Screening of potent drug inhibitors against SARS-CoV-2 RNA polymerase: an in silico approach

利用计算机模拟方法筛选针对SARS-CoV-2 RNA聚合酶的强效药物抑制剂

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Abstract

COVID-19 has emerged as a rapidly escalating serious global health issue, affecting every section of population in a detrimental way. Present situation invigorated researchers to look for potent targets, development as well as repurposing of conventional therapeutic drugs. NSP12, a RNA polymerase, is key player in viral RNA replication and, hence, viral multiplication. In our study, we have screened a battery of FDA-approved drugs against SARS-CoV-2 RNA polymerase using in silico molecular docking approach. Identification of potent inhibitors against SARS-CoV-2 NSP12 (RNA polymerase) were screeened from FDA approved drugs by virtual screening for therapeutic applications in treatment of COVID-19. In this study, virtual screening of 1749 antiviral drugs was executed using AutoDock Vina in PyRx software. Binding affinities between NSP12 and drug molecules were determined using Ligplot(+) and PyMOL was used for visualization of docking between interacting residues. Screening of 1749 compounds resulted in 14 compounds that rendered high binding affinity for NSP12 target molecule. Out of 14 compounds, 5 compounds which include 3a (Paritaprevir), 3d (Glecaprevir), 3h (Velpatasvir), 3j (Remdesivir) and 3l (Ribavirin) had a binding affinity of - 10.2 kcal/mol, -9.6 kcal/mol, - 8.5 kcal/mol, - 8.0 kcal/mol and - 6.8 kcal/mol, respectively. Moreover, a number of hydrophobic interactions and hydrogen bonding between these 5 compounds and NSP12 active site were observed. Further, 3l (Ribavirin) was docked with 6M71 and molecular dynamic simulation of the complex was also performed to check the stability of the conformation. In silico analysis postulated the potential of conventional antiviral drugs in treatment of COVID-19. However, these finding may be further supported by experimental data for its possible clinical application in present scenario.

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