A method based on neural networks is trained and tested on a nonredundant set of -barrel membrane proteins known at atomic resolution with a jackknife procedure. The method predicts the topography of transmembrane strands with residue accuracy as high as 78% when evolutionary information is used as input to the network. Of the transmembrane -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 -barrel membrane proteins.
; s; multiple sequence alignment; pattern recognition; membrane strands;
|Titolo:||Prediction of the transmembrane regions of beta-barrel membrane proteins with a neural network-based predictor|
|Data di pubblicazione:||2001|
|Citazione:||Prediction of the transmembrane regions of beta-barrel membrane proteins with a neural network-based predictor / IACOBONI I; MARTELLI P.G; FARISELLI P; DE PINTO V; CASADIO R. - 10(2001), pp. 779-787.|
|Appare nelle tipologie:||1.1 Articolo in rivista|