Ebook: Computational Modeling of Biological Systems: From Molecules to Pathways

Computational modeling is emerging as a powerful new approach to study and manipulate biological systems. Multiple methods have been developed to model, visualize, and rationally alter systems at various length scales, starting from molecular modeling and design at atomic resolution to cellular pathways modeling and analysis. Higher time and length scale processes, such as molecular evolution, have also greatly benefited from new breeds of computational approaches. This book provides an overview of the established computational methods used for modeling biologically and medically relevant systems.

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Computational modeling is emerging as a powerful new approach to study and manipulate biological systems. Multiple methods have been developed to model, visualize, and rationally alter systems at various length scales, starting from molecular modeling and design at atomic resolution to cellular pathways modeling and analysis. Higher time and length scale processes, such as molecular evolution, have also greatly benefited from new breeds of computational approaches. This book provides an overview of the established computational methods used for modeling biologically and medically relevant systems. Table of Contents Cover Computational Modeling of Biological Systems ISBN 9781461421450 e-ISBN 9781461421467 Contents Part I Molecular Modeling Introduction to Molecular Dynamics: Theory and Applications in Biomolecular Modeling 1 Introduction 2 Statistical Mechanics Background o 2.1 Microstates and the Ensemble Theory o 2.2 The Ensemble Average 3 Molecular Dynamics: The Theory o 3.1 The Force Field o 3.2 Long-Range Interactions o 3.3 Equations of Motion 4 Applications of MD: A Few Examples o 4.1 Calculation of Water Diffusion o 4.2 Characterization of Receptor Flexibility in Virtual Screening 5 Running a Simulation: Preparations and Precautions o 5.1 System Preparation # 5.1.1 Choosing Initial Structures # 5.1.2 pKa Calculation # 5.1.3 Adding Water and Ions o 5.2 Simulation Conditions # 5.2.1 Designing Simulation Protocols # 5.2.2 Dealing with Errors 6 Advanced Simulation Techniques o 6.1 Accelerated Molecular Dynamics o 6.2 Free Energy: The Concept o 6.3 Free-Energy Perturbation o 6.4 Thermodynamic Integration o 6.5 Umbrella Sampling and Other Techniques 7 Outlook References The Many Faces of Structure-Based Potentials: From Protein Folding Landscapes to Structural Characterization of Complex Biomolecules 1 Introduction 2 Structure-Based Models o 2.1 Foundations in Energy Landscape Theory o 2.2 Structure-Based Model as a Baseline 3 Implementation of Structure-Based Models o 3.1 Native Contact Map o 3.2 SBM Potential # 3.2.1 C Model # 3.2.2 All-Atom Model # 3.2.3 Contact Potential o 3.3 Molecular Dynamics with SBM o 3.4 SMOG: Automated Generation of SBM o 3.5 Choosing a Graining: C or All-Atom 4 Applications o 4.1 Folding # 4.1.1 Protein Folding # 4.1.2 Multimeric Folding and Binding o 4.2 Native Basin Dynamics o 4.3 Multiple Basin Models o 4.4 Molecular Modeling 5 Concluding Remarks References Discrete Molecular Dynamics Simulation of Biomolecules 1 Introduction 2 Discrete Molecular Dynamics o 2.1 Algorithm o 2.2 Fine Grid o 2.3 Reduce the Unnecessary Square Root Calculation o 2.4 Paul's O(1) Sorting Approach 3 Development of DMD Force Field for Biomolecules o 3.1 Hydrogen Bonds o 3.2 All-Atom Protein Model o 3.3 Extension of the Force Field for Small Molecules 4 DMD Simulations of Biomolecules o 4.1 Folding of Small, Fast-Folding Proteins o 4.2 Protein-Protein Design o 4.3 Protein Dynamic Coupling and Allosteric Engineering of Kinases 5 Conclusion References Small Molecule Docking from Theoretical Structural Models 1 Docking as a Method for Drug Design 2 Docking Algorithms 3 Scenario for Docking Use 4 Protein Structure Prediction 5 HT Docking from Homology Modeled Structures 6 Increasing Coverage References Homology Modeling: Generating Structural Models to Understand Protein Function and Mechanism 1 Homology Models: Need and Applicability 2 Template Identification o 2.1 Domain Delineation o 2.2 Direct Sequence Homology: BLAST and PSI-BLAST o 2.3 Remote Homology o 2.4 Meta Servers 3 From Alignment to a Structural Model o 3.1 Model Construction o 3.2 Model Refinement o 3.3 Estimating Model Quality 4 Experimental Constraints to Improve/Verify Homology Models 5 Conclusions References Quantum Mechanical Insights into Biological Processes at the Electronic Level 1 Introduction 2 Ab Initio Treatment of Biochemical Systems on the Ground State o 2.1 Theoretical Foundation o 2.2 Navigating Through the Wealth of Ab Initio Methods: Some Quick Recipes o 2.3 Examples of Applications 3 Mixed QM/MM Techniques o 3.1 Theoretical Foundation o 3.2 Examples of Applications 4 Excited States and Electron Detachment o 4.1 Theoretical Foundation o 4.2 Examples of Applications 5 Ground and Excited States Dynamics o 5.1 Theoretical Foundation o 5.2 Examples of Applications 6 Summary References Part II Modeling Macromolecular Assemblies Multiscale Modeling of Virus Structure, Assembly, and Dynamics 1 Introduction 2 Background on Spherical Virus Architecture 3 Mathematical and Geometric Models for Describing Virus Phenomena o 3.1 The Canonical Capsid Model o 3.2 Prediction of the Optimal Subunit Shape o 3.3 Hexamer Complexity as a Predictor of Capsid Properties o 3.4 Limitations of the Canonical Capsid Model 4 Self-Assembly of Virus Capsids 5 Maturation and Mechanical Properties of Virus Capsids 6 Conclusions and Future Directions References Mechanisms and Kinetics of Amyloid Aggregation Investigated by a Phenomenological Coarse-Grained Model 1 Introduction 2 Coarse-Grained Models 3 The Coarse-Grained Phenomenological Model 4 Aggregation of the CGF Peptide Model in Bulk Solution o 4.1 Aggregation Kinetics and Pathways o 4.2 Mechanism of Nucleation o 4.3 Concentration Effects o 4.4 Amyloid Fibril Polymorphism 5 Aggregation in the Presence of Lipid Vesicles and Inert Crowders o 5.1 Effect of Lipid Bilayers on CGF Peptide Aggregation o 5.2 Effect of Surfactants on CGF-Peptide Aggregation o 5.3 Macromolecular Crowding Effect on CGF Peptide Aggregation 6 Conclusion References The Structure of Intrinsically Disordered Peptides Implicated in Amyloid Diseases: Insights from Fully Atomistic Simulations 1 Introduction 2 Simulation Approaches 3 The Alzheimer Amyloid- Peptide o 3.1 A40 and A42 o 3.2 Familial Forms of AD o 3.3 Effect of Solution Conditions on A Structure 4 The IAPP Peptide 5 Conclusions References Part III Modeling Cells and Cellular Pathways Computer Simulations of Mechano-Chemical Networks Choreographing Actin Dynamics in Cell Motility 1 Introduction 2 Mechano-Chemical Networks Regulating Actin Dynamics o 2.1 Stochastic Simulations of Biological Mechano-Chemical Networks # 2.1.1 Reaction-Diffusion Master Equation # 2.1.2 Detailed Modeling of Filopodia and Lamellipodia 3 Filopodia o 3.1 Biological Background o 3.2 Chemistry, Mechanics, and Transport in Filopodia o 3.3 Chemistry o 3.4 Transport o 3.5 Mechanics 4 Lamellipodia o 4.1 Introduction o 4.2 Chemical Feedbacks Regulate Actin Mesh Growth # 4.2.1 Elongation vs. Nucleation of Actin Filaments # 4.2.2 The Antagonism Between Capping and Anti-capping Proteins Affects Actin Network Dynamics # 4.2.3 Transport of Molecules o 4.3 Mechanical Aspects of Lamellipodial Protrusion # 4.3.1 Cell Membrane # 4.3.2 Re-organization of the Actin Network: From Lamellipodia to Filopodia 5 Summary References Computational and Modeling Strategies for Cell Motility 1 Introduction 2 Models for Active Filaments o 2.1 Active Polar Filament Model o 2.2 Active Apolar Filament Models o 2.3 Kinetic Models for Active Fluids 3 Models for Active Gels o 3.1 Isotropic Active Gel Model o 3.2 Active Polar Gel Model o 3.3 Three-Component Active Fluid Model 4 A Phase Field Model for a Cell Surrounded by Solvent o 4.1 Approximate Model 5 Numerical Results and Discussion o 5.1 Activation of a Local Domain in the Cortical Layer o 5.2 Active Regions Alternating on Opposing Sides of the Cell 6 Conclusion References Theoretical Analysis of Molecular Transport Across Membrane Channels and Nanopores 1 Introduction 2 Discrete-State Stochastic Models o 2.1 Molecule/Channel Interactions o 2.2 Intermolecular Interactions 3 Summary and Conclusions References Part IV Modeling Evolution Modeling Protein Evolution 1 Why Model Protein Evolution? 2 The Challenges o 2.1 Modeling Protein Energetics o 2.2 Modeling Selective Constraints o 2.3 Modeling Evolutionary Dynamics 3 The Distribution of Observed Protein Structures 4 Evolution of Thermodynamic Properties 5 Other Evolutionary Processes 6 Conclusion References Modeling Structural and Genomic Constraints in the Evolution of Proteins 1 Molecular Phenotypes 2 Population Dynamics and Statistical Physics 3 Substitution Rate and Mutational Robustness 4 Translation Load 5 Protein Stability and Mutation Bias 6 Protein Size and Marginal Stability 7 Inverse Folding 8 Protein Structure Evolution 9 Conformation Changes 10 Disordered Proteins 11 Conclusions References Modeling Proteins at the Interface of Structure, Evolution,and Population Genetics 1 Introduction 2 Simulation and Forward Evolution 3 Phylogeny and Thermodynamics 4 Population Genetics and Biophysical Constraints in Models for Interspecific Evolution 5 Concluding Thoughts References Index