Bayesian inference of protein ensembles from SAXS data

• Published in: Physical chemistry chemical physics : PCCP 2016-02, Vol.18 (8), p.5832-5838
• Main Authors: Antonov, L. D, Olsson, S, Boomsma, W, Hamelryck, T
• Format: Article
• Language: English
• Subjects: Bayes Theorem; Bayesian analysis; Beryllium; Computer simulation; Inference; Intrinsically Disordered Proteins - chemistry; Macromolecules; Maximization; Models, Molecular; Protein Conformation; Proteins; SAXS; Scattering, Small Angle; X-Ray Diffraction
• ISSN: 1463-9076; 1463-9084
• DOI: 10.1039/c5cp04886a

The inherent flexibility of intrinsically disordered proteins (IDPs) and multi-domain proteins with intrinsically disordered regions (IDRs) presents challenges to structural analysis. These macromolecules need to be represented by an ensemble of conformations, rather than a single structure. Small-angle X-ray scattering (SAXS) experiments capture ensemble-averaged data for the set of conformations. We present a Bayesian approach to ensemble inference from SAXS data, called Bayesian ensemble SAXS (BE-SAXS). We address two issues with existing methods: the use of a finite ensemble of structures to represent the underlying distribution, and the selection of that ensemble as a subset of an initial pool of structures. This is achieved through the formulation of a Bayesian posterior of the conformational space. BE-SAXS modifies a structural prior distribution in accordance with the experimental data. It uses multi-step expectation maximization, with alternating rounds of Markov-chain Monte Carlo simulation and empirical Bayes optimization. We demonstrate the method by employing it to obtain a conformational ensemble of the antitoxin PaaA2 and comparing the results to a published ensemble. A probabilistic method infers ensembles of intrinsically disordered proteins (IDPs) by combining SAXS data with a force field.