In silico evidence of antiviral activity against SARS-CoV-2 main protease of oligosaccharides from Porphyridium sp

Abstract

The coronavirus pandemic (COVID-19) has created an urgent need to develop effective strategies for prevention and treatment. In this context, therapies against protease M^pro, a conserved viral target, would be essential to contain the spread of the virus and reduce mortality. Using combined techniques of structure modelling, in silico docking and pharmacokinetics prediction, many compounds from algae were tested for their ability to inhibit the SARS-CoV-2 main protease and compared to the recent recognized drug Paxlovid. The screening of 27 algal molecules including 15 oligosaccharides derived from sulfated and non-sulphated polysaccharides, eight pigments and four poly unsaturated fatty acids showed high affinities to interact with the protein active site. Best candidates showing high docking scores in comparison with the reference molecule were sulfated tri-, tetra- and penta-saccharides from Porphyridium sp. exopolysaccharides (SEP). Structural and energetic analyses over 100 ns MD simulation demonstrated high SEP fragments-M^pro complex stability. Pharmacokinetics predictions revealed the prospects of the identified molecules as potential drug candidates.

Keywords: Docking; Inhibitors; Microalgae; Polysaccharide; Porphyridium; SARS-CoV-2.

Graphical abstract

Fig. 1

Chemical structures of the three molecules having the best binding energies and the FDA approved drug Paxlovid.

Fig. 2

A close up stick view (A) and surface view (B) of the superimposed Paxlovid position in the active site of M^pro of the solved 3D structure (green) and re-docked structure (blue).

Fig. 3

Binding mode of Redocked Paxlovid as stick representation showing S1, S1′, S2 and S4 subsites (A) and as dot surface representation showing fullness of the active site (B). Visualization of docking results interaction of the inhibitor using (C) Ligplotplus and (D) dan Poseview.

Fig. 4

Binding mode of the penta-sulphated oligosaccharide (CMA3) as stick representation (A) and as dot surface representation showing fullness of the active site (B). Visualization of docking results interaction of the inhibitor using (C) Ligplotplus and (D) dan Poseview.

Fig. 5

Binding mode of the tetra-sulphated oligosaccharide (CMA1) as stick representation (A) and as dot surface representation showing fullness of the active site (B). Visualization of docking results interaction of the inhibitor using (C) Ligplotplus and (D) dan Poseview.

Fig. 6

Analysis of MD simulation trajectories of 100 ns time scale. (A) RMSD plot displaying the molecular vibration of Cα backbone of Receptor + control (red), Receptor + CMA1 (pink) and Receptor + CMA3 (blue). (B) RMSF plots showing the fluctuations of respective amino acids throughout the simulation time 100 ns for Receptor + control (red), Receptor + CMA1 (pink) and Receptor + CMA3 (blue). (C) Number of hydrogen bonds formed between Receptor + control (red), Receptor + CMA1 (pink) and Receptor + CMA3 (blue) during 100 ns simulation time scale. (D) Radius of gyration plots for the deduction of compactness of protein Receptor + control (red), Receptor + CMA1 (pink) and Receptor + CMA3 (blue). Solvent accessible surface area (SAS Area) displaying the ligand bound and unbound area at the binding pocket (cyan), (E) Receptor + CMA1 (pink), (F) Receptor + CMA3 (blue) and (G) Receptor + control (red).

Fig. 7

Free Energy Landscape displaying the achievement of global minima (ΔG, kJ/mol) of (A) receptor in presence of CMA1 with respect to their RMSD (nm) and radius of gyration (Rg, nm); (B). Free Energy Landscape displaying the achievement of global minima (ΔG, kJ/mol) of receptor in presence of CMA3 with respect to their RMSD (nm) and radius of gyration (Rg, nm). (C) Free Energy Landscape displaying the achievement of global minima (ΔG, kJ/mol) of receptor in presence of control with respect to their RMSD (nm) and radius of gyration (Rg, nm).