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E-raamat: Virtual Screening - Principles, Challenges ad=nd Practical Guidelines V48: Principles, Challenges, and Practical Guidelines [Wiley Online]

Series edited by (University of Düsseldorf, Germany), Series edited by (University of Heidelberg, Germany), Series edited by (Swiss Institute of Technology (ETH), Zürich, Switzerland), Edited by (University of Würzburg, Germany)
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Virtual Screening - Principles, Challenges ad=nd Practical Guidelines V48: Principles, Challenges, and Practical GuidelinesSuurem pilt
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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.
List of Contributors
xvii
Preface xxiii
A Personal Foreword xxv
Part One Principles
1(176)
1 Virtual Screening of Chemical Space: From Generic Compound Collections to Tailored Screening Libraries
3(32)
Markus Boehm
1.1 Introduction
3(1)
1.2 Concepts of Chemical Space
4(2)
1.3 Concepts of Druglikeness and Leadlikeness
6(2)
1.4 Diversity-Based Libraries
8(7)
1.4.1 Concepts of Molecular Diversity
8(1)
1.4.2 Descriptor-Based Diversity Selection
9(3)
1.4.3 Scaffold-Based Diversity Selection
12(1)
1.4.4 Sources of Diversity
13(2)
1.5 Focused Libraries
15(5)
1.5.1 Concepts of Focused Design
15(1)
1.5.2 Ligand-Based Focused Design
16(1)
1.5.3 Structure-Based Focused Design
17(1)
1.5.4 Chemogenomics Approaches
18(2)
1.6 Virtual Combinatorial Libraries and Fragment Spaces
20(1)
1.7 Databases of Chemical and Biological Information
21(3)
1.8 Conclusions and Outlook
24(1)
1.9 Glossary
25(10)
References
26(9)
2 Preparing and Filtering Compound Databases for Virtual and Experimental Screening
35(26)
Maxwell D. Cummings
Eric Arnoult
Christophe Buyck
Gary Tresadern
Ann M. Vos
Jorg K. Wegner
2.1 Introduction
35(1)
2.2 Ligand Databases
36(6)
2.2.1 Chemical Data Structures
36(2)
2.2.2 3D Conformations
38(1)
2.2.3 Data Storage
39(1)
2.2.4 Workflow Tools
39(1)
2.2.5 Past Reviews and Recent Papers
40(2)
2.3 Considering Physicochemical Properties
42(1)
2.3.1 Druglikeness
42(1)
2.3.2 Leadlikeness and Beyond
43(1)
2.4 Undesirables
43(3)
2.4.1 Screening Artifacts
44(1)
2.4.2 Pharmacologically Promiscuous Compounds
45(1)
2.5 Property-Based Filtering for Selected Targets
46(6)
2.5.1 Antibacterials
47(2)
2.5.2 CNS
49(2)
2.5.3 Protein-Protein Interactions
51(1)
2.6 Summary
52(9)
References
53(8)
3 Ligand-Based Virtual Screening
61(26)
Herbert Koeppen
Jan Kriegl
Uta Lessel
Christofer S. Tautermann
Bernd Wellenzohn
3.1 Introduction
61(1)
3.2 Descriptors
62(5)
3.3 Search Databases and Queries
67(1)
3.3.1 Selection of Reference Ligands
67(1)
3.3.2 Preparation of the Search Database
68(1)
3.4 Virtual Screening Techniques
68(11)
3.4.1 Similarity Searches
69(1)
3.4.1.1 Similarity Measures
69(1)
3.4.1.2 Practice of Similarity Searches
69(2)
3.4.1.3 Selection of Descriptors
71(1)
3.4.1.4 Data Fusion
72(1)
3.4.2 Similarity Searches in Very Large Chemical Spaces
72(2)
3.4.3 Machine Learning in Virtual Screening
74(1)
3.4.3.1 Unsupervised Methods
75(1)
3.4.3.2 Supervised Methods
75(1)
3.4.3.3 Selected Techniques
76(2)
3.4.3.4 Machine Learning Applications for Virtual Screening
78(1)
3.4.4 Validation of Methods and Prediction of Success
78(1)
3.5 Conclusions
79(8)
References
80(7)
4 The Basis for Target-Based Virtual Screening: Protein Structures
87(28)
Jason C. Cole
Oliver Korb
Tjelvar S.G. Olsson
John Liebeschuetz
4.1 Introduction
87(1)
4.2 Selecting a Protein Structure for Virtual Screening
87(14)
4.2.1 Why Are There Errors in Crystal Structures?
87(5)
4.2.2 Possible Problems That May Occur in a Crystal Structure
91(1)
4.2.2.1 Entirely Incorrect Models
91(1)
4.2.2.2 Sequencing Errors
91(1)
4.2.2.3 Misplaced Side Chains
91(1)
4.2.2.4 Structural Disorder
92(1)
4.2.2.5 Poorly Modeled Cofactors and Ligands
92(2)
4.2.2.6 Erroneous Solvent
94(1)
4.2.3 Structural Relevance
95(1)
4.2.3.1 The Biologically Relevant Unit and Crystal Packing
95(3)
4.2.4 Critical Evaluation of Models: Recognizing Issues in Structures
98(3)
4.3 Setting Up a Protein Model for vHTS
101(8)
4.3.1 Binding Site Definition
101(3)
4.3.2 Protonation
104(1)
4.3.3 Treatment of Solvent in Docking
104(1)
4.3.4 Use of Protein-Based Constraints in Docking
105(1)
4.3.5 Protein Flexibility
106(1)
4.3.5.1 Pose Prediction
107(1)
4.3.5.2 Virtual Screening
108(1)
4.4 Summary
109(1)
4.5 Glossary of Crystallographic Terms
110(5)
4.5.1 R-Factor
110(1)
4.5.2 Resolution
110(1)
4.5.3 2mFo-DFc Map
110(1)
References
110(5)
5 Pharmacophore Models for Virtual Screening
115(38)
Patrick Markt
Daniela Schuster
Thierry Langer
5.1 Introduction
115(1)
5.2 Compilation of Compounds
116(1)
5.2.1 Chemical Structure Generation
116(1)
5.2.2 Conformational Analysis
116(1)
5.3 Pharmacophore Model Generation
117(2)
5.3.1 State of the Art
117(1)
5.3.2 Structure-Based Methods
117(1)
5.3.3 Ligand-Based Methods
118(1)
5.3.4 Limitations of Ligand-Based Methods
119(1)
5.4 Validation of Pharmacophore Models
119(8)
5.4.1 Chemical Databases for Validation
119(2)
5.4.2 Enrichment Assessment
121(1)
5.4.3 Enrichment Metrics
122(2)
5.4.4 Receiver Operating Characteristic Curve Analysis
124(1)
5.4.5 Area Under the ROC Curve
125(2)
5.5 Pharmacophore-Based Screening
127(4)
5.5.1 DS Catalyst
128(1)
5.5.2 Unity (Galahad/Gasp)
128(1)
5.5.3 Ligandscout
129(1)
5.5.4 Moe
130(1)
5.5.5 Phase
130(1)
5.6 Postprocessing of Pharmacophore-Based Screening Hits
131(1)
5.6.1 Lead- and Druglikeness
131(1)
5.6.2 Structural Similarity Analysis
131(1)
5.7 Pharmacophore-Based Parallel Screening
132(1)
5.8 Application Examples for Synthetic Compound Screening
133(3)
5.8.1 17β-Hydroxysteroid Dehydrogenase 1 Inhibitors
133(1)
5.8.2 Cannabinoid Receptor 2 (CB2) Ligands
134(2)
5.8.3 Further Application Examples
136(1)
5.9 Application Examples for Natural Product Screening
136(7)
5.9.1 Cyclooxygenase (COX) Inhibitors
139(1)
5.9.2 Sigma-1 (σ1) Receptor Ligands
139(1)
5.9.3 Acetylcholinesterase Inhibitors
140(1)
5.9.4 Human Rhinovirus Coat Protein Inhibitors
141(1)
5.9.5 Quorum-Sensing Inhibitors
141(1)
5.9.6 Peroxisome Proliferator-Activated Receptor γ Ligands
141(1)
5.9.7 β-Ketoacyl-Acyl Carrier Protein Synthase III Inhibitors
142(1)
5.9.8 5-Lipoxygenase Inhibitors
142(1)
5.9.9 11β-Hydroxysteroid Dehydrogenase Type 1 Inhibitors
142(1)
5.9.10 Pharmacophore-Based Parallel Screening of Natural Products
143(1)
5.10 Conclusions
143(10)
References
144(9)
6 Docking Methods for Virtual Screening: Principles and Recent Advances
153(24)
Didier Rognan
6.1 Principles of Molecular Docking
153(5)
6.1.1 Sampling Degrees of Freedom of the Ligand
154(1)
6.1.1.1 Generation of Multiconformer Ligand Libraries
154(1)
6.1.1.2 Incremental Construction
154(1)
6.1.1.3 Stochastic Methods
155(1)
6.1.2 Scoring Ligand Poses
156(1)
6.1.2.1 Empirical Scoring Functions
156(1)
6.1.2.2 Knowledge-Based Potential of Mean Force
156(1)
6.1.2.3 Force Fields
157(1)
6.1.2.4 Critical Evaluation of Scoring Functions
157(1)
6.2 Docking-Based Virtual Screening Flowchart
158(4)
6.2.1 Ligand Setup
158(1)
6.2.2 Protein Setup
159(1)
6.2.3 Docking
160(2)
6.2.4 Postdocking Analysis
162(1)
6.3 Recent Advances in Docking-Based VS Methods
162(6)
6.3.1 Novel Docking Algorithms
162(2)
6.3.2 Fragment Docking
164(1)
6.3.3 Postdocking Refinement
164(1)
6.3.3.1 Rescoring with Rigorous Scoring Functions
164(1)
6.3.3.2 Topological Scoring by Protein-Ligand Interaction Fingerprint (IFP)
165(1)
6.3.4 Addressing Protein Flexibility
166(2)
6.3.5 Solvated or Dry?
168(1)
6.4 Future Trends in Docking
168(9)
References
169(8)
Part Two Challenges
177(114)
7 The Challenge of Affinity Prediction: Scoring Functions for Structure-Based Virtual Screening
179(44)
Christoph Sotriffer
Hans Matter
7.1 Introduction
179(1)
7.2 Physicochemical Basis of Protein-Ligand Recognition
180(5)
7.3 Classes of Scoring Functions
185(7)
7.3.1 Force Field-Based Methods
185(4)
7.3.2 Empirical Scoring Functions
189(3)
7.3.3 Knowledge-Based Scoring Functions
192(1)
7.4 Interesting New Approaches to Scoring Functions
192(8)
7.4.1 Improved Treatment of Hydrophobicity and Dehydration
192(2)
7.4.2 Development and Validation of SFCscore
194(1)
7.4.3 Consensus Scoring
195(1)
7.4.4 Tailored Scoring Functions
196(3)
7.4.5 Structural Interaction Fingerprints
199(1)
7.5 Comparative Assessment of Scoring Functions
200(3)
7.6 Tailoring Scoring Strategies in Virtual Screening
203(3)
7.6.1 Toward a Strategy for Applying Scoring Functions
203(1)
7.6.2 Retrospective Validation Prior to Prospective Virtual Screening
204(1)
7.6.3 Lessons Learned: Improvements in Scoring Evaluations
205(1)
7.6.4 Postfiltering Results of Virtual Screenings
205(1)
7.7 Caveats for Development of Scoring Functions
206(3)
7.7.1 General Points
206(1)
7.7.2 Biological Data
207(1)
7.7.3 Structural Data on Protein-Ligand Complexes and Decoy Data Sets
207(1)
7.7.4 Cooperarivity and Other Model Deficiencies
208(1)
7.8 Conclusions
209(14)
References
210(13)
8 Protein Flexibility in Structure-Based Virtual Screening: From Models to Algorithms
223(22)
Angela M. Henzler
Matthias Rarey
8.1 How Flexible Are Proteins? - A Historical Perspective
223(2)
8.1.1 Ligand Binding Is Coupled with Protein Conformational Change
223(1)
8.1.2 Types of Flexibility
224(1)
8.2 Flexible Protein Handling in Protein-Ligand Docking
225(11)
8.2.1 Docking Following Conformational Selection
227(1)
8.2.1.1 Protein Flexibility Analysis and Protein Ensemble Generation
227(1)
8.2.1.2 Ensemble-Based Docking Techniques
228(3)
8.2.2 Induced Fit Docking: Single-Structure-Based Docking Techniques
231(1)
8.2.2.1 Consecutive Ligand and Protein Conformational Change
232(2)
8.2.2.2 Simultaneous Ligand and Protein Conformational Change
234(1)
8.2.3 Integrated Docking Approaches
235(1)
8.3 Flexible Protein Handling in Docking-Based Virtual Screening
236(2)
8.3.1 Efficiency of Fully Flexible Docking Approaches in Retrospective
237(1)
8.3.2 Discrimination of Binders and Nonbinders
238(1)
8.4 Summary
238(7)
References
239(6)
9 Handling Protein Flexibility in Docking and High-Throughput Docking: From Algorithms to Applications
245(18)
Claudio N. Cavasotto
9.1 Introduction: Docking and High-Throughput Docking in Drug Discovery
245(1)
9.2 The Challenge of Accounting for Protein Flexibility in Docking
246(4)
9.2.1 Theoretical Understanding of the Problem
246(1)
9.2.2 Docking Failures Due to Protein Flexibility
247(3)
9.3 Accounting for Protein Flexibility in Docking-Based Drug Discovery and Design
250(7)
9.3.1 Receptor Ensemble-Based Docking Methods
252(1)
9.3.2 Single-Structure-Based Docking Methods
253(3)
9.3.3 Multilevel Methods
256(1)
9.3.4 Homology Modeling
257(1)
9.4 Conclusions
257(6)
References
258(5)
10 Consideration of Water and Solvation Effects in Virtual Screening
263(28)
Johannes Kirchmair
Gudrun M. Spitzer
Klaus R. Lied!
10.1 Introduction
263(3)
10.2 Experimental Approaches for Analyzing Water Molecules
266(5)
10.3 Computational Approaches for Analyzing Water Molecules
271(4)
10.3.1 Molecular Dynamics Simulations
271(3)
10.3.2 Empirical and Implicit Considerations of Solvation Effects
274(1)
10.4 Water-Sensitive Virtual Screening: Approaches and Applications
275(6)
10.4.1 Protein-Ligand Docking
275(3)
10.4.2 Pharmacophore Modeling
278(3)
10.5 Conclusions and Recommendations
281(10)
References
282(9)
Part Three Applications and Practical Guidelines
291(68)
11 Applied Virtual Screening: Strategies, Recommendations, and Caveats
293(26)
Dagmar Stumpfe
Jurgen Bajorath
11.1 Introduction
293(1)
11.2 What Is Virtual Screening?
293(1)
11.3 Spectrum of Virtual Screening Approaches
294(1)
11.4 Molecular Similarity as a Foundation and Caveat of Virtual Screening
295(1)
11.5 Goals of Virtual Screening
296(1)
11.6 Applicability Domain
297(2)
11.7 Reference and Database Compounds
299(1)
11.8 Biological Activity versus Compound Potency
300(2)
11.9 Methodological Complexity and Compound Class Dependence
302(1)
11.10 Search Strategies and Compound Selection
302(2)
11.11 Virtual and High-Throughput Screening
304(2)
11.12 Practical Applications: An Overview
306(1)
11.13 LFA-1 Antagonist
307(3)
11.13.1 Similarity Searching
308(1)
11.13.2 Results and Further Calculations
309(1)
11.14 Selectivity Searching
310(4)
11.14.1 Selectivity Searching for Cathepsin K-Selective Inhibitors
311(1)
11.14.2 Selectivity Searching with 2D Fingerprints
312(1)
11.14.3 Identification of Selective Inhibitors
313(1)
11.15 Concluding Remarks
314(5)
References
315(4)
12 Applications and Success Stories in Virtual Screening
319(40)
Hans Matter
Christoph Sotriffer
12.1 Introduction
319(1)
12.2 Practical Considerations
320(1)
12.3 Successful Applications of Virtual Screening
321(26)
12.3.1 Structure-Based Virtual Screening
322(1)
12.3.1.1 Kinases
322(2)
12.3.1.2 Proteases
324(1)
12.3.1.3 Nuclear Receptors
325(2)
12.3.1.4 Short-Chain Dehydrogenases
327(1)
12.3.1.5 G Protein-Coupled Receptors (GPCRs)
327(4)
12.3.1.6 Antiinfectives
331(2)
12.3.1.7 Other Target Proteins
333(3)
12.3.2 Structure-Based Library Design
336(2)
12.3.3 Ligand-Based Virtual Screening
338(1)
12.3.3.1 Ion Channels
339(1)
12.3.3.2 Kinases
340(1)
12.3.3.3 Nuclear Hormone Receptors
341(1)
12.3.3.4 G Protein-Coupled Receptors (GPCRs)
342(3)
12.3.3.5 Other Protein Targets
345(2)
12.4 Conclusion
347(12)
References
348(11)
Part Four Scenarios and Case Studies: Routes to Success
359(132)
13 Scenarios and Case Studies: Examples for Ligand-Based Virtual Screening
361(20)
Trevor Howe
Daniele Bemporad
Gary Tresadern
13.1 Introduction
361(1)
13.2 ID Ligand-Based Virtual Screening
362(1)
13.3 2D Ligand-Based Virtual Screening
363(5)
13.3.1 Examples from the Literature
363(3)
13.3.2 Applications at J&JPRD Europe
366(2)
13.4 3D Ligand-Based Virtual Screening
368(8)
13.4.1 Methods
370(2)
13.4.2 3DLBVS Examples
372(1)
13.4.2.1 CRF1 Antagonists
372(3)
13.4.2.2 Ion Channel Antagonism
375(1)
13.4.2.3 Metabotropic Glutamate Receptor
375(1)
13.5 Summary
376(5)
References
377(4)
14 Virtual Screening on Homology Models
381(30)
Robert Kiss
Gyorgy M. Keseru
14.1 Introduction
381(1)
14.2 Homology Models versus Crystal Structures: Comparative Evaluation of Screening Performance
382(12)
14.2.1 Soluble Proteins
382(10)
14.2.2 Membrane Proteins
392(2)
14.3 Challenges of Homology Model-Based Virtual Screening
394(5)
14.3.1 Level of Sequence Identity
395(1)
14.3.2 Main-Chain Flexibility
396(1)
14.3.3 Side-Chain Conformation: Induced Fit Effects of Ligands
396(1)
14.3.4 Loop Modeling
397(2)
14.4 Case Studies
399(12)
14.4.1 Virtual Screening on the Homology Model of Histamine H4 Receptor
399(3)
14.4.2 Virtual Screening on the Homology Model of Janus Kinase 2
402(2)
References
404(7)
15 Target-Based Virtual Screening on Small-Molecule Protein Binding Sites
411(24)
Ralf Heinke
Urszula Uciechowska
Manfred Jung
Wolfgang Sippl
15.1 Introduction
411(3)
15.1.1 Pharmacophore-Based Methods
412(1)
15.1.2 Ligand Docking
412(1)
15.1.3 Virtual Screening
413(1)
15.1.4 Binding Free Energy Calculations
414(1)
15.2 Structure-Based VS for Histone Arginine Methyltransferase PRMT1 Inhibitors
414(8)
15.2.1 Structure-Based VS of the NCI Diversity Set
415(2)
15.2.2 Pharmacophore-Based VS
417(5)
15.3 Identification of Nanomolar Histamine H3 Receptor Antagonists by Structure- and Pharmacophore-Based VS
422(9)
15.3.1 Generation of Homology Model of the hH3R and hH3R Antagonist Complexes
423(1)
15.3.2 Validation of the Homology Model by Docking Known Antagonists into the hH3R Binding Site
424(1)
15.3.3 Pharmacophore-Based VS
425(4)
15.3.4 Experimental Testing of the Identified Hits
429(1)
15.3.5 Discussion of the Applied VS Strategies
429(2)
15.4 Summary
431(4)
References
432(3)
16 Target-Based Virtual Screening to Address Protein-Protein Interfaces
435(32)
Olivier Sperandio
Maria A. Miteva
Bruno O. Villoutreix
16.1 Introduction
435(2)
16.2 Some Recent PPIM Success Stories
437(1)
16.3 Protein-Protein Interfaces
438(4)
16.3.1 Interface Pockets, Flexibility, and Hot Spots
440(2)
16.3.2 Databases and Tools to Analyze Interfaces
442(1)
16.4 PPIMs' Chemical Space and ADME/Tox Properties
442(5)
16.5 Drug Discovery, Chemical Biology, and In Silico Screening Methods: Overview and Suggestions for PPIM Search
447(3)
16.6 Case Studies
450(7)
16.6.1 PPI Stabilizers: Superoxide Dismutase Type 1
450(2)
16.6.2 PPI Inhibitors: Lck
452(3)
16.6.3 Allosteric Inhibitors: Antitrypsin Polymerization
455(2)
16.7 Conclusions and Future Directions
457(10)
References
458(9)
17 Fragment-Based Approaches in Virtual Screening
467(24)
Danzhi Huang
Amedeo Caflisch
17.1 Introduction
467(1)
17.2 In Silico Fragment-Based Approaches
468(2)
17.3 Our Approach to High-Throughput Fragment-Based Docking
470(9)
17.3.1 Decomposition of Compounds into Fragments
471(1)
17.3.2 Docking of Anchor Fragments
471(1)
17.3.3 Flexible Docking of Library Compounds
472(1)
17.3.4 LIECE Binding Energy Evaluation
472(3)
17.3.5 Consensus Scoring
475(1)
17.3.6 In Silico Screening Campaigns
475(1)
17.3.7 West Nile Virus NS3 Protease (Flaviviral Infections)
475(2)
17.3.8 EphB4 Tyrosine Kinase (Cancer)
477(2)
17.4 Lessons Learned from Our Fragment-Based Docking
479(2)
17.5 Challenges of Fragment-Based Approaches
481(10)
References
482(9)
Appendix A Software Overview 491(10)
Appendix B Virtual Screening Application Studies 501(10)
Index 511
Christoph Sotriffer is Professor for Pharmaceutical Chemistry at the University ofWürzburg, 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 ofWürzburg 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 GÖCH in 2005 and the German Chemical and Pharmaceutical Societies GDCh and DPhG in 2007.
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