Biomolecular Simulations in Structure-Based Drug Discovery

Francesco Luigi Gervasio holds a chair in Biomolecular Modelling and is professor of Chemistry and professor of Structural and Molecular Biology at University College London (UK). Vojtech Spiwok is a researcher of University of Chemistry and Technology, Prague (Czech Republic). He has authored numerous scientific publications on biomolecular simulations with a special emphasis on development and application of enhanced sampling techniques.

• Foreword xiiiPart I Principles 11 Predictive Power of Biomolecular Simulations 3Vojtech Spiwok1.1 Design of Biomolecular Simulations 41.2 Collective Variables and Trajectory Clustering 61.3 Accuracy of Biomolecular Simulations 81.4 Sampling 101.5 Binding Free Energy 141.6 Convergence of Free Energy Estimates 161.7 Future Outlook 20References 212 Molecular Dynamics–Based Approaches Describing Protein Binding 29Andrea Spitaleri and Walter Rocchia2.1 Introduction 292.1.1 Protein Binding: Molecular Dynamics Versus Docking 302.1.2 Molecular Dynamics –The Current State of the Art 312.2 Protein–Protein Binding 322.3 Protein–Peptide Binding 342.4 Protein–Ligand Binding 362.5 Future Directions 382.5.1 Modeling of Cation-p Interactions 382.6 Grand Challenges 39References 39Part II Advanced Algorithms 433 Modeling Ligand–Target Binding with Enhanced Sampling Simulations 45Federico Comitani and Francesco L. Gervasio3.1 Introduction 453.2 The Limits of Molecular Dynamics 463.3 TemperingMethods 473.4 Multiple Replica Methods 483.5 Endpoint Methods 503.5.1 Alchemical Methods 503.6 Collective Variable-Based Methods 513.6.1 Metadynamics 523.7 Binding Kinetics 573.8 Conclusions 59References 604 Markov State Models in Drug Design 67Bettina G. Keller, Stevan Aleksic, and Luca Donati4.1 Introduction 674.2 Markov State Models 684.2.1 MD Simulations 684.2.2 The Molecular Ensemble 694.2.3 The Propagator 694.2.4 The Dominant Eigenspace 704.2.5 The Markov State Model 724.3 Microstates 754.4 Long-Lived Conformations 774.5 Transition Paths 794.6 Outlook 81Acknowledgments 82References 825 Monte Carlo Techniques for Drug Design: The Success Case of PELE 87Joan F. Gilabert, Daniel Lecina, Jorge Estrada, and Victor Guallar5.1 Introduction 875.1.1 First Applications 885.1.2 Free Energy Calculations 885.1.3 Optimization 885.1.4 MC and MD Combinations 895.2 The PELE Method 905.2.1 MC Sampling Procedure 915.2.2 Ligand Perturbation 915.2.3 Receptor Perturbation 915.2.4 Side-Chain Adjustment 935.2.5 Minimization 935.2.6 Coordinate Exploration 935.2.7 Energy Function 945.3 Examples of PELE’s Applications 945.3.1 Mapping Protein Ligand and Biomedical Studies 945.3.2 Enzyme Characterization 96Acknowledgments 97References 976 Understanding the Structure and Dynamics of Peptides and Proteins Through the Lens of Network Science 105Mathieu Fossepre, Laurence Leherte, Aatto Laaksonen, and Daniel P. Vercauteren6.1 Insight into the Rise of Network Science 1056.2 Networks of Protein Structures: Topological Features and Applications 1076.2.1 Topological Features and Analysis of Networks: A Brief Overview 1076.2.2 Centrality Measures and Protein Structures 1106.2.3 Software 1146.3 Networks of Protein Dynamics: Merging Molecular Simulation Methods and Network Theory 1176.3.1 Molecular Simulations: A Brief Overview 1176.3.2 How Can Network Science Help in the Analysis of Molecular Simulations? 1186.3.3 Software 1196.4 Coarse-Graining and Elastic Network Models: Understanding Protein Dynamics with Networks 1206.4.1 Coarse-Graining: A Brief Overview 1206.4.2 Elastic Network Models: General Principles 1236.4.3 Elastic Network Models: The Design of Residue Interaction Networks 1246.5 Network Modularization to Understand Protein Structure and Function 1286.5.1 Modularization of Residue Interaction Networks 1286.5.2 Toward the Design of Meso scale Protein Models with Network Modularization Techniques 1306.6 Laboratory Contributions in the Field of Network Science 1316.6.1 Graph Reduction of Three-Dimensional Molecular Fields of Peptides and Proteins 1326.6.2 Design of Multi scale Elastic Network Models to Study Protein Dynamics 1356.7 Conclusions and Perspectives 140Acknowledgments 142References 142Part III Applications and Success Stories 1637 From Computers to Bedside: Computational Chemistry Contributing to FDA Approval 165Christina Athanasiou and Zoe Cournia7.1 Introduction 1657.2 Rationalizing the Drug Discovery Process: Early Days 1667.2.1 Captopril (Capoten®) 1677.2.2 Saquinavir (Invirase®) 1677.2.3 Ritonavir (Norvir®) 1687.3 Use of Computer-Aided Methods in the Drug Discovery Process 1687.3.1 Ligand-Based Methods 1697.3.1.1 Overlay of Structures 1697.3.1.2 Pharmacophore Modeling 1717.3.1.3 Quantitative Structure–Activity Relationships (QSAR) 1727.3.2 Structure-Based Methods 1737.3.2.1 Molecular Docking – Virtual Screening 1757.3.2.2 Flexible Receptor Molecular Docking 1797.3.2.3 Molecular Dynamics Simulations 1797.3.2.4 De Novo Drug Design 1807.3.2.5 Protein Structure Prediction 1817.3.2.6 Rucaparib (Zepatier®) 1847.3.3 Ab InitioQuantumChemical Methods 1857.4 Future Outlook 186References 1908 Application of Biomolecular Simulations to G Protein–Coupled Receptors (GPCRs) 205Mariona Torrens-Fontanals, TomaszM. Stepniewski, Ismael Rodriguez-Espigares, and Jana Selent8.1 Introduction 2058.2 MD Simulations for Studying the Conformational Plasticity of GPCRs 2078.2.1 Challenges in GPCR Simulations: The Sampling Problem and Simulation Timescales 2088.2.2 Making Sense Out of Simulation Data 2098.3 Application of MD Simulations to GPCR Drug Design:Why Should We Use MD? 2108.4 Evolution of MD Timescales 2148.5 Sharing MD Data via a Public Database 2168.6 Conclusions and Perspectives 216Acknowledgments 217References 2179 Molecular Dynamics Applications to GPCR Ligand Design 225Andrea Bortolato, Francesca Deflorian, Giuseppe Deganutti, Davide Sabbadin,StefanoMoro, and Jonathan S.Mason9.1 Introduction 2259.2 The Role of Water in GPCR Structure-Based Ligand Design 2269.2.1 WaterMap and WaterFLAP 2289.3 Ligand-Binding Free Energy 2309.4 Ligand-Binding Kinetics 2339.4.1 Supervised Molecular Dynamics (SuMD) 2359.4.2 Adiabatic Bias Metadynamics 2389.5 Conclusion 241References 24210 Ion Channel Simulations 247Saurabh Pandey, Daniel Bonhenry, and Rudiger H. Ettrich10.1 Introduction 24710.2 Overview of Computational Methods Applied to Study Ion Channels 24810.2.1 Homology Modeling 24810.2.2 All-atom Molecular Dynamics Simulations 24910.2.2.1 Force Fields 25010.2.3 Methods for Calculation of Free Energy 25110.2.3.1 Free Energy Perturbation 25110.2.3.2 Umbrella Sampling 25110.2.3.3 Metadynamics 25210.2.3.4 Adaptive Biased Force Method 25210.3 Properties of Ion Channels Studied by Computational Modeling 25310.3.1 A Refined Atomic Scale Model of the Saccharomyces cerevisiae K+-translocation Protein Trk1p 25310.3.2 Homology Modeling, Docking, and Mutagenesis Studies of Human Melatonin Receptors 25410.3.3 Selectivity and Permeation in Voltage-Gated Sodium (NaV) Channels 25410.3.4 Study of Ion Conduction Mechanism, Favorable Translocation Path,and Ion Selectivity in KcsA Using Free Energy Perturbation and Umbrella Sampling 25710.3.5 Ion Conductance Calculations 26010.3.5.1 Voltage-Dependent Anion Channel (VDAC) 26110.3.5.2 Calculation of Ion Conduction in Low-Conductance GLIC Channel 26110.3.6 Transient Receptor Potential (TRP) Channels 26310.4 Free Energy Methods Applied to Channels Bearing Hydrophobic Gates 26410.5 Conclusion 270Acknowledgments 271References 27111 Understanding Allostery to Design New Drugs 281Giulia Morra and Giorgio Colombo11.1 Introduction 28111.2 Protein Allostery: Basic Concepts and Theoretical Framework 28211.2.1 The Classic View of Allostery 28311.2.2 The Thermodynamic Two-State Model of Allostery 28311.2.3 From Thermodynamics to Protein Structure and Dynamics 28511.2.4 Entropy in Allostery: The Ensemble Allostery Model 28711.3 Exploiting Allostery in Drug Discovery and Design 28811.3.1 Computational Prediction of Allosteric Behavior and Application to Drug Discovery 28811.3.2 Identification of Allosteric Binding Sites Through Structural and Dynamic approaches 28911.4 Chaperones 29111.5 Kinases 29311.6 GPCRs 29411.7 Conclusions 296References 29612 Structure and Stability of Amyloid Protofibrils of Polyglutamine and Polyasparagine from Molecular Dynamics Simulations 301 Viet HoangMan, Yuan Zhang, Christopher Roland, and Celeste Sagui12.1 Introduction 30112.2 Polyglutamine Protofibrils and Aggregates 30312.2.1 Investigations of Oligomeric Q8 Structures 30312.2.2 Time Evolution, Steric Zippers, and Crystal Structures of 4 × 4 Q8Aggregates 30612.2.3 Monomeric Q40 Protofibrils 30812.3 Amyloid Models of Asparagine (N) and Glutamine(Q) 31112.3.1 Initial Structures 31312.3.2 Monomeric PolyQ βHairpinsAre More Stable than PolyN Hairpins 31412.3.3 N-rich Oligomers Are Most Stable in Class 1 Steric Zippers with 2-by-2 Interdigitation 31512.3.4 PolyQ Oligomers Are Most Stable in Antiparallel Stranded β Sheets with 1-by-1 Steric Zippers 31612.3.5 PolyQ Structures Show Higher Stability than Most Stable PolyN Structures 31712.3.6 Thermodynamic Considerations of Aggregate Formation 31812.4 Summary 319Acknowledgments 320References 32013 Using Biomolecular Simulations to Target Cdc34 in Cancer 325Miriam Di Marco, Matteo Lambrughi, and Elena Papaleo13.1 Background 32513.2 Families of E2 Enzymes 32713.3 Cdc34 Protein Sequence and Structure 32813.4 Cdc34 Heterogeneous Conformational Ensemble in Solution 32913.5 Long-Range Communication in Family 3 Enzymes: A Structural Path from the Ub-Binding Site to the E3 Recognition Site 33013.6 Cdc34 Modulation by Phosphorylation: From Phenotype to Structure 33113.7 The Dual Role of the Acidic Loop of Cdc34: Regulator of Activity and Interface for E3 Binding 33213.8 Different Strategies to Target Cdc34 with Small Molecules 33313.9 Conclusions and Perspectives 334Acknowledgments 336References 336Index 343