رویکردهای محاسباتی برای درک سیستم‌های دینامیکی: تا شدن و مونتاژ پروتئین (جلد ۱۷۰) (پیشرفت در زیست‌شناسی مولکولی و علم ترجمه)

Computational Approaches for Understanding Dynamical Systems: Protein Folding and Assembly, Volume 170 in the Progress in Molecular Biology and Translational Science series, provides the most topical, informative and exciting monographs available on a wide variety of research topics. The series includes in-depth knowledge on the molecular biological aspects of organismal physiology, with this release including chapters on Pairwise-Additive and Polarizable Atomistic Force Fields for Molecular Dynamics Simulations of Proteins, Scale-consistent approach to the derivation of coarse-grained force fields for simulating structure, dynamics, and thermodynamics of biopolymers, Enhanced sampling and free energy methods, and much more. Includes comprehensive coverage on molecular biology Presents ample use of tables, diagrams, schemata and color figures to enhance the reader's ability to rapidly grasp the information provided Contains contributions from renowned experts in the field Copyright Contributors Preface Pairwise-additive and polarizable atomistic force fields for molecular dynamics simulations of proteins Introduction Force field overview Bonded interactions Nonbonded interactions Types of atomistic force fields Pairwise-additive force fields AMBER AMBER-derived implicitly polarized force fields CHARMM OPLS GROMOS Polarizable force fields Fluctuating charge models Induced dipole and multipole models The classical Drude oscillator Conclusions and future directions References Scale-consistent approach to the derivation of coarse-grained force fields for simulating structure, dynamics, and thermod ... Introduction Theoretical background Potential of mean force of a coarse-grained system as a prototype of the effective energy function Steps toward a scale-consistent coarse-grained energy function Factorization of the PMF into Kubo cluster-cumulant functions Analytical scale-consistent approximations to the coarse-grained energy terms Parameterization of the effective energy expressions Force field calibration Implementation The UNIfied COarse-gRaiNed (UNICORN) model of biological macromolecules Features of selected energy terms of the scale-consistent UNICORN model Long-range Upipjel and UBiBjel terms Torsional terms Backbone-local-correlation (Ucorr(3)) terms Molecular dynamics and other conformational-search engine implementation with UNICORN Applications of UNICORN Protein structure prediction Effect of hydrodynamic interactions on folding kinetics Investigation of Hsp70 chaperone cycle Investigation of telomere stability Conclusions Acknowledgments References How to learn from inconsistencies: Integrating molecular simulations with experimental data Introduction Reweighting strategies Maximum entropy Maximum parsimony Bayesian inference or MaxPrior Comparing MaxEnt, MaxPars, and MaxPrior reweighting Interpretation of the results General applicability Imperfect force fields Numerical challenges Experiment-biased simulations Maximum entropy Empirical energy terms Bayesian inference Comparing reweighting with experiment-biased methods Adaptability Forward models Imperfect force fields Force field optimization Background on force field parametrization Refining protein and RNA force fields Proteins RNA Matching time-dependent and time-resolved data Maximum entropy and likelihood in dynamical systems Maximum Caliber Average Block Selection Challenges Balance between simulations and experimental data Interplay between reweighting and force field corrections Using kinetic data to reweight equilibrium ensembles A new generation of force fields Conclusions Acknowledgments References Enhanced sampling and free energy calculations for protein simulations Introduction Collective variable and free energy CV-based sampling Umbrella sampling Metadynamics Steered molecular dynamics CV-free sampling Replica exchange molecular dynamics Accelerated molecular dynamics Combination of enhanced sampling approaches Programs and tutorials Conclusion and outlook Acknowledgments References Long-time methods for molecular dynamics simulations: Markov State Models and Milestoning Introduction Markovian approach: Markov state models for MD simulations Relative RMSD for state assignment Applications of MSMs to enhance sampling in simulations of folding and binding of amyloid peptides Milestoning Conclusions Acknowledgments References Protein thermal stability Introduction Thermal stability in silico Thermophilic proteins Protein stability in crowded environments Conclusion Acknowledgments References Computer simulations of protein-membrane systems Introduction Lipid diversity: The scaffold of biological membranes Membrane proteins: The complexity of biological membranes Lipid rafts and hydrophobic mismatch: The regulation and organization of biological membranes Role of MD simulations in investigating protein-membrane systems Lipid force fields Atomistic force fields for lipids General description of classical force fields Experimental observables for the validation of lipid force fields Area per lipid Membrane thickness and electron density profile Acyl chain order parameters Membrane area compressibility Lateral diffusion coefficient Comparison of the atomistic lipid force fields CHARMM AMBER Slipids OPLS-AA GROMOS Limitations of atomistic lipid force fields The HMMM model Coarse-grained force fields for lipids The MARTINI CG model The all-atom to coarse-grained mapping CG mapping of lipids Other lipid CG models Which lipid FF to choose for a simulation? MD simulation setup and analysis of protein-membrane systems CHARMM-GUI PDB loader and manipulator Mono- and bilayer builder Nanodisc builder Micelle and hex phase builder HMMM builder MARTINI builder Setting up protein-membrane system with other programs Simulations with AMBER FF Simulations with OPLS-AA FF Simulations with GROMOS FF Simulations with MARTINI FF Glycosylation MD simulation software packages Analysis tools for studying protein-membrane systems Visualization and plotting tools Analysis tools Case studies for protein-membrane systems Atomistic simulations of integral membrane proteins G-protein-coupled receptors β2-adrenergic receptor (β2AR) Adenosine A2A receptor (A2AR) Opsin receptor Membrane transporters and channels XylE/LacY transporters Human dopamine transporter (hDAT) Inwardly rectifying potassium (Kir2.1) channel Glycoproteins Mitochondrial membrane protein Atomistic simulations of membrane-associated proteins Amyloids Amyloid β (Aβ) peptide The human islet amyloid polypeptide (hIAPP) α-synuclein (aSyn) Peripheral membrane proteins Fibroblast growth factor (FGFs) Pleckstrin homology (PH) domain Actin-binding proteins (ABPs) Viral fusion proteins Coarse-grained simulations of membrane proteins Curvature induction and membrane remodeling by FAM134B reticulon homology domain assist selective ER-phagy Lipid droplet biogenesis is a liquid phase separation spatially regulated by seipin and membrane curvature Lipid-protein interactions are unique fingerprints for membrane proteins Conclusions and future directions Acknowledgments References Minimalistic coarse-grained modeling of viral capsid assembly Introduction Experimental structure determination methods Models for assembly Confined models Interactions between capsid proteins Energy landscapes of coarse-grained capsid models ``Magic number ́ ́ clusters Non-spherical shells with polyhedral symmetries Open tubes Scaffolding Helical capsids Head-tail assemblies Hierarchical self-assembly of addressable capsids Conclusions and outlook Acknowledgments References Aggregation of disease-related peptides Introduction Computer simulation models for amyloid protein aggregation Structures of small aggregates Exploring the early aggregates of amyloid peptides at quasi-atomic level with hydrodynamics Primary and secondary nucleation from simulations Recent advances in structures of Aβ40/42 oligomers from simulations Conclusions Conflict of interest Acknowledgments References Computational studies of protein aggregation mediated by amyloid: Fibril elongation and secondary nucleation Introduction Computational insights into fibril elongation ``Fast-deposition ́ ́ versus ``lock-and-dock ́ ́ mechanisms Insights into the ``dock-and-lock ́ ́ mechanism of fibril elongation Initial docking of peptides driven by water release Structural rearrangement in the locking step Computational insights into the mechanism of secondary nucleation Experimental background Insights into surface-induced nucleation of peptides Protein/peptide-surface interactions Insights from simulations of nucleation processes on surfaces Computational insights into fibril-dependent secondary nucleation Insights from CG simulations of secondary nucleation Peptide-fibril interactions characterized by simulations at high resolution Summary and outlook Acknowledgments References Aggregation and coacervation with Monte Carlo simulations Introduction Markov chain Monte Carlo simulations Small update MC simulations Folding and aggregation using all-atom MC simulations Exploring fibril formation with lattice models MC simulations of liquid-liquid phase separation Conclusion References Index A B C D E F G H I K L M N O P Q R S T U V W X Z __Computational Approaches for Understanding Dynamical Systems: Protein Folding and Assembly, Volume 170__ in the __Progress in Molecular Biology and Translational Science__ series, provides the most topical, informative and exciting monographs available on a wide variety of research topics. The series includes in-depth knowledge on the molecular biological aspects of organismal physiology, with this release including chapters on Pairwise-Additive and Polarizable Atomistic Force Fields for Molecular Dynamics Simulations of Proteins, Scale-consistent approach to the derivation of coarse-grained force fields for simulating structure, dynamics, and thermodynamics of biopolymers, Enhanced sampling and free energy methods, and much more.