Prediction of the transmembrane regions of beta-barrel membrane proteins with a neural network-based predictor

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