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 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.
Laboratory of Biocomputing, Centro Interdipartimentale per le Ricerche Biotecnologiche (CIRB), Bologna, Italy.
I
I
Jacoboni
P L
PL
Martelli
P
P
Fariselli
V
V
De Pinto
R
R
Casadio
D000465:Algorithms; D001425:Bacterial Outer Membrane Proteins / Q000737:chemistry*; D016208:Databases, Factual; D004926:Escherichia coli / Q000737:chemistry; D005544:Forecasting; D008954:Models, Biological; D016571:Neural Networks, Computer; D018272:Porins / Q000737:chemistry*; D017433:Protein Structure, Secondary; D012241:Rhodopseudomonas / Q000737:chemistry
D001425:Bacterial Outer Membrane Proteins; D018272:Porins