Protein structural domain parsing by consensus reasoning over multiple knowledge sources and methods.
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.
Kulikowski
C A
CA
Computer Science Department, Rutgers University, Piscataway, NJ 08903, USA. kulikows@cs.rutgers.edu
Muchnik
I
I
Yun
H J
HJ
Dayanik
A A
AA
Zhang
D
D
Song
Y
Y
Montelione
G T
GT
D000465:Algorithms; D019295:Computational Biology; D008390:Markov Chains; D017434:Protein Structure, Tertiary; D011506:Proteins / Q000737:chemistry*; D017421:Sequence Analysis
D011506:Proteins