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VASP-E:通过静电等势体体积分析进行特异性注释。

VASP-E: specificity annotation with a volumetric analysis of electrostatic isopotentials.

作者信息

Chen Brian Y

机构信息

Department of Computer Science and Engineering, P.C. Rossin College of Engineering and Applied Sciences, Lehigh University, Bethlehem, Pennsylvania, United States of America.

出版信息

PLoS Comput Biol. 2014 Aug 28;10(8):e1003792. doi: 10.1371/journal.pcbi.1003792. eCollection 2014 Aug.

Abstract

Algorithms for comparing protein structure are frequently used for function annotation. By searching for subtle similarities among very different proteins, these algorithms can identify remote homologs with similar biological functions. In contrast, few comparison algorithms focus on specificity annotation, where the identification of subtle differences among very similar proteins can assist in finding small structural variations that create differences in binding specificity. Few specificity annotation methods consider electrostatic fields, which play a critical role in molecular recognition. To fill this gap, this paper describes VASP-E (Volumetric Analysis of Surface Properties with Electrostatics), a novel volumetric comparison tool based on the electrostatic comparison of protein-ligand and protein-protein binding sites. VASP-E exploits the central observation that three dimensional solids can be used to fully represent and compare both electrostatic isopotentials and molecular surfaces. With this integrated representation, VASP-E is able to dissect the electrostatic environments of protein-ligand and protein-protein binding interfaces, identifying individual amino acids that have an electrostatic influence on binding specificity. VASP-E was used to examine a nonredundant subset of the serine and cysteine proteases as well as the barnase-barstar and Rap1a-raf complexes. Based on amino acids established by various experimental studies to have an electrostatic influence on binding specificity, VASP-E identified electrostatically influential amino acids with 100% precision and 83.3% recall. We also show that VASP-E can accurately classify closely related ligand binding cavities into groups with different binding preferences. These results suggest that VASP-E should prove a useful tool for the characterization of specific binding and the engineering of binding preferences in proteins.

摘要

用于比较蛋白质结构的算法经常被用于功能注释。通过在非常不同的蛋白质之间寻找细微的相似性,这些算法可以识别出具有相似生物学功能的远缘同源物。相比之下,很少有比较算法专注于特异性注释,而在特异性注释中,识别非常相似的蛋白质之间的细微差异有助于发现导致结合特异性差异的小结构变异。很少有特异性注释方法考虑静电场,而静电场在分子识别中起着关键作用。为了填补这一空白,本文描述了VASP-E(基于静电的表面性质体积分析),这是一种基于蛋白质-配体和蛋白质-蛋白质结合位点静电比较的新型体积比较工具。VASP-E利用了一个核心观察结果,即三维实体可用于全面表示和比较静电等势面和分子表面。通过这种综合表示,VASP-E能够剖析蛋白质-配体和蛋白质-蛋白质结合界面的静电环境,识别对结合特异性有静电影响的单个氨基酸。VASP-E被用于研究丝氨酸和半胱氨酸蛋白酶的一个非冗余子集以及巴那斯酶-巴那斯塔和Rap1a-raf复合物。基于各种实验研究确定的对结合特异性有静电影响的氨基酸,VASP-E以100%的精度和83.3%的召回率识别出有静电影响的氨基酸。我们还表明,VASP-E可以将密切相关的配体结合腔准确地分类为具有不同结合偏好的组。这些结果表明,VASP-E应该是一种用于表征蛋白质特异性结合和设计结合偏好的有用工具。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/c1ed/4148194/08c883ba6cc7/pcbi.1003792.g001.jpg

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