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1 Laboratory of Biocomputing, Centro Interdipartimentale per le Ricerche Biotecnologiche (CIRB), Bologna, Italy
2 Laboratory of Biophysics, Department of Biology, University of Bologna, Bologna, Italy
3 Laboratory of Biochemistry and Molecular Biology, Department of Chemical Science, University of Catania, Italy
Reprint requests to: Dr. Rita Casadio, Department of Biology, University of Bologna, Via Irnerio 42, 40126 Bologna Italy; e-mail: casadio{at}kaiser.alma.unibo.it; fax: 39-051-242576.
A method based on neural networks is trained and tested on a nonredundant set of ß-barrel membrane proteins known at atomic resolution with a jackknife procedure. The method predicts the topography of transmembrane ß strands with residue accuracy as high as 78% when evolutionary information is used as input to the network. Of the transmembrane ß-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 ß-barrel membrane proteins.
Keywords: Neural networks; secondary structure predictions; multiple sequence alignment; pattern recognition; membrane ß strands; prediction of membrane porins
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