Trialanine Data

Name: Trialanine Data
License: https://creativecommons.org/licenses/by-sa/3.0/legalcode

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Abstract/Contents

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 computational 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 accurate 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.

Type of resource	software, multimedia
Date created	2013

Subject	protein folding
Genre	Dataset

Related Publication	Kyle A. Beauchamp, Vijay S. Pande, Rhiju Das. Bayesian Energy Landscape Tilting: Towards Concordant Models of Molecular Ensembles, Biophysical Journal, Volume 106, Issue 6, 18 March 2014, Pages 1381-1390, ISSN 0006-3495, http://dx.doi.org/10.1016/j.bpj.2014.02.009. (http://www.sciencedirect.com/science/article/pii/S0006349514001854)
Related item	Title Source code repository
Location	https://purl.stanford.edu/zy655fc6471

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License: This work is licensed under a Creative Commons Attribution Share Alike 3.0 Unported license (CC BY-SA).

Preferred Citation: Beauchamp, Kyle; Das, Rhiju; Pande, Vijay. (2013). Trialanine Data. Stanford Digital Repository. Available at: http://purl.stanford.edu/zy655fc6471

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