Literature DB >> 15947018

Refined phylogenetic profiles method for predicting protein-protein interactions.

Jingchun Sun1, Jinlin Xu, Zhen Liu, Qi Liu, Aimin Zhao, Tieliu Shi, Yixue Li.   

Abstract

MOTIVATION: The increasing availability of complete genome sequences provides excellent opportunity for the further development of tools for functional studies in proteomics. Several experimental approaches and in silico algorithms have been developed to cluster proteins into networks of biological significance that may provide new biological insights, especially into understanding the functions of many uncharacterized proteins. Among these methods, the phylogenetic profiles method has been widely used to predict protein-protein interactions. It involves the selection of reference organisms and identification of homologous proteins. Up to now, no published report has systematically studied the effects of the reference genome selection and the identification of homologous proteins upon the accuracy of this method.
RESULTS: In this study, we optimized the phylogenetic profiles method by integrating phylogenetic relationships among reference organisms and sequence homology information to improve prediction accuracy. Our results revealed that the selection of the reference organisms set and the criteria for homology identification significantly are two critical factors for the prediction accuracy of this method. Our refined phylogenetic profiles method shows greater performance and potentially provides more reliable functional linkages compared with previous methods.

Mesh:

Substances:

Year:  2005        PMID: 15947018     DOI: 10.1093/bioinformatics/bti532

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


  39 in total

Review 1.  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

2.  Computational Methods for Predicting Protein-Protein Interactions Using Various Protein Features.

Authors:  Ziyun Ding; Daisuke Kihara
Journal:  Curr Protoc Protein Sci       Date:  2018-06-21

Review 3.  Emerging methods in protein co-evolution.

Authors:  David de Juan; Florencio Pazos; Alfonso Valencia
Journal:  Nat Rev Genet       Date:  2013-03-05       Impact factor: 53.242

4.  Mapping global and local coevolution across 600 species to identify novel homologous recombination repair genes.

Authors:  Dana Sherill-Rofe; Dolev Rahat; Steven Findlay; Anna Mellul; Irene Guberman; Maya Braun; Idit Bloch; Alon Lalezari; Arash Samiei; Ruslan Sadreyev; Michal Goldberg; Alexandre Orthwein; Aviad Zick; Yuval Tabach
Journal:  Genome Res       Date:  2019-02-04       Impact factor: 9.043

5.  Protein annotation from protein interaction networks and Gene Ontology.

Authors:  Cao D Nguyen; Katheleen J Gardiner; Krzysztof J Cios
Journal:  J Biomed Inform       Date:  2011-05-06       Impact factor: 6.317

6.  Detecting Coevolution of Functionally Related Proteins for Automated Protein Annotation.

Authors:  Alan L Kwan; Susan K Dutcher; Gary D Stormo
Journal:  Proc IEEE Int Symp Bioinformatics Bioeng       Date:  2010

7.  Testing the accuracy of eukaryotic phylogenetic profiles for prediction of biological function.

Authors:  Saurav Singh; Dennis P Wall
Journal:  Evol Bioinform Online       Date:  2008-06-18       Impact factor: 1.625

8.  Exploiting amino acid composition for predicting protein-protein interactions.

Authors:  Sushmita Roy; Diego Martinez; Harriett Platero; Terran Lane; Margaret Werner-Washburne
Journal:  PLoS One       Date:  2009-11-20       Impact factor: 3.240

9.  The conservation and evolutionary modularity of metabolism.

Authors:  José M Peregrín-Alvarez; Chris Sanford; John Parkinson
Journal:  Genome Biol       Date:  2009-06-12       Impact factor: 13.583

10.  Co-evolutionary networks of genes and cellular processes across fungal species.

Authors:  Tamir Tuller; Martin Kupiec; Eytan Ruppin
Journal:  Genome Biol       Date:  2009-05-05       Impact factor: 13.583

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