title: Automatic prediction of protein function.
abstract: Most methods annotating protein function utilise sequence homology to proteins of experimentally known function. Such a homology-based annotation transfer is problematic and limited in scope. Therefore, computational biologists have begun to develop ab initio methods that predict aspects of function, including subcellular localization, post-translational modifications, functional type and protein-protein interactions. For the first two cases, the most accurate approaches rely on identifying short signalling motifs, while the most general methods utilise tools of artificial intelligence. An outstanding new method predicts classes of cellular function directly from sequence. Similarly, promising methods have been developed predicting protein-protein interaction partners at acceptable levels of accuracy for some pairs in entire proteomes. No matter how difficult the task, successes over the last few years have clearly paved the way for ab initio prediction of protein function.
authors.affiliation: Department of Biochemistry and Molecular Biophysics, Columbia University, 650 West 168th Street BB217, New York, New York 10032, USA. rost@columbia.edu
authors.forename: B
authors.initials: B
authors.lastname: Rost
authors.forename: J
authors.initials: J
authors.lastname: Liu
authors.forename: R
authors.initials: R
authors.lastname: Nair
authors.forename: K O
authors.initials: KO
authors.lastname: Wrzeszczynski
authors.forename: Y
authors.initials: Y
authors.lastname: Ofran
mesh_terms: D019295:Computational Biology; D030562:Databases, Protein; D011499:Protein Processing, Post-Translational; D011506:Proteins / Q000502:physiology*; D017385:Sequence Homology; D013329:Structure-Activity Relationship
chemical_list: D011506:Proteins