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

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470

Wei Li1, Gen Li2, Yuzhi Sun3

  • 1Institute of Bioinformatics, Harbin Institute of Technology, Harbin, China.

Current Gene Therapy
|November 14, 2023
PubMed
Abstract

Insights

Researchers identified SARS-CoV-2 virus phosphorylation sites using deep learning, revealing impacts on host cell immune responses. This work aids in understanding viral evolution and developing new anti-SARS-CoV-2 drugs and vaccines.

Area of Science:

  • Virology
  • Immunology
  • Computational Biology

Background:

  • Emergence of SARS-CoV-2 mutant strains necessitates understanding viral infection mechanisms.
  • SARS-CoV-2 infection alters host cell phosphorylation, modulating immune responses.

Purpose of the Study:

  • To analyze SARS-CoV-2 infection phosphorylation sites.
  • To develop a deep learning method for identifying these sites.
  • To provide data for drug and vaccine development against SARS-CoV-2.

Main Methods:

  • Analysis of SARS-CoV-2 virus-infected phosphorylation regions.
  • Development of a transformer-based deep learning model for phosphorylation site identification.

Main Results:

  • Identification of specific phosphorylation sites in SARS-CoV-2 infected host cells.
  • Association analysis linking SARS-CoV-2 infection to altered immune responses and potential cancer progression.

Conclusions:

  • The study provides fundamental data on viral infection mechanisms and host cell modulation.
  • Findings offer insights into SARS-CoV-2 evolution and immune responses.
  • The developed method serves as a screening tool for anti-SARS-CoV-2 drug and vaccine design.
Keywords:
Deep learningSARS-CoV-2immune inflammatory diseases.lung cancerphosphorylationtransformer

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