G Protein-Coupled Receptors: Essential Methods

G Protein-Coupled Receptors Essential Methods Editors David R. Poyner School of Life and Health Sciences, University o...

Author: David Poyner | Mark Wheatley

54 downloads 1741 Views 2MB Size Report

This content was uploaded by our users and we assume good faith they have the permission to share this book. If you own the copyright to this book and it is wrongfully on our website, we offer a simple DMCA procedure to remove your content from our site. Start by pressing the button below!
Report copyright / DMCA form

DOWNLOAD PDF

G Protein-Coupled Receptors Essential Methods Editors

David R. Poyner School of Life and Health Sciences, University of Aston, Birmingham, UK

Mark Wheatley School of Biosciences, Birmingham University, Birmingham, UK

A John Wiley & Sons, Ltd., Publication

G Protein-Coupled Receptors

G Protein-Coupled Receptors Essential Methods Editors

David R. Poyner School of Life and Health Sciences, University of Aston, Birmingham, UK

Mark Wheatley School of Biosciences, Birmingham University, Birmingham, UK

A John Wiley & Sons, Ltd., Publication

This edition first published 2010,  2010 John Wiley & Sons, Ltd Wiley-Blackwell is an imprint of John Wiley & Sons, formed by the merger of Wiley’s global Scientific, Technical and Medical business with Blackwell Publishing. Registered office: John Wiley & Sons Ltd, The Atrium, Southern Gate, Chichester, West Sussex, PO19 8SQ, UK Other Editorial Offices: 9600 Garsington Road, Oxford, OX4 2DQ, UK 111 River Street, Hoboken, NJ 07030-5774, USA For details of our global editorial offices, for customer services and for information about how to apply for permission to reuse the copyright material in this book please see our website at www.wiley.com/wiley-blackwell The right of the author to be identified as the author of this work has been asserted in accordance with the Copyright, Designs and Patents Act 1988. All rights reserved. No part of this publication may be reproduced, stored in a retrieval system, or transmitted, in any form or by any means, electronic, mechanical, photocopying, recording or otherwise, except as permitted by the UK Copyright, Designs and Patents Act 1988, without the prior permission of the publisher. Wiley also publishes its books in a variety of electronic formats. Some content that appears in print may not be available in electronic books. Designations used by companies to distinguish their products are often claimed as trademarks. All brand names and product names used in this book are trade names, service marks, trademarks or registered trademarks of their respective owners. The publisher is not associated with any product or vendor mentioned in this book. This publication is designed to provide accurate and authoritative information in regard to the subject matter covered. It is sold on the understanding that the publisher is not engaged in rendering professional services. If professional advice or other expert assistance is required, the services of a competent professional should be sought. The contents of this work are intended to further general scientific research, understanding, and discussion only and are not intended and should not be relied upon as recommending or promoting a specific method, diagnosis, or treatment by physicians for any particular patient. The publisher and the author make no representations or warranties with respect to the accuracy or completeness of the contents of this work and specifically disclaim all warranties, including without limitation any implied warranties of fitness for a particular purpose. In view of ongoing research, equipment modifications, changes in governmental regulations, and the constant flow of information relating to the use of medicines, equipment, and devices, the reader is urged to review and evaluate the information provided in the package insert or instructions for each medicine, equipment, or device for, among other things, any changes in the instructions or indication of usage and for added warnings and precautions. Readers should consult with a specialist where appropriate. The fact that an organization or Website is referred to in this work as a citation and/or a potential source of further information does not mean that the author or the publisher endorses the information the organization or Website may provide or recommendations it may make. Further, readers should be aware that Internet Websites listed in this work may have changed or disappeared between when this work was written and when it is read. No warranty may be created or extended by any promotional statements for this work. Neither the publisher nor the author shall be liable for any damages arising herefrom. Library of Congress Cataloguing-in-Publication Data G protein-coupled receptors : essential methods / edited by David R. Poyner and Mark Wheatley. p. cm. Includes bibliographical references and index. ISBN 978-0-470-74914-2 (cloth : alk. paper) 1. Cell receptors. 2. Ion channels. I. Poyner, David. II. Wheatley, Mark. QH603.C43G67 2009 612 .01575 – dc22 2009031411 ISBN: 978-0-470-74914-2 A catalogue record for this book is available from the British Library. Typeset in 10/12 Times by Laserwords Private Limited, Chennai, India Printed in Singapore by Markono Pte. Ltd First impression– 2010

Contents Preface Contributors 1 Measurement of Ligand–G Protein-coupled Receptor Interactions Katie Leach, Celine Valant, Patrick M. Sexton and Arthur Christopoulos 1.1 Introduction 1.2 Methods and approaches References

2 Second Messenger Assays for G Protein-coupled Receptors: cAMP, Ca2+ , Inositol Phosphates, ERK1/2 Karen J. Gregory, Patrick M. Sexton, Arthur Christopoulos and Caroline A. Hick 2.1 Introduction 2.2 Methods and approaches 2.3 Troubleshooting References

3 Use of the [35 S]GTPγS Binding Assay to Determine Ligand Efficacy at G Protein-coupled Receptors Elodie Kara and Philip G. Strange 3.1 Introduction 3.2 Methods and approaches 3.3 Troubleshooting Acknowledgements References

4 Quantitative Imaging of Receptor Trafficking Andy R. James, Takeo Awaji, F. Anne Stephenson and Nicholas A. Hartell 4.1 Introduction 4.2 Methods and approaches 4.3 Troubleshooting References

ix xi 1 1 5 27

31

31 33 49 50

53 53 54 66 67 67

69 69 70 80 82

vi

CONTENTS

5 Production of Recombinant G Protein-coupled Receptor in Yeast for Structural and Functional Analysis Richard A.J. Darby, Mohammed Jamshad, Ljuban Grgic, William J. Holmes and Roslyn M. Bill 5.1 Introduction 5.2 Methods and approaches 5.3 Troubleshooting References

6 Monitoring GPCR–Protein Complexes Using Bioluminescence Resonance Energy Transfer Werner C. Jaeger, Kevin D.G. Pfleger and Karin A. Eidne 6.1 Introduction 6.2 Methods and approaches 6.3 Troubleshooting References

7 Using Intramolecular Fluorescence Resonance Energy Transfer to Study Receptor Conformation Cornelius Krasel and Carsten Hoffmann 7.1 Introduction 7.2 Methods and approaches 7.3 Troubleshooting References

8 A Disulfide Cross-linking Strategy Useful for Studying Ligand-induced Structural Changes in GPCRs Jian Hua Li, Stuart D.C. Ward, Sung-Jun Han, Fadi F. Hamdan and Jürgen Wess 8.1 Introduction 8.2 Methods and approaches 8.3 General considerations, caveats and troubleshooting Acknowledgements References

9 Use of Fluorescence Correlation Spectroscopy to Study the Diffusion of G Protein-coupled Receptors Stephen J. Briddon, Jonathan A. Hern and Stephen J. Hill 9.1 Introduction 9.2 Methods and approaches 9.3 Troubleshooting References

85

85 86 106 107

111 111 114 128 128

133 133 136 143 144

147

147 149 162 164 164

169 169 170 190 191

CONTENTS

10 Identification and Analysis of GPCR Phosphorylation Kok Choi Kong, Sharad C. Mistry and Andrew B. Tobin 10.1 Introduction 10.2 Methods Acknowledgements References

11 Measurement and Visualization of G Protein-coupled Receptor Trafficking by Enzyme-linked Immunosorbent Assay and Immunofluorescence Stuart J. Mundell, Shaista P. Nisar and Eamonn Kelly 11.1 Introduction 11.2 Methods and approaches 11.3 Troubleshooting References

12 Substituted Cysteine Accessibility Method (SCAM) George Liapakis and Jonathan A. Javitch 12.1 Introduction 12.2 Methods and approaches 12.3 Troubleshooting References

13 Homology Modelling of G Protein-coupled Receptors John Simms 13.1 Introduction 13.2 Methods and approaches 13.3 Troubleshooting 13.4 Automated methods for generating models of GPCRs References

Appendix Site-directed Mutagenesis and Chimeras

vii

197 197 198 213 213

215 215 217 226 227

229 229 230 247 248

251 251 252 269 269 270

275

Alex Conner, Mark Wheatley and David R. Poyner A.1 Introduction A.2 Why mutagenesis? A.3 Troubleshooting A.4 Conclusion References

Index

275 275 285 285 286

289

Preface

This book describes a number of techniques relating to research on G protein-coupled receptors (GPCRs), written by a number of leading international authorities. In line with the rest of the essential methods series, each chapter contains an overview of the method and this is followed by a series of detailed protocols, providing a bench-side guide. The literature on GPCRs is vast and the pace of investigation shows no sign of slackening. Given that these receptors are not only the largest protein family in the human genome but are also the single biggest target for therapeutic agents, this level of interest is not surprising. For any editor, this poses problems; the range of techniques that can be applied to GPCRs is vast and continues to grow, so it is impossible to cover them all in a single volume. In the case of this book, the techniques that are covered have been selected simply because we think that they will prove to be useful tools in future research and will contribute to increasing our understanding of GPCRs. In addition, they are of interest to the editors. Doubtless, readers wishing to find descriptions of additional methods will be able to find these elsewhere in the GPCR literature. Some of the techniques covered in this volume are very well known, such as mutagenesis or measurement of second messengers. We have included these because they are so fundamental to GPCR research and so might be of interest to the newcomer to the field. Some of the other techniques presented might not be so widely employed, but they have proved their worth in numerous laboratories. Chapters 1 and 2, by Leach et al . and Gregory et al . respectively, review radioligand binding techniques (including detection of allosteric modulators) and measurements of a range of second messengers activated by GPCRs. Measurement of GTPγ S binding as an index of receptor activation is dealt with by Kara and Strange in Chapter 3. Quantitative GPCR imaging is considered in the context of receptor trafficking by James et al . in Chapter 4. In Chapter 5, Darby et al . describe methods for overexpressing GPCRs, concentrating on Pichia pastoris as a host. In Chapter 6, Jaeger et al . consider how bioluminescence resonance energy transfer can be used to look at GPCR complexes, whereas Krasel and Hoffmann describe in Chapter 7 how fluorescence resonance energy transfer can be used to measure receptor conformation. Chapters 8 and 12, by Li et al . and Liapakis and Javitich respectively, review the utility of engineered cysteines for artificial intramolecular disulfide bonds and cysteine scanning accessibility mutagenesis respectively. The new technique of fluorescence correlation spectroscopy

x

PREFACE

is described by Briddon et al . in Chapter 9. Mundell et al . (Chapter 11) and Kong et al . (Chapter 10) consider receptor regulation; how this can be quantified and how GPCR phosphorylation can be measured. Simms reviews techniques for receptor modelling in Chapter 13 and the Appendix (by Conner et al .) considers methods for mutagenesis. We hope this volume will be useful to investigators in GPCR research. David R. Poyner Mark Wheatley

Contributors Takeo Awaji

Richard A.J. Darby

Department of Physiology, Tokyo Women’s Medical University School of Medicine, 8-1 Kawada-cho, Shinjuku-ku, Tokyo 162–8666, Japan

Pharmaceutical & Biological Sciences, School of Life and Health Sciences, Aston University, Aston Triangle, Birmingham B4 7ET, UK

Roslyn M. Bill Pharmaceutical & Biological Sciences, School of Life and Health Sciences, Aston University, Aston Triangle, Birmingham B4 7ET, UK

Karin A. Eidne Laboratory for Molecular Endocrinology – GPCRs, WAIMR and UWA Centre for Medical Research, QEII Medical Centre, Nedlands, Perth, Western Australia 6009, Australia

Karen J. Gregory Stephen J. Briddon Institute of Cell Signalling, School of Biomedical Sciences, University of Nottingham, Nottingham NG7 2UH, UK

Arthur Christopoulos Drug Discovery Biology Laboratory, Department of Pharmacology, Monash University, Clayton 3800, Australia

Alex Conner School of Medicine, Warwick University, Coventry CV4 7AL, UK

Drug Discovery Biology Laboratory, Department of Pharmacology, Monash University, Clayton 3800, Australia

Ljuban Grgic Pharmaceutical & Biological Sciences, School of Life and Health Sciences, Aston University, Aston Triangle, Birmingham B4 7ET, UK

Fadi F. Hamdan Molecular Signaling Section, Laboratory of Bioorganic Chemistry, National Institute of Diabetes and Digestive and Kidney Diseases, Bethesda, MD 20892, USA

xii

CONTRIBUTORS

Sung-Jun Han

Werner C. Jaeger

Molecular Signaling Section, Laboratory of Bioorganic Chemistry, National Institute of Diabetes and Digestive and Kidney Diseases, Bethesda, MD 20892, USA

Laboratory for Molecular Endocrinology – GPCRs, WAIMR and UWA Centre for Medical Research, QEII Medical Centre, Nedlands, Perth, Western Australia 6009, Australia

Nicholas A. Hartell Department of Cell Physiology and Pharmacology, University of Leicester, Leicester LE1 9HN, UK

Jonathan A. Hern Institute of Cell Signalling, School of Biomedical Sciences, University of Nottingham, Nottingham NG7 2UH, UK

Andy R. James Department of Pharmacology, The School of Pharmacy, University of London, London WC1N 1AX, UK

Mohammed Jamshad Pharmaceutical & Biological Sciences, School of Life and Health Sciences, Aston University, Aston Triangle, Birmingham B4 7ET, UK

Caroline A. Hick Drug Discovery Biology Laboratory, Department of Pharmacology, Monash University, Clayton 3800, Australia

Stephen J. Hill Institute of Cell Signalling, School of Biomedical Sciences, University of Nottingham, Nottingham NG7 2UH, UK

Jonathan A. Javitch Center for Molecular Recognition, Columbia University, P&S 11-401, Box 7, 630 West 168th Street, New York, NY 10032, USA

Elodie Kara

Department of Pharmacology, University of Würzburg, 97070 Würzburg, Germany

School of Pharmacy, University of Reading, PO Box 228, Whiteknights, Reading RG6 6AJ, UK

William J. Holmes

Eamonn Kelly

Pharmaceutical & Biological Sciences, School of Life and Health Sciences, Aston University, Aston Triangle, Birmingham B4 7ET, UK

Department of Physiology & Pharmacology, University of Bristol, Medical Sciences Building, University Walk, Bristol BS8 1TD, UK

Carsten Hoffmann

CONTRIBUTORS

Kok Choi Kong

Stuart J. Mundell

Department of Cell Physiology and Pharmacology, University of Leicester, Hodgkin Building, Lancaster Road, Leicester LE1 9HN, UK

Department of Physiology & Pharmacology, University of Bristol, Medical Sciences Building, University Walk, Bristol BS8 1TD, UK

Cornelius Krasel School of Pharmacy, University of Reading, Whiteknights, Reading, Berkshire RG6 6AH, UK

Katie Leach Drug Discovery Biology Laboratory, Department of Pharmacology, Monash University, Clayton 3800, Australia

Jian Hua Li Molecular Signaling Section, Laboratory of Bioorganic Chemistry, National Institute of Diabetes and Digestive and Kidney Diseases, Bethesda, MD 20892, USA

Shaista P. Nisar Department of Physiology & Pharmacology, University of Bristol, Medical Sciences Building, University Walk, Bristol BS8 1TD, UK

Kevin D.G. Pfleger Laboratory for Molecular Endocrinology – GPCRs, WAIMR and UWA Centre for Medical Research, QEII Medical Centre, Nedlands, Perth, Western Australia 6009, Australia

David R. Poyner Pharmaceutical and Biological Sciences, School of Life and Health Sciences, Aston University, Birmingham B4 7ET, UK

George Liapakis Faculty of Medicine, University of Crete, Voutes, Heraklion 71003, Crete, Greece

Patrick M. Sexton

Sharad C. Mistry

John Simms

Department of Cell Physiology and Pharmacology, University of Leicester, Hodgkin Building, Lancaster Road, Leicester LE1 9HN, UK

Drug Discovery Biology Laboratory, Department of Pharmacology, Monash University, Clayton 3800, Australia

Department of Pharmacology, University of Monash, Clayton, Victoria 3800, Australia

xiii

xiv

CONTRIBUTORS

F. Anne Stephenson

Stuart D.C. Ward

Department of Pharmaceutical and Biological Chemistry, The School of Pharmacy, University of London, London WC1N 1AX, UK

Molecular Signaling Section, Laboratory of Bioorganic Chemistry, National Institute of Diabetes and Digestive and Kidney Diseases, Bethesda, MD 20892, USA

Philip G. Strange School of Pharmacy, University of Reading, PO Box 228, Whiteknights, Reading RG6 6AJ, UK

Andrew B. Tobin Department of Cell Physiology and Pharmacology, University of Leicester, Hodgkin Building, Lancaster Road, Leicester LE1 9HN, UK

Celine Valant Drug Discovery Biology Laboratory, Department of Pharmacology, Monash University, Clayton 3800, Australia

Jurgen Wess ¨ Molecular Signaling Section, Laboratory of Bioorganic Chemistry, National Institute of Diabetes and Digestive and Kidney Diseases, Bethesda, MD 20892, USA

Mark Wheatley School of Biosciences, Birmingham University, Birmingham B15 2TT, UK

1 Measurement of Ligand–G Protein-coupled Receptor Interactions Katie Leach, Celine Valant, Patrick M. Sexton and Arthur Christopoulos Drug Discovery Biology Laboratory, Monash Institute of Pharmaceutical Sciences and Department of Pharmacology, Monash University, Parkville, 3052, Australia

1.1 Introduction 1.1.1 Ligand–receptor interactions and the law of mass action Radioligand binding assays take advantage of the ability to detect the decay of radioactive material, which can be incorporated into a ligand of choice. The interaction of such a radioligand with a receptor preparation can subsequently be determined by capturing and measuring the amount of radioactivity present. Radioligand binding assays can be used to estimate molecular parameters, such as the density of receptors present in a tissue or cellular preparation or the affinity of a ligand for binding to a receptor. The simplest scheme that describes the binding of a ligand to its receptor is based on the law of mass action: Kon −− −→ A+R− ← −− AR Koff

where the ligand A binds to the receptor R to form the ligand–receptor complex AR. The rate at which the ligand binds to the receptor, expressed as the number of binding events per unit of time, is dependent on the ligand concentration, the number of

G Protein Coupled Receptors Edited by David R. Poyner and Mark Wheatley  2010 John Wiley & Sons, Ltd.

2

CH 1 MEASUREMENT OF LIGAND–G PROTEIN-COUPLED RECEPTOR INTERACTIONS

unoccupied receptors and the association rate constant Kon . In contrast to an enzymatic reaction, there is no degradation of the product AR; and if this reaction is reversible, then the ligand–receptor complexes can dissociate into free receptor and ligand, which is dependent on the concentration of ligand–receptor complexes and the dissociation rate constant Koff . Although the association and dissociation rates may differ, over time an equilibrium state will arise whereby the rate at which new ligand–receptor complexes are formed will equal the rate at which ligand–receptor complexes break down. At equilibrium, the ratio of the dissociation and association rate constants provides a useful measure of the overall strength with which a ligand interacts with a receptor, the equilibrium dissociation constant Ka (although sometimes referred to as Kd ) and expressed in moles per litre. Ka is also the concentration of ligand that binds half the receptors present. Therefore, at equilibrium, the concentration of ligand–receptor complexes is governed by the total receptor density [RT ], the ligand concentration [A] and the equilibrium dissociation constant of the ligand: [AR] =

[RT ] × [A] [A] + Ka

(1.1)

where [RT ] = [R] + [AR] and Ka = Koff /Kon . Equation 1.1 is often referred to as the Hill–Langmuir binding isotherm and describes equilibrium binding of a ligand to a receptor under the law of mass action, such that a hyperbolic curve will result when binding is plotted against the molar ligand concentration. This was first used by A.V. Hill to describe the binding of oxygen to haemoglobin [1, 2]. If ligand concentrations are expressed in logarithmic space, then a sigmoidal concentration–occupancy curve will be apparent. If binding of a ligand to a receptor at equilibrium follows a simple mechanism, where the binding of one ligand molecule is unaffected by concomitant binding events and where the ligand binds to only a single class of receptor sites, then the concentration–occupancy relationship plotted on a logarithmic scale will follow a sigmoidal curve that approximately spans from 10 to 90% occupancy over a 100-fold, or two log-unit, concentration range. The steepness of the slope of such a curve in linear space, the Hill coefficient, will equal unity.

1.1.2 Competitive interactions at G protein-coupled receptors 1.1.2.1 Antagonist binding Although ligand–G protein-coupled receptor (GPCR) interactions can be quantified by observing the binding of a radiolabelled ligand to a receptor, it is sometimes more practical to measure the ability of a fixed concentration of radioligand to bind to the receptor in the presence of increasing concentrations of an unlabelled ligand, to indirectly determine the interaction of the unlabelled ligand with the receptor. If we consider the binding of ligand A in the presence of a competitor B at equilibrium: Kon B

Kon A

Koff B

Koff A

← −− A + R + B − −− −→ A + BR − −− −→ ← −− AR + B

1.1 INTRODUCTION

3

then we can define the concentration of receptors bound to the radiolabelled ligand A in the presence of an unlabelled competitive ligand B, which was first derived by Gaddum [3, 4]. A form of this relationship can be described by [AR] =

[RT ] × [A] [B] [A] + Ka 1 + Kb

(1.2)

Competitive binding data are commonly expressed as the fractional inhibition of radioligand binding in the presence of the competitor. However, the binding of some ligands does not follow the simple law of mass action and receptor occupancy is not always directly proportional to ligand concentration (see Section 1.1.2.2). Under these circumstances, the Hill slope may vary from unity and must, therefore, be empirically incorporated into any ligand binding equation to derive the steepness of the slope describing the concentration–occupancy relationship, as shown in Equation 1.3: Y =

Top − Bottom + Bottom [B]n 1+ IC50

(1.3)

where Y is radioligand binding, Top is the top asymptote of the curve equal to total binding of the radioligand in the absence of competitor B, Bottom is the bottom asymptote of the curve equal to nonspecific binding, n is the Hill coefficient and IC50 is the concentration of B that inhibits 50% of radioligand binding. If the Hill coefficient equals unity, then the equilibrium dissociation constant of the unlabelled ligand Kb can be determined using the Cheng–Prusoff equation [5]: Kb =

IC50 [A] 1+ Ka

(1.4)

1.1.2.2 Agonist binding Although binding of an antagonist to a receptor will often display a concentration– occupancy relationship that has a Hill coefficient of unity, agonist binding to GPCRs is usually more complex. This has been well characterized in competition assays between agonists and radiolabelled antagonists, which often yield shallow curves, with Hill coefficients less than unity. These shallow curves reflect different receptor states, for which the agonist has different affinities and the (often) biphasic nature of the curves gives rise to a competition curve that spans greater than a twofold concentration range of competitor (see Figure 1.1). The addition of guanine nucleotides such as guanosine diphosphate, guanosine triphosphate (GTP), guanylylimidodiphosphate (GppNHp) and guanosine 5 -O(3-thiotriphosphate) (GTPγ S) often alters the proportion and affinity of the two binding sites, demonstrating that the dispersion of agonist affinity states reflects the formation of a ternary complex consisting of an agonist, a GPCR and a guanine

CH 1 MEASUREMENT OF LIGAND–G PROTEIN-COUPLED RECEPTOR INTERACTIONS % Specific radioligand binding

4

100

One-site binding Two-site binding

80 60 40 20 0 −12

−10

−8 −6 −4 Log [competitor] (M)

−2

Figure 1.1 Theoretical competition binding curve simulated using a one-site binding fit that follows a Hill slope of unity (solid line) in comparison with a two-site binding fit that follows a shallow slope (dashed line).

nucleotide binding protein (G protein). A ternary complex model (TCM) has been proposed to explain the shallow curves observed with agonists versus radiolabelled antagonists in competition binding assays, but requires a number of assumptions that are not often met [6]. For instance, it must be assumed that the G protein is limiting so that not all of the receptors can form a complex with the G protein, enabling both G protein-coupled (high-affinity) and -uncoupled (low-affinity) receptor species to be observed. This is rarely observed in cellular systems used to study many ligand–GPCR interactions, where G protein levels often exceed those of receptor expression levels. More sophisticated extensions of the TCM have been developed to account for the ability of agonists to bind with higher affinity to receptors that have been mutated to exert constitutive activity than to their wild-type counterparts, even in the absence of G proteins [7]. However, in general there is little advantage to using the extended TCM for routine data analysis, as the simpler TCM can adequately approximate the binding of agonists to a receptor.

1.1.3 Allosteric ligands For a number of GPCRs, there are ligands that can bind to the receptor at a site that is topographically distinct from the endogenous, or orthosteric, ligand binding site [8]. These binding sites and the ligands that bind to them are referred to as ‘allosteric’. Since allosteric ligands do not directly compete for binding with the orthosteric ligand, they have the ability to form a ternary complex in which both the orthosteric and the allosteric ligand occupy the receptor (see Figure 1.2). Binding of an allosteric ligand to a receptor may alter the receptor conformation such that binding of the orthosteric ligand is altered, and vice versa. These changes in binding are termed ‘cooperative effects’ [9]. In terms of binding, the allosteric TCM predicts that an allosteric modulator may inhibit (0 < α < 1), enhance (α > 1) or have no effect (α = 1) on the binding of an

1.2 METHODS AND APPROACHES

AR

Kb /α

Ka

5

ARB Ka /α

R

Kb

RB

Figure 1.2 A ternary complex model describing the binding of an orthosteric ligand A and an allosteric modulator B to a receptor R. Ka and Kb are the equilibrium dissociation constants of R for A and B respectively, α is the binding cooperativity between A and B (the selectivity of A for R and RB or of B for R and AR) and, therefore, determines the effect of B on the binding of A and vice versa.

orthosteric ligand. The formation of a ternary complex between the receptor, the orthosteric ligand and the allosteric modulator can be described by

[AR] + [ARB] = [A] + Ka

[RT ] × [A] [B] α[B] 1+ 1+ Kb Kb

(1.5)

Several recent reviews provide detailed information regarding our understanding of such compounds and the methods that can be used to detect and analyse allosteric interactions [8, 10, 11].

1.2 Methods and approaches 1.2.1 General considerations for radioligand binding assays 1.2.1.1 Buffers Acidic, basic or neutral buffers, consisting of pH buffering agents such as trishydroxymethylaminomethane hydrochloride (tris acid), trishydroxymethylaminomethane (tris base) and 4-(2-hydroxyethyl)-1-piperazine-ethanesulfonic acid (HEPES), are all commonly used for radioligand binding assays, usually at concentrations of 10–50 mm. HEPES-based buffers are popular as HEPES is relatively heat stable. For some receptors, ligand binding and receptor activity are highly dependent upon pH, particularly as both the receptor and the ligand may be protonated (acidified) or deprotonated depending on the pH. Many researchers chose to perform radioligand binding assays at pH 7.4, which resembles a physiological environment. The composition of the buffer is particularly important, as certain trace metals can directly interact with GPCRs and alter their behaviour. For instance, 100 mm NaCl is commonly used to maintain a high ionic strength, yet sodium can destabilize receptor–G protein formation by interacting with a highly conserved aspartate residue in transmembrane domain 2 of Family A GPCRs [12], which can have implications for the binding of agonists and inverse agonists. Sodium may also have less specific effects that are due to the ionic strength of the buffer, and comparing the effects of sodium

6


and potassium may be useful to determine the potential impact of this phenomenon. Alternatively, 100 mm N -methyl-d-glucamine may be substituted for sodium to alter ionic strength whilst having no effects on receptor–G protein coupling. Additional ions may also have direct effects on ligand binding or receptor activity. For instance, magnesium is required for efficient coupling between the receptor and G protein and may, therefore, enhance the proportion of high-affinity agonist binding sites. Magnesium is usually present in the buffer at concentrations between 1 and 10 mm, a concentration that can also be used in functional membrane-based assays such as [35 S]GTPγ S binding assays. Calcium can directly bind to members of the Family C GPCRs to alter the binding of ligands [13] and can also activate certain proteases that may enhance degradation of the receptor. Ethyleneglycoltetraacetate (EGTA) may be added to the assay buffer to chelate calcium ions and act as a protease inhibitor, whilst ethylenediaminetetraacetate (EDTA) is useful for chelating additional trace metals, such as magnesium, to prevent ligand oxidation. For these reasons, EDTA is a useful component in the buffer used to prepare membranes for radioligand binding (see Section 1.2.2.1). Additional protease inhibitors may also be added, such as serine-, cysteine- and metallo-protease inhibitors. Finally, the buffer must also be optimal for the ligands to be used in the radioligand binding assay. Some ligands, particularly 5-hydroxytryptamine and catecholamines such as dopamine, epinephrine and norepinephrine, are particularly susceptible to oxidation, so an antioxidant such as ascorbic acid can be added to the buffer to prevent this. Other ligands, such as proteins and peptides, may stick to plastic and glass, and a protein such as bovine serum albumin (0.001–0.1%) may be required to coat these surfaces to reduce adsorption of the ligand.

1.2.1.2 Temperature and incubation time Radioligand binding assays are usually performed at 20–37 ◦ C. Although 37 ◦ C is physiologically relevant, receptors studied in membrane systems may become unstable at high temperatures. If lower temperatures are used, then care must be taken to ensure that equilibrium binding is reached. Therefore, the temperature at which the experiment is performed depends upon the stability of the receptor and ligands and the binding kinetics of the ligand. The law of thermodynamics predicts that equilibrium will be reached faster at higher temperatures, with an approximate doubling in the reaction rate with a 10 ◦ C increase in temperature. With regard to incubation time, this may vary between receptors. The binding of a fixed concentration of radioligand should initially be measured at different time points to determine when equilibrium is attained (see Section 1.2.2.5). The rate at which the ligand–receptor interaction approaches equilibrium is often termed the Kobserved or Kobs , measured in units of inverse time. Kobs is dependent on the association and dissociation rate constants of the ligand and the ligand concentration, with lower ligand concentrations taking longer to reach equilibrium. Thus, binding of a radioligand to a receptor starting from time point 0 will follow Y = [AR]Eq × (1 − e−Kobs t )

(1.6)


7

where Kobs = Kon [A] + Koff and [AR]Eq is binding once equilibrium has been reached (expressed in units of the Y axis, such as disintegrations per minute (dpm)). Low ligand concentrations should, therefore, be used when testing equilibration time. It is recommended that five times the dissociation half-life of any ligand should be allowed in order to reach approximately 97% equilibrium binding with the receptor, which is considered to be sufficient.

1.2.1.3 Ligand depletion Under ideal experimental conditions only a small fraction of the total ligand added will bind to the receptor or to nonspecific sites. Thus, the free-ligand concentration throughout the assay is generally close to the total concentration of ligand added to the assay. However, if a large proportion of the ligand added is bound, either specifically or nonspecifically, then the concentration of free ligand in solution will deviate significantly from the concentration added to the assay. Generally, if less than 10% of the ligand is bound at each given ligand concentration, then ligand depletion is minimal and, therefore, is not a concern. Initial experiments can be carried out to optimize the protein content of the assay and ensure that excess radioligand does not bind to the protein added. This can be achieved by measuring the binding of a fixed radioligand concentration in the presence of different concentrations of the receptor preparation and calculating the percentage of radioligand bound at each receptor concentration. If ligand depletion does appear to be a problem, then the assay format may be altered to overcome this. The assay volume may be increased, but a greater amount of ligand will also be required to obtain the same concentration whilst receptor numbers will remain constant. Alternatively, the free radioligand can be measured in each tube if a centrifugation assay is employed (see Section 1.2.1.4). Otherwise, analysis techniques that account for the differences between the added and free ligand concentration can be used (see Section 1.2.2.2).

1.2.1.4 Separation of bound from free radioligand In order to measure the amount of radioligand bound to the receptor preparation, bound radioligand must usually be separated from free radioligand. The most common separation method is vacuum filtration, whereby the samples are rapidly filtered, generally onto glass-fibre filter paper, and washed to remove radioligand that is weakly bound to the filter or to the receptor preparation. Be aware that high concentrations of membrane can clog the filter pores, leading to slower filtration and washing rates. The wash buffer should be ice-cold and washing should be rapid so as to minimize dissociation of the radioligand from the receptors. Filtration of samples can, however, lead to loss of receptors, as some can pass through the glass-fibre filters. High-speed centrifugation assays may be used to minimize loss of receptor and ensure that a greater proportion of the ligand-bound receptor is collected. However, these assays may require more protein in order to pellet the membrane efficiently. The supernatant can then be removed and the pellet rapidly washed. Centrifugation assays are also useful when ligand depletion is a problem, as the free radioligand concentration can be determined following termination of the assay.

8


1.2.2 General assay protocols 1.2.2.1 Membrane preparations Radioligand binding may be studied in a cell line or tissue that endogenously expresses the receptor of interest, or a recombinant cell system in which receptor expression is induced. These assays may be performed on whole cells, on tissues or on soluble and purified receptors. However, a common approach is to prepare membrane preparations from the cell lines or tissues expressing the receptor (Protocol 1.1). Although the generation of membranes can be more expensive than using live cells, the use of membrane preparations is particularly convenient because the membranes can be frozen and stored for several months and defrosted when required. It is recommended to perform each repeat of a radioligand binding assay on a new membrane preparation made from a new tissue preparation or cell passage number.

PROTOCOL 1.1 Preparation of Membranes from Adherent Mammalian Cells Equipment and Reagents • Lifting buffer; for example, 2 mM EDTA in a phosphate-buffered saline (PBS) solution (150 mM NaCl, 16 mM Na2 HPO4 , 4 mM NaH2 PO4 ) for Chinese hamster ovary (CHO) cells, or PBS alone for less adherent cell lines, such as HEK 293 cells • HEPES-based buffer; for example, 20 mM HEPES, 10 mM EDTA, pH 7.4 • Low-EDTA HEPES-based buffer; for example, 20 mM HEPES, 0.1–1 mM EDTA, pH 7.4 • Low- and high-speed chilled centrifuges • Homogenizer.

Method 1 Grow cells to 80–90% confluence in T175 flasks (175 cm2 ). Generally, one T175 flask will provide sufficient membrane for approximately 50 assay tubes using 15 µg of protein per tube. Remove media and add 10 ml warm lifting buffera to each flask. For cell lines that are particularly adherent, an initial wash with calcium-free PBS may be required to remove any remaining media. For cell lines that require lifting buffer containing EDTA, incubate for 2–5 min at 37 ◦ C. Gently tap the flasks to detach cells and collect the cell suspension in appropriate tubes. Wash each flask with 10 ml PBS and collect the wash. 2 Centrifuge the cell suspension at approximately 200 g (approximately 1200 rpm in most bench-top centrifuges) for 10 min and resuspend the cell pellet in 2–3 ml HEPES- or tris-based bufferb per T175 flask, or an appropriate volume to ensure efficient homogenization (step 3). 3 All subsequent steps should be performed at 4 ◦ C to reduce activation of proteases. Homogenize the cell solution using a homogenizer. Perform three 5–10 s bursts at top speed (∼20 000 rpm) with cooling on ice in between each burst.


9

4 Centrifuge the resulting cell lysate at 600 g (approximately 1700 rpm in most bench-top centrifuges) for 10 min to separate the nuclear fraction and additional cell debris. If a large pellet is obtained, resuspend the pellet and repeat steps 3 and 4, combining the supernatant obtained following each centrifugation step. 5 Transfer the remaining suspension to new tubes and centrifuge at approximately 40 000 g or higher for 1 h. Resuspend the resulting protein pellet in a HEPES- or tris-based buffer,c which can be the buffer that will be used for subsequent radioligand binding assays, or a predominantly HEPES or tris-based buffer with a low concentration (0.1–1 mM) of a chelating agent such as EDTA or EGTA. Resuspend in approximately 0.5–2 ml buffer per T175 flask, depending on the expression level of the receptor and subsequently the amount of protein required for each assay. 6 Homogenize the membrane suspension briefly and dispense into aliquots of a suitable volume. Membranes can be stored at −80 ◦ C, generally for up to 12 months, and the protein content of the preparation can be determined using an appropriate method such as that of Lowry [14] or Bradford [15].

Notes a It

is not recommended to use trypsin for harvesting cells, as receptors may be hydrolysed.

b The

HEPES- or tris-based buffer should contain 1–10 mM EDTA, EGTA or both to reduce proteolysis of the receptor of interest following homogenization steps. c Ten

confluent T175 flasks should generate approximately 10 ml of protein at a concentration of 1–2 mg/ml.

1.2.2.2 Saturation binding assays Saturation binding assays are used to determine the binding of different radioligand concentrations to a receptor at equilibrium directly. These assays can be used to derive direct measurements of the total receptor number or density present in the system under investigation and to determine the affinity of the radioligand for the receptor. However, the radioligand will not only bind specifically to the receptor of interest, but will also bind to additional sites within a membrane or cell preparation or to the tubes used to perform the radioligand binding assay. Therefore, nonspecific binding must be determined in parallel using a high concentration of a competing ligand to displace each radioligand concentration from the receptor. Where possible, the competitive ligand used to define nonspecific binding should not be an unlabelled form of the radioligand, as both compounds will compete for the same nonspecific binding sites. Ideally, at least 100–1000 times the Ka of the competitive ligand used to define nonspecific binding should be used to ensure full receptor occupancy. Protocols 1.2–1.6 for radioligand binding assays describe assays performed in 0.5–1 ml volumes, but are applicable to smaller volume assays that can be scaled down. Traditionally, specific binding is determined by subtracting nonspecific binding from total binding and the data are analysed using Equation 1.7 (see Figure 1.3). However,

10

CH 1 MEASUREMENT OF LIGAND–G PROTEIN-COUPLED RECEPTOR INTERACTIONS 6000

Total Binding Nonspecific Binding Specific binding

DPM

4000

2000

0 0

2

4

6

8

10

[3H]NMS (nM)

Figure 1.3 Saturation radioligand binding. Binding of the radioligand, [3 H]N-methylscopolamine ([3 H]NMS), to the M4 muscarinic acetylcholine (ACh) receptor stably expressed in FlpIn CHO cells, where nonspecific binding was determined in the presence of 10 µm atropine. Membranes were incubated with [3 H]NMS for 1 h at 37 ◦ C before the assay was terminated as described in the methods section.

with the development of more sophisticated computer software, it is now possible to fit the data globally to a model of receptor and nonspecific site occupancy, shown in Equation 1.8. If data are fitted globally, then this means that a family of curves is fitted to a particular model, rather than just a single curve. Parameters common to both datasets can be shared between them, enabling their determination from the relationship between all curves (a comprehensive explanation of global fitting is provided in [16]). The radioligand–receptor complex concentration [AR] that forms at equilibrium at each radioligand concentration is reflected by the specific binding of the radioligand, expressed on the y-axis. We usually call the total number of receptors that are defined by our radioligand as the Bmax , so specific binding of each radioligand concentration in a saturation binding assay is defined by the Hill–Langmuir occupancy equation using the terms shown in Equation 1.7. Note the relationship to Equation 1.1. Y =

Bmax × [A] [A] + Ka

(1.7)

Nonspecific binding is generally linearly proportional to the radioligand concentration and, therefore, is described by the equation for a straight line. As the total binding TB measured in the experiments represents both specific and nonspecific binding, radioligand binding, expressed on the y-axis, to one class of binding sites is defined by TB =

Bmax × [A] + NS × [A] [A] + Ka

(1.8)

where NS is the nonspecific binding. In the presence of ligand depletion, binding is defined by TB =

Bmax × ([A]T − TB) + ([A]T − [TB]) × NS ([A]T − TB) + Ka

(1.9)


11

where [A]T is the total radioligand concentration added. As TB appears on both sides of Equation 1.9 (implicit equation), it cannot be entered into most nonlinear regression programs. The equation can, however, be rearranged into a quadratic equation: √ −b + b2 − 4ac (1.10) TB = 2a where a = −1 − NS b = [A]T [2(NS + 1)] + Ka (NS + 1) + Bmax and c = −[A]T [NS(Ka + [A]T )] + Bmax If total and nonspecific binding data are fitted globally, then nonspecific binding should be shared between datasets so that the fraction of total binding that is nonspecific at each radioligand concentration can be determined. This type of analysis will derive the Ka and the maximum level of binding of the radioligand without the need to subtract nonspecific from total binding. However, the raw data values that are derived from a saturation radioligand binding assay are usually more useful if converted to the amount of radioligand bound to our receptor preparation. We usually define the total number of binding sites in reference to the amount of protein or cells. For instance, 15 µg protein were added into each tube of the saturation assay shown in Figure 1.3; therefore, 1.7 pmol of receptor is expressed per milligram of protein.

PROTOCOL 1.2 Saturation Binding Assays Equipment and Reagents • Membrane preparation (Protocol 1.1) • Binding buffera • 5 ml polypropyleneb assay tubes (Techno-plas) • Radioligand • Competitive antagonist • Wash bufferc • Water bath • Vacuum harvester (Brandel) • Glass-fibre filter paperd (Whatman) • Liquid scintillation cocktaile (PerkinElmer) • Liquid scintillation counter (e.g. Packard Tri-Carb LS counter).

Method 1 Dilute the receptor preparation, radioligand and competitive antagonist in assay buffer to 10× the final concentrations required in the assay, so that each can be diluted by this factor when added to the final assay mix.

12


2 In an appropriate volume of binding buffer (usually between 0.5 and 1 ml), prepare one set of assay tubes containing increasing concentrations of radioligand and one identical set of tubes that also contain a saturating concentration of a competitive ligand to define nonspecific binding (see Table 1.1).

Table 1.1 Tube set-up used to determine saturation binding of [3 H]NMS to the M4 muscarinic ACh receptor using a 500 µl final assay volume, where nonspecific binding is determined in the presence of 10 µM atropine. Final [[3 H]NMS] (M)

[3 H]NMS (µl)

100 µM atropine (10 µM final concentration) (µl)

Buffer (µl)

Membrane (µl)

0

450

50

0

400

50

0

290

50

0

400

50

0

290

50

0

400

50

0

290

50

0

400

50

50

400

50

50

350

50

50

240

50

50

350

50

50

240

50

50

350

50

50

240

50

50

350

50

Total binding 0 1 × 10

0 −11 −11

3.16 × 10

−10

50 (of 1 × 10

−10

160 (of 1 × 10

M)

−10

−9

M)

1 × 10

50 (of 1 × 10

3.16 × 10−10

160 (of 1 × 10−9 M)

−9

50 (of 1 × 10

1 × 10

3.16 × 10

−9

−8

160 (of 1 × 10 50 (of 1 × 10

1 × 10

−8

M)

M)

−8

−7

M)

M)

Nonspecific binding 0

0 −11

1 × 10

−11

50 (of 1 × 10

−10

M)

−10

3.16 × 10

160 (of 1 × 10

1 × 10−10

50 (of 1 × 10−9 M)

3.16 × 10

−10

−9

50 (of 1 × 10

1 × 10

3.16 × 10 −8

1 × 10

160 (of 1 × 10

−9

−8

160 (of 1 × 10 50 (of 1 × 10

−9

M)

M)

−8

−7

M)

M)

M)

3 Start the binding reaction by addition of membrane protein,f bringing the assay to the desired volume and incubate the reaction for sufficient time so as to reach equilibrium binding (at least five times the dissociation half-life of the ligand). 4 At the appropriate time, terminate the reaction by rapid vacuum filtration through glass-fibre filter paper followed by three to four 4 ml washes with ice-cold wash buffer to separate bound from free radioligand. 5 Determine radioactivity by liquid scintillation counting.


13

Notes a For

any radioligand binding assay, the buffer may be a simple buffer containing no or low concentrations of ions with chelating agents such as EDTA and EGTA (e.g. 20 mM HEPES, 1 mM EDTA, 1 mM EGTA, pH 7.4) or the buffer may resemble a more physiological environment or a buffer used for functional assays such as [35 S]GTPγ S binding assays (e.g. 20 mM HEPES, 100 mM NaCl, 10 mM MgCl2 , pH 7.4). Care should be taken when choosing the appropriate acid or base to adjust the pH of the buffer. Sodium hydroxide (NaOH), for instance, will alter the concentration of sodium ions in the solution, which may have effects on the binding of ligands. 1–2 M KOH is more appropriate for adjusting the pH of radioligand binding buffers. b Proteins and peptides are less likely to stick to polypropylene than to polystyrene-based plastics. c PBS

or other simple saline solution, such as 0.9% NaCl.

d

GF/B-grade glass-fibre filter paper has larger pores and, therefore, is generally better suited to cell-based binding assays, whilst GF/C-grade glass-fibre filter paper is better suited for membrane-based assays. However, higher nonspecific binding may be apparent when using GF/C-grade glass-fibre filter paper. e Certain

scintillation cocktails may be better suited to specific applications. For instance, whilst Perkin Elmer’s Ultima Gold is suitable for both aqueous and nonaqueous samples, Irga Safe Plus is more suited to aqueous samples. f The

appropriate protein concentration should be optimized prior to the performance of any ligand binding assay to ensure that ligand depletion does not occur.

1.2.2.3 Heterologous and homologous competition binding experiments The use of high radioligand concentrations in saturation binding experiments can be expensive, whilst many ligands are unavailable in radioactive form, meaning that their affinity for a receptor cannot always be directly determined. Competition binding assays, however, are an alternative means to study interactions between the receptor and an unlabelled ligand under equilibrium conditions, by measuring the binding pattern of a single radioligand concentration in the presence of increasing concentrations of an unlabelled competitor (Protocol 1.3). The concentration of the unlabelled ligand that causes 50% inhibition of radioligand binding is termed the IC50 . However, this does not necessarily mean that this concentration of ligand will bind half the receptors, as the IC50 of the unlabelled ligand will depend upon the ability of the radioligand to bind to the receptor (e.g. its equilibrium dissociation constant), the ability of the unlabelled ligand to bind to the receptor and the concentration of the radiolabelled ligand. Thus, the IC50 will normally be greater than the equilibrium dissociation constant Kb of the unlabelled ligand. However, the Cheng–Prusoff equation (Equation 1.4) can be used to determine the Kb of the competitor in a competition binding experiment. If one of the ligands being used for the assay is an agonist, then it may be desirable to eliminate, or at least minimize, the ability of the G protein to couple to the receptor, so that the interaction between the ligand and the receptor is largely independent of G protein coupling. For this reason, many researchers choose to perform agonist

14


competition binding assays in the presence of guanine nucleotides and, hence, enable the data to be fitted to a simple one-site binding model. However, if the Hill coefficient of the inhibition curve is significantly different from unity, then this may indicate that the agonist binds to two different receptor species, which are usually described as a high- and low-affinity state. Under these circumstances, binding of the competitor to two sites may be the preferred model (see Equation 1.14). An alternative approach to competition binding assays measures the competition between the radioligand and a non-radiolabelled version of the radioligand. This type of competition assay is called homologous competition binding [17]. It can be utilized to determine the affinity of the radioligand for a receptor in addition to the number of binding sites present, if the radioligand, A, and unlabelled competitor, B, share an identical affinity for the receptor. Under these circumstances, binding of the radioligand will be a fraction of the total ligand bound ([A] + [B]) and will be defined by [AR] = where

[A] × [Rt ] [A] + [B] + Ka

(1.11)

Ka = IC50 − [A]

(1.12)

% Specific [3H]CCPA binding

If we consider a heterologous competition binding assay, binding of a radioligand, expressed on the y-axis, in the presence of increasing concentrations of a competitor, expressed on the x-axis, will follow a sigmoidal curve that can be described by Equation 1.3 (see Figure 1.4). Depending on the nonlinear regression program used to analyse the data, the program must be told to define parameters in a logarithmic

100 80 60 40 20 0 −12

−11

−10 −9 −8 −7 Log [R-PIA] (M)

−6

Figure 1.4 Competition binding of the radiolabelled agonist, [3 H]2-chloro-N6 -cyclopentyladenosine ([3 H]CCPA), at the A1 adenosine receptor by increasing concentrations of the unlabelled agonist, N6 -((R)-2-phenylisopropyl)adenosine (R-PIA). Membranes were incubated with [3 H]CCPA and R-PIA for 1 h at 30 ◦ C before termination of the assay as described in the methods section. Data were transformed to the percentage of specific [3 H]CCPA binding and fitted closely to a model that described one-site binding with a Hill coefficient of unity.

15


form if this is how they are to be reported. Alternatively, Equation 1.3 can be recast to reflect this, as shown by the following Y =

Top − Bottom + Bottom 1 + 10(log[B]−log IC50 )n

(1.13)

If the Hill coefficient n is not significantly different from unity, then this may be constrained as such in the analysis. If, however, it appears to vary from unity, then the data may sometimes be fitted best by a two-site binding model: fraction_1 1 − fraction_1 + Bottom Y = (Top − Bottom) + 1 + 10log[B]−log IC50_1 1 + 10log[B]−log IC50_2 (1.14) For analysis of homologous binding data we can use Equation 1.15 to define binding: Y =

PROTOCOL 1.3

Top − Bottom + Bottom 1 + 10log[B]−log[A] + 10log Ka −log[A]

(1.15)

Competition Binding Assays

Equipment and Reagents • Membrane preparation (Protocol 1.1) • Binding buffera • 5 ml polypropylene assay tubes (Techno-plas) • Radioligandb • Unlabelled ligandc • Guanine nucleotided (Sigma) • Wash buffere • Water bath • Vacuum harvester (Brandel) • Glass-fibre filter paper (Whatman) • Liquid scintillation cocktail (PerkinElmer) • Liquid scintillation counter (e.g. Packard Tri-Carb LS counter).

Method 1 Dilute the receptor preparation, radioligand, unlabelled ligand and guanine nucleotides (if required) in assay buffer to 10× the final concentration desired in the assay. 2 In an appropriate volume of binding buffer, add a single concentration of radioligand to all assay tubes and add increasing concentrations of the unlabelled ligand into different tubes. Ensure that you prepare tubes in which no unlabelled ligand is added to define

16


total binding, in addition to tubes that contain no unlabelled ligand but a high concentration of an alternative competitive ligand to define nonspecific binding. If desired, add an appropriate concentration of guanine nucleotides to each tube to minimize G protein coupling to the receptor (see Table 1.2).

Table 1.2 Tube set-up used to determine binding of [3 H]CCPA in the presence of increasing concentrations of R-PIA using a 500 µl final assay volume, where nonspecific binding is determined in the presence of 100 µM R-PIA. 1 mM GppNHp Buffer 20 nM [3 H]CCPA Membrane (100 µM final (µl) (2 nM final (µl) concentration) concentration) (µl) (µl)

Final [R-PIA] R-PIA (M)

0

0

3.16 × 1×

10−10

3.16 × 1×

10−11 10−10

10−9

3.16 ×

10−9

1 × 10−8 3.16 × 1×

10−7

3.16 × 1×

10−8 10−7

10−6

Nonspecific

160 (of 1 × 50 (of 1 ×

10−9 M)

160 (of 1 × 50 (of 1 ×

10−10 M) 10−9 M)

10−8 M)

160 (of 1 ×

10−8 M)

50 (of 1 × 10−7 M) 160 (of 1 × 50 (of 1 ×

10−7 M)

10−6 M)

160 (of 1 ×

10−6 M)

50

350

50

50

50

190

50

50

50

300

50

50

50

190

50

50

50

300

50

50

50

190

50

50

50

300

50

50

50

190

50

50

50

300

50

50

50

190

50

50

50 (of 1 ×

10−5 M)

50

300

50

50

50 (of 1 ×

10−3 M)

50

300

50

50

binding

3 Start the reaction by the addition of membrane and incubate the assay for the desired time so as to reach equilibrium binding. 4 Terminate the reaction and determine radioactivity as described in Protocol 1.2.

Notes a As

described in Protocol 1.2.

b The

radioligand concentration should be approximately equal to its Ka or lower.

c This

will be an unlabelled form of the radioligand for homologous competition binding assays, or an alternative unlabelled ligand for heterologous competition binding assays. d If guanine nucleotides are to be added to the assay, 100 µM GppNHp or 1 mM GTP is generally sufficient to uncouple receptor–G protein complexes. e As



17

1.2.2.4 Equilibrium binding experiments with an allosteric modulator Saturation assays performed in the absence and presence of a putative allosteric modulator are a useful means to determine whether a shift in radioligand affinity in the presence of the modulator is in agreement with an allosteric mode of action (Protocol 1.4). For a competitive ligand, if the ratio of the affinity of the radioligand in the presence (Ka ) and absence (Ka ) of this competitor, otherwise known as the ‘dose ratio’, is determined, then a Schild plot of log[(Ka /Ka ) − 1] against the log competitor concentration should yield a straight line with a slope of unity [18]. If, however, there is negative cooperativity between the putative modulator and the orthosteric ligand, then the slope may differ from unity and a curvilinear plot may result. The curvature in the Schild plot will represent the cooperativity between the two ligands, reflecting a limit in the shift in the radioligand affinity. Positive allosteric interactions, in contrast, can be characterized by an enhancement in the affinity of the radioligand, causing a leftward shift in the radioligand binding curve. Under these circumstances, the dose ratio can be determined as the ratio of radioligand affinities in the absence and presence of the allosteric modulator and a Schild plot of log(Ka /Ka ) against the log modulator concentration may reflect the positive cooperativity [8]. Competition binding assays may also be used to study the direct effects of an allosteric modulator on the binding of a radioligand at the orthosteric site; although the allosteric modulator will not directly compete for binding with the orthosteric radioligand, the cooperativity will mediate alterations in the binding of the radioligand. If we consider the scheme shown in Figure 1.2, then fractional occupancy of a receptor population by the orthosteric ligand [A] in the presence of an allosteric modulator [B] can be described by

ρAR+ARB = [A] + Ka

Bmax × [A] [B] α[B] 1+ 1+ Kb Kb

(1.16)

Note that the apparent affinity Kapp of the radioligand in the presence of the allosteric modulator is defined by [B] 1+ Kb Kapp = Ka (1.17) α[B] 1+ Kb Care must be taken when choosing the radioligand concentration for such assays. If the allosteric modulator has very high negative cooperativity with the radioligand, it may appear competitive if a low radioligand concentration is employed giving rise to low radioligand receptor occupancy. As the radioligand concentration is increased, the ability of the allosteric modulator to inhibit radioligand binding will be reduced, which is seen by an increase in the bottom asymptote of the inhibition curve. Subsequently, full inhibition by the allosteric modulator may not be achieved using high radioligand concentrations, preventing its actions from being distinguished from a competitive antagonist (see Figure 1.5). However, nonequilibrium binding artefacts may greatly

CH 1 MEASUREMENT OF LIGAND–G PROTEIN-COUPLED RECEPTOR INTERACTIONS % Specific [3H]NMS Binding

18

100

[3H]NMS (nM)

80

2 0.2

60 40 20 0 −9

−8

−7

−6

−5

−4

−3

Log [C7/3-phth] (M)

Figure 1.5 Normalized binding of two different concentrations of the radioligand, [3 H]NMS, at the M4 muscarinic ACh receptor in the presence of increasing concentrations of the muscarinic receptor allosteric modulator, heptane-1,7-bis(dimethyl-3 -phthalimidopropyl)ammonium bromide (C7 /3-phth). Membranes were incubated with C7 /3-phth for 1 h at 37 ◦ C before the assay was terminated as described in Section 1.2.

influence the results obtained in such an experiment and care must be taken to interpret the observations correctly [19]. Several alternative approaches have been described to determine the effects that the allosteric modulator can exert [10, 20]. One approach is to compete radioligand binding with an unlabelled competitive ligand in the presence of different concentrations of allosteric modulator [20], which can give detailed information regarding the interaction of the allosteric modulator with both the radioligand and the orthosteric competitor (Protocol 1.4). The assay described in Protocol 1.4 can be used to determine the cooperativity between the allosteric modulator and both the radioligand and unlabelled competitor, where binding of the radioligand [A] in the presence of the competitor [B] and the allosteric modulator [X] is described by

Y = [A] +

Ka KX α[X] + KX

Bmax × [A] [B] [X] β[B] × [X] 1+ + + Kb Kb Kb KX

(1.18)

where Ka , Kb and KX are the equilibrium dissociation constants of the radioligand, competitor and allosteric modulator respectively. Figure 1.6 shows competition binding at the M4 muscarinic ACh receptor in the absence and presence of an allosteric modulator, which exerts weak negative cooperativity with the radioligand, [3 H]NMS. In contrast, however, the same modulator exerts positive cooperativity with ACh, demonstrated by the leftward shift in the apparent ability of ACh to compete for [3 H]NMS binding.

19

% Specific [3H]NMS binding

1.2 METHODS AND APPROACHES [Modulator] (mM) 100

0 0.3

80

1 60

3 10

40 20 0 −10

−9

−8

−7 −6 −5 Log [ACh] (M)

−4

−3

Figure 1.6 Competition of binding of the radioligand, [3 H]NMS, at the M4 muscarinic ACh receptor by increasing concentrations of the agonist, ACh, in the absence and presence of increasing concentrations of an allosteric potentiator of ACh binding. Membranes were equilibrated with ACh and the allosteric modulator for 3 h at 37 ◦ C before termination of the assay as described in Section 1.2.

PROTOCOL 1.4 Orthosteric Radioligand Binding in the Presence of an Allosteric Modulator Equipment and Reagents • Membrane preparation (Protocol 1.1) • Binding buffera • 5 ml polypropylene assay tubes (Techno-plas) • Radioligandb • Unlabelled competitor • Allosteric modulator • Guanine nucleotidesc (Sigma) • Wash bufferd • Water bath • Vacuum harvester (Brandel) • Glass-fibre filter paper (Whatman) • Liquid scintillation cocktail (PerkinElmer) • Liquid scintillation counter (e.g. Packard Tri-Carb LS counter).

20


Method 1 Dilute the receptor preparation, radioligand, unlabelled competitor, allosteric modulator and guanine nucleotides (if required) in assay buffer to 10× the final concentration desired in the assay. 2 In an appropriate volume of binding buffer, add a single concentration of radioligand to all tubes. 3 For a control curve in the absence of allosteric modulator, add increasing concentrations of the unlabelled competitive ligand into different tubes. Remember to prepare tubes in which no unlabelled ligand is added to define total binding, in addition to tubes that contain no unlabelled ligand but a high concentration of an alternative competitive ligand to define nonspecific binding. 4 To a second set of tubes, add increasing concentrations of the unlabelled ligand into different tubes in addition to the allosteric modulator. Several concentrations of modulator should be tested. If each concentration–response curve will be harvested separately, for every curve derived in the presence of the allosteric modulator, prepare tubes in which no unlabelled ligand is added but the allosteric modulator is present, to define total binding in the presence of the modulator, in addition to tubes that contain no unlabelled ligand and no allosteric modulator. This will account for variation between total binding counts on different pieces of filter paper. The total binding in the absence of allosteric modulator can be used to transform data if the allosteric modulator shows negative or positive cooperativity with the radioligand and, therefore, reduces or increases its binding. Also ensure that the vehicle in which the allosteric modulator is prepared exerts no effect on radioligand binding. In addition, prepare tubes that contain no unlabelled ligand but a high concentration of a different competitive ligand to define nonspecific binding (see Table 1.3).

Table 1.3 Tube set-up used to determine the binding of [3 H]NMS to the M4 muscarinic ACh receptor in the presence of increasing concentrations of ACh and an allosteric modulator, using a 500 µl final assay volume, where nonspecific binding is determined in the presence of atropine. Final [ACh] (M)

Allosteric 1 mM GppNHp Buffer 2 nM [3 H]NMS Membrane modulator (100 µM final (µl) (0.2 nM final (µl) (µl) concentration) concentration) (µl) (µl)

ACh (µl)

Control curve in the absence of allosteric modulator 0

0

0

50

350

50

50

1 × 10−9

50 (of 1 × 10−8 M)

0

50

300

50

50

3.16 × 10

−9

160 (of 1 × 10

−8

M)

0

50

190

50

50

0

50

300

50

50

1 × 10−8

50 (of 1 × 10−7 M)

3.16 × 10−8

160 (of 1 × 10−7 M)

0

50

190

50

50

10−7

50 (of 1 × 10 –6 M)

0

50

300

50

50

3.16 × 10 –7

160 (of 1 × 10−6 M)

0

50

190

50

50

0

50

300

50

50

1× 1×

10−6

50 (of 1 ×

10−5 M)

(continued overleaf )

21


Table 1.3 (continued) Allosteric 1 mM GppNHp Buffer 2 nM [3 H]NMS Membrane modulator (100 µM final (µl) (0.2 nM final (µl) (µl) concentration) concentration) (µl) (µl)

Final [ACh] (M)

ACh (µl)

3.16 × 10−6

160 (of 1 × 10−5 M)

0

50

190

50

50

10−5

50 (of 1 × 10−4 M)

0

50

300

50

50

3.16 × 10−5

160 (of 1 × 10−4 M)

0

50

190

50

50

0

50

300

50

50

0

50

190

50

50

0

50

300

50

50

0

50

300

50

50

0

50

300

50

50

1×

1 × 10

−4

3.16 × 10−4 1×

10−3

Nonspecific binding

50 (of 1 × 10

−3

M)

160 (of 1 × 10−3 M) 50 (of 1 ×

10−2 M)

50 (of 100 µM) atropine

Curve in the presence of allosteric modulator Vehicle control

50 vehicle

Total binding

0

0

50

350

50

50

0

0

50

50

300

50

50

50

50

250

50

50

1 × 10−9 3.16 ×

10−9

50 (of 1 × 10−8 M) 160 (of 1 ×

10−8 M)

50

50

140

50

50

50

50

250

50

50

1 × 10−8

50 (of 1 × 10−7 M)

3.16 × 10−8

160 (of 1 × 10−7 M)

50

50

140

50

50

10−7

50 (of 1 × 10−6 M)

50

50

250

50

50

3.16 × 10−7

160 (of 1 × 10−6 M)

50

50

140

50

50

50

50

250

50

50

50

50

140

50

50 50

1× 1×

10−6

3.16 × 10−6 1 × 10

−5

50 (of 1 ×

10−5 M)

160 (of 1 × 10−5 M) 50 (of 1 × 10

−4

50

50

250

50

3.16 × 10−5

160 (of 1 × 10−4 M)

50

50

140

50

50

1 × 10−4

50 (of 1 × 10−3 M)

50

50

250

50

50

3.16 × 10−4

160 (of 1 × 10−3 M)

50

50

140

50

50

10−3

50 (of 1 × 10−2 M)

50

50

250

50

50

50 DMSO

50

250

50

50

1×

Nonspecific binding

50 (of 100 µM) atropine

M)

5 Start the reaction by the addition of membrane and incubate the assay for the desired time so as to reach equilibrium binding. 6 Terminate the reaction and determine radioactivity as described in Protocol 1.2.

Notes a As b


The radioligand concentration should approximately equal its Ka .

c See d

note d in Protocol 1.3.

As described in Protocol 1.2.

22


1.2.2.5 Kinetic radioligand binding assays Kinetic assays can be used to determine the association or dissociation rate constants of a radioligand (Protocol 1.5). This information is useful, for instance, for deciding the correct incubation time for many radioligands to reach equilibrium conditions so that equilibrium dissociation and association constants can be accurately measured in subsequent assays [21]. Kinetic assays can additionally be used to reveal cooperativity between two binding sites, where the binding kinetics of the orthosteric ligand may be modulated. Method 1 in Protocol 1.5 describes measurement of the time taken to reach equilibrium binding, which will be represented by a plateau in the binding curve over time. The rate at which equilibrium is reached is dependent on the radioligand concentration and the rate at which the ligand associates with and dissociates from the receptor. In the presence of a single radioligand concentration, this information can be used to calculate the association rate constant Kon only if the rate at which the ligand dissociates from the receptor Koff is known. If Koff is known, then we can use Equation 1.19 to calculate Kon : Kobs − Koff Kon = (1.19) [A] However, if the Koff of the ligand is unknown, then both Kon and Koff can be determined by measuring the Kobs of multiple concentrations of radioligand. Traditionally, Kobs (in inverse time) is plotted against the inverse radioligand concentration to derive a straight line with a gradient equal to Kon and a y-intercept equal to Koff . Alternatively, the Kobs of at least two different concentrations of radioligand can be fitted globally to the kinetic model shown in Equation 1.6. Otherwise, the rate of radioligand association can be determined by measuring both the association and dissociation of the radioligand in one experiment. A kinetic assay of this kind allows us to measure the dissociation rate of the radioligand directly in inverse time and will yield the Kobs , as described for association experiments; so, if we know the radioligand concentration, then we can define occupancy over time using Equation 1.6.

PROTOCOL 1.5 Measurement of Radioligand Binding Kinetics Equipment and Reagents • Membrane preparation (Protocol 1.1) • Binding buffera • 5 ml polypropylene assay tubes (Techno-plas) • Radioligand • Competitive ligand • Wash bufferb • Water bath • Vacuum harvester (Brandel)

23


• Glass-fibre filter paper (Whatman) • Liquid scintillation cocktail (PerkinElmer) • Liquid scintillation counter (e.g. Packard Tri-Carb LS counter).

Method 1: Association Binding Kinetics 1 Dilute the receptor preparation, radioligand and competitive ligand in assay buffer to 10× the final concentration desired in the assay. 2 Prepare tubes containing the radioligand and identical tubes also containing a saturating concentration of a competitive ligand to define nonspecific binding.c Tubes that contain no radioligand must also be prepared to represent binding of the radioligand at the zero time point (i.e. no radioligand binding). Membrane can be added to these tubes before the start of the assay, as the values obtained from these tubes will purely be representative of background counts that arise from sources other than the radioligand. 3 Stagger the addition of membrane, bringing the assay to the desired final volume, so that binding of the radioligand can be determined at various time points. For instance, add membrane to the first tube at time point 0 and make subsequent membrane additions to successive tubes at 5, 10, 15 and 18 min. Separate bound from free radioligand (as described in Protocol 1.2) at 20 min. Thus, tubes containing membrane added at time points 0, 5, 10, 15 and 18 min will represent 20, 15, 10, 5 and 2 min incubation between the membrane and radioligand respectively, whilst tubes containing membrane but no radioligand will represent time point 0 (see Table 1.4).

Table 1.4 Tube set-up used to determine the binding of [3 H]NMS to the M4 muscarinic ACh receptor over time, using a 1 ml final assay volume, where nonspecific binding is determined in the presence of atropine. Time of [3 H]NMS addition (min)

Association time (min)

2 nM [3 H]NMS (0.2 nM final concentration) (µl)

Buffer (µl)

100 µM Atropine (10 µM final concentration) (µl)

Membrane (µl)

Specific binding 0

20

100

800

0

100

5

15

100

800

0

100

10

10

100

800

0

100

15

5

100

800

0

100

18

2

100

800

0

100

–

0

0

900

0

100 (continued overleaf )

24


Table 1.4 (continued) Time of [3 H]NMS addition (min)

Association time (min)

2 nM [3 H]NMS (0.2 nM final concentration) (µl)

Buffer (µl)

100 µM Atropine (10 µM final concentration) (µl)

Membrane (µl)

100

700

100

100

Nonspecific binding 0

20

5

15

100

700

100

100

10

10

100

700

100

100

15

5

100

700

100

100

18

2

100

700

100

100

–

0

0

800

100

100

4 Determine radioactivity as described in Protocol 1.2.

Method 2: Dissociation Binding Kinetics 1 Dilute the receptor preparation and radioligand in assay buffer to 10× the final concentration desired in the assay. Dilute the competitive ligand to 100 000× its Ka value, as this will be diluted 100× into the assay, so the final concentration of competitive ligand will be 1000× its Ka . 2 Prepare tubes containing the radioligand. Also prepare one set of tubes containing a saturating concentration of a competitive ligand to define nonspecific binding at each time point. 3 Stagger the addition of membrane to each tube so that the membrane is incubated with the radioligand for the same amount of time in each tube prior to the addition of competitor (see Table 1.5).

Table 1.5 Tube set-up to determine the dissociation kinetics of [3 H]NMS from the M4 muscarinic ACh receptor over time, using a 1 ml final assay volume. Time of Time of Dissociation 2 nM [3 H]NMS Buffer Membrane 1 mM Atropine membrane atropine time (0.2 nM final (10 µM addition addition (min) concentration) final (min) (min) (µl) concentration) 0

60

20

100

800

100

10

5

65

15

100

800

100

10

10

70

10

100

800

100

10 (continued overleaf )

25


Table 1.5 (continued) Time of Time of Dissociation 2 nM [3 H]NMS Buffer Membrane 1 mM Atropine (10 µM membrane atropine time (0.2 nM final addition addition (min) concentration) final (min) (min) (µl) concentration) 15

75

5

100

800

100

10

18

78

2

100

800

100

10

20

–

0

100

800

100

0

Note that this tube set-up can be used for both specific and nonspecific binding; however, for nonspecific binding, atropine must be present throughout the entire incubation period that the receptor is incubated with [3 H]NMS, rather than staggering its addition following equilibrium binding of [3 H]NMS.

4 Following equilibrium binding of the radioligand with the receptor,d stagger the addition of a small volume (∼10 µl to minimize adjustment of the assay volume and, thus, prevent changes in the equilibrium binding of the radioligand) of a saturating concentration of a competitive ligand, which will bind to the unoccupied receptors and prevent reassociation of the radioligand with the receptor, or by diluting the sample by at least 100-fold to reduce the free concentration of radioligand by this factor (so that its concentration is far lower than its Ka and binding will subsequently be negligible). 5 At the appropriate time, terminate the reaction to separate bound from free radioligand and determine radioactivity as described in Protocol 1.2.

Notes a As b


As described in Protocol 1.2.

c For

nonspecific binding, ensure that the competitive ligand is present in the assay for the entire incubation period with radioligand. d Be

aware that low ligand concentrations will take longer to equilibrate.

1.2.2.6 Kinetic radioligand binding assays in the presence of an allosteric modulator using isotopic dilution A competitive ligand cannot change the dissociation binding kinetics of an orthosteric ligand as the two cannot occupy the receptor at the same time. However, when an allosteric modulator binds to a receptor, it may alter the receptor conformation such that it forms a ‘new’ receptor species that has a new set of affinities for its orthosteric ligands. This is often reflected in alterations in the association and/or dissociation kinetics of the orthosteric ligand. Kinetic radioligand binding assays can, therefore, be used to reveal important information regarding the binding mechanism of a ligand and are a sensitive way to determine whether two ligands can bind to a receptor


% Specific [3H]NMS binding

26

120

[3H]NMS dissociation

100

[3H]NMS dissociation± C7/3-phth

80 60 40 20 0 0

25

50 75 Time (min)

100

125

Figure 1.7 Dissociation of the radioligand, [3 H]NMS, from the M4 muscarinic ACh receptor in the absence and presence of the muscarinic receptor allosteric modulator, C7 /3-phth. The radioligand was allowed to equilibrate with the receptor for 1 h at 37 ◦ C before its dissociation was observed by the addition of a high concentration (100× the K a ) of atropine in the absence and presence of C7 /3-phth and termination of the assay as described in Section 1.2.

simultaneously (Protocol 1.6). Determination of the dissociation rate of a ligand in the absence and presence of another is the simplest way to measure kinetic effects (see Figure 1.7). The radioligand is usually allowed to reach equilibrium binding and its association with the receptor is prevented, as described previously, in the absence or presence of a putative allosteric modulator (for example, see [20] for protocols).

PROTOCOL 1.6 Measurement of Radioligand Dissociation Kinetics in the Presence of an Allosteric Modulator Equipment and Reagents • Membrane preparation • Binding buffera • 5 ml polypropylene assay tubes (Techno-plas) • Radioligand • Allosteric modulator • Competitive antagonist • Wash bufferb • Water bath • Vacuum harvester (Brandel) • Glass-fibre filter paper (Whatman) • Liquid scintillation cocktail (PerkinElmer) • Liquid scintillation counter (e.g. Packard Tri-Carb LS counter).

REFERENCES

27

Method 1 Dilute the receptor preparation in assay buffer to 10× the final desired assay concentration and dilute the radioligand in assay buffer to approximately 10× its Ka .c Dilute both the competitive ligand and the allosteric modulator in assay buffer to 200× the final desired assay concentration. Further dilute the competitive ligand at a 1 : 1 ratio with either the allosteric modulator or with assay buffer so that each ligand is 100× concentrated. 2 Prepare two sets of tubes containing radioligand, so that radioligand dissociation can be measured in one set and radioligand dissociation in the presence of the allosteric modulator can be measured in the other set. Also prepare tubes containing radioligand in which a saturating concentration of a competitive ligand will be incubated with the receptor throughout the experiment to define nonspecific binding. 3 Stagger the addition of membrane to tubes containing the radioligand and incubate for sufficient time so as to reach equilibrium binding. 4 Stagger the addition of the competitive ligand with and without the putative allosteric modulator. The tube set-up outlined in Table 1.5 can be used for this assay, with the allosteric modulator–competitive ligand mix being added in the appropriate tubes in place of the competitive ligand alone. 5 At the appropriate time, separate bound from free radioligand and determine radioactivity as described in Protocol 1.2.

Notes a As


b As


c Generally,

to observe kinetic effects, higher concentrations of an allosteric modulator will be required than those required to observe cooperative effects in equilibrium binding assays; therefore, reducing receptor occupancy by the radioligand will increase the ability to detect alterations in the binding kinetics of the radioligand.

References 1. Barcroft, J. and Hill, A.V. (1910) The nature of oxyhæmoglobin, with a note on its molecular weight. J. Physiol., 39, 411–428. 2. Hill, A.V. (1910) The possible effects of the aggregation of the molecules of haemoglobin on its dissociation curves. Proc. Physiol. Soc., 40, iv–vii. 3. Gaddum, J.H. (1937) The quantitative effects of antagonistic drugs. J. Physiol., 89, 7P–9P. One of the original publications by Gaddum describing the concept of competitive antagonism. 4. Gaddum, J.H. (1943) Introductory address. Part I. Biological aspects: the antagonism of drugs. Trans. Faraday Soc., 39, 323–332.

28


5. Cheng, Y. and Prusoff, W.H. (1973) Relationship between the inhibition constant (Ki ) and the concentration of inhibitor which causes 50 per cent inhibition (I50 ) of an enzymatic reaction. Biochem. Pharmacol., 22, 3099–3108. 6. De Lean, A., Stadel, J.M. and Lefkowitz, R.J. (1980) A ternary complex model explains the agonist-specific binding properties of the adenylate cyclase-coupled beta-adrenergic receptor. J. Biol. Chem., 255, 7108–7117. The original publication describing a ternary complex model for GPCR activation. 7. Samama, P., Cotecchia, S., Costa, T. and Lefkowitz, R.J. (1993) A mutation-induced activated state of the beta 2-adrenergic receptor. Extending the ternary complex model. J. Biol. Chem., 268, 4625–4636. The original publication describing an extended ternary complex model for GPCR activation. 8. May, L.T., Leach, K., Sexton, P.M. and Christopoulos, A. (2007) Allosteric modulation of G protein-coupled receptors. Annu. Rev. Pharmacol. Toxicol., 47, 1–51. A good review covering the concepts of allosterism and the detection and quantification of allosteric effects. 9. Ehlert, F.J. (1988) Estimation of the affinities of allosteric ligands using radioligand binding and pharmacological null methods. Mol. Pharmacol., 33, 187–194. The original publication describing an allosteric ternary complex model. 10. Christopoulos, A. and Kenakin, T. (2002) G protein-coupled receptor allosterism and complexing. Pharmacol. Rev., 54, 323–374. 11. Langmead, C.J. and Christopoulos, A. (2006) Allosteric agonists of 7TM receptors: expanding the pharmacological toolbox. Trends Pharmacol. Sci., 27, 475–481. 12. Horstman, D.A., Brandon, S., Wilson, A.L. et al. (1990) An aspartate conserved among G-protein receptors confers allosteric regulation of alpha 2-adrenergic receptors by sodium. J. Biol. Chem., 265, 21590–21595. 13. Galvez, T., Urwyler, S., Prézeau, L. et al. (2000) Ca2+ requirement for high-affinity γ -aminobutyric acid (GABA) binding at GABAB receptors: involvement of serine 269 of the GABABR1 subunit. Mol. Pharmacol., 57, 419–426. 14. Lowry, O.H., Rosebrough, N.J., Farr, A.L. and Randall, R.J. (1951) Protein measurement with the folin phenol reagent. J. Biol. Chem., 193, 265–275. 15. Bradford, M.M. (1976) A rapid and sensitive method for the quantitation of microgram quantities of protein utilizing the principle of protein-dye binding. Anal. Biochem., 72, 248–254. 16. Motulsky, H. and Christopoulos, A. (2004) Fitting Models to Biological Data Using Linear and Nonlinear Regression. A Practical Guide to Curve Fitting, Oxford University Press, Oxford. 17. DeBlasi, A., O’Reilly, K. and Motulsky, H.J. (1989) Calculating receptor number from binding experiments using same compound as radioligand and competitor. Trends Pharmacol. Sci., 10, 227–229. 18. Arunlakshana, O. and Schild, H.O. (1959) Some quantitative uses of drug antagonists. Br. J. Pharmacol. Chemother., 14, 48–58. One of the original publications describing concepts of competitive and noncompetitive antagonism and the use of the ‘Schild plot’.

REFERENCES

29

19. Avlani, V., May, L.T., Sexton, P.M. and Christopoulos, A. (2004) Application of a kinetic model to the apparently complex behavior of negative and positive allosteric modulators of muscarinic acetylcholine receptors. J. Pharmacol. Exp. Ther., 308, 1062–1072. 20. Lazareno, S. and Birdsall, N.J. (1995) Detection, quantitation, and verification of allosteric interactions of agents with labeled and unlabeled ligands at G protein-coupled receptors: interactions of strychnine and acetylcholine at muscarinic receptors. Mol. Pharmacol., 48, 362–378. 21. Motulsky, H.J. and Mahan, L.C. (1984) The kinetics of competitive radioligand binding predicted by the law of mass action. Mol. Pharmacol., 25, 1–9.

2 Second Messenger Assays for G Protein-coupled Receptors: cAMP, Ca2+, Inositol Phosphates, ERK1/2 Karen J. Gregory, Patrick M. Sexton, Arthur Christopoulos and Caroline A. Hick Drug Discovery Biology Laboratory, Department of Pharmacology, Monash University, Clayton, Australia

2.1 Introduction Cells respond to their environment through a complex and interdependent series of signal transduction pathways. The largest family of cell surface proteins involved in the transduction of signals across biological membranes comprises G protein-coupled receptors (GPCRs). GPCRs have evolved to recognize a huge variety of different endogenous stimuli, ranging from lipids, peptides, proteins and nucleotides to ions and photons [1]. The predominant mechanism utilized by GPCRs for transducing extracellular stimuli to the intracellular environment involves the coupling of the receptor to intracellular heterotrimeric G proteins [2, 3]. The involvement of an intracellular coupling partner that can vary in subcellular localization, as well as between different cell types, generates enormous diversity in both the strength and nature of the resultant signal. It is these features that explain the prominent role of GPCRs as extracellular chemical sensors and their importance as targets for the development of drugs with wide clinical applications [4]. There is still much to be learnt about how GPCRs function and how they can be selectively modulated. Fortunately, the wide interest in GPCR research has facilitated the development of a huge range of screening G Protein Coupled Receptors Edited by David R. Poyner and Mark Wheatley  2010 John Wiley & Sons, Ltd.

32

CH 2 SECOND MESSENGER ASSAYS FOR G PROTEIN-COUPLED RECEPTORS

techniques. With the need in cellular screening to find robust markers to monitor cells overexpressing the GPCR of interest, second messenger assays have become an essential tool in both facilitating GPCR drug development and understanding basic biology [5]. Second messengers are molecules that relay signals received by receptors on the cell surface to target molecules in the cytosol and the nucleus. Second messengers also serve to amplify the strength of a signal greatly. Activation of a GPCR induces the exchange of guanosine diphosphate for guanosine triphosphate on the G protein α subunit and dissociation of the α subunit from the βγ heterodimer [6]. Consequently, the Gα- and

b ai

as

aq/11

PI3K

g

Grb AC

PLC DAG

ATP

cAMP

PKA

Sos

PIP2 IP3

PKC

Ras

Ca2+ MEK

ERK1/2

Figure 2.1 Major intracellular signalling pathways arising from G protein coupling. The free Gαs subunit activates ACs, resulting in increased levels of intracellular cAMP, leading to the activation of downstream effectors, including protein kinase A (PKA). On the other hand, Gαi subunits inactivate AC, resulting in decreased levels of cAMP. The Gαq/11 family activate PLC, which hydrolyses phosphatidyl-inositol-4,5-bisphosphate (PIP2 ) to give diacylglycerol (DAG) and IP3 . IP3 acts at IP3 receptors present on intracellular stores to mobilize intracellular Ca2+ . Increased levels of intracellular Ca2+ and DAG can activate protein kinase C (PKC), which can activate the small G protein Ras, leading to the activation of the MAPK kinase (MEK), and subsequently activation of extracellular signal-regulated kinases 1 and 2 (ERK1/2). The βγ heterodimer can also activate ERK1/2. It does this through facilitating the localization, and activation of a number of kinases (e.g. PI-3-kinase) and adaptor molecules (e.g. Grb and Sos). ERK1/2 activation can also arise from Gαs coupling, the pathway being dependent on PKA and MEK.


33

Gβγ-subunits stimulate effector molecules, which include adenylyl cyclase (AC), phosphodiesterases, phospholipase A2 (PLA2 ) and phospholipase C (PLC), thereby activating or inhibiting the production of a variety of second messengers [3]. There are four major classes of second messengers for which the majority of GPCR assays have been designed: cyclic nucleotides such as cyclic adenosine monophosphate (cAMP), inositol triphosphates (IP3 ), mitogen-activated protein kinases (MAPKs) and calcium ions. Second messengers are part of the signal transduction pathway that is initiated when GPCRs are activated. It is the coupling of the receptor to the G protein, particularly the Gα subunit, that determines how the stimulus is transduced in the intracellular environment. Although many Gα subunits have been identified, they can be divided into four main families, namely Gq/11 , Gi/o , Gs and G12/13 , each family modulating separate effectors in the signal transduction pathway (see Figure 2.1) [7, 8]. GPCR coupling to Gαq and the Gβγ subunits of Gi/o activate PLC-β, whose main function is to hydrolyse PIP2 to give DAG and IP3 . DAG is necessary for activation of PKC, whilst IP3 activates IP3 receptors mediating the release of calcium from intracellular stores. GPCR coupling to Gαs and Gαi/o is linked to activation and inhibition of AC respectively. AC triggers the production of cAMP from adenosine triphosphate (ATP), with increased levels of cellular cAMP leading to the activation of PKA. The activation of the small monomeric G protein Rho can be indicative of G12/13 coupling [9]; however, there is evidence that Gq/11 family members may also activate Rho [10, 11]. Thus, with the exception of the G12/13 family, second messenger assays for IP3 , calcium and cAMP are relatively specific measurements of functional GPCRs coupling to specific Gα subunits. It is widely accepted that many GPCRs are capable of coupling to multiple G protein families; as such, screening for the activation of a particular pathway may result in a bias for the modulation of a single aspect of receptor behaviour. Thus, from the perspective of ligand screening, it may be pertinent to assay for a marker of receptor activation that is a convergence of multiple pathways. Dissociated Gβγ subunits, as well as the activation of PKC- and PKA-dependent pathways, can result in the activation of MAPKs, particularly the activation by phosphorylation of ERK1/2) [12–21]. Therefore, assaying for receptor-mediated phosphorylation of ERK1/2 presents a measure of receptor function that represents a pathway with multiple inputs [22].

2.2 Methods and approaches 2.2.1 cAMP cAMP is a tightly regulated signalling molecule involved in the transduction of extracellular signals. The signal is initiated by the binding of a ligand to a GPCR followed by dissociation of the Gα from the βγ and direct interaction of the G protein subunit with the enzyme AC [23]. AC is an enzyme family with at least nine different isoforms having been identified [24]. The isoforms are expressed to different levels in different tissues, and although they are all activated by forskolin and interact with Gαs , each is under very distinct regulation [25]. AC is a membrane-bound enzyme that

34


catalyses the conversion of ATP to cAMP, which in turn modifies cellular function by activating cAMP-dependent PKA enabling the phosphorylation of substrate proteins [26]. cAMP can also cause activation of the transcription factor CREB (cAMP response element binding protein), thereby turning on gene transcription [27]. GPCRs may be linked to the generation of cAMP by one of two routes. One group of receptors (such as β-adrenoceptors, A2 adenosine receptors and D1 dopamine receptors) is associated with an increase in AC activity and elevated cAMP levels mediated through Gαs . Activation of the second group of receptors (including A1 adenosine receptors, glutamate receptors and α2 -adrenoceptors) results in an inhibition of AC and a reduction in cAMP levels mediated through Gi/o proteins. There are many methods available to determine cAMP concentration. All of them rely on the construction of cAMP standard curves against which cAMP levels in the samples can be calculated. Stimulation of the cells with forskolin, which directly activates the AC enzyme, is used to assess the maximum cellular cAMP levels independent of receptor activation. Several methods also include an acetylation step, which increases the sensitivity of the assay. The four main methods are discussed below. 1 Radioimmunoassay (RIA) involves the competition between radioactively labelled [125 I]-cAMP and cellular cAMP in the sample for the binding site of a polyclonal cAMP antibody. Traditional RIAs require multiple steps and can involve handling large quantities of [125 I]-cAMP [28, 29]. More recently, a FlashPlate assay has been developed where the cAMP antibody is affixed to scintillant-coated microplate wells and the counting of the bound fraction is dependent upon the distance of the material to the walls of the wells (see www.perkinelmer.com). This eliminates the need to separate the bound from the free antigen. Results can be read on a MicroBeta or TopCount microplate scintillation counter. 2 Competitive immunoassay kits utilize polyclonal antibodies to cAMP that bind the cAMP in the standards or samples, or to cAMP conjugated to a molecule that will produce either a fluorescent or colorimetric change; for example, alkaline phosphatase, β-galactosidase or horseradish peroxidase [30]. The binding is competitive and the strength of signal generated is inversely proportional to the concentration of cAMP in the sample. Results can be read on any standard plate reader. 3 Fluorescence polarization assays utilize an antibody to cAMP and a fluorescein-labelled cAMP tracer [31]. When the tracer is free in solution it rotates rapidly and when excited with polarized light it emits light of low polarization. However, when the tracer is bound to the cAMP antibody the rotation rate is slowed, causing an increase in polarization. Cellular cAMP in the sample competes with the tracer for the antibody, causing polarization values to decrease with increasing cAMP concentrations. Results can be read on a fluorescence polarization plate reader; for example, Fusion or EnVision (Perkin Elmer). 4 AlphaScreen (Amplified Luminescent Proximity Homogeneous Assay) (PerkinElmer) contains donor and acceptor beads which generate a signal when they are in close proximity [32]. Upon laser excitation at 680 nm, ambient oxygen is converted to the singlet state by a photosensitizer contained within the


35

donor bead. One donor bead can emit up to 50 000 singlet oxygen molecules per second. This results in very high signal amplification, allowing assay miniaturization. The acceptor beads contain a thioxene derivative that reacts with the singlet oxygen to generate chemiluminescence at 370 nm. Energy transfer to fluorescent acceptors in the same beads shifts the emission wavelength to 520–620 nm. The singlet oxygen can diffuse approximately 200 nm before it decays. In the case of the cAMP assay, biotinylated cAMP brings the two beads together to generate a signal. Exogenous cAMP from cell lysates competes with the biotinylated cAMP for binding to the acceptor bead, resulting in a decrease in signal with increasing cAMP concentration. Results can be read on a Fusion-α, AlphaQuest microplate analyser or EnVision (Perkin Elmer). The traditional RIA has long been the gold standard for cAMP assays; however, these assays are nonhomogeneous, requiring precipitation and multiple steps, making them unsuitable for handling large numbers of data points, and they have additional implications for health, safety and the environment. The other assay formats available give comparable results to the RIA, and the factors to consider when deciding on an assay format will involve the sensitivity of the assay, the plate reader available, the need for miniaturization and the cost. The AlphaScreen assay is described in Protocol 2.1.

PROTOCOL 2.1

cAMP AlphaScreen Assay

Equipment and Reagents • GPCR-expressing cell line • Versene (Invitrogen) • Phenol-red-free media (Invitrogen) • Haemocytometer • Bench-top centrifuge • Microplate analyser; for example, Fusion-α (PerkinElmer) • Multichannel pipette (Eppendorf) • 384-well, white Optiplates (PerkinElmer) • AlphaScreen cAMP assay kit (PerkinElmer) • Stimulation buffer (50 ml phenol-red-free media (Invitrogen), 50 mg bovine serum albumin (BSA) (Sigma), 100 µl of 500 mM 3-isobutyl-1-methylxanthine (IBMX)a (Sigma)) pH to 7.4 with NaOH • Lysis buffer (0.3% Tween 20 (Sigma), 5 mM 4-(2-hydroxyethyl)-1-piperazineethanesulfonic acid (HEPES) (Sigma), 0.1% BSA (Sigma)) pH to 7.4 with NaOH • Forskolin (Sigma)

36


• 2 × ligand stock solutions in stimulation buffer • cAMP (Sigma) dilutions in lysis bufferb • Topseal adhesive sealing film (PerkinElmer).

Method 1 Maintain GPCR-expressing cells in T75 or T175 tissue culture flasks; when 90% confluent, harvest cells. 2 Detach cells with 3 ml versene for 5 min at 37 ◦ C. 3 Spin the cells at 2500 g for 4 min in a bench-top centrifuge, discard supernatant and resuspend the cells in 1.5 ml of phenol-red-free media. 4 Count the number of cells using a haemocytometer. Spin the cells at 2500 g for 4 min and adjust to required cell densityc with freshly made stimulation buffer. 5 Incubate the cells in stimulation buffer for 30 min at 37 ◦ C and 5% CO2 . 6 Following the recommendations in the AlphaScreen protocol, add 5 µl of agonist/antagonist/forskolind in stimulation buffer to the wells of a white 384-well plate to generate dose–response curves. 7 To generate a cAMP standard curve, add 10 µl of cAMP in lysis buffer to the wells of a white 384-well plate. 8 Add 5 µl of cells to all wells except those containing the cAMP standard curve. 9 Cover the plate with topseal to prevent evaporation and spin the plate at 1500 g for 30 s in a bench-top centrifuge. 10 Incubate plate for 30 min at 37 ◦ C.e 11 Add 10 µl lysis buffer to all wells and spin the plate at 1500 g for 30 s in a bench-top centrifuge. 12 Prepare acceptor beads and donor bead/biotinylated cAMP mix as recommended in the AlphaScreen protocol.f Donor bead/biotinylated cAMP mix must be incubated at room temperature for at least 30 min prior to addition to the plate. 13 Add 5 µl of acceptor beads to all wells and cover the plate with topseal to prevent evaporation. 14 Gently tap the bottom of the plate to mix and incubate the plate in the dark for exactly 30 min at room temperature. 15 Add 5 µl of donor bead/biotinylated cAMP mix to all wells and cover the plate with topseal to prevent evaporation. 16 Gently tap the bottom of the plate to mix and incubate the plate in the dark overnight at room temperature.g 17 Read the plate on a microplate analyser.

37


18 Plot the AlphaScreen signal as a function of concentration of cAMPh (see Figure 2.2). 100

cAMP [nM]

80 60 40 20 0 −10

−9

−8

−7

−6

−5

−4

−3

Log [Drug] M

Figure 2.2 Typical dose–response curve for AlphaScreen cAMP assay. Chinese hamster ovary (CHO) cells stably transfected with the human glucagon-like-peptide (GLP1) receptor, which preferentially couples to the Gs family of G proteins, were stimulated with forskolin (•) and a receptor agonist (). This graph shows log drug concentration against cAMP concentration. The raw fluorescence units measured in the Fusion-α microplate reader (PerkinElmer) have been converted to cAMP concentration by using a cAMP standard curve. Notes a IBMX

is a potent phosphodiesterase inhibitor. It reduces cAMP degradation during the assay, but it also competes with cAMP to bind the anti-cAMP antibodies, causing a decrease in maximal signal of 30%. IBMX concentration should be limited to 0.2–0.25 mM. b For

the cAMP standard curve, make up a 5 mM stock cAMP solution in phosphate-buffered saline (PBS) and store at −20 ◦ C. Make up a 100 µM stock in lysis buffer and keep on ice. Make up fresh serial dilutions to provide a concentration range from 3 × 10−6 to 3 × 10−11 M in 1/2 log units. There is a dilution of 1 : 3 in the assay plate, giving a final concentration range of 1 × 10−6 to 1 × 10−11 M. c Cell

density is very important in this assay and must be optimized for each cell line. Once optimized, the same cell density should be used for all further experiments. As a general guide, 5000–10 000 cells per well (384-well plate) produce a good signal. High cell numbers, >25 000 cells per well, decrease the signal due to the high sensitivity of the AlphaScreen kit. d When

assaying receptors that couple through Gi/o causing inhibition of AC and a reduction in cAMP levels, make up forskolin plus agonist in stimulation buffer. A forskolin concentration producing 50% (EC50 ) of the maximum AC activation will be more sensitive to weak antagonists, while an assay performed at 80% (EC80 ) will give a larger signal window and a more robust assay. Agonists of Gi/o will reverse the effects of forskolin. e Stimulation time is critical for achieving optimal detection of cAMP; once the optimal cell number has been determined, a time course experiment for stimulation should be carried out.

38


f Donor

and acceptor beads are light sensitive and should be handled in a subdued light environment. It is possible to dilute the beads from the standard assay protocol. This should be determined empirically.

g Incubation of the plate after addition of donor bead/biotinylated cAMP mix can be from 4 h to overnight. Longer incubation times increase the signal and the signal-to-noise ratio in the assay. h AlphaScreen signal should be plotted as a function of concentration of cAMP due to the nonlinearity of the response in parts of the curve.

2.2.2 Inositol phosphates Coupling of GPCRs to the Gq/11 family of G proteins results in the activation of PLC, which subsequently hydrolyses PIP2 , a phospholipid that is located in the plasma membrane, to give IP3 (also commonly known as triphosphoinositol; abbreviated InsP3 or IP3 ), and DAG. DAG in turn activates PKC, whilst IP3 acts at IP3 receptors to mobilize Ca2+ from intracellular stores. The accumulation of IP3 is commonly used as a measure of GPCR function. An AlphaScreen-based assay that works on the same principle as the cAMP assay has been developed (see Protocol 2.1); however, in this case, cellular IP3 competes with biotinylated IP3 , with increasing amounts of IP3 corresponding to a decreased signal, assessed by a microplate reader. By far the most commonly used and reliable approach to measure the accumulation of IP3 relies on the incorporation or 3 H-inositol into the membrane phospholipids. Thus, if the agonist–receptor system of interest is coupled to the activation of PLCs, agonist stimulation will result in the hydrolysis of 3 H-inositol phospholipids in the plasma membrane, forming 3 H-inositol phosphates (3 H-IP). 3 H-IP is separated from 3 H-inositol and liquid scintillation counting can then be used to quantify the amount of 3 H-IP in a sample. Anion-exchange chromatography is the most commonly used method to isolate the 3 H-IP formed from 3 H-inositol (see Protocol 2.2, based on [33]). The separation of 3 H-IP from 3 H-inositol is time consuming, laborious and generates a significant amount of radioactive waste. Semi-automated filtration-based anion-exchange chromatography approaches have been described [34, 35] that are suitable for 96- to 384-well plate formats. More recently, the scintillation proximity assay (SPA ) technology has been used in conjunction with immobilized metal ion affinity chromatography [36] or yttrium silicate [37] to separate 3 H-IP in a 96- to 384-well plate format. The positive charge of the metal ions (or yttrium silicate) binds the negatively charged 3 H-IP, facilitating the separation of 3 H-IP from the neutral 3 H-inositol. The radioactivity can be quantified using a β-counter capable of handling 96- to 384-well plates; for example, Topcount. The majority of techniques are based on the incorporation of radiolabelled inositol; as such, the handling of radioactive waste is an important consideration in selecting an assay format. Other important considerations are equipment access, the level of throughput required and the cost.


PROTOCOL 2.2 Cells

Inositol Phosphate Accumulation Assay: Whole

Equipment and Reagents • GPCR-expressing cell line • Sterile 24-well plates (Nunc) • PBS (Invitrogen) • inositol-free Dulbecco’s modified Eagle medium (DMEM) (Invitrogen) • 3 H-myo-inositol (Amersham Biosciences) • 1 M NaOH • 1 M formic acid •

14

C-IP (synthesized to order by American Radiolabeled Chemicals)

• HEPES buffer (110 mM NaCl, 5.4 mM KCl, 1.8 mM CaCl2 ·2H2 O, 1.0 mM MgCl2 ·7H2 O, 25 mM glucose, 58.4 mM sucrose, pH 7.4) • Li+ buffer (same as HEPES buffer with 10 mM LiCl) • 1 M perchloric acid • 10 × ligand stocks • Resin AG 1-X8 (formate form) (anion-exchange columns, Bio-Rad) • Wash solution (60 mM ammonium formate/5 mM sodium tetraborate) • Elution buffer (1 M ammonium formate/0.1 M formic acid) • Scintillation vials • Scintillation cocktail appropriate for aqueous solutions (e.g. Perkin Elmer HiSafe 3) • Vortex • Liquid scintillation counter.

Method 1 Maintain GPCR-expressing cells in T75 or T175 tissue culture flasks; when 90% confluent, harvest cells and seed into 24-well flat-bottom plates. 2 Grow cells in 24-well flat-bottom plates until 70–80% confluent. 24 h prior to assay, remove serum-containing DMEM by aspiration, wash each well twice with 1 ml PBS and replace with serum- and inositol-free DMEM containing 3 H-myo-inositol (1 µCi ml−1 ). 3 Regeneratea and calibrate columns by the addition of (in the following order): distilled water (dH2 O), 1 M NaOH, dH2 O, 1 M formic acid, dH2 O, dH2 O, 40 µl of diluted 14 C-IP standard.b

39

40


4 Add 40 µl of 14 C-IP, and 2 ml elution buffer to three scintillation vials. 5 Aspirate serum-free DMEM from wells,c washing twice with 1 ml HEPES buffer. 6 Add 450 µl of Li+ buffer/welld and then incubate for 15 min at 37 ◦ C. 7 Add 50 µl of 10 × ligand solution to wells and incubate for 30 min at 37 ◦ C. 8 Stop reaction by aspiration of ligand containing assay buffer, and replace with 500 µl of ice-cold 1 M perchloric acid/well. 9 Incubate for 15 min on ice. 10 Transfer supernatant to regenerated anion-exchange columns and allow solution to drip through. 11 Wash columns once with 10 ml of dH2 O, then twice with 10 ml of wash solution. 12 Elute columns with 2 ml elution buffer, collected into scintillation vials.e 13 Add 3 ml of scintillation fluid to each scintillation vial. Cap vials, vortex and count using a dual 14 C/3 H counting protocol. 14 The normalized 3 H-IPb (disintegrations per minute) should be plotted against the log[agonist] M. Concentration–response curves should be fitted to the data. Notes a

When washing and regenerating columns, ensure that each solution has dripped through completely before adding the next solution.

b Addition of 14 C-IP to each column prior to assay provides a control for variations in the efficiency of individual columns; as such, the 3 H-IP detected should be normalized against the corresponding 14 C-IP value for each individual sample. However, 14 C-IP is no longer stocked by most suppliers. c Serum-free

DMEM in step 4 is radioactive and should be disposed of in an appropriate manner.

d LiCl

is added to the HEPES-based buffer during the stimulation period as it inhibits inositol monophosphatases, thus allowing the accumulation of 3 H-inositol phosphates [33]. e

After completion of assay, columns should be washed with 1 M NaOH and dH2 O, so that they may be reused. To maintain efficiency, columns should be kept moist and regenerated prior to every experiment.

2.2.3 Intracellular calcium Calcium ions are probably the most widely assayed intracellular messengers. In response to many different signals, a rise in the concentration of Ca2+ in the cytosol triggers a variety of events, such as muscle contraction, release of neurotransmitters at synapses, secretion of hormones, activation of T cells and B cells, apoptosis and a variety of biochemical changes mediated by PKC [39]. GPCRs can trigger the release of Ca2+ from the endoplasmic reticulum (ER) via the binding of IP3 to IP3 receptors on the surface of the ER. Activation of GPCRs coupled to Gq cause activation of the


41

enzyme PLCβ. PLC-β is a key enzyme in the PIP2 metabolism pathway; it hydrolyses PIP2 into two second messengers: IP3 and DAG [39]. There are many commercially available calcium indicators, and several factors should be taken into consideration when planning an experiment. The range of calcium concentrations that are to be evaluated, the instrumentation available and the loading requirements of the dye must be considered [40]. 1 Fura-2 is a UV-light-excitable ratiometric Ca2+ indicator. Upon binding Ca2+ , fura-2 exhibits an absorption shift that can be observed by scanning the excitation spectrum between 300 and 400 nm, while monitoring the emission at ∼510 nm. Ratiometric measurements of Ca2+ provide an extremely robust assay system, as they are not affected by dye loading, bleaching or illumination intensity. However, the assay does require a microplate instrument containing a UV light source; for example, FLIPR (Molecular Devices). 2 Visible-light-excitable Ca2+ indicators offer several advantages over UV-lightexcitable indicators. The major one is that efficient excitation can be achieved with most laser-based bench-top instrumentation. There is also a reduced interference from sample autofluorescence and less cellular photodamage and light scatter. However, the drawback is that fluorescence intensity depends on several factors not related to calcium concentration, such as acquisition conditions and probe concentration. The two major indicators in this class are Fluo-3 and Fluo-4. Fluo-4 is an analogue of Fluo-3 with the two chlorine substituents replaced by fluorine atoms. Fluo-4 has an increased fluorescence excitation at 488 nm and, consequently, more than double the signal levels of Fluo-3 in microplate screening applications. Fura-2, Fluo-3 and Fluo-4 are available as cell impermeant salts which are useful for microinjection protocols or as acetoxymethyl (AM) esters which can passively diffuse across cell membranes; once inside the cell these esters are cleaved by intracellular esterases to yield cell impermeant fluorescent indicators. AM esters are used in microplate assay formats and are suitable for high-throughput assays requiring robotics and miniaturization. An assay using Fluo-4 is described in Protocol 2.3.

PROTOCOL 2.3

Fluo-4 Calcium Assay

Equipment and Reagents • GPCR-expressing cell line and corresponding complete growth medium without antibiotic • Haemocytometer • Bench-top centrifuge • Fluorescent plate reader; for example, Flex Station (Molecular Devices) • Multichannel pipette (Eppendorf)

42


• 96-well, black clear-bottom plates (Nunc) • 96-well, v-bottomed plates (Nunc) • Buffer A (150 mM NaCl, 2.5 mM KCl, 1.2 mM MgCl2 , 1.5 mM CaCl2 , 10 mM HEPES, 10 mM glucose) • Probenecida (Sigma), prepare a 250 mM solution in 1 M NaOH • Assay buffer: add probenecid to buffer A to give a final concentration of 2.5 mM; adjust assay buffer to pH 7.4 with NaOH • Fluo-4, AM, 1 mM solution in dimethyl sulfoxide (DMSO) (Invitrogen) • Pluronic F-127b , 20% solution in DMSO (Invitrogen) • Load buffer: mix Fluo-4 AM and Pluronic F-127 in equal volumes and add to assay buffer to give a final concentration of 2–4 µM Fluo-4 • Ionomycinc (Sigma) • Uridine 5 -triphosphate (UTP)d (Sigma).

Method 1 Maintain GPCR-expressing cells in T75 or T175 tissue culture flasks; when 90% confluent, harvest cells. 2 Spin the cells at 2500 g for 4 min in a bench-top centrifuge and resuspend the cells in 10 ml of complete growth media without antibiotics. 3 Count the number of cells using a haemocytometer and seed the cells at 30 000–50 000 cells per well in 96-well black clear-bottom plates using 200 µl of cell suspension per well. 4 Incubate the cells in the 96-well plates for 24 h at 37 ◦ C and 5% CO2 . At the time of the assay the cells should be 90–100% confluent. 5 Make up assay buffer on the day of the experiment; warm buffer to 37 ◦ C. 6 Remove media from the cellse by careful aspiration. 7 Wash cells by adding 100 µl prewarmed assay buffer to each well and aspirate. Repeat the wash step. 8 Make up load buffer and load cells by adding 100 µl load buffer to each well. 9 Protect the assay plate from light by wrapping the plate in foil. Return the plate to an incubator at 37 ◦ C and 5% CO2 for 1 h to load. 10 Whilst the plates are loading, prepare the drug plates by making up the appropriate 10× drug dilutions in assay buffer and aliquoting them into a 96-well v-bottomed plate. Include controls in the drug plate, such as assay buffer alone, ionomycin (100 µM) and UTP (1 mM). Keep the drug plate at 37 ◦ C until it is required in the assay. 11 Set up the fluorescent plate reader (excitation at 485 nm and emission at 520 nm, 90 s read time, 20 µl compound transfer after 20 s). Prewarm the machine to 37 ◦ C.

43


12 After the cells have loaded, wash to remove Fluo-4 by adding 100 µl prewarmed assay buffer to each well and aspirating. Repeat the wash step. 13 Add 180 µl assay buffer to each well. 14 Place the assay plate, drug plate and tips into the plate reader and run the assay.

Ca++ mobilisation (raw fluorescence units)

15 With this assay set-up you should see a baseline reading in the first 20 s. Ionomycin should give a steep increase on addition then plateau at this high level. UTP should give a steep increase on addition then gradually return to baseline (see Figure 2.3). log10[agonist] M = −5

50 000 45 000

log10[agonist] M = −6

40 000


35 000


30 000 25 000

Not stimulated Basal

0

60

120

180

time(sec)

Figure 2.3 Representative Ca2+ mobilization trace using Fluo-4 AM indicator dye. This trace is taken from CHO cells stably transfected with the human M5 muscarinic acetylcholine receptor, which preferentially couples to the Gq/11 family of G proteins. Basal levels of fluorescence are seen in the first 10 s. In response to agonist stimulation, Ca2+ mobilization is characterized by an initial peak response, followed by a plateau phase. 16 To calculate a response from your test compounds, first calculate the baseline over the first 10 s, then calculate your maximum response between 20 and 90 s and use the following formula: [(Max response−Baseline)/(Baseline)] × 100 = percentage response. 17 To assay antagonists, preincubate the cells with the antagonist for 90 s in the plate reader before the addition of agonist.f Notes a Probenecid is a cation exchange inhibitor which stops the bound Ca–Fluo-4 complex from being pumped out of the cell. b Pluronic

F-127 is a nonionic detergent which can assist in dispersion of the nonpolar Fluo-4 AM ester in aqueous media, thereby increasing cell loading. c Ionomycin

is a calcium ionophore and can be used to record a maximum calcium response.

d

UTP acts on P2Y receptors endogenously expressed in most cell lines and can be used as an internal control. e When

washing the cells, handle the plates carefully to avoid dislodging the cells from the bottom of the wells.

44


f It may be necessary to perform an agonist dose–response curve in the presence of antagonist to determine the most suitable agonist concentration to use. An agonist concentration producing 50% (EC50 ) of the maximum calcium response will be more sensitive to weak antagonists, while an assay performed at 80% (EC80 ) will give a larger signal window and a more robust assay.

2.2.4 Mitogen-activated protein kinases Activation of cell-surface receptors can result in activation of multiple signalling pathways, many of which are associated MAPK cascades, ultimately resulting in the activation by phosphorylation of ERK1/2 [21, 22]. Gq coupled-receptors generally activate ERK1/2 via PKC and/or PLCβ in both Ca2+ -dependent and -independent manners [41–46]. Gi/o coupled-receptors generally mediate phosphorylated ERK1/2 (pERK1/2) via their βγ subunits [12–14]. Gs activation of ERK1/2 can be via a cAMP/PKA/B-Raf-dependent mechanism [15, 16, 47]. Measurement of a response that is a convergence of multiple pathways and not linked to a specific G protein subtype increases the likelihood of detecting a range of agonists. A read-out of receptor function that is amplified and not biased towards a specific pathway has the propensity to detect weak partial agonism that otherwise may be missed. With respect to ERK1/2, a very specific antibody is commercially available that can be used to determine relative levels of the doubly pERK1/2 using Western blotting (Protocol 2.4) as well as enzyme-linked immunosorbent assay (ELISA)-based assays. ERK1/2 are each phosphorylated at two sites: Thr202/Tyr204 for ERK1 and Tyr185/Thr187 for ERK2 [48]. In a Western blot, pERK1 and pERK2 can separated based on electrophoretic mobility, as they differ in size; thus, changes in pERK1 or pERK2 can be assessed independently of one another. However, both of these techniques are labour intensive and time consuming, requiring multiple washing steps.

PROTOCOL 2.4 pERK1/2 Detection Using Western Blotting-based Approach Equipment and Reagents • GPCR-expressing cell line • Sterile 6- or 12-well flat-bottom plates (Nunc) • 10 × ligand stocks • Liquid nitrogen • PBS (Invitrogen); 137 mM NaCl, 3 mM KCl, 1.5 mM KH2 PO4 , 8 mM Na2 HPO4 • Serum-free DMEM (Invitrogen)


• Lysis buffer (50 mM tris(hydroxymethyl)aminomethane hydrochloride (tris-HCl), 120 mM NaCl, 1 mM ethylenediaminetetraacetate, 50 mM NaF, 10 mM sodium pyrophosphate, 1 mM benzamidine (Sigma), 0.1 mM NaVO4 , 1% (v/v) Igepal (Sigma) and 0.2% (v/v) protease inhibitor cocktail III (Calbiochem)) • 4 × sample buffer (250 mM tris-HCl, 10% (w/v) sodium dodecyl sulfate (SDS), 10% (v/v) β-mercaptoethanol, 30% (v/v) glycerol and 0.05% (w/v) bromophenol blue (Sigma)) • Running buffer (25 mM tris(hydroxymethyl)aminomethane (tris-base), 250 mM glycine, 0.1% (w/v) SDS) • Transfer buffer (25 mM tris-base, 192 mM glycine, 20% (v/v) methanol, pH 8.5) • Protein assay kit (Bio-Rad) • BSA (Sigma) • Mini-Gel apparatus (Bio-Rad) • Gel transfer apparatus (Bio-Rad) • Polyvinylidene difluoride PVDF membrane (Bio-Rad) • Tris-buffered saline containing 0.1% (v/v) Tween-20 (Sigma), 20 mM tris-base, 140 mM NaCl (TBST) • 2 M NaOH • Powdered low-fat skimmed milk • Phospho-ERK antibody (rabbit anti-phospho-p42/44 MAPK antibody, Cell Signalling Technologies) • Total-ERK antibody (rabbit anti-p42/44 MAPK antibody, Cell Signalling Technologies) • Secondary antibody (anti-rabbit horse radish peroxidase conjugated sheep raised immunoglobulin, Chemicon) • ECLTM Western Blotting Analysis System (Amersham) • Hyperfilm chemiluminescence film (Amersham).

Method 1 Maintain GPCR-expressing cells in T75 or T175 tissue culture flasks; when 90% confluent, harvest cells and seed into 6- or 12-well flat-bottom plates. 2 Allow cells to grow in plates until ∼80% confluent. 3 16–24 h prior to stimulation, remove serum-containing DMEM by aspiration, wash twice with 1 ml/well PBS and replace with 1800 µl/well serum-free DMEM. 4 Prepare 10× ligand stocks to construct concentration response curves, or time courses, as ligands will be diluted (1 : 10) upon addition into plate, 200 µl/well. 5 Stimulate cells by addition of 200 µl/well of ligands for desired time at 37 ◦ C.a

45

46


6 After stimulation of cells with ligands, place cells onto ice, aspirate ligand-containing media and replace with 1 ml of ice-cold serum-free media. 7 Scrape cells from wells and transfer the contents of each well to pre-chilled 1.5 ml tubes. 8 Centrifuge (16 800 g) at 4 ◦ C for 1 min, aspirate supernatant, then snap-freeze cell pellet with liquid nitrogen.b 9 Resuspend the pellet in 50–100 µl of lysis bufferc and incubate for 45 min on ice. 10 Centrifuge for 5 min (16 800 g) and transfer the supernatant to a fresh set of 1.5 ml tubes. 11 Determine the protein concentration of each sample using BSA as the standard. 12 Dilute the samples in 15 µl 4× sample buffer, then denature for 5 min at 85 ◦ C. 13 Separate equal amounts of protein (10–20 µg) on a 10% SDS–polyacrylamide mini gel for 80 min at 200 V. 14 Transfer separated proteins to PVDF membrane (pretreated with methanol and pre-equilibrated in transfer buffer), for 35 min at 100 V. 15 Probe the membranes for pERK1/2 with phospho-ERK antibody (0.1% (v/v) in TBST with 5% milk powder) for 45 mind at room temperature, with continual agitation. 16 Wash membranes with TBST three times for 10 min at room temperature, with continual agitation.e 17 Probe with secondary antibody (0.2% (v/v) in TBST containing 5% milk powder) for 1 h at room temperature, with continual agitation. 18 Visualize bands using ECL Western Blotting Analysis System according to manufacturer’s instructions and Hyperfilm chemiluminescence film.f 19 Membranes can then be stripped with 0.2 M NaOH for 45 min, before probing with total-ERK antibody (0.2% (v/v) in TBST with 5% milk powder) for 1 h. Membranes should be washed, exposed to secondary antibody and bands visualized as before. Notes a Receptor-mediated

pERK1/2 is a transient response and the time course for the peak pERK1/2 response should be established. In addition, the utmost care should be taken when handling cells prior and during stimulation, as shear forces and stress can result in activation ERK1/2 pathways.

b

Snap-frozen cell lysates can be stored at −20 ◦ C for at least 1 month.

c Protease

inhibitors should be added as required to the lysis buffer on day of assay for optimal

activity. d Concentrations

and exposure times for antibodies should be optimized, as should film exposure times, as overexposure of film may prevent accurate determination of relative band intensities. e To

limit nonspecific fluorescence, length and number of washes should be optimized between primary and secondary antibody exposure.


47

f Changes in the relative intensity of pERK1 and pERK2 can be quantified using a densitometric approach and may be interpreted singularly or as a whole. Best practice is to ensure loading of equivalent amounts of protein and also to control for the amount of nonphosphorylated ERK1/2 (total-ERK).

More recently, a number of relatively high-throughput assays have been developed for the determination of pERK1/2 based on electrochemiluminescence (MSD, Gaithersburg, MD), infrared fluorescence (LI-COR, Lincoln, NE) and the proximity bead-based AlphaScreen technology (PerkinElmer, Boston, MA) detection systems [49, 50]. The SureFire cellular ERK1/2 AlphaScreen -based assay (TGR Biosciences, Adelaide, Australia), utilizes a two-antibody system, where one antibody is directed against pERK1/2 and the second to an invariant epitope. Only the pERK1/2 will be bound by both antibodies; thus, the AlphaScreen proximity-beads that bind the individual antibodies will only interact when pERK1/2 is present. The SureFire assay is described in Protocol 2.5 and is suitable for the detection of ERK1/2 phosphorylation mediated by both endogenously expressed and transfected (stable and transient) receptors in both adherent and suspended cells [50, 51]. In comparison with the other high-throughput kits available, the SureFire ERK1/2 kit does not require multiple washing steps or cell fixation.

PROTOCOL 2.5 pERK1/2 Detection Using Sure Fire ERK AlphaScreen-based Assay for Adherent Cell Lines Equipment and Reagents • Sterile 96-well plates (Nunc) • PBS (Invitrogen) • Serum-free DMEM (Invitrogen) • 10× ligand stocks • Sure Fire ERK1/2 kit (including lysis buffer, activation buffer, reaction buffer and AlphaScreen beads) (TGR Biosciences) • 384-well, white Optiplates (Perkin Elmer) • Alphascreen plate reader; for example, Fusion-α (Packard BioScience).

Method 1 Maintain GPCR-expressing cells in T75 or T175 tissue culture flasks; when 90% confluent, harvest cells and seed into 96-well flat-bottom plates at a suitable density.a 2 Allow cells to adhere for a minimum of 4 h, before aspiration of serum-containing DMEM, washing twice with PBS (100 µl/well) and then addition of 180 µl/well

48


serum-free DMEM (for interaction studies, reduce assay volume to 160 µl/well),16–24 h prior to assay.b 3 Prepare 10 × ligand stocks to construct concentration response curves, or time courses, as ligands will be diluted (1 : 10) upon addition into plate, 20 µl/well. 4 Stimulate cells with ligands (20 µl/well) for desired time; for antagonist interaction studies, a 30 min pre-incubation is recommended, at 37 ◦ C.c 5 Stop the assay by aspiration of ligand-containing serum-free DMEM and addition of lysis buffer, 100 µl/well, agitate for 1–2 min.d 6 Proceed according to manufacturer’s instructions.e

pERK1/2 (raw fluorescence units)

7 Read plate on an AlphaScreen plate reader, using standard AlphaScreen settings. Raw fluorescence units can be corrected for basal fluorescence and/or normalized to a positive control; for example 10% foetal bovine serum (see Figure 2.4). 20000 15000 10000 5000 0 0

5

10

15 20 time(min)

25

30

Figure 2.4 Typical time course for receptor-mediated ERK1/2 phosphorylation. CHO cells stably transfected with the human M2 muscarinic acetylcholine receptor, which preferentially couples to the Gi/o family of G proteins, were stimulated over the course of 30 min with 10% foetal bovine serum (•) and 1 µm acetylcholine (). Receptor-mediated ERK1/2 phosphorylation was measured in duplicate using the AlphaScreen-based SureFire assay, and was characterized as a transient response, peaking at 5 min and returning to baseline levels by 10 min. Notes cell density should first be determined for different cellular backgrounds, ∼50 000 cell/well in a 96-well plate is recommended for transfected CHO cell lines. At the time of the assay, cells should be 90–100% confluent.

a Optimal

b

Serum starvation prior to assay is recommended, as this ensures all cells are in a nongrowth phase of the cell cycle, it reduces background and increases the signal to noise. However, it is not a mandatory requirement.

c As

pERK1/2 is generally a transient response, a time course should first be established for all ligands, such that consequent dose–response curves can be conducted at the peak time point. lysates and activated lysates may be stored at −20 ◦ C for at least 1 month prior to completing assay. d Cell

2.3 TROUBLESHOOTING

49

e Incorporation of a positive control into the experimental design is recommended that mediates pERK1/2 via a distinct mechanism (e.g. 10% foetal bovine serum). Basal levels of pERK1/2 should also be ascertained by the addition of serum-free media to wells, at the appropriate time point.

2.3 Troubleshooting • The most important aspect of a successful assay is the cells expressing your GPCR of interest. In order to achieve a robust cellular assay your GPCR needs to be expressed at a reasonable level in a cell line with minimal background activity in your chosen assay. GPCRs can be expressed either in a stable cell line or by transient transfection of cells. Transient transfection can give high expression levels and, therefore, a higher signal to background, whereas stable cell lines are important for large-scale screening campaigns where the same cell line is being repeatedly tested. GPCR expression levels in cell lines can often be determined by radioligand binding assays. Transfected receptor DNA constructs can also be modified to incorporate an epitope that is recognized by an antibody to determine expression. • The coupling profile of GPCR to G protein can be cell line dependent; therefore, where possible, it is necessary to look at more than one output of receptor function or to look at function in a variety of cell backgrounds. • The overexpression of GPCRs in cell lines can lead to coupling through nonphysiological pathways. This is a particular problem when investigating the basic biology of GPCRs and their signalling pathways. However, this phenomenon, together with the use of promiscuous G proteins that will couple to any receptor, has led to the development of ultrahigh-throughput screens for GPCRs where the sole interest is the detection of receptor activation or inhibition. • Cell density is a critical factor in all of the assays and must be optimized for each condition. Cell density will not only affect the strength of the signal, but also the level of background noise detected within the assay. • As with all assays, the inclusion of informative controls, both positive and negative, is crucial. Important controls for all assays are measurement of basal levels, effect of vehicle addition and activation of the second messenger by a method independent of the GPCR of interest, whether this be by an endogenously expressed GPCR or nonreceptor mediated; for example, direct activation of ACs by forskolin. • GPCR overexpression can result in high levels of constitutive activity, thus reducing the signal window, or may result in the downregulation of a signalling pathway. Another concern is the influence of signal amplification, whereby all agonists tested may appear as full agonists due to the influence of receptor reserve. Treatment with an antagonist/inverse agonist can decrease basal levels of GPCR activity, therefore increasing the signal-to-noise ratio. Receptor alkylation may be utilized to decrease

50


the number of binding sites for agonists, and may reveal the partial agonists, as receptor reserve is diminished.

References 1. Fredriksson, R., Lagerstrom, M.C., Lundin, L.G. and Schioth, H.B. (2003) The G-protein-coupled receptors in the human genome form five main families. Phylogenetic analysis, paralogon groups, and fingerprints. Mol. Pharmacol., 63, 1256–1272. 2. Bourne, H.R. (1997) How receptors talk to trimeric G proteins. Curr. Opin. Cell Biol., 9, 134–142. 3. Cabrera-Vera, T.M., Vanhauwe, J., Thomas, T.O. et al. (2003) Insights into G protein structure, function, and regulation. Endocr. Rev., 24, 765–781. Review covering G-protein structure, function and regulation. 4. Drews, J. (2000) Drug discovery: a historical perspective. Science, 287, 1960–1964. 5. Thomsen, W., Frazer, J. and Unett, D. (2005) Functional assays for screening GPCR targets. Curr. Opin. Biotechnol., 16, 655–665. 6. Hamm, H.E. (1998) The many faces of G protein signaling. J. Biol. Chem., 273, 669–672. 7. Neer, E.J. (1995) Heterotrimeric G proteins: organizers of transmembrane signals. Cell , 80, 249–157. 8. Neves, S.R., Ram, P.T. and Iyengar, R. (2002) G protein pathways. Science, 31, 1636–1639. Review of G-protein signalling pathways. 9. Riobo, N.A. and Manning, D.R. (2005) Receptors coupled to heterotrimeric G proteins of the G12 family. Trends Pharm. Sci., 26, 146–154. 10. Chikumi, H., Vazquez-Prado, J., Servitja, J. et al. (2002) Potent activation of RhoA by Gαq and Gq -coupled receptors. J. Biol. Chem., 277, 27130–27134. 11. Vogt, S., Grosse, R., Schultz, G. and Offermanns, S. (2003) Receptor-dependent RhoA activation in G12 /G13 -deficient cells: genetic evidence for an involvement of Gq /G11 . J. Biol. Chem., 278, 28743–28749. 12. Faure, M., Voyno-Yasenetskaya, T.A. and Bourne, H.R. (1994) cAMP and beta gamma subunits of heterotrimeric G proteins stimulate the mitogen-activated protein kinase pathway in COS-7 cells. J. Biol. Chem., 269, 7851–7854. 13. Crespo, P., Xu, N., Simonds, W.F. and Gutkind, J.S. (1994) Ras-dependent activation of MAP kinase pathway mediated by G-protein beta gamma subunits. Nature, 369, 418–420. 14. Lopez-Ilasca, M., Crespo, P., Pellici, P.G. et al. (1997) Linkage of G protein-coupled receptors to the MAPK signaling pathway through PI 3-kinase gamma. Science, 275, 394–397. 15. Schmitt, J.M. and Stork, P.J.S. (2002) PKA phosphorylation of Src mediates cAMP’s inhibition of cell growth via Rap1. Mol. Cell , 9, 85–94. 16. Schmitt, J.M. and Stork, P.J.S. (2002) Cyclic AMP-mediated inhibition of cell growth requires the small G protein Rap1. J. Biol. Chem., 277, 43024–43032. 17. Brock, C., Schaefer, M., Reusch, H.P. et al. (2003) Roles of Gβγ in membrane recruitment and activation of p110γ/p101 phosphoinositide 3-kinase γ. J. Cell Biol., 160, 89–99.

REFERENCES

51

18. Azzi, M., Charest, P.G., Angers, S. et al. (2003) β-Arrestin-mediated activation of MAPK by inverse agonists reveals distinct active conformations for G protein-coupled receptors. Proc. Natl. Acad. Sci. U. S. A., 100, 11406–11411. 19. Gutkind, J.S. (1998) The pathways connecting G protein-coupled receptors to the nucleus through divergent mitogen-activated protein kinase cascades. J. Biol. Chem., 273, 1839–1842. 20. Davis, R.J. (1993) The mitogen-activated protein kinase signal transduction pathway. J. Biol. Chem., 268, 14553–14556. 21. Werry, T.D., Sexton, P.M. and Christopoulos, A. (2005) ‘Ins and outs’ of seven-transmembrane receptor signalling to ERK. Trends Endocrinol. Metab., 16, 26–33. 22. Goldsmith, Z.G. and Dhanasekaran, D.N. (2007) G protein regulation of MAPK networks. Oncogene, 26, 3122–3142. 23. Hurley, J.H. (1999) Structure, mechanism, and regulation of mammalian adenylyl cyclase. J. Biol. Chem., 274, 7599–7602. 24. Hanoune, J. and Defer, N. (2001) Regulation and role of adenylyl cyclase isoforms. Annu. Rev. Pharmacol. Toxicol., 41, 145–174. 25. Lai, H.L., Lin, T.H., Kao, Y.Y. et al. (1999) The N terminus domain of type VI adenylyl cyclase mediates its inhibition by protein kinase C. Mol. Pharmacol., 56, 644–650. 26. Skalhegg, B.S. and Tasken, K. (1971) Specificity in the cAMP/PKA signaling pathway; differential expression, regulation, and subcellular localization of subunits of PKA. Front. Biosci., 1, 331–342. 27. Montminy, M. (1997) Transcriptional regulation by cyclic AMP. Annu. Rev. Biochem., 66, 807–822. 28. Steiner, A.L., Kipnis, D.M., Utiger, A. and Parker, C.W. (1969) Radioimmunoassay for the measurement of adenosine 3 , 5 -cyclic phosphate. Proc. Natl. Acad. Sci. U. S. A., 64, 367–373. Original RIA cAMP assay. 29. Steiner, A.L., Parker, C.W. and Kipnis, D.M. (1972) Radioimmunoassay for cyclic nucleotides. I. Preparation of antibodies and iodinated cyclic nucleotides. J. Biol. Chem., 247, 1106–1113. 30. Gabriel, D., Vernier, M., Pfeifer, M.J. et al. (2003) High throughput screening technologies for direct cyclic AMP measurement. Assay Drug Dev. Technol., 1, 291–303. 31. Pope, A.J., Haupts, U.M. and Moore, K.J. (1999) Homogeneous fluorescence readouts for miniaturized high-throughput screening: theory and practice. Drug Discov. Today, 4, 350–362. 32. Ullman, E.F., Kirakossian, H., Singh, S. et al. (1994) Luminescent oxygen channeling immunoassay: measurement of particle binding kinetics by chemiluminescence. Proc. Natl. Acad. Sci. U. S. A., 91, 5426–5430. Description of AlphaScreen methodology. 33. Berridge, M.J., Downes, P.C. and Hanley, M.R. (1982) Lithium amplifies agonist-dependent phosphatidylinositol responses in brain and salivary glands. Biochem. J., 206, 587–595. Anion-exchange chromatography method for IP3 . 34. Chengavala, M., Kostek, B. and Frail, D.E. (1999) A multi-well filtration assay for quantitation of inositol phosphates in biological samples. J. Biochem. Biophys. Methods, 38, 163–170. 35. Tian, Y., Wu, L. and Chung, F. (1997) High throughput 96-well plate assay for receptor-mediated phosphatidylinositol turnover. J. Biomol. Screen., 2, 91–97.

52


36. Liu, J.J., Hartman, D.S. and Bostwick, J.R. (2003) An immobilized metal ion affinity adsorption and scintillation proximity assay for receptor-stimulated phosphoinositide hydrolysis. Anal. Biochem., 318, 91–99. 37. Brandish, P.E., Hill, L.A., Zheng, W. and Scolnick, E.M. (2003) Scintillation proximity assay of inositol phosphates in cell extracts: high-throughput measurement of G-protein-coupled receptor activation. Anal. Biochem., 313, 311–318. 38. Bootman, M.D., Collins, T.J., Peppiatt, C.M. et al. (2001) Calcium signalling - an overview. Semin. Cell Dev. Biol., 12, 3–10. 39. Berridge, M.J. and Irvine, R.F. (1989) Inositol phosphates and cell signalling. Nature, 341, 197–205. 40. Haugland, R.P. (2005) Indicators for Ca2+ , Mg2+ , Zn2+ and other metal ions, The Handbook: A Guide to Fluorescent Probes and Labelling Technologies, 10th edn, Invitrogen Corporation, http://www.invitrogen.com/site/us/en/home/References/Molecular-Probes-The-Handbook.html. Detailed description of calcium-sensitive dyes. 41. Ueda, Y., Hirai, S., Osada, S. et al. (1996) Protein kinase C δ activates the MEK-ERK pathway in a manner independent of Ras and dependent on Raf. J. Biol. Chem., 271, 23512–23519. 42. Schonwasser, D.C., Marais, R.M., Marshall, C.J. and Parker, P.J. (1998) Activation of the mitogen-activated protein kinase/extracellular signal-regulated kinase pathway by conventional, novel, and atypical protein kinase C isotypes. Mol. Cell. Biol., 18, 790–798. 43. Lev, S., Moreno, H., Martinez, R. et al. (1995) Protein tyrosine kinase PYK2 involved in Ca2+ -induced regulation of ion channel and MAP kinase functions. Nature, 376, 737–745. 44. Dikic, I., Tokiwa, G., Lev, S. et al. (1996) A role for Pyk2 and Src in linking G-protein-coupled receptors with MAP kinase activation. Nature, 383, 547–550. 45. Della Rocca, G.J., van Biesen, T., Daaka, Y. et al. (1997) Ras-dependent mitogen-activated protein kinase activation by G protein-coupled receptors. Convergence of Gi - and Gq -mediated pathways on calcium/calmodulin, Pyk2, and Src kinase. J. Biol. Chem., 272, 19125–19132. 46. Kolch, W., Heidecker, G., Kochs, G. et al. (1993) Protein kinase Cα activates RAF-1 by direct phosphorylation. Nature, 364, 249–252. 47. Vossler, M.R., Yao, H., York, R.D. et al. (1989) cAMP activates MAP kinase and Elk-1 through a B-Raf- and Rap1-dependent pathway. Cell , 89, 73–82. 48. Chen, Z., Gibson, T.B., Robinson, F. et al. (2001) MAP kinases. Chem. Rev., 101, 2449–2476. 49. Wong, S.K. (2004) A 384-well cell-based phospho-ERK assay for dopamine D2 and D3 receptors. Anal. Biochem., 333, 265–272. 50. Osmond, R.I.W., Sheehan, A., Borowicz, R. et al. (2005) GPCR screening via ERK 1/2: a novel platform for screening G protein-coupled receptors. J. Biomol. Screen., 10, 730–737. SureFire ERK assay methodology. 51. Lee, H.J., Mun, H., Lewis, N.C. et al. (2007) Allosteric activation of the extracellular Ca2+ -sensing receptor by l-amino acids enhances ERK1/2 phosphorylation. Biochem. J., 404, 141–149.

3 Use of the [35S]GTPγ S Binding Assay to Determine Ligand Efficacy at G Protein-coupled Receptors Elodie Kara and Philip G. Strange School of Pharmacy, University of Reading, Reading, UK

3.1 Introduction The actions of drugs at receptors depend on two events: the binding of the drug to the receptor and the response triggered by the drug at the receptor and in the associated tissue. The binding of the drug is reflected in the affinity with which the drug binds to the receptor. The ability of the drug to alter the activity of signalling systems linked to the receptor is often referred to as ‘efficacy’ and is reflected in differences in the extent and potency of the response. We can envisage a scale of efficacy from positive for agonists to negative for inverse agonists with neutral antagonists having zero efficacy. In this chapter we shall consider methods for assessing efficacy for agonists for the G protein-coupled receptors (GPCRs).

3.1.1 Efficacy and its measurement It is very important to understand and quantify the efficacy of drugs, as this has an important bearing on drug action. For example, several drugs with very low but positive


54

CH 3 USE OF THE [35 S]GTPγ S BINDING ASSAY

efficacy (partial agonists) have been introduced recently and found to have useful therapeutic properties. Examples here are buprenorphine (used for opiate addiction), buspirone (used for anxiety) and aripiprazole (used for schizophrenia) [1]. It seems that the very low agonist efficacy expressed by these drugs is important for their actions, and so systems that can define this efficacy are required. As efficacy refers to the functional effects of a drug it must be defined using functional tests. The effects of a set of drugs are, therefore, tested in a concentration/response experiment using a suitable functional response linked to the receptor. Responses used in the past have typically been intact tissue responses, such as smooth muscle contraction. More recently, however, second messenger assays (e.g. cyclic adenosine monophosphate) have been widely used (see Chapter 2). Newer biophysical assays for changes in receptor conformation (e.g. see [2]) are showing promise as well. For agonists, several measures of efficacy are then accessible from this kind of experiment. First, the maximal effects Emax of the drugs can be determined. Comparisons of the Emax values for a set of drugs provide a useful measure of relative efficacy, but this measure fails where the drugs produce a full (100%) response in the test system. For drugs that produce a full response, termed full agonists, it will be impossible to differentiate differences in relative efficacy, and so other measures are required. One such measure is the KA /EC50 ratio [3–5]. This ratio provides an estimate of the extent to which the agonist response curve is shifted away from the agonist binding curve. The EC50 represents the concentration of agonist that produces a half-maximal response and KA is the dissociation constant for agonist binding to the receptors (also the concentration of agonist that occupies half the receptors). The KA /EC50 ratio provides a good estimate of relative efficacy for agonists with moderate to full efficacy but approaches unity for the weaker agonists and so provides little discrimination in this efficacy range. The two measures may be combined to produce the parameter Emax KA /EC50 . This provides a continuous measure of efficacy over the full range of agonist efficacy (P.G. Strange unpublished results; see also [6]) and so is a very useful parameter. When it is used to analyse real data in the literature it also provides an excellent measure of efficacy [7].

3.2 Methods and approaches 3.2.1 In vitro systems to measure agonist efficacy: the [35 S]GTPγ S binding assay Whereas drug actions at many receptors can be assayed using functional tissue responses, it is useful to use in vitro tests based on cell-free preparations of receptors, expressed in recombinant cells for example. One very popular assay is the guanosine 5 -O-(3-thiotriphosphate) [35 S]GTPγ S binding assay (e.g. see [8]). This assay takes advantage of the normal G protein cycle associated with GPCRs [9]. In the G protein cycle, the inactive G protein (G) with guanosine diphosphate (GDP) bound


55

is converted to the active G protein by combination with the agonist-occupied receptor (AR) to form the ternary complex ARG·GDP. In this complex, GDP release is facilitated and guanosine triphosphate (GTP) binds to activate the G protein. Dissociation of the G protein into α·GTP and βγ subunits occurs and these alter effector activity; for example, adenylyl cyclase. In the [35 S]GTPγ S binding assay, the GTP binding event is assayed using the nonhydrolysable analogue of GTP, [35 S]GTPγ S, and in some cases Eu-labelled GTPγ S (e.g. see [10]). The [35 S]GTPγ S binding assay is described in detail in Protocols 3.1–3.5. The preparation of membranes is initially described (Protocol 3.1). To obtain an optimal level of agonist-stimulated [35 S]GTPγ S binding, it is usually necessary to add GDP to assays. GDP reduces the basal [35 S]GTPγ S binding level by competing for [35 S]GTPγ S binding sites on G proteins and other nucleotide binding proteins. To determine the concentration of GDP to add in your experiment, the stimulation of [35 S]GTPγ S binding over the basal level by saturating agonist concentration should be calculated for different GDP concentrations. This is described in Protocol 3.2; the analysis forms Protocol 3.3.

PROTOCOL 3.1 Preparation of Membranes from Recombinant Cells Expressing G Protein-coupled Receptors Equipment and Reagents • GPCR-expressing cell line • Glass beads (2 mm diameter) (Sigma) • 50 ml plastic tubes (Greiner) • 50 ml Nalgene ultracentrifugation tubes (Fisher) • Ultra-Turrax homogenizer • Centrifuge (for 365 g) • Ultracentrifuge (for 31 500 g) • Buffer: 20 mM N-(2-hydroxyethyl)piperazine-N -ethanesulfonic acid (HEPES), 1 mM ethylene glycol bis(2-aminoethylether)-N,N,N ,N -tetraacetic acid (EGTA), 1 mM ethylenediaminetetraacetic acid (EDTA), pH 7.4. The buffer must be kept at 4 ◦ C until the experiment starts • 10% trichloroacetic acid • Lowry protein assay reagents: buffer A (2% Na2 CO3 , 0.1 M NaOH), buffer B (0.5% CuSO4 · 5H2 O, 1% NaK tartrate) and buffer C (Folin–Ciocalteu’s phenol reagent diluted to 1 : 1 in water).


56

Method The protocol provided here is for adherent cells cultured in 175 cm2 culture flasks; for example, Chinese hamster ovary (CHO) cells expressing a recombinant GPCR. If the cells used are nonadherent, then put them directly in a 50 ml plastic tube and proceed to step 6. In order to obtain a good yield of protein at the end of the experiment, the adherent cells should be ∼95% confluent when the membrane preparation is done. The flasks are handled conveniently in batches of five. 1 Remove the medium from the tissue-culture flasks and discard. 2 Add 7 ml buffer to each flask to wash the cells and discard. 3 Add 5 ml buffer to each flask. Add the glass beads to the first flask and shake. Then, add the beads and cells to the second flask. Repeat this for all five flasks. Cells may also be detached using phosphate-buffered saline (PBS)/4 mM EDTA or using a cell scraper. 4 Take the liquid from flasks and add it to one 50 ml plastic tube (at this step, the total volume is around 25 ml). The samples should be kept in ice throughout. 5 Wash the five flasks with 10 ml buffer, transferring the 10 ml from flask to flask and adding it to the 50 ml tube (the total volume of the tube at this step would be 25 ml from step 4 and 10 ml from step 5, giving a total volume of 35 ml). 6 Homogenize the cells and the buffer in the 50 ml plastic tube with the Ultra-Turrax homogenizer at 24 000 rpm, four times for 5 s each. 7 Centrifuge the tubes at 365 g for 10 min at 4 ◦ C. 8 Remove the supernatant and put it into 50 ml ultracentrifuge tubes. Balance the tubes. 9 Centrifuge at 31 500 g for 1 h at 4 ◦ C. 10 Discard the supernatant. Resuspend the pellet in 2 ml of buffer using the Ultra-Turrax homogenizer at 16 000 rpm for 3 s. 11 Wash the centrifuge tube with 500 µl of buffer and add this to membrane suspension. The total volume of membrane preparation from five 175 cm2 flasks is 2.5 ml. If your cells were ∼95% confluent, then the protein concentration should be around 3 mg ml−1 . 12 Divide into aliquots of 300 µl and store at −80 ◦ C. Special precautions are not required to freeze the samples. 13 Determine the protein concentration in the sample, protein (∼30 µg) should be precipitated using 10% trichloroacetic acid before determining protein concentration as described by Lowry et al. using bovine serum albumin (BSA) (10–100 µg) as a standard [11]. The precipitation step is employed to remove substances that interfere in the protein assay. Briefly, add 1 ml of 10% trichloroacetic to samples, vortex mix and centrifuge 15 min at 2500 g, at room temperature. Discard the supernatant and allow tube to drain well. Ten minutes later, dilute Lowry B solution 1/50 (v/v) in Lowry A solution, to 1 : 50, and add 1 ml of this mixture into the tubes. Vortex mix and incubate 10 min at room temperature. Then add 100 µl of Lowry C and incubate 10 min more.


Finally, add 2 ml of H2 O, vortex mix and incubate 30 min at room temperature. Read the absorbance at 760 nm. From the standard concentration–absorbance curve obtained, determine the protein concentration of your samples.

PROTOCOL 3.2 Determination of the Concentration of GDP Required in the [35 S]GTPγ S Binding Assay To obtain an optimal level of agonist stimulated [35 S]GTPγ S binding, it is usually necessary to add GDP to assays. GDP reduces the basal [35 S]GTPγ S binding level by competing for [35 S]GTPγ S binding sites on G proteins and other nucleotide binding proteins. To determine the concentration of GDP to add in your experiment, the stimulation of [35 S]GTPγ S binding over the basal level by saturating agonist concentration should be calculated for different GDP concentrations.

Equipment and Reagents • 5 ml round-bottom polystyrene tubes (Fisher) • Water bath • Brandel cell harvester (Semat International) (directions below are for a 24-place harvester) • GF/C glass-fibre filters (Whatman – Schleicher and Schuell) • Scintillation tubes and caps • Scintillation fluid (Ultima Gold, Perkin–Elmer) • [35 S]GTPγ S (GE Healthcare) (37 TBq mmol−1 ) • 10 × concentrations of GDP stock solutions • 10 × stock solutions of drugs required to stimulate the receptor • [35 S]GTPγ S binding assay buffer: 20 mM HEPES, 10 mM MgCl2 , 100 mM NaCl, pH 7.4. Keep at 4 ◦ C until the experiment starts • PBS: 140 mM NaCl, 10 mM KCl, 1.5 mM KH2 PO4 , 8 mM Na2 HPO4 . Keep at 4 ◦ C until the experiment starts.

Method 1 Prepare two sets of tubes: one for the assessment of the basal binding level and one for the measurement of the agonist stimulated binding. It is recommended to determine each data point in triplicate. That represents six tubes per concentration of GDP tested; Figure 3.1 shows a suggested rack layout.

57


58

GDP concentrations 1

2

3

4 basal stimulated

Figure 3.1 Rack organization for experiment to determine GDP dependency of [35 S]GTPγ S binding assay. 2 Prepare GDP at different concentrations (e.g. from 10−4 to 10−7 M) in assay buffer. The solutions should be prepared at 10 × the final concentration in the assay, as 100 µl will be added in 1 ml final assay volume. 3 Prepare the agonist solution(s). A saturating concentration should be used. Again, this solution must be prepared at 10 × the final concentration as 100 µl will be added in a final volume of 1 ml. 4 Prepare the protein suspension obtained as in Protocol 3.1. In recombinant systems expressing between 0.5–10 pmol mg−1 of receptors, it is recommended to add 20 µg of protein per tube. As 100 µl of protein solution will be added to the final volume of 1 ml, the suspension prepared must be at 0.2 µg of protein per microliter.a 5 Prepare the [35 S]GTPγ S solution at a concentration of 1 nM. The final concentration in the tubes will be ∼100 pM. The GraphPad radioactivity calculator is accessible online and may help to calculate the accurate amount of [35 S]GTPγ S to use based on the date of manufacture and half-life of 35 S (http://graphpad.com/quickcalcs/radcalcform. cfm). 6 In 5 ml round-bottom polystyrene tubes, add: (a) 700 µl of buffer in the tubes for the assessment of the basal and 600 µl in the tubes for the stimulated points (see Figure 3.1 for suggested layout of rack). (b) 100 µl of GDP in both ‘basal’ and ‘stimulated’ tubes for each concentration. (c) 100 µl of agonist in ‘stimulated’ tubes. (d) 100 µl membrane preparation in all tubes. Vortex mix and incubate in a water bath at 30 ◦ C for 30 min. At this time, the tubes contain:

Buffer GDP Agonist Membrane preparation

Basal

Stimulated

700 µl 100 µl – 100 µl

600 µl 100 µl 100 µl 100 µl


7 Add 100 µl [35 S]GTPγ S solution to all the tubes. Vortex mix and incubate 30 min at 30 ◦ C. 8 Stop the reaction by filtration through Whatman GF/C glass-fibre filters using a Brandel cell harvester. Wash four times with 3 ml ice-cold PBS. 9 Place the filter disk in a scintillation tube and add 2 ml of scintillation liquid. Cover the tube with a cap, vortex mix, and incubate for at least 6 h. 10 Vortex mix the tubes and determine the radioactivity by liquid scintillation spectrometry. Notes For cells expressing at higher levels, less protein could be added (10 g) 1 Resuspend the cell pellet in ice-cold breaking buffer at a ratio of 2 : 1 buffer to pellet (v/w). 2 Pass the cell suspension through an Emulsiflex-C3 cell disrupter fitted with a chilled heat exchanger (Avestin) four times according to the manufacturer’s instructions. Observe the cells under a light microscope to check the extent of cell breakage. The breaking efficiency should be >90% at a homogenizing pressure of 30 000 psi. 3 Remove the unbroken cells and cellular debris by centrifugation at 10 000g for 30 min at 4 ◦ C. 4 Centrifuge the clarified supernatant at 100 000g for 90 min at 4 ◦ C to collect the membrane fraction. 5 Resuspend the membrane pellet in ice-cold buffer A using a glass homogenizer at a ratio of 10 ml buffer per gram of pellet.

Notes a The

amount of breaking buffer used to wash the glass beads can be adjusted so as not to exceed the combined filling volume of the tubes.

5.2.6 Solubilization and purification An important step in the production and ultimate structural and functional characterization of GPCRs is the ability to solubilize the protein in detergent effectively prior to purification. The correct choice of detergent is critical to ensure maximal return of biologically active, correctly folded protein. Detergent purity, its ability to maintain biological activity of the protein over varying solubilizing concentrations, its solubility at the desired working temperature, methods for its removal and potential conflicts with the needs of downstream purification or crystallization all need to be considered. A key parameter is the critical micelle concentration (CMC), which is defined as the lowest concentration above which detergent monomers cluster to form micelles. Proteins then incorporate into these micelles via hydrophobic interactions. Typically, it is necessary to work at or just above the CMC, since micelles form over a narrow concentration range. In order to identify the best detergent for GPCR solubilization, a panel of detergents each of varying concentrations needs to be evaluated. Solubilization screening with a panel of 10 different detergents, ranging from nonionic to zwitterionic molecules, is a good start point (Protocol 5.5). Nonionic detergents, such as n-octyl-β-d-glucopyranoside (β-OG), n-dodecylphosphocholine (DPC) and lauryldimethylamine oxide (LDAO), are often more successful in yielding soluble GPCRs than the zwitterionic cyclofos-4 (CYFOS-4) and foscholineiso9 (FC109). Recent studies have identified several detergents and optimized conditions for general membrane protein solubilization, including SoPIP2;1 using β-OG [27], PagP barrel with CYFOS [28], SERCA with DPC [29] and Arabidopsis thaliana leaf membrane proteins with


101

Brij-n [30]. Furthermore, β-OG, LDAO, C8E4 and n-dodecyl-β-d-maltopyranoside (DDM) are routinely used amongst protein crystallographers and have facilitated numerous crystal structure studies (see http://www.mpibp-frankfurt.mpg.de/michel/ public/memprotstruct.html for an analysis of crystallization conditions for structures published up to 2006). It must be noted that there are no hard and fast rules regarding which detergents to choose, but research is revealing that certain proteins do solubilize better with a particular detergent. For example, White et al. [31] have suggested that membrane proteins possessing greater than 70% hydrophobic residues in their predicted transmembrane domains would solubilize better with β-OG and C8E4 than with other detergents compared with proteins with less than 70% hydrophobic residues. Note that it is also possible to solubilize efficiently in one detergent and then detergent exchange into a second more favourable detergent for downstream protein processing.

PROTOCOL 5.5 GPCR Solubilization and Purification Equipment and Reagents • Fixed-speed roller mixer (Sturat SRT 9) • Reagents for immunoblotting (see Protocol 5.3) • Solubilization buffer (20 mM HEPES pH 7.0, 100 mM NaCl, 10% glycerol) • Homogenizer (homogenizer PTFE pestle/stainless steel rod 15 ml (SLS)) • DC protein assay (Bio-Rad Laboratories) • Fixed-speed flatbed roller (Stuart) • Glass homogenizer (SLS) • BioRad DC protein assay kit • SDS–PAGE sample buffer [21], SDS– PAGE gels and electrophoresis buffers • Breaking buffer (50 mM sodium phosphate, pH 7.4, 5% glycerol, 2 mM EDTA, 100 mM NaCl), store at 4 ◦ C • 1 ml HisTrap HP column (GE Healthcare).

Method 1 Prepare the membranes as outlined in the large-scale section of Protocol 5.4 but omitting the resuspension step 5. 2 Resuspend the membrane pellet in solubilization buffer using a glass homogenizer and add 10 ml of buffer per gram of pellet. Determine the protein concentration using a BioRad DC protein assay kit according to the manufacturer’s instructions and resuspend the membrane pellet to a final concentration of 10 mg protein per millilitre. 3 Proceed to step 4 or freeze in liquid nitrogen and store at −80 ◦ C until required.

102

CH 5 PRODUCTION OF RECOMBINANT G PROTEIN-COUPLED RECEPTOR IN YEAST

4 For small-scale solubilisation, add 50 µl of the membrane fraction to 950 µl of solubilization buffer containing the required detergent concentration and incubate at room temperature for 1 h on a fixed-speed roller mixer. 5 Pellet the nonsolubilized material at 100 000g, 1 h at 4 ◦ C and resuspend any visible pellet in 200 µl breaking buffer. 6 Load the supernatant and the resuspended pellet (mixed 1 : 1 in 2× SDS–PAGE sample buffer) for SDS–PAGE and immunoblot analysis.a 7 For a large-scale solubilization (50–100 ml), add the preferred detergent to final working concentration (1–5%), as determined from the small-scale screen,b incubate and process as above. 8 Use the supernatant for purification of the tagged receptor with IMAC using a 1 ml His Trap column according to manufacturer’s instructions (see Section 5.2.6.1). Notes a Refer

to Protocol 5.3, step 8, for instructions on immunoblotting.

bA

panel of detergents and working concentrations should be screened for effective solubilization of the recombinant protein using the following panel of detergents at 2% w/v on membrane suspensions: DDM, DPC, CYFOS-4, β-OG, foscholineiso9 (FC109), LDAO, pentaethyleneglycol-n-octylether (C8E5), DPC–cholesterolhemisuccinate and docosaethyleneglycol monohexadecylether (Brij 58; all purchased from Anatrace Inc). Analyse the solubilized (supernatant) and nonsolubilized (membrane pellet) material by immunoblot analysis for solubilization efficiency using a primary his monoclonal antibody and an anti-mouse IgG HRP-conjugated secondary antibody (Sigma).

5.2.6.1 Protein purification Having successfully solubilized a GPCR from yeast membranes, the protein is now ready for purification using affinity chromatography dependent upon the tag added to the protein, as discussed in Section 5.2.2.2. One of the most popular methods is purification using immobilized metal affinity chromatography (IMAC). HisTrap HP prepacked Ni-Sepharose columns (GE Healthcare) allow quick and efficient purification and have binding capacities of at least 40 mg His6 -tagged protein per millilitre medium. These columns can be cleaned, stripped, recharged and reused, making them cost effective. Neutral or slightly alkaline buffers, such as sodium phosphate (pH 7–8), containing 0.5–1.0 m NaCl should be considered. Typically, 300 mm NaCl works well to prevent nonspecific, ionically bound host proteins in combination with the selected detergent at the lowest concentration to maintain protein solubilization and minimize aggregation. Addition of imidazole at low concentrations (∼20–30 mm) in the sample, binding and wash buffers is important to further minimize host cell protein binding to the purification matrix. Elution of the protein is best achieved with a linear elution gradient of increasing imidazole to achieve high yield and high purity. Further detailed information can be found at www.gehealthcare.com/hitrap.

103


5.2.7 Analysis of recombinant proteins To date there are X-ray crystal data for only one GPCR, that of bovine rhodopsin and its mutants [1, 32]. Electron microscopy has also been used to study rhodopsin in its native membranes [33, 34]. In addition, at least 13 other GPCRs have been characterized by noncrystallographic methods [35]. Various methods of protein analysis are discussed below, highlighting the basic principles and the advantages and disadvantages of each method. More detailed explanations for all the techniques can be found in the many specialized books available and within other chapters of this book. In particular, biological assays are covered elsewhere, but the importance of determining the amount of active GPCR in a sample, and the efforts to purify it ultimately in a homogeneous, fully functional form, cannot be overstated.

5.2.7.1 Immunoblot analysis Immunoblot analysis has already been mentioned in Protocol 5.3 and is a standard technique. Basically, the method involves the transfer of proteins from an SDS–PAGE gel (separated on the basis of size) to an inert membrane such as nitrocellulose or polyvinylidene fluoride and the subsequent immunodetection of the recombinant GPCR visualized on either X-ray film or digitally captured via a charge-coupled device camera. The most sensitive method of detection uses a peroxidise-tagged secondary antibody developed by chemiluminescence (allowing detection of ’ refers to a submenu located under a main menu title in DeepView. The symbols ‘$>’ refer to commands which should be applied directly to the command line and do not form part of a script. UNIX commands are represented in the main text as italics. Text in Courier New font refers to commands entered at the command line, in a script or the contents of a file and does not form part of the main body of text.

Requirements for Protocol 13.1 • Internet access • a text editor • DeepView installed on a local computer • MODELLER installed on either a local or networked computer.

Method 1 Download the amino acid sequences for the huv1ar (accession number P37288) and rv1ar (accession number P30560) from http://au.expasy.org/sprot/. 2 Download the file 1U19 from the PDB repository (http://www.rcsb.org/pdb/home/home.do). 3 Open the file 1U19.pdb using the File > Open PDB file function in DeepView and save the amino acid sequence using the File > Save > Sequence (FASTA) function. 4 Edit a text file such that it contains, in PIR formata (see Figure 13.1) (i) the sequence of the template (1U19.pdb), (ii) the sequence of the protein to be modelled (huv1ar) and (iii) other homologues obtained from a database search (rv1ar) and save as huv1ar.seq.

256

CH 13 HOMOLOGY MODELLING OF G PROTEIN-COUPLED RECEPTORS

>P1;1U19.pdb structure:1U19.pdb:1:A:348:A::::: MNGTEGPNFYVPFSNKTGVVRSPFEAPQYYLAEPWQFSMLAAYMFLLIMLGFPINFLTLYVTVQHKKLR TPLNYILLNLAVADLFMVFGGFTTTLYTSLHGYFVFGPTGCNLEGFFATLGGEIALWSLVVLAIERYVV VCKPMSNFRFGENHAIMGVAFTWVMALACAAPPLVGWSRYIPEGMQCSCGIDYYTPHEETNNESFVIYM FVVHFIIPLIVIFFCYGQLVFTVKEAAAQQQESATTQKAEKEVTRMVIIMVIAFLICWLPYAGVAFYIF THQGSDFGPIFMTIPAFFAKTSAVYNPVIYIMMNKQFRNCMVTTLCCGKNPLGDDEASTTVSKTETSQV APA* >P1;huv1ar sequence:huv1ar::::::::: MSEAAHVLITGAAGQIGYILSHWIASGELYGDRQVYLHLLDIPPAMNRLTALTMELEDCAFPHLAGFVA TTDPKAAFKDIDCAFLVASMPLKPGQVRADLISSNSVIFKNTGEYLSKWAKPSVKVLVIGNPDNTNCEI AMLHAKNLKPENFSSLSMLDQNRAYYEVASKLGVDVKDVHDIIVWGNHGESMVADLTQATFTKEGKTQK VVDVLDHDYVFDTFFKKIGHRAWDILEHRGFTSAASPTKAAIQHMKAWLFGTAPGEVLSMGIPVPEGNP YGIKPGVVFSFPCNVDKEGKIHVVEGFKVNDWLREKLDFTEKDLFHEKEIALNHLAQGG* >P1;rv1ar sequence:rv1ar:::::::: MSFPRGSQDRSVGNSSPWWPLTTEGSNGSQEAARLGEGDSPLGDVRNEELAKLEIAVLAVIFVVAVLGN SSVLLALHRTPRKTSRMHLFIRHLSLADLAVAFFQVLPQLCWDITYRFRGPDWLCRVVKHLQVFAMFAS AYMLVVMTADRYIAVCHPLKTLQQPARRSRLMIATSWVLSFILSTPQYFIFSVIEIEVNNGTKTQDCWA TFIQPWGTRAYVTWMTSGVFVAPVVVLGTCYGFICYHIWRNIRGKTASSRHSKGDKGSGEAVGPFHKGL LVTPCVSSVKSISRAKIRTVKMTFVIVSAYILCWAPFFIVQMWSVWDENFIWTDSENPSITITALLASL NSCCNPWIYMFFSGHLLQDCVQSFPCCHSMAQKFAKDDSDSMSRRQTSYSNNRSPTNSTGMWKDSPKSS KSIRFIPVST*

Figure 13.1 An example of a PIR formatted file containing the sequences of the template (bRh, PDB identifier 1U19.pdb), the target (huv1ar) and a homologous protein to the target (rv1ar). For clarity, this example contains only three sequences, whereas multiple sequence alignments for homology modelling should contain a number of homologous proteins of the target sequence.

5 Edit a file such that it contains the following MODELLER Python script and save the file as salign.py:b from modeller import* log.verbose() env = environ() env.io.atom_files_directory=’./’ aln = alignment(env, file=’huv1ar.seq’, align_codes=’all’) aln.salign(rr_file=’$(LIB)/as1.sim.mat’, output=’’, max_gap_length=20, gap_function=True, feature_weights=(1., 0., 0., 0., 0., 0.), gap_penalties_1d=(-200, 0), gap_penalties_2d=(3.5, 3.5, 3.5, 0.2, 4.0, 6.5, 2.0, 0.0, 0.0),


257

output_weights_file=’salign.mtx’ similarity_flag=True) aln.write(file=’huv1ar-1U19.pir’, alignment_format=’PIR’)

6 Execute the MODELLER Python script salign.py with the command $> MODHOME/modeller9v1/bin/mod9v1 salign.py

7 Manually edit the alignment file ‘huv1ar-1U19.pir’ with a text editor such that large gaps or regions of uncertainty in the alignment and the corresponding regions in other sequences are removedc (see Figures 13.2 and 13.3). >P1;1U19.pdb structure:1U19.pdb:1 :A:348 :A:::-1.00:-1.00 MNGTEGPNFYVPFSNK------TGVVRSPFEA--------PQYYLAEPWQFSMLAAYMFLLIMLGFPIN FLTLYVTVQHKKLRTPLNYILLNLAVADLFMVFGGFTTTLYTSLHGYFVFGPTGCNLEGFFATLGGEIA LWSLVVLAIERYVVVCKPMSNFRFGENHAIMGVAFTWVMALACAAPPLVGWS--RYIPEGM—-QCSCGI DYYTPHEETNNESFVIYMFVVHFIIPLIVIFFCYGQLVFTV------KEAAA----Q----QQES---------------ATTQKAEKEVTRMVIIMVIAFLICWLPYAGVAFYIFTHQGS---DFGPIFMTIPAFF AKTSAVYNPVIYIM-MNKQFRNCMVTTLCC----------GKNPLG------DDEASTTVSK--------TETSQVAPA--* >P1;huv1ar sequence:huv1ar: : : : :::-1.00:-1.00 MRLSAGPDAGPSGNSSPWWPLATGAGNTSREAEALGEGNGPPRDVRNEELAKLEIAVLAVTFAVAVLGN SSVLLALHRTPRKTSRMHLFIRHLSLADLAVAFFQVLPQMCWDITYRFRGPDWLCRVVKHLQVFGMFAS AYMLVVMTADRYIAVCHPLKTLQQPARRSRLMIAAAWVLSFVLSTPQYFVFS—-MIEVNNVTKARDCWA TFIQPW---GSRAYVTWMTGGIFVAPVVILGTCYGFICYNIWCNVRGKTASR----QSKGAEQAGVAFQ KGFLLAPCVSSVKSISRAKIRTVKMTFVIVTAYIVCWAPFFIIQMWSVWDPMSVWTESENPTITITALL GSLNSCCNPWIYMFFSGHLLQDCVQSFPCCQNMKEKFNKEDTDSMSRRQTFYSNNRSPTNSTGMWKDSP KSSKSIKFIPVST* >P1;rv1ar sequence:rv1ar: : : : :::-1.00:-1.00 MSFPRGSQDRSVGNSSPWWPLTTEGSNGSQEAARLGEGDSPLGDVRNEELAKLEIAVLAVIFVVAVLGN SSVLLALHRTPRKTSRMHLFIRHLSLADLAVAFFQVLPQLCWDITYRFRGPDWLCRVVKHLQVFAMFAS AYMLVVMTADRYIAVCHPLKTLQQPARRSRLMIATSWVLSFILSTPQYFIFSVIEIEVNNGTKTQDCWA TFIQPW---GTRAYVTWMTSGVFVAPVVVLGTCYGFICYHIWRNIRGKTASSRHSKGDKGSGEAVGPFH KGLLVTPCVSSVKSISRAKIRTVKMTFVIVSAYILCWAPFFIVQMWSVWDENFIWTDSENPSITITALL ASLNSCCNPWIYMFFSGHLLQDCVQSFPCCHSMAQKFAKDDSDSMSRRQTSYSNNRSPTNSTGMWKDSP KSSKSIRFIPVST*

Figure 13.2 The salign.py-generated, PIR-formatted multiple sequence alignment file (huv1ar-1U19.pir) containing the sequences for the template (bRh, 1U19.pdb), the target sequence (human V1aR, huv1ar) and a homologous protein to the target sequence (rat V1aR, rv1ar). Highlighted in grey are large gaps or regions of uncertainty in the alignment. Modelling of large inserts requires the use of specialist techniques and should be addressed separately; as such, these regions are deleted from the alignment (see Figure 13.3).

258


>P1;1U19.pdb structure:1U19.pdb:1 :A:287 :A:::-1.00:-1.00 PQYYLAEPWQFSMLAAYMFLLIMLGFPINFLTLYVTVQHKKLRTPLNYILLNLAVADLFMVFGGFTTTL YTSLHGYFVFGPTGCNLEGFFATLGGEIALWSLVVLAIERYVVVCKPMSNFRFGENHAIMGVAFTWVMA LACAAPPLVGWS--RYIPEGM--QCSCGIDYYTPHEETNNESFVIYMFVVHFIIPLIVIFFCYGQLVFT V-ATTQKAEKEVTRMVIIMVIAFLICWLPYAGVAFYIFTHQGS---DFGPIFMTIPAFFAKTSAVYNPV IYIM-MNKQFRNCMVTTLCC* >P1;huv1ar sequence:huv1ar: : : : :::-1.00:-1.00 PPRDVRNEELAKLEIAVLAVTFAVAVLGNSSVLLALHRTPRKTSRMHLFIRHLSLADLAVAFFQVLPQM CWDITYRFRGPDWLCRVVKHLQVFGMFASAYMLVVMTADRYIAVCHPLKTLQQPARRSRLMIAAAWVLS FVLSTPQYFVFS--MIEVNNVTKARDCWATFIQPW---GSRAYVTWMTGGIFVAPVVILGTCYGFICYN I/KSISRAKIRTVKMTFVIVTAYIVCWAPFFIIQMWSVWDPMSVWTESENPTITITALLGSLNSCCNPW IYMFFSGHLLQDCVQSFPCC* >P1;rv1ar sequence:rv1ar: : : : :::-1.00:-1.00 PLGDVRNEELAKLEIAVLAVIFVVAVLGNSSVLLALHRTPRKTSRMHLFIRHLSLADLAVAFFQVLPQL CWDITYRFRGPDWLCRVVKHLQVFAMFASAYMLVVMTADRYIAVCHPLKTLQQPARRSRLMIATSWVLS FILSTPQYFIFSVIEIEVNNGTKTQDCWATFIQPW---GTRAYVTWMTSGVFVAPVVVLGTCYGFICYH I/KSISRAKIRTVKMTFVIVSAYILCWAPFFIVQMWSVWDENFIWTDSENPSITITALLASLNSCCNPW IYMFFSGHLLQDCVQSFPCC*

Figure 13.3 The edited multiple sequence alignment file (huv1ar-1U19.pir) containing the sequences for the template (bRh, 1U19.pdb), the target sequence (huV1aR) and a homologous protein to the target sequence (rV1aR). Large insertions or regions of uncertainty have been deleted and replaced by the symbol ‘/’ (shown in grey) which denotes a break in the protein backbone where a large inserted region has been removed. In addition, the number of residues in field 5 of the structure entry (1U19.pdb) has been changed to reflect the changes made in the alignment. See the MODELLER manual for more information. 8 Use the program DeepView to open and edit the template file so that it corresponds to changes made in the edited sequence alignment (see Figures 13.2 and 13.3). This can be achieved by highlighting the relevant residues in the control panel (Window > Control Panel) and deleting them by using the Build > Remove Selected Residues function. 9 Use DeepView to renumber the edited template file starting from 1 using the Edit > Rename Current Layer function. 10 Save the DeepView edited file using the Save > Layer function. Notes a The

PIR format (see Figure 13.1) for use in MODELLER is as follows. The first line contains the sequence identifier, in the format ‘>P1;seqname’, where seqname is the name of the sequence. The identifier must be unique for all proteins and sequences in the file. The second line contains 10 fields separated by colons. However, only fields 1–6 are required for MODELLER use. Field 1 specifies whether or not the data is from a 3D structure (structure) or is sequence (sequence) data. Field 2 (1U19.pdb, huv1ar and rv1ar respectively) does not have to be unique but must correspond to the variable ‘sequence’ in the MODELLER Python script. Fields 3–6 are the residue


259

and chain identifiers for the first (fields 3 and 4) and last residue (fields 5 and 6) of the structure file. Fields 7–10 are optional; see MODELLER manual for more information. The remainder of the data contains the sequence of protein, with an asterisk marking the end of the entry. The standard upper case, one-letter amino acid codes are used. b

This script will align the sequences in the file huv1ar.seq and outputs the file huv1ar-1U19.pir which contains the sequence alignment in PIR format. The salign module accounts for structural information from the template when constructing a sequence alignment using a gap penalty function that inserts gaps in loop regions that are outside regions of secondary structure; see MODELLER manual for more information regarding format. c A further consideration is the length of inserted regions, denoted by gaps (-) between the target and template sequences. Whereas short insertions in the template alignment may be tolerated (one to four residues), larger insertions may result in ‘knots’ in the final structure. Therefore, it is a prudent step to delete any region of large insertions in the sequence alignment (shaded in grey; see Figures 13.2 and 13.3). It is important to maintain the overall alignment, as changes made to one sequence must also be reflected in the other sequences. Special attention should be paid to ensure that conserved residues are aligned between the sequences. Any errors introduced at this point will seriously affect the final model.

13.2.3 Homology modelling of a GPCR Once a suitable template has been found and an alignment between the target and the template structures has been generated, a model of the target protein can be created (Protocol 13.2). The most common method to generate homology models is through the satisfaction of spatial constraints, much in the same way as NMR structures are generated (Protocol 13.3). An example of a program that uses this style of homology modelling is MODELLER [32]. Spatial restraints can be obtained from a number of sources that include homology-derived restraints and molecular-mechanics-based or statistically derived preferences for bonded and nonbonded interactions. In addition, restraints may also be obtained from a number of external source, such as spin labelling [33], fluorescence labelling [34], cysteine cross-linking [33], engineered Zn-binding sites [35] and double mutant constructs [36]. A number of publicly accessible programs are available to verify homology models, and these generally belong to one of two categories. The first category (e.g. PROCHECK [37] and WHATIF [38]) checks for proper protein stereochemistry, such as symmetry checks, geometry checks and structural packing quality. The second category (e.g.VERIFY3D [39] and PROSAII [40]) checks the fitness of sequence to structure and assigns a score for each residue fitting its current environment. Finally, of course, the best discriminator between good and bad models is through human intervention, and this should be employed at each stage of the modelling process. Generating a homology model using MODELLER consists of the following steps: 1 preparing an input MODELLER Python script; 2 ensuring that all required files, including sequences, structures and/or alignments, are located the same directory;

260


3 executing a MODELLER input to generate the homology models; 4 analysing the output model and scoring.

PROTOCOL 13.2 Generating a Homology Model for the Human Vasopressin1a Receptor Based on the Structure 1U19 The following procedure uses MODELLER to generate five homology models of the huv1ar using the edited alignment generated in Protocol 13.1 and the bRh structure 1U19.

Requirements for Protocol 13.2 • The edited alignment file (huv1ar-1U19.pir) from Protocol 13.1. • The edited structural template 1U19.pdb from Protocol 13.1. • A text editor. • MODELLER installed on either a local or networked computer.

Method 1 Edit a text file such that it contains the following MODELLER Python script and save the file as huv1ar-model.py: from modeller import* from modeller.automodel import* env = environ() a = automodel(env, alnfile=’huv1ar-1U19.pir’, knowns=’1U19.pdb’, sequence=’huv1ar’) a.starting_model = 1 a.ending_model = 5 a.make()

2 Execute the MODELLER Python script with the commanda $> MODHOME/modeller9v1/bin/mod9v1 huv1ar-model.py &

3 Once the models have been generated, order them on the basis of the MODELLER objective function using the following command at the UNIX prompt:b $> grep ‘MODELLER OBJECTIVE FUNCTION:’ huv1ar.B9999*.pdb| sort +1

4 Visually inspect the top, lowest scoring, models using DeepView and observe differences between the side-chain packing of the models. Notes a Once

an alignment between the target and template has been generated, MODELLER can calculate a basic 3D model of the target sequence automatically using the automodel routine.


261

In this script, the line ‘alnfile’ defines the filename for the alignment between target and template in PIR format (huv1ar-1U19A.pir), ‘knowns’ defines the filename of the template structure (1U19.pdb) and sequence defines the name of the target sequence (huv1ar) in the alignment file. The two variables starting_model and ending_model limit the number of models that are calculated (five in the above example). However, more models should be generated (∼200) for homology modelling and only five models were generated with the above script for the purposes of illustration and speed. In addition to a number of housekeeping files (see MODELLER manual for more information), the files containing the coordinates of the models are defined with the suffix B9999000∗ .pdb, where ∗ is the model number. Visual inspection of the models can then be performed using a package such as DeepView. The symbol ‘&’ allows the program to run in the background, which allows the user to log out of the system. b One

method of obtaining a good structure is to use the MODELLER objective function found in the model PDB file. This value is not an absolute measure of model quality and can only be used to rank models calculated from the same alignment. As with most objective functions, the lower the value, the better the model is. Although reliable, the MODELLER objective function should be used in combination with other energy/scoring functions, thus forming a consensus approach to picking the best model. In this way, the relative strengths and weaknesses of the various scoring functions will not bias the results of the modelling exercise. However, it is important to note that, before any external evaluation of the model, one should check the log file from the MODELLER run for runtime errors and restraint violations (see the MODELLER manual). grep is a command-line utility that is found in most UNIX-style operating systems, and given a list of files (huv1ar.B9999∗ .pdb) grep searches for lines of text that match a regular expressions (MODELLLER OBJECTIVE FUNCTION) and outputs only the matching lines. The output of grep is sent, or piped, using the symbol ‘|’ to a second UNIX command called sort. This command is able to order the output of grep numerically, such that it is easy to identify the best (lowest) scoring model.

PROTOCOL 13.3 Generating a Homology Model for the Human Vasopressin1a Receptor Based on the Structure 1U19 Incorporating External Restraints The following procedure uses MODELLER to generate five homology models of the huv1ar using the edited alignment generated in Protocol 13.1 and the bRh structure 1U19. In addition, a number of external distance constraints will also be included which will be incorporated into the final model.

Requirements for Protocol 13.3 • The edited alignment file (huv1ar-1U19.pir) from Protocol 13.1. • The edited structural template 1U19.pdb from Protocol 13.1. • A text editor. • MODELLER installed on either a local or networked computer.

262


Method 1 Edit a text file such that it contains the following MODELLER Python script and save the file as huv1ar-model-user-restraints.py: # Addition of restraints to the default ones from modeller import * from modeller.automodel import* # Load the automodel class log.verbose() env = environ() # directories for input atom files env.io.atom_files_directory = ‘./:../atom_files’ class mymodel(automodel): def special_restraints(self, aln): rsr = self.restraints at = self.atoms # Residues 1 through 10 should be an alpha helix: rsr.add(secondary_structure.alpha(self.residue_range(’1:’ ‘10:’))) # Use a harmonic potential between residues 29 and 57. rsr.add(forms.gaussian(group=physical.xy_distance, feature=features.distance(at[’CA:29’], at[’CA:57’]), mean=6.7, stdev=0.1)) a = mymodel(env, alnfile = ‘huv1ar-1U19.pir ‘, knowns = ‘1U19.pdb’, sequence = ‘huv1ar’) a.starting_model= 1 a.ending_model =1 a.make()

2 Execute the MODELLER Python script with the commanda $> MODHOME/modeller9v1/bin/mod9v1 huv1ar-model-user-restraints.py. &

3 Once the models have been generated, order them on the basis of the MODELLER objective function using the following UNIX command at the UNIX prompt:b $> grep ‘MODELLER OBJECTIVE FUNCTION:’ huv1ar.B9999*.pdb| sort +1

4 Visually inspect the top models using DeepView and observe the effects of the additional distance restraints on the final structure.


263

13.2.4 Loop prediction Unlike the transmembrane domains, loop regions connecting the helices are quite diverse in size and amino acid composition, which makes homology modelling of these regions unreliable. Furthermore, the importance of the loops is such that any model of a GPCR without adequate consideration of these regions would be incomplete. The goal of loop prediction is to identify the correct conformation of a protein fragment from a series of decoys in the context of the remaining protein. This is not a trivial task, as loop regions, which can included both insertions and deletions, often share little sequence homology with the template. Traditionally, two approaches have been used for loop prediction: database methods [41, 42] and ab initio methods [43, 44]. Database methods attempt to find protein fragments from existing structures that are approximately the same size as the desired loop section. This is followed by the evaluation of suitable candidates and optimization by means of an energy function. However, for database methods, loops longer than five residues often have problems identifying near-native conformations in the template library, which limits their usefulness in a number of cases [42]. Ab initio methods, however, do not require a homologous protein fragment, but involve the generation of a large ensemble of candidate loop structures from which a singleconformation or cluster of conformations is picked using an energy-based scoring function. Current methods have shown that ab initio prediction of regions of up to 12 residues give good agreement with experimental data [45]. However, one limit to the effectiveness of ab initio loop prediction is the scoring function [43]. Traditionally, detailed atomic force fields, either with or without a solvation term, have been used successfully to identify native loop fragments. However, although effective, these methods are often computationally intensive and require an initial filter step to remove nonnative loop fragments [44]. One such advance in loop prediction is through the development of knowledge-based or statistical potentials, which can be as effective at predicting the correct loop conformation as more rigorous force-field-based approaches, but at a fraction of the computational cost. A recently developed statistical potential is the distance-scaled, finite ideal-gas reference (DFIRE) potential [46], which was shown to be comparable to that of an Assisted Model Building and Energy Requirement (AMBER) generalized Born surface area (GB/SA) force field for short loops of two to eight residues [47]. Once programs such as DFIRE filter out nonnative loop conformations, traditional physical-based scoring methods can be used to identify the native loop conformation. One program that has been shown to be effective for ab initio loop prediction is RAPPER [44], which is a conformational search algorithm for restraint-based protein modelling. Although RAPPER can be accessed through the Internet (http://mordred.bioc.cam.ac.uk/∼rapper/loop2.php), a Linux binary is also available that allows for greater control over the loop generation process and can be accessed from the UNIX command line. A typical ab initio loop prediction protocol using RAPPER consists of the generation of a large number of loop conformations with idealized stereochemistry for the heavy atoms (N, Cα , C, O). Atomic van der Waals radii are scaled down by 25% to ensure good packing and loop fragments generated that contain clashing atoms are discarded. A distance geometry check is also performed on the final modelled loop fragment such that the N-terminal Cα

264


anchor atom of the framework and the C-terminal Cα atom of the loop fragment are ˚ To ensure conformational diversity of the loop ensemble generated, within 3.7 A. no two fragments are allowed which have a global root-mean-squared deviation ˚ The loop fragments generated are then joined to the backbone of the of ’ refers to commands which should be applied directly to the command line and do


265

not form part of a script. Files with a ‘.key’ are keyfiles used with the TINKER suite of programs and contain arguments for use with the programs contained in this protocol. See the TINKER manual for more information on the keyfile arguments.

Method 1 Obtain and install, under a UNIX-based operating system, the programs RAPPER, DFIRE and TINKER. 2 Download the PDB file 1U19 from the PDB repository (http://www.rcsb.org/pdb/home/home.do). 3 Copy the file 1U19.pdb to the RAPPERHOME/rappermc/distrib directory.a 4 Generate 1000 loop models for the region between residues 98 and 107 of extracellular loop 1 for the PDB file 1U19 using RAPPER with the following command line:a $> RAPPERHOME/rappermc/distrib/rapper_Linux_i386 RAPPERHOME/rappermc/distrib/params_Linux_i386.xml model-loops-benchmark --rapper-dir RAPPERHOME/rappermc --verify-setup false --models 1000 #number of models to be generated # --pdb 1U19.pdb #PDB code of template # --start 98 #start residue # --stop 107 #end residue # --population-max-passes 2000 --divide-and-conquer false --ignore-hetatms true --sidechain-mode smart --sidechain-library RAPPERHOME/rappermc/data/richardson.lib --sidechain-radius-reduction 0.75 --verify-structures true --enforce-strict-anchor-geometry true --verify-structures-for-energy-calculations true --use-contact-filters true --write-individual-models true >&rhod-ecl1& # enables RAPPER to run in the background #

5 Once RAPPER has generated 1000 loop conformations for the region 98–107 of the PDB file 1U19, join the fragment PDB files to the framework PDB file (both are generated by RAPPER) by editing a text file such that it contains the following lines of script and save the file as ‘join’:b cd RAPPERHOME/rappermc/distrib/TESTRUNS foreach pdb (looptest*pdb) RAPPERHOME/rappermc/distrib/rapper_Linux_i386 RAPPERHOME/rappermc/distrib/params_Linux_i386.xml joinpdb

266


--pdb1 framework.pdb --pdb2 $pdb --pdb-out $pdb.pdb --rapper-dir RAPPERHOME/rappermc end

6 Execute the script ‘join’ by using the command $> csh join

7 Whilst in the directory RAPPERHOME/rappermc/distrib/TESTRUNS, execute the following UNIX command to generate a file containing all the complete receptor PDB files:c $> ls * > loops

8 Edit the file ‘loops’ using the UNIX command sed such that it contains the number 10 after each of the entries:d $> sed ‘s/looptest.*/& 10/g’ loop > loop.in

9 Copy the program DFIRE and associated files to the RAPPERHOME/rappermc/distrib/TESTRUNS directory. 10 Execute the program DFIRE using the file loop.in as input and redirect the resulting output to the file loop.out using the following UNIX command line: $> ./dfire < loop.in > loop.out

11 Numerically sort the file loop.out using the UNIX command sort on the basis of the DFIRE score and retain the filenames of the 50 best-scoring loop predictions in a file called best-loops:e $> sort +1 – g loop.out | head – n 50 >best-loops

12 Copy the top 50 best-scoring loop predictions files to a separate directory. 13 Covert these PDB files to TINKER XYZ format by editing a text file such that it contains the following lines of script and save the file as pdb2xyz:f foreach pdb (*pdb) TINKERHOME/tinker/bin/pdbxyz.x – k pdbxyz.key $pdb end

14 Edit a file such that it contains the following statements and save it as ‘pdbxyz.key’: parameters TINKERHOME/tinker/params/amber99.prm


15 Execute the script ‘pdb2xyz’ by using the command $> csh pdb2xyz

16 Once the file format conversion has been completed, minimize the predicted loop using the AMBER99 GB/SA force field as implemented in TINKER by editing a text file such that it contains the following lines of script and save the file as ‘minimize-loop’: foreach xyz (*xyz) TINKERHOME/tinker/bin/minimize.x – k minimize.key $xyz 0.1 end

17 Edit a file such that it contains the following statements and save it as ‘minimize.key’: parameters TINKERHOME/tinker/params/amber99.prm solvate still inactive – 1 1576 # atom numbers to be restrained active – 1577 1729 # atom numbers to minimize inactive – 1730 5468 # atom numbers to be restrained maxiter 100

18 Execute the script ‘minimize-loop’ by using the command $> csh minimize-loop >& loop-minimize &

19 Analyse the resulting minimized loop structures using the AMBER99 GB/SA forcefield using the following UNIX script (analyze) and keyfile (analyze.key): foreach xyz_2 (*.xyz_2) TINKERHOME/tinker/bin/analyze.x – k analyze.key $xyz_2 e > $xyz_2.analysed end

the keyfile ‘analyze.key’ contains the following statements: parameters TINKERHOME/tinker/params/amber99.prm solvate still

20 Order the loop models on the basis of the energy function obtained in the previous step: $> grep ‘Energy’ *xyz_2.analysed | sort +1

21 Retain and visually inspect the top 10 loop structures using DeepView and compare them with the region 98–107 of the structure 1U19.

267

268


Notes a Further

information regarding the functions of the input flags can be obtained using the help function under RAPPER. Whilst a link may be made to the RAPPER programs and associated files, this example is run directly from the RAPPERHOME/rappermc/distrib directory. Depending on the size of the loop to be modelled, the run time of RAPPER may be between hours or days. The last line of the command (>&rhod-ecl1&) allows RAPPER to run as a background job. b The

script ‘join’ uses the UNIX command foreach to merge the framework.pdb file generated by RAPPER with each of the loop decoys (looptest*.pdb). foreach is usually used in place of a standard for statement and essentially performs a task on everything in a set, rather than performing a specific task x times to one file. The output of the script is a set of files with the suffix ∗ pdb.pdb. These file contain the complete receptor protein and should be archived for future use. inspection of the file ‘loops’ should reveal one entry for each of the ∗ pdb.pdb files located in the RAPPERHOME/rappermc/distrib/TESTRUNS directory as shown below.

c Visual

The file ‘loop’ looptest-0.pdb.pdb looptest-100.pdb.pdb looptest-101.pdb.pdb looptest-102.pdb.pdb ... ... looptest-9.pdb.pdb d The

product of the sed command is the file ‘loop.in’. Visual inspection of this file should reveal the same list of entries as found in the ‘loop’ file with the number 10 after each looptest∗ pdb.pdb. The command sed (stream editor) refers to a UNIX utility for parsing text files and applying the operation which has been specified via the command line. The outputs are then directed to either the screen or a file. The number 10 which was applied to the file loop as part of the sed command is arbitrary and is a format requirement for the correct operation of the program DFIRE. The file ‘loop.in’ looptest-0.pdb.pdb 10 looptest-100.pdb.pdb 10 looptest-101.pdb.pdb 10 looptest-102.pdb.pdb 10 ... ... looptest-9.pdb.pdb 10 e The

UNIX command sort is used here to order the output from DFIRE numerically, the results of which are piped to the UNIX command head that retains the top 50 entries and saves them under the filename ‘best-loops’. At this point the directory RAPPERHOME/rappermc/distrib/TESTRUNS should be archived to a permanent storage medium.

f The

PDB files, keyfile script and executable script should all be present in the same directory.

13.4 AUTOMATED METHODS FOR GENERATING MODELS OF GPCRs

269

13.3 Troubleshooting • Knots in final homology structure: Initially, knots, or regions of entangled protein chains, may be a direct result of a poor initial alignment. Visual inspection and editing of the alignment should be performed and adjusted or deleted accordingly. If the alignment is correct and regions of uncertainty have been deleted, then one solution is to calculate independently many models and to ensure that a population exists which is free of tangled protein. In addition, extending the optimization time for the model-building process may reduce the occurrence of the knots. • Python formatting matters: Failure of the MODELLER Python script to initiate may be as a result of a poorly formatted ‘.py’ file. Familiarity with Python expressions may save a lot of time and frustration. There are many resources for learning Python itself, such as a series of comprehensive tutorials at http://www.python.org/doc/.

13.4 Automated methods for generating models of GPCRs Whilst GPCR models can be generated using the techniques described above, a number of automated servers have been developed that provide a fast and reliable route for the creation of homology models and require just the target sequence or alignment with a suitable template. The Expert Protein Analysis System (ExPASy) web site (http://ca.expasy.org/ [48]) can be used for the retrieval of a protein sequence or more complex tasks, including the analysis and identification of related sequences. Several dedicated databases for studying GPCRs, such as the GPCRDB (http://www.gpcr.org/7tm/ [49]), supplement ExPASy-like web sites and include specialized tools for examining this class of proteins. As with manual modelling methods, automated modelling servers often require an alignment between the template and target sequences. To facilitate this, a number of freely accessible servers are available, including T-COFFEE (http://www.igs.cnrs-mrs.fr/Tcoffee/tcoffee cgi/index.cgi) and CLUSTALW (http://www.ebi.ac.uk/Tools/clustalw/). The subsequent alignment can then be submitted to an automated modelling server to generate a homology model of the protein of interest. Two examples of automated modelling servers are MODBASE (http://salilab.org/modbase/ [50]) and SWISS MODEL (http://swissmodel.expasy.org/SWISS-MODEL.html [51]), both of which also act as repositories for previously generated models. Whilst both servers are capable of generating high-quality models of GPCRs, SWISS MODEL does have the advantage of supporting a dedicated GPCR modelling section. In addition, SWISS MODEL also enables the user to choose a specific template from a list of available and suitable structures. The use of different templates can then lead to a series of models that can be filtered further using methods such as experimental data. Whilst the above methods can be used for automatically generating homology models of related sequences, distantly related targets require a subtly different

270


approach. One service is the Structure Prediction Meta Server (http://meta. bioinfo.pl/submit wizard.pl [52]), which provides access to various fold recognition, function prediction and local structure prediction methods. The output from the server includes the PDB codes of the hits, alignments, reliability scores for every server and provides a jury prediction based on the results collected from other services. Allied methods to this include the servers from the Fiser lab (http://www.fiserlab.org/servers table.htm [53]), which provides access to various fold recognition, function prediction and local structure prediction methods. In cases where there is no obvious template for the target, ab initio modelling servers may be used to generate of a series models which can be filtered using knowledge-based approaches. An example of this is the ROBETTA server (http://robetta.bakerlab.org/ [54]), which is an automated front end to the successful ROSETTA method [55]. This service requires only the protein sequence to generate molecular models, and with the success of ROSETTA in recent CASP experiments, this method is a good starting point for generating models which may be tested experimentally. Whilst these methods may appear to be a black-box approach to generate protein structure, a series of checks needs to be performed before the models are used to analyse or suggest experiments. These checks include stereochemistry checks using modelling servers such as WHATIF and PROCHECK and a careful examination of the alignments used to generate models, as well as a visual examination of the model using programs such as DeepView. External to these checks are services such as EVA (http://eva.compbio.ucsf.edu/∼eva/ [56]), which provides a continuous, fully automated and statistically significant analysis of structure prediction servers. This could be checked prior to using a particular server to assess the validity of the modelling routines for the target of choice.

References 1. Gudermann, T., Nurnberg, B. and Schultz, G. (1995) Receptors and G proteins as primary components of transmembrane signal transduction. Part 1. G-protein-coupled receptors: structure and function J. Mol. Med., 73, 51–63. 2. Palczewski, K., Kumasaka, T., Hori, T. et al. (2000) Crystal structure of rhodopsin: a G protein-coupled receptor. Science, 289, 739–745. The first three-dimensional structure of a GPCR, which has enabled the generation of homology models for this family of membrane proteins. 3. Cherezov, V., Rosenbaum, D.M., Hanson, M.A. et al. (2007) High-resolution crystal structure of an engineered human β2 -adrenergic G protein-coupled receptor. Science, 318, 1258–1265. 4. Forrest, L.R., Tavoulari, S., Zhang, Y.W. et al. (2007) Identification of a chloride ion binding site in Na+ /Cl− -dependent transporters. Proc. Natl. Acad. Sci. U. S. A., 104, 12761–12766. 5. Wieman, H., Tøndel, K., Anderssen, E. and Drabløs, F. (2004) Homology-based modelling of targets for rational drug design. Mini Rev. Med. Chem., 4, 793–804. 6. Berman, H.M., Westbrook, J., Feng, Z. et al. (2000) The Protein Data Bank. Nucleic Acids Res. 28, 235–242.

REFERENCES

271

7. Altschul, S.F., Gish, W., Miller, W. et al. (1990) Basic local alignment search tool. J. Mol. Biol., 215, 403–410. 8. Altschul, S.F., Madden, T.L., Schäffer, A.A. et al. (1997) Gapped BLAST and PSI-BLAST: a new generation of protein database search programs. Nucleic Acids Res., 25, 3389–3402. 9. Krogh, A., Brown, M., Mian, I.S. et al. (1994) Hidden Markov models in computational biology: applications to protein modeling. J. Mol. Biol., 235, 1501–1531. 10. Karplus, K., Barrett, C. and Hughey, R. (1998) Hidden Markov models for detecting remote protein homologies. Bioinformatics, 14, 846–856. 11. Eddy, S.R. (1998) Profile hidden Markov models. Bioinformatics, 14, 755–763. 12. Okada, T., Sugihara, M., Bondar, A.N. et al. (2004) The retinal conformation and its environment ˚ crystal structure. J. Mol. Biol., 342, 571–583. in rhodopsin in light of a new 2.2 A 13. Li, J., Edwards, P.C., Burghammer, M. et al. (2004) Structure of bovine rhodopsin in a trigonal crystal form. J. Mol. Biol., 343, 1409–1438. 14. He, X.L., Bazan, J.F., McDermott, G. et al. (2003) Structure of the Nogo receptor ectodomain: a recognition module implicated in myelin inhibition. Neuron, 38, 177–185. 15. Kobe, B. and Deisenhofer, J. (1996) Mechanism of ribonuclease inhibition by ribonuclease inhibitor protein based on the crystal structure of its complex with ribonuclease A. J. Mol. Biol., 264, 1028–1043. 16. Grace, C.R., Perrin, M.H., DiGruccio, M.R. et al. (2004) NMR structure and peptide hormone binding site of the first extracellular domain of a type B1 G protein-coupled receptor. Proc. Natl. Acad. Sci. U. S. A., 101, 12836–12841. The first reported structure of the N-terminal domain of a Family B GPCR. 17. Sun, C., Song, D., Davis-Taber, R.A. et al. (2007) Solution structure and mutational analysis of pituitary adenylate cyclase-activating polypeptide binding to the extracellular domain of PAC1-RS. Proc. Natl. Acad. Sci. U. S. A., 104, 7875–7880. 18. Grace, C.R., Perrin, M.H., Gulyas, J. et al. (2007) Structure of the N-terminal domain of a type B1 G protein-coupled receptor in complex with a peptide ligand. Proc. Natl. Acad. Sci. U. S. A., 104, 4858–4863. 19. Parthier, C., Kleinschmidt, M., Neumann, P. et al. (2007) Crystal structure of the incretin-bound extracellular domain of a G protein-coupled receptor. Proc. Natl. Acad. Sci. U. S. A., 104, 13942–13947. 20. Tsuchiya, D., Kunishima, N., Kamiya, N. et al. (2002) Structural views of the ligand-binding cores of a metabotropic glutamate receptor complexed with an antagonist and both glutamate and Gd3+ . Proc. Natl. Acad. Sci. U. S. A., 99, 2660–2665. 21. Muto, T., Tsuchiya, D., Morikawa, K. and Jingami, H. (2007) Structures of the extracellular regions of the group ii/iii metabotropic glutamate receptors. Proc. Natl. Acad. Sci. U. S. A., 104, 3759–3764. 22. Benner, S.A., Cohen, M.A. and Gonnet, G.H. (1994) Amino acid substitution during functionally constrained divergent evolution of protein sequences. Protein Eng., 7, 1323–1332. 23. Henikoff S. and Henikoff J.G. (1992) Amino acid substitution matrices from protein blocks. Proc. Natl. Acad. Sci. U. S. A., 89, 10915–10919. 24. Ng, P.C., Henikoff, J.G. and Henikoff, S. (2000) PHAT: a transmembrane-specific substitution matrix. Predicted hydrophobic and transmembrane. Bioinformatics, 16, 760–796.

272


25. Muller, T., Rahmann, S. and Rehmsmeier, M. (2001) Non-symmetric score matrices and the detection of homologous transmembrane proteins. Bioinformatics, 17 ( Suppl 1), S182–S189. 26. Forrest, L.R., Tang, C.L. and Honig, B. (2006) On the accuracy of homology modeling and sequence alignment methods applied to membrane proteins. Biophys. J , 91, 508–517. 27. Shafrir, Y. and Guy, H.R. (2004) STAM: simple transmembrane alignment method. Bioinformatics, 20, 758–769. 28. Notredame, C., Higgins, D.G. and Heringa, J. (2000) T-Coffee: a novel method for fast and accurate multiple sequence alignment. J. Mol. Biol., 302, 205–217. 29. Thompson, J.D., Higgins, D.G. and Gibson, T.J. (1994) CLUSTAL W: improving the sensitivity of progressive multiple sequence alignment through sequence weighting, position-specific gap penalties and weight matrix choice. Nucleic Acids Res., 22, 4673–4680. 30. Ballesteros, J.A., Shi, L. and Javitch, J.A. (2001) Structural mimicry in G-protein-coupled receptors: implications of the high-resolution structure of rhodopsin for structure-function analysis of rhodopsin-like receptors. Mol. Pharmacol., 60, 1–19. An interesting article which draws ideas from the structure of bovine rhodopsin to rationalize the function of GPCRs as a whole. 31. Guex, N. and Peitsch, M.C. (1997) SWISS-MODEL and the Swiss-PdbViewer: an environment for comparative protein modeling. Electrophoresis, 18, 2714–2723. 32. Sali, A. and Blundell, T.L. (1993) Comparative protein modelling by satisfaction of spatial restraints. J. Mol. Biol., 234, 779–815. 33. Farrens, D.L., Altenbach, C., Yang, K. et al. (1996) Requirement of rigid-body motion of transmembrane helices for light activation of rhodopsin. Science, 274, 768–770. 34. Gether, U., Lin, S., Ghanouni, P. et al. (1997) Agonists induce conformational changes in transmembrane domains III and VI of the β2 adrenoceptor. EMBO J., 16, 6737–6747. 35. Elling, C.E., Thirstrup, K., Nielsen, S.M. et al. (1997) Metal-ion sites as structural and functional probes of helix–helix interactions in 7TM receptors. Ann. N. Y. Acad. Sci., 814, 142–151. 36. Zhou, W., Flanagan, C., Ballesteros, J.A. et al. (1994) A reciprocal mutation supports helix 2 and helix 7 proximity in the gonadotropin-releasing hormone receptor. Mol. Pharmacol., 45, 165–170. 37. Laskowski, R.A., Rullmannn, J.A., MacArthur, M.W. et al. (1996) AQUA and PROCHECK-NMR: programs for checking the quality of protein structures solved by NMR. J. Biomol. NMR, 8, 477–486. 38. Hooft, R.W., Vriend, G., Sander, C. and Abola, E.E. (1996) Errors in protein structures. Nature, 381, 272. 39. Eisenberg, D., Lüthy, R. and Bowie, J.U. (1997) VERIFY3D: assessment of protein models with three-dimensional profiles. Methods Enzymol., 277, 396–404. 40. Sippl, M.J. (1993) Recognition of errors in three-dimensional structures of proteins. Proteins, 17, 355–362. 41. Fiser, A., Do, R.K. and Sali, A. (2000) Modeling of loops in protein structures. Protein Sci., 9, 1753–1773. 42. Deane, C.M. and Blundell, T.L. (2001) CODA: a combined algorithm for predicting the structurally variable regions of protein models. Protein Sci., 10, 599–612.

REFERENCES

273

43. Soto, C.S., Fasnacht, M., Zhu, J. et al. (2008) Loop modeling: sampling, filtering, and scoring. Proteins, 70, 834–843. 44. de Bakker, P.I., DePristo, M.A., Burke, D.F. and Blundell, T.L. (2003) Ab initio construction of polypeptide fragments: accuracy of loop decoy discrimination by an all-atom statistical potential and the AMBER force field with the generalized Born solvation model. Proteins, 51, 21–40. 45. Zhu, K., Pincus, D.L., Zhao, S. and Friesner, R.A. (2006) Long loop prediction using the protein local optimization program. Proteins, 65, 438–452. 46. Zhou, H. and Zhou, Y. (2002) Distance-scaled, finite ideal-gas reference state improves structure-derived potentials of mean force for structure selection and stability prediction. Protein Sci., 11, 2714–2726. The first report of a statistical potential with comparable accuracy to full atomic force fields. 47. Zhang, C., Liu, S. and Zhou, Y. (2004) Accurate and efficient loop selections using DFIRE-based all-atom statistical potential. Protein Sci., 13, 391–399. 48. Gasteiger, E., Gattiker, A., Hoogland, C. et al. (2003) ExPASy: the proteomics server for in-depth protein knowledge and analysis. Nucleic Acids Res., 31, 3784–3788. 49. Horn, F., Bettler, E., Oliveira, L. et al. (2003) GPCRDB information system for G protein-coupled receptors. Nucleic Acids Res., 31, 294–297. 50. Pieper, U., Eswar, N., Davis, F.P. et al. (2006) MODBASE: a database of annotated comparative protein structure models and associated resources. Nucleic Acids Res., 34, D291–D295. 51. Schwede, T., Kopp, J., Guex, N. and Peitsch, M.C. (2003) SWISS-MODEL: an automated protein homology-modeling server Nucleic Acids Res., 31, 3381–3385. 52. Ginalski, K., Elofsson, A., Fischer, D. and Rychlewski, L. (2003) 3D-Jury: a simple approach to improve protein structure predictions. Bioinformatics, 19, 1015–1018. 53. Rai, B.K., Madrid-Aliste, C.J., Fajardo, J.E. and Fiser, A. (2006) MMM: a sequence-to-structure alignment protocol. Bioinformatics, 22, 2691–2692. 54. Kim, D.E., Chivian, D. and Baker, D. (2004) Protein structure prediction and analysis using the Robetta server. Nucleic Acids Res., 32, W526–W531. 55. Rohl, C.A., Strauss, C.E., Misura, K.M. and Baker, D. (2004) Protein structure prediction using Rosetta. Methods Enzymol., 383, 66–93. 56. Eyrich, V.A., Mart´ı-Renom, M.A., Przybylski, D. et al. (2001) EVA: continuous automatic evaluation of protein structure prediction servers. Bioinformatics, 17, 1242–1243.

Appendix Site-directed Mutagenesis and Chimeras Alex Conner1 , Mark Wheatley2 and David R. Poyner3 1 School

of Medicine, Warwick University, Coventry CV4 7AL, UK of Biosciences, Birmingham University, Birmingham B15 2TT, UK 3 School of Life and Health Sciences, Aston University, Birmingham B4 7ET, UK 2 School

A.1 Introduction The ability to alter the structure of G protein-coupled receptors (GPCRs) at will by site-directed mutagenesis or by generation of receptor chimeras is an essential technique in molecular pharmacology. This appendix will concentrate on the design of mutants for point changes or chimeras, as well as briefly reviewing the main experimental techniques.

A.2 Why mutagenesis? The search for reliable structural data, whether through crystal studies, nuclear magnetic resonance (NMR) or biophysics, is a primary goal towards understanding GPCRs. However, this will not provide information about the movements and contacts made and broken during the transition stages of receptor activation. This also holds true for the contacts made between the receptor and the countless interacting molecules that GPCRs encounter during their surface localization, activation and desensitization. GPCRs are increasingly being recognized not as a simple lock in a ‘lock and key’ mechanism, but as the crux of a fluid receptor-complex heavily dependent on the cellular environment and the available molecular partners. Examples of GPCRs are now widely recognized as functional homodimers and heterodimers [1], and several G Protein Coupled Receptors Edited by David R. Poyner and Mark Wheatley  2010 John Wiley & Sons, Ltd.

276

APPENDIX: SITE-DIRECTED MUTAGENESIS AND CHIMERAS

are known to require accessory proteins [2]. Their pharmacology can be altered by the membrane constituents, including cholesterol-lipid rafts and the invaginated caveolae [3], and the emphasis on non-G protein signalling of receptors is now established [4]. Clearly, exploring the structural and biochemical requirements of integral domains and individual receptors of GPCRs is crucial towards modelling the shape of activation, a key factor both for targeted therapeutics and fundamental receptor biology. By far the most convenient and powerful technique for altering and assessing GPCR biochemistry involves the alteration of individual amino acids or groups of residues in a manner thought to negate their influence or to change the influence in such a way that the role becomes clear. Such mutagenesis has been used successfully for 30 years and can be broadly split into two main approaches. These include a comprehensive random (saturation) mutagenesis, essentially fishing for disrupted or enhanced constraints. The second is a targeted mutagenesis approach, substituting or deleting residues or whole regions within a GPCR based on a hypothesis-led appraisal of key functional parts of the protein.

A.2.1 Random (saturation) mutagenesis Random mutagenesis is a nonprejudiced approach for the isolation of specific but untargeted residues of structural or functional relevance. This technique results in a significant volume of data for analysis and requires a subsequent selection from that pool of data. Originally used in whole organisms more than 50 years ago using radiation or chemical mutagens to alter overt phenotypes, random mutagenesis has recently undergone an upsurge in experimental popularity, moving towards in vitro methods more in line with the directed analysis used elsewhere. This involves the polymerase chain reaction (PCR) misincorporation or ‘gene-shuffling’ through recombination strategies for localized random mutagenesis of specific gene targets or their regulatory elements; see [5] for a review. A recent development in the power of random mutagenesis for the analysis of GPCRs came from the group of Jurgen Wess, studying the M3 muscarinic acetylcholine receptor (M3R). They utilized a combination of low-frequency random mutagenesis of the entire M3R coding sequence, followed by the application of a new yeast genetic screen that allows the recovery of inactivating M3R single-point mutations [6]. This gives a comprehensive understanding of multiple residues in the second extracellular loop, critical for receptor activation [7]. It remains to be seen whether this is more efficient for the systematic analysis of GPCR structure function.

A.2.2 Targeted mutagenesis Targeting residues for mutation takes many forms and is a widely used and powerful form of functional protein analysis leading to the discovery of innumerable specific functional and structural domains required for a host of protein functions. It is clear that targeted mutagenesis does not necessarily need to start with a precise biochemical understanding of the functional or structural regions to be assessed. On a macroscale,

A.2 WHY MUTAGENESIS?

277

a mining approach to narrow down regions of interest has been consistently and successfully used in the GPCR field. Taking two examples, this has led to the discovery of agonist-specific binding domains in the V1a vasopressin receptor [8] and specific signalling loci in the metabotropic glutamate receptors [9]. Mutations altering large areas of a protein can take many forms. The relatively simple deletion mutants are, perhaps, easier to design via single PCRs (often through intermediary cloning vectors) and subcloning. C-terminal deletions have the added possibility of point mutations to introduce stop codons which makes them easier to make, although the possibility of missing this codon might require more than one consecutive stop codon; western blots are useful to show the size differential, confirming the deletion status. Peptide mimetics are, in essence, an extreme form of protein deletion using peptide synthesis technology to create small fragments of a protein, either for competition with the natural protein or to analyse interaction with specified partners in vivo and in vitro. Chimeras are more difficult, but often more revealing when combined with sophisticated data retrieval. Straight hybrids, an N-terminal piece of one receptor fused to the C-terminus of another, are technically challenging but can be made with cloning vectors in a similar way to deletions but with an extra ligation step (see Protocol A.1). The particularly complicated aspect arises when introducing a new piece in the centre of a gene of interest (this is also true for deletions of a central region of a protein). Several methods are used in the literature, including the attractive but costly ‘outsourcing’ to one of a number of companies specializing in specific gene manipulation [10]. A simple PCR/ligation approach can be used when the domains to be fused are derived from very closely related GPCRs. This technique, which incorporates into the oligonucleotides the same sites for restriction endonucleases at each end of the PCR product as needed for the exchange of corresponding domains, was used successfully in an early study of the functional domains of the Family A vasopressin V2 and oxytocin receptors [11] as well as the similar Family B parathyroid hormone and secretin receptors [12]. This is a relatively easy method for chimera production, but relies heavily on the presence or introduction of common restriction endonuclease sites, which is often limited to the formation of constructs from closely related receptors. Where the precise endonuclease sites are not available, these can sometimes be introduced as silent mutations and incorporated into the ends of the oligonucleotides used to generate a fragment for insertion from the donor receptor. This has been used successfully to create chimeras between members of the bradykinin receptor subfamily [13] and less similar rhodopsin and β2 adrenergic receptors [14]. In Protocol A.1, a generic method is described for production of chimeric mutants. A second example has been described [15] to include a more comprehensive three-step PCR method which can used even where the insertion of suitable sites is not feasible. The gene sequence to be inserted (I) can be amplified using oligonucleotides designed to have flanking sequences from the target gene (T) corresponding to the region of insertion. This fragment, when amplified, can be mixed with the gene to be targeted (in its plasmid vector) and the mixture used as a template for a second round of PCR. This requires the separate amplification of two products, including the entire upstream sequence of the target gene and the entire downstream sequence of the target gene both

278


fused to the insert sequence. This is shown in Figure A.1. The final step requires the mixing of both sequences and the subsequent amplification of the entire chimeric gene using the extreme oligonucleotide primers only. This method was described previously [15], although not necessarily for GPCRs, and is one of several similar methods of PCR-based chimeric production used, including blunt cloning (Protocol A.1), the introduction of restriction sites for domain swapping [8] and 5 -overhang plasmid extension [16]. For all of these methods, the precise molar ratios have to be adjusted and titrated for optimum efficiency. It is a very important consideration, particularly when designing chimeras between closely related receptors, to note that several individual point mutations may lead to a significant chimeric effect if the amino acid conservation is high. The technique is much simpler and is described below (Protocol A.2). Furthermore, the problems associated with the construction and analysis of chimeric proteins can outweigh the insight they provide. Predominantly, this includes topology-folding problems and that, due to the spread of important residues which can come together on tertiary/quaternary folding, ‘key areas’ of importance identified through chimeric receptor analysis often require a subsequent alanine sweep of the individual residues.

PROTOCOL A.1 Production of Chimeric Receptors Equipment and Reagents • Standard PCR thermo cycler (many available) • Pfu DNA polymerase (Promega) and 10 × buffer provided with the Pfu polymerase • Oligonucleotide primers (Invitrogen) • 10 mm deoxyribonucleotide triphosphate (dNTP) mixture (Sigma–Aldrich): a mixture of all four dNTPs (2 -deoxyadenosine 5 -triphosphate (dATP), 2 -deoxycytidine 5 -triphosphate (dCTP), 2 -deoxyguanosine 5 -triphosphate (dGTP), 2 -deoxythymidine 5 -triphosphate (dTTP)) • DNA template plasmid at 100 ng µl−1 containing the gene of interest • 0.5 ml sterile PCR tubes (Sarstedt) • 0.8–1.2% agarose gel • Seaplaque agarose (Lonza, UK) • Agarose gel tank (Bio-Rad Laboratories) • 240 V power pack (Bio-Rad Laboratories) • Desktop microcentrifuge (many available) • Gel extraction kit (Qiagen) • T4 DNA ligase (Invitrogen) • Srf I restriction enzyme (Promega, UK)


• Blunt-cloning plasmid pCR-Script (Stratagene, UK) • Competent cells; for example, XL10-GOLD cells (Stratagene, UK) • Blue–white selective agents isopropyl-β-d-thiogalactopyranoside (IPTG) and X-gal (Stratagene, UK) • Microbiological incubator (many available) • Luria–Bertani (LB) medium (Oxoid, UK) • l-Agar (Oxoid, UK) • Ampicillin (sodium salt; Sigma, UK) • Plasmid isolation kit (Wiz-prep; Promega, UK).

Method 1 Identify the fragments required from each receptor and design PCR primers for their amplification, ensuring the upstream sense primer and the downstream antisense primer have suitable restriction sites for the final cloning step into the multiple cloning site of the desired expression vector. Oligonucleotide primer design considerations should follow those described for routine PCR. Each fragment should be amplified separately using a blunt polymerase (e.g. Pfu; others are available). 2 Prepare the amplification mix. This should contain the following: (a) 5–10 ng plasmid (as for general PCR); mini-prep DNA is adequately pure and concentrated for the reaction to be successful. (b) 1–10 pm each primer (in excess). (c) 1–10 U Pfu polymerase (according to manufacturers instructions) with the associated buffer. (d) Excess dNTPs (typically 1 µl at 2.5 mm starting concentration). A final volume of 50 µl is advised, overlaid with an equivalent volume of mineral oil to prevent evaporation/condensation. The melting temperature has to be optimized; however, a general estimate of 60 ◦ C is suitable for most primers. Following an initial denaturation step of 95 ◦ C for 1 min, cycle as follows, for 30 cycles:

95 ◦ C 55–60 ◦ C 75 ◦ C

1 min 1 min 1 min

Typically, two reactions for each desired fragment is sensible. 3 Run all on a 1.2% agarose gel (seaplaque agarose is more pure and has a lower melting temperature; this is desirable for gel elution). Remove the gel fragment using a clean, sterile scalpel, retaining as little gel as possible. 4 Purify the sample using a commercially available resin-retention kit (Promega, UK, Qiagen, UK, and others). This routinely results in clean, eluted samples of between 30

279

280


and 50 µl of DNA at a concentration of 20–200 ng µl−1 ; however, it is necessary to run an aliquot (1 µl is usually sufficient depending on PCR amplification) on a 1.2% agarose gel to confirm band presence, as gel-clean kits can occasionally fail, leading to a loss of DNA. 5 Ligate the two clean bands. Mix approximately 100 ng each fragment, 1–10 U T4 DNA ligase (according to manufacturer’s instructions) and add the appropriate ligase buffer as directed. Use a typical final volume of 10–20 µl. Reports differ as to the optimum length and temperature of ligation. Experience suggests 16 h at 14 ◦ C is particularly efficient, although 1 h at 37 ◦ C is known to be sufficient. 6 Carry out the second PCR step to create the chimeric fragment. Using approximately 1–2 µl of the ligation product as a template, repeat the PCR as above using the N-terminal sense primer and the C-terminal antisense primer of the desired chimera. Include individual primer controls. Run, excise and clean the PCR product as described above. The product can then be ligated into a blunt-ended cloning vector such as the pCR-Script blunt-cloning vector (Stratagene) with blue–white selection. 7 Perform the blunt cloning of the fragment into a plasmid vector. Pre-digest the vector with a restriction site producing blunt ends (any single EcoRVI-containing plasmid can be used, but in this case there is an Srf I site located in the middle of a β-lactamase gene) and then ligate into the plasmid. Digestion and ligation are according to manufacturer’s instructions, but briefly: approximately 100 ng plasmid is digested in appropriate buffer with 5–10 U appropriate restriction enzymes for 1 h at 37 ◦ C followed by a 20 min 75 ◦ C enzyme denaturation step. Ligation can be titrated for efficiency with a starting point of 1 : 10 plasmid : fragment ratio, at concentrations of approximately 10 : 100 ng respectively. The ligation conditions are as described above (step 5). 8 Transform any normal Escherichia coli competent cells (unlike the site-directed point mutagenesis described below, no special considerations are necessary). This is followed by overnight 37 ◦ C incubation on l-agar plates with the required antibiotic selection agent (ampicillin in the case of pCR-Script). To identify successfully ligated transformants,a spread 100 µl of 100 mm IPTG (for β-lactamase promoter) and 100 µl 2% X-gal (β-lactamase substrate) onto the agar, allowing the components to dry individually to prevent aggregation. White colonies are selected (include one blue colony as an unligated plasmid control). 9 Plasmids obtained from overnight cultures are sequenced using appropriate sequencing primers (refer to http://www.stratagene.com/manuals/211190.pdf for a specific pCR-Script protocol (Stratagene, UK)). Subsequent subcloning into an expression vector is routine. Notes of the fragment results in the disruption of the β-lactamase gene. Plating transformants onto X-gal containing agar will result in blue and white colonies for those without inserts and with a disruptive insert respectively.

a Ligation

281


STEP 1

Donor gene Target gene

PCR

STEP 2

PCR

PCR

STEP 3

PCR

Sub-clone into expression vector.

Figure A.1 A three-step PCR process for chimeric-gene formation [15].

A.2.3 Point mutations From the early experiments of the British Canadian chemist Michael Smith in the 1970s (for which he was awarded a Nobel Prize), site-directed point mutations have arguably become the single biggest tool in the identification of biologically important loci of the GPCR superfamily. Their use, combined with bioinformatics techniques and the large number of protein sequences in the GPCR family have, to some extent, circumvented the problems encountered by the lack of further structural data following on from the publication of the rhodopsin crystal structure in 2000 [17, 18]. The choice for residue substitution has been predominantly alanine replacement, either individually or as part of a less-targeted ‘alanine scan’. This is due to alanine residues having a small inert side chain (a single methyl group) without introducing

282


the flexible propensity observed with the smaller glycine residue. Alanine scans have been used in abundance for biological GPCR analysis by ourselves and numerous other groups studying each of the main subfamilies [19–21]. Earlier studies have been comprehensively reviewed [17]. Cysteine-substitution is a second highly used technique with the advantage of using the natural sulfhydryl reactivity and disulfide cross-linking for inter- and intra-molecular analysis. This has been used to observe ligand-binding interactions and activation mechanisms in rhodopsin [22] and other receptors [23]. A similar approach creating intramolecular constraints through zinc binding to histidine substitutions throughout GPCRs from Family A and Family B showed a very clear suggestion of the constraints applied to these receptors upon ligand-induced activation, and was hugely influential in our understanding of the ‘flowering’ effect seen during this conformational change in the GPCR superfamily [24, 25]. There are, of course, no right answers, and the choice of substitution can always be criticized. Alanine scans are usually preferred to glycine as an approximate ‘null

Figure A.2 The Stratagene QuikChange mutagenesis method (based on http://www.stratagene. com/manuals/200518.pdf).


283

substitution’, although it has been suggested (not without justification) that many proteins can tolerate a single alanine substitution, even in functionally important domains. Conversely, the introduction of cysteine or histidine residues clearly create large reactive side chains which could form whole new intramolecular interactions with no clear model to describe them, often resulting in problems with expression, rather than receptor physiology. Unlike chimeric cloning, over the last 10–15 years, a version of the Stratagene QuikChange mutagenesis method (Figure A.2) has become, almost exclusively, the site-directed mutagenesis method of choice. For GPCR research, an analysis of publications including mutagenic technology (targeted) reveals that almost all of the papers used this protocol compared with previous methods. The reason for this is a good one. The method is cheap, quick and reliable and requires no specific plasmid vector, multiple cloning site or multiple ligation/transformation reactions. Plasmid size is limiting, but there is a relatively broad range (predicted to be approximately 2–10 kb). Primer design is a key consideration, but standard, commercially available oligonucleotides provide ample material of sufficient quality for most mutagenesis reactions. An example method is given in Protocol A.2.

PROTOCOL A.2 Site-directed Mutagenesis Method Equipment and Reagents • Standard PCR thermo cycler (many available) • Pfu DNA polymerase (Promega) and 10 × buffer provided with the Pfu polymerase • Oligonucleotide primers (Invitrogen) • 10 mm dNTP mixture(Sigma–Aldrich): a mixture of all four dNTPs (dATP, dCTP, dGTP, dTTP) • DNA template plasmid at 100 ng µl−1 containing the gene of interest • 5 ml sterile PCR tubes (Sarstedt) • 8–1.2% agarose gel • Agarose gel tank (Bio-Rad Laboratories) • 240 V power pack (Bio-Rad Laboratories) • Desktop microcentrifuge (many available) • DpnI restriction enzyme (New England Biolabs) • Competent cells; for example, XL10-GOLD cells (Stratagene, UK) • Microbiological incubator (many available) • LB medium (Oxoid, UK) • l-Agar (Oxoid, UK)

284


• Ampicillin (sodium salt; Sigma, UK) • Plasmid isolation kit (Wiz-prep; Promega, UK).

Method 1 Design appropriate oligonucleotide primers; this is arguably the most important design aspect to site-directed mutagenesis. The key rules include: (a) Incorporating 9–15 nucleotides either side of the mutation. (b) Using codon degeneracy to reduce the number of changed bases; codon usage is not thought to be a major issue. (c) Checking for obvious secondary structure and potential mispriming with a BLAST search. Practically, when many primers are being designed, it is common to perform these checks only if a problem with individual mutagenesis using certain primers is encountered. (d) Trying to avoid multiple runs of individual bases; this has been known to cause mispriming but is often an unavoidable issue. (e) Trying to terminate the primer at the 3 end with a so-called GC clamp;a that is, end the primer at the 3 end with two Gs, Cs or a combination. 2 The amplification mix contains the following: (a) 100 ng plasmid (this is not PCR therefore a significant starting mass is required); mini-prep DNA is adequately pure and concentrated for the reaction to be successful. (b) 10 pm each primer. (c) 5–10 U Pfu polymerase with the associated buffer. (d) Excess dNTPs (typically 1 µl at 2.5 mm final concentration). A final volume of 50 µl is advised. Always include a control with one or both oligonucleotides omitted. 3 The melting temperature has to be optimized, although a general estimate of 60 ◦ C is suitable for most primers. Following an initial denaturation step of 95 ◦ C for 1 min, cycle as follows, for 12 cycles:

95 ◦ C 60 ◦ C 75 ◦ C

1 min 1 min 2 min per kb (14 min for a typical GPCR in a pcDNA3 expression plasmid)b

To digest the template fragment from the amplification mix, add 2–10 U DpnI, pipette up and down and spin briefly in a microcentrifuge for 1 min. Incubate at 37 ◦ C for 1 h.

A.4 CONCLUSION

285

4 Transform any commercially available competent cells,c using typically 1–5 µl amplification mix; include a DpnI-treated plasmid transformation as a control for DpnI digestion). 5 Following transformation, it is advisable to incubate in 0.5–1 ml LB medium (with no selective agent) at 37 ◦ C for 1–1.5 h to promote initial antibiotic resistance in free media. This is followed by overnight 37 ◦ C incubation on l-agar plates with the required antibiotic selection agent (ampicillin in the case of pCR-Script). 6 To identify successful transformants, plasmids obtained from overnight cultures should be sequenced using appropriate sequencing primers as for the chimera production (Protocol A.1, step 9). If restriction sites have been disrupted by the mutation change, then a digestion analysis of transformants can be a useful initial screen to avoid unnecessary sequencing costs. Notes a

The A–T interaction has two hydrogen bonds, whereas the G–C interaction has three. The movement of polymerase from the 3 end of the annealed primer requires a firm double-stranded grip. No such interaction is required at the 5 end. As such, ending with a GC clamp can enhance the amplification step significantly. check that the amplification has been successful it is useful to remove 5 µl and compare with the pre-DpnI control on a 1.2% agarose gel. This is not essential and occasionally the product is at too low a concentration for electrophoretic analysis. b To

c

Competence can be a limiting factor with transformations, and the higher the cell competence, the more chance of success there is.

A.3 Troubleshooting The conditions for transformation of the DpnI-digested amplification reaction (in concert with the oligonucleotide-free control) is a key consideration for successful routine mutagenesis. It has been noted by several researchers (including the first author of this appendix) that sedimentation and precipitation of several components of the amplification reaction is a strong negative influence. Following thawing, concerted vortexing of all components is advisable. Almost all E. coli strains used to produce the initial template are (dam) methylase positive, perfect for DpnI-mediated mutagenesis. On rare occasions, strains do not have this capability and the plasmids produced are not suitable templates.

A.4 Conclusion Mutagenesis is a sophisticated, crucial tool for the structure–function analysis of GPCRs which has provided more information on the pharmacology and signalling of this superfamily than any other technique. It is important to be aware that the information gleaned from these types of study can only be as good as the experimental

286


design. The choice of mutation, from chimera and deletions to individual alanine substitutions, needs a thorough and preferably model-driven process. No technique can be considered best, and the subsequent assaying technique is a major problem due to the promiscuous nature of this receptor family. Furthermore, it is important to consider that, as the primary amino acid structure folds so many times, the mining technique or even multiple residue-substitution can result in mutations far apart in the fully folded protein structure. Following the huge interest in GPCR mutagenesis, it is critical that the data retrieved is not wasted. A recent review discussing roles for information collection, management and integration in structure–function studies of GPCRs insightfully covers the requirement for data-mining and meta-analyses in the field [26]. Also, whilst individual studies have revealed many interesting and useful facets of receptor biology, the future of this research lies in combining these techniques with structural analysis (through crystals and NMR), iterative modelling and detailed spectroscopy (including fluorescence resonance energy transfer and bioluminescence resonance energy transfer). It is important that GPCR mutagenesis is not a stopgap whilst waiting for the breakthrough in three-dimensional GPCR analysis; rather, it is taken forward to assist the understanding of receptor activation, antagonist binding and the specific impact of the local environment on GPCR biology. Genomics suites have increased the turnaround time and, in our experience, the analysis has by far overtaken the molecular biology as the rate-limiting factor. This puts pressure on the high-throughput screening techniques to catch up.

References 1. Milligan, G. (2004) G protein-coupled receptor dimerization: function and ligand pharmacology. Mol. Pharmacol., 66, 1–7. 2. Sexton, P.M., Morfis, M., Tilakaratne, N. et al. (2006) Complexing receptor pharmacology: modulation of family B G protein-coupled receptor function by RAMPs. Ann. N. Y. Acad. Sci., 1070, 90–104. 3. Insel, P.A., Head, B.P., Ostrom, R.S. et al. (2005) Caveolae and lipid rafts: G protein-coupled receptor signaling microdomains in cardiac myocytes. Ann. N. Y. Acad. Sci., 1047, 166–172. 4. Barak, L.S., Wilbanks, A.M. and Caron, M.G. (2003) Constitutive desensitization: a new paradigm for g protein-coupled receptor regulation. Assay Drug Dev. Technol., 1, 339–346. 5. Beukers, M.W. and Ijzerman, A.P. (2005) Techniques: how to boost GPCR mutagenesis studies using yeast. Trends Pharmacol. Sci., 26, 533–539. 6. Li, B., Scarselli, M., Knudsen, C.D. et al. (2007) Rapid identification of functionally critical amino acids in a G protein-coupled receptor Nat. Methods, 4, 169–174. 7. Scarselli, M., Li, B., Kim, S.K. and Wess, J. (2007) Multiple residues in the second extracellular loop are critical for M3 muscarinic acetylcholine receptor activation J. Biol. Chem., 282, 7385–7396.

REFERENCES

287

8. Hawtin, S.R., Wesley, V.J., Parslow, R.A. et al. (2000) Critical role of a subdomain of the N-terminus of the V1a vasopressin receptor for binding agonists but not antagonists; functional rescue by the oxytocin receptor N-terminus. Biochemistry, 39, 13524–13533. 9. Havlickova, M., Blahos, J., Brabet, I. et al. (2003) The second intracellular loop of metabotropic glutamate receptors recognizes C termini of G-protein α-subunits. J. Biol. Chem., 278, 35063– 35070. 10. Gupte, J., Cutler, G., Chen, J.L. and Tian, H. (2004) Elucidation of signaling properties of vasopressin receptor-related receptor 1 by using the chimeric receptor approach. Proc. Natl. Acad. Sci. U. S. A., 101, 1508–1513. 11. Postina, R., Kojro, E. and Fahrenholz, F. (1996) Separate agonist and peptide antagonist binding sites of the oxytocin receptor defined by their transfer into the V2 vasopressin receptor. J. Biol. Chem., 271, 31593–31601. 12. Turner, P.R., Bambino, T. and Nissenson, R.A. (1996) A putative selectivity filter in the G-proteincoupled receptors for parathyroid hormone and secretion. J. Biol. Chem., 271, 9205–9208. 13. Yu, J., Polgar, P., Lubinsky, D. et al. (2005) Coulombic and hydrophobic interactions in the first intracellular loop are vital for bradykinin B2 receptor ligand binding and consequent signal transduction. Biochemistry, 44, 5295–5306. GPCR chimera construction and analysis between close members of a GPCR subfamily. 14. Kim, J.M., Hwa, J., Garriga, P. et al. (2005) Light-driven activation of β2 -adrenergic receptor signaling by a chimeric rhodopsin containing the β2 -adrenergic receptor cytoplasmic loops. Biochemistry, 44, 2284–2292. GPCR chimera construction and analysis between more distant members of a GPCR subfamily. 15. Grandori, R., Struck, K., Giovanielli, K. and Carey, J. (1997) A three-step PCR protocol for construction of chimeric proteins. Protein Eng., 10, 1099–1100. A clear multistep process for unrestricted chimeric construct formation. 16. Hoffmann, C., Soltysiak, K., West, P.L. and Jacobson, K.A. (2004) Shift in purine/pyrimidine base recognition upon exchanging extracellular domains in P2Y 1/6 chimeric receptors. Biochem. Pharmacol., 68, 2075–2086. 17. Kristiansen, K. (2004) Molecular mechanisms of ligand binding, signaling, and regulation within the superfamily of G-protein-coupled receptors: molecular modeling and mutagenesis approaches to receptor structure and function. Pharmacol. Ther., 103, 21–80. A comprehensive review of GPCR mutagenesis before 2004. 18. Lu, Z.L., Saldanha, J.W. and Hulme, E.C. (2002) Seven-transmembrane receptors: crystals clarify. Trends Pharmacol. Sci., 23, 140–146. 19. Conner, A.C., Simms, J., Conner, M.T. et al. (2006) Diverse functional motifs within the three intracellular loops of the CGRP1 receptor. Biochemistry, 45, 12976–12985. 20. Conner, A.C., Simms, J., Howitt, S.G. et al. (2006) The second intracellular loop of the calcitonin gene-related peptide receptor provides molecular determinants for signal transduction and cell surface expression. J. Biol. Chem., 281, 1644–1651. Use of QuikChange mutagenesis for an alanine scan analysis of a GPCR. 21. Conner, M., Hawtin, S.R., Simms, J. et al. (2007) Systematic analysis of the entire second extracellular loop of the V1a vasopressin receptor: key residues, conserved throughout a G-protein-coupled receptor family, identified. J. Biol. Chem., 282, 17405–17412.

288


22. Hubbell, W.L., Altenbach, C., Hubbell, C.M. and Khorana, H.G. (2003) Rhodopsin structure, dynamics, and activation: a perspective from crystallography, site-directed spin labeling, sulfhydryl reactivity, and disulfide cross-linking. Adv. Protein Chem., 63, 243–290. 23. Han, S.J., Hamdan, F.F., Kim, S.K. et al. (2005) Pronounced conformational changes following agonist activation of the M3 muscarinic acetylcholine receptor. J. Biol. Chem., 280, 24870–24879. 24. Sheikh, S.P., Vilardarga, J.P., Baranski, T.J. et al. (1999) Similar structures and shared switch mechanisms of the β2 -adrenoceptor and the parathyroid hormone receptor. Zn(II) bridges between helices III and VI block activation. J. Biol. Chem., 274, 17033–17041. 25. Sheikh, S.P., Zvyaga, T.A., Lichtarge, O. et al. (1996) Rhodopsin activation blocked by metal-ion-binding sites linking transmembrane helices C and F. Nature, 383, 347–350. 26. Shi, L. and Javitch, J.A. (2006) A role for information collection, management, and integration in structure–function studies of G-protein coupled receptors. Curr. Pharm. Des., 12, 1771–1783.

Index

Numbers in italics refer to Protocols A2A adenosine receptors 86, 134–5, 137–8, 144 ab initio modelling 263, 270 acetoxymethyl (AM) esters 41 adenine nucleotides 89 adenosine 14, 86, 134–5, 137–8, 144, 187 adenosine triphosphate (ATP) 33–4 adenylyl cyclase (A) 32–4, 49, 65–6 adherent mammalian cells 8–9 Aequorea victoria 114 affinity chromatography 102, 107 affinity labelling 230 agonists 3–4, 54–5, 61–4, 197, 277 binding assays 5–6, 13–14, 17, 19 binding curve 54 BRET 111, 116, 120 disulfide cross-linking 148, 152–4 FCS 170, 183, 187 FRET 135–6, 143–4 full 49, 54, 66, 134–5, 188 ligand efficacy 53–5, 60–1, 64–7 partial 50, 54, 64, 135 partial inverse 148 response curve 54 SCAM 229–31, 233, 240–1, 245–6 second messenger assays 49–50 trafficking 77, 80, 216–17, 220–1 see also inverse agonists alanine 237, 281–3, 286 scans 281–2 alcohol oxidase (AOX 1) 87, 94–5

alignment of target sequence 252–4, 254–9, 259, 269–70 alkylation 49 allosteric ligands 4–5 allosteric modulator 17–19, 19–21, 25–6, 26–7 alpha2A (α2A ) adrenergic receptor 134–5, 137, 144 AlphaScreen 34–5, 35–8, 47, 47–9 amine 183 amino acids 71, 104, 207, 247, 276, 286 disulfide cross-linking 149, 151–2 FRET 134, 136–8 homology modelling 253, 263 ammonium chloride 79 amplification 89–92, 106 analytical ultracentrifugation (AUC) 104–5 angiotensin 123 anion-exchange chromatography 38 antagonists 2–4, 17, 66, 153, 170, 286 SCAM 229–31, 233, 237–8, 240–1, 246 antibiotics 120–1 anti-receptor antibodies 197–200, 200–1, 202, 203–4 Arabidopsis thaliana 100 aripiprazole 54, 64–5 arrestins 133, 135 ascorbic acid 6 asparagines 237 aspartate 239 association rate 6–7, 22, 23–4 constant 1–2, 7, 22 atropine 10, 12


290 autocorrelation analysis 171 FCS 169–72, 178, 181–2, 187–8, 191 autocorrelation curves 176–8, 182–4, 189 autofluorescence 116, 178 FCS 177–8, 182–4, 189–90 bacteria 85, 92, 139, 148, 163, 200 BRET 116, 119 bathorhodopsin 134 beta1 (β1 ) adrenergic receptor 135, 137, 144 beta2 (β2 ) adrenergic receptor 71, 112, 277 disulfide cross-linking 148, 153–4 FRET 134–5 homology modelling 251–2, 254 SCAM 229, 233–4, 236, 240–1, 246 beta3 (β3 ) adrenergic receptor 181 beta (β) arrestins 112, 116, 123 beta2 (β2 ) bradykinin receptor 143–4 biarsenical fluorophores 138 biochemistry 114, 147, 207–9, 209–13, 276 bioinformatics 281 bioluminescence resonance energy transfer (BRET) 111–24, 124-7, 127–8 controls 123–4 FCS 169 mutagenesis 286 principles 114–16 specificity 122–3 sub-methods 118, 124 biophysics 54, 147–8, 275 biotinylated receptor 201–3, 203–4 bipartite alignment method 253 bradykinin 277 buffers 5–7, 9, 74 FCS 178, 184, 186 recombinant GPCR 92, 94, 102, 105, 107 buprenorphine 54 buspirone 54 calcitonin 111 calcium 6, 32–3, 40–1 Fluo-4 assay 41–4 trafficking 71, 80 cAMP (cyclic adenosine monophosphate) 32–5, 35–8, 44, 54, 120 cAMP response element binding protein (CREB) 34 cannabinoid 64, 71 carazolol 148 catecholamine 6, 247

INDEX

cDNA 117, 119, 120–1, 128, 198 cell culture and transfection 154–6 cell density 49 cellular labelling 138–40 central nervous system (CNS) 69 centrifugation 7, 97 cerebella granule neurones 204 Cerulean 135, 143 chaperons 86, 111 chemiluminescence 103 Cheng–Prusoff equation 3, 13 chimeras 275, 277–8, 278–80, 281, 283, 286 Chinese hamster ovary (CHO) cells 177, 181, 187 phosphorylation 199, 202, 208 cholecystokinin receptor type A (CCKAR) 73 chromophore 103–4 chromophore–opsin complex 134 chymotrypsin 208 circadian clock proteins 113, 117 circular dichroism (CD) 86, 103–5 clonidine 135 cloning 86–92, 95, 97, 106, 139 clozapine 247 coelenterazine 114–15, 117–18, 124 co-immunoprecipitation 113 colorimetric assay 217 competition binding 3–4, 13–15, 15–16 competitive immunoassay 34 concentration–occupancy relationship 2–3 concentration–response curves 64 confocal microscopy 120, 173 FCS 169–70, 172–3, 178, 186 trafficking 70, 73–5, 82, 216 conformational changes in SCAM 245–7 constitutively active mutant (CAM) 246 continuous-wave laser excitation 172–3 critical micelle concentration (CMC) 100 C-termini 73, 198–9, 245, 277 BRET 115, 117, 119 disulfide cross-linking 149–51, 163 FCS 177, 181 FRET 134, 136–7, 143 homology modelling 253–4, 264 Cu–Phen 150–2, 162–3 cyan fluorescent protein (CFP) 136–7, 141 cyclic adenosine monophosphate 54, 120 cyclic adenosine monophosphate (cAMP) 32–5, 35–8, 44, 54, 120 Cys residues 148–9, 151–4, 157, 162–4, 233–4

INDEX

291

database modelling 263 deactivation 112 DeepBlueC 118 DeepView 254–9, 270 degradation 198, 207–8, 221 density (concentration) of receptors 1–3, 7 desensitization 112, 215, 275 detergents 100–1, 113–14 DFIRE 263–4, 264–5, 268 diacylglycerol (DAG) 32, 38, 41, 148 diffusion 169–91 coeffiecients 170–1 digestion of factor Xa 158, 159–60, 162 dimers 164, 191 dissociation constant 1–2, 7, 22, 26, 54 binding kinetics 22, 24–7 distance-scaled, finite ideal-gas reference (DFIRE) 263–4, 264–6, 268 disulfide cross-linking 147–58, 158-9, 159–64, 282 DNA sequencing 234 dopamine 6, 64–6, 135 SCAM 229, 232–4, 237, 244–7 dose-response curves 67, 120 drugs and medication 31–2, 53–4, 65–6, 147, 190, 251

enhanced green fluorescent protein (EGFP) 114–15, 119 enhanced yellow fluorescent protein (EYFP) 135, 143 trafficking 7–3, 75–81 environment 128, 275, 286 trafficking 69–70, 72 enzyme-linked immunosorbent assay (ELISA) 44, 120–1 trafficking 215–27 epinephrine 6 epitope tagging 49, 116, 197–9, 253 disulfide cross-linking 149–51 SCAM 238, 247 trafficking 216–17, 220–1, 223, 226 equilibrium binding experiments 17–19 equilibrium dissociation constant 2, 5, 9, 11, 13, 17–18, 22 Escherichia coli 86, 92, 285 ethanedithiol (EDT) 140 ethanol 181 ethylenediaminetetra acetate (EDTA) 6, 9, 13 ethyleneglycoltetra acetate (EGTA) 6, 9, 13 excitation maxima 135, 177–8, 183 expression vectors 87–9 extended BRET (eBRET) 118, 128 external restraints on homology modelling 261–2 extracellular receptors 79–80, 81 extracellular signal-related kinases 1 and 2 (ERK 1/2) 32–3, 44 phosphorylated 44, 44–7, 47, 47–9

Edman degradation 198, 207–8 efficacy 53–67 measurement 53–4 electrochemiluminescence 47 electron microscopy 103 electrophysiology assays 64–5 electroporation 92, 94 electrostatic potential 232–3, 245 Emerald 135 emission maxima 135, 177, 183 endocytic markers 223 endocytosis 71, 215 endogenous Cys 233–4 endosomes 223 EnduRen 118, 128 engineered cysteine 229–34, 238, 245

Family A 5, 253, 277, 282 Family B 253, 277, 282 Family C 6, 253 Fed-batch fermentation 97 FLAG 120, 198, 217, 221 Fluo-3 41 Fluo-4 41, 41–4 fluorescein 223 fluorescein-based arsenical hairpin binder (FlAsH) 134–40, 140–1, 177 cellular labelling 138–40, 140–1 fluorescein isothiocyanate (FITC) 223 fluorescence 47, 105, 177, 246, 259 BRET 116–17, 121–2 disulfide cross-linking 148, 163 trafficking 69–76, 79–81, 216, 223 fluorescence-activated cell sorting (FACS) 122

cysteine 6, 134, 138, 177, 253, 259, 282–3 engineered 229–34, 238, 245 SCAM 229–48 substitution 282 cytosol 32, 40, 120 FCS 178, 186, 188

292 fluorescence correlation spectroscopy (FCS) 169–84, 184–5, 185–91 calibration of microscope 174–5, 175-6, 176–7 equipment 172–4 principles 170–2 upper cell membrane 179–80 fluorescence polarization assays 34 fluorescence recovery after photobleaching (FRAP) 169, 190 fluorescence resonance energy transfer (FRET) 113–14, 169, 286 compared with BRET 116–17 fluorescence lifetime imaging microscopy (FLIM) 116 intramolecular 133–44 ligand mediated changes 141–2, 142–3, 143 trafficking 73, 75–6 fluorochromes 223 fluorophores biarsenical 138 BRET 113, 115, 117–20, 124 FCS 170, 172, 174, 176–7, 182–3, 186, 190 FRET 134–8 good insertion sites 136–7 trafficking 70, 75, 227 formaldehyde 87 forskolin 33–4, 49 Förster distance 116 Fourier transform infrared spectroscopy (FTIR) 105 FRET fluorescence lifetime imaging microscopy (FRET-FLIM) 116 full agonists 49, 54, 66, 134–5, 188 functional analysis 85–107 Fura-2 41 fusion proteins 120–1 Gα subunit 32–3 Gβγ subunits 32–3 G protein-coupled receptor kinase (GPK) 197 glutamate 234, 237, 253, 277 trafficking 70, 71, 77, 80–1 glutathione-S -transferase (GST) 89, 113, 200, 202 glycine 237, 282 glycosylation 86, 151 Golgi export 217 gondotrophin 123

INDEX

green fluorescent protein (GFP) 89, 119 FCS 170, 179–80, 181, 188 FRET 134–6 trafficking 71–3, 75–81, 216 guanine nucleotides 3–4, 14, 88 guanosine 5 -O-(3-thiotriphosphate) ([35 S] GTPγ S) 3, 6, 54–5, 55–64, 64–7 guanosine diphosphate (GDP) 3, 32, 54–5 guanosine triphosphate (GTP) 3, 32, 55, 183 haemagglutinin (HA) 120, 149, 198–9, 216–17 haloperidol 65 harvesting cells 234, 235–6 HEPES-based buffers 5, 9 heterodimers 275 heterologous cells 234 high-performance liquid chromatography (HPLC) 174, 178, 183 Hill coefficient 2–4, 14–15, 67 Hill–Langmuir binding isotherm 2 Hill–Langmuir occupancy equation 10 histidine 70, 97, 237, 282–3 homodimers 275 homology modelling 251–4, 254–9, 259–60, 260–2, 263–4, 264–8, 269–70 automated methods 269–70 horseradish peroxidise 34, 203 human embryonic kidney (HEK) cells 177, 217, 235, 246 trafficking 73, 75–6, 79–81 human genome 113, 251 hydrogen peroxide 87, 163 image correlation spectroscopy 190 imidazole 102 immobilized metal affinity chromatography (IMAC) 38, 102, 107 immunoblotting 97, 103 immunocytochemistry 198 immunofluorescence 217, 222–3, 226 immunoprecipitation 197–204, 208 incubation time 6–7 infrared fluorescence 47 initiation codon 88–9 inositol phosphates (IP) 32–3, 38, 120 accumulation assay 39–40 inositol triphosphates (IP3 ) 33, 38, 39–40, 41, 148 insect cells 85–6 insulin 80

293

INDEX

interferogram 105 internalization 112, 117, 197 trafficking 215–18, 220–1, 223, 224–6 intracellular signalling 32 inverse agonists 5, 49, 111, 135, 153–4 FCS 183, 188 ligand efficacy 53, 64–5 ionized thiolate 230–1 ionotropic receptors 73 isoleucine 237 isomerization 133, 246 isoproterenol 134, 233, 246 isotopic dilution 25–6 isotopic labelling 106 jellyfish 114 kinases 112, 133 kinetic radioligand binding assays 22, 22–5, 25–6, 26–7 knots in homology structure 269 Kolmogorov–Smirnov test 79 lactose permease 234 lambda scanning 73, 75 laser scanning confocal microscopy 70 law of mass action 1–3 leucine 237 leucine-rich repeats (LRRs) 253 ligands and ligand binding 86, 97, 253, 282 allosteric 4–5 BRET 111, 117, 123 depletion 7, 10–11 design of fluorescent 183–4 efficacy 53–67 FCS 169–70, 172, 182–4, 184–5, 185–8 FRET 133, 135, 141–2, 142–3, 143 inducing structural changes 147–64 orthosteric 4–5, 17–18, 19–21, 25 protection 240–1, 241–4 SCAM 231–4, 236–7, 237–8, 238–40, 240–1, 241–4, 244–7 trafficking 73, 216, 221 ligand–receptor complex (AR) 1–3, 5, 10 ligand–receptor interactions 1–27, 183 light microscopy 70 linear dichroism (LD) 104 lipids 31, 86 live cell antibody labelling 77–8 long-term depression (LTD) 71–2

long-term potentiation (LTP) 71–2 loop predictions 263–4 loop regions 263–4, 264–8 luciferases 113, 116 luminescence 114, 122 luminophores 113, 117, 119–120 Lumio Green 134–5, 138 Lumio Red 134–5 lumirhodopsin 134 lysines 134 lysosomes 215, 223 lysotracker red 223 M1 acetylcholine receptor 143–4 magnesium 6 maltose binding protein (MBP) 89 mammalian cells 85–6, 197 adherent 8–9 BRET 116, 120–1, 124–7 mass spectrometry 207 melanocortin receptors (MC2R) 111, 119 membrane preparation 8, 8–9, 55, 154, 156–7 urea treatment 157 membrane-spanning segments 229–34, 237, 244–7 memory 69 metarhodopsin 134 methane thiosulfate (MTS) 232–3 SCAM 230–4, 238, 238–40, 240–1, 243, 243–4, 248 methane thiosulfate ethylammonium (MTSEA) 230, 233–4, 238, 241, 244–6 methane thiosulfate ethylsulfonate (MTSES) 230, 233, 238, 245 methane thiosulfate ethyltrimethylammonium (MTSET) 230, 233–4, 238, 245 methanol 87, 95 methionine 237 microscopy 70, 103, 116, 223, 224–6 see also confocal microscopy mitogen-activated protein (MAP) kinase 32–3, 44, 65–6, 112 phosphorylation 197 MODELLER 254–9, 259–60, 260–2, 262, 269 molecular iodine 150–2, 163 monensin 75 monoclonal antibody 199 monomeric receptors 191 moxonidine 135 muscarinic acetylcholine 64

294 muscarinic receptor (mAChR) 151–4, 276 disulfide cross-linking 148–54, 164 phosphorylation 197–200, 202, 204, 208 mutagenesis 138, 275–8, 278–80, 280–3, 283–5, 285–6 disulfide cross-linking 148, 152–4 point mutations 281–3 random (saturation) 276 SCAM 230, 232, 234, 236–8, 238–40, 240–1, 247–8 site directed 275, 283–5 targeted 276–8 myc 120, 217 nigericin 75 nitrocellulose 103 nonnative conformations 162 nonnative loops 263 nonspecific binding 9–11 noradrenaline 134–5 norepinephrine 6, 135 normalized binding 17–18 norphenylephrine 135 N-termini 136, 177, 277 BRET 117, 119 disulfide cross-linking 149–51, 154 homology modelling 253–4, 263 large domains 253 phosphorylation 198–200 SCAM 245, 247 trafficking 71, 73, 76, 216–17 nuclear magnetic resonance (NMR) spectroscopy 105–6, 259 mutagenesis 275, 286 octopamine 135 opiates 54, 64 [32 P] orthophosphate 198–9, 204, 204–7, 207–9, 209–13 orthosteric ligands 4–5, 17–18, 19–21, 25 overexpression 49, 121–3, 247 oxymetazoline 135 palmitoylation 112 parathyroid hormone 134–5, 143, 277 partial agonists 50, 54, 64, 135 partial inverse agonists 148 peptide mimetics 277 pH 5, 71–2 trafficking 69–81, 221, 226 pharmacophore 183

INDEX

phenol red 177–8, 190 phenylalanine 237 pHluorins 69–72 phosphatise 216–17 phosphoamino acid (PAA) 198, 207–8 phosphopeptides 198, 207–8 phosphorimager 208 phosphorylated ERK 1/2 44, 44–7, 47, 47–9 phosphorylation 197–200, 200–1, 201–4, 204–7, 208, 209–13, 213 BRET 112 photobleaching 117, 141 FCS 177, 181–2, 188, 190 fluorescence recovery after (FRAP) 169, 190 trafficking 70, 74, 76, 82 photometry 141–2 photon counting 169–70, 172, 174, 177 histogram analysis 191 photophysics 188 photorhodopsin 134 phototoxicity 183, 186 Pichia pastoris 86–8, 93–4, 94–5, 95–7, 98 PIP2 32, 38, 41 plasmids 86–7, 90–1, 92, 95, 199 mutagenesis 278, 282–3 PLC 32, 38 point mutations 281–3 polyacrylamide gel electrophoresis (PAGE) 198 SDS 103, 106, 198, 204, 208 polymerade chain reaction (PCR) 87, 138–9 mutagenesis 276–8, 281 polyvinylidene fluoride 103 potassium 6 pre-bleach 178, 182, 186, 190 preparation of membranes 97–8, 98–100 proline 237 protease digest 208 protein expression measurement 121–2, 128 protein kinase A (PKA) 32, 34, 44, 197 protein kinase C (PKC) 32, 38, 40, 44, 197 proteosomes 215, 223 protonated amine 245 purification 86, 100–1, 101–2, 102–3, 107 disulfide cross-linking 147–8 phosphorylation 197 Python formatting 259, 269 quantitative imaging 69–82 query sequence 251–2

295

INDEX

radioimmunoassay (RIA) 34–5 radioimmunoprecipitation assay (RIPA) 199 radiolabelling 2–3, 198, 204, 207 radioligands 1–3, 5–8, 49, 153 competition binding 13–14, 15–16 equilibrium binding experiments 17 FCS 188 measurement of kinetics 22–5, 26–7 normalized 17–18 orthosteric 4–5, 17–18, 19–21, 25 phosphorylation 202 saturation 9–11, 11–13 SCAM 241, 243–4 separating bound from free 7 trafficking 70, 215–16 RAPPER 263, 264–6, 268 ratiometric analysis 76, 76–7, 80–1, 121 receptor specific antibodies 197–201 recombinant protein analysis 86–7 recycling 217–18, 220–1, 221–2 regions of interest (ROIs) 74–5, 78, 81 Renilla luciferase (Rluc) 114–15, 117–19, 122, 124 Renilla reniformis 114 resensitization 215 resonance energy transfer (RET) 113–16, 119 resorufin-based arsenical hairpin binder (ReAsH) 134–5 restraint-based modelling 263 restriction mapping 234, 248 rhodamine 223 rhodopsin 85–6, 103, 105, 147–8, 153–4 FRET 133, 136, 138 homology modelling 251–2, 254–9, 260 mutagenesis 277, 281–2 SCAM 229, 244–6 Saccharomyces cerevisiae 86–9, 97, 106 transformation 92, 93 saturation binding assays 9–11, 11–13, 13, 122 scaffolding proteins 177, 182 scale-up 86, 97–8, 106 scanning FCS 190 Schild plot 17 scintillation proximity assay (SPA) 38 screening 86, 95–7 sea pansy 114 second extracellular loop 245 second messengers 31–50, 54, 120, 148, 197 secondary structure 244–5

secretin 277 sedimentation equilibration 105 sedimentation velocity techniques 105 sequestration 215 serine 6, 112, 208, 234, 237 serotonin 86, 234 sigmoidal dose–response curve 67 signal peptides 89 signal-regulated kinase 65–6 signal sequence addition 89, 90 signal-to-noise ratio (S:N ) 49, 116–17 FCS 174–5, 178, 182, 190 signal transduction pathways 31, 33, 49 signalling pathways 112, 197 single photon counting 169–70, 172, 174, 177 single transmembrane proteins 111 sodium 5–6, 13 sodium dodecyl sulphate (SDS) 198 PAGE 103, 106, 198, 204, 208 sodium phosphate 102 solubilization 86, 100–1, 101–2, 106–7, 147, 159–60 phosphorylation 202, 204 specific binding 9–10 spectroscopy 86, 133, 286 spot bleaching 181–2, 186, 190 stereochemistry 259, 270 steric block 232–3 sterols 86 stoichiometry 104, 122 stop codon 119, 277 Stratagene QuikChange 282–3 streptavidin 201 subcellular fractionation 70 substituted cysteine accessibility method (SCAM) 229–48 substitution matrices 253 substrates 114–15, 117–18, 124 sulfhydryl reagents 229–32, 233, 241, 245, 282 SureFire 47, 47–9 surface-expressed receptors 71–2, 76–7, 77–8, 78–80, 217–18 surface receptor loss 218–20 synaptic transmission 69, 71, 80 synaptobrevin 80 synaptotagmin 80 tag addition 89, 90 tagging proteins 117, 119–24

296 target sequence 252–4, 259, 269–70 aligning 254–9, 269–70 TC-FlAsH 134, 138 TC-ReAsH 134 temperature 5–7, 91, 128 FCS 178, 187 trafficking 220–1, 223 templates for homology modelling 252, 263, 269–70 alignment 252–3, 254–9, 269–70 selection 252–3, 259 ternary complex model (TCM) 4–5 thiol 183 thioredoxin (TRX) 89 third intracellular loop 200, 202 disulfide cross-linking 149, 151, 163 FRET 134, 136–8, 143–4 threonine 112, 208, 237 thrombin 70 thyroid stimulating hormone receptor (TSHR) 253 time-resolved FRET (TR-FRET) 116 TINKER 264, 264–7 titration assays 122 total binding (TB) 9–11 trace metals 5–6 trafficking 215–18, 218–20, 220–1, 221–2, 222–4, 224–6, 226–7 BRET 117, 128 phosphorylation 197, 199 quantitative imaging 69–82 transfection 49, 154–6, 199, 204, 235 BRET 120–2, 128 disulfide cross-linking 154–6, 162, 164 FCS 178, 181, 186 FRET 140 trafficking 217, 220, 226–7 transferring 223 transformation 92, 92–4, 94–5, 97 transient transfection 49 transmembrane (TM) domains 252–4, 263 transmitter release 71

INDEX

tryptophan 237 tyrosine 208, 237 ubiquitin 112, 122 ultra-violet 41, 183 urea treatment 157 vacuum filtration 7 valine 237 vasopressin 112, 199, 254–9, 260–2, 277 verification 252, 259 visible-light excitation 41 western blotting 44, 44–7, 120, 277 disulfide cross-linking 150–2, 158, 160–2 phosphorylation 198, 201 whole-cell patch clamp methods 77 wild-type proteins 4, 120 SCAM 231–3, 236, 246 trafficking 72–3 xenon 104 X-ray crystallography 105 yeast 85–107 choice 86–7 preparation of membranes 98–100 transformation 92–4 yeast-two-hybrid assays 113 yellow fluorescent protein (YFP) 71–3, 115, 188 FRET 136–7, 141–2, 144 YFpH-GluR2 73, 77, 77–8, 78–9, 81 YFpH fusion proteins 72–3, 73–4, 74–7, 77–8 spectral characterization 73–4 yohimbine 135 zeocin 94–5 zinc 282 zwitterionic molecules 100

G Protein-Coupled Receptors: Essential Methods

G protein-coupled receptors in drug discovery

G Protein-Coupled Receptors in Drug Discovery

Ligand Design for G Protein-coupled Receptors

G Protein-Coupled Receptors in Drug Discovery

Methods in Enzymology: Nuclear Receptors

G Proteins (Methods in Neurosciences)

Essential Demographic Methods

Essential numerical computer methods

The Chemokine Receptors (The Receptors)

Immune Receptors (Methods in Molecular Biology, v748)

The Cannabinoid Receptors (The Receptors)

The Dopamine Receptors (The Receptors)

Toll-Like Receptors: Methods and Protocols

Toll-Like Receptors Methods and Protocols

Thyroid Hormone Receptors. Methods and Protocols

Nuclear Receptors, Volume 364 (Methods in Enzymology)

G Protein-Coupled Receptors: Structure, Signaling, and Physiology

The Cannabinoid Receptors (The Receptors)

The Chemokine Receptors (The Receptors)

The Melanocortin Receptors (The Receptors)

Rhodopsin and G-Protein Linked Receptors, Part A,

Methods for the Discovery and Characterization of G Protein-Coupled Receptors (Neuromethods 60)

Ligand Design for G Protein-coupled Receptors (Methods and Principles in Medicinal Chemistry)

The G Protein-Coupled Receptors Handbook (Contemporary Clinical Neuroscience)

G

PAF Receptors

Essential mathematical methods for physicists

Animal Cell Culture: Essential Methods

Plant Cell Culture: Essential Methods

Cell Biology Assays: Essential Methods

G Protein-Coupled Receptors: Essential Methods

G protein-coupled receptors in drug discovery

G Protein-Coupled Receptors in Drug Discovery

Ligand Design for G Protein-coupled Receptors

G Protein-Coupled Receptors in Drug Discovery

Methods in Enzymology: Nuclear Receptors

G Proteins (Methods in Neurosciences)

Essential Demographic Methods

Essential numerical computer methods

The Chemokine Receptors (The Receptors)

Immune Receptors (Methods in Molecular Biology, v748)

The Cannabinoid Receptors (The Receptors)

The Dopamine Receptors (The Receptors)

Toll-Like Receptors: Methods and Protocols

Toll-Like Receptors Methods and Protocols

Thyroid Hormone Receptors. Methods and Protocols

Nuclear Receptors, Volume 364 (Methods in Enzymology)

G Protein-Coupled Receptors: Structure, Signaling, and Physiology

The Cannabinoid Receptors (The Receptors)

The Chemokine Receptors (The Receptors)

The Melanocortin Receptors (The Receptors)

Rhodopsin and G-Protein Linked Receptors, Part A,

Methods for the Discovery and Characterization of G Protein-Coupled Receptors (Neuromethods 60)

Ligand Design for G Protein-coupled Receptors (Methods and Principles in Medicinal Chemistry)

The G Protein-Coupled Receptors Handbook (Contemporary Clinical Neuroscience)

G

PAF Receptors

Essential mathematical methods for physicists

Animal Cell Culture: Essential Methods

Plant Cell Culture: Essential Methods

Cell Biology Assays: Essential Methods

Recommend Documents