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bioRxiv · 10.1101/002048

Bayesian Energy Landscape Tilting: Towards Concordant Models of Molecular Ensembles

Kyle Beauchamp·Vijay Pande·Rhiju Das
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Abstract

Predicting biological structure has remained challenging for systems such as disordered 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 peptides , {beta}, 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. BELTs principled fxramework thus enables practical predictions for complex biomolecular systems from discordant simulations and sparse data.

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Kyle Beauchamp, Vijay Pande, Rhiju Das. 2014-01-24. Bayesian Energy Landscape Tilting: Towards Concordant Models of Molecular Ensembles. https://doi.org/10.1101/002048

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