IPs-GRUAtt: An attention-based bidirectional gated recurrent unit network for predicting phosphorylation sites of SARS-CoV-2 infection

• Main Authors: Guiyang Zhang (Author), Qiang Tang (Author), Pengmian Feng (Author), Wei Chen (Author)
• Format: Book
• Published: Elsevier, 2023-06-01T00:00:00Z.
• Subjects: article
• Online Access: Connect to this object online.
• Notes: 2162-2531; 10.1016/j.omtn.2023.02.027
• Keywords: MT: Bioinformatics; SARS-CoV-2; phosphorylation; deep learning; bidirectional gated recurrent unit; attention mechanism; Therapeutics. Pharmacology; RM1-950
• Source: Molecular Therapy: Nucleic Acids, Vol 32, Iss , Pp 28-35 (2023)

The global pandemic of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection has generated tremendous concern and poses a serious threat to international public health. Phosphorylation is a common post-translational modification affecting many essential cellular processes and is inextricably linked to SARS-CoV-2 infection. Hence, accurate identification of phosphorylation sites will be helpful to understand the mechanisms of SARS-CoV-2 infection and mitigate the ongoing COVID-19 pandemic. In the present study, an attention-based bidirectional gated recurrent unit network, called IPs-GRUAtt, was proposed to identify phosphorylation sites in SARS-CoV-2-infected host cells. Comparative results demonstrated that IPs-GRUAtt surpassed both state-of-the-art machine-learning methods and existing models for identifying phosphorylation sites. Moreover, the attention mechanism made IPs-GRUAtt able to extract the key features from protein sequences. These results demonstrated that the IPs-GRUAtt is a powerful tool for identifying phosphorylation sites. For facilitating its academic use, a freely available online web server for IPs-GRUAtt is provided at http://cbcb.cdutcm.edu.cn/phosphory/.