ABSTRACT
Molecular modeling tools were applied to design a potential covalent inhibitor of the main protease (Mpro) of the SARS-CoV-2 virus and to investigate its interaction with the enzyme. The compound includes a benzoisothiazolone (BZT) moiety of antimalarial drugs and a 5-fluoro-6-nitropyrimidine-2,4(1.H,3H)-dione (FNP) moiety mimicking motifs of inhibitors of other cysteine proteases. The BZT moiety provides a fair binding of the ligand on the protein surface, whereas the warhead FNP is responsible for efficient nucleophilic aromatic substitution reaction with the catalytic cysteine residue in the Mpro active site, leading to a stable covalent adduct. According to supercomputer calculations of the reaction energy profile using the quantum mechanics/molecular mechanics method, the energy of the covalent adduct is 21 kcal mol-1 below the energy of the reactants, while the highest barrier along the reaction pathway is 9 kcal mol-1. These estimates indicate that the reaction can proceed efficiently and can block the Mpro enzyme. The computed structures along the reaction path illustrate the nucleophilic aromatic substitution (SNAr) mechanism in enzymes. The results of this study are important for the choice of potential drugs blocking the development of coronavirus infection.
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 me- chanics/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. © The Authors 2020. This paper is published with open access at SuperFri.org
ABSTRACT
Molecular dynamics simulations with the QM(DFT)/MM potentials are utilized to discriminate between reactive and nonreactive complexes of the SARS-CoV-2 main protease and its substrates. Classification of frames along the molecular dynamic trajectories is utilized by analysis of the 2D maps of the Laplacian of electron density. Those are calculated in the plane formed by the carbonyl group of the substrate and a nucleophilic sulfur atom of the cysteine residue that initiates enzymatic reaction. Utilization of the GPU-based DFT code allows fast and accurate simulations with the hybrid functional PBE0 and double-zeta basis set. Exclusion of the polarization functions accelerates the calculations 2-fold, however this does not describe the substrate activation. Larger basis set with d-functions on heavy atoms and p-functions on hydrogen atoms enables to disclose equilibrium between the reactive and nonreactive species along the MD trajectory. The suggested approach can be utilized to choose covalent inhibitors that will readily interact with the catalytic residue of the selected enzyme. © The Authors 2020. This paper is published with open access at SuperFri.org