Scoring hidden Markov models to discriminate beta-barrel membrane proteins.
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.
Deng
Yong
Y
School of Electronics & Information Technology, Shanghai Jiao Tong University, Shanghai 200030, PR China.
Liu
Qi
Q
Li
Yi-Xue
YX
D000465:Algorithms; D020816:Amino Acid Motifs; D019295:Computational Biology / Q000379:methods*; D030562:Databases, Protein; D008390:Markov Chains; D008565:Membrane Proteins / Q000737:chemistry*; D020543:Proteome / Q000737:chemistry; D011550:Pseudomonas aeruginosa / Q000737:chemistry / Q000235:genetics; D016415:Sequence Alignment; D017386:Sequence Homology, Amino Acid; D040681:Structural Homology, Protein
D008565:Membrane Proteins; D020543:Proteome