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. 2021 Apr 13:9:e11261.
doi: 10.7717/peerj.11261. eCollection 2021.

Identification of a novel inhibitor of SARS-CoV-2 3CL-PRO through virtual screening and molecular dynamics simulation

Affiliations

Identification of a novel inhibitor of SARS-CoV-2 3CL-PRO through virtual screening and molecular dynamics simulation

Asim Kumar Bepari et al. PeerJ. .

Abstract

Background: The COVID-19 pandemic, caused by the SARS-CoV-2 virus, has ravaged lives across the globe since December 2019, and new cases are still on the rise. Peoples' ongoing sufferings trigger scientists to develop safe and effective remedies to treat this deadly viral disease. While repurposing the existing FDA-approved drugs remains in the front line, exploring drug candidates from synthetic and natural compounds is also a viable alternative. This study employed a comprehensive computational approach to screen inhibitors for SARS-CoV-2 3CL-PRO (also known as the main protease), a prime molecular target to treat coronavirus diseases.

Methods: We performed 100 ns GROMACS molecular dynamics simulations of three high-resolution X-ray crystallographic structures of 3CL-PRO. We extracted frames at 10 ns intervals to mimic conformational diversities of the target protein in biological environments. We then used AutoDock Vina molecular docking to virtual screen the Sigma-Aldrich MyriaScreen Diversity Library II, a rich collection of 10,000 druglike small molecules with diverse chemotypes. Subsequently, we adopted in silico computation of physicochemical properties, pharmacokinetic parameters, and toxicity profiles. Finally, we analyzed hydrogen bonding and other protein-ligand interactions for the short-listed compounds.

Results: Over the 100 ns molecular dynamics simulations of 3CL-PRO's crystal structures, 6LZE, 6M0K, and 6YB7, showed overall integrity with mean Cα root-mean-square deviation (RMSD) of 1.96 (±0.35) Å, 1.98 (±0.21) Å, and 1.94 (±0.25) Å, respectively. Average root-mean-square fluctuation (RMSF) values were 1.21 ± 0.79 (6LZE), 1.12 ± 0.72 (6M0K), and 1.11 ± 0.60 (6YB7). After two phases of AutoDock Vina virtual screening of the MyriaScreen Diversity Library II, we prepared a list of the top 20 ligands. We selected four promising leads considering predicted oral bioavailability, druglikeness, and toxicity profiles. These compounds also demonstrated favorable protein-ligand interactions. We then employed 50-ns molecular dynamics simulations for the four selected molecules and the reference ligand 11a in the crystallographic structure 6LZE. Analysis of RMSF, RMSD, and hydrogen bonding along the simulation trajectories indicated that S51765 would form a more stable protein-ligand complexe with 3CL-PRO compared to other molecules. Insights into short-range Coulombic and Lennard-Jones potentials also revealed favorable binding of S51765 with 3CL-PRO.

Conclusion: We identified a potential lead for antiviral drug discovery against the SARS-CoV-2 main protease. Our results will aid global efforts to find safe and effective remedies for COVID-19.

Keywords: 3CL-PRO; COVID-19; Coronavirus; Gromacs; Main protease; Mpro; SARS-CoV-2; Vina; docking; in silico.

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Conflict of interest statement

The authors declare that they have no competing interests.

Figures

Figure 1
Figure 1. Molecular dynamics simulation of 3CL-PRO’s three crystal structures (6LZE, 6M0K, and 6YB7).
(A) Alignment of three crystal structures. (B) Energy minimization for molecular dynamics simulation. (C) NVT equilibration. (D) NPT equilibration. (E–J) Conformational changes of four amino acid residues at the active site of 3CL-PRO over the simulation period. (K) RMSD (running averages) of alpha carbons. (L) RMSF of alpha carbons. Inset shows fluctuations of a loop region of 6LZE.
Figure 2
Figure 2. TPSA and WLOGP of top 20 ligands plotted on the BOILED-Egg.
Figure 3
Figure 3. Docking conformations, physicochemical properties, and protein-ligand interactions for the best four molecules.
(A) Best docking poses of the ligands from virtual screening. In 6LZE, 11a is the co-crystallized ligand. (B) Interactions of 3CL-PRO and the ligand 11a in 6LZE. (C–N) Structure, physicochemical properties, and protein-ligand interactions of L220477 (C and D), R872172 (F–H), L220477 (I–K), and S51765 (L–N). The colored zone in radar charts (D, G, J, and M) indicates suitable physicochemical space for oral bioavailability. LIPO, lipophilicity (XLOGP3); SIZE, molecular weight (g/mol); POLAR, polarity (TPSA); INSOLU, insolubility (LogS); INSATU, insaturation (fraction Csp3); FLEX, flexibility (number of rotatable bonds).
Figure 4
Figure 4. Spatial fluctuations of protein and ligands during molecular dynamics simulations of complexes.
(A) C-alpha RMSD (running averages) for 3CL-PRO in complexes. (B) Ligand RMSD (running averages) in complexes. (C) C-alpha RMSF for 3CL-PRO in complexes. (D) Ligand RMSF in complexes.
Figure 5
Figure 5. Protein-ligand binding modes in MD simulations of best ligands.
Protein-ligand conformations at every 10 ns of simulation for 11a (A1–A6), L220477 (B1–B6), R872172 (C1–C6), ST074801 (D1–D6), and S517656 (E1–E6).
Figure 6
Figure 6. Analysis of hydrogen bonding interactions for best ligands.
(A–E) Number of hydrogen bonds between the ligand and 3CL-PRO during the simulation period. (F) Occupancy of hydrogen bonding for the best ligands. (G) Occupancy of hydrogen bonding of the ligand with some important residues at the active site of 3CL-PRO.
Figure 7
Figure 7. Key distances (running averages of 20 ps) between the ligand and the key amino acid residues of the target protein.
Distances (in angstrom) are plotted against time for (A) 11a and HIS41, (B) L220477 and ASP187, (C) R872172, (D) ST074801, and (E) S51765.
Figure 8
Figure 8. Protein-ligand interaction energies from molecular dynamics simulations for complexes of best ligands.
(A) Average short-range Coulomb (Coul-SR) and short-range Lennard–Jones (LJ-SR) energies for the complexes. Error bars show standard deviations. (B–F) Coul-SR and LJ-SR for the complexes over the simulation period.

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