Cover -- Title Page -- Copyright -- Contents -- Foreword -- Part I Principles -- Chapter 1 Predictive Power of Biomolecular Simulations -- 1.1 Design of Biomolecular Simulations -- 1.2 Collective Variables and Trajectory Clustering -- 1.3 Accuracy of Biomolecular Simulations -- 1.4 Sampling -- 1.5 Binding Free Energy -- 1.6 Convergence of Free Energy Estimates -- 1.7 Future Outlook -- References -- Chapter 2 Molecular Dynamics-Based Approaches Describing Protein Binding -- 2.1 Introduction -- 2.1.1 Protein Binding: Molecular Dynamics Versus Docking -- 2.1.2 Molecular Dynamics - The Current State of the Art -- 2.2 Protein-Protein Binding -- 2.3 Protein-Peptide Binding -- 2.4 Protein-Ligand Binding -- 2.5 Future Directions -- 2.5.1 Modeling of Cation-p Interactions -- 2.6 Grand Challenges -- References -- Part II Advanced Algorithms -- Chapter 3 Modeling Ligand-Target Binding with Enhanced Sampling Simulations -- 3.1 Introduction -- 3.2 The Limits of Molecular Dynamics -- 3.3 Tempering Methods -- 3.4 Multiple Replica Methods -- 3.5 Endpoint Methods -- 3.5.1 Alchemical Methods -- 3.6 Collective Variable-Based Methods -- 3.6.1 Metadynamics -- 3.7 Binding Kinetics -- 3.8 Conclusions -- References -- Chapter 4 Markov State Models in Drug Design -- 4.1 Introduction -- 4.2 Markov State Models -- 4.2.1 MD Simulations -- 4.2.2 The Molecular Ensemble -- 4.2.3 The Propagator -- 4.2.4 The Dominant Eigenspace -- 4.2.5 The Markov State Model -- 4.3 Microstates -- 4.4 Long-Lived Conformations -- 4.5 Transition Paths -- 4.6 Outlook -- Acknowledgments -- References -- Chapter 5 Monte Carlo Techniques for Drug Design: The Success Case of PELE -- 5.1 Introduction -- 5.1.1 First Applications -- 5.1.2 Free Energy Calculations -- 5.1.3 Optimization -- 5.1.4 MC and MD Combinations -- 5.2 The PELE Method -- 5.2.1 MC Sampling Procedure -- 5.2.2 Ligand Perturbation.

5.2.3 Receptor Perturbation -- 5.2.4 Side-Chain Adjustment -- 5.2.5 Minimization -- 5.2.6 Coordinate Exploration -- 5.2.7 Energy Function -- 5.3 Examples of PELE's Applications -- 5.3.1 Mapping Protein Ligand and Biomedical Studies -- 5.3.2 Enzyme Characterization -- Acknowledgments -- References -- Chapter 6 Understanding the Structure and Dynamics of Peptides and Proteins Through the Lens of Network Science -- 6.1 Insight into the Rise of Network Science -- 6.2 Networks of Protein Structures: Topological Features and Applications -- 6.2.1 Topological Features and Analysis of Networks: A Brief Overview -- 6.2.2 Centrality Measures and Protein Structures -- 6.2.3 Software -- 6.3 Networks of Protein Dynamics: Merging Molecular Simulation Methods and Network Theory -- 6.3.1 Molecular Simulations: A Brief Overview -- 6.3.2 How Can Network Science Help in the Analysis of Molecular Simulations? -- 6.3.3 Software -- 6.4 Coarse-Graining and Elastic Network Models: Understanding Protein Dynamics with Networks -- 6.4.1 Coarse-Graining: A Brief Overview -- 6.4.2 Elastic Network Models: General Principles -- 6.4.3 Elastic Network Models: The Design of Residue Interaction Networks -- 6.5 Network Modularization to Understand Protein Structure and Function -- 6.5.1 Modularization of Residue Interaction Networks -- 6.5.2 Toward the Design of Mesoscale Protein Models with Network Modularization Techniques -- 6.6 Laboratory Contributions in the Field of Network Science -- 6.6.1 Graph Reduction of Three-Dimensional Molecular Fields of Peptides and Proteins -- 6.6.2 Design of Multiscale Elastic Network Models to Study Protein Dynamics -- 6.7 Conclusions and Perspectives -- Acknowledgments -- References -- Part III Applications and Success Stories -- Chapter 7 From Computers to Bedside: Computational Chemistry Contributing to FDA Approval -- 7.1 Introduction.

7.2 Rationalizing the Drug Discovery Process: Early Days -- 7.2.1 Captopril (Capoten®) -- 7.2.2 Saquinavir (Invirase®) -- 7.2.3 Ritonavir (Norvir®) -- 7.3 Use of Computer-Aided Methods in the Drug Discovery Process -- 7.3.1 Ligand-Based Methods -- 7.3.1.1 Overlay of Structures -- 7.3.1.2 Pharmacophore Modeling -- 7.3.1.3 Quantitative Structure-Activity Relationships (QSAR) -- 7.3.2 Structure-Based Methods -- 7.3.2.1 Molecular Docking - Virtual Screening -- 7.3.2.2 Flexible Receptor Molecular Docking -- 7.3.2.3 Molecular Dynamics Simulations -- 7.3.2.4 De Novo Drug Design -- 7.3.2.5 Protein Structure Prediction -- 7.3.2.6 Rucaparib (Zepatier®) -- 7.3.3 Ab Initio Quantum Chemical Methods -- 7.4 Future Outlook -- References -- Chapter 8 Application of Biomolecular Simulations to G Protein-Coupled Receptors (GPCRs) -- 8.1 Introduction -- 8.2 MD Simulations for Studying the Conformational Plasticity of GPCRs -- 8.2.1 Challenges in GPCR Simulations: The Sampling Problem and Simulation Timescales -- 8.2.2 Making Sense Out of Simulation Data -- 8.3 Application of MD Simulations to GPCR Drug Design: Why Should We Use MD? -- 8.4 Evolution of MD Timescales -- 8.5 Sharing MD Data via a Public Database -- 8.6 Conclusions and Perspectives -- Acknowledgments -- References -- Chapter 9 Molecular Dynamics Applications to GPCR Ligand Design -- 9.1 Introduction -- 9.2 The Role of Water in GPCR Structure-Based Ligand Design -- 9.2.1 WaterMap and WaterFLAP -- 9.3 Ligand-Binding Free Energy -- 9.4 Ligand-Binding Kinetics -- 9.4.1 Supervised Molecular Dynamics (SuMD) -- 9.4.2 Adiabatic Bias Metadynamics -- 9.5 Conclusion -- References -- Chapter 10 Ion Channel Simulations -- 10.1 Introduction -- 10.2 Overview of Computational Methods Applied to Study Ion Channels -- 10.2.1 Homology Modeling -- 10.2.2 All-atom Molecular Dynamics Simulations -- 10.2.2.1 Force Fields.

10.2.3 Methods for Calculation of Free Energy -- 10.2.3.1 Free Energy Perturbation -- 10.2.3.2 Umbrella Sampling -- 10.2.3.3 Metadynamics -- 10.2.3.4 Adaptive Biased Force Method -- 10.3 Properties of Ion Channels Studied by Computational Modeling -- 10.3.1 A Refined Atomic Scale Model of the Saccharomyces cerevisiae K+-translocation Protein Trk1p -- 10.3.2 Homology Modeling, Docking, and Mutagenesis Studies of Human Melatonin Receptors -- 10.3.3 Selectivity and Permeation in Voltage-Gated Sodium (NaV) Channels -- 10.3.4 Study of Ion Conduction Mechanism, Favorable Translocation Path, and Ion Selectivity in KcsA Using Free Energy Perturbation and Umbrella Sampling -- 10.3.5 Ion Conductance Calculations -- 10.3.5.1 Voltage-Dependent Anion Channel (VDAC) -- 10.3.5.2 Calculation of Ion Conduction in Low-Conductance GLIC Channel -- 10.3.6 Transient Receptor Potential (TRP) Channels -- 10.4 Free Energy Methods Applied to Channels Bearing Hydrophobic Gates -- 10.5 Conclusion -- Acknowledgments -- References -- Chapter 11 Understanding Allostery to Design New Drugs -- 11.1 Introduction -- 11.2 Protein Allostery: Basic Concepts and Theoretical Framework -- 11.2.1 The Classic View of Allostery -- 11.2.2 The Thermodynamic Two-State Model of Allostery -- 11.2.3 From Thermodynamics to Protein Structure and Dynamics -- 11.2.4 Entropy in Allostery: The Ensemble Allostery Model -- 11.3 Exploiting Allostery in Drug Discovery and Design -- 11.3.1 Computational Prediction of Allosteric Behavior and Application to Drug Discovery -- 11.3.2 Identification of Allosteric Binding Sites Through Structural and Dynamic Approaches -- 11.4 Chaperones -- 11.5 Kinases -- 11.6 GPCRs -- 11.7 Conclusions -- References -- Chapter 12 Structure and Stability of Amyloid Protofibrils of Polyglutamine and Polyasparagine from Molecular Dynamics Simulations -- 12.1 Introduction.