High throughput prediction of critical protein regions using correlated mutation analysis.

摘要

Correlated mutation analysis is an effective approach for predicting functional and structural residue interactions from protein multiple sequence alignments. A prediction pipeline over the Pfam database was developed to predict residue contacts within protein domains. Cross-reference with the PDB showed these contacts are spatially close. Furthermore, we found our predictions to be biochemically reasonable and correspond closely with known contact matrices. This large-scale search for coevolving regions within protein domains revealed that if two sites in an alignment covary, then neighboring sites in the alignment would also typically covary, resulting in clusters of covarying residues. The program PatchD was developed to measure the covariation between disconnected sequence clusters to reveal patch covariation. Patches that exhibited strong covariation identified multiple residues that were generally nearby in the protein structures, suggesting that the detection of covarying patches can be used in addition to traditional CMA approaches to reveal functional interaction partners.