Literature DB >> 18930732

Correlated evolution of interacting proteins: looking behind the mirrortree.

Maricel G Kann1, Benjamin A Shoemaker, Anna R Panchenko, Teresa M Przytycka.   

Abstract

It has been observed that the evolutionary distances of interacting proteins often display a higher level of similarity than those of noninteracting proteins. This finding indicates that interacting proteins are subject to common evolutionary constraints and constitutes the basis of a method to predict protein interactions known as mirrortree. It has been difficult, however, to identify the direct cause of the observed similarities between evolutionary trees. One possible explanation is the existence of compensatory mutations between partners' binding sites to maintain proper binding. This explanation, though, has been recently challenged, and it has been suggested that the signal of correlated evolution uncovered by the mirrortree method is unrelated to any correlated evolution between binding sites. We examine the contribution of binding sites to the correlation between evolutionary trees of interacting domains. We show that binding neighborhoods of interacting proteins have, on average, higher coevolutionary signal compared with the regions outside binding sites; however, when the binding neighborhood is removed, the remaining domain sequence still contains some coevolutionary signal. In conclusion, the correlation between evolutionary trees of interacting domains cannot exclusively be attributed to the correlated evolution of the binding sites or to common evolutionary pressure exerted on the whole protein domain sequence, each of which contributes to the signal measured by the mirrortree approach.

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Mesh:

Year:  2008        PMID: 18930732      PMCID: PMC2678019          DOI: 10.1016/j.jmb.2008.09.078

Source DB:  PubMed          Journal:  J Mol Biol        ISSN: 0022-2836            Impact factor:   5.469


  31 in total

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Authors:  Benjamin A Shoemaker; Anna R Panchenko; Stephen H Bryant
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2.  Predicting protein-protein interaction by searching evolutionary tree automorphism space.

Authors:  Raja Jothi; Maricel G Kann; Teresa M Przytycka
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3.  Structure, function, and evolution of transient and obligate protein-protein interactions.

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4.  Use of receiver operating characteristic (ROC) analysis to evaluate sequence matching.

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5.  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

6.  Predicting protein domain interactions from coevolution of conserved regions.

Authors:  Maricel G Kann; Raja Jothi; Praveen F Cherukuri; Teresa M Przytycka
Journal:  Proteins       Date:  2007-06-01

7.  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

8.  Assessing protein co-evolution in the context of the tree of life assists in the prediction of the interactome.

Authors:  Florencio Pazos; Juan A G Ranea; David Juan; Michael J E Sternberg
Journal:  J Mol Biol       Date:  2005-09-30       Impact factor: 5.469

9.  Why highly expressed proteins evolve slowly.

Authors:  D Allan Drummond; Jesse D Bloom; Christoph Adami; Claus O Wilke; Frances H Arnold
Journal:  Proc Natl Acad Sci U S A       Date:  2005-09-21       Impact factor: 11.205

10.  Phylogenetic tree information aids supervised learning for predicting protein-protein interaction based on distance matrices.

Authors:  Roger A Craig; Li Liao
Journal:  BMC Bioinformatics       Date:  2007-01-09       Impact factor: 3.169

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  33 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.  The human protein coevolution network.

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

3.  Mac-1 Regulates IL-13 Activity in Macrophages by Directly Interacting with IL-13Rα1.

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Journal:  J Biol Chem       Date:  2015-07-09       Impact factor: 5.157

4.  Proteomic and phylogenetic coevolution analyses of pM79 and pM92 identify interactions with RNA polymerase II and delineate the murine cytomegalovirus late transcription complex.

Authors:  Travis J Chapa; Yushen Du; Ren Sun; Dong Yu; Anthony R French
Journal:  J Gen Virol       Date:  2017-02-12       Impact factor: 3.891

5.  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 6.  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

7.  The methanogenic redox cofactor F420 is widely synthesized by aerobic soil bacteria.

Authors:  Blair Ney; F Hafna Ahmed; Carlo R Carere; Ambarish Biswas; Andrew C Warden; Sergio E Morales; Gunjan Pandey; Stephen J Watt; John G Oakeshott; Matthew C Taylor; Matthew B Stott; Colin J Jackson; Chris Greening
Journal:  ISME J       Date:  2016-08-09       Impact factor: 10.302

8.  Development of a novel bioinformatics tool for in silico validation of protein interactions.

Authors:  Nicola Barbarini; Luca Simonelli; Alberto Azzalin; Sergio Comincini; Riccardo Bellazzi
Journal:  J Biomed Biotechnol       Date:  2010-06-07

9.  Structural and functional roles of coevolved sites in proteins.

Authors:  Saikat Chakrabarti; Anna R Panchenko
Journal:  PLoS One       Date:  2010-01-06       Impact factor: 3.240

10.  Covariation of branch lengths in phylogenies of functionally related genes.

Authors:  Wai Lok Sibon Li; Allen G Rodrigo
Journal:  PLoS One       Date:  2009-12-29       Impact factor: 3.240

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