Prediction of β-turns in proteins from multiple alignment using neural network
Prediction of β-turns in proteins from multiple alignment using neural network
复制标题
DOI：
10.1110/ps.0228903
复制
发表时间：
2003-03-01
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
发展了一种基于神经网络的方法，用于通过多序列比对来预测蛋白质的β转角。使用两个具有单个隐层的前馈反向传播网络，其中第一序列结构网络以PSI-BLAST生成的位置特定评分矩阵的形式用多序列比对来训练。来自第一网络的初始预测和PSIPRED预测的二级结构被用作第二结构-结构网络的输入，以改进从第一网络获得的预测。通过使用多序列比对中包含的进化信息，已经实现了预测精度的显著提高。对一组426条非同源蛋白质链进行七倍交叉验证后，最终网络的总预测准确率为75.5%。相应的Q(Pred)、Q(Obs)和Matthews相关系数值分别为49.8%、72.3%和0.43，是所有已发表的P转向预测方法中最好的。Web服务器BetaTPred2(http://www.imtech.res.in/raghava/betatpred2/)就是基于这种方法开发的。
A neural network-based method has been developed for the prediction of beta-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 P-turn prediction methods. The Web server BetaTPred2 (http://www.imtech.res.in/raghava/betatpred2/) has been developed based on this approach.