Virtual drug repurposing study against SARS-CoV-2 TMPRSS2 target
Abstract
Currently, the world suffers from a new coronavirus SARS-CoV-2 that causes COVID-19. Therefore, there is a need for the urgent development of novel drugs and vaccines for COVID-19. Since it can take years to develop new drugs against this disease, here we used a hybrid combined molecular modeling approach in virtual drug screening repurposing study to identify new compounds against this disease. One of the important SARS-CoV-2 targets namely type 2 transmembrane serine protease (TMPRSS2) was screened with NPC's NIH small molecule library which includes approved drugs by FDA and compounds in clinical investigation. We used 6654 small molecules in molecular docking and top-50 docking scored compounds were initially used in short (10-ns) molecular dynamics (MD) simulations. Based on average MM/GBSA binding free energy results, long (100-ns) MD simulations were employed for the identified hits. Both binding energy results as well as crucial residues in ligand binding were also compared with a positive control TMPRSS2 inhibitor, Camostat mesylate. Based on these numerical calculations we proposed a compound (benzquercin) as strong TMPRSS2 inhibitor. If these results can be validated by in vitro and in vivo studies, benzquercin can be considered to be used as inhibitor of TMPRSS2 at the clinical studies.
How strong is the evidence?
Evidence Hub reads this as a preprint, done in computer models (in silico), not in people. Its evidence level is 5 of 5 (Very low · case / narrative / animal / in vitro / in silico). Case reports, narrative reviews, and animal, cell or computer-model studies give the weakest evidence for human health.
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