Discovering natural products as potential inhibitors of SARS-CoV-2 spike proteins

Abstract

The ongoing global pandemic caused by the SARS-CoV-2 virus has demanded the urgent search for effective therapeutic interventions. In response, our research aimed at identifying natural products (NPs) with potential inhibitory effects on the entry of the SARS-CoV-2 spike (S) protein into host cells. Utilizing the Protein Data Bank Japan (PDBJ) and BindingDB databases, we isolated 204 S-glycoprotein sequences and conducted a clustering analysis to identify similarities and differences among them. We subsequently identified 33,722 binding molecules (BMs) by matching them with the sequences of 204 S-glycoproteins and compared them with 52,107 secondary metabolites (SMs) from the KNApSAcK database to identify potential inhibitors. We conducted docking and drug-likeness property analyses to identify several SMs with potential as drug candidates based on binding energy (BE), no Lipinski's rule violation (LV), psychochemical properties within the pink area of the bioavailability radar, and a bioavailability score (BAS) not less than 0.55. Fourteen SMs were predicted through computational analysis as potential candidates for inhibiting the three major types of S proteins. Our study provides a foundation for further experimental validation of these compounds as potential therapeutic agents against SARS-CoV-2.

Keywords: Drug discovery; Natural products; SARS-CoV-2; Spike protein; Virtual screening.

Fig. 1

Schematic representation of the SARS-CoV-2 spike protein, showing its trimeric structure and key domains (S1, S2, RBD). The figure illustrates the conformational changes and cleavage events necessary for viral entry and highlights the hypothesized action points of the identified secondary metabolite (SM) clusters. Cluster 1 targets the RBD-ACE2 interaction to block viral attachment. Cluster 2 modulates the S1/S2 interface to interfere with cleavage events. Clusters 3, 4, and 5 act on the S2 subunit and S2’ cleavage site to inhibit membrane fusion and protease activation. The figure provides a comprehensive visualization of the mechanisms targeted by the SM clusters during the viral entry process.

Fig. 2

Flow diagram of the experiments explaining this research.

Fig. 3

Five clusters of spike proteins were identified based on sequence similarity. A reduction was made to Clusters 1 and 2 for better visualization.

Fig. 4

(a) Host species distribution of binding molecules, and (b) binding molecules distributed across spike proteins.

Fig. 5

Distribution of secondary metabolites in terms of binding energy for each cluster.

Fig. 6

An example of a comprehensive analysis of C00001224 represents bioavailability analysis and protein–ligand interactions. (A) Docked models of the protein–ligand complex showing C00001224 binding within the binding site of the 7E7B chainA protein. The binding pocket is visualized with hydrogen bond regions highlighted: pink represents hydrogen bond donors, and yellow represents hydrogen bond acceptors. (B) Protein–ligand binding sites of C00001224, shown with preserved atomic coloring (carbon: gray, nitrogen: blue, oxygen: red, hydrogen: white). Purple dotted lines represent hydrophobic interactions, green dotted lines represent carbon-hydrogen bonds, while the purple circle denotes the centroid of a key aromatic residue involved in π-alkyl interactions. (C) A 2D interaction map detailing specific interactions between C00001224 and key amino acid residues in the 7E7B Chain A protein’s binding site. Interactions include π-alkyl (pink dashed lines), carbon-hydrogen bonds (green lines), and hydrophobic contacts (purple highlights). (D) Bioavailability radar plot illustrating the key properties of the secondary metabolite C00001224, including lipophilicity (LIPO), molecular weight (SIZE), polarity (POLAR), solubility (INSOLU), saturation (INSATU), and flexibility (FLEX).

Fig. 7

Distribution of SwissADME analysis results on SMs for Cluster 1 categorized into BAS groups.

Fig. 8

Distribution of SwissADME analysis results on SMs for Cluster 2 categorized into BAS groups.

Fig. 9

Comprehensive analysis of C00001492 revealed its bioavailability and protein–ligand interactions. (A) Docked models of the protein–ligand complex showing C00001492 binding within the binding site of the 6LZG chainB protein. The binding pocket is visualized with hydrogen bond regions highlighted: pink represents hydrogen bond donors, and yellow represents hydrogen bond acceptors. (B) Protein–ligand binding sites of C00001492, shown with preserved atomic coloring (carbon: gray, nitrogen: blue, oxygen: red, hydrogen: white). Dark and light purple dotted lines represent π-π stacked and π-alkyl interactions respectively, green dotted lines represent carbon-hydrogen bonds, orange dotted lines represent π-sulfur interactions. (C) A 2D interaction map detailing specific interactions between C00001492 and key amino acid residues in the 6LZG ChainB protein’s binding site. Interactions include π-alkyl (light pink dashed lines), π-π stacked (dark pink dashed lines), carbon-hydrogen bonds (green lines), and π-sulfur (orange lines). (D) Bioavailability radar plot illustrating the key properties of the secondary metabolite C00001492, including lipophilicity (LIPO), molecular weight (SIZE), polarity (POLAR), solubility (INSOLU), saturation (INSATU), and flexibility (FLEX).

Fig. 10

Distribution of SwissADME analysis results on SMs for Cluster 4 categorized into BAS groups.

Fig. 11

Comprehensive analysis of C0000446 represents bioavailability analysis and protein–ligand interactions. (A) Docked models of the protein–ligand complex showing C00000446 binding within the binding site of the 7C53 chainA protein. The binding pocket is visualized with hydrogen bond regions highlighted: pink represents hydrogen bond donors, and yellow represents hydrogen bond acceptors. (B) Protein–ligand binding sites of C00000446, shown with preserved atomic coloring (carbon: gray, nitrogen: blue, oxygen: red, hydrogen: white). Green dotted lines represent conventional hydrogen bond. (C) A 2D interaction map detailing specific interactions between C00000446 and key amino acid residues in the 7C53 ChainA protein’s binding site. Interactions include conventional hydrogen bond (green dashed lines). (D) Bioavailability radar plot illustrating the key properties of the secondary metabolite C00000446, including lipophilicity (LIPO), molecular weight (SIZE), polarity (POLAR), solubility (INSOLU), saturation (INSATU), and flexibility (FLEX).