Transmembrane beta-barrel (TMB) proteins are embedded in the outer membrane of 
Gram-negative bacteria, mitochondria and chloroplasts. The cellular location and 
functional diversity of beta-barrel outer membrane proteins makes them an 
important protein class. At the present time, very few non-homologous TMB 
structures have been determined by X-ray diffraction because of the experimental 
difficulty encountered in crystallizing transmembrane (TM) proteins. The 
transFold web server uses pairwise inter-strand residue statistical potentials 
derived from globular (non-outer-membrane) proteins to predict the 
supersecondary structure of TMB. Unlike all previous approaches, transFold does 
not use machine learning methods such as hidden Markov models or neural 
networks; instead, transFold employs multi-tape S-attribute grammars to describe 
all potential conformations, and then applies dynamic programming to determine 
the global minimum energy supersecondary structure. The transFold web server not 
only predicts secondary structure and TMB topology, but is the only method which 
additionally predicts the side-chain orientation of transmembrane beta-strand 
residues, inter-strand residue contacts and TM beta-strand inclination with 
respect to the membrane. The program transFold currently outperforms all other 
methods for accuracy of beta-barrel structure prediction. Available at 
http://bioinformatics.bc.edu/clotelab/transFold.