Evaluation of methods for predicting the topology of beta-barrel outer membrane proteins and a consensus prediction method.
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.
Bagos
Pantelis G
PG
Department of Cell Biology and Biophysics, Faculty of Biology, University of Athens, Panepistimiopolis, Athens 15701, Greece. pbagos@biol.uoa.gr <pbagos@biol.uoa.gr>
Liakopoulos
Theodore D
TD
Hamodrakas
Stavros J
SJ
D000465:Algorithms; D000704:Analysis of Variance; D001185:Artificial Intelligence; D001425:Bacterial Outer Membrane Proteins / Q000737:chemistry*; D019295:Computational Biology / Q000379:methods*; D003196:Computer Graphics; D003198:Computer Simulation; D003627:Data Interpretation, Statistical; D030562:Databases, Protein; D005069:Evaluation Studies as Topic; D020407:Internet; D008390:Markov Chains; D008565:Membrane Proteins / Q000737:chemistry; D008956:Models, Chemical; D008958:Models, Molecular; D009345:Neisseria meningitidis / Q000235:genetics; D016571:Neural Networks, Computer; D011381:Programming Languages; D011487:Protein Conformation; D017433:Protein Structure, Secondary; D017434:Protein Structure, Tertiary; D011506:Proteins / Q000737:chemistry*; D015203:Reproducibility of Results; D016415:Sequence Alignment; D020539:Sequence Analysis, Protein; D012984:Software; D013211:Staphylococcus aureus / Q000235:genetics
D001425:Bacterial Outer Membrane Proteins; D008565:Membrane Proteins; D011506:Proteins