title: Functional annotations improve the predictive score of human disease-related mutations in proteins.
abstract: Single nucleotide polymorphisms (SNPs) are the simplest and most frequent form of human DNA variation, also valuable as genetic markers of disease susceptibility. The most investigated SNPs are missense mutations resulting in residue substitutions in the protein. Here we propose SNPs&GO, an accurate method that, starting from a protein sequence, can predict whether a mutation is disease related or not by exploiting the protein functional annotation. The scoring efficiency of SNPs&GO is as high as 82%, with a Matthews correlation coefficient equal to 0.63 over a wide set of annotated nonsynonymous mutations in proteins, including 16,330 disease-related and 17,432 neutral polymorphisms. SNPs&GO collects in unique framework information derived from protein sequence, evolutionary information, and function as encoded in the Gene Ontology terms, and outperforms other available predictive methods.
authors.affiliation: Laboratory of Biocomputing, CIRB/Department of Biology, University of Bologna, Bologna 40126, Italy.
authors.forename: Remo
authors.initials: R
authors.lastname: Calabrese
authors.forename: Emidio
authors.initials: E
authors.lastname: Capriotti
authors.forename: Piero
authors.initials: P
authors.lastname: Fariselli
authors.forename: Pier Luigi
authors.initials: PL
authors.lastname: Martelli
authors.forename: Rita
authors.initials: R
authors.lastname: Casadio
mesh_terms: D000015:Abnormalities, Multiple / Q000235:genetics*; D006801:Humans; D009154:Mutation; D011506:Proteins / Q000235:genetics*; D013577:Syndrome
chemical_list: D011506:Proteins