Virtual Screening: Principles, Challenges, and Practical Guidelines (Methods and Principles in Medicinal Chemistry)

Virtual Screening: Principles, Challenges, and Practical Guidelines (Methods and Principles in Medicinal Chemistry)

By: Christoph Sotriffer (editor), Raimund Mannhold (series_editor), Gerd Folkers (series_editor), Hugo Kubinyi (series_editor)Hardback

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Description

Drug discovery is all about finding small molecules that interact in a desired way with larger molecules, namely proteins and other macromolecules in the human body. If the three-dimensional structures of both the small and large molecule are known, their interaction can be tested by computer simulation with a reasonable degree of accuracy. Alternatively, if active ligands are already available, molecular similarity searches can be used to find new molecules. This virtual screening can even be applied to compounds that have yet to be synthesized, as opposed to "real" screening that requires cost- and labor-intensive laboratory testing with previously synthesized drug compounds. Unique in its focus on the end user, this is a real "how to" book that does not presuppose prior experience in virtual screening or a background in computational chemistry. It is both a desktop reference and practical guide to virtual screening applications in drug discovery, offering a comprehensive and up-to-date overview. Clearly divided into four major sections, the first provides a detailed description of the methods required for and applied in virtual screening, while the second discusses the most important challenges in order to improve the impact and success of this technique. The third and fourth, practical parts contain practical guidelines and several case studies covering the most important scenarios for new drug discovery, accompanied by general guidelines for the entire workflow of virtual screening studies. Throughout the text, medicinal chemists from academia, as well as from large and small pharmaceutical companies report on their experience and pass on priceless practical advice on how to make best use of these powerful methods.

About Author

Christoph Sotriffer is Professor for Pharmaceutical Chemistry at the University ofWurzburg, Germany. He graduated as a chemist from the University of Innsbruck, Austria, where he obtained his PhD in 1999. After conducting postdoctoral research at the University of California, San Diego, USA, and the University of Marburg, Germany, he moved to the University ofWurzburg in 2006, where he has built a research group for computational medicinal chemistry. Besides structure-based drug design and virtual screening, his prime scientific interest is the computational analysis and prediction of protein-ligand interactions. His work was awarded by the Austrian Chemical Society GOCH in 2005 and the German Chemical and Pharmaceutical Societies GDCh and DPhG in 2007.

Contents

List of Contributors XVII Preface XXIII A Personal Foreword XXV Part One Principles 1 1 Virtual Screening of Chemical Space: From Generic Compound Collections to Tailored Screening Libraries 3 Markus Boehm 1.1 Introduction 3 1.2 Concepts of Chemical Space 4 1.3 Concepts of Druglikeness and Leadlikeness 6 1.4 Diversity-Based Libraries 8 1.4.1 Concepts of Molecular Diversity 8 1.4.2 Descriptor-Based Diversity Selection 9 1.4.3 Scaffold-Based Diversity Selection 12 1.4.4 Sources of Diversity 13 1.5 Focused Libraries 15 1.5.1 Concepts of Focused Design 15 1.5.2 Ligand-Based Focused Design 16 1.5.3 Structure-Based Focused Design 17 1.5.4 Chemogenomics Approaches 18 1.6 Virtual Combinatorial Libraries and Fragment Spaces 20 1.7 Databases of Chemical and Biological Information 21 1.8 Conclusions and Outlook 24 1.9 Glossary 25 References 26 2 Preparing and Filtering Compound Databases for Virtual and Experimental Screening 35 Maxwell D. Cummings, Eric Arnoult, Christophe Buyck, Gary Tresadern, Ann M. Vos, and Jorg K. Wegner 2.1 Introduction 35 2.2 Ligand Databases 36 2.2.1 Chemical Data Structures 36 2.2.2 3D Conformations 38 2.2.3 Data Storage 39 2.2.4 Workflow Tools 39 2.2.5 Past Reviews and Recent Papers 40 2.3 Considering Physicochemical Properties 42 2.3.1 Druglikeness 42 2.3.2 Leadlikeness and Beyond 43 2.4 Undesirables 43 2.4.1 Screening Artifacts 44 2.4.2 Pharmacologically Promiscuous Compounds 45 2.5 Property-Based Filtering for Selected Targets 46 2.5.1 Antibacterials 47 2.5.2 CNS 49 2.5.3 Protein Protein Interactions 51 2.6 Summary 52 References 53 3 Ligand-Based Virtual Screening 61 Herbert Koeppen, Jan Kriegl, Uta Lessel, Christofer S. Tautermann, and Bernd Wellenzohn 3.1 Introduction 61 3.2 Descriptors 62 3.3 Search Databases and Queries 67 3.3.1 Selection of Reference Ligands 67 3.3.2 Preparation of the Search Database 68 3.4 Virtual Screening Techniques 68 3.4.1 Similarity Searches 69 3.4.2 Similarity Searches in Very Large Chemical Spaces 72 3.4.3 Machine Learning in Virtual Screening 74 3.4.4 Validation of Methods and Prediction of Success 78 3.5 Conclusions 79 References 80 4 The Basis for Target-Based Virtual Screening: Protein Structures 87 Jason C. Cole, Oliver Korb, Tjelvar S.G. Olsson, and John Liebeschuetz 4.1 Introduction 87 4.2 Selecting a Protein Structure for Virtual Screening 87 4.2.1 Why Are There Errors in Crystal Structures? 87 4.2.2 Possible Problems That May Occur in a Crystal Structure 91 4.2.3 Structural Relevance 95 4.2.4 Critical Evaluation of Models: Recognizing Issues in Structures 98 4.3 Setting Up a Protein Model for vHTS 101 4.3.1 Binding Site Definition 101 4.3.2 Protonation 104 4.3.3 Treatment of Solvent in Docking 104 4.3.4 Use of Protein-Based Constraints in Docking 105 4.3.5 Protein Flexibility 106 4.4 Summary 109 4.5 Glossary of Crystallographic Terms 110 4.5.1 R-Factor 110 4.5.2 Resolution 110 4.5.3 2mFo-DFc Map 110 References 110 5 Pharmacophore Models for Virtual Screening 115 Patrick Markt, Daniela Schuster, and Thierry Langer 5.1 Introduction 115 5.2 Compilation of Compounds 116 5.2.1 Chemical Structure Generation 116 5.2.2 Conformational Analysis 116 5.3 Pharmacophore Model Generation 117 5.3.1 State of the Art 117 5.3.2 Structure-Based Methods 117 5.3.3 Ligand-Based Methods 118 5.3.4 Limitations of Ligand-Based Methods 119 5.4 Validation of Pharmacophore Models 119 5.4.1 Chemical Databases for Validation 119 5.4.2 Enrichment Assessment 121 5.4.3 Enrichment Metrics 122 5.4.4 Receiver Operating Characteristic Curve Analysis 124 5.4.5 Area Under the ROC Curve 125 5.5 Pharmacophore-Based Screening 127 5.5.1 DS CATALYST 128 5.5.2 UNITY (GALAHAD/GASP) 128 5.5.3 LIGANDSCOUT 129 5.5.4 MOE 130 5.5.5 PHASE 130 5.6 Postprocessing of Pharmacophore-Based Screening Hits 131 5.6.1 Lead- and Druglikeness 131 5.6.2 Structural Similarity Analysis 131 5.7 Pharmacophore-Based Parallel Screening 132 5.8 Application Examples for Synthetic Compound Screening 133 5.8.1 17b-Hydroxysteroid Dehydrogenase 1 Inhibitors 133 5.8.2 Cannabinoid Receptor 2 (CB2) Ligands 134 5.8.3 Further Application Examples 136 5.9 Application Examples for Natural Product Screening 136 5.9.1 Cyclooxygenase (COX) Inhibitors 139 5.9.2 Sigma-1 (s1) Receptor Ligands 139 5.9.3 Acetylcholinesterase Inhibitors 140 5.9.4 Human Rhinovirus Coat Protein Inhibitors 141 5.9.5 Quorum-Sensing Inhibitors 141 5.9.6 Peroxisome Proliferator-Activated Receptor c Ligands 141 5.9.7 b-Ketoacyl-Acyl Carrier Protein Synthase III Inhibitors 142 5.9.8 5-Lipoxygenase Inhibitors 142 5.9.9 11b-Hydroxysteroid Dehydrogenase Type 1 Inhibitors 142 5.9.10 Pharmacophore-Based Parallel Screening of Natural Products 143 5.10 Conclusions 143 References 144 6 Docking Methods for Virtual Screening: Principles and Recent Advances 153 Didier Rognan 6.1 Principles of Molecular Docking 153 6.1.1 Sampling Degrees of Freedom of the Ligand 154 6.1.2 Scoring Ligand Poses 156 6.2 Docking-Based Virtual Screening Flowchart 158 6.2.1 Ligand Setup 158 6.2.2 Protein Setup 159 6.2.3 Docking 160 6.2.4 Postdocking Analysis 161 6.3 Recent Advances in Docking-Based VS Methods 162 6.3.1 Novel Docking Algorithms 162 6.3.2 Fragment Docking 164 6.3.3 Postdocking Refinement 164 6.3.4 Addressing Protein Flexibility 166 6.3.5 Solvated or Dry? 168 6.4 Future Trends in Docking 168 References 169 Part Two Challenges 177 7 The Challenge of Affinity Prediction: Scoring Functions for Structure-Based Virtual Screening 179 Christoph Sotriffer and Hans Matter 7.1 Introduction 179 7.2 Physicochemical Basis of Protein Ligand Recognition 180 7.3 Classes of Scoring Functions 185 7.3.1 Force Field-Based Methods 185 7.3.2 Empirical Scoring Functions 189 7.3.3 Knowledge-Based Scoring Functions 191 7.4 Interesting New Approaches to Scoring Functions 192 7.4.1 Improved Treatment of Hydrophobicity and Dehydration 192 7.4.2 Development and Validation of SFCscore 194 7.4.3 Consensus Scoring 195 7.4.4 Tailored Scoring Functions 196 7.4.5 Structural Interaction Fingerprints 199 7.5 Comparative Assessment of Scoring Functions 200 7.6 Tailoring Scoring Strategies in Virtual Screening 203 7.6.1 Toward a Strategy for Applying Scoring Functions 203 7.6.2 Retrospective Validation Prior to Prospective Virtual Screening 204 7.6.3 Lessons Learned: Improvements in Scoring Evaluations 205 7.6.4 Postfiltering Results of Virtual Screenings 205 7.7 Caveats for Development of Scoring Functions 206 7.7.1 General Points 206 7.7.2 Biological Data 207 7.7.3 Structural Data on Protein Ligand Complexes and Decoy Data Sets 207 7.7.4 Cooperativity and Other Model Deficiencies 208 7.8 Conclusions 209 References 210 8 Protein Flexibility in Structure-Based Virtual Screening: From Models to Algorithms 223 Angela M. Henzler and Matthias Rarey 8.1 How Flexible Are Proteins? A Historical Perspective 223 8.1.1 Ligand Binding Is Coupled with Protein Conformational Change 223 8.1.2 Types of Flexibility 224 8.2 Flexible Protein Handling in Protein Ligand Docking 225 8.2.1 Docking Following Conformational Selection 227 8.2.2 Induced Fit Docking: Single-Structure-Based Docking Techniques 231 8.2.3 Integrated Docking Approaches 235 8.3 Flexible Protein Handling in Docking-Based Virtual Screening 236 8.3.1 Efficiency of Fully Flexible Docking Approaches in Retrospective 237 8.3.2 Discrimination of Binders and Nonbinders 238 8.4 Summary 238 References 239 9 Handling Protein Flexibility in Docking and High-Throughput Docking: From Algorithms to Applications 245 Claudio N. Cavasotto 9.1 Introduction: Docking and High-Throughput Docking in Drug Discovery 245 9.2 The Challenge of Accounting for Protein Flexibility in Docking 246 9.2.1 Theoretical Understanding of the Problem 246 9.2.2 Docking Failures Due to Protein Flexibility 247 9.3 Accounting for Protein Flexibility in Docking-Based Drug Discovery and Design 250 9.3.1 Receptor Ensemble-Based Docking Methods 252 9.3.2 Single-Structure-Based Docking Methods 253 9.3.3 Multilevel Methods 256 9.3.4 Homology Modeling 257 9.4 Conclusions 257 References 258 10 Consideration of Water and Solvation Effects in Virtual Screening 263 Johannes Kirchmair, Gudrun M. Spitzer, and Klaus R. Liedl 10.1 Introduction 263 10.2 Experimental Approaches for Analyzing Water Molecules 266 10.3 Computational Approaches for Analyzing Water Molecules 271 10.3.1 Molecular Dynamics Simulations 271 10.3.2 Empirical and Implicit Considerations of Solvation Effects 274 10.4 Water-Sensitive Virtual Screening: Approaches and Applications 275 10.4.1 Protein Ligand Docking 275 10.4.2 Pharmacophore Modeling 278 10.5 Conclusions and Recommendations 281 References 282 Part Three Applications and Practical Guidelines 291 11 Applied Virtual Screening: Strategies, Recommendations, and Caveats 293 Dagmar Stumpfe and Jurgen Bajorath 11.1 Introduction 293 11.2 What Is Virtual Screening? 293 11.3 Spectrum of Virtual Screening Approaches 294 11.4 Molecular Similarity as a Foundation and Caveat of Virtual Screening 295 11.5 Goals of Virtual Screening 296 11.6 Applicability Domain 297 11.7 Reference and Database Compounds 299 11.8 Biological Activity versus Compound Potency 300 11.9 Methodological Complexity and Compound Class Dependence 301 11.10 Search Strategies and Compound Selection 302 11.11 Virtual and High-Throughput Screening 304 11.12 Practical Applications: An Overview 306 11.13 LFA-1 Antagonist 307 11.14 Selectivity Searching 310 11.15 Concluding Remarks 314 References 315 12 Applications and Success Stories in Virtual Screening 319 Hans Matter and Christoph Sotriffer 12.1 Introduction 319 12.2 Practical Considerations 320 12.3 Successful Applications of Virtual Screening 321 12.3.1 Structure-Based Virtual Screening 322 12.3.2 Structure-Based Library Design 336 12.3.3 Ligand-Based Virtual Screening 338 12.4 Conclusion 347 References 348 Part Four Scenarios and Case Studies: Routes to Success 359 13 Scenarios and Case Studies: Examples for Ligand-Based Virtual Screening 361 Trevor Howe, Daniele Bemporad, and Gary Tresadern 13.1 Introduction 361 13.2 1D Ligand-Based Virtual Screening 362 13.3 2D Ligand-Based Virtual Screening 363 13.3.1 Examples from the Literature 363 13.3.2 Applications at J&JPRD Europe 366 13.4 3D Ligand-Based Virtual Screening 368 13.4.1 Methods 370 13.4.2 3DLBVS Examples 372 13.5 Summary 376 References 377 14 Virtual Screening on Homology Models 381 Robert Kiss and Gyorgy M. Keseru" 14.1 Introduction 381 14.2 Homology Models versus Crystal Structures: Comparative Evaluation of Screening Performance 382 14.2.1 Soluble Proteins 382 14.2.2 Membrane Proteins 392 14.3 Challenges of Homology Model-Based Virtual Screening 394 14.3.1 Level of Sequence Identity 395 14.3.2 Main-Chain Flexibility 396 14.3.3 Side-Chain Conformation: Induced Fit Effects of Ligands 396 14.3.4 Loop Modeling 397 14.4 Case Studies 399 14.4.1 Virtual Screening on the Homology Model of Histamine H4 Receptor 399 14.4.2 Virtual Screening on the Homology Model of Janus Kinase 2 402 References 404 15 Target-Based Virtual Screening on Small-Molecule Protein Binding Sites 411 Ralf Heinke, Urszula Uciechowska, Manfred Jung, and Wolfgang Sippl 15.1 Introduction 411 15.1.1 Pharmacophore-Based Methods 412 15.1.2 Ligand Docking 412 15.1.3 Virtual Screening 413 15.1.4 Binding Free Energy Calculations 414 15.2 Structure-Based VS for Histone Arginine Methyltransferase PRMT1 Inhibitors 414 15.2.1 Structure-Based VS of the NCI Diversity Set 415 15.2.2 Pharmacophore-Based VS 417 15.3 Identification of Nanomolar Histamine H3 Receptor Antagonists by Structure- and Pharmacophore-Based VS 422 15.3.1 Generation of Homology Model of the hH3R and hH3R Antagonist Complexes 423 15.3.2 Validation of the Homology Model by Docking Known Antagonists into the hH3R Binding Site 424 15.3.3 Pharmacophore-Based VS 425 15.3.4 Experimental Testing of the Identified Hits 429 15.3.5 Discussion of the Applied VS Strategies 429 15.4 Summary 431 References 432 16 Target-Based Virtual Screening to Address Protein Protein Interfaces 435 Olivier Sperandio, Maria A. Miteva, and Bruno O. Villoutreix 16.1 Introduction 435 16.2 Some Recent PPIM Success Stories 437 16.3 Protein Protein Interfaces 438 16.3.1 Interface Pockets, Flexibility, and Hot Spots 440 16.3.2 Databases and Tools to Analyze Interfaces 442 16.4 PPIMs. Chemical Space and ADME/Tox Properties 442 16.5 Drug Discovery, Chemical Biology, and In Silico Screening Methods: Overview and Suggestions for PPIM Search 447 16.6 Case Studies 450 16.6.1 PPI Stabilizers: Superoxide Dismutase Type 1 450 16.6.2 PPI Inhibitors: Lck 452 16.6.3 Allosteric Inhibitors: Antitrypsin Polymerization 455 16.7 Conclusions and Future Directions 457 References 458 17 Fragment-Based Approaches in Virtual Screening 467 Danzhi Huang and Amedeo Caflisch 17.1 Introduction 467 17.2 In Silico Fragment-Based Approaches 468 17.3 Our Approach to High-Throughput Fragment-Based Docking 470 17.3.1 Decomposition of Compounds into Fragments 471 17.3.2 Docking of Anchor Fragments 471 17.3.3 Flexible Docking of Library Compounds 472 17.3.4 LIECE Binding Energy Evaluation 472 17.3.5 Consensus Scoring 475 17.3.6 In Silico Screening Campaigns 475 17.3.7 West Nile Virus NS3 Protease (Flaviviral Infections) 475 17.3.8 EphB4 Tyrosine Kinase (Cancer) 477 17.4 Lessons Learned from Our Fragment-Based Docking 479 17.5 Challenges of Fragment-Based Approaches 481 References 482 Appendix A: Software Overview 491 Appendix B: Virtual Screening Application Studies 501 Index 511

Product Details

  • ISBN13: 9783527326365
  • Format: Hardback
  • Number Of Pages: 550
  • ID: 9783527326365
  • weight: 1172
  • ISBN10: 3527326367

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