Analyzing ensembles of amyloid proteins using bayesian statistics

Abstract

Intrinsically disordered proteins (IDPs) are notoriously difficult to study experimentally because they rapidly interconvert between many dissimilar conformations during their biological lifetime, and therefore cannot be described by a single structure. The importance of studying these systems, however, is underscored by the fact that they form toxic aggregates that play a role in the pathogenesis of many disorders. The first step towards a comprehensive understanding of the aggregation mechanism of these proteins involves a description of their thermally accessible states under physiologic conditions. The resulting conformational ensembles correspond to coarse-grained descriptions of their energy landscapes, where the number of structures in the ensemble is related to the resolution in which one views the free energy surface. Here, we provide step-by-step instructions on how to use experimental data to construct a conformational ensemble for an IDP using a Variational Bayesian Weighting (VBW) algorithm. We further discuss how to leverage this Bayesian approach to identify statistically significant ensemble-wide observations that can form the basis of further experimental studies.