The inference of protein-protein interactions by co-evolutionary analysis is improved by excluding the information about the phylogenetic relationships
The inference of protein-protein interactions by co-evolutionary analysis is improved by excluding the information about the phylogenetic relationships
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DOI：
10.1093/bioinformatics/bti564
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发表时间：
2005-09-01
期刊：
影响因子：
5.8
通讯作者：
Toh, H
中科院分区：
文献类型：
--
作者：
Sato, T;Yamanishi, Y;Toh, H
研究动机：蛋白质相互作用的预测是当前生物信息学的一个重要课题。镜像树方法使用进化信息来预测蛋白质-蛋白质相互作用。然而，已经认识到，通过镜像树方法的预测导致许多误报。我们的研究的动机是解决这个问题，通过改进的方法来提取关于蛋白质pairs.Results的协同进化信息，我们开发了一种新的方法来预测蛋白质-蛋白质相互作用的镜像树方法的框架下的协同进化信息。独创性是使用投影算子从距离矩阵中排除关于源生物之间的系统发育关系的信息。每个距离矩阵被转换成一个向量用于操作。该载体被称为“系统发育载体”。我们提出了三种方法来提取系统发育信息：（1）使用来自与所考虑的蛋白质相同来源的生物的16 S rRNA，（2）平均系统发育向量和（3）分析系统发育向量的主成分。我们研究了所提出的方法来预测相互作用的蛋白质对大肠杆菌的性能，使用实验验证的数据。我们的方法是成功的，它大大减少了预测中的假阳性数量。
Motivation: The prediction of protein-protein interactions is currently an important issue in bioinformatics. The mirror tree method uses evolutionary information to predict protein-protein interactions. However, it has been recognized that predictions by the mirror tree method lead to many false positives. The incentive of our study was to solve this problem by improving the method of extracting the co-evolutionary information regarding the protein pairs.Results: We developed a novel method to predict protein-protein interactions from co-evolutionary information in the framework of the mirror tree method. The originality is the use of the projection operator to exclude the information about the phylogenetic relationships among the source organisms from the distance matrix. Each distance matrix was transformed into a vector for the operation. The vector is referred to as a 'phylogenetic vector'. We have proposed three ways to extract the phylogenetic information: (1) using the 16S rRNA from the same source organisms as the proteins under consideration, (2) averaging the phylogenetic vectors and (3) analyzing the principal components of the phylogenetic vectors. We examined the performance of the proposed methods to predict interacting protein pairs from Escherichia coli, using experimentally verified data. Our method was successful, and it drastically reduced the number of false positives in the prediction.