Computational Biology and Chemistry
TSFSOM: transmembrane segments prediction by fuzzy self-organizing map
ISNN'06 Proceedings of the Third international conference on Advances in Neural Networks - Volume Part III
Scoring hidden Markov models to discriminate β-barrel membrane proteins
Computational Biology and Chemistry
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A novel method is developed to model and predict the transmembrane regions of @b-barrel membrane proteins. It is based on a Hidden Markov model (HMM) with architecture obeying those proteins' construction principles. The HMM is trained and tested on a non-redundant set of 11 @b-barrel membrane proteins known to date at atomic resolution with a jack-knife procedure. As a result, the method correctly locates 97% of 172 transmembrane @b-strands. Out of the 11 proteins, the barrel size for ten proteins and the overall topology for seven proteins are correctly predicted. Additionally, it successfully assigns the entire topology for two new @b-barrel membrane proteins that have no significant sequence homology to the 11 proteins. Predicted topology for two candidates for @b-barrel structure of the outer mitochondrial membrane is also presented in the paper.