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/.