Aminoglycosides as potential inhibitors of SARS-CoV-2 main protease: an in silico drug repurposing study on FDA-approved antiviral and anti-infection agents

Abstract

Background: The emergence and spread of SARS-CoV-2 throughout the world has created an enormous socioeconomic impact. Although there are several promising drug candidates in clinical trials, none is available clinically. Thus, the drug repurposing approach may help to overcome the current pandemic.

Methods: The main protease (M^pro) of SARS-CoV-2 is crucial for cleaving nascent polypeptide chains. Here, FDA-approved antiviral and anti-infection drugs were screened by high-throughput virtual screening (HTVS) followed by re-docking with standard-precision (SP) and extra-precision (XP) molecular docking. The most potent drug's binding was further validated by free energy calculations (Prime/MM-GBSA) and molecular dynamics (MD) simulation.

Results: Out of 1397 potential drugs, 157 showed considerable affinity toward M^pro. After HTVS, SP, and XP molecular docking, four high-affinity lead drugs (Iodixanol, Amikacin, Troxerutin, and Rutin) with docking energies -10.629 to -11.776kcal/mol range were identified. Among them, Amikacin exhibited the lowest Prime/MM-GBSA energy (-73.800kcal/mol). It led us to evaluate other aminoglycosides (Neomycin, Paramomycin, Gentamycin, Streptomycin, and Tobramycin) against M^pro. All aminoglycosides were bound to the substrate-binding site of M^pro and interacted with crucial residues. Altogether, Amikacin was found to be the most potent inhibitor of M^pro. MD simulations of the Amikacin-M^pro complex suggested the formation of a complex stabilized by hydrogen bonds, salt bridges, and van der Waals interactions.

Conclusion: Aminoglycosides may serve as a scaffold to design potent drug molecules against COVID-19. However, further validation by in vitro and in vivo studies is required before using aminoglycosides as an anti-COVID-19 agent.

Keywords: Aminoglycosides; Antibiotics; COVID-19; Docking and simulation; SARS-CoV-2.

Fig. 1

Molecular docking of Amikacin with SARS-CoV-2 M^pro. (A) 2D view of Amikacin binding at the substrate-binding site of M^pro, (B) 3D view of Amikacin binding at the substrate-binding cavity of M^pro, and (C) Molecular interaction between Amikacin and M^pro, showing various kinds of bonds and amino acid residues responsible for the formation of a stable Amikacin-M^pro complex.

Fig. 2

Molecular docking simulation of the Amikacin-M^pro complex. (A) Root mean square deviations (RMSDs) in the Cα-atoms of M^pro only (red), Amikacin-M^pro complex (blue) and Amikacin only (black), during simulation, (B) root mean square fluctuations (RMSFs) in the Cα-atoms of M^pro (teal) as compared to experimentally determined B-factor of M^pro (brown). The vertical lines (green) on X-axis represent the amino acid residue with which Amikacin formed a contact. The light brown and light teal vertical bars represent the secondary structures α-helices and β-sheets respectively, (C) variation in the radius of gyration (rGyr) as a function of simulation time, and (D) variations in molecular surface area (MolSA), solvent accessible surface area (SASA) and polar surface area (PSA) as a function of simulation time.

Fig. 3

Molecular interactions between Amikacin and M^pro during molecular dynamics simulation. (A) Participation of different amino acid residues during simulation. An interaction fraction of >1 shows that the residue was involved in more than one kind of interaction, (B) contribution of amino acid residues in making contacts with Amikacin as a function of simulation time, and (C) percent interaction between different amino acid residues and Amikacin during the simulation. Only residues having interaction with Amikacin for >30% simulation are shown.

Fig. 4

Variation in the secondary structure of M^pro as a function of simulation. (A) Contribution of individual amino acid residues in the formation of protein's secondary structure elements (SSE). Orange and teal bars represent changes in α-helices and β-sheets. (B) Fluctuations in the SSE (%) as a function of simulation, and (C) contribution of individual amino acid residues in the overall secondary structure of M^pro during the simulation.