The beta-barrel outer membrane proteins constitute one of the two known 
structural classes of membrane proteins. Whereas there are several different 
web-based predictors for alpha-helical membrane proteins, currently there is no 
freely available prediction method for beta-barrel membrane proteins, at least 
with an acceptable level of accuracy. We present here a web server (PRED-TMBB, 
http://bioinformatics.biol.uoa.gr/PRED-TMBB) which is capable of predicting the 
transmembrane strands and the topology of beta-barrel outer membrane proteins of 
Gram-negative bacteria. The method is based on a Hidden Markov Model, trained 
according to the Conditional Maximum Likelihood criterion. The model was 
retrained and the training set now includes 16 non-homologous outer membrane 
proteins with structures known at atomic resolution. The user may submit one 
sequence at a time and has the option of choosing between three different 
decoding methods. The server reports the predicted topology of a given protein, 
a score indicating the probability of the protein being an outer membrane 
beta-barrel protein, posterior probabilities for the transmembrane strand 
prediction and a graphical representation of the assumed position of the 
transmembrane strands with respect to the lipid bilayer.