title: Scoring hidden Markov models to discriminate beta-barrel membrane proteins.
abstract: 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.
authors.affiliation: School of Electronics & Information Technology, Shanghai Jiao Tong University, Shanghai 200030, PR China.
authors.forename: Yong
authors.initials: Y
authors.lastname: Deng
authors.forename: Qi
authors.initials: Q
authors.lastname: Liu
authors.forename: Yi-Xue
authors.initials: YX
authors.lastname: Li
mesh_terms: 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
chemical_list: D008565:Membrane Proteins; D020543:Proteome