Improving the prediction of protein secondary structure in three and eight classes using recurrent neural networks and profiles
Improving the prediction of protein secondary structure in three and eight classes using recurrent neural networks and profiles
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DOI：
10.1002/prot.10082
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发表时间：
2002-05-01
影响因子：
2.9
通讯作者：
Baldi, P
中科院分区：
文献类型：
--
作者：
Pollastri, G;Przybylski, D;Baldi, P
二级结构预测正日益成为几种旨在预测蛋白质结构和功能的方法的主力。在这里，我们使用合奏的双向递归神经网络架构，PSI-BLAST衍生的配置文件，和一个大的非冗余训练集，以获得两个新的预测：（a）的SSpro程序的第二个版本的二级结构分类成三个类别和（B）的SSpro 8程序的第一个版本的二级结构分类成八类DSSP程序产生的。我们描述了三个不同的测试集上的SSpro实现了约78%的正确预测的持续性能的结果。我们报告的混淆矩阵，比较PSI-BLAST的BLAST衍生的配置文件，并评估相应的性能改进。SSpro和SSpro 8作为Web服务器实现，可与其他结构特征预测器一起使用：http.1/promoter.ics.uci.edu/ BRNN-PRED/。
Secondary structure predictions are increasingly becoming the workhorse for several methods aiming at predicting protein structure and function. Here we use ensembles of bidirectional recurrent neural network architectures, PSI-BLAST-derived profiles, and a large nonredundant training set to derive two new predictors: (a) the second version of the SSpro program for secondary structure classification into three categories and (b) the first version of the SSpro8 program for secondary structure classification into the eight classes produced by the DSSP program. We describe the results of three different test sets on which SSpro achieved a sustained performance of about 78% correct prediction. We report confusion matrices, compare PSI-BLAST to BLAST-derived profiles, and assess the corresponding performance improvements. SSpro and SSpro8 are implemented as web servers, available together with other structural feature predictors at: http.1/promoter.ics.uci.edu/ BRNN-PRED/.