BACKGROUND: Prediction of the transmembrane strands and topology of beta-barrel 
outer membrane proteins is of interest in current bioinformatics research. 
Several methods have been applied so far for this task, utilizing different 
algorithmic techniques and a number of freely available predictors exist. The 
methods can be grossly divided to those based on Hidden Markov Models (HMMs), on 
Neural Networks (NNs) and on Support Vector Machines (SVMs). In this work, we 
compare the different available methods for topology prediction of beta-barrel 
outer membrane proteins. We evaluate their performance on a non-redundant 
dataset of 20 beta-barrel outer membrane proteins of gram-negative bacteria, 
with structures known at atomic resolution. Also, we describe, for the first 
time, an effective way to combine the individual predictors, at will, to a 
single consensus prediction method.
RESULTS: We assess the statistical significance of the performance of each 
prediction scheme and conclude that Hidden Markov Model based methods, 
HMM-B2TMR, ProfTMB and PRED-TMBB, are currently the best predictors, according 
to either the per-residue accuracy, the segments overlap measure (SOV) or the 
total number of proteins with correctly predicted topologies in the test set. 
Furthermore, we show that the available predictors perform better when only 
transmembrane beta-barrel domains are used for prediction, rather than the 
precursor full-length sequences, even though the HMM-based predictors are not 
influenced significantly. The consensus prediction method performs significantly 
better than each individual available predictor, since it increases the accuracy 
up to 4% regarding SOV and up to 15% in correctly predicted topologies.
CONCLUSIONS: The consensus prediction method described in this work, optimizes 
the predicted topology with a dynamic programming algorithm and is implemented 
in a web-based application freely available to non-commercial users at 
http://bioinformatics.biol.uoa.gr/ConBBPRED.