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Bottaro S, Boomsma W, E Johansson K, et al. Subtle Monte Carlo Updates in Dense Molecular Systems. J Chem Theory Comput. 2012;8(2):695-702doi: 10.1021/ct200641m.
Bottaro, S., Boomsma, W., E Johansson, K., Andreetta, C., Hamelryck, T., & Ferkinghoff-Borg, J. (2012). Subtle Monte Carlo Updates in Dense Molecular Systems. Journal of chemical theory and computation, 8(2), 695-702. https://doi.org/10.1021/ct200641m
Bottaro, Sandro, et al. "Subtle Monte Carlo Updates in Dense Molecular Systems." Journal of chemical theory and computation vol. 8,2 (2012): 695-702. doi: https://doi.org/10.1021/ct200641m
Bottaro S, Boomsma W, E Johansson K, Andreetta C, Hamelryck T, Ferkinghoff-Borg J. Subtle Monte Carlo Updates in Dense Molecular Systems. J Chem Theory Comput. 2012 Feb 14;8(2):695-702. doi: 10.1021/ct200641m. Epub 2012 Jan 18. PMID: 26596617.
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J Chem Theory Comput. 2012 Feb 14;8(2):695-702. doi: 10.1021/ct200641m. Epub 2012 Jan 18.

Subtle Monte Carlo Updates in Dense Molecular Systems.

Journal of chemical theory and computation

Sandro Bottaro, Wouter Boomsma, Kristoffer E Johansson, Christian Andreetta, Thomas Hamelryck, Jesper Ferkinghoff-Borg

Affiliations

  1. Department of Electrical Engineering, Technical University of Denmark, Kgs. Lyngby, Denmark.
  2. Department of Astronomy and Theoretical Physics, Lund University, Lund, Sweden.
  3. Department of Biology, University of Copenhagen, Copenhagen, Denmark.

PMID: 26596617 DOI: 10.1021/ct200641m

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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