MOTIVATION: Transmembrane beta-barrel (TMB) proteins are embedded in the outer 
membranes of mitochondria, Gram-negative bacteria and chloroplasts. These 
proteins perform critical functions, including active ion-transport and passive 
nutrient intake. Therefore, there is a need for accurate prediction of secondary 
and tertiary structure of TMB proteins. Traditional homology modeling methods, 
however, fail on most TMB proteins since very few non-homologous TMB structures 
have been determined. Yet, because TMB structures conform to specific 
construction rules that restrict the conformational space drastically, it should 
be possible for methods that do not depend on target-template homology to be 
applied successfully.
RESULTS: We develop a suite (TMBpro) of specialized predictors for predicting 
secondary structure (TMBpro-SS), beta-contacts (TMBpro-CON) and tertiary 
structure (TMBpro-3D) of transmembrane beta-barrel proteins. We compare our 
results to the recent state-of-the-art predictors transFold and PRED-TMBB using 
their respective benchmark datasets, and leave-one-out cross-validation. Using 
the transFold dataset TMBpro predicts secondary structure with per-residue 
accuracy (Q(2)) of 77.8%, a correlation coefficient of 0.54, and TMBpro predicts 
beta-contacts with precision of 0.65 and recall of 0.67. Using the PRED-TMBB 
dataset, TMBpro predicts secondary structure with Q(2) of 88.3% and a 
correlation coefficient of 0.75. All of these performance results exceed 
previously published results by 4% or more. Working with the PRED-TMBB dataset, 
TMBpro predicts the tertiary structure of transmembrane segments with RMSD <6.0 
A for 9 of 14 proteins. For 6 of 14 predictions, the RMSD is <5.0 A, with a 
GDT_TS score greater than 60.0.
AVAILABILITY: http://www.igb.uci.edu/servers/psss.html.