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- W2610293882 endingPage "1605" @default.
- W2610293882 startingPage "1583" @default.
- W2610293882 abstract "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. © 2017 Wiley Periodicals, Inc." @default.
- W2610293882 created "2017-05-12" @default.
- W2610293882 creator A5023302612 @default.
- W2610293882 date "2017-05-05" @default.
- W2610293882 modified "2023-09-26" @default.
- W2610293882 title "BayesWHAM: A Bayesian approach for free energy estimation, reweighting, and uncertainty quantification in the weighted histogram analysis method" @default.
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- W2610293882 doi "https://doi.org/10.1002/jcc.24800" @default.
- W2610293882 hasPubMedId "https://pubmed.ncbi.nlm.nih.gov/28475830" @default.
- W2610293882 hasPublicationYear "2017" @default.
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