List of Contributors xix
Preface xxvii
Acknowledgments xxix

1 Atomistic Force Fields for Molecular Simulations with Emphasis on the CHARMM Additive and Drude Polarizable Models 1
Suvankar Ghosh, Prabin Baral, Payal Chatterjee, Anastasia Croitoru, Anmol Kumar, Yiling Nan, Xiaojing Teng, Mingjun Yang, Wenbo Yu, and Alexander D. MacKerell Jr.

1.1 Introduction 1
1.2 Additive and Drude Potential Energy Functions 3
1.3 Water 8
1.4 Proteins 9
1.5 Nucleic Acids 14
1.6 Carbohydrates 16
1.7 Lipids 20
1.8 Atomic Ions 24
1.9 Small Molecules 26
1.10 Summary and Future Directions 30

2 Treating Noncovalent Interactions in Biomolecules with QM and QM/MM Models 59
Qiang Cui

2.1 Introduction 59
2.2 General Computational Models 60
2.3 Applications and Discussions 74
2.4 Concluding Remarks 80

3 QM/MM Simulations on Catalytic Mechanisms of Metalloenzymes 101
Tai-Ping Zhou, Shengheng Yan, Xiaoyu Wang, and Binju Wang

3.1 Introduction 101
3.2 QM/MM Methods for Enzyme System 105
3.3 Applications of QM/MM Methods 109
3.4 Conclusions 133

4 Integrative Multiscale Modeling of Biomolecular Interactions: From Mechanistic Understanding to Design 153
Carla Calvó-Tusell, Miguel A. Maria-Solano, Marc Garcia-Borràs, and Ferran Feixas

4.1 Introduction 153
4.2 Integrative Multiscale Molecular Modeling of Biomolecular Interactions and Mechanisms 158
4.3 Case Study I: Time Evolution of the Millisecond Allosteric Activation of IGPS 166
4.4 Case Study II: Protein Energy Networks as a Framework for Allosteric Pathway Discovery and Drug Design in GPCRs 174
4.5 Case Study III: Mechanistically Guided Design of Enzymes for C—N Bond Formation via Multiscale Modeling 176
4.6 Perspective and Outlook 180

5 Methodological Advances in Computational Biomodeling: End-point Free Energy Approaches 193
John Z.H. Zhang and Zhaoxi Sun

5.1 End-point Free Energy Approaches: An Introductory Tale 193
5.2 Methodological Advances 195
5.3 Practical Applications 202

6 Computational Modeling of Protein–Protein/Peptide/RNA Interactions 213
Martin Zacharias

6.1 Introduction 213
6.2 Prediction of Protein–Protein Complexes by Traditional Docking Methods 214
6.3 Structure Prediction of Biomolecular Complexes Using Deep Learning Methods 218
6.4 Prediction of Protein–Peptide and Protein–RNA Complexes Using Deep Learning Methods 221
6.5 Molecular Dynamics Simulation of Proteins in Complex with Proteins, Peptides, and RNA 223
6.6 Binding Free Energy Calculations of Biomolecular Complexes 227
6.7 Peptide and Protein Interaction Design 230
6.8 Conclusions 231

7 Modeling Peptide–Protein Interactions with MELD: A Physics-based Framework for Structure, Affinity, and Design 243
Guadalupe Alvarez, Yisel Martinez-Noa, Bhumika Singh, Jokent Gaza, Qianchen Liu, and Alberto Perez

7.1 Introduction: The Biological and Clinical Relevance of Peptides and Peptide Epitopes 243
7.2 MELD: A Versatile Computational Tool for Modeling Molecular Recognition 248
7.3 Protocols 252
7.4 Case Studies 258
7.5 Conclusions 270

8 Transforming Drug Binding with AI-enhanced Computational Modeling 281
Aamir Mehmood and Dong-Qing Wei

8.1 Introduction 281
8.2 Computational Strategies Optimizing Drug Development 284
8.3 Innovations in Computational Modeling of Receptor–Ligand Binding Dynamics 286
8.4 AI in Transforming Preclinical Drug Development 292
8.5 Computational AI Resources for Drug Development 294
8.6 Receptor–Ligand Interactions Prediction Using AI 299
8.7 Conclusion and Future Direction 303

9 Molecular Simulations-based Predictions of Drug's Residence Times in the Exascale Era: Current Status and Recent Advances 317
Nitin Malapally, Marta Devodier, Estela Suarez, Thomas Lippert, Giulia Rossetti, Paolo Carloni, and Davide Mandelli

9.1 Introduction 317
9.2 MD-based Methods to Estimate RTs 319Table of Contents xi
9.3 Qualitative and Fast Estimates of RTs 320
9.4 Statistical Mechanics-based Methods for Quantitative Estimates of RTs 320
9.5 Predicting RTs in the Exascale Era: A Jülich Perspective 330
9.6 Conclusions 340

10 Binding Thermodynamics and Kinetics of Host–Guest Systems Determined from Long-timescale Molecular Dynamics Simulations 355
Ruben Montes, William Troxel, and Chia-en A. Chang

10.1 Introduction 355
10.2 Background on Molecular Recognition 356
10.3 Molecular Dynamics Techniques 359
10.4 Using Long Classical MD to Investigate Chemical Host–Guest Systems 362
10.5 Advanced Sampling Techniques in Host–Guest Systems 369
10.6 Perspective 371

11 Supervised Molecular Dynamics Approaches to Protein–Ligand (Un)binding 381
Giuseppe Deganutti, Ludovico Pipitò, and Christopher A. Reynolds

11.1 Introduction 381
11.2 Supervised MD (SuMD) Accelerates Ligand Binding Sampling 386
11.3 SuMD Approaches to Provide Functional Hypotheses for Binding and Unbinding Interactions 392
11.4 Conclusion 399

12 Dissipation-corrected Targeted Molecular Dynamics 407
Steffen Wolf

12.1 Introduction 407
12.2 Theory 408
12.3 Applications 418
12.4 Practical Considerations 421
12.5 Conclusion 425

13 Hybrid Gaussian Accelerated Molecular Dynamics and the Weighted Ensemble Methods for Biomolecular Simulations 433
Anugraha Thyagatur, Hung-Yu Wan, Siddharth Sonti, Roland Faller, and Surl-Hee Ahn

13.1 Introduction 433
13.2 Methods 436
13.3 Applications of GaMD-WE 444
13.4 Applications of ParGaMD 446
13.5 Conclusions 449

14 Replica Exchange Gaussian Accelerated Molecular Dynamics for Enhanced Sampling and Free Energy Calculations of Biomolecular Interactions 459
Yu-ming M. Huang

14.1 Introduction 459
14.2 Theory 462
14.3 Simulation Workflow 467
14.4 Applications of Rex-GaMD 469
14.5 Challenges and Future Directions 485

15 Enhanced Sampling of Biomolecular Interactions with Gaussian Accelerated Molecular Dynamics 493
Keya Joshi, Jinan Wang, and Yinglong Miao

15.1 Introduction 493
15.2 Methods 494
15.3 Applications 500
15.4 Conclusion 509

16 Contact Dynamics-based Perturbation Analysis: Revealing Allosteric Communication Pathways in Proteins 517
Harmanpreet Singh and Donald Hamelberg

16.1 Introduction 517
16.2 Methods for Determining Allostery 522
16.3 Allostery Through the Lens of dCNA 526
16.4 Summary and Outlook 531

17 Biophysical Methods for Studying the Adamantyl Amine–Lipid–Influenza A M2 Protein Channel System 543
Kyriakos Georgiou, Maria Kouridaki, and Antonios Kolocouris

17.1 Introduction 543
17.2 The Inhibitor Binding Site of Adamantyl Amines in AM2 546
17.3 Interactions of Adamantyl Amines with AM2 WT Channel 547
17.4 Interactions of Adamantyl Amines with Amantadine-resistant AM2 Mutant Channels 562
17.5 Interactions of AM2–Adamantyl Amine Complexes with Membranes 569
17.6 Challenges for the Design of New Antivirals 575

18 Prediction of Biomolecule Kinetics Using Physics-based Brownian Dynamics to Data-driven Machine Learning Methods 595
Bin Sun, Alec Loftus, and Peter Kekenes-Huskey

18.1 The Enzyme-substrate Interaction in Biological Systems 595
18.2 The Molecular Physics of Binding Events 601
18.3 Experimental Approaches for Characterizing Thermodynamics and Kinetics of Binding 606
18.4 Modeling Diffusion-limited Kinetics with Brownian Dynamics 610
18.5 The Next Frontier 637
18.6 Conclusion 657

Acknowledgments 657
References 658
Index 681