Yinglong Miao, PhD, is an Associate Professor in the Department of Pharmacology, Computational Medicine Program and Lineberger Comprehensive Cancer Center at University of North Carolina - Chapel Hill. His research focuses on computational methods for studying biomolecular interactions, including molecular dynamics simulations, enhanced sampling techniques, and machine learning approaches for drug discovery and biomo?lecular dynamics characterization.

• List of Contributors xixPreface xxviiAcknowledgments xxix1 Atomistic Force Fields for Molecular Simulations with Emphasis on the CHARMM Additive and Drude Polarizable Models 1Suvankar 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 11.2 Additive and Drude Potential Energy Functions 31.3 Water 81.4 Proteins 91.5 Nucleic Acids 141.6 Carbohydrates 161.7 Lipids 201.8 Atomic Ions 241.9 Small Molecules 261.10 Summary and Future Directions 302 Treating Noncovalent Interactions in Biomolecules with QM and QM/MM Models 59Qiang Cui2.1 Introduction 592.2 General Computational Models 602.3 Applications and Discussions 742.4 Concluding Remarks 803 QM/MM Simulations on Catalytic Mechanisms of Metalloenzymes 101Tai-Ping Zhou, Shengheng Yan, Xiaoyu Wang, and Binju Wang3.1 Introduction 1013.2 QM/MM Methods for Enzyme System 1053.3 Applications of QM/MM Methods 1093.4 Conclusions 1334 Integrative Multiscale Modeling of Biomolecular Interactions: From Mechanistic Understanding to Design 153Carla Calvó-Tusell, Miguel A. Maria-Solano, Marc Garcia-Borràs, and Ferran Feixas4.1 Introduction 1534.2 Integrative Multiscale Molecular Modeling of Biomolecular Interactions and Mechanisms 1584.3 Case Study I: Time Evolution of the Millisecond Allosteric Activation of IGPS 1664.4 Case Study II: Protein Energy Networks as a Framework for Allosteric Pathway Discovery and Drug Design in GPCRs 1744.5 Case Study III: Mechanistically Guided Design of Enzymes for C—N Bond Formation via Multiscale Modeling 1764.6 Perspective and Outlook 1805 Methodological Advances in Computational Biomodeling: End-point Free Energy Approaches 193John Z.H. Zhang and Zhaoxi Sun5.1 End-point Free Energy Approaches: An Introductory Tale 1935.2 Methodological Advances 1955.3 Practical Applications 2026 Computational Modeling of Protein–Protein/Peptide/RNA Interactions 213Martin Zacharias6.1 Introduction 2136.2 Prediction of Protein–Protein Complexes by Traditional Docking Methods 2146.3 Structure Prediction of Biomolecular Complexes Using Deep Learning Methods 2186.4 Prediction of Protein–Peptide and Protein–RNA Complexes Using Deep Learning Methods 2216.5 Molecular Dynamics Simulation of Proteins in Complex with Proteins, Peptides, and RNA 2236.6 Binding Free Energy Calculations of Biomolecular Complexes 2276.7 Peptide and Protein Interaction Design 2306.8 Conclusions 2317 Modeling Peptide–Protein Interactions with MELD: A Physics-based Framework for Structure, Affinity, and Design 243Guadalupe Alvarez, Yisel Martinez-Noa, Bhumika Singh, Jokent Gaza, Qianchen Liu, and Alberto Perez7.1 Introduction: The Biological and Clinical Relevance of Peptides and Peptide Epitopes 2437.2 MELD: A Versatile Computational Tool for Modeling Molecular Recognition 2487.3 Protocols 2527.4 Case Studies 2587.5 Conclusions 2708 Transforming Drug Binding with AI-enhanced Computational Modeling 281Aamir Mehmood and Dong-Qing Wei8.1 Introduction 2818.2 Computational Strategies Optimizing Drug Development 2848.3 Innovations in Computational Modeling of Receptor–Ligand Binding Dynamics 2868.4 AI in Transforming Preclinical Drug Development 2928.5 Computational AI Resources for Drug Development 2948.6 Receptor–Ligand Interactions Prediction Using AI 2998.7 Conclusion and Future Direction 3039 Molecular Simulations-based Predictions of Drug's Residence Times in the Exascale Era: Current Status and Recent Advances 317Nitin Malapally, Marta Devodier, Estela Suarez, Thomas Lippert, Giulia Rossetti, Paolo Carloni, and Davide Mandelli9.1 Introduction 3179.2 MD-based Methods to Estimate RTs 319Table of Contents xi9.3 Qualitative and Fast Estimates of RTs 3209.4 Statistical Mechanics-based Methods for Quantitative Estimates of RTs 3209.5 Predicting RTs in the Exascale Era: A Jülich Perspective 3309.6 Conclusions 34010 Binding Thermodynamics and Kinetics of Host–Guest Systems Determined from Long-timescale Molecular Dynamics Simulations 355Ruben Montes, William Troxel, and Chia-en A. Chang10.1 Introduction 35510.2 Background on Molecular Recognition 35610.3 Molecular Dynamics Techniques 35910.4 Using Long Classical MD to Investigate Chemical Host–Guest Systems 36210.5 Advanced Sampling Techniques in Host–Guest Systems 36910.6 Perspective 37111 Supervised Molecular Dynamics Approaches to Protein–Ligand (Un)binding 381Giuseppe Deganutti, Ludovico Pipitò, and Christopher A. Reynolds11.1 Introduction 38111.2 Supervised MD (SuMD) Accelerates Ligand Binding Sampling 38611.3 SuMD Approaches to Provide Functional Hypotheses for Binding and Unbinding Interactions 39211.4 Conclusion 39912 Dissipation-corrected Targeted Molecular Dynamics 407Steffen Wolf12.1 Introduction 40712.2 Theory 40812.3 Applications 41812.4 Practical Considerations 42112.5 Conclusion 42513 Hybrid Gaussian Accelerated Molecular Dynamics and the Weighted Ensemble Methods for Biomolecular Simulations 433Anugraha Thyagatur, Hung-Yu Wan, Siddharth Sonti, Roland Faller, and Surl-Hee Ahn13.1 Introduction 43313.2 Methods 43613.3 Applications of GaMD-WE 44413.4 Applications of ParGaMD 44613.5 Conclusions 44914 Replica Exchange Gaussian Accelerated Molecular Dynamics for Enhanced Sampling and Free Energy Calculations of Biomolecular Interactions 459Yu-ming M. Huang14.1 Introduction 45914.2 Theory 46214.3 Simulation Workflow 46714.4 Applications of Rex-GaMD 46914.5 Challenges and Future Directions 48515 Enhanced Sampling of Biomolecular Interactions with Gaussian Accelerated Molecular Dynamics 493Keya Joshi, Jinan Wang, and Yinglong Miao15.1 Introduction 49315.2 Methods 49415.3 Applications 50015.4 Conclusion 50916 Contact Dynamics-based Perturbation Analysis: Revealing Allosteric Communication Pathways in Proteins 517Harmanpreet Singh and Donald Hamelberg16.1 Introduction 51716.2 Methods for Determining Allostery 52216.3 Allostery Through the Lens of dCNA 52616.4 Summary and Outlook 53117 Biophysical Methods for Studying the Adamantyl Amine–Lipid–Influenza A M2 Protein Channel System 543Kyriakos Georgiou, Maria Kouridaki, and Antonios Kolocouris17.1 Introduction 54317.2 The Inhibitor Binding Site of Adamantyl Amines in AM2 54617.3 Interactions of Adamantyl Amines with AM2 WT Channel 54717.4 Interactions of Adamantyl Amines with Amantadine-resistant AM2 Mutant Channels 56217.5 Interactions of AM2–Adamantyl Amine Complexes with Membranes 56917.6 Challenges for the Design of New Antivirals 57518 Prediction of Biomolecule Kinetics Using Physics-based Brownian Dynamics to Data-driven Machine Learning Methods 595Bin Sun, Alec Loftus, and Peter Kekenes-Huskey18.1 The Enzyme-substrate Interaction in Biological Systems 59518.2 The Molecular Physics of Binding Events 60118.3 Experimental Approaches for Characterizing Thermodynamics and Kinetics of Binding 60618.4 Modeling Diffusion-limited Kinetics with Brownian Dynamics 61018.5 The Next Frontier 63718.6 Conclusion 657Acknowledgments 657References 658Index 681

Yinglong Miao, PhD, is an Associate Professor in the Department of Pharmacology, Computational Medicine Program and Lineberger Comprehensive Cancer Center at University of North Carolina - Chapel Hill. His research focuses on computational methods for studying biomolecular interactions, including molecular dynamics simulations, enhanced sampling techniques, and machine learning approaches for drug discovery and biomo?lecular dynamics characterization.

• List of Contributors xixPreface xxviiAcknowledgments xxix1 Atomistic Force Fields for Molecular Simulations with Emphasis on the CHARMM Additive and Drude Polarizable Models 1Suvankar 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 11.2 Additive and Drude Potential Energy Functions 31.3 Water 81.4 Proteins 91.5 Nucleic Acids 141.6 Carbohydrates 161.7 Lipids 201.8 Atomic Ions 241.9 Small Molecules 261.10 Summary and Future Directions 302 Treating Noncovalent Interactions in Biomolecules with QM and QM/MM Models 59Qiang Cui2.1 Introduction 592.2 General Computational Models 602.3 Applications and Discussions 742.4 Concluding Remarks 803 QM/MM Simulations on Catalytic Mechanisms of Metalloenzymes 101Tai-Ping Zhou, Shengheng Yan, Xiaoyu Wang, and Binju Wang3.1 Introduction 1013.2 QM/MM Methods for Enzyme System 1053.3 Applications of QM/MM Methods 1093.4 Conclusions 1334 Integrative Multiscale Modeling of Biomolecular Interactions: From Mechanistic Understanding to Design 153Carla Calvó-Tusell, Miguel A. Maria-Solano, Marc Garcia-Borràs, and Ferran Feixas4.1 Introduction 1534.2 Integrative Multiscale Molecular Modeling of Biomolecular Interactions and Mechanisms 1584.3 Case Study I: Time Evolution of the Millisecond Allosteric Activation of IGPS 1664.4 Case Study II: Protein Energy Networks as a Framework for Allosteric Pathway Discovery and Drug Design in GPCRs 1744.5 Case Study III: Mechanistically Guided Design of Enzymes for C—N Bond Formation via Multiscale Modeling 1764.6 Perspective and Outlook 1805 Methodological Advances in Computational Biomodeling: End-point Free Energy Approaches 193John Z.H. Zhang and Zhaoxi Sun5.1 End-point Free Energy Approaches: An Introductory Tale 1935.2 Methodological Advances 1955.3 Practical Applications 2026 Computational Modeling of Protein–Protein/Peptide/RNA Interactions 213Martin Zacharias6.1 Introduction 2136.2 Prediction of Protein–Protein Complexes by Traditional Docking Methods 2146.3 Structure Prediction of Biomolecular Complexes Using Deep Learning Methods 2186.4 Prediction of Protein–Peptide and Protein–RNA Complexes Using Deep Learning Methods 2216.5 Molecular Dynamics Simulation of Proteins in Complex with Proteins, Peptides, and RNA 2236.6 Binding Free Energy Calculations of Biomolecular Complexes 2276.7 Peptide and Protein Interaction Design 2306.8 Conclusions 2317 Modeling Peptide–Protein Interactions with MELD: A Physics-based Framework for Structure, Affinity, and Design 243Guadalupe Alvarez, Yisel Martinez-Noa, Bhumika Singh, Jokent Gaza, Qianchen Liu, and Alberto Perez7.1 Introduction: The Biological and Clinical Relevance of Peptides and Peptide Epitopes 2437.2 MELD: A Versatile Computational Tool for Modeling Molecular Recognition 2487.3 Protocols 2527.4 Case Studies 2587.5 Conclusions 2708 Transforming Drug Binding with AI-enhanced Computational Modeling 281Aamir Mehmood and Dong-Qing Wei8.1 Introduction 2818.2 Computational Strategies Optimizing Drug Development 2848.3 Innovations in Computational Modeling of Receptor–Ligand Binding Dynamics 2868.4 AI in Transforming Preclinical Drug Development 2928.5 Computational AI Resources for Drug Development 2948.6 Receptor–Ligand Interactions Prediction Using AI 2998.7 Conclusion and Future Direction 3039 Molecular Simulations-based Predictions of Drug's Residence Times in the Exascale Era: Current Status and Recent Advances 317Nitin Malapally, Marta Devodier, Estela Suarez, Thomas Lippert, Giulia Rossetti, Paolo Carloni, and Davide Mandelli9.1 Introduction 3179.2 MD-based Methods to Estimate RTs 319Table of Contents xi9.3 Qualitative and Fast Estimates of RTs 3209.4 Statistical Mechanics-based Methods for Quantitative Estimates of RTs 3209.5 Predicting RTs in the Exascale Era: A Jülich Perspective 3309.6 Conclusions 34010 Binding Thermodynamics and Kinetics of Host–Guest Systems Determined from Long-timescale Molecular Dynamics Simulations 355Ruben Montes, William Troxel, and Chia-en A. Chang10.1 Introduction 35510.2 Background on Molecular Recognition 35610.3 Molecular Dynamics Techniques 35910.4 Using Long Classical MD to Investigate Chemical Host–Guest Systems 36210.5 Advanced Sampling Techniques in Host–Guest Systems 36910.6 Perspective 37111 Supervised Molecular Dynamics Approaches to Protein–Ligand (Un)binding 381Giuseppe Deganutti, Ludovico Pipitò, and Christopher A. Reynolds11.1 Introduction 38111.2 Supervised MD (SuMD) Accelerates Ligand Binding Sampling 38611.3 SuMD Approaches to Provide Functional Hypotheses for Binding and Unbinding Interactions 39211.4 Conclusion 39912 Dissipation-corrected Targeted Molecular Dynamics 407Steffen Wolf12.1 Introduction 40712.2 Theory 40812.3 Applications 41812.4 Practical Considerations 42112.5 Conclusion 42513 Hybrid Gaussian Accelerated Molecular Dynamics and the Weighted Ensemble Methods for Biomolecular Simulations 433Anugraha Thyagatur, Hung-Yu Wan, Siddharth Sonti, Roland Faller, and Surl-Hee Ahn13.1 Introduction 43313.2 Methods 43613.3 Applications of GaMD-WE 44413.4 Applications of ParGaMD 44613.5 Conclusions 44914 Replica Exchange Gaussian Accelerated Molecular Dynamics for Enhanced Sampling and Free Energy Calculations of Biomolecular Interactions 459Yu-ming M. Huang14.1 Introduction 45914.2 Theory 46214.3 Simulation Workflow 46714.4 Applications of Rex-GaMD 46914.5 Challenges and Future Directions 48515 Enhanced Sampling of Biomolecular Interactions with Gaussian Accelerated Molecular Dynamics 493Keya Joshi, Jinan Wang, and Yinglong Miao15.1 Introduction 49315.2 Methods 49415.3 Applications 50015.4 Conclusion 50916 Contact Dynamics-based Perturbation Analysis: Revealing Allosteric Communication Pathways in Proteins 517Harmanpreet Singh and Donald Hamelberg16.1 Introduction 51716.2 Methods for Determining Allostery 52216.3 Allostery Through the Lens of dCNA 52616.4 Summary and Outlook 53117 Biophysical Methods for Studying the Adamantyl Amine–Lipid–Influenza A M2 Protein Channel System 543Kyriakos Georgiou, Maria Kouridaki, and Antonios Kolocouris17.1 Introduction 54317.2 The Inhibitor Binding Site of Adamantyl Amines in AM2 54617.3 Interactions of Adamantyl Amines with AM2 WT Channel 54717.4 Interactions of Adamantyl Amines with Amantadine-resistant AM2 Mutant Channels 56217.5 Interactions of AM2–Adamantyl Amine Complexes with Membranes 56917.6 Challenges for the Design of New Antivirals 57518 Prediction of Biomolecule Kinetics Using Physics-based Brownian Dynamics to Data-driven Machine Learning Methods 595Bin Sun, Alec Loftus, and Peter Kekenes-Huskey18.1 The Enzyme-substrate Interaction in Biological Systems 59518.2 The Molecular Physics of Binding Events 60118.3 Experimental Approaches for Characterizing Thermodynamics and Kinetics of Binding 60618.4 Modeling Diffusion-limited Kinetics with Brownian Dynamics 61018.5 The Next Frontier 63718.6 Conclusion 657Acknowledgments 657References 658Index 681

Yinglong Miao, PhD, is an Associate Professor in the Department of Pharmacology, Computational Medicine Program and Lineberger Comprehensive Cancer Center at University of North Carolina - Chapel Hill. His research focuses on computational methods for studying biomolecular interactions, including molecular dynamics simulations, enhanced sampling techniques, and machine learning approaches for drug discovery and biomo?lecular dynamics characterization.

• List of Contributors xixPreface xxviiAcknowledgments xxix1 Atomistic Force Fields for Molecular Simulations with Emphasis on the CHARMM Additive and Drude Polarizable Models 1Suvankar 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 11.2 Additive and Drude Potential Energy Functions 31.3 Water 81.4 Proteins 91.5 Nucleic Acids 141.6 Carbohydrates 161.7 Lipids 201.8 Atomic Ions 241.9 Small Molecules 261.10 Summary and Future Directions 302 Treating Noncovalent Interactions in Biomolecules with QM and QM/MM Models 59Qiang Cui2.1 Introduction 592.2 General Computational Models 602.3 Applications and Discussions 742.4 Concluding Remarks 803 QM/MM Simulations on Catalytic Mechanisms of Metalloenzymes 101Tai-Ping Zhou, Shengheng Yan, Xiaoyu Wang, and Binju Wang3.1 Introduction 1013.2 QM/MM Methods for Enzyme System 1053.3 Applications of QM/MM Methods 1093.4 Conclusions 1334 Integrative Multiscale Modeling of Biomolecular Interactions: From Mechanistic Understanding to Design 153Carla Calvó-Tusell, Miguel A. Maria-Solano, Marc Garcia-Borràs, and Ferran Feixas4.1 Introduction 1534.2 Integrative Multiscale Molecular Modeling of Biomolecular Interactions and Mechanisms 1584.3 Case Study I: Time Evolution of the Millisecond Allosteric Activation of IGPS 1664.4 Case Study II: Protein Energy Networks as a Framework for Allosteric Pathway Discovery and Drug Design in GPCRs 1744.5 Case Study III: Mechanistically Guided Design of Enzymes for C—N Bond Formation via Multiscale Modeling 1764.6 Perspective and Outlook 1805 Methodological Advances in Computational Biomodeling: End-point Free Energy Approaches 193John Z.H. Zhang and Zhaoxi Sun5.1 End-point Free Energy Approaches: An Introductory Tale 1935.2 Methodological Advances 1955.3 Practical Applications 2026 Computational Modeling of Protein–Protein/Peptide/RNA Interactions 213Martin Zacharias6.1 Introduction 2136.2 Prediction of Protein–Protein Complexes by Traditional Docking Methods 2146.3 Structure Prediction of Biomolecular Complexes Using Deep Learning Methods 2186.4 Prediction of Protein–Peptide and Protein–RNA Complexes Using Deep Learning Methods 2216.5 Molecular Dynamics Simulation of Proteins in Complex with Proteins, Peptides, and RNA 2236.6 Binding Free Energy Calculations of Biomolecular Complexes 2276.7 Peptide and Protein Interaction Design 2306.8 Conclusions 2317 Modeling Peptide–Protein Interactions with MELD: A Physics-based Framework for Structure, Affinity, and Design 243Guadalupe Alvarez, Yisel Martinez-Noa, Bhumika Singh, Jokent Gaza, Qianchen Liu, and Alberto Perez7.1 Introduction: The Biological and Clinical Relevance of Peptides and Peptide Epitopes 2437.2 MELD: A Versatile Computational Tool for Modeling Molecular Recognition 2487.3 Protocols 2527.4 Case Studies 2587.5 Conclusions 2708 Transforming Drug Binding with AI-enhanced Computational Modeling 281Aamir Mehmood and Dong-Qing Wei8.1 Introduction 2818.2 Computational Strategies Optimizing Drug Development 2848.3 Innovations in Computational Modeling of Receptor–Ligand Binding Dynamics 2868.4 AI in Transforming Preclinical Drug Development 2928.5 Computational AI Resources for Drug Development 2948.6 Receptor–Ligand Interactions Prediction Using AI 2998.7 Conclusion and Future Direction 3039 Molecular Simulations-based Predictions of Drug's Residence Times in the Exascale Era: Current Status and Recent Advances 317Nitin Malapally, Marta Devodier, Estela Suarez, Thomas Lippert, Giulia Rossetti, Paolo Carloni, and Davide Mandelli9.1 Introduction 3179.2 MD-based Methods to Estimate RTs 319Table of Contents xi9.3 Qualitative and Fast Estimates of RTs 3209.4 Statistical Mechanics-based Methods for Quantitative Estimates of RTs 3209.5 Predicting RTs in the Exascale Era: A Jülich Perspective 3309.6 Conclusions 34010 Binding Thermodynamics and Kinetics of Host–Guest Systems Determined from Long-timescale Molecular Dynamics Simulations 355Ruben Montes, William Troxel, and Chia-en A. Chang10.1 Introduction 35510.2 Background on Molecular Recognition 35610.3 Molecular Dynamics Techniques 35910.4 Using Long Classical MD to Investigate Chemical Host–Guest Systems 36210.5 Advanced Sampling Techniques in Host–Guest Systems 36910.6 Perspective 37111 Supervised Molecular Dynamics Approaches to Protein–Ligand (Un)binding 381Giuseppe Deganutti, Ludovico Pipitò, and Christopher A. Reynolds11.1 Introduction 38111.2 Supervised MD (SuMD) Accelerates Ligand Binding Sampling 38611.3 SuMD Approaches to Provide Functional Hypotheses for Binding and Unbinding Interactions 39211.4 Conclusion 39912 Dissipation-corrected Targeted Molecular Dynamics 407Steffen Wolf12.1 Introduction 40712.2 Theory 40812.3 Applications 41812.4 Practical Considerations 42112.5 Conclusion 42513 Hybrid Gaussian Accelerated Molecular Dynamics and the Weighted Ensemble Methods for Biomolecular Simulations 433Anugraha Thyagatur, Hung-Yu Wan, Siddharth Sonti, Roland Faller, and Surl-Hee Ahn13.1 Introduction 43313.2 Methods 43613.3 Applications of GaMD-WE 44413.4 Applications of ParGaMD 44613.5 Conclusions 44914 Replica Exchange Gaussian Accelerated Molecular Dynamics for Enhanced Sampling and Free Energy Calculations of Biomolecular Interactions 459Yu-ming M. Huang14.1 Introduction 45914.2 Theory 46214.3 Simulation Workflow 46714.4 Applications of Rex-GaMD 46914.5 Challenges and Future Directions 48515 Enhanced Sampling of Biomolecular Interactions with Gaussian Accelerated Molecular Dynamics 493Keya Joshi, Jinan Wang, and Yinglong Miao15.1 Introduction 49315.2 Methods 49415.3 Applications 50015.4 Conclusion 50916 Contact Dynamics-based Perturbation Analysis: Revealing Allosteric Communication Pathways in Proteins 517Harmanpreet Singh and Donald Hamelberg16.1 Introduction 51716.2 Methods for Determining Allostery 52216.3 Allostery Through the Lens of dCNA 52616.4 Summary and Outlook 53117 Biophysical Methods for Studying the Adamantyl Amine–Lipid–Influenza A M2 Protein Channel System 543Kyriakos Georgiou, Maria Kouridaki, and Antonios Kolocouris17.1 Introduction 54317.2 The Inhibitor Binding Site of Adamantyl Amines in AM2 54617.3 Interactions of Adamantyl Amines with AM2 WT Channel 54717.4 Interactions of Adamantyl Amines with Amantadine-resistant AM2 Mutant Channels 56217.5 Interactions of AM2–Adamantyl Amine Complexes with Membranes 56917.6 Challenges for the Design of New Antivirals 57518 Prediction of Biomolecule Kinetics Using Physics-based Brownian Dynamics to Data-driven Machine Learning Methods 595Bin Sun, Alec Loftus, and Peter Kekenes-Huskey18.1 The Enzyme-substrate Interaction in Biological Systems 59518.2 The Molecular Physics of Binding Events 60118.3 Experimental Approaches for Characterizing Thermodynamics and Kinetics of Binding 60618.4 Modeling Diffusion-limited Kinetics with Brownian Dynamics 61018.5 The Next Frontier 63718.6 Conclusion 657Acknowledgments 657References 658Index 681