Data for: Umbrella Refinement of Ensembles - An Alternative View on Ensemble Optimization

The elucidation of protein dynamics, especially in the context of intrinsically disordered proteins is challenging and requires the cooperation between experimental studies and computational analysis. Molecular dynamics simulations is an essential tool of investigation but often struggles to accurately quantify the conformational preferences of flexible proteins. To create a quantitatively validated conformational ensemble, such simulations may be refined with experimental data using Bayesian- and maximum entropy methods. In this study, we present a method to optimize a conformational ensemble using Bayes’ theorem in connection with methodology derived from Umbrella Sampling. The resulting method called Umbrella Refinement of Ensembles (URE) reduces the number of parameters to be optimized in comparison to the classical Bayesian Ensemble Refinement and remains methodologically suitable to be used together with the forward formulated Kullback-Leibler divergence. The method is validated using two established systems, an alanine-alanine zwitterion and the chignolin peptide using nuclear magnetic resonance data from the literature.