A computational prediction of SARS-CoV-2 structural protein inhibitors from Azadirachta indica (Neem)

Abstract

The rapid global spread of the Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) has created an unprecedented healthcare crisis. The treatment for the severe respiratory illness caused by this virus is primarily symptomatic at this point, although the usage of a broad antiviral drug Remdesivir has been allowed on emergency basis by the Food and Drug Administration (FDA). The ever-increasing death toll highlights an urgent need for development of specific antivirals. In this work, we have utilized docking and simulation methods to identify small molecule inhibitors of SARS-CoV-2 Membrane (M) and Envelope (E) proteins, which are essential for virus assembly and budding. A total of 70 compounds from an Indian medicinal plant source (Azadirachta indica or Neem) were virtually screened against these two proteins and further analyzed with molecular dynamics simulations, which resulted in the identification of a few common compounds with strong binding to both structural proteins. The compounds bind to biologically critical regions of M and E, indicating their potential to inhibit the functionality of these components. We hope that our computational approach may result in the identification of effective inhibitors of SARS-CoV-2 assembly.Communicated by Ramaswamy H. Sarma.

Keywords: MM-PBSA; SARS-CoV-2; docking; molecular dynamics simulation; natural compound.

Figure 1.

Modeling of SARS-CoV-2 E and M proteins: A) 3D representation of a pentameric form of E-protein, with the N-terminal, transmembrane, and C-terminal domains of each monomer highlighted in blue, green and orange respectively and secondary structure representation is shown on right panel. B) 3D representation of the M-protein, with the N-terminal, transmembrane and C-terminal domains highlighted in blue, green and orange respectively. The C-terminal conserved region is highlighted in red and secondary structure representation is shown on right panel.

Figure 2.

2D interaction diagram of five compounds with the highest binding energies against SARS-CoV-2 E and M proteins. The diagrams were generated with LigPlot⁺⁺.

Figure 3.

3D interaction diagram of five compounds with the highest binding energies against SARS-CoV-2 E and M proteins. The ligands are shown in violet surface representation.

Figure 4.

Molecular Dynamics (MD) simulation results of Protein-ligand complexes: RMSD of protein backbone over time and RMSD of ligands over time for A) E-protein complexes and B) M-protein complexes.

Figure 5.

MM/PBSA per residue energy contribution to the binding energy (only residues scoring ≥1.5 kJ/mol are shown) for individual compounds A) Nimbolin A, B) Nimocin, C) 7-Deacetyl-7-Benzoylgedunin, D) 24-Methylenecycloartanol and E) Cycloeucalenone with E-protein.

Figure 6.

MM/PBSA per residue energy contribution to the binding energy (only residues scoring ≥1.5 kJ/mol are shown) for individual compounds A) Nimocin A, B) 24-Methylenecycloartan-3-one, C) Phytosterol, D) Beta-Amyrin and E) Nimbolin A with M-protein.