Literature DB >> 15994190

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

Tetsuya Sato1, Yoshihiro Yamanishi, Minoru Kanehisa, Hiroyuki Toh.   

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. AVAILABILITY: The R script for the prediction of protein-protein interactions reported in this manuscript is available at http://timpani.genome.ad.jp/~proj/ CONTACT: sato@kuicr.kyoto-u.ac.jp SUPPLEMENTARY INFORMATION: The information is also available at the same site as the R script.

Entities:  

Mesh:

Substances:

Year:  2005        PMID: 15994190     DOI: 10.1093/bioinformatics/bti564

Source DB:  PubMed          Journal:  Bioinformatics        ISSN: 1367-4803            Impact factor:   6.937


  60 in total

1.  Evolutionary rate covariation reveals shared functionality and coexpression of genes.

Authors:  Nathan L Clark; Eric Alani; Charles F Aquadro
Journal:  Genome Res       Date:  2012-01-27       Impact factor: 9.043

2.  ERC analysis: web-based inference of gene function via evolutionary rate covariation.

Authors:  Nicholas W Wolfe; Nathan L Clark
Journal:  Bioinformatics       Date:  2015-08-04       Impact factor: 6.937

3.  Co-evolutionary analysis of domains in interacting proteins reveals insights into domain-domain interactions mediating protein-protein interactions.

Authors:  Raja Jothi; Praveen F Cherukuri; Asba Tasneem; Teresa M Przytycka
Journal:  J Mol Biol       Date:  2006-08-01       Impact factor: 5.469

Review 4.  Practical and theoretical advances in predicting the function of a protein by its phylogenetic distribution.

Authors:  Philip R Kensche; Vera van Noort; Bas E Dutilh; Martijn A Huynen
Journal:  J R Soc Interface       Date:  2008-02-06       Impact factor: 4.118

5.  Specificity in protein interactions and its relationship with sequence diversity and coevolution.

Authors:  Luke Hakes; Simon C Lovell; Stephen G Oliver; David L Robertson
Journal:  Proc Natl Acad Sci U S A       Date:  2007-04-27       Impact factor: 11.205

6.  High-confidence prediction of global interactomes based on genome-wide coevolutionary networks.

Authors:  David Juan; Florencio Pazos; Alfonso Valencia
Journal:  Proc Natl Acad Sci U S A       Date:  2008-01-16       Impact factor: 11.205

7.  A novel method to detect proteins evolving at correlated rates: identifying new functional relationships between coevolving proteins.

Authors:  Nathaniel L Clark; Charles F Aquadro
Journal:  Mol Biol Evol       Date:  2009-12-31       Impact factor: 16.240

8.  The human protein coevolution network.

Authors:  Elisabeth R M Tillier; Robert L Charlebois
Journal:  Genome Res       Date:  2009-08-20       Impact factor: 9.043

9.  Reconstructing ancestral gene content by coevolution.

Authors:  Tamir Tuller; Hadas Birin; Uri Gophna; Martin Kupiec; Eytan Ruppin
Journal:  Genome Res       Date:  2009-11-30       Impact factor: 9.043

10.  Inference of functional relations in predicted protein networks with a machine learning approach.

Authors:  Beatriz García-Jiménez; David Juan; Iakes Ezkurdia; Eduardo Andrés-León; Alfonso Valencia
Journal:  PLoS One       Date:  2010-04-01       Impact factor: 3.240

View more

北京卡尤迪生物科技股份有限公司 © 2022-2023.