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. 2024;24(3):239-248.
doi: 10.2174/0115665232268074231026111634.

Prediction of SARS-CoV-2 Infection Phosphorylation Sites and Associations of these Modifications with Lung Cancer Development

Wei Li  1 , Gen Li  2 , Yuzhi Sun  3 , Liyuan Zhang  3 , Xinran Cui  3 , Yuran Jia  3 , Tianyi Zhao  4
Affiliations

Prediction of SARS-CoV-2 Infection Phosphorylation Sites and Associations of these Modifications with Lung Cancer Development

Wei Li et al. Curr Gene Ther. 2024.

Abstract

Introduction: Since the emergence of SARS-CoV-2 viruses, multiple mutant strains have been identified. Infection with SARS-CoV-2 virus leads to alterations in host cell phosphorylation signal, which systematically modulates the immune response.

Methods: Identification and analysis of SARS-CoV-2 virus infection phosphorylation sites enable insight into the mechanisms of viral infection and effects on host cells, providing important fundamental data for the study and development of potent drugs for the treatment of immune inflammatory diseases. In this paper, we have analyzed the SARS-CoV-2 virus-infected phosphorylation region and developed a transformer-based deep learning-assisted identification method for the specific identification of phosphorylation sites in SARS-CoV-2 virus-infected host cells.

Results: Furthermore, through association analysis with lung cancer, we found that SARS-CoV-2 infection may affect the regulatory role of the immune system, leading to an abnormal increase or decrease in the immune inflammatory response, which may be associated with the development and progression of cancer.

Conclusion: We anticipate that this study will provide an important reference for SARS-CoV-2 virus evolution as well as immune-related studies and provide a reliable complementary screening tool for anti-SARS-CoV-2 virus drug and vaccine design.

Keywords: Deep learning; SARS-CoV-2; immune inflammatory diseases.; lung cancer; phosphorylation; transformer.

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