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• W3167293810 endingPage "e2021010" @default.
• W3167293810 startingPage "e2021010" @default.
• W3167293810 abstract "The coronavirus disease of 2019 (COVID-19) has become a global pandemic with rapid rate of transmission and fatalities worldwide. Scientists have been investigating a host of drugs that may be rechanneled to fight this malaise. Thus, in this current computational study we carried out molecular docking experiments to assess the bridging potentials of some commercial drugs such as chloroquine, hydroxychloroquine, lopinavir, ritonavir, nafamostat, camostat, famotidine, umifenovir, nitazoxanide, ivermectin, and fluvoxamine at the interface between human ACE2 and the coronavirus spike glycoprotein complex. This is aimed at ascertaining the ability of these drugs to bridge and prevent the complexing of these two proteins. The crystal structure of human ACE2 and the coronavirus spike glycoprotein complex was retrieved from protein database, while the selected drugs were retrieved from PubChem data base. The proteins and drugs were prepared for docking using Cresset Flare software. The docking was completed via AutoDock Vina module in Python Prescription software. The best hit drugs with each receptor were selected and their molecular interactions were analyzed using BIOVIA’s Discovery Studio 2020. The best hit compounds on the human ACE2 were the lopinavir (-10.1 kcal/mol), ritonavir (-8.9 kcal/mol), and nafamostat (-8.7 kcal/mol). Ivermectin, nafamostat, and camostat with binding energy values -9.0 kcal/mol, -7.8 kcal/mol, and -7.4 kcal/mol respectively were the hit drugs on the coronavirus spike glycoprotein. Nafamostat showed a dual bridging potential against ACE2 and spike glycoprotein, and could therefore be a promising lead compound in the prevention and control of this disease." @default.
• W3167293810 created "2021-06-22" @default.
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• W3167293810 date "2021-06-14" @default.
• W3167293810 modified "2023-10-16" @default.
• W3167293810 title "Blocking the interactions between human ACE2 and coronavirus spike glycoprotein by selected drugs: a computational perspective" @default.
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• W3167293810 doi "https://doi.org/10.5620/eaht.2021010" @default.
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