Literature DB >> 18275083

Analysis of the residue-residue coevolution network and the functionally important residues in proteins.

Byung-Chul Lee1, Keunwan Park, Dongsup Kim.   

Abstract

It is a common belief that some residues of a protein are more important than others. In some cases, point mutations of some residues make butterfly effect on the protein structure and function, but in other cases they do not. In addition, the residues important for the protein function tend to be not only conserved but also coevolved with other interacting residues in a protein. Motivated by these observations, the authors propose that there is a network composed of the residues, the residue-residue coevolution network (RRCN), where nodes are residues and links are set when the coevolutionary interaction strengths between residues are sufficiently large. The authors build the RRCN for the 44 diverse protein families. The interaction strengths are calculated by using McBASC algorithm. After constructing the RRCN, the authors identify residues that have high degree of connectivity (hub nodes), and residues that play a central role in network flow of information (C(I) nodes). The authors show that these residues are likely to be functionally important residues. Moreover, the C(I) nodes appear to be more relevant to the function than the hub nodes. Unlike other similar methods, the method described in this study is solely based on sequences. Therefore, the method can be applied to the function annotation of a wider range of proteins.

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Year:  2008        PMID: 18275083     DOI: 10.1002/prot.21972

Source DB:  PubMed          Journal:  Proteins        ISSN: 0887-3585


  26 in total

1.  Force-clamp spectroscopy detects residue co-evolution in enzyme catalysis.

Authors:  Raul Perez-Jimenez; Arun P Wiita; David Rodriguez-Larrea; Pallav Kosuri; Jose A Gavira; Jose M Sanchez-Ruiz; Julio M Fernandez
Journal:  J Biol Chem       Date:  2008-08-07       Impact factor: 5.157

2.  Correction for phylogeny, small number of observations and data redundancy improves the identification of coevolving amino acid pairs using mutual information.

Authors:  Cristina Marino Buslje; Javier Santos; Jose Maria Delfino; Morten Nielsen
Journal:  Bioinformatics       Date:  2009-03-10       Impact factor: 6.937

3.  Evolutionary coupling analysis identifies the impact of disease-associated variants at less-conserved sites.

Authors:  Donghyo Kim; Seong Kyu Han; Kwanghwan Lee; Inhae Kim; JungHo Kong; Sanguk Kim
Journal:  Nucleic Acids Res       Date:  2019-09-19       Impact factor: 16.971

Review 4.  Phylomedicine: an evolutionary telescope to explore and diagnose the universe of disease mutations.

Authors:  Sudhir Kumar; Joel T Dudley; Alan Filipski; Li Liu
Journal:  Trends Genet       Date:  2011-07-20       Impact factor: 11.639

5.  Functionally important positions can comprise the majority of a protein's architecture.

Authors:  Sudheer Tungtur; Daniel J Parente; Liskin Swint-Kruse
Journal:  Proteins       Date:  2011-03-04

6.  Networks of high mutual information define the structural proximity of catalytic sites: implications for catalytic residue identification.

Authors:  Cristina Marino Buslje; Elin Teppa; Tomas Di Doménico; José María Delfino; Morten Nielsen
Journal:  PLoS Comput Biol       Date:  2010-11-04       Impact factor: 4.475

7.  Correlated mutations: a hallmark of phenotypic amino acid substitutions.

Authors:  Andreas Kowarsch; Angelika Fuchs; Dmitrij Frishman; Philipp Pagel
Journal:  PLoS Comput Biol       Date:  2010-09-16       Impact factor: 4.475

8.  Genome-wide hepatitis C virus amino acid covariance networks can predict response to antiviral therapy in humans.

Authors:  Rajeev Aurora; Maureen J Donlin; Nathan A Cannon; John E Tavis
Journal:  J Clin Invest       Date:  2008-12-22       Impact factor: 14.808

9.  Comparing the functional roles of nonconserved sequence positions in homologous transcription repressors: implications for sequence/function analyses.

Authors:  Sudheer Tungtur; Sarah Meinhardt; Liskin Swint-Kruse
Journal:  J Mol Biol       Date:  2009-10-08       Impact factor: 5.469

10.  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

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