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  Computational screening of FDA approved drugs of fungal origin that may interfere with SARS-CoV-2 spike protein activation, viral RNA replication, and post-translational modification: a multiple target approach

Singh, R., Gautam, A., Chandel, S., Sharma, V., Ghosh, A., Dey, D., et al. (2021). Computational screening of FDA approved drugs of fungal origin that may interfere with SARS-CoV-2 spike protein activation, viral RNA replication, and post-translational modification: a multiple target approach. In Silico Pharmacology, 9(1): 27. doi:10.1007/s40203-021-00089-8.

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Singh, R, Autor
Gautam, A1, Autor           
Chandel, S, Autor
Sharma, V, Autor
Ghosh, A, Autor
Dey, D, Autor
Roy, S, Autor
Ravichandiran, V, Autor
Ghosh, D, Autor
Affiliations:
1IMPRS From Molecules to Organisms, Max Planck Institute for Developmental Biology, Max Planck Society, ou_3376131              

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 Zusammenfassung: Coronavirus spread is an emergency reported globally, and a specific treatment strategy for this significant health issue is not yet identified. COVID-19 is a highly contagious disease and needs to be controlled promptly as millions of deaths have been reported. Due to the absence of proficient restorative alternatives and preliminary clinical restrictions, FDA-approved medications can be a decent alternative to deal with the coronavirus malady (COVID-19). The present study aims to meet the imperative necessity of effective COVID-19 drug treatment with a computational multi-target drug repurposing approach. This study focused on screening the FDA-approved drugs derived from the fungal source and its derivatives against the SARS-CoV-2 targets. All the selected drugs showed good binding affinity towards these targets, and out of them, bromocriptine was found to be the best candidate after the screening on the COVID-19 targets. Further, bromocriptine is analyzed by molecular simulation and MM-PBSA study. These studies suggested that bromocriptine can be the best candidate for TMPRSS2, Main protease, and RdRp protein.

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 Datum: 2021-04
 Publikationsstatus: Online veröffentlicht
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 Identifikatoren: DOI: 10.1007/s40203-021-00089-8
PMID: 33842191
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Titel: In Silico Pharmacology
Genre der Quelle: Zeitschrift
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Ort, Verlag, Ausgabe: -
Seiten: 16 Band / Heft: 9 (1) Artikelnummer: 27 Start- / Endseite: - Identifikator: -