Open Access
Open access
Supercomputing Frontiers and Innovations, volume 7, issue 3

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

Publication typeJournal Article
Publication date2020-09-01
Quartile SCImago
Q3
Quartile WOS
Impact factor
ISSN24096008, 23138734
Computer Science Applications
Hardware and Architecture
Computational Theory and Mathematics
Information Systems
Computer Networks and Communications
Software
Abstract
We illustrate modern modeling tools applied in the computational design of drugs acting as covalent inhibitors of enzymes. We take the Main protease (M pro ) from the SARS-CoV-2 virus as an important present-day representative. In this work, we construct a compound capable to block M pro , 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.

Citations by journals

1
SAR and QSAR in Environmental Research
SAR and QSAR in Environmental Research, 1, 100%
SAR and QSAR in Environmental Research
1 publication, 100%
1

Citations by publishers

1
Taylor & Francis
Taylor & Francis, 1, 100%
Taylor & Francis
1 publication, 100%
1
  • We do not take into account publications that without a DOI.
  • Statistics recalculated only for publications connected to researchers, organizations and labs registered on the platform.
  • Statistics recalculated weekly.
Metrics
Share
Found error?