Table of Contents
List of Contributors XIII
Preface XVII
A Personal Foreword XIX
Part I Binding Thermodynamics 1
1 Statistical Thermodynamics of Binding and Molecular Recognition Models 3
Kim A. Sharp
1.1 Introductory Remarks 3
1.2 The Binding Constant and Free Energy 3
1.3 A Statistical Mechanical Treatment of Binding 4
1.3.1 Binding in a Square Well Potential 6
1.3.2 Binding in a Harmonic Potential 7
1.4 Strategies for Calculating Binding Free Energies 9
1.4.1 Direct Association Simulations 9
1.4.2 The Quasi-Harmonic Approximation 10
1.4.3 Estimation of Entropy Contributions to Binding 11
1.4.4 The MoleculeMechanics Poisson–Boltzmann Surface AreaMethod 13
1.4.5 Thermodynamic Work Methods 14
1.4.6 Ligand Decoupling 15
1.4.7 Linear Interaction Methods 15
1.4.8 Salt Effects on Binding 16
1.4.9 Statistical Potentials 17
1.4.10 Empirical Potentials 18
References 19
2 Some Practical Rules for the Thermodynamic Optimization of Drug Candidates 23
Ernesto Freire
2.1 Engineering Binding Contributions 25
2.2 Eliminating Unfavorable Enthalpy 25
2.3 Improving Binding Enthalpy 26
2.4 Improving Binding Affinity 27
2.5 Improving Selectivity 28
2.6 Thermodynamic Optimization Plot 28
Acknowledgments 30
References 31
3 Enthalpy–Entropy Compensation as Deduced from Measurements of Temperature Dependence 33
Athel Cornish-Bowden
3.1 Introduction 33
3.2 The Current Status of Enthalpy–Entropy Compensation 34
3.3 Measurement of the Entropy and Enthalpy of Activation 34
3.4 An Example 35
3.5 The Compensation Temperature 38
3.6 Effect of High Correlation on Estimates of Entropy and Enthalpy 39
3.7 Evolutionary Considerations 40
3.8 Textbooks 40
References 42
Part II Learning from Biophysical Experiments 45
4 Interaction Kinetic Data Generated by Surface Plasmon Resonance Biosensors and the Use of Kinetic Rate Constants in Lead Generation and Optimization 47
U. Helena Danielson
4.1 Background 47
4.2 SPR Biosensor Technology 48
4.2.1 Principles 48
4.2.2 Sensitivity 49
4.2.3 Kinetic Resolution 50
4.2.4 Performance for Drug Discovery 51
4.3 From Interaction Models to Kinetic Rate Constants and Affinity 53
4.3.1 Determination of Interaction Kinetic Rate Constants 53
4.3.2 Determination of Affinities 54
4.3.3 Steady-State Analysis versus Analysis of Complete Sensorgrams 54
4.4 Affinity versus Kinetic Rate Constants for Evaluation of Interactions 55
4.5 From Models to Mechanisms 56
4.5.1 Irreversible Interactions 57
4.5.2 Induced Fit 57
4.5.3 Conformational Selection 58
4.5.4 Unified Model for Dynamic Targets 58
4.5.5 Heterogeneous Systems/Parallel Reactions 59
4.5.6 Mechanism-Based Inhibitors 60
4.5.7 Multiple Binding Sites and Influence of Cofactors 61
4.6 Structural Information 61
4.7 The Use of Kinetic Rate Constants in Lead Generation and Optimization 62
4.7.1 Structure–Kinetic Relationships 62
4.7.2 Selectivity/Specificity and Resistance 63
4.7.3 Chemodynamics 63
4.7.4 Thermodynamics 64
4.8 Designing Compounds with Optimal Properties 65
4.8.1 Correlation between Kinetic and Thermodynamic Parameters and Pharmacological Efficacy 65
4.8.2 Structural Modeling 66
4.9 Conclusions 67
Acknowledgments 67
References 67
5 NMR Methods for the Determination of Protein–Ligand Interactions 71
Bernd W. Koenig, Sven Schünke, Matthias Stoldt, and Dieter Willbold
5.1 Experimental Parameters from NMR 72
5.2 Aspects of Protein–Ligand Interactions That Can Be Addressed by NMR 77
5.2.1 Detection and Verification of Ligand Binding 77
5.2.2 Interaction Site Mapping 78
5.2.3 Interaction Models and Binding Affinity 80
5.2.4 Molecular Recognition 81
5.2.5 Structure of Protein–Ligand Complexes 82
5.3 Ligand-Induced Conformational Changes of a Cyclic Nucleotide Binding Domain 84
5.4 Ligand Binding to GABARAP Binding Site and Affinity Mapping 86
5.5 Transient Binding of Peptide Ligands to Membrane Proteins 88
References 90
Part III Modeling Protein–Ligand Interactions 99
6 Polarizable Force Fields for Scoring Protein–Ligand Interactions 101
Jiajing Zhang, Yue Shi, and Pengyu Ren
6.1 Introduction and Overview 101
6.2 AMOEBA Polarizable Potential Energy Model 102
6.2.1 Bond, Angle, and Cross-Energy Terms 102
6.2.2 Torsional Energy Term 103
6.2.3 Van der Waals Interactions 103
6.2.4 Permanent Electrostatic Interactions 103
6.2.5 Electronic Polarization 104
6.2.6 Polarization Energy 105
6.3 AMOEBA Explicit Water Simulation Applications 106
6.3.1 Small-Molecule Hydration Free Energy Calculations 106
6.3.2 Ion Solvation Thermodynamics 108
6.3.3 Binding Free Energy of Trypsin and Benzamidine Analogs 110
6.4 Implicit Solvent Calculation Using AMOEBA Polarizable Force Field 113
6.5 Conclusions and Future Directions 115
References 116
7 Quantum Mechanics in Structure-Based Ligand Design 121
Pär Söderhjelm, Samuel Genheden, and Ulf Ryde
7.1 Introduction 121
7.2 Three MM-Based Methods 122
7.3 QM-Based Force Fields 123
7.4 QM Calculations of Ligand Binding Sites 125
7.5 QM/MM Calculations 126
7.6 QM Calculations of Entire Proteins 127
7.6.1 Linear Scaling Methods 128
7.6.2 Fragmentation Methods 129
7.7 Concluding Remarks 133
Acknowledgments 134
References 134
8 Hydrophobic Association and Volume-Confined Water Molecules 145
Riccardo Baron, Piotr Setny, and J. Andrew McCammon
8.1 Introduction 145
8.2 Water as a Whole in Hydrophobic Association 146
8.2.1 Background 146
8.2.2 Computational Modeling of Hydrophobic Association 150
8.2.2.1 Explicit versus Implicit Solvent: Is the Computational Cost Motivated? 152
8.3 Confined Water Molecules in Protein–Ligand Binding 153
8.3.1 Protein Hydration Sites 153
8.3.2 Thermodynamics of Volume-Confined Water Localization 154
8.3.3 Computational Modeling of Volume-Confined Water Molecules 156
8.3.4 Identifying Hydration Sites 158
8.3.5 Water in Protein–Ligand Docking 160
Acknowledgments 161
References 161
9 Implicit Solvent Models and Electrostatics in Molecular Recognition 171
Tyler Luchko and David A. Case
9.1 Introduction 171
9.2 Poisson–Boltzmann Methods 173
9.3 The Generalized Born Model 175
9.4 Reference Interaction Site Model of Molecular Solvation 176
9.5 Applications 179
9.5.1 The ‘‘MM-PBSA’’ Model 180
9.5.2 Rescoring Docking Poses 182
9.5.3 MM/3D-RISM 182
Acknowledgments 185
References 185
10 Ligand and Receptor Conformational Energies 191
Themis Lazaridis
10.1 The Treatment of Ligand and Receptor Conformational Energy in Various Theoretical Formulations of Binding 191
10.1.1 Double Decoupling Free Energy Calculations 192
10.1.2 MM-PB(GB)SA 192
10.1.3 Mining Minima 193
10.1.4 Free Energy Functional Approach 194
10.1.5 Linear Interaction Energy Methods 195
10.1.6 Scoring Functions 196
10.2 Computational Results on Ligand Conformational Energy 196
10.3 Computational Results on Receptor Conformational Energy 198
10.4 Concluding Remarks 199
Acknowledgments 199
References 199
11 Free Energy Calculations in Drug Lead Optimization 207
Thomas Steinbrecher
11.1 Modern Drug Design 207
11.1.1 In Silico Drug Design 210
11.2 Free Energy Calculations 212
11.2.1 Considerations for Accurate and Precise Results 215
11.3 Example Protocols and Applications 217
11.3.1 Example 1: Disappearing an Ion 219
11.3.2 Example 2: Relative Ligand Binding Strengths 221
11.3.3 Applications 223
11.4 Discussion 226
References 227
12 Scoring Functions for Protein–Ligand Interactions 237
Christoph Sotriffer
12.1 Introduction 237
12.2 Scoring Protein–Ligand Interactions: What for and How to? 237
12.2.1 Knowledge-Based Scoring Functions 238
12.2.2 Force Field-Based Methods 240
12.2.3 Empirical Scoring Functions 242
12.2.4 Further Approaches 244
12.3 Application of Scoring Functions: What Is Possible and What Is Not? 246
12.4 Thermodynamic Contributions and Intermolecular Interactions: Which Are Accounted for and Which Are Not? 248
12.5 Conclusions or What Remains to be Done and What Can be Expected? 254
Acknowledgments 255
References 255
Part IV Challenges in Molecular Recognition 265
13 Druggability Prediction 267
Daniel Alvarez-Garcia, Jesus Seco, Peter Schmidtke, and Xavier Barril
13.1 Introduction 267
13.2 Druggability: Ligand Properties 267
13.3 Druggability: Ligand Binding 268
13.4 Druggability Prediction by Protein Class 270
13.5 Druggability Predictions: Experimental Methods 270
13.5.1 High-Throughput Screening 270
13.5.2 Fragment Screening 271
13.5.3 Multiple Solvent Crystallographic Screening 272
13.6 Druggability Predictions: Computational Methods 272
13.6.1 Cavity Detection Algorithms 272
13.6.2 Empirical Models 273
13.6.2.1 Training Sets 273
13.6.2.2 Applicability and Prediction Performance 274
13.6.3 Physical Chemistry Predictions 275
13.7 A Test Case: PTP1B 276
13.8 Outlook and Concluding Remarks 278
References 278
14 Embracing Protein Plasticity in Ligand Docking 283
Manuel Rueda and Ruben Abagyan
14.1 Introduction 283
14.2 Docking by Sampling Internal Coordinates 284
14.3 Fast Docking to Multiple Receptor Conformations 285
14.4 Single Receptor Conformation 285
14.5 Multiple Receptor Conformations 286
14.5.1 Exploiting Existing Experimental Conformational Diversity 286
14.5.2 Selecting ‘‘Important’’ Conformations 288
14.5.3 Generating In Silico Models 288
14.6 Improving Poor Homology Models of the Binding Pocket 289
14.7 State of the Art: GPCR Dock 2010 Modeling and Docking Assessment 290
14.8 Conclusions and Outlook 290
Acknowledgments 292
References 292
15 Prospects of Modulating Protein–Protein Interactions 295
Shijun Zhong, Taiji Oashi, Wenbo Yu, Paul Shapiro, and Alexander D. MacKerell Jr.
15.1 Introduction 295
15.2 Thermodynamics of Protein–Protein Interactions 297
15.3 CADD Methods for the Identification and Optimization of Small-Molecule Inhibitors of PPIs 298
15.3.1 Identifying Inhibitors of PPIs Using SBDD 299
15.3.1.1 Protein Structure Preparation 299
15.3.1.2 Binding Site Identification 300
15.3.1.3 Virtual Chemical Database 302
15.3.1.4 Virtual Screening of Compound Database 302
15.3.1.5 Rescoring 304
15.3.1.6 Final Selection of Ligands for Experimental Assay 306
15.3.2 Lead Optimization 307
15.3.2.1 Ligand-Based Optimization 307
15.3.2.2 Computation of Binding Free Energy 308
15.4 Examples of CADD Applied to PPIs 308
15.4.1 ERK 309
15.4.2 BCL6 311
15.4.3 S100B 313
15.4.4 p56Lck Kinase SH2 Domain 313
15.5 Summary 315
Acknowledgments 315
References 315
Index 331