期刊
JOURNAL OF COMPUTATIONAL CHEMISTRY
卷 38, 期 18, 页码 1583-1605出版社
WILEY
DOI: 10.1002/jcc.24800
关键词
weighted histogram analysis method; histogram reweighting; free energy surfaces; Bayesian inference-umbrella sampling
资金
- National Science Foundation CAREER Award [DMR-1350008]
- Direct For Mathematical & Physical Scien
- Division Of Materials Research [1841800] Funding Source: National Science Foundation
The weighted histogram analysis method (WHAM) is a powerful approach to estimate molecular free energy surfaces (FES) from biased simulation data. Bayesian reformulations of WHAM are valuable in proving statistically optimal use of the data and providing a transparent means to incorporate regularizing priors and estimate statistical uncertainties. In this work, we develop a fully Bayesian treatment of WHAM to generate statistically optimal FES estimates in any number of biasing dimensions under arbitrary choices of the Bayes prior. Rigorous uncertainty estimates are generated by Metropolis-Hastings sampling from the Bayes posterior. We also report a means to project the FES and its uncertainties into arbitrary auxiliary order parameters beyond those in which biased sampling was conducted. We demonstrate the approaches in applications of alanine dipeptide and the unthreading of a synthetic mimic of the astexin-3 lasso peptide. Open-source MATLAB and Python implementations of our codes are available for free public download. (C) 2017 Wiley Periodicals, Inc.
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