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Biomolecular Simulations in Structure-Based Drug Discovery


Biomolecular Simulations in Structure-Based Drug Discovery


Methods and Principles in Medicinal Chemistry 1. Aufl.

von: Francesco L. Gervasio, Vojtech Spiwok, Raimund Mannhold, Helmut Buschmann, Jörg Holenz

124,99 €

Verlag: Wiley-VCH
Format: EPUB
Veröffentl.: 04.01.2019
ISBN/EAN: 9783527806850
Sprache: englisch
Anzahl Seiten: 420

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Beschreibungen

A timely and topical survey of modern simulation tools and their applications in real-life drug discovery, allowing for better and quicker results in structure-based drug design. The first part of this practical guide for industry professionals describes common tools used in the biomolecular simulation of drugs and their targets. A critical analysis of the accuracy of the predictions, the integration of modeling with other experimental data combined with numerous case studies from different therapeutic fields enable users to quickly adopt these new methods for their current projects. The second part then shows how these tools can be applied to drug discovery and development projects. Modeling experts from the pharmaceutical industry and from leading academic institutions present real-life examples for important target classes such as GPCRs, kinases and amyloids as well as for common challenges in structure-based drug discovery. With its inclusion of novel methods and strategies for the modeling of drug-target interactions in the framework of real-life drug discovery and development, this application-oriented reference is tailor-made for medicinal chemists and those working in the pharmaceutical industry.
Foreword xiii Part I Principles 1 1 Predictive Power of Biomolecular Simulations 3Vojtech 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 29Andrea 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 45Federico 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 67Bettina G. Keller, Stevan Aleksic, 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 87Joan 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 105Mathieu 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 165Christina 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) 205Mariona 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 225Andrea 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 247Saurabh 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 Protein Trk1p 253 10.3.2 Homology Modeling, Docking, and Mutagenesis Studies of Human Melatonin Receptors 254 10.3.3 Selectivity and Permeation in Voltage-Gated Sodium (NaV) Channels 254 10.3.4 Study of Ion Conduction Mechanism, Favorable Translocation Path,and Ion Selectivity in KcsA Using Free Energy Perturbation and Umbrella Sampling 257 10.3.5 Ion Conductance Calculations 260 10.3.5.1 Voltage-Dependent Anion Channel (VDAC) 261 10.3.5.2 Calculation of Ion Conduction in Low-Conductance GLIC Channel 261 10.3.6 Transient Receptor Potential (TRP) Channels 263 10.4 Free Energy Methods Applied to Channels Bearing Hydrophobic Gates 264 10.5 Conclusion 270 Acknowledgments 271 References 271 11 Understanding Allostery to Design New Drugs 281Giulia Morra and Giorgio Colombo 11.1 Introduction 281 11.2 Protein Allostery: Basic Concepts and Theoretical Framework 282 11.2.1 The Classic View of Allostery 283 11.2.2 The Thermodynamic Two-State Model of Allostery 283 11.2.3 From Thermodynamics to Protein Structure and Dynamics 285 11.2.4 Entropy in Allostery: The Ensemble Allostery Model 287 11.3 Exploiting Allostery in Drug Discovery and Design 288 11.3.1 Computational Prediction of Allosteric Behavior and Application to Drug Discovery 288 11.3.2 Identification of Allosteric Binding Sites Through Structural and Dynamic approaches 289 11.4 Chaperones 291 11.5 Kinases 293 11.6 GPCRs 294 11.7 Conclusions 296 References 296 12 Structure and Stability of Amyloid Protofibrils of Polyglutamine and Polyasparagine from Molecular Dynamics Simulations 301 Viet HoangMan, Yuan Zhang, Christopher Roland, and Celeste Sagui 12.1 Introduction 301 12.2 Polyglutamine Protofibrils and Aggregates 303 12.2.1 Investigations of Oligomeric Q8 Structures 303 12.2.2 Time Evolution, Steric Zippers, and Crystal Structures of 4 × 4 Q8Aggregates 306 12.2.3 Monomeric Q40 Protofibrils 308 12.3 Amyloid Models of Asparagine (N) and Glutamine(Q) 311 12.3.1 Initial Structures 313 12.3.2 Monomeric PolyQ ?HairpinsAre More Stable than PolyN Hairpins 314 12.3.3 N-rich Oligomers Are Most Stable in Class 1 Steric Zippers with 2-by-2 Interdigitation 315 12.3.4 PolyQ Oligomers Are Most Stable in Antiparallel Stranded ? Sheets with 1-by-1 Steric Zippers 316 12.3.5 PolyQ Structures Show Higher Stability than Most Stable PolyN Structures 317 12.3.6 Thermodynamic Considerations of Aggregate Formation 318 12.4 Summary 319 Acknowledgments 320 References 320 13 Using Biomolecular Simulations to Target Cdc34 in Cancer 325Miriam Di Marco, Matteo Lambrughi, and Elena Papaleo 13.1 Background 325 13.2 Families of E2 Enzymes 327 13.3 Cdc34 Protein Sequence and Structure 328 13.4 Cdc34 Heterogeneous Conformational Ensemble in Solution 329 13.5 Long-Range Communication in Family 3 Enzymes: A Structural Path from the Ub-Binding Site to the E3 Recognition Site 330 13.6 Cdc34 Modulation by Phosphorylation: From Phenotype to Structure 331 13.7 The Dual Role of the Acidic Loop of Cdc34: Regulator of Activity and Interface for E3 Binding 332 13.8 Different Strategies to Target Cdc34 with Small Molecules 333 13.9 Conclusions and Perspectives 334 Acknowledgments 336References 336Index 343  
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). He is a member of the management committee CCPBioSim (UK Collaborative Computational Project for Biomolecular Simulation). Professor Gervasio has authored over 80 scientific publications in high impact journals and has organized numerous conferences and workshops in the field of biomolecular simulations. Vojtech Spiwok is a researcher of University of Chemistry and Technology, Prague (Czech Republic). He has authored 37 scientific publications on biomolecular simulations with a special emphasis on development and application of enhanced sampling techniques.

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