Scoring hidden Markov models to discriminate \(\beta\)-barrel membrane proteins. (English) Zbl 1088.92015
Summary: A new method is presented for identification of ß-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, the 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.
MSC:
| 92C40 | Biochemistry, molecular biology |
| 62P10 | Applications of statistics to biology and medical sciences; meta analysis |
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