title: Protein function prediction: towards integration of similarity metrics.
abstract: Genomic centers discover increasingly many protein sequences and structures, but not necessarily their full biological functions. Thus, currently, less than one percent of proteins have experimentally verified biochemical activities. To fill this gap, function prediction algorithms apply metrics of similarity between proteins on the premise that those sufficiently alike in sequence, or structure, will perform identical functions. Although high sensitivity is elusive, network analyses that integrate these metrics together hold the promise of rapid gains in function prediction specificity.
authors.affiliation: Department of Molecular and Human Genetics, 1 Baylor Plaza, Baylor College of Medicine, Houston, TX 77030, USA.
authors.forename: Serkan
authors.initials: S
authors.lastname: Erdin
authors.forename: Andreas Martin
authors.initials: AM
authors.lastname: Lisewski
authors.forename: Olivier
authors.initials: O
authors.lastname: Lichtarge
mesh_terms: D003198:Computer Simulation; D030562:Databases, Protein; D058977:Molecular Sequence Annotation / Q000379:methods; D011506:Proteins / Q000737:chemistry* / Q000378:metabolism*; D012984:Software
chemical_list: D011506:Proteins