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Published online 11 May 2004

Nucleic Acids Research, 2004, Vol. 32, No. 8 2566-2577

Predicting transmembrane beta-barrels in proteomes

Henry R. Bigelow*,1, Donald S. Petrey2, Jinfeng Liu1,3,4, Dariusz Przybylski1,5 and Burkhard Rost1,3,6

1 CUBIC, Department of Biochemistry and Molecular Biophysics, Columbia University, 650 West 168th Street BB217, New York, NY 10032, 2 Howard Hughes Medical Institute and Department of Biochemistry and Molecular Biophysics, Columbia University, New York, NY 10032, 3 North East Structural Genomics Consortium (NESG), Department of Biochemistry and Molecular Biophysics, Columbia University, 650 West 168th Street BB217, New York, NY 10032, 4 Department of Pharmacology, Columbia University, 630 West 168th Street, New York, NY 10032, 5 Department of Physics, Columbia University, 538 West 120th Street, New York, NY 10027 and 6 Columbia University Center for Computational Biology and Bioinformatics (C2B2), Russ Berrie Pavilion, 1150 St Nicholas Avenue, New York, NY 10032, USA

*To whom correspondence should be addressed. Tel: +1 212 305 4018; Fax: +1 212 305 7932; Email: bigelow{at}cubic.bioc.columbia.edu
Correspondence may also be addressed to Burkhard Rost. Tel: +1 212 305 4018; Fax: +1 212 305 7932; Email: rost{at}cubic.bioc.columbia.edu

Received January 21, 2004; Revised March 14, 2004; Accepted April 12, 2004

Very few methods address the problem of predicting beta-barrel membrane proteins directly from sequence. One reason is that only very few high-resolution structures for transmembrane beta-barrel (TMB) proteins have been determined thus far. Here we introduced the design, statistics and results of a novel profile-based hidden Markov model for the prediction and discrimination of TMBs. The method carefully attempts to avoid over-fitting the sparse experimental data. While our model training and scoring procedures were very similar to a recently published work, the architecture and structure-based labelling were significantly different. In particular, we introduced a new definition of beta- hairpin motifs, explicit state modelling of transmembrane strands, and a log-odds whole-protein discrimination score. The resulting method reached an overall four-state (up-, down-strand, periplasmic-, outer-loop) accuracy as high as 86%. Furthermore, accurately discriminated TMB from non-TMB proteins (45% coverage at 100% accuracy). This high precision enabled the application to 72 entirely sequenced Gram-negative bacteria. We found over 164 previously uncharacterized TMB proteins at high confidence. Database searches did not implicate any of these proteins with membranes. We challenge that the vast majority of our 164 predictions will eventually be verified experimentally. All proteome predictions and the PROFtmb prediction method are available at http://www.rostlab.org/ services/PROFtmb/.


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