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Title: Efficiently sampling conformations and pathways using the concurrent adaptive sampling (CAS) algorithm

Abstract

Molecular dynamics (MD) simulations are useful in obtaining thermodynamic and kinetic properties of bio-molecules but are limited by the timescale barrier, i.e., we may be unable to efficiently obtain properties because we need to run microseconds or longer simulations using femtoseconds time steps. While there are several existing methods to overcome this timescale barrier and efficiently sample thermodynamic and/or kinetic properties, problems remain in regard to being able to sample un- known systems, deal with high-dimensional space of collective variables, and focus the computational effort on slow timescales. Hence, in this work, a new sampling method, called the “Concurrent Adaptive Sampling (CAS) algorithm,” has been developed to tackle these three issues and efficiently obtain conformations and pathways. The method is not constrained to use only one or two collective variables, unlike most reaction coordinate-dependent methods. Instead, it can use a large number of collective vari- ables and uses macrostates (a partition of the collective variable space) to enhance the sampling. The exploration is done by running a large number of short simula- tions, and a clustering technique is used to accelerate the sampling. In this paper, we introduce the new methodology and show results from two-dimensional models and bio-molecules, suchmore » as penta-alanine and triazine polymer« less

Authors:
ORCiD logo [1]; ORCiD logo [2];  [1]
  1. Stanford Univ., CA (United States)
  2. Pacific Northwest National Lab. (PNNL), Richland, WA (United States)
Publication Date:
Research Org.:
Pacific Northwest National Lab. (PNNL), Richland, WA (United States)
Sponsoring Org.:
USDOE Office of Science (SC), Advanced Scientific Computing Research (ASCR) (SC-21)
OSTI Identifier:
1398164
Alternate Identifier(s):
OSTI ID: 1375660
Report Number(s):
[PNNL-SA-125912]
[Journal ID: ISSN 0021-9606]
Grant/Contract Number:  
[AC05-76RL01830]
Resource Type:
Accepted Manuscript
Journal Name:
Journal of Chemical Physics
Additional Journal Information:
[ Journal Volume: 147; Journal Issue: 7]; Journal ID: ISSN 0021-9606
Publisher:
American Institute of Physics (AIP)
Country of Publication:
United States
Language:
English
Subject:
97 MATHEMATICS AND COMPUTING; 59 BASIC BIOLOGICAL SCIENCES; macromolecular; molecular dynamics simulation; enhanced ssa

Citation Formats

Ahn, Surl-Hee, Grate, Jay W., and Darve, Eric F. Efficiently sampling conformations and pathways using the concurrent adaptive sampling (CAS) algorithm. United States: N. p., 2017. Web. doi:10.1063/1.4999097.
Ahn, Surl-Hee, Grate, Jay W., & Darve, Eric F. Efficiently sampling conformations and pathways using the concurrent adaptive sampling (CAS) algorithm. United States. doi:10.1063/1.4999097.
Ahn, Surl-Hee, Grate, Jay W., and Darve, Eric F. Mon . "Efficiently sampling conformations and pathways using the concurrent adaptive sampling (CAS) algorithm". United States. doi:10.1063/1.4999097. https://www.osti.gov/servlets/purl/1398164.
@article{osti_1398164,
title = {Efficiently sampling conformations and pathways using the concurrent adaptive sampling (CAS) algorithm},
author = {Ahn, Surl-Hee and Grate, Jay W. and Darve, Eric F.},
abstractNote = {Molecular dynamics (MD) simulations are useful in obtaining thermodynamic and kinetic properties of bio-molecules but are limited by the timescale barrier, i.e., we may be unable to efficiently obtain properties because we need to run microseconds or longer simulations using femtoseconds time steps. While there are several existing methods to overcome this timescale barrier and efficiently sample thermodynamic and/or kinetic properties, problems remain in regard to being able to sample un- known systems, deal with high-dimensional space of collective variables, and focus the computational effort on slow timescales. Hence, in this work, a new sampling method, called the “Concurrent Adaptive Sampling (CAS) algorithm,” has been developed to tackle these three issues and efficiently obtain conformations and pathways. The method is not constrained to use only one or two collective variables, unlike most reaction coordinate-dependent methods. Instead, it can use a large number of collective vari- ables and uses macrostates (a partition of the collective variable space) to enhance the sampling. The exploration is done by running a large number of short simula- tions, and a clustering technique is used to accelerate the sampling. In this paper, we introduce the new methodology and show results from two-dimensional models and bio-molecules, such as penta-alanine and triazine polymer},
doi = {10.1063/1.4999097},
journal = {Journal of Chemical Physics},
number = [7],
volume = [147],
place = {United States},
year = {2017},
month = {8}
}

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