Literature DB >> 16301205

Inferring functional information from domain co-evolution.

Yohan Kim1, Mehmet Koyutürk, Umut Topkara, Ananth Grama, Shankar Subramaniam.   

Abstract

MOTIVATION: Co-evolution is a powerful mechanism for understanding protein function. Prior work in this area has shown that co-evolving proteins are more likely to share the same function than those that do not because of functional constraints. Many of the efforts founded on this observation, however, are at the level of entire sequences, implicitly assuming that the complete protein sequence follows a single evolutionary trajectory. Since it is well known that a domain can exist in various contexts, this assumption is not valid for numerous multi-domain proteins. Motivated by these observations, we introduce a novel technique called Coevolutionary-Matrix that captures co-evolution between regions of two proteins. Instead of using existing domain information, the method exploits residue-level conservation to identify co-evolving regions that might correspond to domains.
RESULTS: We show that the Coevolutionary-Matrix method can detect greater number of known functional associations for the Escherichia coli proteins when compared with earlier implementations of phylogenetic profiles. Furthermore, co-evolving regions of proteins detected by our method enable us to make hypotheses about their specific functions, many of which are supported by existing biochemical studies.

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Year:  2005        PMID: 16301205     DOI: 10.1093/bioinformatics/bti723

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


  12 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.  Detecting genetic interactions using parallel evolution in experimental populations.

Authors:  Kaitlin J Fisher; Sergey Kryazhimskiy; Gregory I Lang
Journal:  Philos Trans R Soc Lond B Biol Sci       Date:  2019-06-03       Impact factor: 6.237

Review 3.  Algorithmic and analytical methods in network biology.

Authors:  Mehmet Koyutürk
Journal:  Wiley Interdiscip Rev Syst Biol Med       Date:  2010 May-Jun

4.  MS_RHII-RSD, a dual-function RNase HII-(p)ppGpp synthetase from Mycobacterium smegmatis.

Authors:  Maya S Murdeshwar; Dipankar Chatterji
Journal:  J Bacteriol       Date:  2012-05-25       Impact factor: 3.490

5.  Proteins with complex architecture as potential targets for drug design: a case study of Mycobacterium tuberculosis.

Authors:  Bálint Mészáros; Judit Tóth; Beáta G Vértessy; Zsuzsanna Dosztányi; István Simon
Journal:  PLoS Comput Biol       Date:  2011-07-21       Impact factor: 4.475

6.  Why should we care about molecular coevolution?

Authors:  Francisco M Codoñer; Mario A Fares
Journal:  Evol Bioinform Online       Date:  2008-02-14       Impact factor: 1.625

7.  Improved homology-driven computational validation of protein-protein interactions motivated by the evolutionary gene duplication and divergence hypothesis.

Authors:  Christian Frech; Michael Kommenda; Viktoria Dorfer; Thomas Kern; Helmut Hintner; Johann W Bauer; Kamil Onder
Journal:  BMC Bioinformatics       Date:  2009-01-19       Impact factor: 3.169

8.  Effect of reference genome selection on the performance of computational methods for genome-wide protein-protein interaction prediction.

Authors:  Vijaykumar Yogesh Muley; Akash Ranjan
Journal:  PLoS One       Date:  2012-07-26       Impact factor: 3.240

9.  Discovering functional linkages and uncharacterized cellular pathways using phylogenetic profile comparisons: a comprehensive assessment.

Authors:  Raja Jothi; Teresa M Przytycka; L Aravind
Journal:  BMC Bioinformatics       Date:  2007-05-23       Impact factor: 3.169

10.  Prediction of evolutionarily conserved interologs in Mus musculus.

Authors:  Sailu Yellaboina; Dawood B Dudekula; Minoru Sh Ko
Journal:  BMC Genomics       Date:  2008-10-08       Impact factor: 3.969

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