title: Protein structural domain parsing by consensus reasoning over multiple knowledge sources and methods.
abstract: Domain parsing, or the detection of signals of protein structural domains from sequence data, is a complex and difficult problem. If carried out reliably it would be a powerful interpretive and predictive tool for genomic and proteomic studies. We report on a novel approach to domain parsing using consensus techniques based on Hidden Markov Models (HMMs) and BLAST searches built from a training set of 1471 continuous structural domains from the Dali Domain Dictionary (DDD). Validation on an independent test sample of family-matched structural domain sequences from the Scop database yields a consensus prediction performance rate of 75.5%, well above the 58% obtained by simple agreement of methods.
authors.affiliation: Computer Science Department, Rutgers University, Piscataway, NJ 08903, USA. kulikows@cs.rutgers.edu
authors.forename: C A
authors.initials: CA
authors.lastname: Kulikowski
authors.forename: I
authors.initials: I
authors.lastname: Muchnik
authors.forename: H J
authors.initials: HJ
authors.lastname: Yun
authors.forename: A A
authors.initials: AA
authors.lastname: Dayanik
authors.forename: D
authors.initials: D
authors.lastname: Zhang
authors.forename: Y
authors.initials: Y
authors.lastname: Song
authors.forename: G T
authors.initials: GT
authors.lastname: Montelione
mesh_terms: D000465:Algorithms; D019295:Computational Biology; D008390:Markov Chains; D017434:Protein Structure, Tertiary; D011506:Proteins / Q000737:chemistry*; D017421:Sequence Analysis
chemical_list: D011506:Proteins