title: Inference of protein function from protein structure.
abstract: Structural genomics has brought us three-dimensional structures of proteins with unknown functions. To shed light on such structures, we have developed ProKnow (http://www.doe-mbi.ucla.edu/Services/ProKnow/), which annotates proteins with Gene Ontology functional terms. The method extracts features from the protein such as 3D fold, sequence, motif, and functional linkages and relates them to function via the ProKnow knowledgebase of features, which links features to annotated functions via annotation profiles. Bayes' theorem is used to compute weights of the functions assigned, using likelihoods based on the extracted features. The description level of the assigned function is quantified by the ontology depth (from 1 = general to 9 = specific). Jackknife tests show approximately 89% correct assignments at ontology depth 1 and 40% at depth 9, with 93% coverage of 1507 distinct folded proteins. Overall, about 70% of the assignments were inferred correctly. This level of performance suggests that ProKnow is a useful resource in functional assessments of novel proteins.
authors.affiliation: UCLA-DOE Institute for Genomics and Proteomics, Los Angeles, CA 90095, USA.
authors.forename: Debnath
authors.initials: D
authors.lastname: Pal
authors.forename: David
authors.initials: D
authors.lastname: Eisenberg
mesh_terms: D020816:Amino Acid Motifs; D000595:Amino Acid Sequence; D001499:Bayes Theorem; D001665:Binding Sites; D016208:Databases, Factual; D030562:Databases, Protein; D017510:Protein Folding; D011506:Proteins / Q000737:chemistry* / Q000378:metabolism*; D016415:Sequence Alignment; D020539:Sequence Analysis, Protein; D012984:Software
chemical_list: D011506:Proteins