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. 2023 Feb:153:106449.
doi: 10.1016/j.compbiomed.2022.106449. Epub 2022 Dec 23.

SARS-CoV-2 proteases Mpro and PLpro: Design of inhibitors with predicted high potency and low mammalian toxicity using artificial neural networks, ligand-protein docking, molecular dynamics simulations, and ADMET calculations

Roman S Tumskiy  1 , Anastasiia V Tumskaia  2 , Iraida N Klochkova  2 , Rudy J Richardson  3
Affiliations

SARS-CoV-2 proteases Mpro and PLpro: Design of inhibitors with predicted high potency and low mammalian toxicity using artificial neural networks, ligand-protein docking, molecular dynamics simulations, and ADMET calculations

Roman S Tumskiy et al. Comput Biol Med. 2023 Feb.

Abstract

The main (Mpro) and papain-like (PLpro) proteases are highly conserved viral proteins essential for replication of the COVID-19 virus, SARS-COV-2. Therefore, a logical plan for producing new drugs against this pathogen is to discover inhibitors of these enzymes. Accordingly, the goal of the present work was to devise a computational approach to design, characterize, and select compounds predicted to be potent dual inhibitors - effective against both Mpro and PLpro. The first step employed LigDream, an artificial neural network, to create a virtual ligand library. Ligands with computed ADMET profiles indicating drug-like properties and low mammalian toxicity were selected for further study. Initial docking of these ligands into the active sites of Mpro and PLpro was done with GOLD, and the highest-scoring ligands were redocked with AutoDock Vina to determine binding free energies (ΔG). Compounds 89-00, 89-07, 89-32, and 89-38 exhibited favorable ΔG values for Mpro (-7.6 to -8.7 kcal/mol) and PLpro (-9.1 to -9.7 kcal/mol). Global docking of selected compounds with the Mpro dimer identified prospective allosteric inhibitors 89-00, 89-27, and 89-40 (ΔG -8.2 to -8.9 kcal/mol). Molecular dynamics simulations performed on Mpro and PLpro active site complexes with the four top-scoring ligands from Vina demonstrated that the most stable complexes were formed with compounds 89-32 and 89-38. Overall, the present computational strategy generated new compounds with predicted drug-like characteristics, low mammalian toxicity, and high inhibitory potencies against both target proteases to form stable complexes. Further preclinical studies will be required to validate the in silico findings before the lead compounds could be considered for clinical trials.

Keywords: ADMET; COVID-19; Heterocyclic compounds; In silico drug design; Molecular docking; Molecular dynamics simulation; Mpro/PLpro inhibitors; Nirmatrelvir; Pyrazolopyridazines; Tetrazoles.

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Conflict of interest statement

Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have influenced, or appear to have influenced, the work reported in this paper.

Figures

Image 1
Graphical abstract
Fig. 1
Fig. 1
Workflow for creation and screening of the virtual ligand library.
Fig. 2
Fig. 2
(A) Tanimoto similarity coefficients (Tc) for the 16 89-series compounds with the highest PLP docking scores from GOLD and 3 reference compounds (94–00, GRL0617, and ML188). Bar colors: gray = Tc_color (pharmacophore similarity); red = Tc_shape (shape similarity from overlap of molecular volumes); blue = Tc_combi (Tc_color + Tc_shape). Tc values are relative to compound 89–00. Tc_color and Tc_shape range from 0 (no similarity) to 1 (complete similarity). Tc_combi ranges from 0 (no similarity to 2 (complete similarity). (B) 2D pharmacophoric structure of 89–00 showing its 3 types of pharmacophores. (C) Color codes for the 6 pharmacophores assessed (red = acceptor, blue = donor, green = hydrophobe, yellow = rings, orange = anion, magenta = cation). Tc values were calculated using ROCS 3.5.0.2: OpenEye Scientific Software, Santa Fe, NM, http://www.eyesopen.com [77].
Fig. 3
Fig. 3
Docking poses of compounds 89–07 (cyan) and 89–38 (pink) within the active site of Mpro (PDB ID 7L0D). (A) Ribbon view depicted on the left; magnified hydrophobicity surface on the right (colors: blue = greatest polarity; red = greatest hydrophobicity; white = intermediate). (B) 2D interactions of 89–07. (C) 2D interactions of 89–38.
Fig. 4
Fig. 4
The docking poses of compounds 89–00 (pink, right magnified view), 89–40 (dark blue, right magnified view) and 89–27 (forest green, left magnified view) within the Mpro dimer (chain A, dark red; chain B, khaki; drug pockets: P0 (yellow), P1 (purple), P2 (green), P3 (red).
Fig. 5
Fig. 5
2D interactions of 89–00 (A), 89–40 (B), and 89–27 (C) within potential allosteric sites of the Mpro dimer.
Fig. 6
Fig. 6
Docking poses of compounds 89–07 (cyan) and 89–38 (pink) within the active site of PLpro (PDB ID 7LBR) and hydrophobicity surface of PLpro substrate binding site (magnified view) with 89–07, 89–38 and GRL0617 (in orange) (A) Ribbon view depicted on the left; magnified hydrophobicity surface on the right (colors: blue = greatest polarity; red = greatest hydrophobicity; white = intermediate). (B) 2D interactions of 89–07. (C) 2D interactions of 89–38.
Fig. 7
Fig. 7
Trajectories (100 ns) from MD simulations of Mpro dimer complexes with ligands 89–00, 89–07, 89–32, 89–38, and reference compound ML188 bound to the active site of the dimer A-chain. (A) Rg of dimer complexes. (B) RMSD-Cα of A-chain complexes. (C) RMSD-Ligand: movement of the ligand relative to the A-chain. Trajectory colors: magenta, 89–00; green, 89–07; blue, 89–32; dark blue, 89–38; and orange, ML188.
Fig. 8
Fig. 8
Statistical comparisons of mean-value parameters from 100 ns MD simulations of ligand complexes with Mpro dimers. (A) Rg of dimers. (B) RMSD-Cα of A-chain complexes. (C) RMSD-Ligand: movement of ligand relative to A-chain. Values are means ± SEM (n = 5 × 20 ns blocks) computed by block averaging to correct for time-series autocorrelation. There were no statistically significant differences among all pairwise comparisons of means for Rg or RMSD-Cα (one-way ANOVA, Tukey multiple comparisons test, p > 0.05). For RMSD-Ligand values, there were statistically significant differences between 5 pairs of means: *p < 0.05; ***p ≤ 0.0005 (one-way ANOVA with Brown-Forsythe and Welch corrections for unequal variances and Dunnett's T3 multiple comparisons test). Bar colors: magenta, 89–00; green, 89–07; blue, 89–32; dark blue, 89–38; and orange, ML188.
Fig. 9
Fig. 9
MD trajectories of PLpro monomer active site complexes with selected ligands 89–00, 89–07, 89–32, 89–38, and reference compound GRL0617 during 100 ns. (A) Rg of complexes. (B) RMSD-Cα of complexes: movement of the protein backbone. (C) RMSD-Ligand: movement of the ligand relative to the protein. Trajectory colors: magenta, 89–00; green, 89–07; blue, 89–32; dark blue, 89–38; and red, GRL0617.
Fig. 10
Fig. 10
Statistical comparisons of mean-value parameters from 100 ns MD simulations of ligand complexes with monomeric PLpro. (A) Rg. (B) RMSD-Cα of complexes. (C) RMSD-Ligand: movement of ligand relative to the protein. Values are means ± SEM (n = 5 × 20 ns blocks for (A) and (C); n = 10 × 10 ns blocks for (B) computed by block averaging to correct for time-series autocorrelation. Because of the similar mean values and the high variance for 89–00, there were no statistically significant differences among all pairwise comparisons of means for Rg (p > 0.05). For RMSD-Ca and RMSD-Ligand values, there were statistically significant differences between 4 pairs of means: *p < 0.05; **p < 0.01; ***p = 0.0001 (one-way ANOVA with Brown-Forsythe and Welch corrections for unequal variances and Dunnett's T3 multiple comparisons test). Bar colors: magenta, 89–00; green, 89–07; blue, 89–32; dark blue, 89–38; and red, GRL0617.
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