title: Bayesian energy landscape tilting: towards concordant models of molecular ensembles.
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 (3)J 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.
authors.affiliation: Computational Biology Center, Memorial Sloan-Kettering Cancer Center, New York, New York.
authors.forename: Kyle A
authors.initials: KA
authors.lastname: Beauchamp
authors.affiliation: Departments of Chemistry, Computer Science, and Structural Biology and Biophysics Program, Stanford University, Stanford, California. Electronic address: pande@stanford.edu.
authors.forename: Vijay S
authors.initials: VS
authors.lastname: Pande
authors.affiliation: Departments of Biochemistry and Physics and Biophysics Program, Stanford University, Stanford, California. Electronic address: rhiju@stanford.edu.
authors.forename: Rhiju
authors.initials: R
authors.lastname: Das
mesh_terms: D000465:Algorithms; D001499:Bayes Theorem; D008390:Markov Chains; D008956:Models, Chemical; D009010:Monte Carlo Method; D009842:Oligopeptides / Q000737:chemistry*; D058105:Polymerization
chemical_list: D009842:Oligopeptides; C039944:alanyl-alanyl-alanine