A neural-network based method for prediction of γ-turns in proteins from multiple sequence alignment

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Abstract
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 http://www.imtech.res.in/raghava/gammapred/.)