Prediction of β‐turns in proteins from multiple alignment using neural network

Abstract

A neural network‐based method has been developed for the prediction of β‐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 β‐turn prediction methods. The Web server BetaTPred2 ( http://www.imtech.res.in/raghava/betatpred2/ ) has been developed based on this approach.