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Predicting protein structure using hidden Markov models (1997)

Abstract
We discuss how methods based on hidden Markov models performed in the fold-recognition section of the CASP2 experiment. Hidden Markov models were built for a representative set of just over one thousand structures from the Protein Data Bank (pdb). Each CASP2 target sequence was scored against this library of hmms. In addition, an hmm was built for each of the target sequences, and all of the sequences in pdb were scored against that target model, with a good score on both methods indicating a high probability that the target sequence is homologous to the structure. The method worked well in comparison to other methods used at CASP2 for targets of moderate difficulty, where the closest structure in pdb could be aligned to the target with at least 15% residue identity. y To whom correspondence should be addressed. Mailing address: Computer Engineering, UCSC, Santa Cruz, CA 95064 USA. Phone: 1-408-459-4250, Fax: 1-408-459-4829. Mail to other UCSC authors may be similarly addresse...

Publication details
Download http://citeseer.ist.psu.edu/342060.html
Source ftp://ftp.cse.ucsc.edu/pub/karplus/casp-final.ps.gz
Publisher unknown
Contributors The Pennsylvania State University CiteSeer Archives
Repository CiteSeer (United States)
Keywords Kevin Karplus,Kimmen Sjolander,Christian Barrett,Melissa Cline,David Haussler,Richard Hughey,Liisa Holm,Chris Sander,Ebi England Predicting protein structure using hidden Markov models
Language Englisch
Relation oai:CiteSeerPSU:37769, oai:CiteSeerPSU:13898, oai:CiteSeerPSU:342060, oai:CiteSeerPSU:73310