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PreprintCited by 93Advanced Physical and Chemical Molecular InteractionsProtein Structure and DynamicsAdvanced Chemical Physics Studies

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WESTPA 2.0: High-performance upgrades for weighted ensemble simulations and analysis of longer-timescale applications

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Russo, John D., Zhang, She, Leung, Jeremy M. G., Bogetti, Anthony T., Thompson, Jeff P., DeGrave, Alex J., Torrillo, Paul A., Pratt, A. J., Wong, Kim F., Xia, Junchao, Copperman, Jeremy, Adelman, Joshua L., Zwier, Matthew C., LeBard, David N., Zuckerman, Daniel M., Chong, Lillian T.·2021·Journal of Chemical Theory and Computation
DOI 10.1101/2021.12.05.471280PMC PMC8825686

Preprint — published as 10.1021/acs.jctc.1c01154 · Paper page

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Abstract

The weighted ensemble (WE) family of methods is one of several statistical-mechanics based path sampling strategies that can provide estimates of key observables (rate constants, pathways) using a fraction of the time required by direct simulation methods such as molecular dynamics or discrete-state stochastic algorithms. WE methods oversee numerous parallel trajectories using intermittent overhead operations at fixed time intervals, enabling facile interoperability with any dynamics engine. Here, we report on major upgrades to the WESTPA software package, an open-source, high-performance framework that implements both basic and recently developed WE methods. These upgrades offer substantial improvements over traditional WE. Key features of the new WESTPA 2.0 software enhance efficiency and ease of use: an adaptive binning scheme for more efficient surmounting of large free energy barriers, streamlined handling of large simulation datasets, exponentially improved analysis of kinetics, and developer-friendly tools for creating new WE methods, including a Python API and resampler module for implementing both binned and “binless” WE strategies. Table of Contents/Abstract Image: For the manuscript “WESTPA 2.0: High-performance upgrades for weighted ensemble simulations and analysis of longer-timescale applications” by Russo et al .

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Metadata

Authors
Russo, J. D., Zhang, S., Leung, J. M. G., Bogetti, A. T., Thompson, J. P., DeGrave, A. J., Torrillo, P. A., Pratt, A. J., Wong, K. F., Xia, J., Copperman, J., Adelman, J. L., Zwier, M. C., LeBard, D. N., Zuckerman, D. M., & Chong, L. T.
Affiliations
  • Oregon Health & Science University (US)
  • University of Pittsburgh (US)
  • Drake University (US)
Corresponding author
Chong, Lillian T.
ORCID
Venue
Journal of Chemical Theory and Computation
Publisher
openRxiv
Published
2021-12-06
Volume
18
Issue
2
Pages
638-649
ISSN
1549-9618 (ISSN-L)
DOI
10.1101/2021.12.05.471280
PMC
PMC8825686
OpenAlex
W4226013321
Cited by
93
References
1
Citations by year
2026: 19 · 2025: 27 · 2024: 24 · 2023: 11 · 2022: 11 · 2021: 1
FWCI
11.07
Type
journal article
Language
en
Open access
Licence
cc-by-nc-nd
Locations
  • Journal of Chemical Theory and Computation (publishedVersion, cc-by-nc-nd)
  • bioRxiv (Cold Spring Harbor Laboratory) (acceptedVersion)
  • PubMed (publishedVersion)
  • PubMed Central (acceptedVersion)
Funders
  • National Science Foundation (2112871, 1807301)
  • University of Pittsburgh
  • Amazon Web Services
  • National Institutes of Health
  • National Institute of General Medical Sciences (R01GM115805)
  • Division of Chemistry (CHE-1807301)
Topics
  • Physical Sciences › Chemistry › Physical and Theoretical Chemistry › Advanced Physical and Chemical Molecular Interactions
  • Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Protein Structure and Dynamics
  • Physical Sciences › Physics and Astronomy › Atomic and Molecular Physics, and Optics › Advanced Chemical Physics Studies
Keywords
path sampling, rate constants, reaction pathways, activation barrier, adaptive binning, Python interface, resampling

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