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. 2024 Jun 22;17(7):821.
doi: 10.3390/ph17070821.

Identification of South African Plant-Based Bioactive Compounds as Potential Inhibitors against the SARS-CoV-2 Receptor

Nqobile Monate Mkolo  1 , Clarissa Marcelle Naidoo  1 , Rose Kadye  2 , Chikwelu Lawrence Obi  1 , Benson Chucks Iweriebor  1 , Oyinlola Oluwunmi Olaokun  1 , Earl Prinsloo  2 , Muhammad Sulaiman Zubair  3
Affiliations

Identification of South African Plant-Based Bioactive Compounds as Potential Inhibitors against the SARS-CoV-2 Receptor

Nqobile Monate Mkolo et al. Pharmaceuticals (Basel). .

Abstract

The expected progress in SARS-CoV-2 vaccinations, as anticipated in 2020 and 2021, has fallen short, exacerbating global disparities due to a lack of universally recognized "safe and effective" vaccines. This study focuses on extracts of South African medicinal plants, Artemisia annua and Artemisia afra, to identify metabolomic bioactive compounds inhibiting the binding of the SARS-CoV-2 spike protein to ACE2 receptors. The extracts were monitored for cytotoxicity using a resazurin cell viability assay and xCELLigence real-time cell analyzer. Chemical profiling was performed using UPLC-MS/MS, orthogonal projection to latent structures (OPLS), and evaluated using principle component analysis (PCA) models. Identified bioactive compounds were subjected to in vitro SARS-CoV-2 enzyme inhibition assay using standard methods and docked into the spike (S) glycoprotein of SARS-CoV-2 using Schrodinger® suite followed by molecular dynamics simulation studies. Cell viability assays revealed non-toxic effects of extracts on HEK293T cells at lower concentrations. Chemical profiling identified 81 bioactive compounds, with compounds like 6″-O-acetylglycitin, 25-hydroxyvitamin D3-26,23-lactone, and sesaminol glucoside showing promising binding affinity. Molecular dynamics simulations suggested less stable binding, but in vitro studies demonstrated the ability of these compounds to interfere with SARS-CoV-2 spike protein's binding to the human ACE2 receptor. Sesaminol glucoside emerged as the most effective inhibitor against this interaction. This study emphasizes the importance of multiplatform metabolite profiling and chemometrics to understand plant extract composition. This finding is of immense significance in terms of unravelling metabolomics bioactive compounds inhibiting the binding of the SARS-CoV-2 spike protein to ACE2 receptors and holds promise for phytotherapeutics against SARS-CoV-2.

Keywords: 25-hydroxyvitamin D3-26,23-lactone; 6″-O-acetylglycitin; Artemisia afra; Artemisia annua; angiotensin-converting enzyme 2 (ACE2); anti-COVID bioactive compounds; sesaminol glucoside; spike (S) glycoprotein; tryptanthrin.

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Conflict of interest statement

The authors declare no conflicts of interest.

Figures

Figure 1
Figure 1
Viability of HEK293T cells after 24 h of treatment with A. annua and A. afra leaves’ extracts at different concentrations ranging from 250 to 7.81 µg/mL using absorbance for resorufin and fluorescence-based detection.
Figure 2
Figure 2
Real-time monitoring of HEK293T cells after treatment with A. annua and A. afra leaves’ extracts at different concentrations ranging from 250 to 7.81 µg/mL using the xCELLigence RTCA analyzer.
Figure 3
Figure 3
Base peak intensity (BPI) chromatograms obtained from the positive ion UPLC-MS/MS analyses of (a) A. annua and (b) A. afra samples.
Figure 4
Figure 4
The scores scatter plot of the PCA model of A. annua and A. afra metabolite groups.
Figure 5
Figure 5
(a) The scores scatter plot of OPLS-DA model of A. annua and A. afra metabolites. (b) The distribution of VIP values (VIP > 1.5) of A. annua and A. afra metabolites. (c) The loading plot of PLS-DA model of A. annua and A. afra metabolites. Key: The metabolites with red color were labeled as significant compounds (VIP > 1.5) and the metabolites with green color were labeled as non-significant compounds (VIP < 1.5).
Figure 5
Figure 5
(a) The scores scatter plot of OPLS-DA model of A. annua and A. afra metabolites. (b) The distribution of VIP values (VIP > 1.5) of A. annua and A. afra metabolites. (c) The loading plot of PLS-DA model of A. annua and A. afra metabolites. Key: The metabolites with red color were labeled as significant compounds (VIP > 1.5) and the metabolites with green color were labeled as non-significant compounds (VIP < 1.5).
Figure 6
Figure 6
Volcano plot for group A and group B. The range of Y > 1.30 and X > 1 was a significant increase. The range of Y > 1.30 and X < −1 was a significant decrease.
Figure 7
Figure 7
Hierarchical cluster analysis of metabolome data from group c (A. annua) and group d (A. afra) of metabolites. Hierarchical clustering analysis (HCA) was performed using the complete linkage algorithm of the program Cluster 3.0 (Stanford University) and the results are visualized using heatmap 1.0.12 (Raivo Kolde).
Figure 8
Figure 8
Molecular interactions plot of the three potential compounds: (a) 6″-O-acetylglycitin; (b) 25-hydroxyvitamin D3-26,23-lactone; (c) sesaminol glucoside with Optimal Binding Affinities against SARS-CoV-2 spike S glycoprotein. (d) The predicted binding site of the test compounds (stick) on the S glycoprotein spike protein.
Figure 9
Figure 9
(a) Root mean square deviation (RMSD) plot of selected compounds with best binding affinity against spike S glycoprotein over 50 ns of molecular dynamics simulations. (b) Root mean square fluctuation (RMSF) plot of selected compounds with best binding affinity against spike S glycoprotein over 50 ns of molecular dynamics simulations.
Figure 10
Figure 10
Inhibition effects of interaction between SARS-CoV-2 S1 spike protein and ACE-2 receptor by (a) A. annua and A. afra extracts and (b) compounds of 25-hydroxyvitamin D3 monohydrate, sesaminol glucoside, and 6″-O-Acetylglycitin.
Figure 11
Figure 11
The principal component analysis (PCA) score plot of the QC samples, the X axis indicates the number of QC samples, the Y axis indicates the range of RSD.
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