Subtle Monte Carlo updates in dense molecular systems

Sandro Bottaro; Wouter Krogh Boomsma; Kristoffer Enøe Johansson; Christian Andreetta; Thomas Wim Hamelryck; Jesper Ferkinghoff-Borg

doi:10.1021/ct200641m

Subtle Monte Carlo updates in dense molecular systems

Sandro Bottaro, Wouter Krogh Boomsma, Kristoffer Enøe Johansson, Christian Andreetta, Thomas Wim Hamelryck, Jesper Ferkinghoff-Borg

20 Citations (Scopus)

Abstract

Although Markov chain Monte Carlo (MC) simulation is a potentially powerful approach for exploring conformational space, it has been unable to compete with molecular dynamics (MD) in the analysis of high density structural states, such as the native state of globular proteins. Here, we introduce a kinetic algorithm, CRISP, that greatly enhances the sampling efficiency in all-atom MC simulations of dense systems. The algorithm is based on an exact analytical solution to the classic chain-closure problem, making it possible to express the interdependencies among degrees of freedom in the molecule as correlations in a multivariate Gaussian distribution. We demonstrate that our method reproduces structural variation in proteins with greater efficiency than current state-of-the-art Monte Carlo methods and has real-time simulation performance on par with molecular dynamics simulations. The presented results suggest our method as a valuable tool in the study of molecules in atomic detail, offering a potential alternative to molecular dynamics for probing long time-scale conformational transitions.

Original language	English
Journal	Journal of Chemical Theory and Computation
Volume	8
Issue number	2
Pages (from-to)	695-702
Number of pages	8
ISSN	1549-9618
DOIs	https://doi.org/10.1021/ct200641m
Publication status	Published - 14 Feb 2012

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title = "Subtle Monte Carlo updates in dense molecular systems",

abstract = "Although Markov chain Monte Carlo (MC) simulation is a potentially powerful approach for exploring conformational space, it has been unable to compete with molecular dynamics (MD) in the analysis of high density structural states, such as the native state of globular proteins. Here, we introduce a kinetic algorithm, CRISP, that greatly enhances the sampling efficiency in all-atom MC simulations of dense systems. The algorithm is based on an exact analytical solution to the classic chain-closure problem, making it possible to express the interdependencies among degrees of freedom in the molecule as correlations in a multivariate Gaussian distribution. We demonstrate that our method reproduces structural variation in proteins with greater efficiency than current state-of-the-art Monte Carlo methods and has real-time simulation performance on par with molecular dynamics simulations. The presented results suggest our method as a valuable tool in the study of molecules in atomic detail, offering a potential alternative to molecular dynamics for probing long time-scale conformational transitions.",

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T1 - Subtle Monte Carlo updates in dense molecular systems

AU - Bottaro, Sandro

AU - Boomsma, Wouter Krogh

AU - Johansson, Kristoffer Enøe

AU - Andreetta, Christian

AU - Hamelryck, Thomas Wim

AU - Ferkinghoff-Borg, Jesper

PY - 2012/2/14

Y1 - 2012/2/14

N2 - Although Markov chain Monte Carlo (MC) simulation is a potentially powerful approach for exploring conformational space, it has been unable to compete with molecular dynamics (MD) in the analysis of high density structural states, such as the native state of globular proteins. Here, we introduce a kinetic algorithm, CRISP, that greatly enhances the sampling efficiency in all-atom MC simulations of dense systems. The algorithm is based on an exact analytical solution to the classic chain-closure problem, making it possible to express the interdependencies among degrees of freedom in the molecule as correlations in a multivariate Gaussian distribution. We demonstrate that our method reproduces structural variation in proteins with greater efficiency than current state-of-the-art Monte Carlo methods and has real-time simulation performance on par with molecular dynamics simulations. The presented results suggest our method as a valuable tool in the study of molecules in atomic detail, offering a potential alternative to molecular dynamics for probing long time-scale conformational transitions.

AB - Although Markov chain Monte Carlo (MC) simulation is a potentially powerful approach for exploring conformational space, it has been unable to compete with molecular dynamics (MD) in the analysis of high density structural states, such as the native state of globular proteins. Here, we introduce a kinetic algorithm, CRISP, that greatly enhances the sampling efficiency in all-atom MC simulations of dense systems. The algorithm is based on an exact analytical solution to the classic chain-closure problem, making it possible to express the interdependencies among degrees of freedom in the molecule as correlations in a multivariate Gaussian distribution. We demonstrate that our method reproduces structural variation in proteins with greater efficiency than current state-of-the-art Monte Carlo methods and has real-time simulation performance on par with molecular dynamics simulations. The presented results suggest our method as a valuable tool in the study of molecules in atomic detail, offering a potential alternative to molecular dynamics for probing long time-scale conformational transitions.

U2 - 10.1021/ct200641m

DO - 10.1021/ct200641m

M3 - Journal article

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JO - Journal of Chemical Theory and Computation

JF - Journal of Chemical Theory and Computation

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Subtle Monte Carlo updates in dense molecular systems

Abstract

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