In Silico Discovery of Candidate Drugs against Covid-19
1
Institute of Molecular Bioimaging and Physiology, National Research Council (IBFM-CNR), Via F.Cervi 93, 20090 Segrate-Milan, Milan, Italy
2
Department of Physics “Giuseppe Occhialini”, University of Milan-Bicocca Piazza dell’Ateneo Nuovo, 1 - 20126, Milan, Italy
*
Authors to whom correspondence should be addressed.
Viruses 2020, 12(4), 404; https://doi.org/10.3390/v12040404
Received: 17 March 2020
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Revised: 1 April 2020
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Accepted: 4 April 2020
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Published: 6 April 2020
(This article belongs to the Special Issue Computational Biology of Viruses: From Molecules to Epidemics)
Previous studies reported that Angiotensin converting enzyme 2 (ACE2) is the main cell receptor of SARS-CoV and SARS-CoV-2. It plays a key role in the access of the virus into the cell to produce the final infection. In the present study we investigated in silico the basic mechanism of ACE2 in the lung and provided evidences for new potentially effective drugs for Covid-19. Specifically, we used the gene expression profiles from public datasets including The Cancer Genome Atlas, Gene Expression Omnibus and Genotype-Tissue Expression, Gene Ontology and pathway enrichment analysis to investigate the main functions of ACE2-correlated genes. We constructed a protein-protein interaction network containing the genes co-expressed with ACE2. Finally, we focused on the genes in the network that are already associated with known drugs and evaluated their role for a potential treatment of Covid-19. Our results demonstrate that the genes correlated with ACE2 are mainly enriched in the sterol biosynthetic process, Aryldialkylphosphatase activity, adenosylhomocysteinase activity, trialkylsulfonium hydrolase activity, acetate-CoA and CoA ligase activity. We identified a network of 193 genes, 222 interactions and 36 potential drugs that could have a crucial role. Among possible interesting drugs for Covid-19 treatment, we found Nimesulide, Fluticasone Propionate, Thiabendazole, Photofrin, Didanosine and Flutamide.
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Keywords:
bioinformatics; covid-19; drugs; gene network
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MDPI and ACS Style
Cava, C.; Bertoli, G.; Castiglioni, I. In Silico Discovery of Candidate Drugs against Covid-19. Viruses 2020, 12, 404. https://doi.org/10.3390/v12040404
AMA Style
Cava C, Bertoli G, Castiglioni I. In Silico Discovery of Candidate Drugs against Covid-19. Viruses. 2020; 12(4):404. https://doi.org/10.3390/v12040404
Chicago/Turabian StyleCava, Claudia, Gloria Bertoli, and Isabella Castiglioni. 2020. "In Silico Discovery of Candidate Drugs against Covid-19" Viruses 12, no. 4: 404. https://doi.org/10.3390/v12040404
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