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.
Prediction of the transmembrane regions of β-barrel membrane proteins with a neural network-based predictor / Jacoboni I.; Martelli P.L.; Fariselli P.; De Pinto V.; Casadio R.. - In: PROTEIN SCIENCE. - ISSN 0961-8368. - STAMPA. - 10:4(2001), pp. 779-787. [10.1110/ps.37201]
Prediction of the transmembrane regions of β-barrel membrane proteins with a neural network-based predictor
Jacoboni I.;Martelli P. L.;Fariselli P.;Casadio R.
2001
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.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.