Skip to main navigation Skip to search Skip to main content

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

  • Wei Li
  • , Gen Li
  • , Yuzhi Sun
  • , Liyuan Zhang
  • , Xinran Cui
  • , Yuran Jia*
  • , Tianyi Zhao*
  • *Corresponding author for this work
  • Harbin Institute of Technology
  • Harbin Medical University
  • School of Computer Science and Technology, Harbin Institute of Technology
  • School of Medicine and Health, Harbin Institute of Technology

Research output: Contribution to journalArticlepeer-review

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.

Original languageEnglish
Pages (from-to)239-248
Number of pages10
JournalCurrent Gene Therapy
Volume24
Issue number3
DOIs
StatePublished - 2024
Externally publishedYes

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

  1. SDG 3 - Good Health and Well-being
    SDG 3 Good Health and Well-being

Keywords

  • Deep learning
  • SARS-CoV-2
  • immune inflammatory diseases
  • lung cancer
  • phosphorylation
  • transformer

Fingerprint

Dive into the research topics of 'Prediction of SARS-CoV-2 Infection Phosphorylation Sites and Associations of these Modifications with Lung Cancer Development'. Together they form a unique fingerprint.

Cite this