title: Enhanced Sampling of an Atomic Model with Hybrid Nonequilibrium Molecular Dynamics-Monte Carlo Simulations Guided by a Coarse-Grained Model.
abstract: Molecular dynamics (MD) trajectories based on a classical equation of motion provide a straightforward, albeit somewhat inefficient approach, to explore and sample the configurational space of a complex molecular system. While a broad range of techniques can be used to accelerate and enhance the sampling efficiency of classical simulations, only algorithms that are consistent with the Boltzmann equilibrium distribution yield a proper statistical mechanical computational framework. Here, a multiscale hybrid algorithm relying simultaneously on all-atom fine-grained (FG) and coarse-grained (CG) representations of a system is designed to improve sampling efficiency by combining the strength of nonequilibrium molecular dynamics (neMD) and Metropolis Monte Carlo (MC). This CG-guided hybrid neMD-MC algorithm comprises six steps: (1) a FG configuration of an atomic system is dynamically propagated for some period of time using equilibrium MD; (2) the resulting FG configuration is mapped onto a simplified CG model; (3) the CG model is propagated for a brief time interval to yield a new CG configuration; (4) the resulting CG configuration is used as a target to guide the evolution of the FG system; (5) the FG configuration (from step 1) is driven via a nonequilibrium MD (neMD) simulation toward the CG target; (6) the resulting FG configuration at the end of the neMD trajectory is then accepted or rejected according to a Metropolis criterion before returning to step 1. A symmetric two-ends momentum reversal prescription is used for the neMD trajectories of the FG system to guarantee that the CG-guided hybrid neMD-MC algorithm obeys microscopic detailed balance and rigorously yields the equilibrium Boltzmann distribution. The enhanced sampling achieved with the method is illustrated with a model system with hindered diffusion and explicit-solvent peptide simulations. Illustrative tests indicate that the method can yield a speedup of about 80 times for the model system and up to 21 times for polyalanine and (AAQAA)3 in water.
authors.affiliation: Department of Chemistry, and ‡Department of Biochemistry and Molecular Biology, University of Chicago , Chicago, Illinois 60637, United States.
authors.forename: Yunjie
authors.initials: Y
authors.lastname: Chen
authors.affiliation: Department of Chemistry, and ‡Department of Biochemistry and Molecular Biology, University of Chicago , Chicago, Illinois 60637, United States.
authors.forename: Benoît
authors.initials: B
authors.lastname: Roux
mesh_terms: D000465:Algorithms; D056004:Molecular Dynamics Simulation; D009010:Monte Carlo Method; D010455:Peptides / Q000737:chemistry / Q000378:metabolism; D012997:Solvents / Q000737:chemistry; D014867:Water / Q000737:chemistry
chemical_list: D010455:Peptides; D012997:Solvents; D014867:Water; C019529:polyalanine