Literature DB >> 18056067

An integrated system for studying residue coevolution in proteins.

Kevin Y Yip1, Prianka Patel, Philip M Kim, Donald M Engelman, Drew McDermott, Mark Gerstein.   

Abstract

UNLABELLED: Residue coevolution has recently emerged as an important concept, especially in the context of protein structures. While a multitude of different functions for quantifying it have been proposed, not much is known about their relative strengths and weaknesses. Also, subtle algorithmic details have discouraged implementing and comparing them. We addressed this issue by developing an integrated online system that enables comparative analyses with a comprehensive set of commonly used scoring functions, including Statistical Coupling Analysis (SCA), Explicit Likelihood of Subset Variation (ELSC), mutual information and correlation-based methods. A set of data preprocessing options are provided for improving the sensitivity and specificity of coevolution signal detection, including sequence weighting, residue grouping and the filtering of sequences, sites and site pairs. A total of more than 100 scoring variations are available. The system also provides facilities for studying the relationship between coevolution scores and inter-residue distances from a crystal structure if provided, which may help in understanding protein structures. AVAILABILITY: The system is available at http://coevolution.gersteinlab.org. The source code and JavaDoc API can also be downloaded from the web site.

Mesh:

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Year:  2007        PMID: 18056067     DOI: 10.1093/bioinformatics/btm584

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


  30 in total

1.  Statistical Coupling Analysis-Guided Library Design for the Discovery of Mutant Luciferases.

Authors:  Mira D Liu; Elliot A Warner; Charlotte E Morrissey; Caitlyn W Fick; Taia S Wu; Marya Y Ornelas; Gabriela V Ochoa; Brendan S Zhang; Colin M Rathbun; William B Porterfield; Jennifer A Prescher; Aaron M Leconte
Journal:  Biochemistry       Date:  2017-12-28       Impact factor: 3.162

2.  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

3.  Patterns of [FeFe] hydrogenase diversity in the gut microbial communities of lignocellulose-feeding higher termites.

Authors:  Nicholas R Ballor; Jared R Leadbetter
Journal:  Appl Environ Microbiol       Date:  2012-05-25       Impact factor: 4.792

4.  Identifying and seeing beyond multiple sequence alignment errors using intra-molecular protein covariation.

Authors:  Russell J Dickson; Lindi M Wahl; Andrew D Fernandes; Gregory B Gloor
Journal:  PLoS One       Date:  2010-06-28       Impact factor: 3.240

5.  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

6.  Identification of coevolving residues and coevolution potentials emphasizing structure, bond formation and catalytic coordination in protein evolution.

Authors:  Daniel Y Little; Lu Chen
Journal:  PLoS One       Date:  2009-03-10       Impact factor: 3.240

7.  Integration of evolutionary features for the identification of functionally important residues in major facilitator superfamily transporters.

Authors:  Jouhyun Jeon; Jae-Seong Yang; Sanguk Kim
Journal:  PLoS Comput Biol       Date:  2009-10-02       Impact factor: 4.475

8.  Mapping the sequence mutations of the 2009 H1N1 influenza A virus neuraminidase relative to drug and antibody binding sites.

Authors:  Sebastian Maurer-Stroh; Jianmin Ma; Raphael Tze Chuen Lee; Fernanda L Sirota; Frank Eisenhaber
Journal:  Biol Direct       Date:  2009-05-20       Impact factor: 4.540

9.  Protein fragments: functional and structural roles of their coevolution networks.

Authors:  Linda Dib; Alessandra Carbone
Journal:  PLoS One       Date:  2012-11-05       Impact factor: 3.240

10.  CLAG: an unsupervised non hierarchical clustering algorithm handling biological data.

Authors:  Linda Dib; Alessandra Carbone
Journal:  BMC Bioinformatics       Date:  2012-08-08       Impact factor: 3.169

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