In-silico analysis on potential anti-SARS-CoV-۲ protease agents by structure-based docking and cheminformatics

Trying to treat COVID-۱۹ patients has caused serious problems for the scientists. There are many routinely used drugs in clinical settings without definite effects, and more studies should be done so as to reach a successful treatment for COVID-۱۹. Our aim is to evaluate four suggested chemicals using virtual analysis tools based on the drug-screening approach and application of cheminformatics, pharmacotoxicology and docking. Four repurposed drugs: rizatriptan, dasabuvir, pravastatin, and empagliflozin are used in the study. The ۳D structure of COVID-۱۹ Main Protease (M Pro) was obtained from protein data bank (PDB) with PDB code: ۶LU۷, as the target of binding site screening. Besides, cheminformatics, pharmacotoxicology and human proteins targets for each drug was evaluated using SwissADME interface, SwissTarget Prediction web server, toxicity estimation software tool (T.E.S.T) and Toxtree-v۳.۱.۰.۱۸۵۱ offline software. The docking scores (DOS) were -۱۳۹.۳۹۹, -۱۲۵.۷۰۷, -۱۰۲.۱۸۳ and -۹۹.۶۶۴۲ for dasabuvir, rizatriptan, empagliflozin and pravastatin, respectively. In addition, the quantitative structure-activity relationship (QSAR) and pharmacotoxicologic evaluations show dasabuvir has more acceptable results than the others. Human protein target-exploration show that rizatriptan interacts with G protein-coupled receptor and kinase enzymes, pravastatin targets the ۳-hydroxy-۳-methylglutaryl coenzyme A (HMG-CoA) reductase, while empagliflozin interacts with sodium/glucose cotransporters (SLC). But, dasabuvir targets human protein with too low scores. Virtual screening applied to four potential anti-COVID-۱۹ drugs shows that dasabuvir may be a safer and efficient agent, regarding pharmacotoxicology and therapeutic purposes. However, virtually screened agent/s should be evaluated by experimental models for ultimate confirmation.

کلیدواژه ها:

۳C-like proteinase ، Coronavirus ، Cheminformatics ، Molecular Docking Simulation ، Quantitative Structure-Activity Relationship