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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\u2013host interactome networks based on the graphical representation of virus\u2013host 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\u2013host interaction merges with virus\u2013protein and host\u2013protein 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\u2013host 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\u2013host 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.<\/jats:p>","DOI":"10.1038\/s41598-022-13373-0","type":"journal-article","created":{"date-parts":[[2022,6,2]],"date-time":"2022-06-02T09:03:14Z","timestamp":1654160594000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["The impact of the suppression of highly connected protein interactions on the corona virus infection"],"prefix":"10.1038","volume":"12","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-0444-3250","authenticated-orcid":false,"given":"Felipe","family":"Torres","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8580-1912","authenticated-orcid":false,"given":"Miguel","family":"Kiwi","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9078-7120","authenticated-orcid":false,"given":"Ivan K.","family":"Schuller","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2022,6,2]]},"reference":[{"issue":"1","key":"13373_CR1","doi-asserted-by":"publisher","first-page":"126","DOI":"10.3390\/ijms18010126","volume":"18","author":"ME Gonz\u00e1lez","year":"2017","unstructured":"Gonz\u00e1lez, M. 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