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HP35(NLE-NLE) Trajectory Data
Beauchamp, Kyle2011Molecular dynamics simulations of the villin headpiece protein (fast folding NLE-NLE mutant).
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Trialanine Data
Beauchamp, Kyle2013Predicting 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.
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Inferring protein structure and dynamics from simulation and experiment [electronic resource]
Beauchamp, Kyle A.2013.An atomic-scale understanding of biological molecules remains a grand challenge for the physical and biological sciences. Here, I describe how molecular dynamics simulations can be used to directly connect to biophysical experiments. I first describe the use of Markov state models to connect simulated and measured protein kinetics, allowing studies of protein folding at the atomic scale. I then introduce the use of NMR measurements, such as chemical shifts and scalar couplings, for the evaluation of molecular dynamics force field quality. Finally, I propose a new statistical technique that can be used to combine both simulation and experiment into accurate models of conformational ensembles. Such models are shown to be free of force field bias and can be used to investigate the structural and equilibrium properties of biomolecules. In sum, the present work demonstrates how statistically-sound methods of inference can forge a direct connection between simulation and experiment.
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