Computational Modeling of the SARS-CoV-2 Main Protease Inhibition by the Covalent Binding of Prospective Drug Molecules

Authors

  • Alexander V. Nemukhin Lomonosov Moscow State University
  • Bella L. Grigorenko Department of Chemistry, M.V. Lomonosov Moscow State University, Moscow, Russia
  • Igor V. Polyakov Department of Chemistry, M.V. Lomonosov Moscow State University, Moscow, Russia
  • Sofya V. Lushchekina N.M. Emanuel Institute of Biochemical Physics, Russian Academy of Sciences, Moscow, Russia

DOI:

https://doi.org/10.14529/jsfi200303

Abstract

We illustrate modern modeling tools applied in the computational design of drugs acting as covalent inhibitors of enzymes. We take the Main protease (Mpro) from the SARS-CoV-2 virus as an important present-day representative. In this work, we construct a compound capable to block Mpro, which is composed of fragments of antimalarial drugs and covalent inhibitors of cysteine proteases. To characterize the mechanism of its interaction with the enzyme, the algorithms based on force fields, including molecular mechanics (MM), molecular dynamics (MD) and molecular docking, as well as quantum-based approaches, including quantum chemistry and quantum mechanics/molecular mechanics (QM/MM) methods, should be applied. The use of supercomputers is indispensably important at least in the latter approach. Its application to enzymes assumes that energies and forces in the active sites are computed using methods of quantum chemistry, whereas the rest of protein matrix is described using conventional force fields. For the proposed compound, containing the benzoisothiazolone fragment and the substitute at the uracil ring, we show that it can form a stable covalently bound adduct with the target enzyme, and thus can be recommended for experimental trials.

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Published

2020-11-07

How to Cite

Nemukhin, A. V., Grigorenko, B. L., Polyakov, I. V., & Lushchekina, S. V. (2020). Computational Modeling of the SARS-CoV-2 Main Protease Inhibition by the Covalent Binding of Prospective Drug Molecules. Supercomputing Frontiers and Innovations, 7(3). https://doi.org/10.14529/jsfi200303

Issue

Vol. 7 No. 3 (2020)

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Authors retain copyright and grant the journal right of first publication with the work simultaneously licensed under a Creative Commons Attribution-Non Commercial 3.0 License that allows others to share the work with an acknowledgement of the work's authorship and initial publication in this journal.

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