WG-ICRN: Protein 8-state secondary structure prediction based on Wasserstein generative adversarial networks and residual networks with Inception modules.

Abstract

Protein secondary structure is the basis of studying the tertiary structure of proteins, drug design and development, and the 8-state protein secondary structure can provide more adequate protein information than the 3-state structure. Therefore, this paper proposes a novel method WG-ICRN for predicting protein 8-state secondary structures. First, we use the Wasserstein generative adversarial network (WGAN) to extract protein features in the position-specific scoring matrix (PSSM). The extracted features are combined with PSSM into a new feature set of WG-data, which contains richer feature information. Then, we use the residual network (ICRN) with Inception to further extract the features in WG-data and complete the prediction. Compared with the residual network, ICRN can reduce parameter calculations and increase the width of feature extraction to obtain more feature information. We evaluated the prediction performance of the model using six datasets. The experimental results show that the WGAN has excellent feature extraction capabilities, and ICRN can further improve network performance and improve prediction accuracy. Compared with four popular models, WG-ICRN achieves better prediction performance.