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Driving Structural Transitions in Molecular Simulations Using the Nonequilibrium Candidate Monte Carlo.
Kurut, Anil; Fonseca, Rasmus; Boomsma, Wouter.
Afiliação
  • Kurut A; Department of Computer Science, University of Copenhagen , 2100 Copenhagen Ø, Denmark.
  • Fonseca R; Department of Molecular and Cellular Physiology, Stanford University , Stanford, California 94305, United States.
  • Boomsma W; Department of Computer Science, University of Copenhagen , 2100 Copenhagen Ø, Denmark.
J Phys Chem B ; 122(3): 1195-1204, 2018 01 25.
Article em En | MEDLINE | ID: mdl-29260565
Hybrid simulation procedures which combine molecular dynamics with Monte Carlo are attracting increasing attention as tools for improving the sampling efficiency in molecular simulations. In particular, encouraging results have been reported for nonequilibrium candidate protocols, in which a Monte Carlo move is applied gradually, and interleaved with a process that equilibrates the remaining degrees of freedom. Although initial studies have uncovered a substantial potential of the method, its practical applicability for sampling structural transitions in macromolecules remains incompletely understood. Here, we address this issue by systematically investigating the efficiency of the nonequilibrium candidate Monte Carlo on the sampling of rotameric distributions of two peptide systems at atomistic resolution both in vacuum and explicit solvent. The studied systems allow us to directly probe the efficiency with which a single or a few slow degrees of freedom can be driven between well-separated free-energy minima and to explore the sensitivity of the method toward the involved free parameters. In line with results on other systems, our study suggests that order-of-magnitude gains can be obtained in certain scenarios but also identifies challenges that arise when applying the procedure in explicit solvent.

Texto completo: 1 Base de dados: MEDLINE Idioma: En Ano de publicação: 2018 Tipo de documento: Article

Texto completo: 1 Base de dados: MEDLINE Idioma: En Ano de publicação: 2018 Tipo de documento: Article