Virtual Screening in Drug Discovery

Transcription

1 Virtual Screening in Drug Discovery edited by Juan Alvarez and Brian Shoichet Taylor &. Francis Taylor & Francis Group Boca Raton London New York Singapore A CRC title, part of the Taylor & Francis imprint, a member of the Taylor & Francis Group, the academic division of T&F Informa pic.

2 Contents PART 1: Perspectives on Virtual Screening 1 Chapter 1 Virtual Screening: Scope and Limitations Gerhard Klebe 1.1 Introduction Strategies to Virtual Screening Development of a Reliable Pharmacophore Hypothesis Series of Consecutive Hierarchical Filters to Match with the Pharmacophore Hypothesis Docking, Scoring, and Visual Inspection Virtual Screening: Matured as a Routine Tool? Peptide Bond Flip and Interstitial Water Molecules Changes of Protonation States Induced upon Ligand Binding Water, the Nasty, Frequently Ignored Binding Factor Protein Plasticity or "How to Hit a Mobile Target?" Conclusions and Outlook 19 Acknowledgments 19 References 20 Chapter 2 Addressing the Virtual Screening Challenge: The Flex* Approach Marcus Gastreich, Christian Lemmen, Hans Briem, and Matthias Rarey 2.1 Introduction Elementary Models for Docking and Structural Alignment Calculations Conformations Molecular Interactions Scoring Basic Algorithmic Concepts Placing Molecular Fragments The Incremental Construction Phase Bringing It All Together: Base Selection Base Placement Incremental Construction - Postoptimization Advanced Concepts for VS Tools Combinatorial Libraries Docking under Pharmacophore Constraints Protein Flexibility 33

3 2.4.4 Multimolecule Alignment Details to Be Taken into Account Covalent Binders Flexible Ring Systems Water Molecules and Ions Stereoisomerism Supporting the Workflow From an Algorithmic Engine to a VS Machine Scripting the Screening Process Parallel VS A Database of VS Results Mining VS Results A Practical Example VS on a Cyclin-Dependent Kinase Target 3" An Initial Test Reproducing and Cross-Docking Crystal Structures VS for CDK2 Inhibitors Results Alignment-Based Screening for CDK2 Inhibitors 41 Acknowledgments 42 Notes 43 References 43 Chapter 3 An Analysis of Critical Factors Affecting Docking and Scoring Emanuele Perola, W. Patrick Walters, and Paul S. Charifson 3.1 Introduction 4" 3.2 The Importance of Test Set Selection 4 ( Complex Selection Composition of the Test Set Complex Preparation 5: 3.3 Evaluation of Highly Regarded Docking Programs ICM (MolSoft LLC) Glide (Schrodinger, LLC) GOLD (Cambridge Crystallographic Data Centre) Results and Discussion Conclusions Scoring Function Analysis Piecewise Linear Potential (PLP) ChemScore GlideScore Potential of Mean Force (PMF) PMF MMFF OPLS-AA Correlations between Scoring Functions 66

4 3.4.9 Scoring Function Accuracy The Effects of Training Set Selection Conclusions Evaluation of Docking/Scoring Combinations for Virtual Screening Results and Discussion Conclusions Overall Conclusions and Perspectives 79 References 81 PART II: Compound and Hit Suitability for Virtual Screening 87 Chapter 4 Compound Selection for Virtual Screening Tudor I. Oprea, Cristian Bologa, and Marius Olah 4.1 The Role of Leads in Drug Discovery The Leadlike Concept Compound Processing Prior to Virtual Screening Assembling the Virtual Compound Collection Cleaning Up the VCC FILTER for Leadlikeness Similarity Search if Known Active Molecules Are Available D Structure Generation Conclusions 103 Acknowledgment 104 Notes 104 References 104 Chapter 5 Experimental Identification of Promiscuous, Aggregate-Forming Screening Hits Susan L. McGovern 5.1 Introduction Overview Conceptual Advances Protocols Ill Kinetic Assays Sensitivity to Detergent Inhibition of Dissimilar Enzymes Sensitivity to Enzyme Concentration Incubation Effect Other Kinetic Properties Light Scattering Overview 119

5 DLS Instruments Methods Data Analysis Problems and Troubleshooting Conclusions 122 Acknowledgments 123 References 123 PART HI: Ligand-Based Virtual Screening Approaches 125 Chapter 6 Data Mining Approaches for Enhancement of Knowledge-Based Content of De Novo Chemical Libraries Nikolay P. Savchuk and Konstantin V. Balakin 6.1 Introduction Classification QSAR in Virtual Screening Data Analysis and Visualization Preparatory Stages Knowledge Database Molecular Descriptors Enhancement of Target-Specific Informational Content of Virtual Compound Selections Sammon Mapping Modeling CYP450-Mediated Drug Metabolism Prediction of Toxicity for Human Fibroblasts Other Filters De Novo Design of Chemical Libraries Conclusions 151 Acknowledgments 151 References 152 Chapter 7 Pharmacophore-Based Virtual Screening: A Practical Perspective John H. van Drie 7.1 Introduction Motivations Driving the Early Evolution: Historical Developments Concepts and Definition of Terms D Database Virtual Screening, in silico Screening, 3D Database Searching Hits, Hit List Pharmacophore Structure-Activity Relationship Search Query 162

6 7.3.7 Feature, Mapping, Multiple Mappings Dyads, Triads, Tetrads Overlay Scaffold Selectivity Success Rate Enrichment Ratio Geometric Object, Geometric Constraint Projected Point, Receptor Point, Dummy Atoms, Outriggers Steric Constraint, Forbidden Region, Shape-Enhanced Pharmacophores Rigid Match, Rigid Search vs. Flexible Match, Flexible Searching Pharmacophore Discovery D, ID Similarity, Similarity Searching Cluster, Clustering SMILES, MOL Files, SD Files Step 1 Constructing 3D Databases Where Do these Lists of Molecules Come from? What Types of Data-Cleaning Must One Perform on the Given Structures? How Does One Handle Chirality, in Particular Chiral Centers with Unspecified Chirality? How Does One Generate the 3D Information? How Does One Handle Conformational Flexibility? Step 2 Pharmacophore Discovery How Should the Dataset Be Selected? How Should the Conformational Analysis Be Performed? How Can One Detect Candidate Pharmacophores? What Combinations of Features and Constraints Should Be Used? How Should One Sift through the Candidate Pharmacophores? Are Steric Constraints Important? How Should One Construct Them? Which Computational Approaches Work Best? How Do They Compare? Step 3 What Does One Do with the Output of a Virtual Screen? Refine Query See if Data that Tests Hypothesis Exists Computational Postprocessing of Hits Biological Testing Case Studies Dl Agonists (Abbott) Fibrinogen Antagonists (Merck) Protein Kinase C Agonists (NCI) 185

7 7.7.4 HIV Integrase Inhibitors (NCI) Muscarinic M3 Antagonists (Astra) oc4pl Antagonists (Biogen) Estrogen ERoc Receptor (Organon) What Are the Components of the Art of Pharmacophore-Based Virtual Screening that Are Crucial for Success? Open Issues and Future Directions Conclusions Postscript 198 Acknowledgments 201 References 202 Chapter 8 Using Pharmacophore Multiplet Fingerprints for Virtual High Throughput Screening Robert D. Clark, Peter C. Fox, and Edmond J. Abrahamian 8.1 Introduction Overview Conceptual Advances Bitsets vs. Bitmaps Multiplet Mapping Accommodating a Range Bitmap Size Stochastic Cosine Application Preparing the Dataset Specifying Tuplet Generation Parameters Creating Multiplet Fingerprints Examination of Tuplets for a Class of Active Compounds Clustering Actives By Pharmacophoric Similarity Creating Tuplet Hypotheses and Screening a Database 219 Acknowledgments 222 Notes 224 References 224 PART IV: Important Considerations Impacting Molecular Docking 227 Chapter 9 Potential Functions for Virtual Screening and Ligand Binding Calculations: Some Theoretical Considerations Kim A. Sharp

8 9.1 Introduction Types of Scoring/Binding Potentials Basic Theory of Absolute and Relative Binding Affinity Absolute Binding Affinity Relative Binding Affinity Screening Potential or Free Energy Calculation? Separability of Binding Free Energy Terms What to Put into Free Energy-Based Scoring Functions Linear Interaction Energy Methods Statistical Potentials Internal Conformational Changes Effect of Multiple Unbound Ligand Conformations General Effect of Internal Coordinate Changes Conclusions 246 Acknowledgment 246 References 246 Chapter 10 Solvation-Based Scoring for High Throughput Docking Thomas S. Rush III, Eric S. Manas, Gregory J. Tawa, and Juan C. Alvarez 10.1 Introduction and Scope of the Problem Evaluating the Free Energy of Binding between Small Molecules and Proteins Scoring Functions for High Throughput Docking Force-Field-Based Schemes Empirically-Based Schemes Knowledge-Based Schemes Continuum-Based Schemes Full Solvation-Based Scoring for High Throughput Docking The Benefits of Full Solvation-Based Scoring A Full Solvation-Based Scoring Function for High Throughput Docking Implementing Solvation-Based Scoring in a Tiered High Throughput Docking Scheme Conclusions and Future Directions 272 Acknowledgments 273 References 273 [Chapter 11 Classification of Ligand-Receptor Complexes Based on Receptor Binding Site Characteristics Marguerita S.L. Lim-Wilby, Teresa A. Lyons, Michael Dooley, Anne Goupil-Lamy, Sunil Patel, Christoph Schneider, Remy Hoffmann, Hugues-Olivier Bertrand, and Osman F. Giiner 11.1 Introduction 279

9 11.2 Considerations in Binding Site Definitions Conceptual Advances in Using Binding Site Information Class I Small, Well-Defined Binding Sites Class II - Large, Weil-Defined Binding Sites Class III Open Binding Site, with Well-Defined Subsites Class IV Large Binding Site, with No Well-Defined Subsites Class V Shallow and Superficial Binding Site Class VI - Ill-Defined Binding Site at Hinge Region Protocols for Addressing Binding Sites According to Classes Check that There Are No Obvious Problems with Ligand-Receptor Interactions Check X-ray Pose of Ligand Prepare Ligand for Native Docking Prepare Receptor Structure for Docking Define Binding Site Select Level of Site Partitioning Find Best Docking Parameters Number of Conformational Trials per Ligand Option to Perform Minimization with Molecular Mechanics on Saved Poses Select Scoring Functions Conclusions 296 Acknowledgments 297 References 297 PART V: Docking Strategies and Algorithms 301 Chapter 12 A Practical Guide to DOCK 5 Demetri T. Moustakas, Scott C.H. Pegg, and Irwin D. Kuntz 12.1 Introduction Target Preparation X-ray Structures Structures from Other Sources Generating Matching Points Grid Construction Ligand Preparation Conformation Electrostatics Docking DOCK Scoring Functions Bump Filtering 308

10 Contact Scoring Energy Scoring Solvation Scoring DOCK Score Optimization Rigid Docking Generation of Orientations Scoring Ligand Orientations Bump Filtering Contact Scoring Energy Scoring Solvation Scoring Optimizing Ligand Orientations Flexible Docking Ligand Anchor Perception Rigid Docking of the Anchor Flexible Growth Conformational Sampling Scoring and Optimization Refinement of Results Clustering Solvation Rescoring Evaluation of Docking Results Reproduction of Crystal Structures Types of Failures Ranking of Docked Molecules DOCK Resources 323 Acknowledgments 324 References 324 Chapter 13 Pharmacophore-Based Molecular Docking: A Practical Guide Diane Joseph-McCarthy, Iain J. McFadyen, Jinming Zou, Gary Walker, and Juan C. Alvarez 13.1 Introduction Molecular Docking Ligand Flexibility Target Flexibility PhDock and MCSS2SPTS PhDock Database Generation Physical Property Filtering Conformer Generation D Pharmacophore Generation and Overlays Database Statistics The Docking Process Protein Preparation Site Point Selection 336

11 Scoring Functions Available Docking Run Parameters Postprocessing Docking Output HIV-1 Protease Test Case (A Cross-Docking Example) DHFR Enrichment Studies DHFR Actives Database for Enrichment-Factor Calculations Decoy Database DOCK 4.0 vs. PhDock Enrichment Factors DHFR Actives Seeded into Entire ACD Database Evaluation of Various Scoring Schemes Conclusions 343 Acknowledgments 344 References 344 Chapter 14 Fragment-Based High Throughput Docking Peter Kolb, Marco Cecchini, Danzhi Huang, and Amedeo Caflisch 14.1 Introduction Overview Defining the Binding Site Generating a Pose Generation of Ligand Conformations Defining Ligand Positions Incremental Methods Ranking the Poses Objective Function Binding Energy Function and Postprocessing Solvation Protein Flexibility Technical Improvements Current Limitations Search Strategy Scoring Function Multiple-Step Docking Protocols Our Docking Approach Preparation of the Library of Compounds Fragment Choice Protein Preparation First Checks Charged Residues Adding Hydrogens Binding Site Definition Conserved Water Molecules Reference Structure 371

12 Running SEED Running FFLD 372 Acknowledgments 373 L References 374 apter 15 Protein-Ligand Docking and Virtual Screening with GOLD Jason C. Cole, J. Willem M. Nissink, and Robin Taylor 15.1 Introduction 379,15.2 Scoring Functions GoldScore External H-Bond Energy External vdw Energy Internal Strain and H-Bond Energy Other Aspects of GoldScore ChemScore Gaussian Smoothing Metal Coordination Parameterization Modification of ChemScore for Docking Search Strategy Search Space; Ligand and Protein Flexibility Genetic Algorithm Parameters Chromosome Composition and Ligand Placement Annealing Tuning the Genetic Algorithm Use of Torsion Distributions Prediction Reliability Validation Sets for Testing Docking Programs Interpreting Validation Results GOLD Validation Results Current Validation Results for Alternative Genetic Algorithm Settings Importance of Mediating Water Molecules Performance on Different Classes of Proteins Prediction of Ligand Affinity; Virtual High Throughput Screening GOLD in the Literature 402 Program Infrastructure Protein and Ligand Preparation Atom Typing Hydrogen Atom Placement Binding-Site Definition Constraints and Restraints Covalent Constraints Distance Restraints Similarity-Based (Pharmacophore) Restraints 406

13 H-Bonding Restraints Dealing with Large Numbers of Ligands Parallel Processing Selection of the Best Results Customization Future Work 411 Acknowledgments 411 References 411 Chapter 16 A Brief History of Glide: A New Paradigm for Docking and Scoring in Virtual Screening Thomas A. Halgren, Robert B. Murphy, and Richard A. Friesner 16.1 Introduction Computational Methodology Protein and Ligand Preparation Scoring in Glide Docking Accuracy Accuracy in Virtual Screening Extra-Precision Glide Thymidine Kinase (lkim) CDK-2 (Idm2; laql) p38 MAP Kinase (Ia9u; Ibl7; Ikv2) Optimizing Glide's Performance Preparing the Protein Correctly for Glide Choosing the Protein Site or Sites Preparing the Site or Sites Making Sure the Site Properly Accommodates the Cocrystallized Ligand Choosing the Enclosing Box Preparing the Ligands Correctly for Glide Optimizing the vdw Scale Factors Using Glide to Screen Large Databases Dividing the Screen Over Multiple Processors Using glide_sort to Work Up the Results Dealing with a Subjob that Fails Using Glide XP to Improve Pose Quality or Enhance Early Enrichment Using Multiple Receptor Sites to Deal with Receptor Flexibility Conclusions 449 Note 450 References 450 Index 453