title: Reducing phylogenetic bias in correlated mutation analysis.
abstract: 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.
authors.affiliation: Compugen Ltd, 72 Pinchas Rosen, Tel Aviv 69512, Israel.
authors.forename: Haim
authors.initials: H
authors.lastname: Ashkenazy
authors.forename: Yossef
authors.initials: Y
authors.lastname: Kliger
mesh_terms: D020816:Amino Acid Motifs / Q000235:genetics*; D004252:DNA Mutational Analysis / Q000379:methods*; D003627:Data Interpretation, Statistical; D008957:Models, Genetic; D010802:Phylogeny; D011506:Proteins / Q000235:genetics*; D016415:Sequence Alignment
chemical_list: D011506:Proteins