Scoring hidden Markov models to discriminate -barrel membrane proteins

A new method is presented for identification of @b-barrel membrane proteins. It is based on a hidden Markov model (HMM) with an architecture obeying these proteins' construction principles. Once the HMM is trained, log-odds score relative to a null model is used to discriminate @b-barrel membrane proteins from other proteins. The method achieves only 10% false positive and false negative rates in a six-fold cross-validation procedure. The results compare favorably with existing methods. This method is proposed to be a valuable tool to quickly scan proteomes of entirely sequenced organisms for @b-barrel membrane proteins.

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