Reducing phylogenetic bias in correlated mutation analysis

Correlated mutation analysis (CMA) is a sequence-based approach for ab initio protein contact map prediction. The basis of this approach is the observed correlation between mutations in interacting amino acid residues. These correlations are often estimated by either calculating the Pearson's c...

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Bibliographic Details
Published in:Protein engineering, design and selection design and selection, 2010-05, Vol.23 (5), p.321-326
Main Authors: Ashkenazy, Haim, Kliger, Yossef
Format: Article
Language:English
Subjects:
Amino Acid Motifs - genetics
coevolution
coevolving residues
covariation
Data Interpretation, Statistical
DNA Mutational Analysis - methods
Models, Genetic
molecular evolution
Phylogeny
Proteins - genetics
Sequence Alignment
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Summary:Correlated mutation analysis (CMA) is a sequence-based approach for ab initio protein contact map prediction. The basis of this approach is the observed correlation between mutations in interacting amino acid residues. These correlations are often estimated by either calculating the Pearson's correlation coefficient (PCC) or the mutual information (MI) between columns in a multiple sequence alignment (MSA) of the protein of interest and its homologs. A major challenge of CMA is to filter out the background noise originating from phylogenetic relatedness between sequences included in the MSA. Recently, a procedure to reduce this background noise was demonstrated to improve an MI-based predictor. Herein, we tested whether a similar approach can also improve the performance of the classical PCC-based method. Indeed, performance improvements were achieved for all four major SCOP classes. Furthermore, the results reveal that the improved PCC-based method is superior to MI-based methods for proteins having MSAs of up to 100 sequences.
ISSN:1741-0126
1741-0134
DOI:10.1093/protein/gzp078