Watson-Crick pairings. First of all, there are four isosteric base combinations for a Watson-Crick pair, so that if you observe all of them, you already know there have been at least three distinct events in phylogeny. Moreover, Watson-Crick base-pairs are usually not isolated; they tend to come in bunches, since they are most often organized into continuous double helices. In order to prove the existence of a helix, you can safely add the observed numbers of base combinations at each potential base-pair. In contrast, for a given tertiary interaction, there are usually few, typically two, isosteric pairings, and they most often have no neighbors along the sequence. This is why you have to estimate the number of events, and this is why we were successful for Group I when others were not. We had ordered the molecules phylogenetically to begin with and were able to count events, like Carl Woese and Gary Olson initially did when analyzing ribosomal RNA. Question: Does Gutell do it? I don't think he does. Response: No - well, sometimes, typically not. He seems to rely more on mutual information, which by itself seldom goes beyond secondary structure. Let us now briefly go back to the melting curves of some Group I intron mutants [Fig. 15]. Does destabilizing an interaction by introducing mutations tell us whether that interaction is part of the secondary or tertiary structure of the molecule being probed? I already mentioned the problem of P7 and P3. At least one of them has to be a pseudoknot and therefore, according to our "early melting" criterion, part of the tertiary structure. Let us then look at the data for P7 [Fig. 15A]. The basepair being probed (number 2) was G:C in the original, wild-type molecule; the mutants are C:G (pair-reversal,) A:U, G:U, and A:C, and you can see that as soon as you change that pair, you begin destabilizing tertiary structure. As long as we agree that anything that disappears during the early melting transition is tertiary structure, P7 is clearly part of the tertiary structure. In contrast, when we look at the data for a helix such as P9 (Fig. 15C; the same types of mutations were introduced into a G:C pair), we observe that the G:C to A:U or G:U mutations have no effect on the early melting peak. Only by introducing an A:C mismatch can you destabilize the P9

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F. Michel

helix sufficiently to affect the initial unfolding transition. Thus, a reasonable interpretation is that P9 is actually part of the secondary structure of the molecule. With regard to P3 [Fig. 15B], I'm not sure the answer is that clear, but it is certainly more a part of the secondary structure than P7 is. To some extent, melting profiles do provide us with a criterion.

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In Fig. 20, the actual value of O is seen to be neither 0 nor 1, but 0.46. When similar experiments were carried out with RNAs bearing mutations in the P9.0a base-pair, a nearly identical (0.42) O-value was obtained. What is the most likely explanation for this? Well, simply that there must be a multiplicity of pathways that lead to the folded state. In some of these pathways, these interactions are part of early folding intermediates, whereas in others, they form late, something that does not come as a surprise when you remember that in our experiments renaturation takes place within the melting range. Admittedly, multiple folding pathways have

162

F. Michel

also been observed very far from the melting range in experiments carried out in Sarah Woodson's and Jamie Williamson's labs. Still, you would definitely expect that within the melting range of a molecule, when conditions are ideal for conformational searching, there are going to be multiple ways to fold.

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The folding conditions I have just described, in which the molecule is placed within its melting range and all tertiary interactions appear to form simultaneously by an all-or-none process, is very different from the renaturation conditions described by Sarah Woodson, for instance. Except perhaps when urea is added, the conditions used in most laboratories for renaturing the Tetrahymena Group I intron, whose tertiary structure unfolds at around 65°C, are very far from its melting range. What I am now going to try to argue is that in nature, RNA molecules, such as Group I and Group II introns, fold under conditions that are in fact much closer to the conditions we have been using. They are very close to their melting range; i.e., they have very small AG's.

Some Questions Concerning RNA Folding

163

I first began to realize that this had to be the case while working with the sun Y Group I intron, which used to be fashionable around ten years ago, but no longer seems to be. Like other introns, the sunYintron is excised after transcription, and as is often the case with Group I introns, the piece that gets removed (some 1,033 nucleotides) includes not only a ribozyme that directs and catalyzes splicing, but also an open reading-frame (a potential protein-coding sequence devoid of stop codons), which actually specifies a protein. What is somewhat unusual - though not unique - about the sunY intron is that its reading-frame is located entirely downstream of the catalytic core of the ribozyme. Is this a problem? Well, it could be, because as soon as it is synthesized, the ribozyme core becomes liable to catalyze splicing, provided it can find a suitable 3' splice-site. Now, those were precisely the times when we were finding out about the rules for the recognition of 3' splice-sites, and on paper, there was not merely one, but several possible splice-junctions immediately 3' of the ribozyme core. Since it seemed reasonable to look for alternative splicing in the sun Y system, we chose to begin by generating truncated intron transcripts in vitro (they were intended to mimic transcription intermediates in vivo) and soon enough we were able to observe splicing, not just to the proximal sites we had predicted, but to several additional ones. The core seemed quite active and could very easily find a surrogate 3' splice-site. But what was it doing in vivo? I phoned David Shub, a microbiologist and biochemist who had discovered the sunY intron and asked him about the possibility of alternative splicing in vivo. His answer was, "In fact, I have been working on that problem. We have tried very hard to observe alternative splicing in vivo and have been totally unable to find it." This was quite puzzling, so our next experiment was to transcribe the entire intron. I was expecting to see both proximal and distal splicing, but could only observe distal splicing. All proximal reactions were abolished (we had similarly predicted that a number of other group I introns had alternative, proximal splice sites, and in those introns, alternative splicing events were eventually detected in vivo by PCR, a very sensitive method for amplifying nucleic acid molecules that was not available at the time we were tackling this problem). Eventually, I found the catch, which was this [Fig. 21]: The truncated transcripts were very active, but were far more sensitive to the magnesium concentration than the full-length transcript was. They required at least 10 to 15mM Mg + for full activity, as opposed to 3mM for the complete intron, and were essentially inactive at 5mM or less. Now, what you need to know is that the physiological concentration of magnesium - in fact, the concentration of unbound, available magnesium ions - is much less than lOmM. It seems to be much closer to

164

F. Michel

2-3mM, which is barely sufficient for full-length transcripts to be active in vivo. In E. coli cells - I have not yet mentioned that this intron is found in the T4 bacteriophage, and therefore supposedly works in E. coli - the medium is simply not favorable enough for the ribozyme core to fold until the intron is completely synthesized, whereas in vitro, truncated molecules appear fully active under suitable conditions.

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Some Questions Concerning RNA Folding

165

We also eventually found what the missing part was. It consists of a small piece of RNA (lower right in Fig. 22) located immediately upstream from the 3' splicesite, and makes a small number of interactions with the ribozyme core. As far as we know, only two of these interactions matter, in the sense that they seem necessary and sufficient to stabilize the entire ribozyme molecule. One of them is a small pseudoknot (between the L7.2 and L9.2 terminal loops) and the other (P9.0a) an isolated Watson-Crick base-pair. The experimental evidence consists as usual of the melting curves (whether obtained from optical or kinetic measurements) of basesubstituted molecules.

Figure 22. Schematic representation of the three-dimensional organization of the sunY intron. The conserved Group I ribozyme core (P3 to P8) is boxed. Large arrows point to 5' and 3' intron-exon junctions. Known interactions between the 3' terminal domain and the rest of the intron are shown as dashed lines.

Now at the same time we were measuring melting temperatures, David Shub and Ming Xu in Albany were determining relative splicing efficiencies in E. coli cell extracts (Michel et al., 1992), and it is quite interesting to compare the two

166

F. Michel

datasets. Let us assume (a bit naively) that the ability of a population of molecules to carry out splicing directly reflects its ability to fold within a short time window after synthesis (splicing must stand in competition with RNA degradation). Since the time window within which folding competes efficiently with degradation after synthesis must be of the same order of magnitude as the time necessary for transcription of the entire intron, which is estimated to take between 20 and 50 sec, we will assume it to be 1 minute. Thus, when the relative splicing efficiencies of mutants P7.2xP9.2 CC:GC and GC:GG (the wild-type interaction is GC:GC) were estimated from reverse transcription of in vitro extracted RNA to (respectively) be 0.08 and 0.06, our interpretation is that some 7% of the mutant molecules managed to fold during 1 min. Assuming kinetic and thermodynamic parameters determined in vitro apply to in vivo conditions, let us now estimate the folding rate of wild-type molecules under conditions that allow 7% of the mutant molecules to fold. The mutant AAG*s have not been determined, but may be estimated from measured AAG°s (+5.5 and +5.6 kcal/mol; Jaeger et al., 1993) if O is assumed to be the same as for kinetically probed interactions, that is -0.45. The folding rate of wild-type molecules is then estimated to be 3.6 min"1. More than 97% of wild-type molecules get folded during the one-minute time window, which should effectively free the wild-type sequence from selective pressure. Moreover, since AAG* = AAG° for the wild-type molecule (Fig. 2a in Jaeger et al., 1993), which is consistent with the fact that the transition state must be a nearly entirely folded molecule, AG° may be estimated from the ratio of 3.6 min"1 to the values that were measured within the melting range [Fig. 18]. The outcome - a mere -2.2kcal/mol - indicates that in vivo, the stability of the wild-type intron fold is marginal, just enough for 97% of the molecules to be folded at equilibrium. It appears that the conditions under which renaturation takes place actually correspond to the early melting range (this calculation is based on the assumption that the near equivalence between AAG* = AAG° extends beyond the range in which it was established in Jaeger et al., 1993. In actual fact, AG° is likely to be somewhat larger, since kinetic traps should progressively form as one gets away from the melting range.) Interestingly, I have been watching the literature for globular proteins, and the in vivo AGs also appear to be quite small; in the range of-5 to -10 kcal. All of this makes sense if we assume that natural selection is sensing only thermodynamic stability. As long as you are very close to 0, you will have strong selection against mutations; but if you go as far as -10, a single mutation gets you back only to -5, so that there will be no selection against it. Thus, the molecule will in fact drift back to this threshold of say, -5 AG values, estimated for globular proteins. In the example I just gave you for catalytic RNAs, they are exactly as what you would expect in this

Some Questions Concerning RNA Folding

167

perhaps a bit naive picture of natural selection working primarily on thermodynamic stability. I would like to emphasize that there is nothing particularly special about the sunY Group I intron. We also worked on the td intron, which is found in the T4 bacteriophage and has very different organization in terms of the respective locations of the ribozyme core and protein-coding sequence. The td intron does not have to delay folding in order to avoid alternative splicing, yet it yields essentially the same set of thermodynamic estimates. It is no more stable than the sun Y intron. My point is that at least in the case of self-splicing Group 1 and Group II molecules, the overall fold stands very close to its in vivo melting point, so that in fact folding takes place very close to melting conditions. This has a number of implications that we can discuss. One of them is that kinetic traps in three-dimensional folding seem to be rather unlikely under such conditions, which should be ideal for conformational searching.

0.01

11-nt motif

Figure 23. Recognition efficiency of diverse GNRA loops by the 11-nucleotide GAAA receptor motif (from Costa and Michel, 1997.)

168 F. Michel I will mention a number of points that I regard open questions we might now discuss. One of them is whether there is anything special about self-splicing introns. I have been trying to imply that what I am describing is general for RNA. In fact, self-splicing introns are somewhat peculiar molecules. Many of these introns have been selected to move around from one host to the next, so that a fair fraction of them must be very self-sufficient. A hint of this is provided by the distribution of GNRA loops and their receptors in self-splicing molecules. Maria Costa in my lab did selection experiments to find the best receptor for each GNRA loop. She found that for GAAA, there was an incredibly good receptor - an 11nucleotide motif - which gave rise to an unusually stable combination. The interaction was both tight and specific [Fig. 23]. Note that the ordinate scale is logarithmic; the GAAA loop stands out completely for the 11 -nucleotide receptor. Question: For what kind of receptor is this? Response: I can show you the motif. It is a small piece of RNA with a defined threedimensional structure when interacting with the GAAA loop. The structure was determined by crystallography. Question: // is a receptor with a short oligo? Response: It is formed by two strands. There are helices on either side and a small internal loop in secondary structure representation. Question: So this motif is a receptorfor that oligo? Response: For the GAAA terminal loop, and, it seems, the best possible one. We know this from in vitro selection. Question: / am lost. What is a receptor for what? You have this short oligo and this loop... I'm just lost in the logic. Response: Well, it does not really matter. It is a matter of definition.

Some Questions Concerning RNA Folding 169 Comment: // is actually a tertiary interaction in the molecule. It is not a short oligo that is being bound. It is actually the interaction of one part of the molecule with another. In fact, the interaction is very weak in completely separate molecules. Response: Rather weak, yes. Comment: It has to be in cis, so it is just a simple tertiary motif. Response: Luc Jaeger showed that it could be in trans, provided you have two of them. Question: What do you mean by "receptor?" Comment: The word receptor is basically these two motifs interacting with each other. It is tertiary structure. They make the mold better. We call it a receptor and a GNRA loop. Question: Have you used something like SELEX to improve on this? Response: That is exactly what I have been saying. experiment.

Maria Costa did this very

Question: But you have a natural one right there. Response: We initially uncovered the motif in natural molecules, then we did the selection experiments and found that the natural solution was the best possible one for the GAAA loop. It was spectacularly better than anything else for that loop. We know that there is a very specific, perfect match between the two partners. The point I wanted to make is that the 11-nucleotide receptors are very common in self-splicing introns. I can show you a Group II intron in which within same molecule you have three GAAA loops and three 11-nucleotide receptors [Fig. 24]. Eleven-nucleotide receptors are very common in self-splicing introns, but I have never found them in either ribosomal RNA or ribozymes that were selected for activity in vitro (though not for stability or performance in folding.) There is definitely something rather special about these self-splicing introns. This is what attracted me to them in the first place. 1 was interested in molecular evolution. I was looking for an experimental system that would look like a self-contained world.

170 F. Michel Question: Why is it that after ten years, you and Eric have not given us a 3-D model of the Group II intron? Comment: [unintelligible] Response: Well, we have modeled part of the molecule. Question: When did that come out? Response: In the EMBO Journal, last year (Costa et al., 2000). Question: But why not the entire intron? Response: Well, because I hate to be wrong. Question: Is Group II harder to model? Why? Response: We do not feel confident about our current model. There are things I do not like. In the case of the Group I intron, it seemed like a crystallization process; everything started falling together and making sense in a matter of a few weeks. Take the interactions of GNRA loops... I was supposed to be rather good at comparative sequence analysis, and I had never spotted them before we started modeling. It was really cooperation between the two of us. Eric started modeling, then I looked at where the GNRA loops might go. This was a very rough model to start with. Still, it helped me to find the receptors and thus provide some feedback to refine coordinates. It was a very collaborative endeavor and we have never reached the same consensus with Group II. Question: You seemed to make a statement to the effect that - perhaps I misunderstood — the natural selection of these UNA sequences might have arisen because of stability considerations; because of say, 6 to 8 or Wkcals or something like that. Is it possible that selection pressure could be coming purely from kinetics? Response: Yes, of course. Our idea that thermodynamic stability dominates may be pretty naive, but at the same time, it is worth asking the question. Comment: This guy showed that you could in fact improve folding speed.

Some Questions Concerning RNA Folding 171 Response: ...by destabilizing. Adding urea did the job, and in vitro selection did it, and the Woodson and Thirumalai labs showed that by going closer to the melting range you improve folding, and that is my entire point. Question: ...Somewhat of a general question: According to you, has the plausible evolution of RNA sequences been dictated by stability or by kinetics, or are they related? Response: I believe they are related. Still, it may depend on the molecule. The point I am trying to make is that the selection pressure has been different for different types of natural RNA molecules. Ribosomal RNA, as we see it today has not been under the same type of selection pressures as some of the Group I and Group II introns. Question: Is this based on experiments? Response: No; it is based on statistics. Question: How many statistics? Response: Well, quite a large quantity. For example, I know of at least four series, each with numerous sequence examples, of different instances of the 11 -nucleotide motif in Group I; at least four series in Group II, only 1 in RNase P, and not a single example in the huge collection of ribosomal RNA sequences available. Question: They are available, but have the kinetics and stability been determined? Response: We know that ribosomal RNA requires proteins in order to fold. Some Group I and Group II introns do require proteins, although not as massively as ribosomal RNA does. Comment: This comment will tie to Jamie Williamson's talk. Jamie Williamson's group showed that if you destabilize some of these interactions, folding speeds up, because the intermediates are also less stable. The same thing is true for these loop-loop interactions that you identified. RNA-RNA interactions that help stabilize the native structure, at least in the Group I introns, also stabilize misfolded intermediates. Hence, the folding kinetics are slower, although the ultimate stability of the final structure is improved. My pet theory, although not proven, is that by the time you get to the size of a ribosome, this no longer works. Because if you had to assemble the whole thing based on RNA-RNA interactions, you would never arrive. An evolutionary solution to this

172 F. Michel problem, as Jamie Williamson nicely pointed out, is to supplant the RNA-RNA interactions with RNA-protein interactions, which are also more specific. Response: I wonder if what you are describing is due to the fact that you are very far from the melting range. Comment: // turns out that for me, and I believe that Jamie Williamson is also more or less in agreement, when adjusting at physiological temperatures for Tetrahymena, which would be between 27°C and 30°C, we cannot arrive at the physiological folding rates merely by adjusting the ionic conditions. Although we can certainly improve our folding rates, that is not sufficient. Question: This picture [Figure 24] is tree-like. There are many helices there, but it is tree-like. I have the impression that this molecule is deliberately trying very hard to avoid non-tree-like helices. Why is that so? Response: Yes. I think that is because it would be dramatic for folding if you made tertiary interactions before you were done with secondary structure. This helps me make one of my points. If you define the secondary structure in this way, without pseudoknots, it is always physically viable. You will never have problems physically folding the molecule in this way into the structure that probably actually exists at 50°C. As soon as you make tertiary interactions, you get into possible topological problems. My point is that only when you are done with the secondary structure, which is most probably going to form co-transcriptionally, should you attempt three-dimensional folding. In the case of the sunY intron, folding is clearly postponed until the entire molecule is synthesized. Then, folding is entirely cooperative. It is an all-or-none process, taking full advantage of the potential for conformational search. Comment: If this were two-dimensional space, you would be right. In threedimensional space, there is a difference. I think that evolutionarily, this thing could build out more physically. What you say just cannot be true, because threedimensionally, it does not know what is secondary or not. Response: If you start making those pseudoknots before you have completed the secondary structure - I got trapped more than once using models of wires and wood - if you make those interactions, oe-a' and P-P' in Fig. 24, you are going to end up with real knots before you are done with the secondary structure of domain I.

Some

Questions

Concerning

RNA

Folding

173

Comment: / agree, but the reason it evolved could possibly be that the parts appeared separately and were then glued together by something like gene duplication. And they could have evolved this way. It cannot be accidental just for that reason. There must be a structural mechanism forcing it to be tree-like. Comment: One possible answer is that you can envisage folding this even in three dimensions by a succession of interactions that are local in sequence.

Figure 24. A group II intron from the cytochrome oxidase gene of the alga Pylaiella littoralis with three GAAA terminal loops and three 11-nucleotide receptor motifs (from Fontaine et a!., 1997).

Response: That is another point I wanted to raise. What is the role of internal loops in the folding process? What is the role of these small internal loops everywhere in the molecule during the folding process? Are they part of the secondary or tertiary

174 F. Michel structure? Should we regard the final structure of the internal loop as secondary or tertiary? We know the answer, in part. We know that for terminal loops, the GAAA loops for instance, they are clearly part of the secondary structure. They are so stable that they are going to survive the early melting transition. In fact, they will melt much later. For internal loops, the answer is not clear at all. It has been published that the 11nucleotide receptor changes state when it meets the GAAA loops. This is an induced fit. What I do not know is what the stability of this alternate state might be. Does it survive close to the melting range of tertiary structure? I have no idea whether the receptor is disordered or ordered, and to what extent, when close to the melting range. This is another point I would like to know about. Comment: / would like to come back to the issue of selection again. You are emphasizing folding close to the melting temperature. Conjecture could be made based on some expectation that what you want are sequences that have similar architecture, that fold with relatively similar speeds over a wide temperature range to accommodate various species; thermophiles, mesophiles, and so on. So you want to optimize speed close to the melting temperature not just for your system, but also for some range of temperatures. Response: Yes; thank you for bringing this up. Some of those introns work in E. coli, and of course, E. coli does not grow only at 37°C. It has to do splicing presumably at a whole range of temperatures. Is there any kind of homeostasis? You would posit from what I am saying that there should be some kind of homeostasis with respect to the stability of macromolecules, especially RNA. I was recently delighted to find out from the literature that when you submit E. coli to a cold shock, the bug synthesizes a number of so-called cold-shock proteins, at least three of which have proven to be RNA helicases. That is exactly what you would want; proteins that are going to destabilize the RNA and bring it back close to the melting range. Question: I just want to make sure that I understand this picture [Figure 24]; do the additional arrows on this tree-like structure correspond to pseudoknots? Response: Yes; you have one called ot-a', another large one is P-(3', and a shorter one ee'. Question: ... and at least two or three others?

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175

Response: There are three that are more than one base-pair; the others are single, isolated base-pairs. Question: So according to this notation, what about EBS1-IBS1, EBS2-IBS2 - are they not pseudoknots ? Response: It depends. They exist in the precursor molecule; the initial transcript, but they are absent from the excised intron; that is, in the absence of the 5' exon. Question: Regarding the parameters that may influence the folding, what do you think about the speed of transcription? Response: In the case of the sunY intron, if you have a maximum of 55 base-pairs per second, it will still take between 15 and 20 seconds to synthesize the entire intron, which is far more than necessary for splicing if the core were folded. The problem exists. Many of those introns exist in bacteria. I have a phylogenetic tree of Group II introns showing there are an incredible number of them being sequenced in bacterial genomes; organelle introns will soon be a minority. Note also that in bacteria, transcription is normally coupled with translation. Comment: During the course of the elongation of a definite Group I or Group II intron there might be regions of the sequence where the speed can change. It could be imagined that a polymer is somehow weighed in such a way that folding has some time to occur, so that the phase-space should also mention the course of the speed of elongation along the sequence, which makes things even more complicated. Response: If I am correct that three-dimensional folding occurs close to the melting range, then it does not matter much whether synthesis starts with the 5'-end or the 3'-end. The fact that you transcribe from one end to another at a given speed matters only for secondary structure. If I am correct about three-dimensional foldings; i.e., that it is postponed until the entire molecule is synthesized, it means that even though the molecule we are dealing with is a complete transcript, our in vitro experiments reasonably simulate in vivo conditions. Question: Are there data in E. coli about the deficiency of activity of Group I introns? Response: I showed you T4 bacteriophage introns that work in E. coli.

176 F. Michel Question: Are there data about the temperature? Response: No. I do not know what the reasonable temperature range is for T4 bacteriophage infection. David Shub and Ming Xu did all the in vivo work. We only dealt with T4 DNA, which we soon regretted, because we did not know about the nucleotide modifications, which prevent restriction enzymes from cutting. Question: What is the free-energy stabilization range for the three-dimensional tertiary structure, compared with that of the secondary structure? Response: The computed AH's ranged from 150 to 300 kcal/mol, depending on the intron. I computed 300 kcal for Group II introns. Under physiological conditions, when I tried to estimate the AG for three-dimensional tertiary structure in the sunY intron, I found from -2 to -4kcal/mol. But we must remember that the determination is quite indirect. A fraction of spliced transcripts is a very indirect measure of what may be going on. I cannot really put values on thermodynamic parameters. What we think we know is that as soon as you lose more than 2kcal/mol, you begin to get an in vivo phenotype that is going to be severely counter-selected. Question: / am interested in making a comparison with a protein. In an average small globular protein, the amount of AG stabilization is a few kcal per mole of the molecule. Is this a similar case? Response: Yes. It is very similar. Question: In that case, the AG is between the tertiary structure and the structure in which the tertiary structure has been disrupted, but the secondary is still more or less conserved. Response: Yes, exactly. Question: Are these small AGs also pseudoknots? Response: Yes. Question: ...even when you have real canonical pairing? Response: AH is large, but so is AS.

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177

Comment: // is just adding; it is much more than 4kcal. Response: That depends on the temperature. There is one question I have, perhaps for Sarah Woodson or Jamie Williamson. I have been implying that there is usually a single domain of three-dimensional folding. This is very different from what you have described in your kinetic folding experiments, in which you first have folding of the P4-P6 domain, then of P3-P7. On the other hand, when I looked at equilibrium data for the Tetrahymena intron, I think the evidence for separate domains is rather scant. In Turner's lab, there is a single detectable early transition, and they also did chemical modification on both sides. I do not know whether they looked for substructure in the early melting peak, but they clearly did not see it. There are also data from the Cech lab, where folding is followed as a fraction of magnesium concentration. The difference in magnesium concentration between P4-P6 and the rest of the molecule is very small. The values are 0.7 and 0.85, so there is considerable overlap. It is not obvious to me that there are really separate domains within the overall tertiary structure near equilibrium. I was expecting someone to raise this issue, so I am doing it myself. Comment: // is a matter of resolution. If you look at what Sarah Woodson showed; if you look at the small angle-scattering experiments, it is quite clear there is an intermediate; so it is a question of resolution. The experiments, as I see them, are not clean or precise enough to distinguish between equilibrium intermediates. I would be willing to bet a nickel or (a euro). Response: Yes, if these intermediates exist in the melting range. Comment: /'// give you a euro on January 2nd, if I 'm wrong.

References 1. 2.

Dumas, J.P., Ninio, J., Efficient algorithms for folding and comparing nucleic acid sequences. Nucleic Acids Res. 10, 197-206 (1982). Michel, F., Westhof, E., Modelling of the Three-dimensional Architecture of Group I Catalytic Introns Based on Comparative Sequence Analysis. J. Mol. Biol. 216, 585-610(1990).

178 F. Michel 3.

4.

5. 6. 7. 8.

9.

10. 11.

12.

13.

Michel, F., Jaeger, L., Westhof, E., Kuras, R., Tihy, F., Xu, M.-Q., Shub, D.A., Activation of the catalytic core of a group I intron by a remote 3' splice junction. Genes & Development 6, 1373-1385 (1992). Jaeger, L., Westhof, E., Michel, F., Monitoring of the cooperative unfolding of the sunY group I intron of bacteriophage T4. The active form of the sunY ribozyme core is stabilized by multiple interactions with 3' terminal intron components. J. Mol. Biol. 234, 331-346(1993). Jaeger, L., Michel, F., Westhof, E., Involvement of a GNRA tetraloop in long-range RNA tertiary interactions. J. Mol. Biol. 236, 1271-1276 (1994). Costa, M., Michel, F., Frequent use of the same tertiary motif by self-folding RNAs. EMBO J. 14, 1276-1285 (1995). Costa, M., Michel, F., Rules for RNA recognition of GNRA tetraloops deduced by in vitro selection: comparison with in vivo evolution. EMBO J. 16, 3289-3302 (1997). Costa, M., Fontaine, J.-M., Loiseaux-de Goer, S., Michel, F., A group II selfsplicing intron from the brown alga Pylaiella littoralis is active at unusually low magnesium concentrations and forms populations of molecules with a uniform conformation. J. Mol. Biol. 274, 353-364 (1997). Costa, M., Christian, E.L., Michel, F., Differential chemical probing of a group II self-splicing intron identifies bases involved in tertiary interactions and supports an alternative secondary structure model of domain V. RNA 4, 1055-1068 (1998). Rivas, E., Eddy, S.R., A dynamic programming algorithm for RNA structure prediction includingpseudoknots. J. Mol. Biol., 285, 2053-2058 (1999). Brion, P., Michel, F., Schroeder, R., Westhof, E., Analysis of the cooperative thermal unfolding of the td intron of bacteriophage T4. Nucleic Acids Res. 27, 2494-2502 (1999). Brion, P., Schroeder, R., Michel, F., Westhof, E., Influence of specific mutations on the thermal stability of the td group I intron in vitro and on its splicing efficiency in vivo: a comparative study. RNA 5, 947-958 (1999). Costa, M., Michel, F., Westhof, E. A three-dimensional perspective on exon binding by a group II self-splicing intron. EMBO J. 19, 5007-5018 (2000).

RNA FOLDING IN RIBOSOME ASSEMBLY JAMES R. WILLIAMSON Department of Molecular Biology, The Scripps Research Institute, La Jolla, CA, USA

I want to extend what has been discussed to include the possibility of having proteins direct the folding. As we will see, that is a critical component of what is going on in assembly for the ribosome. Figure 1 is a crystal structure of a piece of RNA with this protein bound to it that was done in our laboratory. I will try to describe the role of a particular protein in guiding some critical steps in RNA folding as the ribosome assembles. The things we are talking about for ribosome assembly are applicable to the formation of all kinds of RNA complexes.

Figure 1. RNA-proIein interactions in the assembling ribosome.

Figure 2 is an example from a paper from Ian Mattaj's lab. Certain small nuclear RNAs are exported from the nuclear compartment into the cytoplasm, and there is a defined series of steps that must take place for that to occur. A Cap binding complex (CBC) has to bind to RNA, and only then does another protein, an adaptor protein called PIIAX, bind to the complex, and only then does the exporting complex, a Ran-GTP XpOl, form the functionally competent intermediate for

180 J. R. Williamson transport through the nuclear complex. All of the things discussed here having to do with an ordered, step-wise formation of a complex apply to the formation of any complex in general. It is a biologists' view of this, which is a bunch of blobs. What I hope to be able to give you is a molecular picture for at least one such step in a complex assembly pathway. What is different about the CBC RNA complex that allows PHAX to bind? The basic issue we are trying to understand is the nature of the changes in the complex as things proceed along the assembly pathway.

Ran-GTP Xpo1

Nucleus

Cytoplasm

t

•""U assembly

A

disassembly

CBC *~~\J

U-snRNA

Ohno, Segref. Bachi. Wilm 8 Mattaj. Cell 101.187-198 (2000) Figure 2. Assembly of sn-RNPs for nuclear export.

Question: Is there experimental evidence for this sequence of steps? Response: Yes; Ian Mattaj's lab has identified each of these complexes. Question: What is the time-gap between each step? Response: That is not clear; they did not measure that at all, but we will talk more about time. This past year has seen spectacular progress in understanding the structure of the ribosome. We now have a 5A crystal structure of the full 70S ribosome done by Harry Noller, and there is a 2.4A crystal structure of the large subunit (50S) done by Tom Steitz and Peter Moore, and a structure of around 3A of the 30S subunit, done by Venki Ramakrishnan, as well as by Ada Yonath's laboratory [Fig. 2]. The 70S

RNA Folding in Ribosome Assembly 181 ribosome is almost 3 million Daltons, the 50S approximately 2 million, and the 30S approximately one million. What people have known ever since they isolated and purified the components of ribosomes is that two-thirds of the mass of the ribosome is made of RNA. The RNA is color-coded in red and white in this figure, and the proteins are coded in blue. What we see here is where the two subunits come together at the 70S subunit, and we are looking at the faces that form the interface, most of which consists of RNA. The best analogy for the ribosome is to think of it as an orange with a bunch of twigs stuck to it on the outside. The proteins are really on the outside, and the functionally important parts of the RNA on the inside. Seeing this structure has been quite remarkable, because we knew the RNA was critical, but how critical it was turned out to be quite a surprise. The RNA carries out all the important functional aspects, but what is the role of the proteins? One of the things they are clearly doing is helping in the assembly and stabilization of the ribosome.

70S ribosome

Figure 3. Anatomy of the bacterial ribosome. This describes a molecular inventory for the 70S ribosomal subunit [Fig. 3]. I will now focus exclusively on the small subunit. There is a 16S RNA that is 1,500 nucleotides in length. This is quite large compared to most of the RNAs discussed earlier (see chapter by Woodson). Bound to that are 21 small subunit proteins called S proteins, numbered SI through S21. Most of them are in the range of 10 to 20kDa. For the most part they are very small proteins and bound to this very large, complicated RNA structure. One of the things I think is an emerging theme that has been experimentally verified is that as the RNA molecules get larger and larger.

182 J. R. Williamson they actually fold to their final form slowly [Fig. 4]. Sarah Woodson's talk introduced the time-scales: rapid formation of hair-pins occurs on the microsecond time-scale, tRNA folds on the millisecond time scale, and group I introns, which are about 400 nucleotides, fold on the second-to-minute time-scale. Here we have something that is four times the size of a group I intron; what will keep it folding on a proper track?

rmB ope ron rJ'

16S

23S tRNA

18,700 rlbosomes/cell 40 minute doubling time

5S TITJ

468 ribosomes/minule 8 ribosomes/second 1,1 rRNAtranscripts/SQcond/oporon

7 rRNA operons Transcription rate - 55 nt per second ~5500 nt transcript -100 seconds per transcript (0.01 transcripts/sec) -50% ol bacterial transcription is rRNA Neidhnrt, Escherichia coS and Salmonella lypimtirium, Ch. 1. (1987).

Figure 4. Log phase bacterial ribosome biogenesis.

To give you an idea of the magnitude of this problem, I would like to discuss the demand for ribosomes inside bacterial cells. The role of a group I intron, 400 nucleotides long, is to self-splice, and it does so once it performs one turnover in its life. The ribosome has to get made, stay stable, and be propagated from generation to generation. Ribosomes pass on by cytoplasmic inheritance from one cell generation to the next. There are three ribosomal RNAs, and they are all transcribed as a part of one large operon. There is one big transcript approximately 5,500 nucleotides long. If you were to do an inventory of how many ribosomes there are in a bacterial cell, you would find that there are almost 20,000. So about 25% of the dry weight of a bacterium consists of ribosomes. Ribosomes are responsible for making all the proteins in the cells. Bacteria are essentially packed with ribosomes. If a bacterium has a 40-minute doubling time, that means we must have 468 new ribosomes per minute, or about eight per second. The demand for ribosomes is so great in bacteria that there is not just one of these operons, there are seven of them;

RNA Folding in Ribosome Assembly 183 seven operons constantly transcribing RNA. So we need about one transcript per second per operon to meet the demand for slow-phase bacterial growth. It is well known that the transcription rate for RNA polymerase is approximately 55 nucleotides per second. This means it takes RNA polymerase approximately 100 seconds to transit the entire operon. As a side-note, 50% of the transcription inside the bacterium actually concerns making ribosomal RNA.

What is the steady state concentration of assembling ribosomes? ki D *Assume transcription is rate limiting for assembly

fo R*

P-

R k, [D] =

ki = 1.1 transcripts/(sec*operon) [D] • 7 operons/cell kz-0.01 transcripts/sec

1.1 transcripts/sec ® 55 nt/sec implies RNAP every 50 ntl

~k

[R*]« = 770 /cell _„, , ., - 5 % of ribosomes are assembly intermediates

XgyXro^Craw^ ' - j — — i — —|— —r 1 50«

50 nl

50 (It

Figure 5. Steady-state concentration of ribosome assembly. If we devise a very simple, steady-state hypothesis for what has to happen, we assume that the rate-limiting step for assembly of a ribosome is actually the making of the RNA. Then everything else is just fast, and we can come up with a sort of steady-state concentration of ribosome assembly intermediates [Fig. 5]. If we make one transcript per second per operon and assume it takes around 100 seconds to transit the whole operon, wc can come up with a steady-state concentration of about 700 ribosomes per cell, or around 5% of the ribosomes in the process of actually being assembled. This (equation on right) represents a lower-limit for the concentration in the population of intermediates. So if assembly is slower, this number goes way up. In addition, if you look at the demand for RNA synthesis, if you have 1.1 transcripts at 55 nucleotides per second, it means you have an RNA polymerase every 50 nucleotides. If you measure the RNA polymerase footprint, it is approximately 50 nucleotides. I may have dropped a factor of two somewhere in this "back of the envelope analysis," but basically, the ribosomal operon is

184 J. R. Williamson completely loaded with RNA polymerases. They are chugging along, and then nascent transcripts emerge from each polymerase. We anticipate (as shown in Hcrve Isambert's talk) that these ribosomes are assembling co-transcriptionally.

C3> 23 S i UNA 1000 kDa 2904 nucleotides

5SlRNA -40kOa 120 nucleotides

•

•

• • • • • • • • • • •

34 "L" proteins 5 to 25 kOa •

•

•

• •

• • • • • • • * • • • # •

•

IBSrRNA 500 kDa 1542 nucleotides

• •

30 S subunit 0.9 MDa

a

21

" s " P' 0,eins 8 to 60 kDa

Figure 6. Components of the 70S ribosome.

Figure 7. The structure of the 30S liposomal subunil. Shown here [Fig. 7] is the tertiary RNA structure, rendered in red. You can also see that there are proteins in this figure. The solvent face is on the right, and on the left is the face that faces the 50S subunit. As you can see, most of the proteins are

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located on the periphery; on the outside, and not so much at the center. The mRNA would thread through the upper portion (left), which is also the decoding region; the heart of where the mRNA is decoded into the protein sequence. Of course, peptidebond formation occurs on the large subunit, but this same area on this subunit is where codon/anti-codon interaction occurs. One thing that has become clear (and a good example was given by Sarah Woodson with regard to the group I intron) is that these large RNAs seem to fold into quite large and fairly stable domains. There are three big domains in the 30S subunit; the 5' domain (in blue), the central domain (in light yellow), and the 3' domain (light purple) [Fig. 7]. I will use the rest of my time to discuss experimental approaches that are used to understand the kinds of conformational changes in protein-binding events that occur during assembly of the central domain. In a sense, we have taken a bit of a reductionist approach. Pictured here is the central domain, which is also called the platform region. The 3' domain is also called the head, and the 5' domain is called the body [Fig. 8]. Again, the mRNA threads through the interstices, where the three domain-areas meet. The red helix is also called the decoding helix. The mRNA decoding occurs roughly in that red region. The central domain is more or less the anvil on which the genetic code is read out. Question: Can you explain the central domain that seems to be protruding into the body? Response: Yes. This area in light blue is an interesting intra-subunit interaction, and I can show you exactly where that is, but we are not going to talk about that at all. We are going to talk about the folding of this globular part, which is important for inter-domain interactions. But I'm actually going to just cut it off as fast as I can. I consider this to be absolutely remarkable and heroic work, done 25 years ago by Nomura [Fig. 10]. He showed two things: First, that you can purify all the ribosomal proteins to homogeneity and then reconstitute them onto the 16S RNA, to get a functional 30S subunit. The second thing he did was use this reconstitution of the 30S subunit to demonstrate the order in which the proteins were incorporated into the nascent ribosome. He showed that there are several proteins that interact with the RNA in the absence of any other proteins; these are termed primary binding proteins. There is another set of proteins that require prior binding of one protein, now known as secondary binding proteins. Tertiary binding proteins then

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require prior incorporation of two or more proteins. During Nomura's time, the domain organization that I am showing here was not appreciated.

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Figure 9. Domain structure of the 30S ribosomal subunit.

RNA Folding in ltibosome Assembly 187 Question: Do these proteins interact among themselves? Response: That is part of the story; the short answer is yes, some do, whereas most of them do not. Nomura did not appreciate the domain structure, as in Figures 8 and 9 and I have redrawn his classic map in this form to reflect the domain organization. There is a primary organizer for each domain, and I will now talk about the' central domain, which is organized by the binding of the S15 protein. After S15 binds, a pair of proteins, S6 and S18, bind cooperatively, followed by S l l and S21. The question just asked about this inter-domain interaction turns out to be part of S8, which seems to potentiate the biding of proteins to the 5' domain. It does not do anything in the central domain; it binds to the lower helix, and I will cut that off.

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I will go through the experimental approaches used to answer these questions. We have the assembly pathway of Figure 10. How does S15 recognize the RNA? Why is the binding of S6 and S18 proteins so cooperative? Furthermore, how does S15 exert its effect such that after S15 binds, S6 and S18 can bind? There arc two possible answers for the latter question: Either there are protein-protein interactions between them, or the effect of S15 is mediated at the level of stabilizing the RNA structure in order to make the binding site right for S6 and S18.

188 J. R. Williamson I will distill all these data into a very brief slide [Fig. 11]. A number of research groups had localized the binding of S15 to one region. We identified the minimal binding site as a three-way junction deriving from the central domain. One of the consequences of the fact that the secondary structures in RNA are so thermodynamically stable is that we can frequently dissect pieces out and ensure that they fold properly simply by adding what we call stable tetra-loops to cap the ends of the helices. We can measure the binding constant of S15 for the constructs to be 5 nanomolar. We have captured the thermodynamic essence of binding. Furthermore, these nucleotides boxed in blue are conserved, and if you mutate them S15 no longer binds. These are conserved in 16S RNA, because they form the S15 binding-site.

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How does S15 recognize 16S rRNA?

How is the binding of S6 and S18 cooperative?

How does S15 direct the binding ofS6andS18?

Figure 11. Key questions in 30S central domain assembly. We can consider this minimal S15 binding site as a three-way helical junction, drawn schematically here [Fig. 12]. Early on, we had a hint that S15 induced a conformational change in this three-way junction. We wanted to find a way to quantify this three-way junction, so we collaborated with Paul Hagerman, who is at the Denver Health Science Center, in Colorado. Paul Hagerman adapted a wellknown technique, called transient electric birefringence [Fig. 13], to allow us to measure the angle between two helices. This is a beautiful application of a simple experiment.

RNA Folding in Ribosome Assembly 189

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Figure 12. The minimal S15 binding. It consists of a simple apparatus and a cell containing the RNA solution. You apply a strong electric field of around 1000 volts per centimeter across the cell. The molecules tend to align along the electric field, and that induces birefringence, which is simply a difference in the refractive index in a direction perpendicular or parallel to the cell. The birefringence may then be read with these polarizer and analyzer filters. First the voltage is turned on, then the molecules are aligned, after which birefringence is induced. Then you turn the voltage off, and the molecules go from their somewhat aligned orientation along the field back to a very random orientation. The rate at which they do this is related to the hydrodynamic radius of the molecule. Hagerman's approach was very clever. He said that since we want to know the angle between two of these arms, the rotational correlation time should be made extremely sensitive to this angle by extending a pair of helices. This was done by adding approximately 200 base-pairs of RNA helix while keeping the central junction, since it contains the S15 binding site. The longest rotational correlation time for this asymmetric molecule exquisitely depends on the angle between these two very long helices. These decays are shown schematically in Figure 13. If you have a linear molecule you can get a very long decay, whereas if the molecule is bent you obtain a much shorter decay. Hagerman developed a hydrodynamic theory to approximate these molecules as a series of small spheres and analytically

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calculated the expected rotational correlation times such that it was possible to turn these decay lifetimes of the TEB into an intracellular angle.

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Figure 13. Measurement of intcrhelical angles using iransieni electric birefringence. While the experiment is quite simple, the theory is quite complex. Basically, we make three constructs, one in which we have each pair of arms in turn extended to be 200 base-pairs, and using this simple optical technique, we measure the angles between all the helices. The results of these experiments are shown in Figures 14 and 15. In the absence of the protein, we find the angles between the helices to all be approximately 120°. The error bar is on the order of 10° or 20° (Paul Hagerman would say 10°). In the presence of the S15 protein, these two helices (purple and yellow) become co-linear, and the blue helix forms an acute angle with respect to this. So there is a big conformational change occurring in this very localized region in the 16S RNA. I will say more about that in a moment.

RNA Folding in Ribosome Assembly 191

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Figure 15. The minimal S15 binding site. Once we knew there was a scissoring motion between these two arms, we could set up a very convenient fluorescence assay to monitor the conformational change [Fig. 16]. This has also allowed us to measure other thermodynamics and kinetics. We can synthesize our RNA out of three separate pieces: attach a fluorescein dye to one end and a cy-3 dye to another end. This makes a donor and acceptor fluorophore pair. We can then attach something else to the third arm, for instance biotin, which allows us to immobilize the RNA on a surface. The basic idea is that the two arms undergo this scissoring motion in response to S15 and magnesium ions. When the protein is not bound we have the open conformation, and the

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distance between the two chromophores is well beyond the Forster distance for efficient energy transfer between the two dyes. If we excite the fluorescein donor, we see the fluorescein fluorescence as green. However, once we have the closed conformation, there is close proximity of the two chromophores, we get efficient energy transfer, and see the orange cy-3 fluorescence if we excite the fluorescein dye.

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We have done a variety of solution experiments. I will discuss just one, which we did in collaboration with Steve Chu's lab: single molecule fluorescence. They built a microscope with which you can look at this 10-micron area, and each of the blobs corresponds to an individual molecule that has been immobilized on the surface [Fig. 17]. You have to integrate the fluorescence over about 5 milliseconds in order to see it, so this might be on the order of about 10,000 photons, but each photon came from an individual molecule. We washed the S15 protein at approximately the KD concentration in the solution over these immobilized RNA molecules. When doing bulk fluorescence, in order to do a titration you usually measure the fluorescence intensity of the donor as a function of the protein concentration. Here we can actually cap the molecules. At the Kp, half the

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molecules should have a protein and half should not. In fact, we see that half the molecules are green, indicating they arc in the open conformation, and half of them

are orange. Basically, we are able to reproduce things that can be done in solution. But the power of single-molecule methods is that you can get at the details of the ensemble; you can just read them out. Just recently, experiments have been done in which we can do autocorrelation analysis of the donor wavelength; autocorrelation of the acceptor or crosscorrelation between the donor and the acceptor. If you watch one molecule, you see that the donor fluorescence will go on, then blink off; go on and off, etc. You see a stochastic change of state. If you do autocorrelation analysis, you get the decay rate, which allows you to measure the opening and closing rates. We have done that as a function of the ion concentration, which reveals that it is actually not a simple opening and closing. Ion-driven folding is more complicated than a simple twostate transition. This is interesting because we could never have seen that by doing steady-state fluorescence. This is a very powerful process now in use in a number of labs throughout the world.

individual RNA junctions immobilized on a surface in the presence of S15

Ha, Zhuang, Kim, Orr, Williamson & Chu, PNAS 96,9077 (1999) Figure 17. Single molecule fluorescence.

194 J. R. Williamson The conformational change I just described is in these three helices. Within these two that are scissored together [Fig. 17] I have drawn the protein contacts to the rest of the central domain. After S15 binds, two other proteins, S6 and SI8, bind. Note that they footprint in two separate places, as does SI 1. Those proteins that bind downstream from S15 have bi-partite footprints separated by the two arms that undergo this scissoring motion. One of the things S15 does is consolidate the global structure of this RNA, bringing together two downstream parts of the proteinbinding-sites in the assembly of the domain. This is an important point, because the degrees of freedom of large amounts of RNA are being restricted, and the parts that are attached to this junction are undergoing motions on the tens of Angstroms scale. This has dramatic consequences in terms of consolidating the structure.

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We were able to do deletion analysis of the central domain and start chopping off various pieces, coming up with an interesting observation [Fig. 18J: We could chop off this whole region (right-hand area of figure on top-left) and still bind all the proteins that bind to the central domain. As already mentioned, we can chop off the

RNA Folding in Ribosome Assembly 195 helix, which is the S8 binding site, and that does not affect the other proteins. Now we have half the central domain that still binds most of the proteins. If we chop off the helix where S l l binds, we get a small piece (bottom middle), which binds the three proteins shown below (bottom middle), and this (bottom right) is our minimal S15 site. What is it that S15 does to this structure (bottom middle) that makes it so that S6 and S18 can bind? It is not so clear.

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What I like about this series of RNPs is that I could show you the assembly pathway. Here we have a series of RNA constructs that correspond to intermediates in this discrete assembly process [Fig. 19]. We have an intermediate, where one protein is bound, where three proteins are bound, and where four proteins are bound, and we can put them in a bottle and measure the transitions between the states along this cascade. This is the power of the reductionist approach.

196 J. R. Williamson Question: Are S6 and SI8 binding RNA, or are they binding SI5? Response: Those are in fact the two possibilities. We will ask and answer that in a moment. We solved the crystal structure of this piece (second from right), and mere months after it was finished, the structure of the whole 30S subunit came out. The structure that I m going to show you of our piece is basically identical to what is in the 30S subunit.

Figure 20. The T. thermophilusS\5,

S6. S18-RNA complex.

Here is the structure [Fig. 20], with the S15 protein and the 3-helix junction, in which I described the conformational change. S6 and S18 are at the top, so we have a little bit of both. S6 and S18 clearly form an intimate heterodyne, and that is likely to be why their binding is cooperative. You never sec S6 without S18. However, there are no protein-protein contacts between S15 and S6 or SI8. This means that the effect mediated by S15, which allows the next two proteins to bind, is exerted entirely through stabilizing the RNA structure. There is something

RNA Folding in Ribosome Assembly 197 unstable about the RNA in this region (middle region of figure on right). Once S15 binds, it stabilizes the proper conformation, so that the next two proteins may bind. The S15 structure [Fig. 20] is that of a helical protein. Three helices form the RNA binding face. We see specific-binding amino-acid contacts, and there is a pink loop that folds down. We are actually getting contacts to this loop that we did not anticipate. We cut this pink loop off, and S15 did not seem to care. However, in the complex, we saw contacts between S15 and the residues, and S6 and S18 binding above (not shown). We have now done extensive thermodynamic analysis of the binding of S6 and S18 to the pre-formed S15 complex. We did this with purified components from Aquifex aeolicus, which is a hyper-thermophile that we expressed in E. coli. That is not really important. All these proteins are virtually conserved throughout bacterial sequences. Here are the two possible explanations for why S6 and S18 bind cooperatively [Fig. 21J. We could have the two proteins binding together to form a heterodimer, which then binds to the S15 RNA complex (left), or you could imagine a sequential and weak binding of either protein in either order (right). As I will show you in a minute, it turns out that the two proteins actually form a pre-formed heterodimer.

Model 1- S6 and S18 bind as pre-formed heterodimer:

Model 2- sequential binding of S6 and S18:

Figure 21. Two possible mechanisms for binding of S6 and S18 to the S15-RNA complex.

This is an example of how we measure the affinity of these two things, called a gel-shift experiment [Fig. 221. The reaction we are monitoring is the binding of S15

198 J. R. Williamson to RNA (top right). Looking at either curve, the top describes electrophoresis in a direction that goes from top to bottom. The free RNA moves at the described mobility. When the protein comes on board, you get a complex at a different mobility. All we do is measure the fraction of the RNA that appears in the S15 RNA complex, and we get a KD that is around 5 nanomolar, which is the same in Thermus thermophilus and in E. coli.

[S15] M 40-C, 20 mM K-HEPES, pH 7.6, 330 mM KCI

Figure 22. Polyacrylamide gel-shift assay for S15 binding lo RNA.

To measure the binding of S6 and S18, we carried out an isothermal titration calorimetry (ITC) experiment [Fig. 23]. One of the proteins is put into a thermostatically controlled cell, say S6, in the sample cell, and then SI 8 is introduced into the syringe. You inject a small aliquot into the cell and directly measure the heat that evolves due to the positive or negative binding enthalpy between the two proteins. You keep titrating-in the protein until you have formed a 1:1 complex. Then you sum this up and get the AH, and you can also fit what the KD is within reasonable limits [Fig. 24]. We inject a small amount of protein and get a spike; we actually get some heat evolved. We integrate that, wait a while, and

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inject another aliquot. We are basically doing a titration of S6 into SI8. We eventually add more S6 and no more heat evolves, so we have a 1:1 complex. It turns out to happen at a 1:1 ratio. The KD for the formation of this dimer is about 8 nanomolar, and the AH is about 16 Kcal per mole, which is very typical of a proteinprotein interaction. We can use this preformed heterodimer to do another gel-shift, which is a sort of super-shift experiment, to monitor the binding of the heterodimer to the S15 RNA complex [Fig. 25]. Again, we have electrophoresis from the top to the bottom; shown is the free RNA at a certain mobility, and the pre-formed S15 RNA complex. We have saturated the RNA with S15, and now we titrate-in increasing amounts of the pre-formed heterodimer, and see a higher-order complex coming in. We can measure the KD for this complex, which is approximately 5 nanomolar.

As ligand is injected through syringo into samplo coll, heat is generated or consumed. The ITC keeps a constant temperature difference between the reference and sample cell.

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I will now describe the interesting thing that gets to the very heart of the cooperativity in this binding. I previously described the ITC experiment, in which a

200 J. R. Williamson lot of heat is evolved as we bind S6 and S18 to the RNA, with S15 bound. I have done three other experiments that are also shown here [Fig. 26], and there is no heat. If we titrate-in S6 without S18, or S18 without S6, there is no binding. Or if we titrate the pre-formed heterodimcr into the RNA, where there is no bound S15, there is no binding. This means there is an absolute hierarchy and required assembly order for these proteins. There are no parallel assembly pathways. We must find S15 first, because S6 and S18 do not bind to this RNA by themselves, and we cannot bind either S6 or S18 alone without its other partner [Fig. 27]. We have to pre-form the heterodimer and wait for S I 5 to bind; only then can we bind to the assembling particle. The ribosome assembly pathway has a built-in obligate ordered pathway to literally ensure that you do not form other interactions until the first interactions that are needed to nucleate the folding pathway have been established.

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Figure 24. Isothermal titration calorimetry of the S6:S 18 complex. What is the molecular basis for that occurrence? Here is the complex that was just shown [Fig. 28], in which S15 is making contacts at the lower junction and at

UNA Folding in Ribosome Assembly 201 the upper junction, shown in pink (left). A highlight of this region is on the right. S15 is doing two things: there are amino-acid side-chain contacts to this pink loop, and in addition, there is what we call an inter-helical base-pair. This is a nucleotide from the middle of the green helix that is stuck into the middle of the pink helix. We get a non-Watson Crick base-pair, but it stacks perfectly well into the pink helix, which we call helix 23a. This is a very laminar structure. It is a little like plywood; you are basically locking the helix parallel to helix 22 by this base-pair. That interaction is buttressed by the interactions of S15 to this tetra-loop structure. Apparently this structure by itself is unstable and cannot bind the proteins in the absence of this inter-helical base or the S15 interaction. At this point we really understand at the molecular level the structural basis for the obligate hierarchical assembly that we see in this part of the assembly of the central domain.

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outside and the three helices (top left) form this long, continuous coaxial stack (right). The helix that makes up the inter-domain contact that was asked about earlier is at the bottom right. The figure on the right is really a globular structure. The protein binding-site consists of those three helices on the left that form a coaxial stack. All the other parts of the figure on the left form the globular folded domain (right), and this other region that is folded up (left) actually contains the functionally important parts of the 30S subunit. The 790 loop, one of the most conserved sequences in the 30S subunit, is part of the P-site, where one of the tRNAs binds during protein synthesis. The other helix is called the Dahlberg switch helix, which undergoes a base-pairing register shift as the ribosome translocates from one codon to the next. These are functionally important and potentially flexible regions of the central domain and have nothing to do with protein binding.

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204 J. R. Williamson We can further subdivide the central domain into two subdomains: a primary subdomain responsible for protein binding, which apparently folds first and forms the template upon which this functionally important, secondary subdomain that can now assemble (highlighted in blue). Everything I have described is literally the folding of the two three-way junctions (in white) in order to set up this RNP scaffold, upon which the part in blue is now assembled. In general, what can we say from looking at the binding of this small number of proteins? We can write a kind of mechanism for the assembly of the central domain [Fig. 29]. The top left figure is the helices in the central domain that we have already looked at. The second figure (to the right) describes the conformational change. After S15 binds, we sec another conformational change in the third figure. On the bottom left, we still do not understand the basis for S l l requiring S6 and S18 binding; that study is ongoing. Once all the proteins are bound (bottom right), the second domain folds up to form the P-site in the encoding region.

secondary subdomain functionally important Figure 29. A hierarchy of RNA subdomains.

RNA Folding in Ribosome Assembly

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What we see here is that the mechanism literally consists of an alternating series of conformational changes in protein-binding events. The protein-binding events seem to consolidate relatively unstable parts of the RNA structure. You have something that is very flexible; it folds and adopts the bound conformation, then the protein comes along and locks it down and reduces the unfolding rate of the RNA in that local region so that the next step can occur. To return to what I started talking about earlier, one might expect such a large RNA to fold extremely slowly, but the biology of ribosome biogenesis is such that we know these things have to fold accurately and fast. It seems that the assembly process is encoded in the sequence; it is mediated through the formation of the local RNA structure; conformational changes that are locked down by protein-binding events. That is how you can inexorably close in onto a unique folded structure, even though you have this very large, complicated sequence that has to encode that function. Question: Is this assembly of protein folds co-transcriptional? Response: It has to be. It has been shown that the 5' domain proteins seem to assemble first, followed by the central domain proteins, then the 3' domain proteins. That even happens in vitro. In part, there is a similarity between the cotranscriptional folding and the folding initiated from denatured RNA... Question: Does RNA folding go along with the assembly ofproteins? Response: Yes, absolutely. We have used small pieces, with which we are less concerned about the formation of the secondary structure. For a piece such as this, transcription would take a few seconds. It is fast, compared to most things we are worried about. Protein binding rates in vitro are on the order of 105 molar/second, which is several orders of magnitude slower than diffusion. However, inside the cell, protein concentrations are reasonably high, so one might expect the actual rates would be on the order of seconds. It probably takes 25 seconds to go through 16S RNA. As was shown in Herve Isambert's discussion, as soon as you get a local secondary structure, it will form. That happens very fast. Then you need to make sure that inappropriate structures are not formed. I should say that one of the reasons RNA folding slows-down as the length increases is that you increase the probability of forming misfolded structures. Peter Schuster used a simple structure to very nicely show that you could have slow folding by the unfolding of something

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that was improperly folded. Sarah Woodson then showed that for the group I intron there is the alt P3, an inappropriate helix that forms late, and a bunch of stuff has to unfold in order for that to resolve itself. The more structure that forms, the larger the activation barrier becomes for unfolding. For the ribosome, it becomes critical that you not get stuck in something that is misfolded. If you make the whole thing and you have a wrong helix in the 5' domain, you will never get this thing unassembled. In part, what the ribosomal proteins are doing is making sure that all of this happens; but they are not chaperones, because they are incorporated into the final product. Question: Is this the same for eukaryotes? Response: It is much more complicated in eukaryotes, for a variety of reasons. There are small nucleolar RNAs that are complementary to these sequences, which bind and are important for RNA processing, as well as the modification. Helicases are also involved, and it is much more complicated. In part, it could be that they seize control of the orchestration of all these events. With a bacterial ribosome you can get away with this chaotic assembly pathway. By making the mechanism more complicated, you also can exert more control. Perhaps that is the reason for these elaborations in eukaryotic ribosomes. Question: Once you admit the constraint that every time a proper contact is made it is left alone, in a sense, you are making rules for the proteins. It would seem that they should also ignore those traps in their hierarchical pathway, is that correct? Response: Right. That sort of depends on the energy function used to calculate it. You can always find traps, depending on what your potential function is. Question: Do you have any idea why, among all the stabilizers, G-A mismatches, rather than say, a G-C base-pair? Response: That is an interesting question. We're talking about the inter-helix basepair shown in Figure 28. It turns out that this pair was missed by phylogenetic comparison, because it does not co-vary. It is not clear that it actually matters exactly what the base-pair is. There are probably some geometric constraints with regard to which way the bases are pointing. I think either of the nucleotides in the bulge nucleotide (on left) could fit into a pocket and form a stable structure. There are probably cases in which one nucleotide is in and the other is out, which is why it

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could be missed in phylogeny. I do not think there is anything magical about this. It is not conserved in phylogeny; however, the existence of the bulge and the fact there are two nucleotides is conserved. This is an interesting point that we do not quite understand. It is a detail that we are currently thinking about. We have deleted one of the nucleotides and found that S15 does not care at all. However, S6 and S18 do not incorporate into the complex, and that shows us that this is critical for binding. Question: Do all ribosomes have exactly the same structure in a given cell? Response: Yes; while that may not be exactly true, we know that they all have to perform the same function, and that has to do with high fidelity; they are all making proteins. Where the ribosomes might differ is that they can have all sorts of translation factors bound, and you can modulate the specificity of protein synthesis by binding all types of things. The translation apparatus goes way beyond the confines of the ribosome. The other thing is that if you have antibiotics bound, you can bind them to the RNA and they can disrupt protein synthesis, probably in a very heterogeneous way. Question: Does that depend on the location within the cell? Response: There is binding of ribosomes to the membrane, so those could be somewhat different. Question: [inaudible] Response: There are plenty of cases in which you have mutants. If you treat them with certain antibiotics you disrupt ribosome assembly, and you can find shards of ribosomes laying around. Question: Is there a homolog of SI 5 protein in eukaryotic ribosomes? Response: No. It turns out that the three-way junction structure is completely different in eukaryotic ribosomes. I do not think there is an S15 homolog. Comment: It might be related to the fact that in eukaryotes the proteins are assembled in the ribosomes in a different way.

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Response: Yes; that could be the case. Thai whole relationship is interesting. Some ribosomal proteins are conserved in prokaryotes and eukaryotes, some are unique to prokaryotes, some unique to eukaryotes. Some are uniquely conserved in archaeobacteria or eukaryotes, and some others in archaeobacteria and bacteria. There are all kinds of different ways this might be orchestrated, and the assembly pathway may depend on what Kingdom you are in. That is a good point. Comment: Concerning ribosome heterogeneity, I believe there were data in the 1970s on the dispersion of translation rates on ribosomes. Response: In vitro? Comment: / believe it was in vivo. There was a time when people were measuring protein synthesis rates. Response: On a homogeneous messenger RNA? Comment: Yes, I believe so. Response: One thing I will say about the ribosome field is that the vastness of the literature is humbling, and most of it is not accessible on computer. Question: You 've checked the Stanford database? Response: Yes, but most of that is literature that has to do with individual mutations and chemical modifications and biochemical data, not biological data. There is a huge amount of interesting stuff out there that I, unfortunately, do not have at my fingertips. Question: What exactly is known about the in vivo assembly map? Response: I think all that is known is that roughly, you see the 5'-to-3' assembly. It is a rather difficult experiment to do. One experiment that I know of is essentially an isotope chase experiment, where you grow bacteria, throw in tritiated amino acids and 32P nucleotides and measure the rate of incorporation of 32P and tritium into ribosomes, and you can see some of the 5' proteins come on before. But it is a hellish experiment and I think the dynamic range for the measurement of fractions is

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not very large. It would be good to repeat those experiments, since they were probably done twenty years ago. Question: [inaudible] Response: In principle, you could use FRET by labeling various proteins, absolutely. Then you presumably would have to use GFP fusions, and then, of course, someone could say "the GFP is not perturbing the folding pathway." But it is a good idea that is under consideration. Question: Are you working with E. coli? Response: We have worked with E.coli, Bacillus starothermophilus, thermophilys and Aquifex acolius as a matter of convenience.

Thermus

Question: But the full 70S subunit was Halobacterium? Response: The 50S subunit was Halobacterium, which is an archaeobacterium, although just recently; the eubacterial one came from Ada Yonath's lab. Question: How do you translate the archaeobacterial information to E. coli, for example? Response: Most of it is conserved. You just look at the RNAs, and they are conserved. There are certain expansion sequences... Comment: But the proteins are not. Response: Many of them are. At least half the ribosomal proteins have direct homologues in eubacteria. There is a variety that does not. I think if you look at Yonath's paper that came out in Cell, you will see that they go into great detail about the correspondences between ribosomal proteins and who substitutes for whom in which structure. I have not yet absorbed that paper, but I know that information is in there. Comment: / assume they had to wait until the structure came out before they could really take that apart.

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Response: Yonath just recently did the eubacterial 50S, using molecular replacement from the Archaea structure. So that has been in the database for some time, perhaps almost a year.

FROM RNA SEQUENCES TO FOLDING PATHWAYS AND STRUCTURES: A PERSPECTIVE HERVE ISAMBERT LDFC, Institut de Physique, Strasbourg, France

My talk today concerns RNA folding. Our group is trying to understand the process of RNA folding, going from its primary nucleotide sequence to its secondary structure. The approach we are developing is somewhat complementary to those Michael Zuker and Peter Schuster presented earlier today. We are interested in modeling RNA folding and unfolding kinetics. The idea behind modeling RNA folding kinetics is that in principle it may not only be used to predict RNA secondary structure, but also to potentially learn something about the folding pathways of these molecules. In addition, attempting to model RNA folding kinetics also allows us to predict more complex secondary structures, including pseudo-knots, which are secondary structures that do not resemble trees [Fig. 1]. Naturally, in developing such a dynamic approach it is helpful to have a tool for visualizing what the algorithm is predicting. For this reason, we have adapted the software "RNAmovie," designed by the Bielefeld group, in Germany. This software displays RNA folding pathways, including pseudo-knots, in a movie format and helps to analyze data predicted with the actual folding algorithms. Our primary goal is not only to decode and predict RNA secondary structures, but also to decode their folding pathways. For example, I will discuss the folding pathways of the group 1 intron, which has already been mentioned several times today. We have other objectives beyond this primary goal, such as trying to model and understand the dynamics of antisense hybridization of at least partially complementary molecules. I will show some results that involve the HIV-1 initiation complex. Another topic that interests us is micro-mechanical unfolding of RNA, which essentially concerns a secondary structure being pulled apart by what turns out to be minute forces applied to single molecules by a "large" apparatus. Some very elegant experiments of this type have been conducted in our laboratory at the Institute of Physics in Strasbourg, France.

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Figure I. Decoding RNA folding pathways: Proposed stability exchange between two competing helices forming sequentially during transcription of hepatitis delta virus ribozyme. The strong, yet transient helix P8 guides the nucleation ot"P4.

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Beyond these attempts to decode the information stored in the RNA primary sequence, which generates secondary structures, we are also interested in a sort of reverse engineering approach, in which the idea is to design artificial sequences that exhibit interesting or puzzling behaviors with respect to folding pathways. For instance, we are trying to develop some bi-stable RNA molecular toys, both by computer simulation and in real laboratory experiments. First 1 will present a short visual demonstration of the software we have adapted for use in studying these molecules, which will give you some idea of the software tool we are using. Of course, it is a visualization tool, and you may have questions, which I will be glad to try to answer. This is an RNA molecule being synthesized from its 5'-end. As you can see, the molecule is in fact folding while it is being synthesized. You can see green helices being formed, and orange stretches linked by thin blue lines, which correspond to pseudo-knots. You have to imagine that these orange helices are essentially identical to the other helices in the model. It is just for purposes of visualization that they are somewhat pulled apart like this. You can also see that some of these helices are also transient; they appear at one point, then disappear. We are studying these transient helices, which are quite interesting. The software we use to display these structures was developed by the Bielefeld group. We did have to add the pseudo-knot feature, which was not in the original software package, but that was a minor addition. A good example of transient helices is shown by the intermediate structure of this molecule, which is actually very stable, because it has very strong helices, as well as the pseudo-knot, which is also seen to be very long. In principle, this molecule is so stable that nothing can really happen to it. We see that merely by synthesizing just a few more bases downstream from the sequence a new helix begins to nucleate. At this point, the previous orange helix can very easily be removed by replacing its base-pairs with all the other base-pairs from this new stem. There are definitely transient stems in this particular molecule. My guess is that this is quite a general feature, which is why I asked Michael Zuker whether he had looked at several point-mutations, since one would expect that if these transient stems are really encoded in the primary sequence there would also be higher-order correlations between these complementary mutations, perhaps even complementary mutations that do not appear in the native structure. Question: Is this assuming that the modified bases are being created almost instantaneously after synthesis? Response: There are no modified bases here.

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Question: If one wanted to include that in the model, would it be biologically correct to assume that they are modified almost immediately after synthesis? Response: I do not know the answer to your question. I have asked this question myself many times; the answer is no, probably not. Question: No? Response: Probably not; I don't know. We can also look at another problem, one that is different from that of a single molecule being synthesized. In particular, we can follow the hybridization of two partially complementary nucleic acid sequences. One interesting example is the formation of the initiation complex for reverse transcription of the HIV-1 retrovirus, which involves its hybridization with a tRNA molecule. As many of you know, these retroviruses are reverse-transcribed into double-stranded DNA and then introduced into the host cell genome. These retroviruses must all be recognized by a reverse transcriptase, which carries out that task. It turns out that the reverse transcriptase does not actually recognize these retroviruses by themselves, but only after they have been partially hybridized to a tRNA molecule that is presumably not initially made for this purpose, but that just happened to have been hijacked along the way. This is also a question that interests us. These two molecules, although you would not imagine it from this first picture, have rather long extents of complementary regions. We are studying the dynamic process of hybridization between these two molecules. In order to do so, we join them with this inert linker. If we want to model molecules with modified bases, we could use the same process. First we fold the two molecules separately, and once we are satisfied with the two separately folded states, allow cross-hybridization to occur, with a long extension between the two molecules. You can also see that there is a fair amount of tRNA unfolding going on during this process. The preceding were a few examples of what it is possible to do. How do we actually do this? In order to model RNA folding kinetics, first we must know the free-energies of various structures. There certainly is one very important contribution to the overall free-energy of a molecule, which may be fairly complex, as shown here. This contribution derives from the formed helices, as previously mentioned in some of the other talks. There exist some fairly good models, such as nearest-neighbor models between consecutive stacks. We did not

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invent these numbers; we are using the same tables everyone else does. This part is certainly well known. The other part we incorporate into the model accounts for the overall conformational entropy loss of the molecule during the folding process. We have devised and developed a fairly crude model for the second part, [Fig. 1]. Using this approach, we take this complex molecule and essentially throw out all the details, retaining only the fact that it basically looks like a mixed assembly of stiff rods (blue), which correspond to the helices, and ideal springs, which correspond to the single-strands (black). At this level of complexity, we can compute values by means of basic polymer physics theory, evaluating the entropic costs of the molecule. In the case of usual, tree-like structures, everything is fairly simple, because the overall thermodynamic weight is simply factorized into independent contributions that correspond to the various parts, which in this example consist of two helices and two loops. Everything comes easily, and in principle, we obtain the same results as with the usual approach. On top of these nested structures, we can also estimate the entropic cost of making more complex structures, including pseudo-knots. A simple example is shown, in which two helices are connected by three single-strands. In this conformation, the orientation of the two helices is no longer independent, indicating that there is an entropic price to be paid, which may be estimated using this model. So we can compare structures. But does that mean that we can predict structure? That is not yet the case. The problem is that, as Peter Schuster mentioned earlier today, we start with one sequence and try to predict its structure within a huge structural space. To illustrate this point, it is quite useful to begin with an example of a tRN A sequence and all the possible helices that can be formed from it, in which each line has two segments that correspond to a possible helix. Thus, ACCA can pair with UGGU, and so on. When this is enumerated you obtain many, many helices. This is probably fairly obvious, and also the easy part. The complex part is to find the combination of helices that make a good fold, overall. In addition to the helices that generate the well-known clover-leaf shape, the actual shape is surrounded by other structures. The task of finding the "good" structure is not all that trivial. Remarkably, when limited to the sub-space of tree-like structures, it is possible to exhaustively search all such structures to find the absolute minimum free-energy configurations. It is indeed impressive that algorithms may be designed to essentially enumerate all this structure space. But there are limitations. An obvious one is that by this method, pseudo-knots - which do in fact occur in RNA molecules - cannot be included, a priori. The space including pseudo-knots is much larger. If one would

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like to learn something about all the pseudo-knotted structures, it would seem necessary to abandon the idea of carrying out an exhaustive search of the entire structural space. We had to try something else. Although we were not the first to do so, instead of enumerating the entire structure space, we attempted to devise a reasonable model of the actual wandering around of the sequence within the structure space; i.e., to model the kinetics of the molecule. In order to do so, we had to introduce connections between these states. These connections turned out to connect structures that differed by only one helix. The whole space is then seen to have neighborhood relations. We also had to model and evaluate the kinetic rates between those states. The reason that we could actually do this is that experimental results have shown that the time-limiting steps within the structure-space do indeed involve the formation or dissociation of entire helices and follow kinetic rules involving the barrier between the current ground-state and some intermediate structure, which in this simple case would imply a pinch in the loop in between. Assuming that the kinetics all around the huge structural space follows those laws, the picture we now have is that if one can evaluate those barriers, one can model the wandering of the molecule, as shown by the green arrows. In this random walk, many states are ignored, so it is clear that not all states can be explored, but this is probably what actual RNA molecules do; they do not explore all states either. In the algorithm we developed, when we have a sequence, we first enumerate all stems, as for the tRNA example. We calculate the rate of formation for each of these stems, if they are not already formed in the current structure, or of their dissociation, if they are already formed. Question: Do you include the nucleation step in this case, or is it just proportional to the AG? Response: The nucleation step, if I understand correctly, derives from these rates, k0, which give you the actual time clock for RNA folding. These numbers were extracted from previous experiments. They are a bit like base-stacking interactions, which cannot be estimated ad initio, but have been measured, and we use those numbers. The relevant question might be: "These numbers were measured for some specific molecules, so what is the justification for using them in general?" Comment: In the nucleation step, you already start with some given structure; you are simply changing the combinatorics. Nucleation does not appear here.

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Response: Nucleation does appear at the barrier, and to compute those rates you need to go to some barrier, which essentially is the nucleation step, and we calculated those structures as well. But at each stage, when, for instance, we decided to form another helix, we found another structure, and in principle, as you mentioned, all those rates had changed and we had to recalculate everything again, which is what we do. Question: In the previous case you had entropic factors; have they disappeared now? Response: No, they are all buried here. That is the difference between the freeenergy of these states and the free-energy of the barrier, which is not drawn in this figure. Question: Yes, but the power law, such as with 3/2 power? Response: Yes, they are all here. That is this curve, which comprises everything, loops and all. Even though it may not be included in the figures, the free-energies that comprise loops, and things of that nature, are included. Question: Just to clarify, in part you need these nucleation steps because you only allow complete opening and closing rather than say, a local extension or a shifting or a sliding-over of base-pairs to go from one helix to another - is this correct? Response: You need the nucleation steps to change the overall topology of the current structure. As for local extension - shifting or sliding over base-pairs which do not change the structure topology - we know that they generally occur at much higher rates. This allows us to assume that those degrees of freedom have essentially reached equilibrium. So for each structure topology, we shift and slide over the competing base-pairs in order to find the optimum {i.e., lowest minimum free-energy) configuration., We have to recalculate everything at each stage, which is CPU time-consuming, and is why we have developed a rather elaborate algorithm to hasten this process. Let me finish by saying that when we follow the pathway of this molecule, we obtain a measurement of the time-lapse during the simulation, because we evaluate the lifetime of the current structure by adding all those rates to make a transition from this current structure. When we sum up those rates, we obtain the total number of possible transitions per unit time, so the inverse of those rates is the actual mean lifetime of the molecule. We then have a measurement of

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the time spent between each transition and do the actual transition stochastically, by picking out the next transition according to its weight. We are modeling the dynamics in this way. Question: You capitalize on the cooperativity of zipping and it becomes one step? Response: Yes, that's right. We are wandering around this structure space, and sometimes we will be trapped into a short cycle. At each time, for instance, if we are in a structure defined as Ti, we have to recalculate everything and ask where to go from there. Very often what happens is that the transition leads to a state that has already been visited in the past. So time is wasted in recalculating something that had already been calculated. The idea is to try to speed up this process. Question: What you have developed is not specific to your method? Response: No, it is not specific, and the method we have developed is actually quite general. Today I'm only reporting results obtained for RNA, but in principle, it can be used for many other problems. The idea is to go from this straightforward algorithm to a more complex one, which turns out to be an exact clustering algorithm. When you are in Ti, you recall the states you visited in the past, so you already know everything about those states and all the connections between them. Then you pick the next state, after having summed over all possible pathways within this set of clustered states that are memorized. The first question is "Which is the actual state within this clustered state from which I will now choose a new one?" This can be done statistically by summing over all the different pathways. In this example we chose state j , from which we pick a new state outside the cluster. When we calculate all these statistics over all the pathways, we can also calculate all the time averages that we want. Because we are doing kinetics, we can only measure time averages. There is certainly one average you must know, and that is the average time it took you to go from i to j , and then to exit from j . That is the quantity to be measured and it is precisely evaluated. You also might be interested in the time average fraction of pseudo-knots visited while wandering around in this cluster, etc. This may be done using an exact and efficient algorithm in 0(n 2 ) operations, where n is the number of states in the cluster. When you choose a new state from outside this cluster, it must be included in the new cluster; in other words, the cluster must be updated by

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including the new state. In order for the whole method to remain bounded, you also have to get rid of one state. This can also be done in 0(n 2 ) operations. In the results of this method, and specifically, what may be learned about RNA using this method, the effective speeding-up of this exact clustering-algorithm reveals that several orders of magnitude are gained for a cluster that contains several states. More than a thousand-fold speed-up is gained when you study short molecules such as these. It turns out that it is essential to be able to simulate these molecules for a very long time, since they are trapped for hours, or even for several days. The gain is still several hundred-fold for this natural RNA molecule, the Hepatitis Delta virus ribozyme. As one would guess, it becomes less and less efficient as you use larger and larger molecules. I will further discuss the previously mentioned Group I intron, in which there is still a four-fold speed-up, which in practice makes a lot of difference when simulating those molecules. One thing that may be evaluated with this method is the occurrence of pseudoknots in random RNA sequences. No one knows those numbers exactly. What we find is rather novel and also quite unexpected: even short molecules have pseudoknots. However, this may not come as a surprise to many RNA biologists. We also find that this fraction of pseudo-knots increases slightly with the GC content of these molecules, as one might also expect. It can attain about 25% of the base-pairs involved in pseudo-knots for high GC content, which is clearly non-negligible. What is more unexpected is that the curves for 50-, 100-, and 150-base-pair-long random sequences pretty much collapse into the same curve. This means that the number of pseudo-knots is roughly independent of the length of the molecule. This is something that was not foreseen, at least not by theoretical physicists, who have argued that pseudo-knots should be essentially negligible for short molecules, becoming problematic only for very long sequences. We find this not to be the case and that pseudo-knots are typically non-negligible, even for small molecules, perhaps less so for extremely short molecules. In addition, we find them to be independent of the length of the molecule. Due to lack of time, I can only say a few words about mechanical unfolding. In Strasbourg we are experimentally and numerically developing ideas for the purpose of studying the mechanical unfolding of RNA molecules. We are not the only people in the world doing this. The question we are addressing is how to find out what may be deduced from these experiments on secondary structures, since once it has been pulled apart the structure of the molecule it is not always obvious. We designed some toy molecules, whose purpose is to help address this question. These are three different molecules, consisting of the same two stems, one of which is rather weak and the other fairly strong: GC and AU. We arrange these stems

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differently in each of the three molecules. In principle, we pull one end of GC and one end of the AU stem, or both ends, either AU or GC first, before we would expect the rest of the molecule to unzip. Question: How quickly does this occur? Is it a fast force or a slow force? Response: It is fairly slow experimentally. You pull the molecule apart within a few seconds. You can also test the equilibrium by doing the reverse experiment; that is, to let it fold. This process is not exactly reversible, so we do not quite reach equilibrium either. I will have to skip the actual experiment, which is quite complex and a long story. Instead, I will jump to the actual results to show you that these experiments converge rather nicely with the numerics shown. In these two examples we look at different helices with the same structures. In one example the strong stem must be broken first, then the weak stem is broken. In another example the opposite must be done; the weak stem must be broken before the strong stem. This is shown both in the numerics and the experiments. A striking feature seems to be disagree between the two: the overall slope, which actually comes from the spring constant of the optical tweezers. There are two different optical tweezers in the actual experiments whereas in the numerics carried out before the experiments, the spring constants were the same. What we see in both experiments and in the numerics is that the molecule shows two plateaus, corresponding to the consecutive opening first of the weak stem at rather weak force, and then of the strong stem at a stronger force. For the other molecule, the strong stem cannot be broken until the critical force is reached. Once that happens, the second stem cannot resist and breaks as well. This gives you a larger drop, which is also indicated with the numerics. However, there are certain limitations with this approach. The third molecule is very different from the two just described, yet its trace is very similar to the molecule described. Hence, many details may be observed and measured with these unfolding experiments, but certainly other structural features are missed. I will finish by presenting some new data on the group I intron, returning to the primary goal of our approach, which is to decode RNA folding pathways. The lowest free-energy states we find are pretty close to the known native structure, in particular the pseudo-knots in P3, as well as some other pseudo-knots. There are certainly other bases that are probably incorrectly predicted, but overall there is around 85% agreement between the predicted structure and the known native structure. Perhaps more interesting is the prediction we may make concerning

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certain trapped intermediate structures, which are much less well known in the literature. A word of caution: In the animation of the folding pathway, you will only see the new minimum free-energy structures as they are attained in the simulation. You will miss all the other structures that are actually visited in the prediction algorithm, and that information might prove rather important. With this in mind, the freeenergy of this molecule is described. We also used the clustering approach and its time average features described earlier to follow particular helices, in this case P9, P3, and some other helices, for which one can compute the average time fraction of probability that one has with those particular helices, with a rate between 0 and 1. If it is 1, the helix is always there, and if it is 0, the helix is essentially never there. These patterns indicate that although you have found the actual native structure, there is a certain period of time - a few seconds to perhaps one minute - during which the molecule is not in the correct conformation and probably inactive. We also find that some pathways are misfolding; that is, the molecule is essentially trapped at the end of synthesis, indicating that the absence of P3 in the trapped states seems to agree with Sarah Woodson's work. Question: These experiments require applied force to open RNA molecules. What is your mechanical mode? You compared your results with some numerical model; what is that model? Response: The numerical model is the dynamic model of RNA folding I first described, but in addition to that, a force is added. You must apply work to the system and force it to stretch. In these pulling experiments, there is a molecule, and in addition to carrying out the kinetics described, a fixed constraint is enforced between the two ends. The constraint is then slowly increased to trigger unfolding of the molecule. Upon return, the molecule exerts a force that can be evaluated on the apparatus. If you do only that, you obtain a pretty bad match with the actual experiments, because it amounts to assuming an ideal apparatus to measure the force, which is not the case. Optical tweezers are not ideal, since they have some intrinsic stiffness. What should be modeled is some sort of system in which a bead is included and a fixed constraint applied to the composite system of the "apparatus + molecule." That is how you can match those experiments. The model consists ofthe molecule and the apparatus itself, which is quite important; otherwise the data do not fit. Question: Is the bead also dynamic?

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Response: Right. The bead also has some time to respond. This must also be taken into account. The experiments themselves also need some acquisition time, and all of this must be taken into account in order to interpret the data. That way you obtain fairly good agreement with the actual experiments. Question: Would it depend on the kinds of transitions that you make with the RNAs as they go through large barriers, or are they much more sensitive, compared with the time of reaction to the bead? If some stage takes more than milliseconds, it would react much differently from how it would if it took microseconds, right? Response: Exactly. That could be the case, but not in our very simple example. In principle, yes, you would have a very different reaction. Question: Do you have an argument to explain why the number of pseudo-knots does not increase with size? Response: Not yet, apart from hand-waving arguments, but we do not have a very profound explanation yet. Question: Looking at your simulations, it appears that your RNA is growing from one hairpin. It seems as if you are growing it from one end. Where is the rest of the RNA? Is it unfolded? Is it unstructured? Are you not showing the unstructured part? Response: Everything is shown. Question: But at the beginning only a few nucleotides are shown. Does it start to grow at that point? Response: beginning.

This is simply because the sequence was not started right at the

Question: I'm a bit confused by your model. grows? Response: Right.

Do you assume that it folds as it

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Question: So it is not a folding of RNA; rather, it is basically a co-transcriptional folding of RNA? Response: That is right. My guess is that it's also the way those molecules fold. Question: If you were to take an RNA, melt it and then cool it, would that be a different process ? Response: You would get something else. We studied that for some examples and obtained very different results. That is perhaps one of the reasons why group I seems to have better folding rates in vivo than in vitro, were you to do such coolingdown and heating-up of the sample; so they do not actually fold in the same process. There might also be proteins involved, but that didn't necessarily have to be the case. Comment: I just wanted to have some clarification that it is co-transcriptional folding. Response: Yes, in one case. But you can certainly also devise some models to support this. As seen from the hybridization of the two molecules already shown, there is no synthesis or transcription of those molecules, so you can look at the dynamic hybridization process between the two. Question: While the molecule is being synthesized? Response: No; not in this case. You recall that above I showed two molecules that hybridized with no synthesis involved in the process. It is just to show you that you can address different, but related, problems. Question: But you cannot simulate this molecule folding from a completely formed chain? Response: Yes, I certainly can, and have already done so. I do not have the sequence here, but I could certainly show you something that looks like a very long chain that collapses upon itself. Question: Do you get the same result? Do you get the same final structure?

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Response: For this particular molecule, I have not done the extensive statistics needed to give you an answer, but I have studied other molecules and gotten very different answers in the quench experiments, for which the whole sequence is there and allowed to fold suddenly, starting from any region of the sequence, compared with co-transcriptional folding. Question: Which of the two is closer to the known X-ray structures? Response: I was not referring to the final structure. The final structures in these cases were actually identical, but the pathways were different. Question: Are the final structures always identical no matter whether you do it by growing RNA or by pulling it? Response: Not in all cases; we are now involved in trying to design bistable molecules that are trapped by their own synthesis in one particular state, so you can certainly bias this folding by the mere synthesis of this molecule, which is what we are now exploring.

AN EVOLUTIONARY PERSPECTIVE ON THE DETERMINANTS OF PROTEIN FUNCTION AND ASSEMBLY OLIVIER LICHTARGE Department of Molecular & Human Genetics, Baylor College of Medicine, Houston, TX, USA

Most of us work in this field because it is at the interface of mathematics, physics, biology, and medicine. It is particularly exciting therefore to address an audience as equally diverse. I will describe and discuss our work using evolution as a computational and analytical tool to locate functional sites within protein structures, and the residues that most directly mediate their activity and specificity. This approach is a computational method, known as Evolutionary Trace (ET), and based on the same biological paradigm as mutational analysis in the laboratory. Specifically, it uses mutations and assays to identify functionally important residues, except that here the mutations and assays have already occurred during the course of evolution. First I will explain how the method works, then show you various examples that illustrate its applications. The general context of this work is the fundamental problem of integrating sequence, structure, and function information. Because of the data pouring forth from genome sequencing projects, as well as those soon to come from the Structural Genomics Initiative, this is an especially pressing issue. However, the difficulty of the problem cannot be overstated. Consider that after thirty years of effort, and despite the fact that the underlying process is deterministic, the sub-problem of predicting structures from sequences remains unsolved. Another sub-problem, predicting functions from sequences, should be even harder, since the underlying process there involves evolution (random chance), and is therefore nondeterministic. In view of these difficulties, we focus on a much narrower sub-problem: Given a structure, how to locate its functional sites? Since functional sites mediate all protein functions, the answer to this question should have important applications. If you understand which amino-acids are important in a protein, and how they come together to form a functional surface, you gain basic insights into molecular recognition, as well as into such functions as catalysis, signaling, transport, and so

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on.... In turn, experiments to modulate biological activity, protein engineering, and drug design can focus on the most relevant targets. Another attractive aspect of this problem is that its solution is not trivial. A protein structure just looks like a big collection of atoms in space, and simple observation is inadequate to recognize its functional sites, especially those that involve large macromolecular binding interfaces. One approach is mutational analysis, whereby you systematically change each residue, then assay the mutant protein to decide whether your mutation had functional consequences. In this way, you can map out all the active sites. Unfortunately, mutations are costly and laborious. To assay their function, you obviously must have appropriate biological tests, but this is far from trivial. A given protein may well have five different functions that are unknown to you a priori. How then can you design the appropriate assays needed to grasp the complex functional roles of specific residues? You cannot. Ideally, what is needed is a cheap, scalable method for characterizing the key determinants of protein function. Comment: You can do a mutation that will completely destroy your protein. Response: Yes, replacing certain amino-acids that are important for structural reasons can destroy the protein's function. In essence, structural and functional importance can overlap. But people would then also focus on surface functional sites, where mutations are less likely to have drastic packing consequences.

The evolutionary trace method The problem appears solvable at the outset. Intuitively, you hypothesize that functional sites evolve through variations on a conserved architecture. This is much like a paleontologist recognizing different species from the similarities and differences in their mandibles or teeth. The similarities point to common ancestral functions that are often retained, and the differences point to functional characteristics that are often unique to each descendent species. If you apply this logic to the active site of a protein, you might expect that the location, basic structure, and function of divergently related active sites will be conserved over evolution, but also that each divergent protein will have acquired some variations that mediate unique and novel functional variations. If this simplistic transposition of the macroscopic world to the molecular scale is correct, it predicts that by comparing active sites among divergently related

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proteins we should be able to recognize two types of residues: Those that underlie the fundamental, conserved architecture of the site, which should be mostly invariant, and those that impart species-specific functional modulation, which should vary among functionally distinct species. Hence, just by looking at the sequences, it should be possible to distinguish functional site residues that are completely invariant from those that vary in class-specific patterns; i.e., that are invariant within a given class but that vary among different classes. This, in essence, solves the problem of how to identify active sites; what needs to be done is gather sequences, classify them into various functional groups, and then identify residues that are invariant within each functional group. By construction, these class-specific residues have the property of changing the protein's function whenever they vary during evolution. This is the sine qua non of functional importance. This procedure may be repeated for every single position, identifying a set of class-specific residues and mapping them onto the structure. Hopefully, they will cluster at a site where any variation is linked to functional change. Question: [inaudible] Response: No, actually it does not. I am trying to minimize the number of assumptions. The logic of the argument is simply that if these hypotheses are true, then maybe that will be observed. Question: When? Response: We just take one mutation at a time. Double mutations are much less likely to appear than individual ones. Question: // seems to me that comparing different proteins, you might also find residues that are important for general protein folding. How do you make these distinctions? Response: We do not, but in truth, I am still not sure how to quantify the difference between structural and functional importance. While conceptually intuitive, structure and function are complex, intertwined concepts that may be difficult to separate and in the extreme, misleading. Some of the examples we work on have residues that are functionally important in allosteric pathways, and cause misfolding upon mutation.

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Question: As far as I know, in almost all cases, the active sites will be one of the largest cavities on the surface of the protein. If you know the structure, is there anything that helps to find the active site? Response: That is often true. Small ligands usually bind in cavities. Even so, the residues that contribute most directly to binding in the cavity may not be obvious. Moreover, other functional sites, such as those in protein-protein, protein-RNA, and DNA interfaces involve very large, flat interfaces. So outlining cavities solves only part of the problem, and does so at a lower resolution than we hope to achieve. Question: My comments concern the notion of function. The list you showed is really redundant. If you see what people in supramolecular chemistry like J.-M. Lehn consider, you have three functions: recognition, binding, and catalysis, and even they are already redundant. This is one aspect of your talk that is not very clear so far, because obviously, the question of function is not something you can solve by looking at the protein itself, because it is determined by interaction within the system itself. This should be considered more carefully. My question is the following: If I understand correctly, I have a protein, I know its structure, and I want to know about its functions, in general. Are you going to say anything about it? In a way, this structure is unique; there is only one such sequence and only one such structure. Or are you only going to be able to say something in comparison? Response: As you see, the method depends on the number of sequences you have to compare. If you have only one sequence, you really do not have any evolutionary information. Comment: People learn about a given function, in such a way that they generally don't have to make comparisons. You can learn about a function by working on a protein; a structure, and by setting up an experiment, without making comparisons. Response: These questions will be easier for me to answer in the concrete context of specific examples, so allow me to come back to those issues a little later. For the moment, let me just point out that we have not yet solved the problem. I only said that we can identify a cluster of important residues, defined as invariant within functional classes, if you can define those functional classes. Thus, the problem may be solved if and only if we can define functional classes. But this is

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not straightforward; how can we take sequences and split them into different functional subgroups? There are three possibilities: • • •

expert bias experiments approximations

We choose the last one: to use sequence identity trees as approximations of functional classifications. This simplistic choice may not necessarily be the best one, but it does imply that each node in the tree is a virtual assay that defines functional subgroups, and if so, we now have a fully determined algorithm. Specifically, we can take a set of sequences, calculate a sequence identity tree, approximate a functional tree with that sequence identity tree, thereby dividing the group into functional classes, identify class-specific residues, and eventually map them to the structure.

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Figure 4. The origin of DNA binding specificity in hormone receptors. A trace of intracellular hormone receptors identifies two qualitatively different groups of functionally important residues. Those in Group 1 are mostly invariant and bind nearly invariant bases that they contact similarly in different structures of the protein-DNA complex. Those in Group 2 are highly variable; they contact more variable bases, some of which are outside the strict consensus response elements, with more or less flexibility in various structures. This is consistent with the view that Group 1 determines the basic recognition structures of all hormone receptors, which are then modulated by Group 2 to match the specific variation of their target response elements.

Let's start with the problem of specificity. 1 have already told you about intracellular nuclear receptors. The largest eukaryotic family of transcriptional regulators, they help switch genes on and off. Some homodimerize head-to-head onto a stretch of DNA that forms a palindromic response element. Others heterodimerize head-to-tail or tail-to-tail on DNA response-elements that are double

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or inverted repeats. Some even bind single response elements as monomers. The structural unit that binds to the DNA is actually a small part of the entire hormone receptor; i.e., the DNA binding domain. We traced this domain and identified the DNA binding site. Since the tree has a hierarchy that may tell you something about functional importance, we can perhaps identify the amino-acids that are classspecific very early in the tree, shown as Group 1 in Figure 4, and that are presumably the most important during evolution. Or we can consider those that become class-specific a little later, and which may be a little less important, shown as Group 2 in Figure 4. We can trace an entire hierarchy of residues. If you look at how Group 1 residues bind response elements in three structures of different nuclear hormone receptor-DNA complexes, it turns out that they always contact the same bases. These bases are themselves nearly invariant among DNA response elements. On the other hand, Group 2 trace residues are appreciably more variable and contact bases that are themselves variable during evolution; sometimes they even fall outside the classical response element. Thus, as we assumed in our first hypothesis, this binding site evolves through variations on a conserved theme. Moreover, there is co-variation at the protein/DNA interface, where the most important amino-acids contact invariant bases and the variable, less important amino-acids contact the variable bases. Remarkably, these variations are often drastic and entirely non-conservative of sidechain character. It is then possible to study how those highly variable residues vary during evolution and bind to variable bases, and to exactly map these variations onto the evolutionary tree. The result is a protein-DNA "specificity key," because of the hypothesis that those residues are very important in modulating the specificity that explicitly shows which residues are necessary at the DNA interface for each branch of evolution. Thus, if you wish to confer the DNA-binding activity of a PPAR receptor to an estrogen receptor, you would just go to those residues in estrogen and switch them to what they are in PPAR. While at this point it is somewhat speculative, note that this is exactly the experimental protocol we followed in RGS proteins: we identified trace residues and swapped them in order to swap activity. Question: Are you saying that there is actually a unique code of amino-acids versus DNA base? Response: That is not what I am suggesting. In fact, it is very hard to conceive how proteins with different conformations, and that therefore bind DNA through distinct interactions, would nevertheless converge on a single code. So even if you

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are willing to say that DNA always has the same conformation - which it doesn't a universal recognition code is unlikely. Question: Basically, you are saying that there are amino-acids that conserve interactions with specific DNA bases, and less conserved amino-acids that interact with less-conserved DNA bases. This would imply that there is a code. Response: Yes, absolutely, but the point is that this code is specific to that protein family; type II zinc fingers. Another family of DNA-binding proteins may have a completely different code. Question: Do you think that by looking at a protein sequence you can predict what the recognition site would be? Response: That's a great question, and logic suggests that the answer should be yes. In fact, we are currently trying to think about this in the context of G proteincoupled receptors, but it is certainly not a straightforward problem. A second problem I would like to address is whether two proteins perform the same function. This starts with a case in which ET apparently fails, and it is relevant to a previous question: "Which proteins do you choose in your experiment?" Note that although we traced the DNA binding-site, we utterly failed to identify a dimerization site between the dimer components. There is no signal at the dimer interface. Remember however, that this trace included all nuclear hormone receptors. So, besides those that homodimerize head-to-head, there are also those that dimerize head-to-tail, or that are monomers. There is no reason, of course, that a dimerization interface would be conserved in the latter proteins, so it is not. The remedy is to restrict the trace to only those proteins that homodimerize. You can then immediately recover the homodimer interface. In other words, you can select your sequences any way you wish and test whether they share a common functional surface. This allows you to manipulate the tree in order to set up a number of algebraic manipulations of functional sites and to test whether other receptors use that dimer interface. You start with the steroid sequence and add the other branches to it one at a time. You can construct your computer experiment to look at PPAR receptors, which destroys most of the interface. This is also true of RXR and other non-steroid receptors, except for RAR receptors. This could be a statistical fluke, or it could indicate that RAR receptors use the dimerization

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interface typically associated with steroid receptors for some aspect of their function. To summarize, we were able to identify protein-DNA binding sites, to suggest how DNA recognition specificity is encoded, to identify subgroup-specific active sites, and to find sites that may be shared by distant branches of a sequence family. This is an example of using subgroup analysis to identify which among various members of a family have a structural intersection of common active sites. Basically, it allows us to conduct computational experiments using the data already acquired by evolution.

G protein-coupled receptors 1 would now like to explore functional annotation in the context of G proteincoupled receptors (GPCR). These receptors have seven membrane-spanning helices connected by internal and external loops. They are divided into five main classes, within which sequence identity is low but significant, and between which sequence identity is not detectable. We would like to understand where ligands bind GPCRs, where the conformational switch is located, whether GPCRs dimerize, and where they couple to G proteins. This information would help target mutations, design drugs, interpret ligand-binding affinity, predict G protein coupling, create constitutively active receptors, and modify G protein targets for assay purposes. Ideally, we would like to repeat the algebra I showed you above: First, establish a trace of all G protein-coupled receptors in order to understand what is important for all of them, then do a trace of a given family of GPCRs. Second, trace a specialized GPCR family, in order to identify the residues that are important to that family. Finally, subtract the former from the latter, so as to extract only those residues that are important uniquely in the given GPCR. To do this, we should compare many GPCRs. To be sure that these comparisons make sense, we would first like to make sure these GPCRs have related structures and functions. This is difficult, because sequence identity is poor (there can be substantial structure variation or even greater function variation). We tackle this problem by showing similarities in related GPCRs (positive control) as well as in unrelated ones (the test case of interest), but also show that there are no similarities in non-GPCRs (negative control). What types of similarities should we look at? The answer is similarities in terms of the functional importance of residues, as measured by evolutionary trace. For example, in the fifth transmembrane helix, the graph of an evolutionary trace drawn from the N- to the C-

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terminus shows peaks for residues that are very important, and valleys for those that are not. You can see that among Class A receptors, which are easy to align, peaks and valleys tend to be aligned, indicating some form of correlation that grows toward the C-terminus, the region where all GPCRs couple to the G protein, hence where they are most likely to have an identical function. This suggests that matching peaks and valleys might allow us to recover alignment in instances when they are unknown. Question: / was just wondering if there was something special about this particular helix, or how you chose it? Response: Well, in truth, it is the best and therefore the most illustrative data for this general alignment schema. But the work I will show next is done on all helices, with no bias or greater weight given to any. Question: Why did you focus on the helix rather than on the loops? Response: The helices define the transmembrane domain - all of it. The loops are intra- or extracellular and therefore define other domains which in any case are extraordinarily variable among GPCRs. Since I am initially trying to understand general rules that apply all receptors, it makes sense to focus on the helices. We can study loops later, one family at a time, and extract rules that apply to each ligand family. Figure 5a shows the correlation of trace ranks over all seven transmembrane helices of bioamine receptors with other members of class A. The correlation is small but not nil, consistent with the great functional diversity of those receptors. The correlation is also very sensitive to the alignment. It drops drastically if you offset alignments by ±1. At ±2, it becomes negative. It then recovers somewhat, but never fully, since ±4 residues is a full rotation of the helix axis and all the internal residues are again facing inside, while the external ones are again facing lipids. Internal residues will be more conserved and external ones less so. In other words, there is a structure-based correlation of about 0.18. In conclusion, this positive-control study of trace-rank correlation among class A receptors shows it to be markedly sensitive to the alignment, suggesting that such analysis would be useful for aligning class A with class B and C receptors, which we cannot otherwise align based on sequence identity.

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Figure 5c shows the same analysis when offsetting an alignment of class B helices with those of Class A. A best-candidate alignment emerges, which we defined as the zero offset, such that evolutionary trace rank correlation is of the same magnitude as within class A, although the sequence identity of the best alignment has dropped to 12% [Figure 5d], well below the normal range in which you can make comparisons or alignments. In class C receptors [Figures 5e and 5f), as before, there is still an alignment that maximizes correlation, but now there is no significant sequence identity above the noise threshold of-8%.

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Figure 5. Optimal alignment of GPCRs. Panels a through g show the extent of correlation between trace ranks of various GPCRs or bacteriorhodopsin, for different alternative offsets. Panels b through h show the corresponding sequence identity. See text for details.

This pervasive ability to identify a correlation is becoming suspicious and may be telling us that whatever the protein, it is always possible to find an alignment with a large rank-correlation. So we need a negative control. We used bacterial rhodopsin. This protein is a non-G protein-coupled ion-pump or light-sensor found in archaebacteria. It also has seven helices and folds, much like visual rhodopsin. However, evolutionary trace-rank correlations identify no correlation between Class A and bacteriorhodopsin. Nevertheless, as shown in Figure 5g, the correlation magnitude is at the level of the noise. Thus, the negative control is truly negative. Question: Are you using the sequence of all seven transmembrane helices? Are you also including loops?

244 0. Lichtarge Response: helices.

No, these studies all focus on the membrane domain and its seven

Question: / am assuming that this general problem of structural alignment is in fact common to all the examples you have gone through, a/though it is most obvious in the case of the transmembrane helices. So in fact, how easy is it in general to align the sequences you work with? Response: It is easy to align sequences above 30%, sometimes 25%. Below this, it becomes very hard. Whether the alignment approach that we are using in GPCRs applies to other proteins is an important question that remains to be explored.

Figure 6. Identification of GPCR ligand-bincling sites, a. A trace of visual rhodopsin shows that functionally important residues (in red) cluster internally in the rhodopsin structure, b. A trace of nearly 250 receptors from Classes A and B (in yellow) reveals a cluster of trace residues that form a site of common importance to all these GPCRs. As expected, it is especially prominent close to the G protein coupling site, which is expected to be common to all GPCRs. c. Subtracting h from a yields a small set of trace residues that are specific to visual rhodopsins and that precisely map out the binding site of the light-sensitive retinal chromophore.

These results suggest that GPCRs have a common structure and perhaps common functional determinants. So it is legitimate to trace them jointly. A trace of class A and class B receptors together is shown in Figure 6a, and a trace limited to visual rhodopsinsin in Figure 6b. Subtracting the globally important residues yields those that are important uniquely to rhodopsin, shown in Figure 6c.

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Remarkably, they cluster precisely around the retinal binding-site, which of course is unique and specific to rhodopsin. If you repeat this in other receptors, it is always possible to identify clusters of unique trace residues, but with significant variations in location, suggesting that there are significant differences in the details of ligand-coupling. With rhodopsin, as you tolerate more and more residues, you end up with a large cluster in the lower half of the transmembrane domain that is important to all GPCRs. On the other hand, a trace unique to rhodopsin forms a funnel to this cluster, which we believe is probably the conformational switch that controls GPCR activation.

Proteome-scale ET During the last few minutes I would like to go from the anecdotal world of a few proteins to the entire proteome. Basically, if ET works, it is worthwhile generalizing. However, a number of issues are in the way, such as scalability, objective criteria for success (statistics), and automation into an efficient pipeline. First, I would really like to focus on statistics, because one of the problems we have had until now is that we have only been able to identify a trace analysis as being important using visualization. We look at the structure and notice a cluster. In practice this is certainly useful, but for large-scale applications we would like to have a quantitative and objective way to assess significance. For that we look at how different the clustering of trace residues is from residues that are randomly picked. For example, random residues in pyruvate decarboxylase do not cluster; in fact they scatter all over, whereas trace residues form one very large main cluster. Random residues form more clusters, each of which is quite small. Therefore we repeatedly picked residues in proteins and built histograms that approximated the random distribution of the total number of clusters that would be expected by chance, and of the size of the largest cluster that would be expected by chance. Comparison with actual traces allows us quantify significance. We can do that in proteins of different molecular weights, and the significance threshold decreases linearly. So our experiment was to pick the proteins, blast them, retrieve these sequences, align them, then run the trace and look at the statistics. These traces were not optimized in any special way, except for the obvious sequence fragments, which were removed. The proteins were chosen to be diverse. Nineteen had alpha-beta folds, fifteen had alphas, seven had betas, and two had small domains. Some of them were all eukaryotic, some were eukaryotic-prokaryotic, and some were only prokaryotic. Their functions were also extremely varied. Overall,

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at a 5% threshold of significance, trace clusters were found to be significant in 45 out 46 proteins. Moreover, in cases when the real functional site is known, trace clusters accurately overlapped them. This therefore demonstrates that the evolutionary trace method applies not in just a few special cases, but can in fact be widely applied to the entire set of proteins in the PDB. Notice also that since trace residues are determined using only the sequence, they map onto a structure in clusters. This illustrates the cooperative nature of folding and function on evolutionarily important residues. Let us now summarize these results and ask why this evolutionary approach works. First, we can rank the importance of sequence over the course of evolution, and this evolutionary importance appears to be directly linked to structural and functional importance, although as yet it makes no clear distinction between one and the other. Functional sites may then be identified as clusters of the most important residues. This allows us to predict ligand binding pockets and specificity determinants in a number of blind tests. Specifically, we can anticipate mutation outcomes, low-resolution quaternary structure, and remote homology (GPCR). This can be used to target mutations to relevant sites and for functional annotation by figuring out which remote homologs may share functions. These results are statistically significant and we hope they can be applied to the PDB at large. The problem is to understand how these fairly detailed but general results can emerge from a simple comparison of sequences and can pull out residues that are invariant within groups but variable between them. My view is that it is because the evolutionary trace approach is quite different from a typical algorithm. Normal bioinformatics computations carry out retrospective analyses based on analogy (if sequence A is kind of like sequence B, then protein A is kind of like protein B, and therefore the structure and function of A are kind of like those of B). But this is far different from what is done in a laboratory, where prospective analysis based on deductive analysis leads you to take a sequence and mutate it so that A is made to be unlike B. An assay of function then tells you whether or not A and B are still similar. From that result you deduce the logical relationship between the sequence and the function. The point is that ET exactly follows the same laboratory paradigm as these prospective experiments. First, by comparing sequences pair-wise and looking at all residue variations, we have a large number of sequence mutations at our disposal, exactly as if we had infinite time and resources to create them in the lab. Moreover, these mutations all produce proteins that fold and function sufficiently well to produce an organism that survives natural selection. Next, we need to couple these mutations to assays. This is done through our second hypothesis; that the sequence identity tree approximates

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a functional tree. If so, it literally means that every node in the tree is a virtual functional assay that distinguishes the function of the top branch from the function of the bottom branch. In a tree of 100 proteins, there are 99 nodes, hence 99 virtual functional assays. This enables us to categorize every single mutation that ever occurred during evolution by their functional effects, from the perspective of natural selection. ET is therefore like experimental mutational analysis that simply uses all the mutations that occurred during evolution, as well as approximating all the assays carried out by evolution. Thus ET can integrate our increasing sequence and structure databases into a meta-database of annotated functional sites which, we hope, will lead experiment and theory to relevant parts of a protein. Biology is now confronted with an avalanche of facts and data. Before we can build theories based on these facts we must devise methods to efficiently sift through them and sort those that are relevant to one another. I have shown you an approach that uses a tree classification as a filtering device to extract residues that are most directly relevant to function and structure. We hope to be able to target mutations to these residues and predict their outcomes, leading to many novel and useful applications that should extend to the entire proteome. Taking a step further back, as previously noted, one of the most fundamental challenges in biology is to understand the relationship between sequence, structure, and function. This problem is normally tackled using mathematics, statistics, and physics, but as we pointed out earlier, in biology, random chance and natural selection can result in opportunistic discoveries of novel functional niches, such that we cannot know for sure what a sequence does, at least not without a comprehensive description of its context. Thus, one may not always be able to deterministically connect sequence, structure, and function. A complementary approach employed in the laboratory is to test possible hypotheses with experiments. Of course, one can then look at evolution as the greatest repository of a staggering number of experiments. The advantage of Evolutionary Trace is that it directly exploits these experiments by focusing on the single feature that is central to biology that is not found in any of the other quantitative sciences: Evolution. I thank some current and past members of my laboratory: Anne Philippi, Srinivasan Madabushi, Mat Sowa, Ivana Mihalek, Hui Yao, David Kristensen, Ivica Res, and Dan Morgan, as well as my collaborator and colleague at Baylor College of Medicine, Ted Wensel. Question: Your results are very impressive. My question concerns only clarifying which problem you have solved. If you say prediction of function, this could mean

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J) you have a protein, you know what it does, and want to clarify what particular residues in this protein would be important to achieve this function; 2) you have a protein, you know its sequence and structure, but you don't have the faintest idea what it does. Which problems do you think you can solve? How would you tackle my second point? Response: The method directly addresses the first problem: You have a biologically active protein; you think you know the function of that protein, and wonder what the molecular basis for that particular function is; you do the trace and identify a set of evolutionarily important residues, which by inference are probably linked to your function. Therefore you can focus all your mutational experiments, essentially in order to convince yourself that this is indeed a correct link. That is the kind of problem we clearly address, and for which we have data. The second problem may be looked at in two ways: 1) if you have enough sequence information, you can do a trace on it and perhaps determine an active site on which you can focus, and see if it reminds you of any other known active sites. Active sites are starting to be analyzed as irreducible 3-dimensional functional elements, to the extent that you can build a database that relates those that are irreducible. Given a function, you can search for those elements in a protein, or given other elements, see if there is a relationship between those you already know. There are problems, however: If you are dealing with a catalytic site, you are probably in good shape, because they are not very flexible, therefore you may be able to recognize structural mimicry. If you are dealing with a protein/protein interface, where there may be many conformational changes, it could be much harder to use simple geometric comparisons to identify the underlined function similarity. There is no doubt that in nature, local structural convergence occurs locally; so a given protein region may mimic a region in a different protein, thereby triggering an immune response against the host, or perform the function of that other protein. Comment: // is a very interesting technique. Basically there are three steps: 1) identify the residues by their trace; 2) work out the statistical significance, which basically tells whether the overall traces are good enough, but does not help to focus on any particular patch (usually there are very many scattered and clustered patches, but within the structure); 3) look at the structure and decide whether a patch is important. The question is whether you can develop a statistical procedure that will tell whether a given patch is more significant than another.

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Response: Yes. In the case of the largest patch, we have a formalism that already works (that is what we used here.) For the second, third, and fourth largest patches, the question is whether they are really significant. And I think that what we are doing may be extended to consider these secondary patches as well. There are probably even better formalisms to basically address the whole issue. Question: When you look at the mutation frequency in those traces and compare it to the mutation frequency around the traces, which will be higher? Response: Typically, the mutation frequency will be higher in the residues that are least important. The mutation frequency will be lower in the residues that are more important. This is a generality and does not necessarily apply to specific residues. The point is that mutation frequency may be quite high in some trace residues, so a simple statistical analysis of mutation frequency will not allow you to resolve the functional site as well as you would like. Question: / understand that one approach would be to go along the protein chain and look at the mutation frequencies (the peaks and the troughs). How does that correlate with what you have done? Response: We did do that initially, finding that the functional sites we identified appeared larger, blurry, and smeared over the protein, with some extension. The problem is that you are basically neglecting some of the available information, so you get a low-resolution answer. Comment: You might have mentioned the spectacular success of this kind of approach done a long time ago. But I completely agree that there is much less than there is in the whole tree. Response: We used to believe that conserved residues are more important for function. That is basically true, and part of the story. Conserved residues are important, but if you look across the whole family, there are some that seem not to be conserved. Those that co-vary with the function are also important. So if you look at conserved residues (example: the active site), they will be conserved throughout the whole family. But if you look at the SH3 domain, the binding site co-varies with the ligands that the protein binds. So if you look at co-variation, you will see the residues that actually recognize various ligands.

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Comment: Obviously, nature does not provide unbiased data, and some of the most productive controls for your experiment might not be present in today's organisms, but would be provided by bench experiments. My question concerns the patches that you showed with false-positives; you proposed that they might become non-false positives later on, as more data accumulate. However, another possibility could be that they reflect structural or regular patterns on upstream macromolecules, such as DNA, RNA and mRNA. I would like to have your opinion on that. Response: Yes, it is very hard to know whether something is important when you do not have a strong context in which to infer why it is important. There are many processes in nature according to which a particular residue would end up not being important. Even when you have a context, even when you have something that is important for the binding site, it may be a supposition of importance. Comment: Your approach is based on the assumption that sequence phytogeny is a good representation of functional phytogeny. Sequence phytogeny is more or less defined, because sequence is digital, but the function is more difficult to quantify. I think that function may be either chemical or biological. A good example is the case of hemoglobin, which had been thought of as an oxygen-binder, but it is now shown to be an enzyme. Even such a well-known protein as hemoglobin is known to have a completely different biological function, although it is chemically similar. The concepts of function and functional phytogeny can be very complex. Do you have any comment on this? Response: Those are questions for which you almost need the answer in order to build the functional classification, and many of those functions remain hidden to us. I think that what you can do is identify what is important, carry out experiments, and see if you can start teasing apart the various functions by focusing on the most relevant residues. You need to go back to experimentation. You can only go so far, because a given interface may be involved in many different functions, and considering the overlap, you will not know that until you start testing, using various assays. Question: So to be on the safe side, would you admit that sequence phytogeny corresponds to chemical functions, to make a conservative statement?

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Response: I guess that seems reasonable, and chemistry would be a nice way to put it. But I am still not sure that I agree. The most accurate statement 1 can make is that we use sequence phylogenies to approximate functional classifications. But there are currently no true, correct, and experimentally tested functional classifications that we can use as a gold standard, because we only have a few crude assays to use in trying to understand a myriad of functional aspects of any given protein in terms of its folding, structural, dynamical, biochemical, cellular, physiological, degradation, and other characteristics. I am perplexed, as you may be, as to how one might begin richly describing such a complex classification. I believe that sequence identity does begin to approximate it, and our results suggest that we are on the right path, even if this is a gross approximation. Does this approximation reduce to simply describing chemistry? I think only partially, depending on the protein family. Question: Could you comment on the evolutionary tree? How long are the branches to get a given group? Do you depend on the a prioris of the evolution of proteins? Did you try several methods in the building of a tree with a specific example? Response: Not thoroughly. I've kept a pragmatic point of view (like evolution): if it works, it's good enough. When it starts failing, you go back and you ask yourself what approximations you made that are not reasonable. And maybe at that point you have to start asking yourself why the tree does not work. In one case, we did obtain a nonsense result for one residue. It appeared to be unimportant, but was surrounded by many others that were. This inconsistency suggested an error, and when we examined the trace we found that a single sequence with many atypical variations was responsible for that residue's poor ranking. It turned out that this sequence is an oncogene. Hence it was misclassified in the tree as being similar to its evolutionary relatives, while in fact it performs a completely different biological function. In other words, in that instance, the correct sequence identity tree yielded a false functional classification. So the trees we use are really approximations and can doubtless be improved. Comment: To continue this question: You used importance functions, and if your definition of weights is not one-to-one in the evolutionary tree, it could completely change that importance function, which could change your conclusions entirely.

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Response: If you do a different experiment, you might change the result. But our experimental results match known biological data, so we feel that the current treebuilding algorithms, are good enough for the time-being

General references Lichtarge, O., Sowa, M.E. (2002) Evolutionary Predictions of Binding Surfaces and Interactions. Curr. Op. Struct. Biol. 12:21-27. Lichtarge, O., Sowa, M.E., Philippi, A. (2002). Evolutionary Traces of Functional Surfaces Along the G protein Signaling Pathway. Methods Enzym.. 344:536-556.

Methodological references Lichtarge, O., Bourne, R, Cohen, F.E. (1996). The Evolutionary Trace Methods Defines the Binding Surfaces Common to a Protein Family. J. Mol. Biol 257:342358. Lichtarge, O., Yamamoto, K.R., Cohen, F.E. (1997). Identification of Functional Surfaces of the Zinc Binding Domains of Intracellular Receptors. J. Mol. Biol. 274:325-337. Madabushi, S., Yao, H.,Marsh, M., Philippi, A., Kristensen, D., Sowa, M.E., Lichtarge, O. (2002). Structural Clusters of Evolutionary Trace Residues are Statistically Significant and Widespread in Proteins J. Mol. Biol. 316:139-153. Yao, H., David M. Kristensen, D.M., Mihalek, I., Sowa, M.E., Shaw, C , Kimmel, M., Kavraki, L., Lichtarge, O. (2003) An accurate, scalable method to identify functional sites in protein structures. J. Mol. Biol. 326:255-261.

Specific applications Lichtarge, O., Bourne, H.R., Cohen, F.E. (1996). Evolutionarily Conserved Gapy Binding Surfaces Support a Model of the G Protein-Receptor Complex. Proc. Nat. Acad. Sci. USA. 93:7507-7511. Onrust, R., Herzmark, P., Chi, P., Garcia, P., Lichtarge, O., Kingsley, C , Bourne, H.R. (1997). Receptor and (3y binding sites in the a subunit of the retinal G transducing protein. Science 275:381-384. Sowa, M.E., He, W., Wensel T.G. and Lichtarge O. (2000) Identification of a General RGS-Effector Interface. Proc. Nat. Acad. Sci. USA. 97:1483-1488.

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of Protein ...

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Sowa, M.E., Wei He, Slep, K.C., Kercher, M.A., Lichtarge, O., Wensel, T.G. (2001). Prediction and Confirmation of an Allosteric Pathway for Regulation of RGS Domain Activity. Nature Struct. Biol. 8:234-237. Madabushi, S., Philippi, A., Meng, E.C., Lichtarge, O. Signaling Determinants Reveal Functional Subdomains in the Transmembrane Region of G Protein-Coupled Receptors. (Submitted).

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SOME RESIDUES ARE MORE EQUAL THAN OTHERS. APPLICATION TO PROTEIN CLASSIFICATION AND STRUCTURE PREDICTION ALEXANDER KISTER & IZRAIL GELFAND Department of Mathematics, Rutgers University, Piscataway, NJ, USA

"All animals are equal but some animals are more equal than others." .. .George Orwell, "Animal Farm"

It is well-known that not all residues are equally significant in their degree of contribution to the stability of protein structure. In view of this, we suggest a new approach to the classification and prediction of the structure of proteins, based on the following premise: A small set of specific residues may be used to assign a query protein to a proper group in the protein hierarchy and to predict their secondary and tertiary structure.

Introduction One of the main challenges in the life sciences today is to understand how genomic sequences determine the geometric structure of proteins {e.g., see [1]). Knowledge of the three-dimensional structure of proteins provides valuable insights into their functional properties, since their function is largely determined by their structure [2]. The ability to classify a genomic or amino-acid sequence into its proper protein family allows one to predict, with some degree of approximation, its structure and function. This is an essential prerequisite to using genomic information to explain enzymatic processes that underlie cell behavior, understanding the molecular basis of disease, and achieving rational drug design. With more than fifty complete genomes already sequenced and at least a hundred more close to completion [3], the gap between known sequences and solved structures (collected at the Protein Data Bank [4] and classified in the SCOP database [5]) is quickly widening. Consequently, the task of predicting structure from the amino-acid sequence has taken center stage in the "post-genomic" era. Direct approaches to structure determination include X-ray crystallography and nuclear magnetic resonance, among other techniques. However, such methods are expensive, time-consuming, and not always applicable, especially since, for a large number of proteins, only the primary sequences are known.

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N-end.

k+1

i+1

r

... C-end

a)

b)

V c) Figure 1. Schematic representation of a typical variable immunoglobulin domain, a) P-sheet strands are numbered sequentially, since they are presented in a sequence. Strands 2, 3, 7, and 8 are shown: b) Chain-fold of immunoglobulin heavy-chain variable domain (PDB code line). Drawing done using Molscript [26]. P-sheet strands are shown as ribbons; c) Arrangement of strands in two main P-sheets. The interlocked pairs of strands, ft i+l) and (k, k+l) correspond to strands 2, 3 and 7, 8.

Some Residues are more Equal than Others: Application to Protein ...

257

The potential of alternative methods of protein comparison and classification is not yet settled, and there is an urgent need for more reliable approaches to such bioinformatics problems. Alternative approaches based on theoretical study of the nature of the sequence/structure relationship may be immensely useful in dealing with the wealth of data on newly sequenced genomic sequences. There exist both local and global points of view with respect to the relationship between the linear sequence of amino-acids and their resulting three-dimensional protein structure. The former point of view postulates that just a few critical residues, some 10-20% of the sequence, play the most critical role in determining the characteristics of a fold, whereas the latter considers all residues in the sequence to be crucial [6-7]. The local model received considerable support when Chothia and Lesk showed that rather different amino-acids sequences share the same fold, i.e., the same major secondary structure in the same arrangement with the same chain topology [8]. In our recent article with Chothia and Lesk, we discussed why structure changes more slowly than sequence during protein evolution [9]. For related proteins, structural similarities arise during the course of their evolution from a common ancestor, whereas for proteins with very low homology, fold similarity may be due to physical and chemical factors that favor certain arrangements for secondary structure units and chain topology. One possible explanation for the structural similarity of proteins with widely divergent sequences (homology of 20% or less) is that a few essential residues at specified key positions define the structure of a molecule, whereas residues located at other positions play an auxiliary role and do not have a major effect on the fold. In reality, all residues make some contribution to structure stability, but the relative importance of these contributions may be very different. This is also borne out by site-specific mutagenesis experiments, which reveal that substitutions of residues at various positions may have quite variable effects on the structure and stability of proteins [10-13]. It may therefore be suggested that some residues are more equal than others. In this work we show that these "more-equal-than-others" key residues have very important properties. Knowledge of a small set of key residues merely allows one i. to classify a given protein into an appropriate group of proteins; ii. to predict the main structural characteristics of a query protein, such as a protein fold, a supersecondary structure, and the coordinates of key residues. A protein group can be as 'narrow' as for example, a protein family, or as 'wide' as a set of several superfamilies from different folds. The validity of our

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approach has been demonstrated for 'narrow' groups of proteins, such as the family of the variable domains of immunoglobulin-like proteins [14-16] and for the cadherin superfamily [17]. In this work we will describe a 'wide' group of proteins. This group combines so-called sandwich-like proteins (SP). The overall goal of this research is to identify a small set of key positions in the SP group. Residues at the key positions should have similar structure and sequence characteristics across all SPs. Knowledge of the structural characteristics and 3dimensional coordinates of the key residues coupled with the ability to identify key residues within a query sequence allows us not only to assign a query protein to an SP, but also to make specific predictions regarding its secondary and tertiary structures. Residues at the key positions will be referred to as sequence determinants, since they determine the group affiliation and essential structural characteristics of the proteins. Investigation of structural and sequence features common to SPs is divided into two parts: the search for positions whose residues have the same structural role across all SPs, and the search for the sequence determinants of SPs - a subset of conserved positions whose residues share both structural and chemical properties in all these proteins.

Identifying invariant structural characteristics in a group of non-homologous sandwich-like proteins The proteins of 69 superfamilies in 38 protein folds have been described as 'sandwich-like proteins' (see folds 1.2.1 - 1.2.37 in SCOP [5], release 1.59). Spatial structures of SPs are composed of (3-strands that form two main (3-sheets [Fig. 1]. Although the general architecture of SPs is relatively uniform, the number of strands and their arrangement in space varies widely [18-21]. In addition to two 'main' sandwich sheets, many SPs contain several 'auxiliary' beta sheets. Comparison of SPs sequences in various superfamilies reveals neither functional homology nor significant sequence homology. In fact, some SPs share so little homology (less than 10-15%) as to be undetectable even with the most advanced homology search algorithms, such as HHMer [20]. Our working assumption is that non-homologous proteins grouped together on the basis of common architecture share common features at the level of supersecondary structure. To reveal these structural regularities, we analyzed the hydrogen-bonds between strands that make up two main sheets. It was found that despite a seemingly unlimited number of arrangements of strands resulting in the

Some Residues are more Equal than Others: Application to Protein . ..

259

sandwich-like structure, there exists a rigorously defined constraint on the arrangement of strands in the sheets that holds true for some 95% of SPs. This constraint may be stated as follows: In any given sandwich-like protein structure there exist two pairs of strands, (i, i+l) and {k, k+l) such that: i. ii. iii. iv. v.

the strands of each pair are adjacent to each other in the sequence (Fig. la); strand i is located in one main sheet and i+l in the other; strand k is found in one main sheet and k+l in the other; strands i and k are located within the same sheet, are anti-parallel to each other, and are linked by hydrogen bonds; likewise, strands £+1 and i+l are located within the other main sheet, are anti-parallel to each other, and form hydrogen bonds with each other.

The two pairs of strands are usually found in the middle of the sheets. Interestingly, the two pairs of strands form a sandwich-like substructure within SPs (Fig. 1). This regulation, termed "the rule of interlocked pairs of strands," defines the invariant feature of SPs at the supersecondary structure level.

Identification of SP sequence determinants The identification of sequence determinants is contingent upon proper structurebased sequence alignment of proteins. An essential element of our method is that it involves alignment not of whole sequences, but of strands with a similar structural role in their respective proteins. A group of homologous proteins comprising a protein family are generally characterized by a similar number and arrangement of strands. For this reason, secondary structure determination and alignment of corresponding strands in homologous proteins is generally straightforward. On the other hand, recognition of corresponding strands in a group of proteins as diverse as a collection of SPs from different superfamilies is not a trivial problem. The number and arrangement of strands in the sheets vary widely in SP structures. Therefore, structure-based alignment of non-homologous proteins involves a prerequisite step: determining which strands play an analogous structural role in their respective sequences. Armed with knowledge of the invariant supersecondary features of SPs, we are able to align corresponding strands from different proteins. It follows from the rule of interlocked pairs of strands that four strands, i, i+l, k, and k+l, which have similar structural properties, were found in all SPs. Thus, in our procedure, i strands from all structures were aligned with each other, as were all i+1

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strands, and so forth. In order to find conserved positions in the i, i+l, k, and k+l strands, we characterized each residue with respect to its (i) residue-residue contacts, (ii) hydrogen bonds, and (iii) residue surface exposure. Since strand alignment is based on the structural properties of residues, the first residue in the /* strand of one sequence can possess structural properties similar to (and be aligned with), for example, the 3rd residue of the /* strand of another sequence. See, for example, the first residue, S, in the / strand (PDB code line in table 1) and the third residue, T, in the i strand (PDB code lcgt). Thus, in the common numbering system, the i strand starts at position 3 in the line protein. A consequence of introducing a common numbering system based on the structural alignment of residues is that strands can start at positions other than position 1 and that their lengths can vary for different sequences. It is important to note that no "gaps" are allowed within strands, since strands are viewed as indivisible structural units. Adjacent residues within a strand are always assigned sequential position numbers. However, gaps between strands are a common occurrence. This analysis enabled us to align residues with similar structural properties. It follows that residues that occupy identical positions in the strands have the same structural role in various molecules. This allows us to compare non-homologous proteins, for example, from different superfamilies and dissimilar geometrical folds. The advantage of the structure-based approach is that it makes a common system of residue numbering possible for widely divergent sequences. The structure-based sequence alignment method employed here was developed in our previous work [14]. For the alignment of residues from the i, i+l, k, and k+l strands, the structures were culled from the "SP folds" (Table 1). These proteins belong to different superfamilies and possess no major homology. Analysis of the structurally aligned sequences in the "SP common system of numbering" revealed that in i strands, only positions 6-8 are always occupied in all known SPs. This means, for example, that residues found at position 6 of SP sequences all share similar structural characteristics. The same applies to residues at positions 7 and 8. In the remaining three strands of the invariant substructure, the following positions were taken by structurally similar residues in all SP structures: in i+l strands, positions 4-6; in k strands, positions 8-10; and in k+l strands, positions 6-8. These twelve positions are occupied by residues with structurally-similar properties in their respective SP structures. The residues at these positions lie at the center of the interface between the (3-sheets and form the common geometric core of SP structures.

Some Residues are more Equal than Others: Application to Protein . ..

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Table 1. Structure-based sequence alignment of i, i+l, k, and k+\ strands. 'Fold' proteins are classified as per the SCOP database (release 1.59), i.e., with three numbers identifying the protein: the protein-fold, the superfamily, and the family, respectively, Str - PDB codes of the structure. Conserved hydrophobic and aromatic positions (see text) are in boldface. Each vertical column in the table, starting with the third one, corresponds to a specific position in one of the four strands (i, i+\, k and k+\). Fold

Str

1-1.1 2_2_2 3_1_1 6_1_1 7_1_1 8_1_1 9_1_1 11_1_4 12_1_5 13_1_1 14_2_1 15_1-1

1INE

1TF4 G T F R Y W F T ICG T Q V T V R F V V N 1HO E Y S Q A D 1AA C D T V T WI 1RL W H K F T V V V L 1CZ Y T H L S L F 1SV A S V A R I P L 1G6 E V N WV E S 1LO X V Y R V C V S 1PG S V K T 1 K M F I K 1DK V T F L V C

no

16_1_1 B 1SL 18_1_1 U 1FU 19_1_1 X 20_1_1

1G13

21-1-1

1I9B 1BH G 1BV P 1AO L 1AL Y

22_1_1 23-1-1 24_1_1 26_1_1 27_1_1 28_1_1 29_1_1 30_1„1 31_1_1 33_1_1 34_1_1 36J_1 38_1„1

1SPP 1CB 8

10 1 2 3 4 5 6 7 8 9

10 1 2 3 4 5 6 7 8 9 MS WV R Q

S L K L S C A A

I T V S S A Y

L

N

Q A Y S L T F

V H F V A

P V W N E T F E F I

P Y V E L F I

L F C P V

G S WN P

K V E L WL V G Y A N V Y V K I G F G I

L K V E L L I KS

L T WS G N V T L S V -

T V V L R I A R G D V V L T P D L Y I Y A Q V T F C S T E C V W T L Q M V Q A I F Y T

1IAZ 1DU 5 1YG S

RKI

A V G V D N

1P35 1NL S 1HS 6 1QE X

V L MMF N I

N L D F F D I S V WV

F A V T V Y

G K V D L V L E K E

N E V D V V F

R C L S

L E I D V

F L I T F pi

V V Y T

T V L F V T Y S L P K S E F V V S P S I R Q R F S C

D L A A Y

A

K Q L E F K F F K K H A R Y L A R C G T Y D Y H C V L E I T L M a I F I K A I R Y F K I

T L R K R

E I L S I L L F V R L R K K G N T E L K I

YT

G E Y I L V P S P A N L K A S

VV

D V K Y R V W K Y Q F K V WA L S L V S E I A T C R I

R Y N G KI

G S W

R L R I T I A I

Y A I V WV

F E A D I

MMI Y L

L H V H N

Q V V F Y

N P L A I

H Y WG L R

F Y V C F I A S L C L K L L V S I G I EI

P T

E T D

G Q S I H L G G M T V K V K R C A V L I K H

T WT A L N T Y

A L L Y N G Q K

P MS F L

Y S T F T C P A

V F Q S G P I R S V

E WV R V G L I L S W S F T S K L T Y T A E V S V S

E E E F K V N

R F K L

YE

A E S V Y R L F D H WN V V L D M K R Q V I Q L

D S L Y V S A

1011

A L Y F C A S

DC R R E V Q F R I

Q I T T V G

V T L Q F

N

D L Y Y V E I

rV T V K V V Y

1 L M W E A V T V K

L

N L Y L Q L N

WY Y D V S V

F K V T L ML L

T

101112 1 2 3 4 5 6 7 8 9

N G T L Y L T G N V

N R

k + 1

k

i + 1

I 1 2 3 4 5 6 7 8 9

L V A L MS

S V D I P L F H M A K L L V T C Q S

A Y I K V F Y Y V A V C V L G R A L F Y A K I Y K F I Q Y V T A T I S

S V F V N V T D EI

I F L R D

K T A V L S I

RDL

V G V L A Y L M S N Y K V V F C L R MS F L S F E Y N V V A S F E A T F T I A L V V G M R A A V K V A

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A. Kister & I. Gelf and

Table 2. Identifying four groups of SP proteins within eleven distinct genomes. Sequence determinants of four groups of sandwich-like proteins. The four groups are classified as per the SCOP database: 1) PL protein family lipoxygenase N-terminal domain; 2) AT protein family: alpha-toxin C-terminal domain; 3) AD corresponds to a 30-kD adipocyte complement-related protein; 4) TR corresponds to TRANCE/RANKL cytokine protein domain (such as position 4 in i strand in the PL family and 10 in the i strand of the AT family). The table presents family-specific sets of conserved positions for each of the four protein groups (PL, AT, AD, and TR). Assignment of genomic sequences to each of the four protein groups: Thefirstcolumn lists the names of organisms from which the genomes are derived; the second column contains the numbers of proteins sequenced from the respective genomes. The numbers of sequences belonging to each group of proteins (PL, AT, AD, and TR) found in the genome using our method of sequence determination (MSD) appear in the "MSD" columns. "HMM" columns show the number of sequences of the respective groups of proteins found using Hidden Markov models (see SCOP database).

PL

AT AD TR HM HM HM proteins HMM MSD M MSD M MSD M MSD

genome Arabidopsis thaliana Clostridium acetobutylicum

25617 8

11

4

3672 0

1

1 2

Clostridium perfringens

2660 0 2

Mesorhizobium loti

6752

1

1 2

1

0

Pseudomonas aeruginosa 5567 0 0 Caenorhabditis elegans

20448 5

5

9

2

1 0 0

0

0

0

0

0

0

0

0

3

1 0 0

0

0 0 0

0 0 0

0

0

0 0 0

0

Drosophila melanogaster 14335 2 5

0 0

0 0

1 1

Escherichia coliKl2

4289 0

0

0

1

0

0

0

0

Escherichia coli 0157H7

5361 0

1

0

1

0

0

0

0

Bacillus halodurans

4066 0 0

00

00

Lactococcus lactis

2266 0 1

0 0

1 1

00 0 0

Inspection of amino-acid frequencies in these twelve positions showed that two of three positions in each strand are the conserved hydrophobic positions of SPs: positions 6 and 8 in i strands, 4 and 10 in k strands, and 6 and 8 in k+l strands. They are occupied either by aliphatic (A, V, L, and I), aromatic (W, Y, and F), or nonpolar amino-acid residues (M and C). Residues at these eight conserved positions were termed SP sequence determinants. of all SP sequence

determinants.

Residues V, L, I, and F accounted for 80%

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Protein classification and structure prediction based on sequence determinants This work is based on the premise that structure and sequence determinants may be used to classify proteins and predict their structure. A group of proteins may be characterized by sets of residues at conserved positions; the sequence determinants. Since the residue content of the conserved positions and the amino-acid distances between them are known for a group of proteins, it is possible to scan a novel protein sequence in order to ascertain whether it contains the sequence determinants of a given protein group. If a query sequence contains all the sequence determinants of a given protein group, can it be assigned to that group? In addition to the possibility of assigning primary sequences to their proper protein classes, our approach also allows making a number of specific predictions with respect to the structural properties of proteins. As follows from the definition of sequence determinants, they are characterized by a number of secondary and 3-D structural characteristics, including the coordinates of their Coc atoms. Thus, residues in the query sequence that match the sequence determinants by virtue of their chemical properties and location in the amino-acid sequence may be assumed to have all the structural characteristics of the sequence determinants as well. Knowledge of the secondary structural properties and coordinates of the Coc atoms of residues at conserved positions allows prediction of the protein fold and main features of the supersecondary structure (arrangement of strands), as well as construction of a fairly detailed 3-D model of the query sequence.

Using sequence determinants to classify proteins: an illustration Knowledge of the sequence determinants of protein groups has led to the development of a computer algorithm for the classification of proteins. To assign a query sequence to its proper protein family, it is necessary to match a subset of query sequence residues with the sequence determinants of a protein group. In order to classify sequences, we implemented an algorithm based on appropriate modification of dynamic programming [17]. This algorithm matches the sequence determinants of a given protein group one-by-one with residues of the query sequence. Once a match has been obtained for the sequence determinant closest to the beginning of the sequence, the algorithm seeks a match for the second determinant in the query sequence, and so on. If all the sequence determinants

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match, the protein is assigned to the group. A small number of residues of a given query sequence uniquely identify its group affiliation. Some data concerning the extraction of the proteins of several SP protein families/domains from the genomes of various organisms are presented below. As described above, sandwich-like proteins are characterized by a certain invariant substructure consisting of two interlocked pairs of adjacent strands, i, i+l and k, k+\. Eight conserved positions - the sequence determinants common to all SPs - were found in these strands. However, in addition to those eight positions, SP families have "family-specific" conserved positions. For various protein groups, there were between 1 and 3 of these "extra" specific conserved positions within the four strands. The results of applying a search algorithm that uses the sequence determinants of four protein families to all sequenced proteins of eleven different genomes are presented in Table 2. The "MSD" column of the table provides data on how many proteins of the given family were found in the respective genome by the application of our algorithm. For comparison purposes, the "HMM" column indicates the number of the proteins of a family found using the HMM search procedure, considered to be the most powerful of all currently used methods [20]. Overall, both methods found approximately the same number of SPs in the 11 genomes. All but one of the sequences found by HMM were detected by our approach. However, our method revealed a number of additional sequences that may be putatively assigned to the four families. For the most part, these "additional" proteins are labeled "unrecognized proteins" in the genome. We suggest that our approach can identify even those SPs that are hidden from the HMM search procedure. Further investigation is necessary to tell whether these candidate sequences indeed qualify to link the respective SP families. Our approach also provides an independent check on the accuracy of the HMM-based algorithm. The assignment of a query protein to a protein family yields the structural characteristics of that protein. Thus, all the proteins found have sandwich-like structures. A set of residues aligned with sequence determinants constitutes the geometrical core of the given protein family and allows us to assign coordinates for the C a atoms for these residues. Based on this substructure, we can construct the entire 3-D structure of the protein by applying commonly used homology-based structural prediction methods [21].

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Discussion A direct corollary of our approach is that the complexity of protein-sequence search algorithms and 3-D structure predictions may be dramatically reduced. Instead of carrying out searches using whole protein sequences, we can now search using predefined sets of several key residues. This is analogous to searching for a criminal suspect by his fingerprints rather than using a long list of non-unique descriptors. Our data on sandwich-like proteins shows that the proposed search algorithm compares favorably with powerful and widely used techniques based on Hidden Markov Models. Another advantage of carrying out structure-based analysis is that it often allows us to not only predict the affiliation of a particular protein and outline its secondary and 3-D structure, but also to make "educated guesses" regarding the functional roles of various portions of its sequence. It is evident that the ability to pinpoint parts of a protein sequence that are likely to participate in protein binding (for example) can prove invaluable in planning mutagenesis experiments, or for rational drug design. Overall, our approach may be called "naturalistic" in the Linnaean sense; our aim is to construct a kind of protein "classification key" whereby each protein family, superfamily, group of superfamilies, etc. would be characterized by a limited set of highly specific structure and sequence characteristics. Upon encountering a new protein sequence, one would be able to quickly "scan" it for the presence of the characteristic features and assign it to its proper classification category. The strength of this approach lies in its predictive power; upon attributing a query sequence to a particular protein group, it would be possible to make highly specific predictions about its structural properties. Reasoning by analogy with known structures, one can also speculate about the function of various parts of the sequence and predict, for instance, that a certain portion would be involved in protein-protein recognition, etc. By analogy with zoology, if a new species of animal possessed one or more distinguishing characteristics of ruminants, for example, and was therefore classified as such, one could immediately predict that the newly found mammal would only have molars on its upper jaw (structure prediction), as well as what the functions of each of the four parts of its stomach would be (prediction of functional properties of various parts of the structure.)

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References 1. 2. 3. 4.

5. 6. 7. 8. 9.

10. 11. 12. 13. 14. 15. 16. 17. 18. 19. 20. 21.

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STRUCTURE-FUNCTION RELATIONSHIPS IN POLYMERASES MARC DELARUE Unite de Biochimie Slriiclurale, Instilui Pasleur-CNRS, Paris, France

My talk today will focus on DNA polymerases, starting with a brief overview of the topics to be covered. First I will review how protein sequence analysis may be used to identify and cluster various DNA polymerase families. Next I will describe the crystal structure of a template-independent DNA polymerase that was recently solved in our laboratory. Then I will discuss the open/closed conformational transition in DNA polymerases, a feature common to all polymerase families. To do this I will rely on a simplified version of normal mode analysis. If time allows, I will discuss the role of electrostatics in the active site, where there are metal ions and charged substrate molecules.

Figure 1. The central dogma (reproduced with permission of Garland Science).

268 M. Delarue The physiological role of polymerase is essential in all the kingdoms of life. The so-called central dogma of molecular biology, which was written on a blackboard by Jim Watson in the 1950s [Fig. 1], simply states that DNA makes DNA makes RNA makes protein. Of course, not all the players in the process were known at that time, but over the years, especially during the 1960s, they came to be identified. One of them, DNA polymerase, is required when a cell divides and must make copies of its DNA for the daughter cells. Transcription of DNA into RNA requires RNA polymerase. The machinery that translates RNA into protein is the ribosome, about which you will no doubt hear more during this conference. Fig. 1 shows the celebrated double helix of Watson and Crick, which immediately suggests the mechanism for transferring information from one strand to the other. Here is the famous Watson-Crick pair, which best demonstrates the notion of complementarity and base-pairing ...and on the seventh day of hard work they just sat back, relaxed, and sipped tea...

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Figure 2. DNA polymerization is directional (reproduced with permission of Garland Science). DNA polymerization is directional, going from 5' to 3' [Fig. 2]. The template strand, which directs the copying process, and the primer strand, which will be elongated, are shown here. A nucleotide triphosphate arrives, the 3'-hydroxyl end of the primer attacks the alpha phosphate of the dNTP, and a new base is incorporated opposite the template base. In this figure there is a very simple nucleotide binary code. The small boxes represent the pyrimidine bases, while the purine bases are represented by larger boxes. Whenever there is a small box on one strand, there will

Structure-Function Relationships in Polymerases 269 be a large one on the opposite strand, due to steric complementarity. In reality of course, there is hydrogen-bonding between purines and pyrimidines, not just steric complementarity. However, as we shall see shortly, faithful copying of DNA relies on more than steric and hydrogen-bonding complementarity. On the right side of the figure is a general view of a polymerase, with a kind of canyon in which the different substrates bind.

Figure 3. The replication fork (reproduced with permission of Garland Science).

Since replication is unidirectional and the two strands must be replicated simultaneously, the cell uses a slightly different mechanism for each [Fig. 3]. There is no problem for 5'-3' synthesis, although in reality the replication machinery consists of more than just a polymerase molecule. A "clamp" is required to maintain the progression of the process, so that the polymerases do not continuously fall on and off the strand. A helicase is also needed to unwind the DNA. Singlestrand DNA must be protected by single-strand binding proteins. However, in order to carry out synthesis in the 3'-to-5' direction, Okazaki primers (RNA fragments

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synthesized by a primase) are also necessary. These RNA primers are later removed, and still another polymerase arrives to fill in the gaps between the extended Okasaki fragments. A ligase finishes off the job by joining the various DNA pieces. This is quite a lot of machinery, and there is even more in eukaryotic cells. One might say that studying only a polymerase is very reductionist, and that the structures and roles of all the players implicated in the replication process must be known in order to understand the whole picture. Nevertheless, certain bacterial polymerases acting by themselves in vitro are able to elongate a primer in a template-primer duplex, as Tag polymerase does in PCR procedures carried out every day in molecular biology labs all over the world. One can therefore justifiably state that solving the structure of active Taq polymerase was by itself a major achievement in the pursuit of understanding the DNA replication machinery, especially when it was shown in 1998 by G. Waksman and coll. to be active in the crystal state.

1. Classification of polymerases by sequence analysis Polymerases are absolutely necessary in all living organisms, including viruses, prokaryotes, and eukaryotes. By the end of the 1980s, a growing number of polymerase sequences (essentially viral, since there were no large-scale sequencing projects at the time) were available in the databases. In trying to organize these sequences, we found that it was possible to classify DNA polymerases. This work, which was published by Olivier Poch, Noel Tordo, Dino Moras, Pat Argos, and me in 1990 still holds true, and has contributed to setting the stage in the polymerase field. What did this work show? We were able to identify a few strictly conserved residues, namely aspartates and glutamates, that were scattered along the sequence [Fig. 4]. The aspartates are not actually isolated, but part of a specific stretch of sequence, called a motif. For instance, hydrophobic residues are always present here in this motif, and here they surround this conserved aspartate. At the time, there was only one known polymerase structure, the Klenow fragment off. coli pol I. Examined in the context of this 3-D structure, it becomes immediately apparent that, while situated at very different places in the sequence, the three different aspartates of motifs A and C are very close to each other in 3-dimensional space. Indeed, being the ligands of two

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functionally important Mg++ ions, they identify the active site of the polymerase; however this was not known at the time.

Figure 4. The pol 1 and pol u families. These motifs were located using the so-called profile method, which had just been described by Gribskov et al. (1987). Once sequences are aligned, a positionspecific mutation table is drafted, based on multi-alignment. Using the profile, the database sequence is searched for hits above a certain threshold, such as three sigmas above the mean. If found, new protein sequences are added to the original profile, a new profile is derived, and the analysis is run again. This process is iterated until no new sequence is detected to enrich the profile. At present, this is done more automatically, for instance, using Psi-Blast, a program written by Eugene Koonin and colleagues, at the NCB1, in the United States. It is interesting to look at a simplified representation of the structure of the Klenow fragment. The Klenow fragment off. coli DNA pol I is really just that part of pol 1 from which the N-terminal domain, which exhibits 5'-3' exonuclease activity, has been removed (see fig. 5).

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