Scoring hidden Markov models to discriminate beta-barrel membrane proteins

Yong Deng, Qi Liu, Yi-Xue Li
Computational Biology and Chemistry 2004, 28 (3): 189-94
A new method is presented for identification of beta-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 beta-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 beta-barrel membrane proteins.

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