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Protein secondary structure is crucial to creating an information bridgebetween the primary and tertiary (3D) structures. Precise prediction ofeight-state protein secondary structure (PSS) has significantly utilized in thestructural and functional analysis of proteins in bioinformatics. Deep learningtechniques have been recently applied in this research area and raised theeight-state (Q8) protein secondary structure prediction accuracy remarkably.Nevertheless, from a theoretical standpoint, there are still lots of rooms forimprovement, specifically in the eight-state PSS prediction. In this study, wehave presented a new deep convolutional neural network (DCNN), namely PS8-Net,to enhance the accuracy of eight-class PSS prediction. The input of thisarchitecture is a carefully constructed feature matrix from the proteinssequence features and profile features. We introduce a new PS8 module in thenetwork, which is applied with skip connection to extracting the long-terminter-dependencies from higher layers, obtaining local contexts in earlierlayers, and achieving global information during secondary structure prediction.Our proposed PS8-Net achieves 76.89%, 71.94%, 76.86%, and 75.26% Q8 accuracyrespectively on benchmark CullPdb6133, CB513, CASP10, and CASP11 datasets. Thisarchitecture enables the efficient processing of local and globalinterdependencies between amino acids to make an accurate prediction of eachclass. To the best of our knowledge, PS8-Net experiment results demonstratethat it outperforms all the state-of-the-art methods on the aforementionedbenchmark datasets.

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