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Title: Quantitative comparison of adaptive sampling methods for protein dynamics

Abstract

Adaptive sampling methods, often used in combination with Markov state models, are becoming increasingly popular for speeding up rare events in simulation such as molecular dynamics (MD) without biasing the system dynamics. Several adaptive sampling strategies have been proposed, but it is not clear which methods perform better for different physical systems. Here, we present a systematic evaluation of selected adaptive sampling strategies on a wide selection of fast folding proteins. The adaptive sampling strategies were emulated using models constructed on already existing MD trajectories. We provide theoretical limits for the sampling speed-up and compare the performance of different strategies with and without using some a priori knowledge of the system. The results show that for different goals, different adaptive sampling strategies are optimal. In order to sample slow dynamical processes such as protein folding without a priori knowledge of the system, a strategy based on the identification of a set of metastable regions is consistently the most efficient, while a strategy based on the identification of microstates performs better if the goal is to explore newer regions of the conformational space. Interestingly, the maximum speed-up achievable for the adaptive sampling of slow processes increases for proteins with longer foldingmore » times, encouraging the application of these methods for the characterization of slower processes, beyond the fast-folding proteins considered here.« less

Authors:
ORCiD logo [1];  [1];  [1]; ORCiD logo [1]; ORCiD logo [1]
+ Show Author Affiliations
  1. Rice Univ., Houston, TX (United States)
Publication Date:
Research Org.:
Oak Ridge National Laboratory (ORNL), Oak Ridge, TN (United States). Oak Ridge Leadership Computing Facility (OLCF)
Sponsoring Org.:
USDOE Office of Science (SC); National Science Foundation (NSF); Welch Foundation; National Library of Medicine (NLM)
OSTI Identifier:
1565762
Alternate Identifier(s):
OSTI ID: 1579277
Grant/Contract Number:  
AC05-00OR22725; CHE-1265929; CHE-1738990; PHY-1427654; CCF-1423304; T15LM007093; OCI 0959097; CNS 0821727
Resource Type:
Accepted Manuscript
Journal Name:
Journal of Chemical Physics
Additional Journal Information:
Journal Volume: 149; Journal Issue: 24; Journal ID: ISSN 0021-9606
Publisher:
American Institute of Physics (AIP)
Country of Publication:
United States
Language:
English
Subject:
37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CHEMISTRY; Chemistry; Physics; Protein dynamics; Molecular dynamics; Protein folding; Adaptively biased molecular dynamics; Free energy landscapes

Citation Formats

Hruska, Eugen, Abella, Jayvee R., Nüske, Feliks, Kavraki, Lydia E., and Clementi, Cecilia. Quantitative comparison of adaptive sampling methods for protein dynamics. United States: N. p., 2018. Web. doi:10.1063/1.5053582.
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Hruska, Eugen, Abella, Jayvee R., Nüske, Feliks, Kavraki, Lydia E., & Clementi, Cecilia. Quantitative comparison of adaptive sampling methods for protein dynamics. United States. https://doi.org/10.1063/1.5053582
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Hruska, Eugen, Abella, Jayvee R., Nüske, Feliks, Kavraki, Lydia E., and Clementi, Cecilia. Fri . "Quantitative comparison of adaptive sampling methods for protein dynamics". United States. https://doi.org/10.1063/1.5053582. https://www.osti.gov/servlets/purl/1565762.
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@article{osti_1565762,
title = {Quantitative comparison of adaptive sampling methods for protein dynamics},
author = {Hruska, Eugen and Abella, Jayvee R. and Nüske, Feliks and Kavraki, Lydia E. and Clementi, Cecilia},
abstractNote = {Adaptive sampling methods, often used in combination with Markov state models, are becoming increasingly popular for speeding up rare events in simulation such as molecular dynamics (MD) without biasing the system dynamics. Several adaptive sampling strategies have been proposed, but it is not clear which methods perform better for different physical systems. Here, we present a systematic evaluation of selected adaptive sampling strategies on a wide selection of fast folding proteins. The adaptive sampling strategies were emulated using models constructed on already existing MD trajectories. We provide theoretical limits for the sampling speed-up and compare the performance of different strategies with and without using some a priori knowledge of the system. The results show that for different goals, different adaptive sampling strategies are optimal. In order to sample slow dynamical processes such as protein folding without a priori knowledge of the system, a strategy based on the identification of a set of metastable regions is consistently the most efficient, while a strategy based on the identification of microstates performs better if the goal is to explore newer regions of the conformational space. Interestingly, the maximum speed-up achievable for the adaptive sampling of slow processes increases for proteins with longer folding times, encouraging the application of these methods for the characterization of slower processes, beyond the fast-folding proteins considered here.},
doi = {10.1063/1.5053582},
journal = {Journal of Chemical Physics},
number = 24,
volume = 149,
place = {United States},
year = {Fri Dec 28 00:00:00 EST 2018},
month = {Fri Dec 28 00:00:00 EST 2018}
}
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https://doi.org/10.1063/1.5053582
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