BACKGROUND: Hidden Markov models (HMMs) have been extensively used in 
computational molecular biology, for modelling protein and nucleic acid 
sequences. The design of the model architecture and the algorithms for parameter 
estimation and decoding are extremely important for improve the performance of 
HMM. In topology prediction of transmembrane beta-barrels proteins (TMBs), the 
Baum-Welch algorithm is widely adapted for HMM training but usually leads to a 
sub-optimal model in practice. In addition, all the existing HMM-based 
predictors are only designed to model the transmembrane segment without a 
submodel to model the signal peptide (SP) for full-length sequences. It is not 
convenient for users to investigate the structures of full-length TMB sequences.
RESULTS: We present here, an HMM that combine a transmembrane barrel submodel 
and an SP submodel for both topology and SP predictions. A new genetic algorithm 
(GA) is presented here to training the model, at the same time the 
Posterior-Viterbi algorithm is adopted for decoding. A dataset including 33 TMBs 
that is the most so far in literature are collected for model training and 
testing. Results of self-consistency and jackknife tests shows the GA has better 
global performance than the Baum-Welch algorithm. Results of jackknife tests 
show that this method performs better than all well known existing methods for 
topology predictions. Furthermore, it provides a function to predict SP in 
full-length TMBs sequences with fairish accuracy.
CONCLUSION: We show that our combined HMM-based method is a better choice for 
TMB topology prediction, which implements topology predictions with higher 
accuracy and additional SP predictions for full-length TMB sequences.