Journal Article

A new method for revealing correlated mutations under the structural and functional constraints in proteins

Byung-Chul Lee and Dongsup Kim

in Bioinformatics

Volume 25, issue 19, pages 2506-2513
Published in print October 2009 | ISSN: 1367-4803
Published online July 2009 | e-ISSN: 1460-2059 | DOI:
A new method for revealing correlated mutations under the structural and functional constraints in proteins

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Motivation: Diverse studies have shown that correlated mutation (CM) is an important molecular evolutionary process alongside conservation. However, attempts to find the residue pairs that co-evolve under the structural and/or functional constraints are complicated by the fact that a large portion of covariance signals found in multiple sequence alignments arise from correlations due to common ancestry and stochastic noise.

Results: Assuming that the background noise can be estimated from the coevolutionary relationships among residues, we propose a new measure for background noise called the normalized coevolutionary pattern similarity (NCPS) score. By subtracting NCPS scores from raw CM scores and combining the results with an entropy factor, we show that these new scores effectively reduce the background noise. To test the effectiveness of this method in detecting residue pairs coevolving under the structural constraints, two independent test sets were performed, showing that this new method performs better than the most accurate method currently available. In addition, we also applied our method to double mutant cycle experiments and protein–protein interactions. Although more rigorous tests are required, we obtained promising results that our method tended to explain those data better than other methods. These results suggest that the new noise-reduced CM scores developed in this study can be a valuable tool for the study of correlated mutations under the structural and/or functional constraints in proteins.



Supplementary information: Supplementary data are available at Bioinformatics online.

Journal Article.  5028 words.  Illustrated.

Subjects: Bioinformatics and Computational Biology

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