Prediction of protein secondary structure based on deep residual convolutional neural network

Proteins play a vital role in organisms, which suggests that in-depth study of the function of proteins is helpful to the application of proteins in a more accurate and effective way. Accordingly, protein structure will become the focus of discussion and research for a long time. In order to fully extract the effective information from the protein structure and improve the classification accuracy of the protein secondary sequence, a deep residual network model using different residual units was proposed to predict the secondary structure. This algorithm uses sliding window method to represent amino acid sequences and combines the powerful feature extraction ability of resent network. In this paper, the parameters of the neural network are debugged through experiments, and then the extracted features are classified and verified. The experimental results on CASP9, CASP10, CASP11 and CASP12 data sets imply that the improved deep residual network model based on different residual units can express amino acid sequences more accurately, which is more superior than existing methods.