%A Harpreet Kaur
%A G.P.S. Raghava
%O Copyright of this article belongs to Wiley.
Supplemental material: See www.proteinscience.org.

%J Protein science : a publication of the Protein Society
%T Prediction of beta-turns in proteins from multiple alignment using neural network.
%X 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.
%N 3
%K ?-Turns; prediction; neural networks; multiple alignment; secondary structure; Web server
%P 627-34
%V 12
%D 2003
%I Wiley
%L open252