Biomolecular Simulations in Structure-Based Drug Discovery

A guide to applying the power of modern simulation tools to better drug design

Biomolecular Simulations in Structure-based Drug Discovery offers an up-to-date and comprehensive review of modern simulation tools and their applications in real-life drug discovery, for better and quicker results in structure-based drug design. The authors describe common tools used in the biomolecular simulation of drugs and their targets and offer an analysis of the accuracy of the predictions. They also show how to integrate modeling with other experimental data.

Filled with numerous case studies from different therapeutic fields, the book helps professionals to quickly adopt these new methods for their current projects. Experts from the pharmaceutical industry and academic institutions present real-life examples for important target classes such as GPCRs, ion channels and amyloids as well as for common challenges in structure-based drug discovery. Biomolecular Simulations in Structure-based Drug Discovery is an important resource that:

-Contains a review of the current generation of biomolecular simulation tools that have the robustness and speed that allows them to be used as routine tools by non-specialists
-Includes information on the novel methods and strategies for the modeling of drug-target interactions within the framework of real-life drug discovery and development
-Offers numerous illustrative case studies from a wide-range of therapeutic fields
-Presents an application-oriented reference that is ideal for those working in the various fields

Written for medicinal chemists, professionals in the pharmaceutical industry, and pharmaceutical chemists, Biomolecular Simulations in Structure-based Drug Discovery is a comprehensive resource to modern simulation tools that complement and have the potential to complement or replace laboratory assays for better results in drug design.

Auteur
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.

Contenu

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 DynamicsBased 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 ProteinProtein Binding 32

2.3 ProteinPeptide Binding 34

2.4 ProteinLigand 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 LigandTarget 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....