title: A family of evolution-entropy hybrid methods for ranking protein residues by importance.
abstract: In order to identify the amino acids that determine protein structure and function it is useful to rank them by their relative importance. Previous approaches belong to two groups; those that rely on statistical inference, and those that focus on phylogenetic analysis. Here, we introduce a class of hybrid methods that combine evolutionary and entropic information from multiple sequence alignments. A detailed analysis in insulin receptor kinase domain and tests on proteins that are well-characterized experimentally show the hybrids' greater robustness with respect to the input choice of sequences, as well as improved sensitivity and specificity of prediction. This is a further step toward proteome scale analysis of protein structure and function.
authors.affiliation: Department of Molecular and Human Genetics, Baylor College of Medicine, One Baylor Plaza T921, Houston, TX 77030, USA.
authors.forename: I
authors.initials: I
authors.lastname: Mihalek
authors.forename: I
authors.initials: I
authors.lastname: Res
authors.forename: O
authors.initials: O
authors.lastname: Lichtarge
mesh_terms: D000595:Amino Acid Sequence; D000596:Amino Acids; D000818:Animals; D019277:Entropy; D019143:Evolution, Molecular; D006801:Humans; D008957:Models, Genetic; D008958:Models, Molecular; D011506:Proteins / Q000737:chemistry / Q000235:genetics; D011972:Receptor, Insulin / Q000737:chemistry* / Q000235:genetics; D016415:Sequence Alignment
chemical_list: D000596:Amino Acids; D011506:Proteins; D011972:Receptor, Insulin