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从分别获取的实验测量中推断联合构象分布。

Inference of Joint Conformational Distributions from Separately Acquired Experimental Measurements.

机构信息

Department of Biomedical Engineering, University of Virginia, Charlottesville, Virginia 22904-4259, United States.

Department of Molecular Physiology, University of Virginia, Charlottesville, Virginia 22903, United States.

出版信息

J Phys Chem Lett. 2021 Feb 18;12(6):1606-1611. doi: 10.1021/acs.jpclett.0c03623. Epub 2021 Feb 8.

Abstract

Flexible proteins serve vital roles in a multitude of biological processes. However, determining their full conformational ensembles is extremely difficult because this requires detailed knowledge about the heterogeneity of the protein's degrees of freedom. Label-based experiments such as double electron-electron resonance (DEER) are very useful in studying flexible proteins, as they provide distributional data on heterogeneity. These experiments are typically performed separately, so information about correlation between distributions is lost. We have developed a method to recover correlation information using nonequilibrium work estimates in molecular dynamics refinement. We tested this method on a simple model of an alternating-access transporter for which the true joint distributions are known, and it successfully recovered the true joint distribution. We also applied our method to the protein syntaxin-1a, where it discarded physically implausible conformations. Our method thus provides a way to recover correlation structure in separate experimental measurements of conformational ensembles and refines the resulting structural ensemble.

摘要

柔性蛋白质在多种生物过程中起着至关重要的作用。然而,确定它们的完整构象集合极其困难,因为这需要详细了解蛋白质自由度的异质性。基于标记的实验,如双电子电子共振(DEER),在研究柔性蛋白质时非常有用,因为它们提供了关于异质性的分布数据。这些实验通常是分开进行的,因此会丢失分布之间的相关性信息。我们开发了一种使用分子动力学精修中的非平衡功估计来恢复相关信息的方法。我们在一个简单的交替访问转运蛋白模型上测试了这种方法,该模型具有已知的真实联合分布,并且成功地恢复了真实的联合分布。我们还将我们的方法应用于蛋白突触融合蛋白 1a,其中它丢弃了不合理的构象。因此,我们的方法为恢复构象集合的分离实验测量中的相关结构提供了一种方法,并改进了得到的结构集合。

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引用本文的文献

本文引用的文献

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Hybrid Refinement of Heterogeneous Conformational Ensembles Using Spectroscopic Data.
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