Journal
PHYSICAL CHEMISTRY CHEMICAL PHYSICS
Volume 18, Issue 8, Pages 5832-5838Publisher
ROYAL SOC CHEMISTRY
DOI: 10.1039/c5cp04886a
Keywords
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Funding
- Danish Council for Independent Research for Natural Sciences [DFF-4002-00151]
- University of Copenhagen Excellence Programme for Interdisciplinary Research (UCPH-DSIN)
- Villum Foundation
- Villum Fonden [00007369] Funding Source: researchfish
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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 multistep 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.
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