Lead discovery of SARS-CoV-2 main protease inhibitors through covalent docking-based virtual screening
- Publication type:
- Journal article
- Metadata:
-
- Autoren
- Giorgio Amendola
- Roberta Ettari
- Previti Santo
- Carla Di Chio
- Anna Messere
- Salvatore Di Maro
- Stefan J Hammerschmidt
- Collin Zimmer
- Robert A Zimmermann
- Tanja Schirmeister
- Maria Zappala
- Sandro Cosconati
- Autoren-URL
- https://www.webofscience.com/api/gateway?GWVersion=2&SrcApp=fis-test-1&SrcAuth=WosAPI&KeyUT=WOS:000644728600048&DestLinkType=FullRecord&DestApp=WOS_CPL
- DOI
- 10.1021/acs.jcim.1c00184
- eISSN
- 1549-960X
- Externe Identifier
- Clarivate Analytics Document Solution ID: RT8TP
- PubMed Identifier: 33784094
- ISSN
- 1549-9596
- Ausgabe der Veröffentlichung
- 4
- Zeitschrift
- JOURNAL OF CHEMICAL INFORMATION AND MODELING
- Paginierung
- 2062 - 2073
- Datum der Veröffentlichung
- 2021
- Status
- Published
- Titel
- Lead Discovery of SARS-CoV-2 Main Protease Inhibitors through Covalent Docking-Based Virtual Screening
- Sub types
- Article
- Ausgabe der Zeitschrift
- 61
Data source: Web of Science (Lite)
- Other metadata sources:
-
- Autoren
- Giorgio Amendola
- Roberta Ettari
- Santo Previti
- Carla Di Chio
- Anna Messere
- Salvatore Di Maro
- Stefan J Hammerschmidt
- Collin Zimmer
- Robert A Zimmermann
- Tanja Schirmeister
- Maria Zappalà
- Sandro Cosconati
- DOI
- 10.1021/acs.jcim.1c00184
- eISSN
- 1549-960X
- ISSN
- 1549-9596
- Ausgabe der Veröffentlichung
- 4
- Zeitschrift
- Journal of Chemical Information and Modeling
- Sprache
- en
- Online publication date
- 2021
- Paginierung
- 2062 - 2073
- Datum der Veröffentlichung
- 2021
- Status
- Published
- Herausgeber
- American Chemical Society (ACS)
- Herausgeber URL
- http://dx.doi.org/10.1021/acs.jcim.1c00184
- Datum der Datenerfassung
- 2023
- Titel
- Lead Discovery of SARS-CoV-2 Main Protease Inhibitors through Covalent Docking-Based Virtual Screening
- Ausgabe der Zeitschrift
- 61
Data source: Crossref
- 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) M<sup>pro</sup> 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. <i>In vitro</i> 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 <i>in silico</i> screening of large chemical libraries of potential covalent binders against the SARS-CoV-2 M<sup>pro</sup> enzyme.
- Addresses
- DiSTABiF, University of Campania Luigi Vanvitelli, Via Vivaldi 43, Caserta 81100, Italy.
- Autoren
- Giorgio Amendola
- Roberta Ettari
- Santo Previti
- Carla Di Chio
- Anna Messere
- Salvatore Di Maro
- Stefan J Hammerschmidt
- Collin Zimmer
- Robert A Zimmermann
- Tanja Schirmeister
- Maria Zappalà
- Sandro Cosconati
- DOI
- 10.1021/acs.jcim.1c00184
- eISSN
- 1549-960X
- Externe Identifier
- PubMed Identifier: 33784094
- Funding acknowledgements
- Ministero dell?Istruzione, dell?Universit? e della Ricerca: FFABR_RU_2017
- Open access
- false
- ISSN
- 1549-9596
- Ausgabe der Veröffentlichung
- 4
- Zeitschrift
- Journal of chemical information and modeling
- Schlüsselwörter
- Humans
- Protease Inhibitors
- Antiviral Agents
- Pandemics
- Molecular Docking Simulation
- COVID-19
- SARS-CoV-2
- Sprache
- eng
- Medium
- Print-Electronic
- Online publication date
- 2021
- Paginierung
- 2062 - 2073
- Datum der Veröffentlichung
- 2021
- Status
- Published
- Datum der Datenerfassung
- 2021
- Titel
- Lead Discovery of SARS-CoV-2 Main Protease Inhibitors through Covalent Docking-Based Virtual Screening.
- Sub types
- Research Support, Non-U.S. Gov't
- Journal Article
- Ausgabe der Zeitschrift
- 61
Data source: Europe PubMed Central
- 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.
- Autoren
- Giorgio Amendola
- Roberta Ettari
- Santo Previti
- Carla Di Chio
- Anna Messere
- Salvatore Di Maro
- Stefan J Hammerschmidt
- Collin Zimmer
- Robert A Zimmermann
- Tanja Schirmeister
- Maria Zappalà
- Sandro Cosconati
- Autoren-URL
- https://www.ncbi.nlm.nih.gov/pubmed/33784094
- DOI
- 10.1021/acs.jcim.1c00184
- eISSN
- 1549-960X
- Ausgabe der Veröffentlichung
- 4
- Zeitschrift
- J Chem Inf Model
- Schlüsselwörter
- Antiviral Agents
- COVID-19
- Humans
- Molecular Docking Simulation
- Pandemics
- Protease Inhibitors
- SARS-CoV-2
- Sprache
- eng
- Country
- United States
- Paginierung
- 2062 - 2073
- Datum der Veröffentlichung
- 2021
- Status
- Published
- Datum, an dem der Datensatz öffentlich gemacht wurde
- 2021
- Titel
- Lead Discovery of SARS-CoV-2 Main Protease Inhibitors through Covalent Docking-Based Virtual Screening.
- Sub types
- Journal Article
- Research Support, Non-U.S. Gov't
- Ausgabe der Zeitschrift
- 61
Data source: PubMed
- Autoren
- Giorgio Amendola
- Roberta Ettari
- Santo Previti
- Carla Di Chio
- Anna Messere
- Salvatore Di Maro
- Stefan J Hammerschmidt
- Collin Zimmer
- Robert A Zimmermann
- Tanja Schirmeister
- others
- Zeitschrift
- Journal of chemical information and modeling
- Artikelnummer
- 4
- Paginierung
- 2062 - 2073
- Datum der Veröffentlichung
- 2021
- Herausgeber
- American Chemical Society
- Datum der Datenerfassung
- 2021
- Titel
- Lead discovery of SARS-CoV-2 main protease inhibitors through covalent docking-based virtual screening
- Sub types
- article
- Ausgabe der Zeitschrift
- 61
Data source: Manual
- Beziehungen:
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