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.
Protein Secondary Structure Prediction Using 3D Graphs and Relation-Aware Message Passing Transformers
Banaras Hindu University, Varanasi, India（班纳拉锡大学）；Indiana University, Bloomington（印第安纳大学）；Indian Institute of Technology Jodhpur, India（印度理工学院朱罗尔分校）；Indian Institute of Technology Patna, India（印度理工学院帕坦分校）
Integrating Secondary Structures Information into Triangular Spatial Relationships (TSR) for Advanced Protein Classification
The School of Computing and Informatic, University of Louisiana at Lafayette（计算与信息学院，路易斯安那州立大学拉法叶分校）；Department of Chemistry, University of Louisiana at Lafayette（化学系，路易斯安那州立大学拉法叶分校）
Towards Multiscale Graph-based Protein Learning with Geometric Secondary Structural Motifs
Department of Mathematics and Scientific Computing and Imaging (SCI) Institute University of Utah（数学与科学计算与成像研究所（SCI）大学）；Department of Mathematics, UCLA（数学系，加州大学洛杉矶分校）；Department of Mathematics, UC Davis（数学系，加州大学戴维斯分校）
Multi-objective Genetic Programming with Multi-view Multi-level Feature for Enhanced Protein Secondary Structure Prediction
National Frontiers Science Center for Industrial Intelligence and Systems Optimization（国家工业智能与系统优化前沿科学中心）；Key Laboratory of Data Analytics and Optimization for Smart Industry (Northeastern University)（智能工业数据解析与优化关键实验室（东北大学））；Liaoning Engineering Laboratory of Data Analytics and Optimization for Smart Industry（辽宁智能工业数据解析与优化工程实验室）；Liaoning Key Laboratory of Manufacturing System and Logistics Optimization（辽宁制造系统与物流优化重点实验室）
Institute of Systems and Physical Biology, Shenzhen Bay Laboratory（深圳湾实验室系统与物理生物学研究所）；School of Electronic and Computer Engineering, Peking University（北京大学电子与计算机工程学院）；Ribopeutic Inc（Ribopeutic公司）
