Prediction of the location and type of β‐turns in proteins using neural networks

Abstract

A neural network has been used to predict both the location and the type of β‐turns in a set of 300 nonhomologous protein domains. A substantial improvement in prediction accuracy compared with previous methods has been achieved by incorporating secondary structure information in the input data. The total percentage of residues correctly classified as β‐turn or not‐β‐turn is around 75% with predicted secondary structure information. More significantly, the method gives a Matthews correlation coefficient (MCC) of around 0.35, compared with a typical MCC of around 0.20 using other β‐turn prediction methods. Our method also distinguishes the two most numerous and well‐defined types of β‐turn, types I and II, with a significant level of accuracy (MCCs 0.22 and 0.26, respectively).