In-silico Study of the Developed Hydroxychloroquine-based ACE2 Inhibitor Molecules Against COVID-19: Molecular Modeling and Docking

Authors

  • K. Zaher Laboratory of Preparation Modification and Application of Multiphase Polymeric Materials (LMPMP), University Ferhat Abbas Setif-1, Algeria
  • N. E. Masango Laboratory of Preparation Modification and Application of Multiphase Polymeric Materials (LMPMP), University Ferhat Abbas Setif-1, Algeria
  • W. Sobhi Laboratory of Applied Biochemistry, Faculty of Nature and Life Sciences, University Ferhat Abbas Setif-1 | Research Center of Biotechnology, Algeria
  • K. E. Kanouni Laboratory of Preparation Modification and Application of Multiphase Polymeric Materials (LMPMP), University Ferhat Abbas Setif-1, Algeria
  • A. Semmeq National Center for Scientific Research | Laboratory of Theoretical Physics and Chemistry, University of Lorraine, France
  • Y. Benguerba Laboratory of Preparation Modification and Application of Multiphase Polymeric Materials (LMPMP), University Ferhat Abbas Setif-1, Algeria
Volume: 11 | Issue: 4 | Pages: 7336-7342 | August 2021 | https://doi.org/10.48084/etasr.4244

Abstract

In the present study, we will verify the action of hydroxychloroquine-based derivatives on ACE2 which is considered to be the main portal of entry of the SARS-CoV-2 virus and constitutes an exciting target given its relative genetic stability compared to viral proteins. Thus, 81 molecules derived from hydroxychloroquine by substitutions at 4 different positions were generated in-silico and then studied for their affinity for ACE2 by molecular docking. Only 4 molecules were retained because of their affinity and bioavailability demonstrated by molecular dynamics and molecular docking calculations using COSMOtherm and Materials Studio software.

Keywords:

Hydroxychloroquine, molecular modeling, Covid-19, ACE2, affinity

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[1]
K. Zaher, N. E. Masango, W. Sobhi, K. E. Kanouni, A. Semmeq, and Y. Benguerba, “In-silico Study of the Developed Hydroxychloroquine-based ACE2 Inhibitor Molecules Against COVID-19: Molecular Modeling and Docking”, Eng. Technol. Appl. Sci. Res., vol. 11, no. 4, pp. 7336–7342, Aug. 2021.

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