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• W2952534433 abstract "Predicting biological structure has remained challenging for systems such as dis- ordered proteins that take on myriad conformations. Hybrid simulation/experiment strategies have been undermined by difficulties in evaluating errors from computa- tional model inaccuracies and data uncertainties. Building on recent proposals from maximum entropy theory and nonequilibrium thermodynamics, we address these issues through a Bayesian Energy Landscape Tilting (BELT) scheme for computing Bayesian “hyperensembles” over conformational ensembles. BELT uses Markov chain Monte Carlo to directly sample maximum-entropy conformational ensembles consistent with a set of input experimental observables. To test this framework, we apply BELT to model trialanine, starting from disagreeing simulations with the force fields ff96, ff99, ff99sbnmr-ildn, CHARMM27, and OPLS-AA. BELT incorporation of limited chemical shift and 3J measurements gives convergent values of the peptide’s α, β, and PPII conformational populations in all cases. As a test of predictive power, all five BELT hyperensembles recover set-aside measurements not used in the fitting and report accu- rate errors, even when starting from highly inaccurate simulations. BELT’s principled framework thus enables practical predictions for complex biomolecular systems from discordant simulations and sparse data." @default.
• W2952534433 created "2019-06-27" @default.
• W2952534433 creator A5032418986 @default.
• W2952534433 creator A5073969524 @default.
• W2952534433 creator A5086503378 @default.
• W2952534433 date "2014-01-24" @default.
• W2952534433 modified "2023-09-27" @default.
• W2952534433 title "Bayesian Energy Landscape Tilting: Towards Concordant Models of Molecular Ensembles" @default.
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• W2952534433 doi "https://doi.org/10.1101/002048" @default.
• W2952534433 hasPublicationYear "2014" @default.
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