In the present study, an attempt has been made to develop a method for predicting γ-turns in proteins. First, we have implemented the commonly used statistical and machine-learning techniques in the field of protein structure prediction, for the prediction of γ-turns. All the methods have been trained and tested on a set of 320 nonhomologous protein chains by a fivefold cross-validation technique. It has been observed that the performance of all methods is very poor, having a Matthew’s Correlation Coefficient (MCC) ≤ 0.06. Second, predicted secondary structure obtained from PSIPRED is used in γ-turn prediction. It has been found that machine-learning methods outperform statistical methods and achieve an MCC of 0.11 when secondary structure information is used. The performance of γ-turn prediction is further improved when multiple sequence alignment is used as the input instead of a single sequence. Based on this study, we have developed a method, GammaPred, for γ-turn prediction (MCC = 0.17). The GammaPred is a neural-network-based method, which predicts γ-turns in two steps. In the first step, a sequence-to-structure network is used to predict the γ-turns from multiple alignment of protein sequence. In the second step, it uses a structure-to-structure network in which input consists of predicted γ-turns obtained from the first step and predicted secondary structure obtained from PSIPRED. (A Web server based on GammaPred is available at .)
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