title: Automated construction of structural motifs for predicting functional sites on protein structures.
abstract: Structural genomics initiatives are beginning to rapidly generate vast numbers of protein structures. For many of the structures, functions are not yet determined and high-throughput methods for determining function are necessary. Although there has been extensive work in function prediction at the sequence level, predicting function at the structure level may provide better sensitivity and predictive value. We describe a method to predict functional sites by automatically creating three dimensional structural motifs from amino acid sequence motifs. These structural motifs perform comparably well with manually generated structural motifs and perform better than sequence motifs. Automatically generated structural motifs can be used for structural-genomic scale function prediction on protein structures.
authors.affiliation: Stanford University, 251 Campus Drive X-215, Stanford, CA 94305-5479, USA. mliang@smi.stanford.edu
authors.forename: M P
authors.initials: MP
authors.lastname: Liang
authors.forename: D L
authors.initials: DL
authors.lastname: Brutlag
authors.forename: R B
authors.initials: RB
authors.lastname: Altman
mesh_terms: D020816:Amino Acid Motifs; D001499:Bayes Theorem; D001665:Binding Sites; D002135:Calcium-Binding Proteins / Q000737:chemistry / Q000235:genetics; D019295:Computational Biology; D003198:Computer Simulation; D030562:Databases, Protein; D015394:Molecular Structure; D011506:Proteins / Q000737:chemistry* / Q000235:genetics; D040901:Proteomics / Q000706:statistics & numerical data
chemical_list: D002135:Calcium-Binding Proteins; D011506:Proteins