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Optimal data collection for correlated mutation analysis
Author(s) -
Ashkenazy Haim,
Unger Ron,
Kliger Yossef
Publication year - 2008
Publication title -
proteins: structure, function, and bioinformatics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.699
H-Index - 191
eISSN - 1097-0134
pISSN - 0887-3585
DOI - 10.1002/prot.22168
Subject(s) - sequence (biology) , mutation , computational biology , multiple sequence alignment , computer science , sequence analysis , biology , genetics , data mining , sequence alignment , gene , peptide sequence
The main objective of correlated mutation analysis (CMA) is to predict intraprotein residue–residue interactions from sequence alone. Despite considerable progress in algorithms and computer capabilities, the performance of CMA methods remains quite low. Here we examine whether, and to what extent, the quality of CMA methods depends on the sequences that are included in the multiple sequence alignment (MSA). The results revealed a strong correlation between the number of homologs in an MSA and CMA prediction strength. Furthermore, many of the current methods include only orthologs in the MSA, we found that it is beneficial to include both orthologs and paralogs in the MSA. Remarkably, even remote homologs contribute to the improved accuracy. Based on our findings we put forward an automated data collection procedure, with a minimal coverage of 50% between the query protein and its orthologs and paralogs. This procedure improves accuracy even in the absence of manual curation. In this era of massive sequencing and exploding sequence data, our results suggest that correlated mutation‐based methods have not reached their inherent performance limitations and that the role of CMA in structural biology is far from being fulfilled. Proteins 2009. © 2008 Wiley‐Liss, Inc.