The inference of protein-protein interactions by co-evolutionary analysis is improved by excluding the information about the phylogenetic relationships

Tetsuya Sato, Yoshihiro Yamanishi, Minoru Kanehisa, Hiroyuki Toh

    Research output: Contribution to journalArticle

    95 Citations (Scopus)

    Abstract

    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.

    Original languageEnglish
    Pages (from-to)3482-3489
    Number of pages8
    JournalBioinformatics
    Volume21
    Issue number17
    DOIs
    Publication statusPublished - Sep 1 2005

    All Science Journal Classification (ASJC) codes

    • Statistics and Probability
    • Biochemistry
    • Molecular Biology
    • Computer Science Applications
    • Computational Theory and Mathematics
    • Computational Mathematics

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