Functional annotations improve the predictive score of human disease-related mutations in proteins
Article first published online: 12 MAY 2009
© 2009 Wiley-Liss, Inc.
Volume 30, Issue 8, pages 1237–1244, August 2009
How to Cite
Calabrese, R., Capriotti, E., Fariselli, P., Martelli, P. L. and Casadio, R. (2009), Functional annotations improve the predictive score of human disease-related mutations in proteins. Hum. Mutat., 30: 1237–1244. doi: 10.1002/humu.21047
- Issue published online: 22 JUL 2009
- Article first published online: 12 MAY 2009
- Accepted manuscript online: 12 MAY 2009 12:00AM EST
- Manuscript Accepted: 1 MAY 2009
- Manuscript Received: 16 DEC 2008
- missense mutation;
- support vector machine;
- Gene Ontology;
- disease-related SNP
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. Hum Mutat 30:1–8, 2009. © 2009 Wiley-Liss, Inc.