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• W4281752183 abstract "Several highly effective Covid-19 vaccines are in emergency use, although more-infectious coronavirus strains, could delay the end of the pandemic even further. Because of this, it is highly desirable to develop fast antiviral drug treatments to accelerate the lasting immunity against the virus. From a theoretical perspective, computational approaches are useful tools for antiviral drug development based on the data analysis of gene expression, chemical structure, molecular pathway, and protein interaction mapping. This work studies the structural stability of virus-host interactome networks based on the graphical representation of virus-host protein interactions as vertices or nodes connected by commonly shared proteins. These graphical network visualization methods are analogous to those use in the design of artificial neural networks in neuromorphic computing. In standard protein-node-based network representation, virus-host interaction merges with virus-protein and host-protein networks, introducing redundant links associated with the internal virus and host networks. On the contrary, our approach provides a direct geometrical representation of viral infection structure and allows the effective and fast detection of the structural robustness of the virus-host network through proteins removal. This method was validated by applying it to H1N1 and HIV viruses, in which we were able to pinpoint the changes in the Interactome Network produced by known vaccines. The application of this method to the SARS-CoV-2 virus-host protein interactome implies that nonstructural proteins nsp4, nsp12, nsp16, the nuclear pore membrane glycoprotein NUP210, and ubiquitin specific peptidase USP54 play a crucial role in the viral infection, and their removal may provide an efficient therapy. This method may be extended to any new mutations or other viruses for which the Interactome Network is experimentally determined. Since time is of the essence, because of the impact of more-infectious strains on controlling the spread of the virus, this method may be a useful tool for novel antiviral therapies." @default.
• W4281752183 created "2022-06-13" @default.
• W4281752183 creator A5071555016 @default.
• W4281752183 creator A5074204745 @default.
• W4281752183 creator A5090788376 @default.
• W4281752183 date "2022-06-02" @default.
• W4281752183 modified "2023-09-25" @default.
• W4281752183 title "The impact of the suppression of highly connected protein interactions on the corona virus infection" @default.
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• W4281752183 doi "https://doi.org/10.1038/s41598-022-13373-0" @default.
• W4281752183 hasPubMedId "https://pubmed.ncbi.nlm.nih.gov/35654986" @default.
• W4281752183 hasPublicationYear "2022" @default.
• W4281752183 type Work @default.
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• W4281752183 hasAuthorship W4281752183A5071555016 @default.
• W4281752183 hasAuthorship W4281752183A5074204745 @default.

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