http://crdd.osdd.net/open/id/eprint/252 2012-01-05T15:05:30Z info:eu-repo/semantics/descriptiveMetadata Prediction of beta-turns in proteins from multiple alignment using neural network. Kaur, Harpreet Raghava, G.P.S. QR Microbiology A neural network-based method has been developed for the prediction of beta-turns in proteins by using multiple sequence alignment. Two feed-forward back-propagation networks with a single hidden layer are used where the first-sequence structure network is trained with the multiple sequence alignment in the form of PSI-BLAST-generated position-specific scoring matrices. The initial predictions from the first network and PSIPRED-predicted secondary structure are used as input to the second structure-structure network to refine the predictions obtained from the first net. A significant improvement in prediction accuracy has been achieved by using evolutionary information contained in the multiple sequence alignment. The final network yields an overall prediction accuracy of 75.5% when tested by sevenfold cross-validation on a set of 426 nonhomologous protein chains. The corresponding Q(pred), Q(obs), and Matthews correlation coefficient values are 49.8%, 72.3%, and 0.43, respectively, and are the best among all the previously published beta-turn prediction methods. The Web server BetaTPred2 (http://www.imtech.res.in/raghava/betatpred2/) has been developed based on this approach. Wiley 2003-03 Article PeerReviewed application/pdf http://crdd.osdd.net/open/252/1/raghava2002.3.pdf http://onlinelibrary.wiley.com/doi/10.1110/ps.0228903/pdf Kaur, Harpreet and Raghava, G.P.S. (2003) Prediction of beta-turns in proteins from multiple alignment using neural network. Protein science : a publication of the Protein Society, 12 (3). pp. 627-34. ISSN 0961-8368 http://crdd.osdd.net/open/252/ info:eu-repo/semantics/objectFile info:eu-repo/semantics/humanStartPage