A neural network method for prediction of β-turn types in proteins using evolutionary information
A neural network method for prediction of β-turn types in proteins using evolutionary information
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DOI：
10.1093/bioinformatics/bth322
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发表时间：
2004-11-01
期刊：
影响因子：
5.8
通讯作者：
Raghava, GPS
中科院分区：
文献类型：
--
作者：
Kaur, H;Raghava, GPS
目的：预测β-转角是蛋白质二级结构预测的重要组成部分。最近，一个高度准确的神经网络为基础的方法Betatpred 2已被开发用于预测β-转角蛋白质使用位置特异性评分矩阵（PSSM）产生的PSI-BLAST和二级结构信息预测PSIPRED。然而，Betatpred 2的主要局限性在于它仅预测β-转角和非β-转角残基，并且不提供不同β-转角类型的任何信息。因此，有必要使用基于多序列比对的方法来预测β-turn类型，这将有助于整体三级结构predict.Results：在目前的工作中，已经开发了一种方法用于预测β-turn类型I，II，IV和VIII。对于每个转弯类型，使用了具有单个隐藏层的两个连续前馈反向传播网络，其中第一个序列到结构网络已经在单个序列以及PSI-BLAST PSSM上进行了训练。第一个网络的输出与PSIPRED预测的二级结构沿着被用作第二级结构到结构网络的输入。该网络已经通过7倍交叉验证在426条蛋白质链的非同源数据集上进行了训练和测试。已经观察到，通过使用多序列比对，每个转弯类型的预测性能得到显著改善。通过使用二级结构到结构网络和PSIPRED预测的二级结构信息，性能得到进一步提高。已经观察到，I型和II型β转弯比IV型和VIII型β转弯具有更好的预测性能。最终的网络产生的总体精度为74.5%，93.5%，67.9%和96.5%，MCC值分别为0.29%，0.29%，0.23%和0.02%的I，II，IV和VIII型β-转弯，分别是优于随机预测。
Motivation: The prediction of beta-turns is an important element of protein secondary structure prediction. Recently, a highly accurate neural network based method Betatpred2 has been developed for predicting beta-turns in proteins using position-specific scoring matrices (PSSM) generated by PSI-BLAST and secondary structure information predicted by PSIPRED. However, the major limitation of Betatpred2 is that it predicts only beta-turn and non-beta-turn residues and does not provide any information of different beta-turn types. Thus, there is a need to predict beta-turn types using an approach based on multiple sequence alignment, which will be useful in overall tertiary structure prediction.Results: In the present work, a method has been developed for the prediction of beta-turn types I, II, IV and VIII. For each turn type, two consecutive feed-forward back-propagation networks with a single hidden layer have been used where the first sequence-to-structure network has been trained on single sequences as well as on PSI-BLAST PSSM. The output from the first network along with PSIPRED predicted secondary structure has been used as input for the second-level structure-to-structure network. The networks have been trained and tested on a non-homologous dataset of 426 proteins chains by 7-fold cross-validation. It has been observed that the prediction performance for each turn type is improved significantly by using multiple sequence alignment. The performance has been further improved by using a second level structure-to-structure network and PSIPRED predicted secondary structure information. It has been observed that Type I and II beta-turns have better prediction performance than Type IV and VIII beta-turns. The final network yields an overall accuracy of 74.5, 93.5, 67.9 and 96.5% with MCC values of 0.29, 0.29, 0.23 and 0.02 for Type I, II, IV and VIII beta-turns, respectively, and is better than random prediction.