A Hidden Markov Model method, capable of predicting and discriminating beta-barrel outer membrane proteins.

ABSTRACT:

Background

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

SUBMITTER: Bagos PG 

PROVIDER: S-EPMC385222 | biostudies-literature | 2004 Mar

REPOSITORIES: biostudies-literature