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RESEARCH PRODUCT
Lead Discovery of SARS-CoV-2 Main Protease Inhibitors through Covalent Docking-Based Virtual Screening
Tanja SchirmeisterRobert A. ZimmermannCarla Di ChioCollin ZimmerGiorgio AmendolaSanto PrevitiAnna MessereRoberta EttariStefan HammerschmidtSandro CosconatiSalvatore Di MaroMaria Zappalàsubject
Coronavirus disease 2019 (COVID-19)General Chemical Engineeringmedicine.medical_treatmentSevere acute respiratory syndrome coronavirus 2 (SARS-CoV-2)In silicoComputational biologyLibrary and Information Sciences01 natural sciencesMolecular Docking SimulationAntiviral AgentsArticleDocking (dog)0103 physical sciencesmedicineHumansProtease InhibitorsPandemicsVirtual screeningProtease010304 chemical physicsbusiness.industrySARS-CoV-2COVID-19General Chemistry0104 chemical sciencesComputer Science ApplicationsMolecular Docking Simulation010404 medicinal & biomolecular chemistryTarget proteinbusinessdescription
During almost all 2020, coronavirus disease 2019 (COVID-19) pandemic has constituted the major risk for the worldwide health and economy, propelling unprecedented efforts to discover drugs for its prevention and cure. At the end of the year, these efforts have culminated with the approval of vaccines by the American Food and Drug Administration (FDA) and the European Medicines Agency (EMA) giving new hope for the future. On the other hand, clinical data underscore the urgent need for effective drugs to treat COVID-19 patients. In this work, we embarked on a virtual screening campaign against the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) Mpro chymotrypsin-like cysteine protease employing our in-house database of peptide and non-peptide ligands characterized by different types of warheads acting as Michael acceptors. To this end, we employed the AutoDock4 docking software customized to predict the formation of a covalent adduct with the target protein. In vitro verification of the inhibition properties of the most promising candidates allowed us to identify two new lead inhibitors that will deserve further optimization. From the computational point of view, this work demonstrates the predictive power of AutoDock4 and suggests its application for the in silico screening of large chemical libraries of potential covalent binders against the SARS-CoV-2 Mpro enzyme.
year | journal | country | edition | language |
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2021-03-01 | Journal of Chemical Information and Modeling |