Issue 8, 2016

Bayesian inference of protein ensembles from SAXS data


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.

Graphical abstract: Bayesian inference of protein ensembles from SAXS data

Article information

Article type
17 Aug 2015
28 Oct 2015
First published
28 Oct 2015
This article is Open Access
Creative Commons BY license

Phys. Chem. Chem. Phys., 2016,18, 5832-5838

Author version available

Bayesian inference of protein ensembles from SAXS data

L. D. Antonov, S. Olsson, W. Boomsma and T. Hamelryck, Phys. Chem. Chem. Phys., 2016, 18, 5832 DOI: 10.1039/C5CP04886A

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