Correlation analysis for protein evolutionary family based on amino acid position mutations and application in PDZ domain.
Correlation analysis for protein evolutionary family based on amino acid position mutations and application in PDZ domain.
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DOI：
10.1371/journal.pone.0013207
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发表时间：
2010-10-06
期刊：
影响因子：
3.7
通讯作者：
Huang RB
中科院分区：
文献类型：
--
作者：
Du QS;Wang CH;Liao SM;Huang RB
人们普遍认为，特定方向的突变是由蛋白质家族的功能限制引起的，某些位置的定向突变控制着蛋白质家族的进化方向。不同位置的突变，即使相隔很远，也相互耦合并形成进化网络。寻找残基之间的控制突变位置和突变网络对于蛋白质合理设计和酶工程来说首先很重要。开发了一种计算方法，即氨基酸位置保守突变相关分析（CMCA）来预测相互突变的位置并找到蛋白质家族中的进化网络。氨基酸位置突变函数是CMCA测量某个位置残基突变的基本方程，来源于蛋白质进化家族的MSA（多重结构比对）数据库。然后根据氨基酸位置突变方程构造位置守恒相关矩阵和位置突变相关矩阵。与基于位置保守性统计分析的传统SCA（统计耦合分析）方法不同，CMCA 侧重于位置突变的相关性分析。以CMCA方法对蛋白家族的PDZ结构域进行研究，结果很好地说明了PDZ蛋白家族的远距离变构机制，并发现了残基之间的功能突变网络。我们期望 CMCA 方法可以在蛋白质工程研究中得到应用，并提出改善酶的生物活性和理化性质的新策略。
It has been widely recognized that the mutations at specific directions are caused by the functional constraints in protein family and the directional mutations at certain positions control the evolutionary direction of the protein family. The mutations at different positions, even distantly separated, are mutually coupled and form an evolutionary network. Finding the controlling mutative positions and the mutative network among residues are firstly important for protein rational design and enzyme engineering. A computational approach, namely amino acid position conservation-mutation correlation analysis (CMCA), is developed to predict mutually mutative positions and find the evolutionary network in protein family. The amino acid position mutative function, which is the foundational equation of CMCA measuring the mutation of a residue at a position, is derived from the MSA (multiple structure alignment) database of protein evolutionary family. Then the position conservation correlation matrix and position mutation correlation matrix is constructed from the amino acid position mutative equation. Unlike traditional SCA (statistical coupling analysis) approach, which is based on the statistical analysis of position conservations, the CMCA focuses on the correlation analysis of position mutations. As an example the CMCA approach is used to study the PDZ domain of protein family, and the results well illustrate the distantly allosteric mechanism in PDZ protein family, and find the functional mutative network among residues. We expect that the CMCA approach may find applications in protein engineering study, and suggest new strategy to improve bioactivities and physicochemical properties of enzymes.
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