  Protein secondary structure is crucial to creating an information bridge
between the primary and tertiary (3D) structures. Precise prediction of
eight-state protein secondary structure (PSS) has significantly utilized in the
structural and functional analysis of proteins in bioinformatics. Deep learning
techniques have been recently applied in this research area and raised the
eight-state (Q8) protein secondary structure prediction accuracy remarkably.
Nevertheless, from a theoretical standpoint, there are still lots of rooms for
improvement, specifically in the eight-state PSS prediction. In this study, we
have presented a new deep convolutional neural network (DCNN), namely 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 in the
network, which is applied 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.
Our proposed PS8-Net achieves 76.89%, 71.94%, 76.86%, and 75.26% Q8 accuracy
respectively on benchmark CullPdb6133, CB513, CASP10, and CASP11 datasets. 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, PS8-Net experiment results demonstrate
that it outperforms all the state-of-the-art methods on the aforementioned
benchmark datasets.
