Prediction of protein secondary structure based on an improved channel attention and multiscale convolution module - Jin, Xin; Guo, Lin; Jiang, Qian; Wu, Nan; Yao, Shaowen (Frontiers in Bioengineering and Biotechnology, 2022)
Prediction of the protein secondary structure is a key issue in protein science. Protein secondary structure prediction (PSSP) aims to construct a function that can map the amino acid sequence into the secondary structure so that the pro-tein secondary structure can be obtained according to the amino acid sequence. Driven by deep learning, the predic-tion accuracy of the protein secondary structure has been greatly improved in recent years. To explore a new tech-nique of PSSP, this paper introduces the idea of an adversarial game into the prediction of the secondary structure, and a conditional generative adversarial network (GAN)-based prediction model is proposed. In this work, we introduce a new multiscale convolution module and an improved channel attention (ICA) module into the generator to generate the secondary structure, and then a discriminator is designed to conflict with the generator to learn the complicated features of proteins. In addition, we also propose a PSSP method based on the proposed multiscale convolution mod-ule and ICA module. The experimental results indicate that the conditional GAN-based protein secondary structure prediction (CGAN-PSSP) model is workable and worth studying further because of the strong feature-learning ability of adversarial learning.