Prediction of the transmembrane regions of beta-barrel membrane proteins with a neural network-based predictor |
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Authors: | Jacoboni I Martelli P L Fariselli P De Pinto V Casadio R |
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Affiliation: | Laboratory of Biocomputing, Centro Interdipartimentale per le Ricerche Biotecnologiche (CIRB), Bologna, Italy. |
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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. |
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Keywords: | Neural networks secondary structure predictions multiple sequence alignment pattern recognition membrane β strands prediction of membrane porins |
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