Computational Approaches for Understanding Dynamical Systems: Protein Folding and Assembly: Volume 170

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