PS8-Net: A Deep Convolutional Neural Network to Predict the Eight-State Protein Secondary Structure

Abstract

Protein secondary structure is crucial to creating an information bridge between the primary and tertiary structures. Precise prediction of eight-state protein secondary structure (PSS) has been significantly utilized in the structural and functional analysis of proteins. Deep learning techniques have been recently applied in this area and raised the eight-state (Q8) protein secondary structure prediction accuracy remarkably. Nevertheless, from a theoretical standpoint, there are still many rooms for improvement, specifically in the eight-state PSS prediction. In this study, we have presented a new deep convolutional neural network called PS8- Net, to enhance the accuracy of eight-class PSS prediction. The input of this architecture is a carefully constructed feature matrix from the proteins sequence features and profile features. We introduce a new PS8 module with skip connection to extracting the long-term inter-dependencies from higher layers, obtaining local contexts in earlier layers, and achieving global information during secondary structure prediction. This architecture enables the efficient processing of local and global interdependencies between amino acids to make an accurate prediction of each class. To the best of our knowledge, our proposed PS8-Net experiment results demonstrate that it outperforms all the state-of-the-art methods on the benchmark CullPdb6133, CB513, CASP10, and CASP11 datasets.