Fitness Landscapes, Error Thresholds, and Cofactors in Aptamer Evolution

The Aptamer Handbook

Edited by

Sven Klussmann

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The Aptamer Handbook

Functional Oligonucleotides and Their Applications

Edited bySven Klussmann

The Editor

Dr. Sven KlussmannNoxxon Pharma AGMax-Dohrn-Str. 8–1010589 BerlinGermany

All books published by Wiley-VCH are care-fully produced. Nevertheless, authors, editors,and publisher do not warrant the informationcontained in these books, including thisbook, to be free of errors. Readers are advisedto keep in mind that statements, data, illus-trations, procedural details or other itemsmay inadvertently be inaccurate.

Library of Congress Card No.: Applied for

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

Bibliographic information published byDie Deutsche BibliothekDie Deutsche Bibliothek lists this publicationin the Deutsche Nationalbibliografie;detailed bibliographic data is available in theInternet at <http://dnb.ddb.de>.

c 2006 WILEY-VCH Verlag GmbH & Co.KGaA, Weinheim

All rights reserved (including those of trans-lation into other languages). No part of thisbook may be reproduced in any form – byphotoprinting, microfilm, or any other means– nor transmitted or translated into amachine language without written permis-sion from the publishers. Registered names,trademarks, etc. used in this book, even whennot specifically marked as such, are not to beconsidered unprotected by law.

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Typesetting Hagedorn Kommunikation,ViernheimPrinting Betz Druck GmbH, DarmstadtBookbinding Litges & Dopf BuchbindereiGmbH, HeppenheimCover Christian Mihm, Berlin

ISBN-13: 978-3-527-31059-3ISBN-10: 3-527-31059-2

Contents

Part 1 History and Theoretical Background

1 In Vitro Selection of Functional Oligonucleotides and the Originsof Biochemical Activity 3

James M. Carothers and Jack W. Szostak

1.1 Introduction 3

1.2 A Brief History of In Vitro Selection 4

1.3 Lessons from the Aptamers, Ribozymes, Deoxyribozymes Generatedby In Vitro Selection 6

1.4 Synthetic Approaches to Understanding the Natural Originsof Function 14

1.5 Recent Technological Developments and Future Directions 17

1.6 Conclusion 22

Acknowledgments 23

References 23

2 Mathematical Models on RNA Evolution, Simulations In Silico,and Concepts for In Vitro Selection 29

Peter Schuster

2.1 From Early Experiments and Theories to the Concept ofNeutral Networks 29

2.1.1 Evolution in the Test Tube 29

2.1.2 Kinetic Theory of the Evolution of Molecules 30

2.1.3 Sequence Space and Shape Space 31

2.2 RNA Structures, Thermodynamics and Kinetic Folding 33

2.2.1 Secondary Structures of Minimum Free Energies 33

2.2.2 Inverse Folding 34

2.2.3 Suboptimal Conformations and Kinetic Folding 36

2.2.4 Cofolding and DNA Parameters 38

2.3 Neutral Networks and In Silico Evolution of Molecules 39

2.3.1 Neutral Networks in Sequence Space 39

2.3.2 RNA Evolution In Silico 42

2.3.3 Lessons from Evolution In Silico 45

VContents

The Aptamer Handbook. Edited by S. KlussmannCopyright c 2006 WILEY-VCH Verlag GmbH & Co. KGaA, WeinheimISBN 3-527-31059-2

VI Contents

2.4 Designed and Natural RNA Switches 47

2.5 Outlook on Future Problems in RNA Design 49

Acknowledgments 50

References 50

3 Fitness Landscapes, Error Thresholds, and Cofactorsin Aptamer Evolution 54

�dam Kun, Marie-Christine Maurel, Mauro Santos, and E�rs Szathm�ry

3.1 Introduction 54

3.2 Functionality Landscapes Inferred from Examples 57

3.2.1 Fitness Landscape 57

3.2.2 Damage Selection Experiments with Ribozymes 58

3.2.3 Construction of the Fitness Landscape 62

3.2.3.1 Compatible Structure 63

3.2.3.2 Mispairs 63

3.2.3.3 Critical Sites 63

3.2.3.4 Predicted Structure 64

3.2.4 Case Study: The Fitness Landscape of the Neurospora VS Ribozyme 64

3.2.4.1 Compatible Structure of the VS Ribozyme 64

3.2.4.2 Allowed Mispairs in the VS Ribozyme 66

3.2.4.3 Critical Sites in the VS Ribozyme 66

3.2.4.4 Predicted Structure for the VS Ribozyme 67

3.2.4.5 Properties of the Estimated Fitness Landscape for the VS Ribozyme 68

3.3 Error Thresholds Inferred from Functional Landscapes:The “Realistic” Error Threshold of the Neurospora VS Ribozyme 68

3.4 Looking for Catalytic Partners: Cofactors and Aptamers 71

3.4.1 Co-ribozymes (cofactor-assisted ribozymes) 74

3.4.2 Aptazymes 79

3.5 The Use of Coenzymes: From the RNA World to the Protein Worldvia Translation and the Genetic Code 79

3.6 Outlook 84

Acknowledgments 85

References 85

Part 2 In Vitro Selection of Target-binding Oligonucleotides

4 Aptamers to Small Molecules 95

Heiko Fickert, Iris G. Fransson, and Ulrich Hahn

4.1 Introduction 95

4.2 Aptamers to Nucleotides/Nucleosides/Nucleobases 95

4.3 Aptamers to Cofactors 97

4.4 Aptamers to Amino Acids 99

4.5 Aptamers to Carbohydrates 102

4.6 Aptamers to Natural Products 105

4.7 Aptamers to Organic or Fluorescent Dyes 109

4.8 The Chimeric Approach for Aptamer Selection 112

4.9 Conclusion 112

Acknowledgments 113

References 113

5 Aptamers to Antibiotics 116

Christina Lorenz and Ren�e Schroeder

5.1 Introduction 116

5.2 RNA-binding Antibiotics 119

5.3 Aptamers to Tetracyclines 119

5.4 Aptamers to Streptomycin 122

5.5 Aptamers to Aminoglycosides 124

5.6 Aptamers to Chloramphenicol 125

5.7 Aptamers to the Peptide Antibiotic Viomycin 126

5.7.1 The Peptide Antibiotic Viomycin as a Primordial Lead Molecule 127

5.8 What Have We Learned From the Antibiotic-binding Aptamers? 128

Acknowledgments 128

References 128

6 Aptamers to Proteins 131

Shahid M. Nimjee, Christopher P. Rusconi, and Bruce A. Sullenger


6.2 Properties of Aptamers as Protein Inhibitors 134

6.3 Cytokines/Growth Factors 140

6.3.1 Vascular Endothelial Growth Factor (VEGF) 140

6.3.2 Human Interferon g 142

6.3.3 Angiopoietin-2 142

6.3.4 Basic Fibroblastic Growth Factor 142

6.3.5 Platelet-derived Growth Factor 143

6.4 Nucleic Acid Binding Proteins 144

6.4.1 HIV-1 Tat 144

6.4.2 HIV-1 Rev 145

6.4.3 HIV Reverse Transcriptase 145

6.4.4 Transcription Factor E2F 145

6.4.5 Nuclear Factor Kappa B 146

6.5 Serine Proteases 147

6.5.1 Hepatitis C Virus–NS3 (HSV–NS3) 147

6.5.2 Human Neutrophil Elastase 148

6.5.3 Thrombin 149

6.5.4 Factor VIIa 151

6.5.5 Factor IXa 151

6.6 Antibodies/Immunoglobulins 152

6.6.1 Anti-insulin Receptor Antibody MA20 152

6.6.2 Monoclonal Antibody (MAb) to Acetylcholine Receptor 153

VIIContents

6.6.3 Immunoglobulin E 153

6.6.4 Cytotoxic T Cell Antigen 4 154

6.7 Cell Surface Receptor/Cell Adhesion Molecules 155

6.7.1 P-Selectin 155

6.7.2 L-Selectin 155

6.7.3 Prostate-specific Membrane Antigen 156

6.7.4 Trypanosoma cruzi 156

6.8 Complement Proteins – Human Complement C5 157

6.9 Extracellular Membrane Protein – Tenascin-C 157

6.10 Lipoproteins – Human Non-pancreatic Secretory Phospholipase A2 157

6.11 Prion Proteins – Prion Protein PrPSc 158

6.12 Peptides 158

6.12.1 Ghrelin 158

6.12.2 Neuropeptide Calcitonin Gene-related Peptide 1 159

6.12.3 Gonadotropin-releasing Hormone 159

6.12.4 Neuropeptide Nociceptin/Orphanin FQ 160

6.13 Conclusion 160

References 161

7 Aptamers to Nucleic Acid Structures 167

Jean-Jacques Toulm�, Fabien Darfeuille, Carmelo Di Primo, and Eric Dausse


7.2 Targeting Double-stranded Nucleic Acids 168

7.3 Loop–Loop Interactions 170

7.3.1 RNA–RNA Kissing Complexes 170

7.3.2 DNA–RNA Kissing Complexes 173

7.3.3 Double RNA–RNA Kissing Loops 176

7.3.4 Apical Loop–Internal Loop Interactions 178

7.4 Chemically Modified Aptamers Recognizing RNA Targets 180

7.5 Biological Properties of Aptamers Targeted to Nucleic Acids 184

7.6 Conclusion 185

Acknowledgments 187

References 187

8 Riboswitches: Natural Metabolite-binding RNAs ControllingGene Expression 191

Adam Roth, R�diger Welz, and Ronald R. Breaker


8.2 Genetic Control by Riboswitches 192

8.3 Aptamer Domains of Riboswitches 194

8.4 Natural Aptamers Specific for Guanine and Adenine 196

8.5 High-resolution Aptamer Structures 200

8.6 The Glycine Riboswitch 201

References 205

VIII Contents

Part 3 In Vitro Selection of Short, Catalytically Active Oligonucleotides

9 Catalytically Active RNA Molecules: Tools in Organic Chemistry 211

Barbara-Sylvia Weigand, Andreas Zerressen, J�rg C. Schlatterer,

Mark Helm, and Andres J�schke


9.2 Catalytic Biopolymers 212

9.3 De Novo Creation of Ribozymes 213

9.4 The Catalytic Spectrum of Ribozymes 215

9.5 Summary and Outlook 224

References 224

10 Deoxyribozymes: Catalytically Active DNA Molecules 228

Kenny Schlosser, Simon A. McManus, and Yingfu Li

10.1 Initial Demonstration of DNA’s Catalytic Ability 228

10.1.1 DNAzymes that Cleave RNA 229

10.1.2 Deoxyribozymes that Join DNA 231

10.1.3 Catalytic DNA for Porphyrin Metallation 232

10.2 A Tale of Two Deoxyribozymes that Cleave RNA 233

10.2.1 In Vitro Selection and Secondary Structures of 10-23 and 8-17 234

10.2.2 10-23 as Gene Therapeutics 235

10.2.3 Other Uses of 10-23 239

10.2.4 Utilities of 8-17 240

10.2.5 Recurrence of 8-17 from Several In Vitro Selection Experiments 241

10.3 Other Deoxyribozymes 245

10.3.1 Other RNA-cleaving Deoxyribozymes 245

10.3.2 RNA-ligating Deoxyribozymes 246

10.3.3 DNA-cleaving DNA Enzymes 248

10.3.4 DNA-modifying DNA Enzymes 249

10.3.5 DNA Enzymes that Catalyze the Formation ofPhosphorothioester Bond 252

10.3.6 Deoxyribozymes for Thymine Dimer Repair 253

10.3.7 DNA Enzymes with Foreign Functionalities 254

10.4 Outlook 256

References 257

Part 4 Applications and Outlook

11 In Vivo and In Vitro Target Validation with Nucleic Acid Aptamersas Pharmacological Probes 265

P. Shannon Pendergrast and David M. Epstein


11.2 Target Validation with Aptamers as Pharmacological Probes 265

11.3 Limitations of Target Validation by Gene or mRNA Knockout 268

IXContents

11.4 Target Validation Using Nucleic Acid Aptamers 270

11.4.1 In Vitro Target Validation with Aptamers against Intracellular Targets 271

11.4.2 In Vivo Target Validation with Aptamers against Intracellularand Extracellular Targets 272

11.5 Summary 277

References 277

12 Intramers for Protein Function Analysis and Drug Discovery 280

Michael Famulok and G�nter Mayer


12.2 Intramers: Intracellular Aptamers 281

12.3 Aptamers as Probes for Inhibitor Screening 284

12.4 Summary 287

Acknowledgments 287

References 287

13 Aptazymes: Allosteric Ribozymes and Deoxyribozymes as Biosensors 290

Scott M. Knudsen and Andrew D. Ellington


13.1.1 Oligonucleotide-dependent Aptazymes 291

13.1.2 Activation by Non-nucleic Acid Effectors 291

13.2 Creating Aptazymes via Rational Design and In VitroSelection Methodologies 292

13.2.1 Rational Design of Aptazymes 292

13.2.2 In Vitro Aptazyme Selection 293

13.3 Effector Activation 295

13.4 Aptazyme Structural and Functional Diversity 299

13.5 Uses of Aptazymes in Biology and Biotechnology 303

13.5.1 Aptazymes as Biosensors 303

13.5.2 Aptazymes as Molecular Logic Gates 306

13.5.3 Aptazyme Arrays 306

13.5.4 Aptazyme Use In Vivo 308

Acknowledgments 309

References 309

14 Conversion of Aptamers into Small-Molecule Lead Compounds 311

Andreas Jenne


14.2 Rational Drug Design 312

14.3 Biochemical Screening 313

14.4 Summary and Outlook 320

Acknowledgments 322

References 322

X Contents

15 Aptamers as Ligands for Affinity Chromatographyand Capillary Electrophoresis Applications 324

Eric Peyrin


15.2 Aptamers as Ligands in Affinity Liquid Chromatography(and Electrochromatography) 325

15.2.1 General Principles of Affinity Chromatography 325

15.2.2 Separation/Purification of Proteins 326

15.2.3 Separation of Small Molecules 329

15.2.4 Target-specific Chiral Separation 332

15.3 Aptamers as Ligands in Affinity Capillary Electrophoresis 335

15.3.1 General Principles of Affinity Capillary Electrophoresis 335

15.3.2 Affinity Capillary Electrophoresis for Target (Protein) Quantification 336

15.4 Concluding Remarks 340

References 341

16 Aptamers for In Vivo Imaging 343

Sandra Borkowski and Ludger M. Dinkelborg

16.1 In Vivo Imaging: Modalities and Requirements 343

16.1.1 Imaging Modalities 343

16.1.2 Requirements for Imaging 345

16.2 Aptamers for In Vivo Imaging 346

16.2.1 Oligonucleotide Properties for In Vivo Applications 346

16.2.2 Comparison of Different Classes of Targeting Agents 348

16.2.3 Aptamer Targets for Imaging 349

16.3 Labeling of Aptamers 351

16.3.1 SPECT Isotopes 351

16.3.2 PET Isotopes 353

16.4 Oligonucleotides in SPECT and PET Imaging 354

16.4.1 Non-targeting Aptamers 354

16.4.2 Antisense Oligonucleotides 356

16.4.3 Targeting Aptamers 359

16.5 Outlook 361

References 361

17 Properties of Therapeutic Aptamers 363

Sharon T. Cload, Thomas G. McCauley, Anthony D. Keefe, Judith M. Healy,

and Charles Wilson


17.2 Aptamer Targets 363

17.2.1 Cell Surface Targets 366

17.2.2 Intracellular Targets 367

17.2.3 Extracellular Targets 368

17.3 Aptamer Binding Characteristics 370

17.3.1 Aptamer Affinity 370

XIContents

17.3.2 Aptamer Specificity 372

17.3.3 Aptamer Binding Kinetics 373

17.3.4 Binding versus Function 375

17.4 Chemical Modification of Aptamers 376

17.4.1 2l-Modifications 376

17.4.2 Capping the 3l End 378

17.4.3 Capping the 5l End 379

17.4.4 Phosphate Substitutions 379

17.4.5 Base Modifications 380

17.4.6 Polyethylene Glycol 381

17.4.7 Lipid Tags 381

17.4.8 Peptide Tags 382

17.5 Routes of Administration of Aptamers 382

17.5.1 Parenteral Administration 382

17.5.2 Comparison to Biologics 383

17.6 Opportunities for Alternative Aptamer Formulations 383

17.6.1 Depot 384

17.6.2 Topical 384

17.6.3 Oral 385

17.6.4 Pulmonary 386

17.6.5 Ocular 387

17.7 Aptamer Pharmacokinetics and Biodistribution 387

17.7.1 Key Pharmacokinetic and Biodistribution Parameters 388

17.7.2 Factors Governing Pharmacokineticsand Metabolic Stability of Aptamers 389

17.7.3 Biodistribution of Aptamers 392

17.7.4 Bioanalytical Methods for Aptamer Quantification 395

17.7.5 Summary of Pharmacokinetic and BiodistributionProperties of Aptamers 396

17.8 Toxicity Profile of Aptamers 397

17.9 Immunogenicity of Aptamers 398

17.10 Aptamer Manufacture 398

17.10.1 Contributions to the Costs of Aptamer Synthesis 398

17.10.2 Manufacturing Infrastructure 399

17.10.3 Advantages of Chemical versus Biological Synthesis 399

17.11 Examples of Therapeutic Aptamers in Development 400

17.11.1 Antithrombin Aptamer ARC183 400

17.11.2 Anticomplement C5 Aptamer ARC187 401

17.11.3 Anti-L-Selectin Aptamer 402

17.11.4 Anti-PDGF-BB Aptamer ARC127 403

17.12 Future Prospects for Aptamer Therapeutics 405

References 406

XII Contents

18 Spiegelmers for Therapeutic Applications – Use of Chiral Principlesin Evolutionary Selection Techniques 417

Dirk Eulberg, Florian Jarosch, Stefan Vonhoff, and Sven Klussmann

18.1 Evolutionary Selection Techniques 417

18.2 Chirality 419

18.2.1 Discovery and Consequences of Nature’s Handedness 419

18.2.2 Mirror-Image Proteins 420

18.2.3 Mirror-Image Nucleic Acids 421

18.3 Mirror-Image Evolutionary Techniques: Selection–Reflection 422

18.3.1 d-Peptide Aptamers 424

18.3.2 Functional Mirror-Image Oligonucleotides: Spiegelmers 424

18.3.2.1 Proof of Principle 426

18.3.2.2 Catalytically Active Spiegelmers: Spiegelzymes 427

18.3.2.3 Domain Approach 428

18.3.2.4 Bioactive Spiegelmers 429

18.3.2.5 Spiegelmer Activity in Vivo 431

18.3.2.6 Pharmacological Properties of Spiegelmers 432

18.3.2.7 Production of Spiegelmers 434

18.4 Summary 437

Acknowledgments 439

References 439

19 Applications in the Clinic: The Anti-VEGF Aptamer 443

Tony Realini, Eugene W.M. Ng, and Anthony P. Adamis


19.2 Rationale for Targeting VEGF 443

19.3 VEGF and Human Disease 445

19.3.1 Cancer 445

19.3.2 Age-Related Macular Degeneration 446

19.3.3 Diabetic Retinopathy 448

19.4 The VEGF Therapeutic Dilemma 449

19.4.1 VEGF and Human Physiology 449

19.4.2 Overcoming the Dilemma 450

19.5 VEGF Inhibition 450

19.6 Enter Macugen 452

19.6.1 Preclinical Studies 452

19.6.2 Macugen Clinical Trials 453

19.7 The Future 456

References 457

XIIIContents

Epilogue A Personal Perspecitve: Aptamers after 15 Years 461

Larry Gold

The Beginning 461

The First Patent 462

Creation of NeXagen and NeXstar 462

Diagnostic Imaging 463

Aptamer Therapeutics 464

Aptamer-based Diagnostics at SomaLogic 465

Do Natural Aptamers Exist? 465

Conclusions – SELEX Lessons for Drug Discovery 466

Acknowledgments 468

References 468

Index 471

XIV Contents

Preface

Why a book about aptamers? This question may be raised by all readers who arealready familiar with aptamers, since quite a few reviews of outstanding qualityhave been published in the scientific literature. But the very same questionmay be asked by any reader who is – although scientifically interested – not famil-iar with aptamers at all. When I was asked by the publisher whether I would beinterested in editing a book on aptamers I asked myself a different question: Is itreally true that there is not a single textbook on aptamers so far – more than 15years after they had first been described? Indeed, I realized that this was the case!Although the interested reader will find more than 110 000 hits when typing theterm “aptamer” into the internet search machine Google, although hundreds andhundreds of publications are listed in the scientific databases, although many pa-tents have been filed and granted, although several companies worked and stillwork on and with aptamers, and although last but not least a viable aptamer-based drug (Macugen) has been clinically developed, entered the market, andnow helps many patients to manage the devastating disease of age-related macu-lar degeneration, to date there has been no textbook that summarizes the greatopportunities associated with aptamers.The idea of The Aptamer Handbook is to present a detailed view on the many

facets of aptamers and especially their applications. It is conceptually designedfor a very broad audience that not only comprises the pure scientific disciplinesof molecular biology, biochemistry, and chemistry. It also addresses fields thatare usually more application-oriented such as pharmacology and medicine, butit may also be helpful for managers of the pharma and biotech industries who(should) consider new and innovative technologies to be used or establishedmore broadly. The huge amount of work invested by many brilliant scientistsand the money that has been spent through either public or commercial fundingso far, has helped to create an extensive, broad, and solid basis of knowledge aboutand around aptamers that should be recognized by an increasing number ofpeople.Even though considerable preliminary work had been carried out earlier, the

starting shot was set in 1990 when in vitro evolutionary selection techniqueswere used in the groups of Joyce, Gold, and Szostak to identify unique RNA-based structures that displayed new or altered functionalities: binding to a target

XVPreface


molecule and enzymatic activity, respectively. In the Nature publication of Elling-ton and Szostak the target-binding RNA molecules were named “aptamers”,while in their Science publication Tuerck and Gold termed the process used toidentify aptamers “SELEX” (Systematic Evolution of Ligands by EXponential en-richment). In subsequent years aptamers were raised against almost any type oftarget imaginable: small molecules, peptides, proteins or even ribosomal and viralparticles. While many people believed that only antibodies could bind to targetswith high affinity and specificity it had to be recognized that aptamers – oligonu-cleotide structures – could do these jobs as well, sometimes even better.The first chapter by Carothers and Szostak gives a general overview of aptamers

and places their discovery into the context of discussion about the origins of lifeand the RNA world. The following two chapters provide the reader insight into (invitro) evolution and fitness landscapes from a theoretical point of view. The sec-ond part of the book comprises five chapters, each dealing with aptamers thatbind to certain types of targets such as small molecules, antibiotics, proteins,and nucleic acid structures. The last in the row introduces aptamer motifs called“riboswitches” that were evolved by nature itself. These “natural aptamers” areembedded in messenger RNAs and can directly sense small molecules and aretherefore able to serve as regulatory elements in gene control. In the smaller,third part of the book two chapters describe the catalytic functionalities of RNAand DNA oligonucleotides. Although these so-called ribozymes or deoxyribo-zymes are not classified as aptamers, they can be obtained by in vitro evolutionaryselection methods and exert their functions through the selected three-dimen-sional structures.The fourth part of the book presents an overview of the many applications of

aptamers. These range from aptamers as in vitro tools for target validation outsidethe cell and within the cell (intramers) over so-called aptazymes, which can beused as biosensors and are made of a combination of aptamers and (deoxy)ribo-zymes, to aptamers as lead structures for small molecule development, and as li-gands in affinity chromatography applications. Furthermore, these versatile mole-cules can exert their function in vivo as well. Due to their ability to bind and there-by block important disease targets, aptamers can be and are used as in vivo imag-ing agents and therapeutics. For these applications aptamers usually have to bechemically modified in order to render them biostable; aptamers that are builtfrom mirror-image nucleotides (so-called spiegelmers) already display a nativebiostability and do not need further stabilization. The last chapter introduces Ma-cugen, the antivascular endothelial growth factor aptamer that was approved bythe US regulatory authorities for the treatment of age-related macular degenera-tion in December 2004. Finally, Larry Gold, who was the first to transfer SELEXfrom an academic lab into the environment of a biotech company, summarizeshis personal view on aptamers in an epilogue.I hope that this book will help the interested reader to get a comprehensive im-

pression of the fascinating field of aptamers. The different topics were selected tolight up as many different areas of aptamer research as possible, knowing that

XVI Preface

completeness is very difficult to achieve if possible at all. Further, I hope to attractthe best and brightest to join the field to push the limits further.I am very grateful to all who made this book possible. Many thanks to the

authors for their excellent chapters, to Jerry Joyce for his superb foreword, toChristian Mihm for the cover artwork, and last but not least thank you to thestaff at Wiley-VCH, especially Frank Otmar Weinreich and Steffen Pauly, fortheir continuous support.

Sven KlussmannBerlin, December 2005

XVIIPreface

Foreword

It has been 15 years since the term “aptamer” and the acronym “SELEX” werecoined. With the field of directed molecular evolution now transitioning fromits adolescence to young adulthood, it is an appropriate time to take stock ofwhat aptamer science has to offer, both now and for the future. In this mono-graph, the first ever completely devoted to the subject of aptamers, you willfind a well-chosen set of contributions from leading investigators in the field, de-scribing the methods and applications of aptamer technology. This is not a labora-tory manual, but neither is it a collection of review articles; it is a handbook that ismeant to give you an appreciation for the principles and practice of in vitro selec-tion as applied to functional nucleic acids. Whether you already are or will be apractitioner yourself, or simply want to know what all the fuss is about, thisbook is something that you will want to attack with a highlighter pen and scratchpaper on the side. Evolution is a very powerful process, but it is surprisingly easyto carry out in a modern laboratory. You too can evolve molecules for fun andprofit.The first aptamer, although it was not referred to as such, actually was created

almost 40 years ago, before the advent of recombinant DNA technology (“B.C.,before cloning”, as Sydney Brenner likes to say). In the late 1960s, Sol Spiegel-man realized that the three fundamental processes of Darwinian evolution – am-plification, mutation, and selection – could be applied to a population of RNA mo-lecules in vitro. Amplification of RNA was achieved by employing an RNA-depen-dent RNA polymerase, the replicase protein of Qb bacteriophage. Mutation oc-curred as a result of the intrinsic error rate of the polymerase in copyingvariants of Qb genomic RNA. Selection was based on the ability of particularRNAs to serve as efficient templates for the production of complementaryRNAs and, in turn, for the production of additional copies of themselves. “(Goforth and) multiply, with the biological proviso that (you) do so as rapidly as pos-sible,” Spiegelman famously declared. The result, following multiple rounds ofselective amplification and mutation, was a population of evolved RNA moleculesthat were amplified much more efficiently by the replicase compared with theirancestors.Discussion of Spiegelman’s pioneering work usually focuses on the perhaps un-

surprising result that the evolved RNAs were truncated variants of Qb genomic

XIXForeword


RNA that, by virtue of their smaller size, could be copied more rapidly than thewild type. A more subtle point, however, is that the evolved RNAs also were se-lected to be efficient ligands for the replicase protein, which recognizes particularfeatures of RNA secondary and tertiary structure in both the positive- and nega-tive-stranded RNA. Thus the evolved RNAs were both an aptamer for the replicaseprotein and a substrate for the protein, leading to the production of progenyRNAs.One of the great advances in the history of life on Earth was the transition from

an “RNA world,” in which both genetic and functional properties resided withinRNA, to a DNA and protein world, in which genotype and phenotype were rele-gated to separate macromolecules. Another critical advance in directed molecularevolution was the development of techniques that decoupled amplification of nu-cleic acid molecules from selection based on their functional properties. Thismade it possible to select RNAs that are a ligand for any protein, for example,T4 DNA polymerase, as demonstrated by Craig Tuerk and Larry Gold. RNAscould even be selected that bound to small molecules, as shown by Andrew El-lington and Jack Szostak.In the early 1980s, following the discovery of catalytic RNA by Thomas Cech

and Sidney Altman, one wondered what it would take to coax Qb replicase to am-plify RNA molecules that included a ribozyme or some other functional motif.Fred Kramer and colleagues had shown that it was possible to sneak exogenousnucleotides into variants of Qb genomic RNA that could be amplified in vitro.Those familiar with the details of the system knew, however, that it was only amatter a time – and usually not much time – before the insert would be trimmedor spit out entirely, resulting in a more efficient amplicon. What was needed wasa general-purpose RNA amplification method that would be indifferent to the se-quence being amplified.Then came polymerase chain reaction (PCR), soon followed by reverse tran-

scriptase PCR (RT-PCR), and everything changed. A population of nucleic acidmolecules could be asked to do anything the investigator had the nerve to askthem to do: bind a target molecule, bind a target molecule but not some closelyrelated molecule, catalyze a reaction, catalyze a reaction only after binding tosome other target molecule, and so on. In retrospect, most of the early effortswere rather timid, but soon the gloves came off and it seemed that nearly every-thing was fair game. Literally, of course, the gloves were kept on a bit longer be-cause RNA molecules are highly susceptible to degradation by biological nu-cleases, limiting their potential applications. This limitation was overcome by car-rying out directed evolution with RNA analogs that are nuclease resistant, yet canbe amplified by RT-PCR. Particularly intriguing in this regard are “Spiegelmers,”which first are selected as natural RNAs that bind the enantiomer of the desiredtarget, then are prepared as the corresponding non-natural enantiomer of RNAfor binding to the actual target. These reverse aptamers are aptly named becausethey are the mirror (Spiegel) of their biological counterparts, and in recognition ofSpiegelman’s contributions to initiating the practice of in vitro Darwinian evolu-tion.

XX Foreword

Aptamer science has now reached maturity, not just as a result of its longevityand accumulated knowledge, but through its growing impact on biology andmedicine. In December 2004 the first aptamer compound was approved for clin-ical use. As discussed in the chapter by Anthony Adamis and colleagues, Macu-gen (pegaptanib) is a chemically modified RNA aptamer that binds tightly andspecifically to vascular endothelial growth factor. It has become a preferred treat-ment for the neovascular form of age-related macular degeneration. Other chap-ters describe aptamers that are being developed for various therapeutic applica-tions, medical imaging, clinical diagnostics, drug target validation, biosensor ap-plications, and process chemistry. All this and more awaits you on the pages thatfollow.Darwinian evolution in nature has provided a bounty of functional macromole-

cules. However, just as synthetic organic chemistry has taken us beyond the smallmolecules that can be harvested as natural products, directed evolution has ex-panded upon the set of macromolecules to include compounds that have been tai-lored for our own purposes. This is not intelligent design – quite the opposite infact – but in this book you will see how the vision and skill of the experimenter,combined with the power of an evolutionary search, can lead to some remarkablediscoveries.

Gerald F. JoyceDepartments of Chemistry and Molecular Biology and

The Skaggs Institute for Chemical BiologyThe Scripps Research Institute

La Jolla, California, USA

XXIForeword

XXIIIList of Contributors


List of Contributors

Anthony P. AdamisEyetech Pharmaceuticals, Inc.3 Times Square, 12th FloorNew YorkNew York 10036USA

Sandra BorkowskiResearch Laboratories of Schering AGM�llerstrasse 17813342 BerlinGermany

Ronald R. BreakerDepartment of Molecular,Cellular and Developmental BiologyYale UniversityP.O. Box 208103New HavenConnecticut 06520-8103USA

James M. CarothersDept. of Molecular Biology,and Center for Computationaland Integrative Biology 7215Simches Research CenterMassachusetts General Hospital185 Cambridge StreetBostonMassachusetts 02114USA

Sharon T. CloadArchemix Corp,300 Third StreetCambridgeMassachusetts 02139USA

Eric DausseINSERM U386European Institute of Chemistryand BiologyUniversit� Victor Segalen146, rue L�o-Saignat33076 Bordeaux cedexFrance

Fabien DarfeuilleINSERM U386European Institute of Chemistryand BiologyUniversit� Victor Segalen146, rue L�o-Saignat33076 Bordeaux cedexFrance

Ludger M. DinkelborgResearch Laboratories of Schering AGMuellerstrasse 17813342 BerlinGermany

XXIV List of Contributors

Carmelo Di PrimoINSERM U386European Institute of Chemistryand BiologyUniversit� Victor Segalen146, rue L�o-Saignat33076 Bordeaux cedexFrance

Andrew D. EllingtonUniversity of Texas at Austin2500 Speedway MBB 3.424AustinTexas 78712USA

David M. EpsteinArchemix Corp,300 Third StreetCambridgeMassachusetts 02139USA

Dirk EulbergNOXXON Pharma AGMax-Dohrn-Strasse 8–1010589 BerlinGermany

Michael FamulokRheinische Friedrich-WilhelmsUniversit�t BonnKekul�-Institut f�rOrganische Chemie und BiochemieGerhard-Domagk-Strasse 153121 BonnGermany

Heiko FickertUniversit�t HamburgMartin-Luther-King-Platz 620146 HamburgGermany

Iris G. FranssonUniversit�t HamburgMartin-Luther-King-Platz 620146 HamburgGermany

Larry GoldSomaLogic Inc.1745 38th StreetBoulderColorado 80301USA

Ulrich HahnUniversit�t HamburgMartin-Luther-King-Platz 620146 HamburgGermany

Judith M. HealyArchemix Corp,300 Third StreetCambridgeMassachusetts 02139USA

Mark HelmInstitut f�r Pharmazie undMolekulare BiotechnologieRuprecht-Karls-Universit�t HeidelbergIm Neuenheimer Feld 36469120 HeidelbergGermany

Florian JaroschNOXXON Pharma AGMax-Dohrn-Strasse 8–1010589 BerlinGermany

XXVList of Contributors

Andres J�schkeInstitut f�r Pharmazie undMolekulare BiotechnologieRuprecht-Karls-Universit�t HeidelbergIm Neuenheimer Feld 36469120 HeidelbergGermany

Andreas JenneNascaCell IP GmbHMax-Lebsche-Platz 3181377 MunichGermany

Anthony D. KeefeArchemix Corp,300 Third StreetCambridgeMassachusetts 02139USA

Sven KlussmannNOXXON Pharma AGMax-Dohrn-Strasse 8–1010589 BerlinGermany

Scott M. KnudsenUniversity of Texas at Austin2500 Speedway MBB 3.424AustinTexas 78712USA

�d�m KunCollegium BudapestInstitute for Advanced StudySzenth�roms�g u. 21014 BudapestHungaryandDepartment of Plant Taxonomyand EcologyE�tv�s UniversityP�zm�ny P�ter s�t�ny 1/c1117 BudapestHungary

Yingfu LiDepartment of Biochemistry andBiomedical Sciences andDepartment of ChemistryMcMaster University1200 Main Street WestHamiltonOntario L8N 3Z5Canada

Christina LorenzMax F Perutz LaboratoriesUniversity Departments of theVienna BiocenterDepartment of BiochemistryDr. Bohrgasse 9/51030 ViennaAustria

Marie-Christine MaurelInstitute Jacques Monod –University Paris VIBiochemistry of Evolution andMolecular Adaptability2, place Jussieu75251 Paris Cedex 05France

G�nter MayerRheinische Friedrich-WilhelmsUniversit�t BonnKekul�-Institut f�rOrganische Chemie und BiochemieGerhard-Domagk-Strasse 153121 BonnGermany

Thomas G. McCauleyArchemix Corp,300 Third StreetCambridgeMassachusetts 02139USA

XXVI List of Contributors

Simon A. McManusDepartment of Biochemistryand Biomedical Sciences andDepartment of ChemistryMcMaster University1200 Main Street WestHamiltonOntario L8N 3Z5Canada

Eugene W.M. NgEyetech Pharmaceuticals, Inc.3 Times Square, 12th FloorNew YorkNew York 10036USA

Shahid M. NimjeeUniversity Program of GeneticsDuke University Medical CenterBox 2601, DUMCDurhamNorth Carolina 27710USA

P. Shannon PendergrastArchemix Corp,300 Third StreetCambridgeMassachusetts 02139USA

Eric PeyrinD�partement de PharmacochimieMol�culaireUMR 5063 CNRSInstitut de Chimie Mol�culairede Grenoble FR 2607Universit� Joseph FourierUFR de Pharmacie de GrenobleAvenue de Verdun38240 MeylanFrance

Adam RothDepartment of Molecular,Cellular and Developmental BiologyYale UniversityP.O. Box 208103New HavenConnecticut 06520-8103USA

Tony RealiniWest Virginia University Eye Institute1 Stadium DriveP.O. Box 9193MorgantownWest Virginia 26505USA

Christopher P. RusconiRegado Biosciences, Inc.P.O. Box 14688Research Triangle ParkNorth Carolina 27709USA

Mauro SantosCollegium BudapestInstitute for Advanced StudySzenth�roms�g u. 21014 BudapestHungaryandDepartament de Gen�ticai de MicrobiologiaGrup de Biologia EvolutivaUniversitat Aut�noma de Barcelona08193 Bellaterra (Barcelona)Spain

J�rg C. SchlattererInstitut f�r Pharmazie undMolekulare BiotechnologieRuprecht-Karls-Universit�t HeidelbergIm Neuenheimer Feld 36469120 HeidelbergGermany

XXVIIList of Contributors

Kenny SchlosserDepartment of Biochemistryand Biomedical Sciences andDepartment of ChemistryMcMaster University1200 Main Street WestHamiltonOntario L8N 3Z5Canada

Ren�e SchroederMax F Perutz LaboratoriesUniversity Departments of theVienna BiocenterDepartment of BiochemistryDr. Bohrgasse 9/51030 ViennaAustria

Peter SchusterInstitut f�r Theoretische Chemieder Universit�t WienW�hringerstrasse 171090 ViennaAustria

Bruce A. SullengerDepartment of SurgeryDivision of Experimental SurgeryDuke University Medical CenterBox 2601, DUMCDurhamNorth Carolina 27710USA

E�rs Szathm�ryCollegium BudapestInstitute for Advanced StudySzenth�roms�g u. 21014 BudapestHungaryandResearch Group of Theoretical Biologyand EcologyE�tv�s UniversityP�zm�ny P�ter s�t�ny 1/c1117 BudapestHungary

Jack W. SzostakDept. of Molecular Biology,and Center for Computationaland Integrative Biology 7215Simches Research CenterMassachusetts General Hospital185 Cambridge StreetBostonMassachusetts 02114USA

Jean-Jacques Toulm�INSERM U386European Institute of Chemistryand BiologyUniversit� Victor Segalen146, rue L�o-Saignat33076 Bordeaux cedexFrance

Stefan VonhoffNOXXON Pharma AGMax-Dohrn-Strasse 8–1010589 BerlinGermany

XXVIII List of Contributors

Barbara-Sylvia WeigandInstitut f�r Pharmazie undMolekulare BiotechnologieRuprecht-Karls-Universit�t HeidelbergIm Neuenheimer Feld 36469120 HeidelbergGermany

R�diger WelzDepartment of Molecular,Cellular and Developmental BiologyYale UniversityP.O. Box 208103New HavenConnecticut 06520-8103USA

Charles WilsonArchemix Corp,300 Third StreetCambridgeMassachusetts 02139USA

Andreas ZerressenInstitut f�r Pharmazie undMolekulare BiotechnologieRuprecht-Karls-Universit�t HeidelbergIm Neuenheimer Feld 36469120 HeidelbergGermany

3Fitness Landscapes, Error Thresholds, and Cofactors in AptamerEvolution�dam Kun, Marie-Christine Maurel, Mauro Santos, and E�rs Szathm�ry

3.1Introduction

The idea that RNA was genetic as well as enzymatic material goes back to earlierspeculations concerning the possible role of RNA in the origin of life (Woese, 1967;Crick, 1968; Orgel, 1968). Although the idea was clearly expressed, and the molec-ular nature of RNA – with genetically specified positioning in three dimensions ofdifferent chemical building blocks – should have convinced everybody of the po-tential enzymatic capacity of RNA based almost entirely on first principles, theacceptance of the idea – in line with the deeply non-theoretical nature of molecularbiology – came only after the discoveries of RNA self-splicing and the enzymaticactivity of RNase P RNA (Kole and Altman, 1981; Zaug and Cech, 1982). The em-pirical finding that RNA can be a catalyst as well as an information carrier made itstrongly plausible that the first genetic systems could indeed have consisted ofRNA alone (Pace and Marsh, 1985; Sharp, 1985; Orgel, 1986).White (1976) had formerly argued that the so-called nucleotide coenzymes (like

NAD, NADP, FAD, FMN, etc.) are fossils of an earlier metabolic stage whenRNAs acted as enzymes. It is interesting to note that whereas in White’s paperthe emphasis was on metabolism, in Gilbert’s (1986) manifesto for the “RNAworld” interconversions of RNA molecules are the focus. A much lesser known– but very rigorous – attempt was that of G�nti (1979), who put early enzymaticRNAs into his minimal cell model (see Fernando et al., 2005, for a review on mod-els of minimal cells): the so-called chemoton (see G�nti, 2003a,b). In that model itis assumed that replicative ribozymes catalyze steps of an autocatalytic metaboliccycle, surrounded by a growing membrane. This approach was complemented bya thorough confirmation of White’s insights on coenzymes as remnants of earlyribozymes (Kor�nyi and G�nti, 1981). In G�nti’s theoretical world the RNA worldwas in full bloom by the advent of the experimental demonstration of ribozymeactivity in natural systems.Analysis of a “bag of genes” enclosed in compartments led to the stochastic cor-

rector model (Szathm�ry and Demeter, 1987), demonstrating that selection at the

54 3 Fitness Landscapes, Error Thresholds, and Cofactors in Aptamer Evolution


compartment (protocell) level is strong enough to oppose (correct) the adverseconsequences of within-cell competition among replicative, unlinked genes.The difficulty of internal competition within an early genomic set was firstpointed out by Eigen (1971). The stochastic corrector not only solves the competi-tion problem, but is also a theoretical construct to account for selection for ribo-zyme functions in reproducing compartments (Fig. 3.1), as explicitly stated in thepaper.

553.1 Introduction

Fig. 3.1 The stochastic corrector model(Szathm�ry and Demeter, 1987). Differenttemplates (open and closed circles) contributeto the well-being of the compartments (proto-cells) in that they catalyze steps of metabo-lism, for example. During protocell growthtemplates replicate at differential expectedrates, but stochastically. Upon division (p)

there is chance assortment of templates intooffspring compartments. Stochastic replicationand reassortment generate variation amongprotocells, on which natural selection at thecompartment level can act and oppose to(correct) internal deterioration due to within-cell competition. Such compartmentationselects for efficient ribozyme variants.

However, a burning open question, also realized by Eigen (1971), still remains;namely, the problem of the error threshold of replication (that is, a sharply de-fined threshold beyond which heredity breaks down and evolutionary adaptationbecomes impossible). This concept is the flip side of the coin on which we see“mutational load” on one side. Ever since the pioneering works of Haldane(1937) it was clear to population geneticists that too high a mutational load(the decrease in average fitness of a population due to recurrent deleterious mu-tations) could kill a population, but Eigen looked at this problem from anotherangle. If you fix the mutation rate, how long can a replicator grow before it canno longer be maintained by natural selection, despite it being a fast replicator?Early replicators must have been error prone; therefore, they could not havebeen very long (the size of a tRNA is usually assumed). Does the stochastic cor-rector model push up the error threshold so that genomes composed of several tomany different genes can be maintained by selection? The answer is encouraging,but not sufficient (Zintzaras et al., 2002): the origin of a sizeable genome is still aproblem (see also Santos et al., 2004).We outline here a possible resolution to this conundrum. In the first part of this

chapter we analyze existing ribozymes to obtain a “function landscape,” which as-signs an activity value (ideally) to each mutant sequence. Then we use this func-tion landscape as a proxy for the fitness landscape; the crucial assumption beingthat ribozyme activity affects protocell fitness, hence protocell fitness translatesback to some average ribozyme fitness. In conclusion, we argue that the positionof the error threshold was previously estimated to be too severe: neutral and com-pensatory mutations crucially modify the picture. This result strengthens the pos-sibility of an RNA world in protocells (Szathm�ry, 1990a).In the late 1908s one of us was interested in putting the important theoretical

considerations of an RNA world to experimental test. Contemporary natural ribo-zymes almost exclusively conform to Gilbert’s vision of an RNA world, rather thanlending support to a metabolically complex RNA era, as envisaged by Benner et al.(1987, 1989). It was clear that a proof of the principle of the general enzymaticcapability of RNAs should come from novel experiments. It was suggested thata protocol similar to the production of catalytic antibodies should be followed(Szathm�ry, 1989, 1990b). The suggested method envisaged selection of RNAsby binding to transition state analogs of a given reaction, linked to an affinitychromatography column. Amplification would have happened at the DNA levelafter reverse transcription of the best-binding RNA molecules. Szathm�ry(1989) also pointed out that the same protocol could possibly be used to obtainRNA “aptamers” (as we call them now) that could specifically bind aminoacids, thus enabling the community to test certain ideas (primarily the stereo-chemical one) of the origin of the genetic code.Following this line, in the second half of the chapter we summarize experimen-

tal work on co-ribozymes (cofactor-assisted ribozymes) and aptazymes (aptamersattached to ribozymes). Finally, we discuss ideas around how aptamers and nu-cleotides could have been relevant for the origin of the genetic code and transla-tion.


3.2Functionality Landscapes Inferred from Examples

3.2.1Fitness Landscape

Fitness landscapes or adaptive landscapes are often used in evolutionary biologyto envisage the relationship between genotypes – or phenotypes – and Darwiniansuccess (fitness). The idea of studying evolution by visualizing the distribution offitness values as a kind of landscape was introduced by Wright (1932). MaynardSmith (1970) was the first to coin the term “protein space” for a high-dimensionalspace in which each sequence of length N amino acids (out of 20N possible se-quences) represents one point and is next to 19N points representing all theone-mutant neighbors of each other. In order to produce an adaptive landscapein sequence space a fitness value has to be assigned to each sequence, and anevolving population of proteins typically climbs uphill in the fitness landscape.This concept has since been used by a number of authors (for example Eigen,1985; Schuster, 1986, 1987).The simplest theoretical fitness landscape is the single-peaked fitness landscape

used in Eigen’s (1971) study of the error-threshold in a replicating population ofRNA sequences. In this landscape one sequence (the “master sequence”) has thehighest fitness value, and all other sequences have the same or lower fitness. Thisbiologically rather unrealistic fitness landscape still attracts considerable theoreti-cal interest, mainly because it can be tackled analytically (Drossel, 2001). In evo-lutionary optimization methods such as genetic algorithms (see references inFlamm et al., 1999), as well as the use of the concept of a potential or energy func-tion in physics (for example spin-glasses; Bonhoeffer and Stadler, 1993), fitnesslandscapes have also been applied to the study of biological evolution. However,the structure and characteristics of these landscapes are quite unlikely to matchwith the fitness landscapes of biological systems.The N-K model of Kauffman (Kauffman, 1993) describes a landscape where K

out of N elements are involved in some epistatic interaction. The model producesa rugged fitness landscape which was believed to resemble molecular fitness land-scapes on the basis of its ruggedness. Mutational additivity usually holds for posi-tions in biological sequences that do not interact, and such mutational additivityhas been demonstrated for several proteins (Tekada et al., 1989; Sarai and Tekada,1989; Sandberg and Terwilliger, 1993; Serrano et al., 1993; Zhang et al., 1995;Skinner and Terwilliger, 1996; Nikolova et al., 1998; Aita et al., 2001, 2002). Inthese cases, the most realistic of the N-K landscapes is the Mount Fuji type fitnesslandscapes (also known as multiplicative fitness landscapes), where each elementin a sequence individually and independently contributes to the fitness and thereis a single fittest sequence.

573.2 Functionality Landscapes Inferred from Examples

3.2.2Damage Selection Experiments with Ribozymes

A wealth of information has been accumulated on ribozymes since their discoverynearly 20 years ago. Most of the studies can be fitted into one of the three mainlines of research: (1) characterization of known ribozymes (that is, inferring thestructure and mechanisms of catalysis; Lilley, 1999); (2) modification of naturalribozymes to be used in therapeutics (Sullenger and Gilboa, 2002); and (3) invitro evolution of novel ribozymes (Joyce, 1998, 2002; Landweber et al., 1998; Spir-in, 2002). The characterization of ribozymes frequently involved mutagenesis ex-periments, where the enzymatic activity of certain mutants was measured inorder to get insight into either the structure of the molecule or the mechanismof catalysis. While not directed toward the study of fitness landscapes, these ex-periments certainly contain a wealth of empirical information necessary for as-sembling the realistic fitness landscape of the studied ribozyme. Albeit all natu-rally occurring ribozymes are being studied extensively, there are only a few in-stances where the realistic fitness landscape can be conveniently investigated.Group I and group II introns, as well as the RNAase P, have to be excluded be-cause of their rather large size. Furthermore, it is inevitable to employ an RNAfolding algorithm in any sensible investigation of the fitness landscapes of ribo-zymes. Therefore, ribozymes with a pseudo-knot in their structure also have tobe excluded because most conventional folding algorithms cannot satisfactorilycope with pseudo-knots. This requirement singles out the hepatitis delta virus,which contains such structural elements (Perrotta and Been, 1991).On the other hand, the hammerhead, hairpin, and Neurospora VS self-cleaving

ribozymes can be separated into a substrate and a trans-cleaving ribozyme. Withrespect to these three ribozymes the trans-cleaving enzyme does not contain apseudo-knot structure. The hammerhead can be separated into a 13-mer enzymeand a 41-mer oligonucleotide substrate (Jeffries and Symons, 1989). The hairpincan be separated into a 50-mer enzyme and a 15-mer substrate (Fedor, 2000). Thetrans-acting ribozyme is 144 nucleotides long for the Neurospora VS ribozyme(Fig. 3.2), and the substrate is 20 nucleotides long (Guo and Collins, 1995). Werestrict our further analysis to the trans-acting ribozyme, and assume that the sub-strate is the same as the natural one. Unfortunately for our study, many of themutagenesis experiments have been directed towards the substrate and sub-strate-binding regions (Joseph et al., 1993; Joseph and Burke, 1993; Nishikawaet al., 1997; Ananvoranich and Perreault, 1998; P�rez-Ruiz et al., 1999) in orderto produce new RNA- or DNA-cleaving ribozymes to be used in therapeutics(Yu et al., 1998; And�ng et al., 1999; And�ng et al., 2004; Zhang et al., 2004).Based mainly on experiments with the VS and the hairpin ribozymes the follow-ing general conclusions can be derived:x Structure is important. From experimental data on the VS ribo-zyme Lilley and co-workers (Lafontaine et al., 2002c) state that“the secondary structure of the ribozyme is important, but thenature of most individual base pairs is not. Many can be reversed


or replaced by a different pair without major loss of activity, solong as a base pair is retained at a given position.” Similarly, inthe hairpin ribozyme all base pairs can be altered (except basepair G11:C/U-2) as long as the base pairing is maintained (Fedor,2000).

x There are critical regions in the molecule. For the single-strandedregions the structure has to be maintained, but at many suchpositions the nature of the base located there is also important.For example, most of the bases in the four loops of the hairpinribozyme are essential, and any change in those positionsseverely reduces activity (Siwkowski et al., 1997; Shippy et al.,1998). For the VS ribozyme 16 such critical sites were identified(Lafontaine et al., 2002a): these sites are located around the activesite, the substrate-binding region, and in the two- or three-wayjunctions.

x Structure can be varied slightly. The structure of the naturallyoccurring ribozymes can be slightly varied, as there are regionsthat are not crucial to function. For example, the stem–loop IV ofthe VS ribozyme is virtually completely dispensable, but thejunction 3–4–5 must be formed (albeit after the completeremoval of stem–loops IV and V the ribozyme still has detectableactivity; Sood and Collins, 2002). Similarly, in the hairpin ribo-zyme the helices H1 and H4 can be shortened and greatlyextended without any loss of activity (Fedor, 2000; Sargueil et al.,1995).


Fig. 3.2 Sequence and secondary structure ofthe enzyme part of the Neurospora VS ribozyme(numbering according to Beattie et al., 1995).Roman numbers indicate the regions of the

ribozyme. Capitalized nucleotides indicatepositions for which mutagenesis studies areavailable. Bold nucleotides indicate criticalsites.

While the previous general conclusions can be easily incorporated into a model of afitness landscape, one general difficulty stills remain; namely, the combined effectof multiple mutations. Most mutagenesis experiments have investigated only sin-gle mutations (or mutations involving a base pair) in the vicinity of the wild type insequence space and rarely report the activity of double or higher order mutants. Inthose few instances where the effects of multiple mutations were evaluated, theactivities of the single-point mutants were not always included. The only remark-able exception is the study of Lehman and Joyce (1993) from an initial pool of theTetrahymena ribozyme, where they found that in general the mutational effectswere multiplicative (which implies mutational additivity for ribozyme activity).Table 3.1 summarizes the available experimental information on multiple mu-

tational effects for some of the known nucleolytic ribozymes. The plot of the mea-sured enzymatic activities of the double mutants on the estimated activities fromthe single mutants (Fig. 3.3) clearly suggests that mutational effects are nearlymultiplicative, with a slight positive synergy. Such positive synergy was alsofound for chemical modifications of the hairpin ribozyme (Klosermeier and Mill-ar, 2002). Accordingly, the fitness of a molecule containing n mutations (wn) couldbe estimated as:

wmultiplicativen =

Yn

i= 1

wi

wn = wmultiplicativen p (axb)

(3.1)

where wi is the fitness of a single-error variant, wnmultiplicative is the fitness of an

n-error variant assuming multiplicative effects, and a and b are parameters tobe fitted given the data (Fig. 3.3). We stress, however, that although the data setcontains information from three different ribozymes the number of points isstill quite small. Therefore, some care should be taken when translating the em-pirical available information to a fitness function.Besides these synergistic effects there are also examples of mutations that

“rescue” enzymatic activity to some extent. Mutations that result in the loss ofcatalytic activity also exist (Table 3.2). Mutants containing these and other point


Fig. 3.3 Plot of the mea-sured enzymatic activities ofthe double mutants on theestimated activities from thesingle mutants for theNeurospora VS ribozyme.

mutations might or might not have detectable activity. To our knowledge theseinteractions are impossible to predict, thus they can only be incorporated into adefinition of a fitness landscape if known from experiments. In conclusion, theeasiest way to deal with multiple mutations is to assume mutational indepen-dence (multiplicative effects), although it slightly overestimates the decrease in fit-ness due to multiple mutations. A more realistic assumption can come from tak-ing the synergy into account, albeit more data would be highly welcome. If rescuemutations or other such effects are known of the ribozyme, then they can also beincorporated to increase the realism of the fitness landscape.


Table 3.1 Available experimental data for the evaluation ofmultiple mutant effects on fitness

Mutant 1 Activity Mutant 2 Activity Activity ofthe doublemutant

Estimatedactivity ofthe doublemutant

Reference

U39Ca 0.289 A11G 0.32 0.093 0.095 Joseph et al., 1993

G21Ua 0.01 A20U 0.9 0.01 0.009 Sargueil et al., 2000

G21Ua 0.01 A20G 0.2 0.003 0.002 Sargueil et al., 2000

G21Ua 0.01 A20C 0.6 0.15 0.06 Sargueil et al., 2000

G21Ua 0.01 A43G 0.4 I 0.001 0.004 Sargueil et al., 2000

G21Ua 0.075 A43G 0.085 I 0.001 0.0064 Siwkowski et al., 1997;Sargueil et al., 2000

G21Ua 0.075 A43U 0.002 I 0.001 0.00015 Siwkowski et al., 1997;Sargueil et al., 2000

A7Ca 1 A20C 0.81 1.04 0.81 Anderson et al., 1994

A730Cb 0.32 A731C 0.39 0 0.12 Kumar et al., 1992

G726Ab 0 A730C 0.32 0 0 Kumar et al., 1992

U752Cc 0.80 U753C 0.42 0.52 0.336 Lafontaine et al., 2001b

G722C;C763Gc

0.81 C723G;-G762C

0.84 0.75 0.680 Beattie et al., 1995

G716Cc 0.21 U717A 0.21d 0.02 0.044 Beattie et al., 1995

C662Gc 0.23 A661U 0.23d 0.06 0.053 Beattie et al., 1995

a Hairpin ribozyme (numbering follows Butcher and Burke, 1994a,b).b Hepatitis delta virus (numbering according to Makino et al., 1987).c Neurospora VS ribozyme (numbering according to Beattie et al., 1995).d No data available. The activity of mutant 2 is assumed to be equal tothe activity of mutant 1.

3.2.3Construction of the Fitness Landscape

Based on the foregoing general conclusions we propose a method to generate afitness landscape for any ribozyme for which enough mutagenesis data are avail-able. The result of the method is an algorithm which assigns a relative activity toeach of the 4N possible RNA sequences of length N. For the sake of simplicity werestrict the sequence space to sequences of a given length; however, the methodcould be applied if insertions and deletions were also considered. The algorithmconsists of four steps: (1) compatible structure, (2) mispairs, (3) critical sites, and(4) predicted structure. In each step we calculate an activity value pertaining to thegiven step. The relative activity and the fitness of the sequence (Asequence) isthe product of the individual activities calculated at each step (that is, Asequence =Astructure q Amispair q Acritical q Aenergy).


Table 3.2 Single null and multiple mutations that in somecases “rescue” enzymatic activity to some extent

Mutation thatabolishesactivity

Multiple mutant Relativeactivity

Ribozyme Reference

A43C A43C; G21U 0 Hairpin Siwkowski et al., 1997a;Sargueil et al., 2000

G726A G726A; A730C 0 HDV Kumar et al., 1992

G726A G726A; G727C; A731C 0.02 HDV Kumar et al., 1992

G726A G726A; G728A; C729G 0 HDV Kumar et al., 1992

G726A G726A; G727U; C729G 0.01 HDV Kumar et al., 1992

G726C G726C; G728U; A730C 0 HDV Kumar et al., 1992

G726U G726U; G727U; A731C 0.04 HDV Kumar et al., 1992

G727C G726A; G727C; A731C 0.02 HDV Kumar et al., 1992

G727C G727C; C729A; A731G 0 HDV Kumar et al., 1992

G728C G727A; G728C 0.03 HDV Kumar et al., 1992

G728C G728C; A731U 0.01 HDV Kumar et al., 1992

G728C G728C; A730G 0.06 HDV Kumar et al., 1992

G728C G727A; G728C; C729U 0.04 HDV Kumar et al., 1992

C763A C763A; A766G 0 HDV Kumar et al., 1992

C763A C763A; G764A 0 HDV Kumar et al., 1992

C763G C763G; A765U 0 HDV Kumar et al., 1992

C763G C763G; A765U 0 HDV Kumar et al., 1992

3.2.3.1 Compatible StructureIn order to have any enzymatic activity the molecule should fold into the structureof the ribozyme. Here a number of alternative structures can also be considered.A sequence is said to be compatible with a structure if for every base pair (i,j) in

the structure the bases at the ith and jth positions in the sequence can form oneof the allowed base pairs (that is, A:U, U:A, G:C, C:G, U:C, G:U). Enforcing strictcompatibility might result in an overestimation of the negative effects of mispairmutations. Some mispair mutants retain relatively high level of activity; for exam-ple the C662G mispair mutation in the VS ribozyme decreases activity to 23% ofthe wild type (Beattie et al., 1995). Thus, even sequences with partial compatibilityshould be considered compatible in this step. The negative effect of mispairs istaken into account in the next step.If a sequence is not compatible – even when considering the possibility of mis-

pairs – with any of the possible structures, then it has no activity and its fitness isset to 0. The activity factor for this step (Astructure) is the activity associated with thestructure to which the sequence can fold. The activities of the various possiblestructures can be different.

3.2.3.2 MispairsWhen a sequence is perfectly compatible with a structure (that is, there are nomispairs in it) then Amispair = 1, otherwise every single allowed mispair decreasesactivity to some extent. Every mispair has an associated relative enzymatic activity(Amispair,i), and the activity factor for this step is the product of the activities of theindividual mispairs: Amispair = PAmispair,i.If synergistic effects are taken into account, then they should be incorporated in

this step and/or the next step. Some regions of the ribozyme can show differentsensitivity to mispairs, thus the associated relative enzymatic activity (Amispair,i) willbe different.

3.2.3.3 Critical SitesThe nature of nucleotides at critical sites of the molecule is taken into account inthis third step of the algorithm. These sites are well studied so we can nearly as-sign a measured activity to every possible nucleotide at these positions. In fact, allpossible single mutants of the single-stranded regions of the hairpin ribozymehave been analyzed (Siwkowski et al., 1997; Shippy et al., 1998).As before, the product of the individual activities (Acritical,i) gives the activity fac-

tor for this step (Acritical = PAcritical,i). If synergistic effects are taken into account,then they should be incorporated in this step and/or the previous step. Further-more, if other epistatic effects were present, they would likely affect positions in-volved in this step.


3.2.3.4 Predicted StructureThe last step in establishing the fitness of a sequence is to predict the secondarystructure the sequence will fold into and contrast it with the structure resolved inthe first step. The folding can be done with any available RNA folding routine, asfor example the MFold (Zuker et al., 1999) or the Vienna RNA Package (Hofackeret al., 1994). It has to be noted at this stage that the predicted minimum free en-ergy structure of the wild-type ribozyme sequence does not always correspondwith the actual secondary structure. In this case that structure can also be ac-cepted as a good structure. Furthermore, if mispairs are allowed then they haveto be taken into account during structure comparisons. When the predictedand the target structure are the same Aenergy = 1, otherwise Aenergy = 0. Thisstep is undoubtedly the most costly in terms of CPU time.

3.2.4Case Study: The Fitness Landscape of the Neurospora VS Ribozyme

The Neurospora VS ribozyme (Saville and Collins, 1990) can be used as a modelsystem to show the usefulness of the algorithm in generating the functional fitnesslandscape of the molecule (Fig. 3.2). There is a wealth of mutagenesis informationavailable for this ribozyme (Beattie et al., 1995; Lafontaine et al., 2001a,b, 2002a,b,d;Rastogi et al., 1996; Rastogi and Collins, 1998; Sood and Collins, 2002): out of the144 positions in the ribozyme (stem–loops II–VI; nucleotides 640–783 according tothe conventional numbering), 87 positions have documented mutants (excludingdeletions and insertions) and the total number of mutants studied so far is 183.The ribozyme has six regions (Fig. 3.2), where stem–loop I contains the cleav-

age site and is the substrate of the reaction, while regions II–VI perform the cat-alysis. The naturally occurring VS ribozyme is self-cleaving; however, it can be di-vided into a trans-acting ribozyme plus a substrate system (Guo and Collins,1995). Unlike trans-cleaving versions of the hammerhead, hairpin, and HDV ribo-zymes, the VS ribozyme does not use long stretches of complementary base pair-ing to associate with its substrate. Nonetheless, the ribozyme–substrate interac-tion is quite tight (Guo and Collins, 1995; Lafontaine et al., 2001b). The pseu-do-knot forming between the loops of the substrate and the stem–loop V of theribozyme is crucial for substrate biding and orientation (Rastogi et al., 1996;Andersen and Collins, 2001).

3.2.4.1 Compatible Structure of the VS RibozymeThe length of stem–loop IV and the distal part of stem–loop VI can be varied to agreat extent. The length of stem–loop V can be varied slightly (Lafontaine et al.,2002b), but the length of stem–loop III cannot be changed (Lafontaine et al.,2002b). There are no data available on stem–loop II. Furthermore, the A718bulge in region III can be complemented (that is, replaced by a base pair, withthe insertion of U after position 660) without significant loss of activity (Beattieet al., 1995; Lafontaine et al., 2002b). These changes constitute the basis of


other acceptable structures beside the wild-type structure. Table 3.3 lists variousstructures and their activities.In order to maintain the wild-type total length N = 144 constant, all structures

including deletions or insertions were slightly modified in our study. Single-stranded regions were removed from or added to the beginning and the end ofthe structure in all possible combinations. We allowed some mispairs in the struc-ture, but a mispair can only be considered if the following criteria are met (in whatfollows all numbers refer to positions in the wild-type sequence. In the alternativestructures these positions change according to the shift due to the insertion ordeletion):x Two mispairs cannot be adjacent. There are six experimentallytested mutants that have mispairs at adjacent positions. Four ofthem have absolutely no activity ([G727C, U728A], [G722C,C723G], [G762C, C763G], [A759U, C760G]). In the case of theremaining two mutants ([G716C, U717A], [A661U, C662G]) theactivities are 0.02 and 0.06, respectively.

x The pairs [653:771], [654:770], and [655:769] in stem–loop II haveto be paired. The mispair mutants of the region ([G653C],[C771G], [G655C]) do not have measurable activities, except forthe mutant [C769G] whose relative enzymatic activity is 0.18.

x The base pairs adjacent to a junction have to be paired. The stableforming of the junction is critical for enzymatic activity. If thebases next to a junction do not form a base pair, then the struc-ture of the junction changes considerably and enzymatic activitywould vanish. Accordingly, the following base pairs have to bepaired: [658:721], [663:715], [666:685], [687:709], [722:763].


Table 3.3 Enzymatic activities of different structures of theNeurospora VS ribozyme

Structure Astructure Reference

Original structure 1.000

Deletion of the A652 bulge 0.013 Beattie et al., 1995

Deletion of the A718 bulge 0.136 Lafontaine et al., 2002b

Pairing of the A718 bulge 0.820 Lafontaine et al., 2002b

Length 8 stem III 0.087 Lafontaine et al., 2002b

Length 7 stem V 0.045 Lafontaine et al., 2002b

Length 6 stem IV 0.470 Lafontaine et al., 2002b

Length 4 stem IV 0.590 Lafontaine et al., 2002b

Length 8 stem VI 0.970 Lafontaine et al., 2001b

Length 6 stem VI 1.000 Lafontaine et al., 2001b

x A stem–loop cannot contain more than two mispairs. Generally,two mispairs in the same region greatly reduce activity (0.78[G704C, U706A], 0.54 [U670A, C672G], 0.28 [A748U, U750A], 0.25[A735U, U737], 0.07 [A690U, C692G], 0.06 [A661U, C662G], and0.02 [G716C, U717A]) or abolish it completely (see the adjacentmispairs mentioned above or the mutant [G679C, A681U]). Weassume that if a stem–loop contains more than two mispairs, thenthe resulting molecule would not have any enzymatic activity.

3.2.4.2 Allowed Mispairs in the VS RibozymeWe have distinguished various kinds of different mispairs according to their posi-tion. First, a mispair next to the active site (that is, at positions [731:754] and[729:758]) decreases activity to Amispair,i = 0.025 e 0.011. Second, a mispair nextto the loop of stem V (at positions [695:701]) decreases activity to Amispair,i =0.05. Thus, mutants [U695G] and [A701C] have enzymatic activities of 0.06 and0.04, respectively. Third, a mispair inside a stem decreases activity to 0.2.The experimentally tested mispairs that do not fall into any of the previous ca-

tegories have the following activities: 0.29 [C773G], 0.23 [C662G], 0.21 [G716C],0.12 [G650C], 0.05 [A720U], 0.00 [U659A]. The mean activity of these mutant is0.15 e 0.095, but it should be stressed that these mispairs are located in impor-tant parts of the ribozyme. There are no experimentally tested mutants in thefunctionally less important parts, and it would be reasonable to assume a slightlyless severe decrease of activity. Finally, a mispair next to the loops of stem IV andVI will probably not decrease activity to a great extent. For these mispairs we haveassumed Amispair,i = 0.80.

3.2.4.3 Critical Sites in the VS RibozymeCritical sites in the VS ribozyme are located in the junctions, in the substratebinding region, and in the A730 internal loop (Table 3.4). Thus, the two junctionsof the ribozyme (3–4–5 and 2–3–6) play an important role in the formation ofthe 3D structure of the molecule (Lafontaine et al., 2001a,b). Some critical posi-tions exist in both junctions: positions 656, 657, 665, 686, 710, 712, 713, 767and 768. The other nucleotides of the junctions can be changed to some othernucleotide with minor loss of activity.The substrate interacts with the enzyme through base pairing with bases 697–

699 located in the loop at the end of region V. Therefore, these positions are alsoconsidered as critical for the function of the molecule. Finally, the internal loop ofregion VI (positions 730, 755, 756, and 757) shows the greatest susceptibility tonucleotide substitution (Lafontaine et al., 2001b; Sood and Collins, 2002) and vir-tually any change in the sequence leads to severe reduction in cleavage rate. Thisloop is quite probably the active site of the VS ribozyme. For those positions nu-cleotide A756 appears to be the most important, particularly the amino group atlocation 6 of the purine base (Lafontaine et al., 2002d).


3.2.4.4 Predicted Structure for the VS RibozymeThe minimum free energy (MFE) structure of the original sequence of the VSribozyme is not the experimentally determined secondary structure. The twostructures differ in only three base pairs in stem–loop VI, and the energy differ-ence between the two is a mere 0.3 kcal. But it has to be noted that the nuclearmagnetic resonance structure of the isolated stem–loop VI is the same as in theMFE structure (Flinders and Dieckmann, 2004). Thus both structures might playan important role in the catalysis. Accordingly, we accept the sequence if it foldseither to the MFE or the experimentally predicted structure of the wild-type VSribozyme. We have used the RNA folding algorithm (Hofacker et al., 1994) forsecondary structure predictions.As previously indicated, the enzymatic activity of the molecule is simply the

product of the activity factors estimated in the above four steps. If the resultingactivity was less than the lowest activity that can be reliably measured (that is,Asequence J 10–3), then it was set to 0.


Table 3.4 Activities of the critical sites in the Neurospora VS ribozyme

Critical site Acritical,i

A U C G

656 1.000 0.002 0.003 0.061

657 1.000 0.063 0.063a 0.063a

665 0.014 0.01b 1.000 0.01

686 0.006 1.000 0.006a 0.006a

697 0.000 0.001 0.000 1.000

698 1.000 0.000 0.041 0.000

699 0.006 0.018 1.000 0.000

710 0.002 1.000 0.029 0.002

712 1.000 0.005 0.005b 0.006

713 0.019 1.000 0.136 0.019

730 1.000 0.036 0.055 0.011

755 0.840 0.170 1.000 0.019

756 1.000 0.001 0.002 0.002

757 0.044 0.018 0.014 1.000

767 1.000 0.047 0.047a 0.047a

768 0.573 0.015 0.573c 1.000

a No data available. We assume that enzymatic activity is the same asfor the known mutant.

b No data available. We assume that enzymatic activity is the same asfor the known mutant with the lower activity.

c No data available. We assume that enzymatic activity is the same asfor the known mutant with the higher activity.

3.2.4.5 Properties of the Estimated Fitness Landscape for the VS RibozymeTo characterize the fitness landscape some additional analyses were made. Allpossible one- or two-mutant molecules around the original sequence were gener-ated and their fitness recorded. Molecules containing more than two mutationscould not be completely enumerated in reasonable time, thus only one millionrandomly chosen sequences with 4, 5, 6, 7, 8, 9, and 10 mutations were evaluated(Figure 3.4).Of the 432 possible single mutants, 114 (26.4%) had the same activity as

the wild-type ribozyme (that is, they are “selectively neutral” neighbors), 222(51.4%) had an activity higher than 0.1, and 254 (58.8%) still retained “measur-able” enzymatic activity. Of the 92 664 possible double mutants only 6,579(7.1%) had the same activity as the wild type, whereas 29 096 (31.4%) still retainedsome enzymatic activity (that is, for 68.6% of the sequences Asequence J 10–3).In addition, we generated 10 million random sequences (a very tiny fraction of

the sequence space, which contains approximately 4.973 q 1086 sequences). Noneof them had any enzymatic activity.

3.3Error Thresholds Inferred from Functional Landscapes:The “Realistic” Error Threshold of the Neurospora VS Ribozyme

As previously pointed out, the formal description by Eigen (1971) of the muta-tion–selection dynamics of a population of biological sequences led to the realiza-tion of one of the greatest paradox of prebiotic evolution: the error rate poses alimit to the length of the information that can be selectively maintained withinthe system. A stable cloud of mutants (that is, a quasi-species) can form arounda master sequence as long as the maximum chain length (N) is below the criticalerror rate per site per replication (m*) as determined by the following expression:


Fig. 3.4 Fraction of all possiblemutants of the original se-quence folding into a perfectribozyme (circles), and aribozyme with some activity(square) as function of thenumber of point-mutationsintroduced into the sequences.

N Iln s

m�(3.2)

where s is the selective superiority of the master. Very roughly then, assuming ascustomary that lns z 1, Eq. (2) states that the maximum selectively maintainableamount of information (N) is about the inverse of the mutation rate per base perreplication. Experimental evidence suggests that without the aid of peptide en-zymes the upper bound of copying fidelity per nucleotide per replication couldnot be higher than 0.99 (Johnston et al., 2001), and is quite likely significantlylower than this figure. Accordingly, the maximum N would be lower than 100 nu-cleotides.However, Eigen’s model is based on the assumption that the whole genotype –

the master sequence – has to be maintained for functionality. This assumptionmight be justifiable in a DNA–protein world, but in the RNA world the enzymaticactivity of a molecule was mainly based on its three-dimensional structure ratherthan on the exact order of its building blocks (except for a few critical sites). It is awell-established fact that the number of possible RNA secondary structures is con-siderably less than the number of possible sequences (Schuster et al., 1994; Stadlerand Haslinger, 1999), and it is also possible that two molecules with completelydifferent sequences share the same secondary structure (Huynen, 1996; Huynenet al., 1996; van Nimwegen et al., 1999). In other words, when RNA structure isconsidered it might be feasible to maintain ribozyme functionality (phenotype) atmutational rates that would not allow the preservation of the master sequence.In order to determine the “realistic” error threshold for the VS ribozyme we

have explored the dynamics of a population of RNA molecules with N = 144 atvarious mutation rates per nucleotide per replication (m). At the replication stepa sequence is picked at random according to its fitness. Thus, the probability pjof choosing sequence i with enzymatic activity Ai is:

pi =Ai

SAj(3.3)

where SAj is the sum of activities for all sequences in the population. The nextstep is to copy the chosen sequence with error rate m (only point mutationswere considered). Because a quarter of the times (assuming equal probabilityfor each nucleotide) no effective change will occur in the position even thoughthere is a mutational event, the effective mutation rate is m* = 0.75 m.The new sequence then replaces a randomly chosen sequence, which allows

keeping a constant population of molecules and is also equivalent to the assump-tion that the rate of degradation is the same for all molecules and independent ofenzymatic activity (Bonhoeffer and Stadler, 1993). As a final point we should em-phasize here that the occurrence of thresholds for error propagation was origin-ally derived as a deterministic kinetic theory that is only valid in the limitedcase of an infinite number of molecules. Alves and Fontanari (1998) have ex-tended it to finite populations and found that the critical error rate per site per

693.3 Error Thresholds Inferred from Functional Landscapes

replication decreases linearly with 1/N. In our present case we extrapolated to aninfinite population size by recording the time to extinction (that is, the number ofgenerations when no functional ribozymes remained in the population) at variouserror rates and fitting a straight line to those last few points which still showed adownward trend. The error threshold is then the intersection of the line with theerror rate axis.As shown in Fig. 3.5 the “realistic” error threshold for the VS ribozyme was es-

timated to be m* = 0.052 (this figure refers to the “effective” mutation rate; seeabove). To compare this figure with the error threshold that would be obtainedwithout considering secondary structure we have considered two different fitnesslandscapes: Mount Fuji and single-peaked landscapes.In Mount Fuji landscape we have assigned an activity value to all possible nu-

cleotides at a given position, with the wild-type nucleotides at each position hav-ing a value Ai = 1 for the enzymatic activity. For those positions where experimen-tal data are available we considered that value, otherwise we either used the valuefor the same position derived for another nucleotide (if more than one such valuewere available, we used the lowest of the two) or used a predefined value. In thelast case we considered two scenarios: those mutants have uniformly either Ai =0.8 or Ai = 0.2. Therefore, in the first case we assumed that those positions arenot functionally important, whereas in the second case they are. In no case canthe enzymatic activity of the molecule be higher than 1. The fitness value of a


Fig. 3.5 Error threshold for the VS ribozyme.Maximum (downward triangles), mean(square) and minimal (upward triangles) ob-served times to extinction are plotted as afunction of the error rate. The population size

was 5000 molecules. A generation is defined asa number of replication steps equal to thepopulation size. For each error rate 10 repli-cates were obtained. A line is fitted to the lastfour data points (solid line; R= –0.904).

sequence was then calculated as the product of the individual activities, and theresulting “Mount Fuji error thresholds” were m* = 0.032 (Ai = 0.2) and m* =0.025 (Ai = 0.8), which are substantially lower than the “realistic” error thresholdfrom the functional landscape. Mount Fuji landscape retains some characteristicsof the functional landscape – the fitness effect of the single-stranded regions – butit is no longer possible to have compensatory mutations by changing one basepair into another. In fact, every mutation affecting a helical region counts as amispair, which causes the error catastrophe to occur at lower value.For the single-peaked landscape we used the same assumption as in Eigen’s

model. Thus, the wild-type sequence has Ai = 1 at each position and all other se-quences have Ai = 0.217, which is the average activity of all experimentally testedone-point mutants. The “single-peaked error threshold” was found to be m* =0.023, lower than in either of the previous cases. The reason is that this landscaperetains no information about the structure, and no neutral mutations are possible.By using Eq. (2) above and assuming lns = 1, the maximal error rate for the VS

ribozyme would be m* z 0.007. This figure is nearly an order of magnitude lowerthan the one we got by using a realistic fitness landscape. Furthermore, accordingto the Eigen’s model the error rate of 0.052 would permit a ribozyme of maximumlength 20 to be maintained. In summary, it is quite obvious that the inclusion ofstructural information, as well as information derived from experimental data,crucially alleviates the burden imposed by Eigen’s (1971). This is the first reportof a realistic error threshold calculated for an existing ribozyme. To the best of ourknowledge there is only one other indication of an error threshold calculatedusing a structural landscape. Huynen and co-workers (1996) used the secondarystructure of the phenylalanine tRNA (N = 76) as their object of investigation, andassumed that the fitness decrease of a mutant is proportional to the differencebetween the target structure and the structure of the mutant sequence. They re-ported that the error threshold for the tRNAPhe is 0.0031. This seems to be too low,as even the Eigen’s model predicts a higher error threshold (m* z 0.01). This lowvalue might be an artefact of the folding algorithm, which for tRNAs often pre-dicts a minimum free energy structure unlike the known cloverleaf structures.There is as yet no reported replicase ribozyme. The most promising result thus

far is a ribozyme that can extend a sequence by 14 nucleotides according to a tem-plate (Johnston et al., 2001). This ribozyme works with a 0.967 copying fidelity. Ifa functional replicase ribozyme had the same fidelity, then it could replicate theVS ribozyme without the threat of the error catastrophe.

3.4Looking for Catalytic Partners: Cofactors and Aptamers

The discovery that present-day living cells use RNA catalysts to hydrolyze RNAmolecules or to perform the complex reactions of excision, ligation, and cycliza-tion supporting a limited catalytic diversity, raises the question of the respectivedomains of RNA and protein catalysis. The recent advances in RNA catalysis

713.4 Looking for Catalytic Partners: Cofactors and Aptamers

using the SELEX method make it possible to enhance the catalytic capabilities ofRNA with small molecules as catalytic partners. In this way some RNAs may beanalogous to protein in catalytic competence.Purine nucleotides, and in particular those containing adenine, participate in a

large variety of cellular biochemical processes (Maurel and D�cout, 1999). Theirbest-known function is that of monomeric precursors of RNAs and DNAs. Never-theless, derivatives of adenine are universal cofators. They serve in biological sys-tems as source of energy (ATP), allosteric regulators of enzymatic activity and reg-ulation signals (cyclic AMP). They are also found as acceptors during oxidativephosphorylation (ADP), as components of coenzymes (such as in FAD, NAD,NADP, coenzyme A; Fig. 3.6), as transfer agents of methyl groups of S-adenosyl-methionine, as possible precursors of polyprenoids in C5 (adenosylhopane)(Neunlist et al., 1987), and – last but not least – adenine 2451 conserved withinthe large rRNA in the three kingdoms, would be involved in catalysis duringthe formation of the peptide bond (Muth et al., 2000, 2001; Green and Lorsch,2002).On the other hand, biosynthesis of the amino acid histidine, which would have

appeared late in evolution, begins with 5-phosphoribosyl-1-phosphate (PRPP) thatforms Nl-(5-phosphoribosyl)-ATP by condensation with ATP. This reaction is akinto the initial reaction of purine biosynthesis. Finally, the ease with which purinebases are formed in prebiotic conditions (Or�, 1960) suggests that these baseswere probably essential components of an early genetic system. The nucleotidesthat by post-transcriptional modification can acquire the majority of functionalgroups present in amino acids possess a great potential diversity that is expressedin the modified bases of tRNAs and rRNAs and also at the level of ribonucleotidecoenzymes (several coenzymes derive from AMP; Fig. 3.7). In particular manycoenzymes are nucleotide analogs and the role of these cofactors at all steps ofthe current metabolism, and their distribution within the three kingdoms, sug-gests that a great variety of nucleotides were present in the last common ancestor.It as been suggested (White, 1976; Tr�moli�res, 1980) that coenzymes and mod-ified nucleotides that were present before the appearance of the translationmachinery may have played a prominent role in primeval catalysis.Proteins would have appeared only at a later stage, coenzymes and ribozymes

being fossil traces of past catalysts. Indeed, in the living cell, only a minority ofenzymes function without coenzyme; they are mostly hydrolases, and apartfrom this group, 70% of the enzymes require a coenzyme. If metal coenzymesinvolved in catalysis are considered, the number of enzymes that depend oncoenzymes increases further. Present-day coenzymes, indispensable cofactorsfor many proteins, would be living fossils of catalysts of primitive metabolism(Maurel and Haenni, 2005).Most coenzymes are nucleotides (NAD, NADP, FAD, coenzyme A, ATP, etc.) or

contain heterocyclic nitrogen bases that can originate from nucleotides (thiaminepyrophosphate, tetrahydrofolate, pyridoxal phosphate, etc.). Consequently theefficiency of selected ribozymes can be further expanded if coenzymes and/ornucleotide analogs containing functionalities (thiols, amino groups, imidazolyl



Fig. 3.6 Coenzyme structures.

moieties, etc.) are used. Modern selections yield various co-assisted dependentribozymes, justifying an RNA-based metabolism.

3.4.1Co-ribozymes (cofactor-assisted ribozymes)

Aptamers are capable of recognizing targets as small as metal ions (most RNAenzymes are metallo-ribozymes using metal as cofactors). They can interactwith a wide variety of molecules that are important for metabolism, includingamino acids, porphyrins, nucleotide factors, coenzymes, small peptides, andshort oligonucleotides (Illangasekare and Yarus, 1997; Jadhav and Yarus, 2002a;Joyce, 2002; McGinness et al., 2002; Reader and Joyce, 2002) (Table 3.5).The first aptamer selected for a biological cofactor was an ATP-binding RNA

(Sassanfar and Szostak, 1993; Fig. 3.8) showing a change in the conformationof the RNA and number of close contacts between the ATP and RNA.Since the ATP motif also binds adenosine and NAD+, the idea was that it could

serve to bind adenosine-derived cofactors as well. The presence of adenosine inmany common biological cofactors (ATP, CoA, FAD, NAD+, SAM, coenzymeB12) has been postulated to reflect an evolutionary origin for modern metabolism.It is even possible to consider that catalytic groups that were part of nucleicenzymes were incorporated in specific amino acids rather than being “retained”as coenzymes. This could be the case for imidazole, the functional group of his-tidine, whose present synthesis in the cell is triggered by a nucleotide (Maurel andNinio, 1987; Benner et al., 1989; Maurel, 1992). Further, the use of organic cofac-tors was illustrated by a DNA enzyme that requires histidine as a cofactor duringRNA cleavage (Szazani et al., 2004).A small aptamer recognizing anionic moieties has also been obtained by Szos-

tak and co-workers (Szazadni et al., 2004). In this case the significant interactionsare with the phosphate of ATP (Kd of 5 mmol/L compared with the AMP Kd of 5.5


Fig. 3.7 List of coenzymes derived from AMP.


Table 3.5 Small targets of RNA aptamers

Target Minimal size Kd (mmol/L) Reference

Adenine – 10 Meli et al., 2002

Guanine 32 1.3 Kiga et al., 1998

Guanosine – 32 Connell and Yarus, 1994

l-Adenosine 58 1.7 Klussmann et al., 1996

Theophylline 38 0.1 Jenison et al., 1994

Caffeine – 3 500 Jenison et al., 1994

Riboflavin 35 0.5 Burgstaller and Famulok, 1994

NMN/NAD – 2.5 Lauhon and Szostak, 1995

d-ATP 40 14 Sassanfar et al., 1993

Cyanocobalamin 35 88 nmol/L Lorsch and Szostak, 1994

l-Valine – 12 000 Majerfeld and Yarus, 1994

l-Citrulline 44 65 Famulok, 1994

l-Arginine – 0.33 Geiger et al., 1996

d-Arginine 38 135 Nolte et al., 1996

Biotin 31 6 Wilson et al., 1998

Fig. 3.8 ATP andthe corresponding aptamer.

mmol/L). This is of particular interest as two messenger RNA riboswitches fromEscherichia coli are known to discriminate between phosphorylated small mole-cules.The first self-incorporation of a coenzyme into a ribozyme was performed in

1995 by Breaker and Joyce (1995), substituting neatly alternative coenzymesinto the primary structure of group I intron by replacing the guanosine substratewith natural coenzymes or analogs (Fig. 3.9a and b respectively).Looking for significant metabolic reactions and for coenzyme-dependent ribo-

zymes, the primary biological cofactor used in acyltransfer reactions, coenzymeA (CoA), has been the target of RNA pools leading to a 52-nucleotide minimalaptamer (Burke and Hoffmann, 1998) which recognizes the adenosine moietyof CoA and binds others ATP analogs (Fig. 3.10). The selection of coenzymesynthetase ribozymes is of particular interest in an RNA-catalyzed energy metabo-lism. Yarus, exploring the origin of ribonucleotide coenzymes, demonstrated theRNA-catalyzed formation of three common coenzymes CoA, NAD, and FAD fromtheir precursors, 4l-phosphopantetheine, NMN, and FMN, respectively (Huang etal., 2000). A ribozyme capable of utilizing CoA for the synthesis of acyl-CoA wasselected in vitro. The co-ribozyme isolated, that is an acyl-CoA synthetase, pro-duced acetyl-CoA and butyryl-CoA (Jadhav and Yarus, 2002b).The use of organic cofactors has been illustrated by a hairpin ribozyme that

requires adenine as a cofactor during RNA reversible self-cleavage reaction(Meli et al., 2003). This may lead to a better understanding of prebiotic cofactorsin primeval catalysis. Our working hypothesis is based on the demonstration of


Fig. 3.9 Self-incorporation of coenzyme (adapted from Breaker and Joyce, 1995).

esterase activity in a nucleoside analog, the N6-ribosyladenine (Fuller et al., 1972;Maurel and Ninio, 1987). The activity, due to the presence of an imidazole groupthat is free and available for catalysis, is comparable to that of histidine placed inthe same conditions (Fig. 3.11). We have studied the kinetic behavior of this typeof catalyst (Ricard et al., 1996) and have shown that the catalytic effect increasesgreatly when the catalytic element, the pseudohistidine, is placed in a favorableenvironment within a macromolecule (D�cout et al., 1995). Moreover, primitivenucleotides were not necessarily restricted to the standard nucleotides encoun-tered today, and because of their replicative and catalytic properties, the N6 andN3 substituted derivatives of purines could have constituted essential links be-tween the nucleic acid world and the protein world.Following this line of investigation we started the selection of ribozymes depen-

dent on adenine. Starting from a heterogeneous population of RNAs with 1015

variants (a population of 1015 different molecules) we have selected five popula-tions of RNAs capable of specifically recognizing adenine after ten generations(Meli et al., 2002). When cloned, sequenced, and modeled, the best one amongthe individuals of these populations, has a shape reminiscent of a claw capableof grasping adenine. Following this result we have isolated from a degeneratedhairpin ribozyme, by in vitro selection, two adenine-dependent ribozymes capableof triggering reversible cleavage reactions (Fig. 3.12). One of them is also activewith imidazole alone (Meli et al., 2003).A quarter of classified enzymatic reactions are redox reactions involved in var-

ious biological events, such as metabolism of biological molecules, detoxification,energy production, and regulation of protein functions. An RNA molecule bind-ing hemin and exhibiting a peroxidase activity has been reported (Travascio et al.,1999). In this case, RNA and DNA of the same nucleotide sequence are capable offorming comparable cofactor-binding sites promoting catalysis.


Fig. 3.10Secondary struc-tures of coenzyme–RNA aptamers.

Recently, Tsukiji et al. (2004) reported a ribozyme that exhibits activity analo-gous to alcohol dehydrogenase (ADH). The ADH ribozyme was selected in vitrofrom a pool of random RNA sequences in the presence of NAD+ and Zn2+,based on the ability to convert a benzyl alcohol derivative to the correspondingaldehyde that was covalently attached to the 5l end of the RNA pool. In fact,the ribozyme oxidizes the benzyl alcohol to benzaldehyde in an NAD+ andZn2+-dependent manner, with a rate acceleration at least 7 orders of magnitudehigher than the uncatalyzed reaction.


Fig. 3.11 (a) Adenine. (b) Comparison of modified adenosine and histidine.(c) Catalytic activity of adenine residue.

Fig. 3.12 Adenine-dependent hairpin ribozymes (ADHR). Arrowheads: cleavage sites. Graydots: degenerated (mutated) sites. Vertical bars: separation between the primer binding regionand the ribozyme sequence.

3.4.2Aptazymes

Joining aptamer and ribozyme yields aptazymes. Several examples of new apta-zymes have been obtained from random-sequence RNA selections. Breaker et al.merged sequences of ATP aptamers into the hammerhead ribozyme to designallosteric ribozyme controlled by ATP (Tang and Breaker, 1997). The same ideawas used by Araki et al. (1998) with the hammerhead ribozyme and a flavin-spe-cific RNA aptamer. FMN binding affects the necessary conformation for catalyticactivity. The aptazymes exhibit high activation such as this ATP-dependent ligaseshowing 830-fold activation and a theophylline-dependent ligase that shows 1600-fold activation (Robertson and Ellington, 1999, 2000).These allosteric ribozymes composed of two independent structural domains

can activate kinetic reactivity and regulation providing molecular switches madeof RNA (Soukup and Breaker, 1999). Furthermore, these kinds of selected ribo-zymes can have allosteric responses that are orders of magnitude greater thanthose seen for protein enzymes. The allosteric selection strategy also providesnovel RNA molecular switches responding to cNMP targets. For instance Koizu-mi et al. (1999) demonstrated specific activation of ribozymes cleavage with cGMPand cAMP exhibiting an 5000-fold activation in the presence of the effector com-pounds.One of the most popular biotech applications of riboswitches is developing in

gene therapy, allowing patients to take pills to switch genes on or off. On theother hand, natural aptamers are involved in riboswitch regulation of transcrip-tion termination and translation initiation in bacteria. Either FMN or thiamineacts on bacterial riboswitch RNAs located within the 5l-untranslated region(5l-UTR). Binding of the target ligand alters the conformation resulting in achange in gene expression (Winkler and Breaker, 2003). Recently Winkler et al.(2004) demonstrated that glmS ribozyme responds to glucosamine 6-phosphate(GlcN6P), a crucial compound in sugar metabolism in all three kingdoms oflife and in cell wall biosynthesis in Gram-positive microorganisms, for geneticpurposes. Also in prokaryotes, mRNA binds coenzyme B12 to modulate gene ex-pression involved in cobalamine transport protein. The complexity of riboswitchstructures is supported by numerous short helices and conserved sequences ele-ments, allowing a great functional diversity in modern cells as well as possiblediversity of ancient RNA functions.

3.5The Use of Coenzymes:From the RNA World to the Protein World via Translation and the Genetic Code

Although conclusive evidence for an RNA world does not exist, there is a widelyshared belief that such a world did exist. The main obstacle to the acceptance ofthis appealing idea is that we do not understanding the origin of nucleic acid

793.5 The Use of Coenzymes

replication of long templates (which would give unlimited hereditary potential:Maynard Smith and Szathm�ry, 1995), despite some excellent results concerningthe non-enzymatic replication of short pieces of nucleic acid analogues (von Kie-drowski, 1986), and exponential, non-autonomous amplification of somewhatlonger oligonucleotides (the SPREAD procedure; Luther et al., 1998). It is the suc-cessful selection for aptamers and ribozymes (see various chapters of this book),more than anything else, that convinces people of the plausibility of an RNAworld. In line with an early suggestion (Szathm�ry, 1989, 1990b), it now seemsthat transition state stabilization is a crucial component of successful catalysisby ribozymes (Rupert et al., 2002).In the previous section we gave an overview of coenzyme usage by aptamers

(co-ribozymes). In this final section we comment on the evolutionary significanceof these findings. We think, in the footsteps of White (1976), Kor�nyi and G�nti(1981), and G�nti (2003a), that coenzymes have played a crucial link between theRNA and RNA–protein worlds. If we assume that coenzymes were already of sig-nificance in primordial metabolism in the RNA world, then it is difficult to seehow they could have been widely replaced by any other molecules in metabolicevolution. One can replace RNA enzymes by protein enzymes (see MaynardSmith and Szathm�ry, 1995, for a discussion) through evolution one by one,but any one coenzyme takes part in so many different reactions that unlessthere is a very strong selective force, replacement is unnecessary and/or unima-ginable. The fact that ribozymes can make use of cofactors has been shown ina number of experiments. Roth and Breaker (1998) demonstrated the use of his-tidine in one DNA enzyme and recently, Tsukiji et al. (2004) showed that a se-lected ribozyme can catalyze reduction of an aldehyde using the NADH cofactor.The replacement of ribozymes by protein enzymes must have been driven by

the higher catalytic versatility of the latter (Benner et al., 1987), given the factthat the 20 amino acids offer significantly more functional groups than thefour nucleotides (Table 3.5). Thus if such enzyme takeover indeed has takenplace then modern metabolism is a “palimpsest” (Benner et al., 1989). Yet, inspec-tion of Table 3.5 reveals that when complemented by coenzymes, the catalytic ver-satility of ribozymes approaches that of proteins, at least based on the diversity offunctional groups. But of course it makes a big difference how often an enzymeneeds such an aid. Presumably proteins in a rich metabolism need coenzymesless often and/or use them more efficiently than ribozymes.It now seems that all critical steps of protein synthesis can be catalyzed by

ribozymes, including amino acid binding (Famulok and Szostak, 1992) and acti-vation (Kumar and Yarus, 2001), peptide formation, as it happens in contempor-ary ribosomes; Noller et al., 1992), or in artificial systems (e.g. Illangasekare andYarus, 1999; Sun et al., 2002). The next logical question then is how and whyamino acids have been introduced into the RNA world in the first place. We can-not review the entire relevant literature here, but rather briefly present ourview here that amino acids were among critical coenzymes in the RNA world(Szathm�ry, 1990c, 1993, 1996, 1999), and that proteins are evolved descendantsof amino acid coenzymes.


Before one accepts this line of argument another question must be considered:why did evolution not raise the number of nucleotides in replicable templates?One constraint is replicability. For example, N6-ribosyl-adenine could be a chemi-cally feasible substitute for the amino acid histidine (Maurel and Ninio, 1987;D�cout and Maurel, 1993), but it could not be inserted into RNA by conventionalreplication. From the chemical point of view even replicable alternative base pairsdo not look unfeasible: Piccirilli et al. (1990) have shown that alternative basepairs can be designed, synthesized, and incorporated into nucleic acids. Furtheradditions to the genetic alphabet have also been proposed (reviewed bySzathm�ry, 2003). To be sure, the requirement of replicability puts a severe con-straint on the diversity of these novel building blocks, but it does seem reasonableto propose that increasing the size of the genetic alphabet in templates would alsoincrease the size of the catalytic alphabet when such molecules are used an en-zymes. A theoretical analysis predicts that the increase in catalytic efficiency ofribozyme-like molecules should be slower than exponential with the number ofletters in the alphabet (Szathm�ry, 1991, 1992). But this advance comes at aprice: copying fidelity of these molecules should decrease faster than exponential,so there must be an optimal alphabet size, where the product of catalytic effi-ciency and copying fidelity (a measure of fitness, cf. Eigen, 1971) is maximal(reviewed in Szathm�ry, 2003). Despite the technical difficulties involved, itwould be important to test this prediction in experimental systems.Assuming that the trade-off between replicative fidelity and enzymatic effi-

ciency holds then the canonical solution to the problem of increasing catalytic ver-satility should come by the addition of coenzymes to ribozymes, rather than byincreasing the alphabet size of templates (cf. Szathm�ry and Maynard Smith,1997). Once again there are two options. Coenzymes can be either covalentlylinked to ribozymes or they can be used by reversible binding. Most experimentalattempts demonstrated the first option (reviewed by Jadhav and Yarus, 2002b) butwe emphasize that this is more the result of the applied selection protocol, whereselection on cells is not an option. Note that it is possible to obtain nucleic acidenzymes where cofactors are reversibly rather than covalently bound (Roth andBreaker, 1998; Tsukiji et al., 2004). In general we favor the reversible alternative,for the following reason. Imagine a ribozyme (R) catalyzing a step in metabolism,using a cofactor (C). If the cofactor is bound covalently (R–C), then the followingconstraints hold: (1) there must be a selective charging activity, either within theribozyme or performed by another ribozyme, specific to the first ribozyme (R + Cp R–C); (2) each cofactor molecule is stuck with its own ribozyme molecule; (3)coenzymes cannot be used as cosubstrates at various points in metabolism, sincethey are irreversibly linked to particular ribozyme molecules. The last point is per-haps the most important one. First, it holds for the contemporary nucleotide-likecoenzymes that in any particular reaction they are not catalysts but cosubstrates:only the coenzyme coupling is catalytic, when the coenzyme is recycled by otherreactions restoring its original state (G�nti, 2003a). Schematically it holds that A +C p B + C* and D + C* p E + C, where C and C* are the two states of the samecoenzyme (e.g. NAD and NADH), and A, B, D, and E are metabolic intermedi-


ates. Suppose that a full reaction looks as follows: A + R–C p B + R–C*. Becauseof the constraints on this ribozyme R, specific for the A p B conversion, it cannotcatalyze the reaction D p E that would restore the original state of its coenzymepart C*. Therefore, the only possibility for the coenzyme coupling would be areaction such as Rl + D + R–C*p Rl + E + R–C, where Rl is a different ribozyme,specific for the D p E reaction! We conclude that efficient use of coenzymes andthe realization of coenzyme coupling require in general that coenzymes be rever-sibly bound by most ribozymes, where the latter are specific to different indivi-dual reactions in the network. (Coenzymes in the contemporary protein worldwork this way.) A good question still is whether we should find coenzymessuch as C mostly in free form or bound to a nucleic acid moiety Rll (which, ac-cording to the above reasoning, in general need not be a ribozyme). In the scenar-io of White (1976) and Jadhav and Yarus (2002a,b) the favored alternative is anR–C option. This is consonant with the fact that typically the nucleotide partsact as “handles” (H = Rll) only and do not take part in catalysis (Benner et al.,1987). A good reason for this solution is that RNA handles can be more readilyrecognized and bound by other RNA molecules than anything else (Szathm�ry,1990c, 1993). Thus, rather than forcing all coenzyme-using ribozymes to self-charge the non-nucleic acid part C, they can bind the H–C molecule reversibly,for which there is only one synthetic reaction per coenzyme: S + H + C p S +H–C, where S is a coenzyme synthetase. Occasionally it may happen that the han-dle is self-charging (H + C p H–C), but in general one would favor specific but asshort handles as possible, which hence could not self-charge, therefore in generalone would expect synthetases (S) to catalyze the H + C p H–C reaction.The link between these general considerations on coenzymes and the way out

of the RNA world is that, according to our favored scenario, amino acids had beenutilized as coenzymes of ribozymes before the genetic code was used in the codedsynthesis of proteins in translation. As emphasized before, there is evidence thathistidine can complement a nucleic acid enzyme (Roth and Breaker, 1998).Furthermore, various short peptides turn out to be useful in a nucleic acid context(Bergstrom et al., 2001; Viladkar, 2002). We urge for more experiments where theuse of various amino acids as coenzymes of ribozymes can be tested.The rationale for amino acids (aa) acting alone or linked to handles (H–aa),

favoring the latter, is similar to the one revealed above for coenzymes in general(Szathm�ry, 1993, 1996, 1999). One can imagine that amino acids are introducedinto ribozymes via specific post-replicational modification. Suppose that a ribo-zyme R uses two different amino acids, aa1 and aa2, at two different points inthe molecule. This would need two modifying ribozymes M1 and M2, specificfor aa1 and aa2 and for R, similar to tRNA modification after transcription(Bj�rk, 1995). Even if another ribozyme Rl uses the same two amino acids aa1and aa2, a different pair of modifying ribozymes M1l and M2l would be neededto produce the functional form of Rl. Introduction of more and more amino acidsinto more and more ribozymes by post-replicational modification is therefore animpasse (Szathm�ry and Maynard Smith, 1997).


A feasible alternative is to bind amino acids reversibly. The simplest known ap-tamer for isoleucine (Lozupone et al., 2003) is significantly longer than a shortRNA hairpin, such as the anticodon stem and loop of tRNA, although the latterwould be a specific and stable handle for a linked amino acid (Szathm�ry, 1999),provided it would not have to self-charge. A small ribozyme self-charging Phe is29 nucleotides long (Illangasekare and Yarus, 1999), which is also considerablylonger than an anticodon stem–loop. Thus amino acids could be bound specifi-cally and reversibly via oligonucleotide handles by the ribozymes using them. Sta-bility and reversible binding of such handles would have favored an RNA hairpin(Szathm�ry, 1999).A consequence of the above reasoning is that the anticodon hairpin is likely to

have been the earliest adapter. A remarkable other benefit is that genetic codingpops out for free: different handles could have been bound safely to the sameamino acid, but charging of different amino acids to the same handles wouldhave been forbidden. The origin of the unambiguous but degenerate nature ofthe genetic code lies in the use of such coding coenzyme handles (CCHs), accord-ing to our view (Szathm�ry, 1990c). The crucial difference between the CCH hy-pothesis and the similar, in many ways, idea of Wong (1991) about selection forRNA peptidation is that in the latter coding does not naturally emerge due to thefact that oligopeptides rather than amino acids are assumed to be linked to RNA.As discussed above, many aptamers that bind amino acids are self-charging, but

this is not necessary, as shown by a series of remarkable experiments performedin Hiroaki Suga’s lab. First, they selected for a ribozyme that self-acylates at the 3lend of its integral tRNA part (Saito et al., 2001a). The leader sequence at the 5lend can be cut off by RNAse P from this pre-tRNA, which in turn is able to ami-noacylate the tRNA part in trans (Saito et al., 2001b). The cognate amino acid Pheis recognized by a U/A-rich (codonic/anticodonic!) region, whereas the 3l end oftRNA is recognized by a triplet in the ribozyme. Perhaps even more interesting isthe next series of experiments, in which a somewhat similar bifunctional ribo-zyme charging Gln was selected for (Lee et al., 2000). One domain of this ribo-zyme recognizes an activated glutaminyl ester and charges it to its own 5l-OHgroup (thus forming a covalent intermediate). The other domain binds tRNAand transfers the aminoacyl group to its 3l end. The first domain can be separatedfrom the other and can be significantly simplified. The glutamine-binding do-main can be reduced to 29 nucleotides and can aminoacylate the other domainin trans (Lee and Suga, 2001). Interestingly, a paired cognate codon:anticodontriplet is essential in this domain for the recognition of Gln.These experiments lend support to the idea that short adapter molecules (han-

dles) could have been specifically charged by synthetase ribozymes (Szathm�ry,1993, 1999). Where on the adapter this was carried out is an open question.Woese (1967) and Szathm�ry (1999) suggested that the primordial site foramino acid linking may have been nucleotide 37, immediately adjacent to the an-ticodon. Not only is this position universally modified, but several amino acidsplay a part in the process, in a way that makes sense in the light of scenariosabout evolution of the genetic code (cf. Szathm�ry, 1999). As suggested by


Wong (1991), primordial amino acids may have been N-linked, because the esterbond is too labile.Surprising suggestive evidence came to light recently. In E. coli the tRNAAsp mo-

lecule is modified in its wobble position. The queuosine residue is glutamylatedby a truncated version of glutamyl-tRNA synthetase (Blaise et al., 2004; Salazaret al., 2004) that recognizes the anticodon stem and loop of tRNAAsp. This findingraises the possibility of a role of other synthetase-related molecules in tRNA mod-ifications as well as the possibility of similar activity in the RNA world, catalyzedby ribozymes. Testing this idea again calls for further experimentation.We mentioned that, perhaps surprisingly, codons and/or anticodons tend to

show up in aptamers selected to bind amino acids. Does nature want to tell ussomething or is this merely due to chance? The most recent analysis (Caporasoet al., 2005) favors the latter view. As was noted by Szathm�ry (1999), it is notonly codons but also anticodons that show up more frequently than random inselected aptamers. Indeed now the data set (including aptamers for Gln, Tyr,Leu, Ile, Phe, His, Trp, Arg) seems to be biased towards anticodons, as advocatedin the CCH model (Szathm�ry, 1993).We note that there are still many puzzles around tRNA to be sorted out. Krzy-

zaniak et al. (1994) found that Phe and Met can be specifically charged to theircognate tRNAs in the absence of enzymes at high pressure. Should this resultbe confirmed, then it shows that at least these two tRNAs are barophylic apta-mers, which raises interesting thoughts in connection with the deep-sea hot-spring scenario for the origin of life (reviewed by Hazen et al., 2002) and theneed to select for extremophilic aptamers in general. Another odd result is thathistidine and its anticodon GpUpG act similarly as catalysts in certain in vitro me-tabolic reactions (Shimizu, 2004). Clearly, it would be very important to test andpossibly generalize these observations. We are content that many surprising re-sults will come to light concerning aptamers and their role in evolution.

3.6Outlook

Certain stages of evolution can only be reconstructed by inference from results ofcomparative and experimental analysis. The ultimate goal is to construct scenar-ios for certain critical transitions that are plausible and preferably make somepredictions that were not part of the initial assumptions and that can be tested.Analysis of present-day living systems gives us strong hints that an RNA worldin some form existed for a while.Reconstruction of that world can only be partial, and should be rather based on

experiments that prove certain principles. Aptamers eminently serve this purposefor the RNA world: the experimental results of their in vitro evolution can be jus-tifiably called breathtaking. In this chapter we have addressed two key issues: howthe fitness of RNA replicators is affected by mutations, and how coenzymes couldhave complemented the RNA world and how they, in a literary sense, could have


betrayed it by paving a way out of it into the protein world. None of these issuescan be taken as settled, however. Although we have put forward a detailed analysisof the function landscape of an RNA enzyme, and have used this to infer the fit-ness landscape of RNA replicators, many more data are needed in order to be con-clusive. In a similar vein, hypotheses centered on the evolutionary role of coen-zymes should be more rigorously tested. More ribozymes utilizing cofactors, in-cluding various amino acids, should be selected for. Ribozymes that chargeother RNA molecules with amino acids should be analyzed to see whether codo-nic or anticodonic triplets predominate in the critical binding sites. Ultimately,some crucial steps in the takeover of ribozymes by protein enzymes need to bere-enacted in experiments and detailed models.

Acknowledgments

�.K. is supported by a postdoctoral fellowship from the Hungarian ScientificResearch Fund (OTKA D048406). M.S. is supported by Fundaci�n Ram�n Areces(Spain). E.Sz. is supported by the Hungarian Scientific Research Fund (OTKA).The sponsorship from COST-European Cooperation in the field of Scientificand Technical Research, Internal Project number D27/0003/02 “Emergence andSelection of Networks of Catalytic Species” (URL: http://cost.cordis.lu/src/extranet/publish/D27WGP/d27–0003–02.cfm), and from COST D27 workinggroup (D27/0005/03) entitled “Etiology, Replication, Activity and Persistenceof RNA” (URL: http://cost.cordis.lu/src/extranet/publish/D27WGP/d27–0005–03.cfm), is very much appreciated.

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Fitness Landscapes, Error Thresholds, and Cofactors in Aptamer Evolution

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