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

Citation
I. Jacoboni et al., Prediction of the transmembrane regions of beta-barrel membrane proteins with a neural network-based predictor, PROTEIN SCI, 10(4), 2001, pp. 779-787
Citations number
45
Language
INGLESE
art.tipo
Article
Categorie Soggetti
Biochemistry & Biophysics
Journal title
PROTEIN SCIENCE
ISSN journal
0961-8368 → ACNP
Volume
10
Issue
4
Year of publication
2001
Pages
779 - 787
Database
ISI
SICI code
0961-8368(200104)10:4<779:POTTRO>2.0.ZU;2-A
Abstract
A method based on neural networks is trained and tested on a nonredundant s et of beta -barrel membrane proteins known at atomic resolution with a jack knife procedure. The method predicts the topography of transmembrane beta s trands with residue accuracy as high as 78% when evolutionary information i s 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 correc tly 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 fi ll the gap of the prediction of beta -barrel membrane proteins.