A method based on neural networks is trained and tested on a nonredundant set of 
beta-barrel membrane proteins known at atomic resolution with a jackknife 
procedure. The method predicts the topography of transmembrane beta strands with 
residue accuracy as high as 78% when evolutionary information is used as input 
to the network. Of the transmembrane beta-strands included in the training set, 
93% are correctly assigned. The predictor includes an algorithm of model 
optimization, based on dynamic programming, that correctly models eight out of 
the 11 proteins present in the training/testing set. In addition, protein 
topology is assigned on the basis of the location of the longest loops in the 
models. We propose this as a general method to fill the gap of the prediction of 
beta-barrel membrane proteins.