In silico identification of approved drugs for potential prophylactic uses against SARS-CoV-2 infection
Abstract
The outbreak of Severe Acute Respiratory Syndrome Corona Virus (SARS-CoV-2) responsible for Coronavirus disease 2019 (COVID-19) constitute a major public health threat and economic burden. Despite the multifold research all over the world. COVID-19 has remained without a treatment. Hence, the urgent need to repurpose the FDA-approved drugs through in silico computational technique to identify inhibitors of SARS-CoV-2. Bioinformatics techniques were used to screen 2015 FDA- approved drugs against SARS-CoV-2 proteins and associated human protein, angiotensin-converting enzyme 2 (ACE2) to identify drug for new indication. The ligands were downloaded from Selleckchem drug data bank in SDF format. Open Babel was used to separate the ligands into their compounds and prepared for docking simulation using shell script and saved as PDBQT. Experimental crystal structures of the receptors (ACE2 and Spike proteins) were retrieved from Protein Data Bank (PDB), Autodock tools was used to prepare the proteins and saved as PDBQT files. Docking protocols were validated, molecular docking simulations were performed on a Linux platform using AutoDock Vina in quadruplicate. The binding energies (BEs) kcal/mol were calculated and expressed as mean ± standard deviation. Discovery studio was used for visualization. Density Functional Theory (DFT) was used to determine the predictive maximal inhibitory concentration (pIC50) of best hits. Ninety-one (91) new hits were identified for prophylaxis.…
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