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.

中文翻译：

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PS8-Net：预测八态蛋白质二级结构的深度卷积神经网络

蛋白质二级结构对于在一级和三级 (3D) 结构之间建立信息桥梁至关重要。八态蛋白质二级结构（PSS）的精确预测在生物信息学中蛋白质的结构和功能分析中得到了显着利用。深度学习技术最近被应用于该研究领域，并显着提高了八态（Q8）蛋白质二级结构预测的准确性。尽管如此，从理论的角度来看，仍有很多改进的空间，特别是在八态 PSS 预测方面。在这项研究中，我们提出了一种新的深度卷积神经网络 (DCNN)，即 PS8-Net，以提高八类 PSS 预测的准确性。该架构的输入是从蛋白质序列特征和轮廓特征精心构建的特征矩阵。我们在网络中引入了一个新的 PS8 模块，它与跳过连接一起应用于从更高层提取长期相互依赖性，在早期层中获取局部上下文，并在二级结构预测过程中获得全局信息。我们提出的 PS8-Net 在基准 CullPdb6133、CB513、CASP10 和 CASP11 数据集上分别达到了 76.89%、71.94%、76.86% 和 75.26% 的 Q8 准确率。这种架构能够有效处理氨基酸之间的局部和全局相互依赖性，从而对每个类别进行准确预测。据我们所知，