Bayesian inference of protein ensembles from SAXS data
Bayesian inference of protein ensembles from SAXS data
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DOI：
10.1039/c5cp04886a
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发表时间：
2016-02-28
影响因子：
3.3
通讯作者：
Hamelryck, T.
中科院分区：
文献类型：
--
作者：
Antonov, L. D.;Olsson, S.;Hamelryck, T.
固有无序蛋白质(IDPs)和带有固有无序区域(IDR)的多结构域蛋白质固有的灵活性给结构分析带来了挑战。这些大分子需要由一系列构象来代表，而不是单一的结构。小角X射线散射(SAXS)实验捕获了一组构象的系综平均数据。我们提出了一种从SAXS数据进行集成推理的贝叶斯方法，称为贝叶斯集成SAXS(BE-SAXS)。我们解决了现有方法的两个问题：使用有限的结构集合来表示潜在的分布，以及选择该集合作为初始结构池的子集。这是通过构象空间的贝叶斯后验公式来实现的。BE-SAXS根据实验数据修正了结构先验分布。它使用多步期望最大化，交替进行马尔可夫链蒙特卡罗模拟和经验贝叶斯优化。我们通过使用该方法来获得抗毒素PaaA2的构象集成，并将结果与已发表的集成进行比较，从而展示了该方法。
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.