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. 2024 May 15;10(1):FSO917.
doi: 10.2144/fsoa-2023-0112. eCollection 2024.

SARS-CoV-2-induced phosphorylation and its pharmacotherapy backed by artificial intelligence and machine learning

Fouzia Qamar  1 , Zubair Sharif  2 , Jawaria Idrees  3 , Asif Wasim  4 , Sana Haider  4 , Saad Salman  4
Affiliations

SARS-CoV-2-induced phosphorylation and its pharmacotherapy backed by artificial intelligence and machine learning

Fouzia Qamar et al. Future Sci OA. .

Abstract

Aims: To investigate the role of phosphorylation in SARS-CoV-2 infection, potential therapeutic targets and its harmful genetic sequences. Materials & Methods: Data mining techniques were employed to identify upregulated kinases responsible for proteomic changes induced by SARS-CoV-2. Spike and nucleocapsid proteins' sequences were analyzed using predictive tools, including SNAP2, MutPred2, PhD-SNP, SNPs&Go, MetaSNP, Predict-SNP and PolyPhen-2. Missense variants were identified using ensemble-based algorithms and homology/structure-based models like SIFT, PROVEAN, Predict-SNP and MutPred-2. Results: Eight missense variants were identified in viral sequences. Four damaging variants were found, with SNPs&Go and PolyPhen-2. Promising therapeutic candidates, including gilteritinib, pictilisib, sorafenib, RO5126766 and omipalisib, were identified. Conclusion: This research offers insights into SARS-CoV-2 pathogenicity, highlighting potential treatments and harmful variants in viral proteins.

Keywords: monoclonal antibodies; nucleocapsid protein; phosphorylation; proteomics; spike protein.

Plain language summary

This study explores the process called phosphorylation, which involves adding phosphate groups to certain proteins, influences the way the SARS-CoV-2 virus causes disease. The virus manipulates host enzymes to help it spread and survive. Researchers used data analysis techniques to identify the proteins that play a role in this process, aiming to find potential targets for treatments. They analyzed genetic sequences of key virus proteins and used various tools to predict harmful mutations. The study found several promising compounds that could be used to target the virus. Further research and experiments are needed to confirm their effectiveness as COVID-19 treatments.

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Conflict of interest statement

The authors have no competing interests or relevant affiliations with any organization or entity with the subject matter or materials discussed in the manuscript. This includes employment, consultancies, honoraria, stock ownership or options, expert testimony, grants or patents received or pending, or royalties.

Figures

Figure 1.
Figure 1.
Therapeutic targets identified through interaction networks, artificial intelligence and machine learning.
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