A neural-network based method for prediction of γ-turns in proteins from multiple sequence alignment
A neural-network based method for prediction of γ-turns in proteins from multiple sequence alignment
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DOI：
10.1110/ps.0241703
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发表时间：
2003-05-01
期刊：
影响因子：
8
通讯作者：
Raghava, GPS
中科院分区：
文献类型：
--
作者：
Kaur, H;Raghava, GPS
在本研究中，已作出了尝试，以发展一种方法来预测蛋白质中的γ-转角。首先，我们已经实现了蛋白质结构预测领域中常用的统计和机器学习技术，用于预测伽马转角。所有的方法都经过了训练和测试的一组320个非同源蛋白质链的五倍交叉验证技术。已经观察到，所有方法的性能都非常差，具有小于或等于0.06的马修相关系数（MCC）。其次，从PSIPRED获得的预测二级结构用于γ-转角预测。已经发现，机器学习方法优于统计方法，并且在使用二级结构信息时实现了0.11的MCC。当使用多序列比对而不是单个序列作为输入时，伽马转弯预测的性能进一步提高。基于这项研究，我们已经开发了一种方法，GammaPred，用于伽马转弯预测（MCC = 0.17）。GammaPred是一种基于神经网络的方法，分两步预测伽马转弯。在第一步中，使用序列到结构网络来预测来自蛋白质序列的多个比对的γ转角。在第二步中，它使用结构到结构网络，其中输入由从第一步获得的预测的γ转角和从PSIPRED获得的预测的二级结构组成。（基于GammaPred的网络服务器可在http：//www.imtech.res.in/raghava/gammapred/上获得）。
In the present study, an attempt has been made to develop a method for predicting gamma-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 gamma-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) less than or equal to 0.06. Second, predicted secondary structure obtained from PSIPRED is used in gamma-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 gamma-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 gamma-turn prediction (MCC = 0.17). The GammaPred is a neural-network-based method, which predicts gamma-turns in two steps. In the first step, a sequence-to-structure network is used to predict the gamma-turns from multiple alignment of protein sequence. In the second step, it uses a structure-to-structure network in which input consists of predicted gamma-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/).