Prediction of the transmembrane regions of β‐barrel membrane proteins with a neural network‐based predictor

Abstract

A method based on neural networks is trained and tested on a nonredundant set of β‐barrel membrane proteins known at atomic resolution with a jackknife procedure. The method predicts the topography of transmembrane β strands with residue accuracy as high as 78% when evolutionary information is used as input to the network. Of the transmembrane β‐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 β‐barrel membrane proteins.