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基于共进化数据的相互作用网络方法突显蛋白质家族中的关键残基:以G蛋白偶联受体为例。

Coevolutionary data-based interaction networks approach highlighting key residues across protein families: The case of the G-protein coupled receptors.

作者信息

Baldessari Filippo, Capelli Riccardo, Carloni Paolo, Giorgetti Alejandro

机构信息

Department of Biotechnology, Università di Verona, Ca Vignal 1, strada Le Grazie 15, I-37134 Verona, Italy.

Computational Biomedicine Section, IAS-5/INM-9, Forschungzentrum Jülich, Wilhelm-Johnen-straße, D-52425 Jülich, Germany.

出版信息

Comput Struct Biotechnol J. 2020 May 15;18:1153-1159. doi: 10.1016/j.csbj.2020.05.003. eCollection 2020.

Abstract

We present an approach that, by integrating structural data with Direct Coupling Analysis, is able to pinpoint most of the interaction hotspots (i.e. key residues for the biological activity) across very sparse protein families in a single run. An application to the Class A G-protein coupled receptors (GPCRs), both in their active and inactive states, demonstrates the predictive power of our approach. The latter can be easily extended to any other kind of protein family, where it is expected to highlight most key sites involved in their functional activity.

摘要

我们提出了一种方法,通过将结构数据与直接耦合分析相结合,能够在一次运行中精确找出非常稀疏的蛋白质家族中的大多数相互作用热点(即生物活性的关键残基)。将该方法应用于处于活性和非活性状态的A类G蛋白偶联受体(GPCR),证明了我们方法的预测能力。该方法可以很容易地扩展到任何其他类型的蛋白质家族,预计它能突出显示参与其功能活性的大多数关键位点。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/d648/7260681/0c021de8cc9d/ga1.jpg

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