Prediction of Protein Secondary Structure based on Multi-scale Convolutional Neural Network

Abstract

In the field of bioinformatics, the prediction of secondary structure of proteins is very important. It can be obtained from the prediction of primary structure (amino acid sequence) and can provide reference for the prediction of tertiary structure of proteins. Amino acid sequences of proteins are encoded with several features and then combined into the prediction network. Convolutional neural network has excellent performance in text and sequence information extraction. The amino acid sequence of protein is also a special sequence, so the convolutional neural network can be used to extract the information in the sequence. Moreover, the influence of amino acids on the formation of secondary structure varies with different distances, so in the experiment, convolutional neural networks with convolution nuclei of different sizes were used to form multi-scale convolution blocks to extract amino acid sequence information. At the same time, the sliding window technique is also used to show the interaction between the sequences, and a long amino acid sequence is divided into some amino acid fragments and input into the model. Finally, the accuracy of Q8 on the dataset CB6133_filtered reaches 71%.