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Prediction of the transmembrane regions of beta-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 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.

SUBMITTER: Jacoboni I

PROVIDER: S-EPMC2373968 | biostudies-other | 2001 Apr

REPOSITORIES: biostudies-other

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

Jacoboni I I Martelli P L PL Fariselli P P De Pinto V V Casadio R R

Protein science : a publication of the Protein Society 20010401 4

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 p ...[more]

PMID: 11274469

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