Biomolecular simulations in drug discovery. (2018)

• Record Type: Book
• Further Information: Note: Francesco L. Gervasio, Vojtech Spiwok, Raimund Mannhold, Helmut Buschmann, Jörg Holenz.
• Authors: Gervasio, Francesco L; Spiwok, Vojtech; Mannhold, Raimund, 1948-; Buschmann, Helmut; Holenz, Jörg, 1968-
• Contents: Foreword xiii Part I Principles 1 1 Predictive Power of Biomolecular Simulations 3 ; Vojtech Spiwok 1.1 Design of Biomolecular Simulations 4 1.2 Collective Variables and Trajectory Clustering 6 1.3 Accuracy of Biomolecular Simulations 8 1.4 Sampling 10 1.5 Binding Free Energy 14 1.6 Convergence of Free Energy Estimates 16 1.7 Future Outlook 20 References 21 2 Molecular Dynamics–Based Approaches Describing Protein Binding 29 ; Andrea Spitaleri and Walter Rocchia 2.1 Introduction 29 2.1.1 Protein Binding: Molecular Dynamics Versus Docking 30 2.1.2 Molecular Dynamics –The Current State of the Art 31 2.2 Protein–Protein Binding 32 2.3 Protein–Peptide Binding 34 2.4 Protein–Ligand Binding 36 2.5 Future Directions 38 2.5.1 Modeling of Cation-p Interactions 38 2.6 Grand Challenges 39 References 39 Part II Advanced Algorithms 43 3 Modeling Ligand–Target Binding with Enhanced Sampling Simulations 45 ; Federico Comitani and Francesco L. Gervasio 3.1 Introduction 45 3.2 The Limits of Molecular Dynamics 46 3.3 TemperingMethods 47 3.4 Multiple Replica Methods 48 3.5 Endpoint Methods 50 3.5.1 Alchemical Methods 50 3.6 Collective Variable-Based Methods 51 3.6.1 Metadynamics 52 3.7 Binding Kinetics 57 3.8 Conclusions 59 References 60 4 Markov State Models in Drug Design 67 ; Bettina G. Keller, Stevan Aleksi c, and Luca Donati 4.1 Introduction 67 4.2 Markov State Models 68 4.2.1 MD Simulations 68 4.2.2 The Molecular Ensemble 69 4.2.3 The Propagator 69 4.2.4 The Dominant Eigenspace 70 4.2.5 The Markov State Model 72 4.3 Microstates 75 4.4 Long-Lived Conformations 77 4.5 Transition Paths 79 4.6 Outlook 81 Acknowledgments 82 References 82 5 Monte Carlo Techniques for Drug Design: The Success Case of PELE 87; Joan F. Gilabert, Daniel Lecina, Jorge Estrada, and Victor Guallar 5.1 Introduction 87 5.1.1 First Applications 88 5.1.2 Free Energy Calculations 88 5.1.3 Optimization 88 5.1.4 MC and MD Combinations 89 5.2 The PELE Method 90 5.2.1 MC Sampling Procedure 91 5.2.2 Ligand Perturbation 91 5.2.3 Receptor Perturbation 91 5.2.4 Side-Chain Adjustment 93 5.2.5 Minimization 93 5.2.6 Coordinate Exploration 93 5.2.7 Energy Function 94 5.3 Examples of PELE’s Applications 94 5.3.1 Mapping Protein Ligand and Biomedical Studies 94 5.3.2 Enzyme Characterization 96 Acknowledgments 97 References 97 6 Understanding the Structure and Dynamics of Peptides and Proteins Through the Lens of Network Science 105; Mathieu Fossepre, Laurence Leherte, Aatto Laaksonen, and Daniel P. Vercauteren 6.1 Insight into the Rise of Network Science 105 6.2 Networks of Protein Structures: Topological Features and Applications 107 6.2.1 Topological Features and Analysis of Networks: A Brief Overview 107 6.2.2 Centrality Measures and Protein Structures 110 6.2.3 Software 114 6.3 Networks of Protein Dynamics: Merging Molecular Simulation Methods and Network Theory 117 6.3.1 Molecular Simulations: A Brief Overview 117 6.3.2 How Can Network Science Help in the Analysis of Molecular Simulations? 118 6.3.3 Software 119 6.4 Coarse-Graining and Elastic Network Models: Understanding Protein Dynamics with Networks 120 6.4.1 Coarse-Graining: A Brief Overview 120 6.4.2 Elastic Network Models: General Principles 123 6.4.3 Elastic Network Models: The Design of Residue Interaction Networks 124 6.5 Network Modularization to Understand Protein Structure and Function 128 6.5.1 Modularization of Residue Interaction Networks 128 6.5.2 Toward the Design of Meso scale Protein Models with Network Modularization Techniques 130 6.6 Laboratory Contributions in the Field of Network Science 131 6.6.1 Graph Reduction of Three-Dimensional Molecular Fields of Peptides and Proteins 132 6.6.2 Design of Multi scale Elastic Network Models to Study Protein Dynamics 135 6.7 Conclusions and Perspectives 140 Acknowledgments 142 References 142 Part III Applications and Success Stories 163 7 From Computers to Bedside: Computational Chemistry Contributing to FDA Approval 165; Christina Athanasiou and Zoe Cournia 7.1 Introduction 165 7.2 Rationalizing the Drug Discovery Process: Early Days 166 7.2.1 Captopril (Capoten®) 167 7.2.2 Saquinavir (Invirase®) 167 7.2.3 Ritonavir (Norvir®) 168 7.3 Use of Computer-Aided Methods in the Drug Discovery Process 168 7.3.1 Ligand-Based Methods 169 7.3.1.1 Overlay of Structures 169 7.3.1.2 Pharmacophore Modeling 171 7.3.1.3 Quantitative Structure–Activity Relationships (QSAR) 172 7.3.2 Structure-Based Methods 173 7.3.2.1 Molecular Docking – Virtual Screening 175 7.3.2.2 Flexible Receptor Molecular Docking 179 7.3.2.3 Molecular Dynamics Simulations 179 7.3.2.4 De Novo Drug Design 180 7.3.2.5 Protein Structure Prediction 181 7.3.2.6 Rucaparib (Zepatier®) 184 7.3.3 Ab InitioQuantumChemical Methods 185 7.4 Future Outlook 186 References 190 8 Application of Biomolecular Simulations to G Protein–Coupled Receptors (GPCRs) 205; Mariona Torrens-Fontanals, TomaszM. Stepniewski, Ismael Rodriguez-Espigares, and Jana Selent 8.1 Introduction 205 8.2 MD Simulations for Studying the Conformational Plasticity of GPCRs 207 8.2.1 Challenges in GPCR Simulations: The Sampling Problem and Simulation Timescales 208 8.2.2 Making Sense Out of Simulation Data 209 8.3 Application of MD Simulations to GPCR Drug Design:Why Should We Use MD? 210 8.4 Evolution of MD Timescales 214 8.5 Sharing MD Data via a Public Database 216 8.6 Conclusions and Perspectives 216 Acknowledgments 217 References 217 9 Molecular Dynamics Applications to GPCR Ligand Design 225; Andrea Bortolato, Francesca Deflorian, Giuseppe Deganutti, Davide Sabbadin, StefanoMoro, and Jonathan S.Mason 9.1 Introduction 225 9.2 The Role of Water in GPCR Structure-Based Ligand Design 226 9.2.1 WaterMap and WaterFLAP 228 9.3 Ligand-Binding Free Energy 230 9.4 Ligand-Binding Kinetics 233 9.4.1 Supervised Molecular Dynamics (SuMD) 235 9.4.2 Adiabatic Bias Metadynamics 238 9.5 Conclusion 241 References 242 10 Ion Channel Simulations 247; Saurabh Pandey, Daniel Bonhenry, and Rudiger H. Ettrich 10.1 Introduction 247 10.2 Overview of Computational Methods Applied to Study Ion Channels 248 10.2.1 Homology Modeling 248 10.2.2 All-atom Molecular Dynamics Simulations 249 10.2.2.1 Force Fields 250 10.2.3 Methods for Calculation of Free Energy 251 10.2.3.1 Free Energy Perturbation 251 10.2.3.2 Umbrella Sampling 251 10.2.3.3 Metadynamics 252 10.2.3.4 Adaptive Biased Force Method 252 10.3 Properties of Ion Channels Studied by Computational Modeling 253 10.3.1 A Refined Atomic Scale Model of the Saccharomyces cerevisiae K+-translocation
• Edition: 1st
• Publisher Details: Weinheim : Wiley-VCH
• Publication Date: 2018
• Extent: 1 online resource
• Subjects: 615.190113; Drug development -- Computer simulation; Computational biology; Pharmaceutical chemistry
• Languages: English
• ISBNs: 9783527806850
• Related ISBNs: 9783527806843; 9783527806867
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