Protein Secondary Structure Prediction Using Convolutional Bidirectional GRU

Abstract

In this paper, a protein secondary structure prediction method based on convolutional bidirectional GRU Model (CBi-GRU model) is adopted, which combines the advantages of sliding window in extracting local features of data. The use of CNN and Bi-GRU in the construction of the model improves the feature expression and data utilization, and improves the performance of the model. Protein data from FoxChase Institute were used, and high quality, complete and representative CullPDB dataset, CB513, CASP10 and CASP11 datasets were selected to train, test and validate the model. The results show that the proposed method achieves good prediction performance on CASP10 and CASP11 datasets, and the prediction accuracy of Q8 is 76.2% and 76.4%, respectively. Compared with RaptorX-SS, DeepCNF, CGAN-PSSP and other methods, the Q8 evaluation indicators are improved. Compared with the latest research data, our Q8 prediction accuracy is improved by 2% and 5.1%, which shows the effectiveness and superiority of the proposed model.