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- Title
A Hidden Markov Model method, capable of predicting and discriminating beta-barrel outer membrane proteins.
- Authors
Bagos, Pantelis G; Liakopoulos, Theodore D; Spyropoulos, Ioannis C; Hamodrakas, Stavros J
- Abstract
Integral membrane proteins constitute about 20-30% of all proteins in the fully sequenced genomes. They come in two structural classes, the alpha-helical and the beta-barrel membrane proteins, demonstrating different physicochemical characteristics, structure and localization. While transmembrane segment prediction for the alpha-helical integral membrane proteins appears to be an easy task nowadays, the same is much more difficult for the beta-barrel membrane proteins. We developed a method, based on a Hidden Markov Model, capable of predicting the transmembrane beta-strands of the outer membrane proteins of gram-negative bacteria, and discriminating those from water-soluble proteins in large datasets. The model is trained in a discriminative manner, aiming at maximizing the probability of correct predictions rather than the likelihood of the sequences.
- Publication
BMC bioinformatics, 2004, Vol 5, p29
- ISSN
1471-2105
- Publication type
Journal Article
- DOI
10.1186/1471-2105-5-29