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Lead Discovery of SARS-CoV-2 Main Protease Inhibitors through Covalent Docking-Based Virtual Screening.
Amendola, Giorgio; Ettari, Roberta; Previti, Santo; Di Chio, Carla; Messere, Anna; Di Maro, Salvatore; Hammerschmidt, Stefan J; Zimmer, Collin; Zimmermann, Robert A; Schirmeister, Tanja; Zappalà, Maria; Cosconati, Sandro.
  • Amendola G; DiSTABiF, University of Campania Luigi Vanvitelli, Via Vivaldi 43, Caserta 81100, Italy.
  • Ettari R; Department of Chemical, Biological, Pharmaceutical, and Environmental Sciences, University of Messina, Viale Annunziata, Messina 98168, Italy.
  • Previti S; Department of Chemical, Biological, Pharmaceutical, and Environmental Sciences, University of Messina, Viale Annunziata, Messina 98168, Italy.
  • Di Chio C; Department of Chemical, Biological, Pharmaceutical, and Environmental Sciences, University of Messina, Viale Annunziata, Messina 98168, Italy.
  • Messere A; DiSTABiF, University of Campania Luigi Vanvitelli, Via Vivaldi 43, Caserta 81100, Italy.
  • Di Maro S; DiSTABiF, University of Campania Luigi Vanvitelli, Via Vivaldi 43, Caserta 81100, Italy.
  • Hammerschmidt SJ; Institute of Pharmaceutical and Biomedical Sciences, University of Mainz, Staudingerweg 5, Mainz 55128, Germany.
  • Zimmer C; Institute of Pharmaceutical and Biomedical Sciences, University of Mainz, Staudingerweg 5, Mainz 55128, Germany.
  • Zimmermann RA; Institute of Pharmaceutical and Biomedical Sciences, University of Mainz, Staudingerweg 5, Mainz 55128, Germany.
  • Schirmeister T; Institute of Pharmaceutical and Biomedical Sciences, University of Mainz, Staudingerweg 5, Mainz 55128, Germany.
  • Zappalà M; Department of Chemical, Biological, Pharmaceutical, and Environmental Sciences, University of Messina, Viale Annunziata, Messina 98168, Italy.
  • Cosconati S; DiSTABiF, University of Campania Luigi Vanvitelli, Via Vivaldi 43, Caserta 81100, Italy.
J Chem Inf Model ; 61(4): 2062-2073, 2021 04 26.
Article in English | MEDLINE | ID: covidwho-1157886
Semantic information from SemMedBD (by NLM)
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ABSTRACT
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.
Subject(s)

Full text: Available Collection: International databases Database: MEDLINE Main subject: Protease Inhibitors / COVID-19 Type of study: Diagnostic study / Prognostic study / Screening study Topics: Vaccines Limits: Humans Language: English Journal: J Chem Inf Model Journal subject: Medical Informatics / Chemistry Year: 2021 Document Type: Article Affiliation country: Acs.jcim.1c00184

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Full text: Available Collection: International databases Database: MEDLINE Main subject: Protease Inhibitors / COVID-19 Type of study: Diagnostic study / Prognostic study / Screening study Topics: Vaccines Limits: Humans Language: English Journal: J Chem Inf Model Journal subject: Medical Informatics / Chemistry Year: 2021 Document Type: Article Affiliation country: Acs.jcim.1c00184