Steroidal lactones from Withania somnifera effectively target Beta, Gamma, Delta and Omicron variants of SARS-CoV-2 and reveal a decreased susceptibility to viral infection and perpetuation: A Polypharmacology Approach

doi:10.21203/rs.3.rs-2449581/v1

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Steroidal lactones from Withania somnifera effectively target Beta, Gamma, Delta and Omicron variants of SARS-CoV-2 and reveal a decreased susceptibility to viral infection and perpetuation: A Polypharmacology Approach

https://doi.org/10.21203/rs.3.rs-2449581/v1

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published 27 Feb, 2024

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Prevention from disease is presently the cornerstone of the fight against COVID-19. With the rapid emergence of novel variants of SARS-CoV-2 viz. B.1.1.7 (UK), B.1.351 (South Africa), P.1 (Brazil), delta variant B.1.617.2 (India) and the recently emerged Omicron variant B.1.1.529 (South Africa), there is an urgent need for novel prophylactic agents that have the ability to strengthen and fortify the immune system. To this effect, Withania somnifera (WS) and its phytoconstituent(s) have immense untapped immunomodulatory and therapeutic potential. The in vitro and in vivoanticancer potential of WS is well documented. Recent studies have indicated that the prognosis of COVID patients having cancer as a co-morbidity is poor as the viral infection has been shown to upregulate epithelial-to-mesenchymal transition (EMT) and promote metastasis. WS methanolic extract (WSME) was characterized using HPLC and Withaferin A as standard. In vitro cytotoxicity of WSME was determined against cancer (MDA-MB-231) and normal (Vero) cell lines by MTT assay. Molecular dynamics simulations of Withanolide A with SARS-CoV-2 main protease, spike receptor-binding domain and Withanolide B with SARS-CoV spike glycoprotein and SARS-CoV-2 papain-like protease were performed using Schrödinger. Withaferin A was identified in WSME. WSME displayed potent cytotoxicity against cancer cells. Stability of complexes followed the order 6M0J-Withanolide A>6W9C-Withnaolide B>5WRG-Withanolide B>6LU7-Withanolide A. Maximum stable interaction(s) were observed between Withanolides A and B with SARS-CoV-2 and SARS-CoV spike glycoproteins, respectively. Withanolides A and B have the potential to be tested further in vitro and in vivo as novel antiviral agents against SARS-CoV-2 and SARS-CoV.

Withania somnifera

in vitro cytotoxicity assay

MD simulation

SARS-CoV-2 main protease

papain-like protease

The ongoing coronavirus pandemic has engulfed the world by its novel and evolved variants. These variants have been classified as ‘variants of concern’ by WHO, a label given to a variant if it is more contagious, deadlier than previous strains and more resistant to vaccines and treatment strategies. The delta variant causes more infections and spreads faster than the original SARS-CoV-2 strain of the virus that causes COVID-19. Likewise, Omicron may spread more easily than other variants, including Delta. To fully understand these changes and respond accordingly, researchers need to reliably identify where the variants are, when they appear, and how prevalent they become over time. The paucity of knowledge about SARS-CoV-2 and its specific virulence factors has severely hampered the therapeutic management of the disease. The emergence of the novel strains of SARS-CoV-2 have put a question mark on the efficacy of the currently developed vaccines and the vaccinations attained thereof.

A number of studies have been undertaken to investigate the efficacy of a number of traditional medicinal plants against COVID-19 [1–8]. Multiple bioactive compounds from traditional medicinal plants have been reported to target numerous SARS-CoV-2 specific proteins viz. main protease (3CLpro), papain-like protease (PLpro), and spike protein [4,5,8–10].

In the genome of SARS-CoV-2 beta variant, there seems to be approximately 21 mutations out of which 9 are present in the spike glycoprotein, making it the most dreadful variant [11]. Intriguingly, the binding affinities of the beta and gamma variants with the mutations N417/K848/Y501-RBD with angiotensin-converting enzyme 2 (ACE2) was found to be substantially lower than that of N501Y-RBD with ACE2 present in both the variants [12–13]. These mutations are thought to strengthen the interaction of RBD with ACE2 receptors and aid in the virus ability to evade the immunological response.

Due to evolving SARS-CoV-2 lineages which often differ from one another by a few mutations, the phylogenetic classification has become very challenging. A new variant of concern was identified in early January, 2021, with key mutations in L18F, T20N, P26S, D138Y, R190S, K417T, E484K, N501Y, D614G, H655Y, T1027I, and V1176F. This P.1 variant also known as gamma variant spread across 45 countries. The second deadly wave was traced back to a strain known as B.1.617.2 (the delta variant) with key mutations in T19R, (G142D*), 156del, 157del, R158G, L452R, T478K, D614G, P681R, D950N of the spike glycoprotein [14].

Since the virus has shown resilience against control and management, the conventional ‘single drug-single target-single disease’ approach has failed to bear fruit. Thus, an agent that can simultaneously rev up the immune system of the host and target various life cycle stages of SARS-CoV-2 is the need of the hour. Thus, taking the polypharmacology approach, an immunomodulatory agent that can also sum up as an antiviral agent may contribute to effective management of COVID-19 via a synergistic/additive way.

‘Ayurveda’ heals body, mind, and soul. Undoubtedly Ayurveda is the oldest uninterrupted therapeutic institution. Plants are responsible for providing a wide variety of useful products, including nourishment, energy, textiles, condiments, essential oils, resins, gums, and scents. In terms of scientific research, plants have been recognized as a possible source for the production of a diverse spectrum of chemical compounds. Bioengineered or naturally occurring components that can restore/enhance immune function and stimulate various aspects of adaptive or innate immunity are referred to alternatively as immunomodulators, immunorestoratives, immunoadjuvants, immunostimulants, immunosuppressants and immunoaugmentors. The utilization of plants as immunomodulatory material is still in its emerging stage. A range of phytochemicals are known to stimulate the hematopoietic system and maturation of immune cells. To this effect, WS and its phytoconstituents are known immunomodulatory, anti-inflammatory and anti-tumorigenic agents and are also thought of as potential antiviral agents by virtue of their critical roles in slowing COVID-19 progression and protecting vital organs can be viewed as (Fig. 1). A number of studies have focussed on evaluating the potential of WS and its phytoconstituents as anti-viral entry and replication agents [15–19] while others have focussed on the potential of WS and its phytoconstituents as immunomodulatory and ‘polypharmacology’ agents [20–22].

The present study provides conclusive evidence about the role of Withanolide A and B from WS as selective inhibitors of SARS-CoV-2 entry and replication into host cells. Binding energies and dissociation constants of Withanolide A and B with respect to the spike glycoprotein, main protease and papain-like protease of the beta variant were comparatively assessed with those of the newly evolved variants viz. delta, gamma and omicron using molecular docking. Since 3D structure of spike glycoprotein of the newly evolved omicron variant was not available at the time of writing the manuscript, the same was generated using homology modeling. MD simulations of Withanolide A with SARS-CoV-2 main protease and spike receptor-binding domain and Withanolide B with SARS-CoV spike glycoprotein (PDB ID: 5WRG) and SARS-CoV-2 papain-like protease using Schrödinger software were also performed.

2.1. Reagents and chemicals

Dulbecco’s Modified Eagle Medium Nutrient Mixture F-12 (DMEM/F-12), fetal bovine serum (FBS), antibiotics penicillin and streptomycin solution were purchased from Sigma-Aldrich, USA. Standard withaferin A (WFA) was purchased from Cayman Chemicals, Michigan, USA. All other chemicals and reagents used were of analytical grade.

2.2. Preparation and HPLC characterization of WSME

The present study was carried out at Cell and Tissue Culture Lab, Department of Biochemistry, Era's Lucknow Medical College and Hospital, Era University, Lucknow. Commercially obtained WS stems were purchased from Chowk market, Lucknow, India. The plant extract was validated using HPLC analysis. The extract preparation was performed as per our previously reported study [23].

2.2.1. Preparation of the standard for HPLC Analysis

Withaferin-A (WFA) working standard was prepared in 1 mL of HPLC grade methanol and further dilutions were performed according to the already published protocol [23]. Calibration curves for standard WFA were found to be linear at concentrations of 50 (23.53 mg), 100 (47.06 mg) and 200 µM (94.12 mg).

2.2.2. Preparation of WSME for HPLC Analysis

100 mg of WSME working solution was dissolved in 1 mL of methanol further dilutions were performed according to the already published protocol [23].

2.2.3. Procedure

HPLC was performed on a Waters Alliance 515 HPLC system, equipped with two Waters 1525 pumps, a Waters Pump Control Module, degasser, injector, and a Waters 2998 photodiode array detector (Waters, Milford, MA, USA). For all separations, an ODS-2 Hypersil C18 reversed-phase column (250 × 4.6 mm, 5 µm particle size, maintained at 25°C) was used. The mobile phase consisted of water (solvent A) and acetonitrile (solvent B) which was applied in the following gradient elution for 55 min: 95% A, 5% B for 0–5 min, 85% A, 15% B for 5 min, 60% A, 40% B for 40 min, 10% A, 90% B for 10 min, 50% A, 50% B for 2 min, 95% A, 5% B for 3 min. The flow rate and sample volume were set at 1.0 mL/min and 10 µL, respectively. All separations were monitored at 243 nm.

2.3. Culture of human breast cancer cell line

Recent studies has demonstrated that COVID-19 infection leads to cancer progression via EMT promotion [24] and the role of WS and its phytoconstituents in reduction of cancer-related fatigue and improvement of quality of life [25]. Interestingly, since cancer and viral diseases are known to present with similar symptoms and signs [26–29], it was thought that agents/extract(s) having anticancer activity might also behave as potential antiviral agents. Hence, a need was felt to assess the anticancer potential of the extract.

Human triple-negative breast cancer cell line MDA-MB-231 was obtained from the cell repository center of the National Centre for Cell Sciences, Pune, India. MDA-MB-231 cells were maintained in DMEM/F-12 (1:1) media supplemented with 10% heat-inactivated FBS, 1% antibiotic antimycotic solution and maintained at 37°C with 5% CO₂ in an incubator.

2.4. 3-(4,5-dimethylthiazol-2-yl)-2,5-diphenyltetrazolium bromide (MTT) assay on MDA-MB-231 cells

The cell viability of WSME against human breast cancer cells MDA-MB-231 was evaluated by MTT reduction assay following a previously published protocol [23]. The cellular morphological changes were observed under an inverted phase-contrast microscope (Nikon Eclipse TS100, Japan).

2.5. Homology Modeling

The surface glycoprotein protein sequence of Omicron variant (BA.1) of SARS-CoV-2 with accession number UHO53131 was retrieved from National Center for Biotechnology Information (NCBI) Virus (https://www.ncbi.nlm.nih.gov/labs/virus/vssi/#/), a freely accessible national library for protein sequences. A BLASTp (Basic Local Alignment Search Tool Program) algorithm was implemented to produce a tertiary structure of protein from the templates selected from the Protein Data Bank (PDB) as reported previously [30]. Sequences of proteins that were more similar to the query sequence, were selected as templates. The SWISS-MODEL workspace was used to model the 3-D structure of spike protein of Omicron variant [31]. After optimization, structure validation and quality analysis of modeled structure was performed using Molprobity web server (http://molprobity.biochem.duke.edu/). The overall model quality was measured via the addition of H-atoms, all-atom contact analysis, Ramachandran and rotamer analyses and covalent-geometry analyses. The visualization of the three-dimensional modeled structure of the Omicron variant of SARS-CoV-2 was performed in Accelrys Biovia Discovery Studio version 2017 R2 (San Diego, CA, USA).

2.6. Molecular Docking

Molecular docking analysis of Withanolides A and B interaction with spike glycoprotein of beta, delta, gamma and omicron variants were done using AutoDock 4.0/ADT version 4.2.6. The binding energies and docking poses were further validated using AutoDock Vina version 1.1.2 programs. Both softwares employ a grid-based approach to enable quick assessment of the binding energy of trial conformations, allowing searching of the broad conformational area accessible to a ligand surrounding a protein. A Lamarckian genetic algorithm is employed as the main approach for conformational searching. The detailed algorithm of both softwares as well as the method used have been discussed in a previously reported study [15].

2.7. Molecular Dynamics (MD) Simulation

MD simulation studies were carried out using Schrödinger software on Maestro molecular modeling platform (Schrödinger Release 2019–4: Maestro, Schrödinger, LLC, New York, NY, 2019. version 12.2.012).

The best-docked poses generated in AutoDock Vina were used as output files followed by generation of ligand-protein complexes in Pymol which were used as input files to run MD simulation. Using system builder of Desmond, the solvation of selected protein-ligand complexes was performed by the TIP3P water model. A crystallographic simulation box with a buffer distance of 10 Å between the box edge and atoms of the complex was generated by addition of an appropriate number of counter ions; the system was then neutralized and 0.15 M NaCl was maintained inside the simulation box. MD simulation was performed using the NPT equilibrated assemble at 300 K and an atmospheric pressure of 1.013 bars while OPLS3eforce field parameters were implemented in the simulation runs. During the 100ns simulation, a number of frames were recorded and saved to the trajectory. For the analysis of the trajectory obtained, a simulation interaction diagram was used.

The interacting coordinates of protein(s)-bound with their ligand were further recorded and saved in the form of trajectories. The perpetuations in root mean square deviation (RMSD) of Cα atoms, root means square fluctuation (RMSF) of each residue in the complex determining the changes in the rigidity and flexibility and radius of gyration (Rg) depicting the overall structural variations in the protein upon binding of selected ligand were analyzed. Additionally, solvent accessible surface area (SASA) of hydrophilic and hydrophobic residues along with intramolecular and intermolecular hydrogen bond formation of all the systems were also analyzed and calculated.

A full-scale molecular simulation was performed for about 100ns for Withanolide A with SARS-CoV-2 main protease (PDB ID: 6LU7), SARS-CoV-2 spike receptor-binding domain bound with ACE2 (PDB ID: 6M0J), Withanolide B with SARS-CoV spike glycoprotein (PDB ID: 5WRG) and papain-like protease of SARS CoV-2 (PDB ID: 6W9C).

2.8. Statistical Analysis

Since the work involved validation of the in silico results through in vitro biological activity evaluation of the WSME against MDA-MB-231 cells, the calculation of sample size was not applicable to this study. Cell viability data were expressed as the mean ± SD from three independent experiments. Statistical evaluation was determined by one-way ANOVA followed by Dunnett’s Multiple Comparison Test using GraphPad Prism software (Version 8.01). A p-value < 0.05 was considered statistically significant and that of > 0.05 was considered statistically insignificant.

3.1. Phytochemical Characterization of WSME

Qualitative phytochemical characterization of WSME constituents was carried out using HPLC. From the analysis, the concentration of major constituent, Withaferin A, in the extract was found to be higher in comparison to other constituents, thus accounting for the higher anticancer activity of the methanolic extract due to the presence of the same (Fig. 3, R_t = 11.272 min). The analytical HPLC method used in the study provided a good baseline resolution of peaks of withanolides present in WSME with reference to the Withaferin A standard (R_t = 11.340 min) as depicted in Fig. 2. The concentration of WFA in the WSME (100 mg/mL) was found to be 130.98 µM.

Fig. 4(a) depicts a significant decline in the number of viable cells along with the color intensity of wells containing higher doses of WSME as compared to control wells containing MDA cells. WSME had a potent cytotoxic effect on MDA cells with an IC₅₀ value with 66 µg/mL, whereas no significant effect was observed on Vero cells even on doses ranging upto 400 µg/mL (Fig. 4(b)).

Figure 5(A-D) depicts the morphological analysis of untreated versus treated cells in a dose-dependent manner with WSME (25, 50 and 75 µg/mL) on MDA cells. WSME displayed cytotoxic as well as cytostatic effect on human breast cancer cells MDA-MB-231. Furthermore, the treated cells showed an altered morphology when observed under an inverted microscope. However, no such effect was observed on normal kidney epithelial Vero cells (Fig. 5 (A’-D’)).

3.2. Molprobity Scoring of Modeled structure of Omicron Variant of SARS-CoV-2

The MolProbity score is a typical number that represents the overall quality statistics of proteins. In the present study, the Molprobity scores of the Omicron modeled structure (Fig. 6(a)) came out to be 1.42 as shown in Table 1, which was well within the allowed range. The score is a combination of the clashscore, rotamer, and Ramachandran evaluations, normalized to the same scale as X-ray resolution. Observed Rama distribution Z-score value (-0.48 ± 0.14), helps to see if the input structure's z-score falls within the range of scores found for native proteins of similar size. As evident from Fig. 6 (b), the modeled protein Ramachandran plot revealed that 93.53% of the residues were within the 'preferred 98% contour range’ and there were 31 Ramachandran outliers (0.90%) (Table 1). Table 1, depicts that the number of serious clashes per 1000 atoms were reported to be 1.53 (Goal value: -4 < x < 2).

Table 1

Molprobity analysis of Modeled spike glycoprotein structure of omicron variant of SARS-CoV-2
All-Atom Contacts	Clashscore, all atoms:	1.53		99th percentile* (N = 1784, all resolutions)
All-Atom Contacts	Clashscore is the number of serious steric overlaps (> 0.4 Å) per 1000 atoms.
Protein Geometry	Poor rotamers	40	1.33%	Goal: <0.3%
	Favored rotamers	2807	93.47%	Goal: >98%
	Ramachandran outliers	31	0.90%	Goal: <0.05%
	Ramachandran favored	3210	93.53%	Goal: >98%
	Molprobity score	1.42		100th percentile* (N = 27675, 0 Å − 99 Å)
	Cβ deviations > 0.25 Å	32	0.99%	Goal: 0
	Bad bonds:	7 / 27630	0.03%	Goal: 0%
	Bad angles:	194 / 37587	0.52%	Goal: <0.1%
Peptide Omegas	Cis Non-Prolines:	2/3267	0.06%	Expected: ≤1 per chain, or ≤ 5%
Peptide Omegas	Twisted Non-Proline:	1/3267	0.03%	Goal: 0

NOTE: In the two column results, the left column gives the raw count, right column gives the percentage.

*100th percentile is the best among structures of comparable resolution; 0th percentile is the worst. For clashscore the comparative set of structures was selected in 2004, for Molprobity score in 2006

Molprobity score combines the clashscore, rotamer, and Ramachandran evaluations into a single score, normalized to be on the same scale as Xray resolution

3.3 Comparative Assessment of Binding Energies of Withanolide A and B with Spike Glycoprotein of SARS-CoV-2 Beta, Delta, Gamma and Omicron Variants

As reported in our previously published study, the spike glycoprotein (PDB ID: 6M0J) of beta variant of SARS-CoV-2 displayed good binding kinetics with Withanolides A (B.E.: -9.1, K_d: 214.02 nM) and B (B.E.: -8.88, K_d: 308.96 nM) by AutoDock v4.2.6 and further validated using AutoDock Vina. In the present study, the binding of spike glycoprotein(s) of gamma, delta and omicron SARS-CoV-2 variants were docked with Withanolide A and B [11].

SARS-CoV-2 spike glycoprotein of gamma variant (PDB ID: 7EKC) displayed significant binding with Withanolides A (B.E.: -4.94, K_d: 240.74 µM) and B (B.E.: -5.44, K_d: 102.53 µM). However, the delta variant (PDB ID: 7V7N) was found to display a much stronger binding affinity with Withanolides A (B.E.: -8.63, K_d: 475.1 nM) and B (B.E.: -9.27, K_d: 159.47 nM) which was found in agreement with the previous study reported.

The most recent ‘omicron’ variant of SARS-CoV-2 has grabbed global attention. Therefore, targeting it might prove crucial in validating the already reported results. In accordance to the previous reports, it is evident from Table 2 that Withanolide A (B.E.: -7.67, K_d: 2.4 µM) and B (B.E.: -8.09, K_d: 1.17 µM) possessed potent binding affinity to spike glycoprotein of omicron variant. The results were further validated using AutoDock Vina as presented in Table 2.

Table 2

Comparative assessment of Withanolide A and B with Beta, Delta, Gamma and Omicron variants of SARS-CoV-2 spike glycoprotein
S.No.	Variants of SARS-CoV-2	PDB IDs	Ligands	Binding Energy (B.E.) using AutoDock Vina	Binding Energy (B.E.) using AutoDock v4.2.6	K_d (Dissociation constant)
1.	Beta*	6M0J	Withanolide A	-9.3	-9.1	214.02 nM
2.	Beta*	6M0J	Withanolide B	-9.2	-8.88	308.96 nM
3.	Gamma	7EKC	Withanolide A	-8.6	-4.94	240.74 µM
4.	Gamma	7EKC	Withanolide B	-7.5	-5.44	102.53 µM
5.	Delta	7V7N	Withanolide A	-10.0	-8.63	475.1 nM
6.	Delta	7V7N	Withanolide B	-9.6	-9.27	159.47 nM
7.	Omicron(BA.1)	Modeled Structure	Withanolide A	-9.4	-7.67	2.4 µM
8.	Omicron (BA.1)	Modeled Structure	Withanolide B	-9.7	-8.09	1.17 µM

3.4. MD simulation studies

A molecular dynamic study was performed in order to validate and explore the time-dependent interaction of protein-ligand complexes along with confirmation of complex stability. Based on the interaction study of SARS-CoV-2 with WS phytoconstituents performed in previously reported paper [15], selection of best-docked complexes were done to run MD simulation. During a 100ns MD simulation, the dynamic stability of complexes were defined/analysed using computational parameters like P-RMSF, protein-ligand RMSD, protein-ligand contacts, P-RMSF, ligand torsion profile and ligand properties.

3.4.1 Stability complex analysis of root mean square deviations of protein (RMSD-P), ligand root means square fluctuation (L-RMSF) and protein root mean square fluctuation (P-RMSF)

The distance measured between the protein backbones of the superimposed protein is defined as the RMSD. The RMSD analysis is evaluated to ensure the conformational stability of the protein backbone and ligand-protein complex. RMSD-P is primarily used to identify the movements of various atoms in the protein when the ligand is present in the receptor's active site. RMSD-P plot score was calculated for 100ns. The RMSD-P plot for Withanolide A with SARS-CoV-2 main protease was observed to be in the range of 1.2–10.1 Å (Fig. 7a), while for SARS-CoV-2 spike receptor-binding domain bound with ACE2, the value was in the range of 1.2 to 3.8 Å (Fig. 7b). The RMSD-P plot for Withanolide B with SARS-CoV spike glycoprotein was observed to be in the range of 2.6–9.6 Å (Fig. 7c), while for SARS CoV-2 papain-like protease it was found in the range 1.5-5.0 Å (Fig. 7d).

An analysis of Fig. 7a reveals that initially up to almost 60ns, the viral main protease (PDB ID: 6LU7) and ligand (Withanolide A) both showed maximum fluctuations. After 60ns, the protein and ligand both maintained stability between 66-100ns and 59-94ns, respectively. Similarly, Fig. 7b evidently indicate that the RBD of viral spike glycoprotein (PDB ID: 6M0J) and ligand Withanolide A initially showed fluctuations in RMSD due to translational motion, but after 10ns an equilibrated motion of both was observed up to 100ns. On the other hand, Withanolide B stabilized in binding pocket of SARS-CoV spike glycoprotein (PDB ID: 5WRG) throughout the 100ns MD simulation with very less Cα-atom fluctuation ranging from 2.6–9.6 Å as is evident from Fig. 7c. In Fig. 7d, rotational motion was observed in SARS-CoV-2 papain-like protein (PDB ID: 6W9C)-Withanolide B complex within the binding pocket showing that Withanolide B had a snug fit in the protein binding cavity. Thus, these results strongly indicate the stable binding of Withanolides A and B to spike receptor-binding domain bound with ACE2 and active site of papain-like protease of SARS CoV-2, respectively during the simulation time of 100ns. On the other hand, binding of Withanolide A to SARS CoV-2 main protease maintained stability during the initial simulation time of 60ns.

Moreover, the RMSD-L for Withanolide A with SARS-CoV-2 main protease (PDB ID: 6LU7) was found in the range of 1.1–25.2 Å (Fig. 7a), while for Withanolide A with SARS-CoV-2 spike receptor-binding domain bound with ACE2 (PDB ID: 6M0J) was observed in the range of 0.8–16.2 Å (Fig. 7b). RMSD-L for Withanolide B with SARS-CoV spike glycoprotein (PDB ID: 5WRG) was seen in the range of 1.1–4.7 Å (Fig. 7c), while for Withanolide B with papain-like protease of SARS CoV-2 (6W9C) was obtained within the range 1.8–5.9 Å (Fig. 7d). The results revealed good stability of complexes viz. Withanolide A and B to SARS-CoV-2 spike receptor-binding domain bound with ACE2 (PDB ID: 6M0J) and SARS CoV-2 papain-like protease (PDB ID: 6W9C) respectively, by equilibrated motion throughout 100ns MD simulation.

Snapshots depicting interacting amino acids at different ns in Withanolide B-5WRG, Withanolide A-6LU7, Withanolide A-6M0J and Withanolide B-6W9C have been furnished as supplementary tables SA, SB, SC and SD, respectively. Interestingly, for all the above interactions, the ligand molecules were found to fluctuate within the binding pocket of the respective protein thereby suggesting that the studied ligand(s) did not move out of the binding pockets of the given protein during the simulation run time.

RMSF is calculated to reveal the structural stability of protein backbone and ligand complex. The L-RMSF analysis suggested that binding of Withanolide A to SARS-CoV-2 main protease (PDB ID: 6LU7) was stable over the course of simulation. Atoms 9, 14, 15, 17–20, 23–25, 27 and 33 of Withanolide A were plotted in a graph showed little fluctuation with RMSF > 9.9 Å when bound to SARS-CoV-2 main protease (PDB ID: 6LU7) (Fig. 8a); whereas, with SARS-CoV-2 RBD complexed with ACE2 (PDB ID: 6M0J), atoms ranging from 9–12, 15, 18, 19, 23–25, 27, 30, 32, 33 of Withanolide A displayed high fluctuations with RMSF > 4.7 Å (Fig. 8b). While, L-RMSF of Withanolide B bound with SARS-CoV spike glycoprotein (PDB ID: 5WRG) revealed that atoms 8, 14 and 16–18 showed major fluctuations with RMSF > 1.6 Å (Fig. 8c). Similarly, Withanolide B with SARS-CoV-2 papain-like protease (PDB ID: 6W9C) showed that atoms 8–10, 16, 18, 27–29 displayed fluctuations with RMSF > 2.6 Å (Fig. 8d).

Furthermore, P-RMSF is calculated to measure the binding stability and structural flexibility of all Cα atoms of protein over the full course of simulation. In the present study, RMSF calculation was conducted to understand the local conformational changes of the analyzed complexes. For Withanolide A binding with SARS-CoV-2 main protease (PDB ID: 6LU7), all the amino acid residues in the protein were with P-RMSF below 9.7 Å (Fig. 9a). On the other hand, when Withanolide A bound to SARS-CoV-2 RBD complexed with ACE2 (PDB ID: 6M0J), the residues were found to lie below the P-RMSF of 4.3 Å (Fig. 9b). Similarly, Withanolide B, when complexed with 5WRG, revealed a P-RMSF of 10.0 Å (Fig. 9c) and when complexed with 6W9C showed a P-RMSF with 6.4 Å (Fig. 9d). A protein secondary structure analysis of P-RMSF suggested that Withanolide A with 6LU7 displayed higher flexibility in the loop region, followed by beta-strand and alpha-helix regions. Majority of the contacting amino acids were found to be present in the alpha-helix regions (green lines), therefore indicating greater stability of the complex (Fig. 9a). On the other hand, Withanolide A when complexed with 6M0J revealed that all the amino acids involved in hydrogen bond formation lay in the beta-strand region (Fig. 9b), suggesting lesser stability of the protein-ligand complex.

3.5. Protein-Ligand Contact Analysis

Protein-ligand interaction was monitored throughout the course of MD simulation. As is evident from Figs. 10a and 11a, Withanolide A formed hydrogen bonds directly with 6LU7 at amino acid positions Arg76, Lys137, Thr190, Ala191, Thr196, Asp197, Thr199, Leu232, Lys236, Tyr237, Asn238, Tyr239, Gln273, Met276, Asn277, Ala285, Leu287 and Asp289. The active site residues of enzyme 3CL-pro SARS-CoV-2 viz. Arg188, Pro168, Ala191, Gln192 and Thr190 were found from review of literature and were found to comparable with that present in Fig. 11(a). ⁽³²⁾ Additionally, the previous study reports that Withanolide A was found to bind near or at the active site of 6LU7 and the interacting amino acid was Arg188 [15]. However, with 6M0J the direct hydrogen bonds were formed at positions Leu73, Gln102, Gly104, Asp350, Arg393 and Asn394 (Figs. 10b and 11b). Both ligands also formed hydrophobic as well as ionic interactions with different amino acids. Srivastava et al. (2022) have reported Lys5, Gln127, Tyr126, Val125, Ser139, Lys137, Glu288 and Glu290 as the interacting amino acids in 6LU7-Withanolide A complex [15].

Interestingly, Withanolide A formed more hydrogen bonds with 6LU7, thereby accounting for greater stability of the complex. On the other hand, Withanolide B formed hydrogen bonds directly with 5WRG at positions A:Asp976, B:Gln947, B:Asn951, B:Arg977, B:Ser985, C:Arg977 and C:Gln987 and with 6W9C at A:Asn109, A:Gly160, A:Gln269, B:Asn109, B:Gly160, C:Asn109 and C:Val159 positions (Figs. 8, 9c and 9d) over the course of simulation. The interacting amino acids between 5WRG-Withanolide B have been found to be Gln984, Thr980, Leu983, Gly981, Arg977, Phe952, Tyr738, Asp976 as reported in the previous study [15]. A study has confirmed that Withanolide B interacts with the active site residue Gln269 present in 6W9C [33].

In the case of 5WRG, C:Gln987 formed water-mediated interaction with Withanolide B for 54% of the simulation time. Additionally, A:Asp976 interacted with the –OH and -O group of sugar which was found to be stable for over 53% and 48% of the simulation time (Fig. 10c). However, C:Arg977 and B:Arg977 formed water bridge and hydrogen bond for only about 39% and 59%, respectively of the entire simulation run (Fig. 10c). Figure 6d revealed that A:Gly160 and B:Asn109 interacted through hydrogen bonding with 43% and 79%, respectively, of the simulation period. In contrast, Withanolide A had no contact in the complete range of simulation time that indicated its weak interaction with the core binding pocket of 6LU7 and 6M0J as shown in Fig. 10a and b.

3.6. Ligand Properties

During MD simulation of 100ns the properties of ligands were analyzed using the first frame reference confirmation. The RMSD of the Withanolide A with 6LU7 and 6M0J were evaluated with values ranging between 0.3–0.9 Å and 0.2–1.1Å, respectively, along with reference conformation time t = 0. However, the calculated RMSD for Withanolide B in complex with 5WRG and 6W9C were found to be in the range of 0.2–1.7 Å and 0.3–1.8 Å, respectively. As is evident from Fig. 12, no intramolecular hydrogen bond (intraHB) were detected in any of the protein-ligand interactions. Therefore, this indicated that the bonds in the ligands were free to interact with protein. Radius of gyration is an indicator of compactness of protein structure. The variation in compactness of Withanolide A with 6LU7 and 6M0J was found to be in the range of 4.71-5.0 Å and 4.6–4.9 Å, respectively whereas for Withanolide B with 5WRG and 6W9C it was found within the range of 4.8–5.1 Å and 4.7–5.1 Å. Molecular surface area (MolSA) was calculated with a 1.4 Å probe radius that was equivalent to van der Waals surface area ranging between 398.7-412.8 Å² for Withanolide A and 396.6-410.2 Å² for Withanolide B. Theoretically, calculating the interaction between the ligand and solvent during the simulation run is determined by solvent accessible surface area (SASA). In the present study, the value of SASA for Withanolide A with 6LU7 and 6M0J ranged between 181.9-604.7 Å² and 155.0-397.2 Å², respectively and 23.2-108.2 Å² and 91.8-248.5 Å², respectively for Withanolide B with 5WRG and 6W9C. Polar surface area (PSA) was also evaluated to determine solvent accessible surface area in the ligands contributed only by oxygen and nitrogen atoms. They were found within the range of 140.4-163.2 Å² and 122.9-142.8 Å² for Withanolide A in complex with 6LU7 and 6M0J, respectively and 119-3-137.2 Å² and 117.3–140.0 Å² for Withanolide B in complex with 5WRG and 6W9C, respectively.

3.7. Ligand Torsion profile

The plot for ligand torsions sums up the conformational evolutions/changes in the ligand throughout the simulation trajectory of 100ns for each rotatable bond (RB). Figure 13 depicts the 2D schematic of Withanolide A and B with color-coded RB. Each RB torsion is further accompanied by a dial plot and bar plots in the same color. The conformation of the torsion throughout the course of the simulation is explained by dial plots. Additionally, the probability density of the torsion is summarized by the bar plots as shown in Fig. 13.

Despite clinical success with anti-spike vaccines, the effectiveness of neutralizing antibodies and vaccines has been compromised by rapidly evolving SARS-CoV-2 variants. B.1.1.7 (UK), B.1.351 (South Africa), P.1 (Brazil), delta B.1.617.2 (India) and the recently emerged Omicron have mutations in the N-glycosylation pattern and receptor binding domain (RBD) of the spike protein in order to evade the host immune response. The viral SARS-CoV-2 Spike (S) protein binds to host receptor ACE2 expressed majorly in lung, intestine, heart, kidney and alveolar epithelial cells. This interaction is facilitated through the binding of RBD region present on the S1 subunit of viral S protein. However, S2 subunit aids in the viral fusion and release of (+) sense ssRNA into the host [34]. During the replication stage, around 15 non-structural proteins (Nsps) are processed from two large polyproteins, Pp1a (4405 amino acids) and Pp1ab (7096 amino acids). In addition, two proteases viz. the papain-like protease (PLpro), encoded within nsp3 and 3-chymotrypsin-like ‘main’ protease (3CLpro) are encoded by nsp5. PLpro processes nsp1, nsp2 and nsp3, whereas 3CLpro cleaves the remaining 13 nsps. The final assembly of the 15 Nsps into the cytoplasmic, ER membrane-bound multicomponent replicase-transcriptase complex (RTC), helps in the regulation of viral genome as well as subgenomic mRNA replication, transcription and maturation [35]. However, prevention from disease is presently the mainstay of the fight against COVID-19 and there is an urgent need for novel prophylactic agents that are equally effective against all emerging SARS-CoV-2 variants. Therefore, the use of natural products with inherent antiviral properties in circumventing the current spike in the third wave of COVID-19 is the need of the hour. Several studies have reported the rejuvenating properties of natural products and managing respiratory disorders with medicinal plants [15, 36–39].

Molecular docking and simulation studies are in silico approaches offering an absolutely fundamental starting point for further research in terms of binding energy and stability of protein-ligand complexes. Researchers have recently begun using simulation and modeling techniques for drug development and prediction of efficacy of phytochemicals against SARS-CoV-2. In addition, the use of specific molecular modeling and simulation tools have paved new ways for testing of suitable drug candidates with higher accuracy and sensitivity.

The current in silico study provides a deeper insight into the efficacy and mechanism of action of the selected WS ligands. The selected ligands viz. Withanolides A and B displayed great potential against SARS-CoV-2 and SARS-CoV proteins. The host receptor ACE2 is crucial for the commencement of viral infection. Critical to viral entry is the attachment of the RBD of SARS-CoV-2 S protein to the host ACE2 [40]. Previous research has demonstrated that cells lacking expression of the ACE2 protein are resistant to infection by SARS-CoV-2 [41]. Secondly, SARS-CoV-2 main protease, also known as 3CL protease, is a cysteine protease with a molecular weight of 33.8 kDa that facilitates the final maturation of functional polypeptides needed for the assembly of replication-transcription machinery [42]. 3CLpro is accountable for cleaving host transmembrane protease serine 2 (TMPRSS2), which is necessary for viral entry into host cell, and an in silico study has predicted the interaction of withanone (Withanolide) with the protein [43]. A study has validated that Ashwagandha-derived Withanolides viz. camostat mesylate, withanoside-V, withanoside-IV, methoxy withaferin A, Withanolide B, withaferin A, withanone, 12-deoxywithastramonolide and Withanolide A possess potent ability to inhibit the activity of both TMPRSS2 and 3CLpro [44]. Our previous study has reported that Withanolide A exhibited strong binding with 6LU7 (BE: -8.93 kcal/mol, K_d: 285.01 nM) using AutoDock 4.2.6 (Table 3). Whereas, binding interaction with 6M0J (BE: -9.1, K_d: 214.02 nM) was reported to be stronger when compared with the reference drug arbidol (BE: -6.71, K_d: 12.0 µM) (Table 3) [15]. The findings are comparable to a study detailing the dual action of withanone in inhibiting SARS-CoV-2 entry into the host cells as well as treating COVID-19 [45]. However, Withanolide B displayed 1000x stronger binding to 6W9C (BE: −10.3 kcal/mol, K_d: 28.32 nM) and 5WRG (BE: − 9.4, K_d: 129.59 nM) as compared to reference drugs, cinacalcet (BE: -6.44, K_d: 19.17 µM) and hydroxychloroquine (BE: -5.25, K_d: 142.18 µM), respectively [15]. A recent study has predicted the antiviral potential of WS withanolides namely withacoagin and Withanolide B to block spike glycoprotein and RNA dependent RNA polymerase (RdRp) with a high binding affinity [46]. Comparison of previous study with the present one has confirmed the binding of Withanolides A and B at specific sites on the target proteins. The common interacting amino acids in Withanolide A-6LU7 complex were Lys5, Lys137, Arg188, Glu290, Asp289, Glu288, Leu287, Leu286 and Ser44, Lys562, Asn394, Arg393, Leu391, Phe390, Asp382, Asp350, Trp349, Ala348, Phe40, Ala99 in Withanolide A-6M0J complex. Support in evidence of the above results has been found in a recent study wherein Arg188 was also found to be one of the interacting amino acids in the binding of Withanolide A at the active site of SARS-CoV-2 main protease [47]. Similarly, in the present study, Withanolide B was found to contact 5WRG atTyr738, Asp976, Thr980, Gln947, Ser950, Asn951, Phe952, Arg977, Gly981, Gln984, Ser985, Phe741, Gln987, Leu983 and 6W9C at Ser85, Asp108, Asn109, Thr158, Val159, Gly160, Glu161, Leu162, Gln269 and His89. This indicates the crucial importance of these residues in facilitating the protein-ligand complex formation and modulating the structure and activity/function of SARS-CoV-2 proteins. Furthermore, the above findings underline the salient features of binding pocket(s) accessible for ligand interaction within the target protein and helps define the best spatial orientation of the conformers for novel anti-SARS-CoV-2 drug design.

Table 3

Binding energies of WS phytoconstituents and SARS-CoV-2 target proteins using molecular docking along with their interacting amino acids (Reproduced from Srivastava et al., 2022 [15]).
		AutoDock v4.2.6			AutoDock Vina			iGEMDOCK v2.1
S.No	Protein-Ligand Complexes	BE (kcal/mol)	K_d	Interacting amino acids	BE (kcal/mol)	K_d	Interacting amino acids	T.E. (kcal/mol)	VDW	HB	EI	Interacting amino acids
1.	6M0J-Withanolide A	-3.68	1.99 mM	Arg466, Phe464, Ala352, Trp353, Asn354, Arg355	-8.2	900.88 nM	Leu441, Phe374, Leu368, Phe342, Trp436, Asn343, Val367, Phe338, Gly339, Asn440, Ser373, Ser371	-85.08	-73.05	-12.03	0	Ser371, Ser373, Phe374, Ser375, Trp436, Asn437, Asn440, Leu441, Phe342, Asn343
2.	6W9C-Withanolide B	-10.3	28.32 nM	Gln269, Asn109, Asn109, Leu162, Glu161, Asn109, Leu162, Glu161, Gly160, Val159, His89, Thr158	-10.4	22.51 nM	His89, Thr158, Gly160, Asn109, Gln269, Leu162, Val159	-104.57	-96.14	-8.43	0	Thr158, Leu162, Glu161, Gly160, His89, Asp108, Ser85, Ala86, Gly160, Val159, Asn109
3.	5WRG-Withanolide B	-9.4	129.59 nM	Gln984, Thr980, Gln984, Leu983, Thr980, Gly981, Arg977, Phe952, Tyr738, Asp976, Thr980, Leu983, Gln984	-10.4	23.67 nM	Ser950, Asn951, Gly981, Arg977, Thr980, Arg977, Asp976, Asp976, Phe952, Gln984, Leu983, Gln947, Phe741, Ser985, Tyr738	-100.62	-94.31	-6.32	0	Lys715, Asp757, Ala753, Arg761, Arg758, Ala754, Leu846, Gly653, Ile652, Pro651, Tyr300
4.	6LU7-Withanolide A	-5.26	139.0 µM	Lys5, Gln127, Tyr126, Val125, Ser139, Lys137, Glu288, Glu290	-8.5	592.38 nM	Asp289, Thr199, Glu290, Arg131, Lys137, Tyr239, Leu272, Leu287, Leu286, Met276, Gly275	-91.61	-70.18	-21.43	0	Gly179, Asn180, Phe181, Asn84, Cys85, Arg40, Phe185, Tyr54, Asn53, Pro52, Arg188
Note: BE: Binding Energy, K_d: Dissociation Constant, T.E.: Total Energy, VDW: Van der Waals Energy, HB: Hydrogen Bonding Energy and EI: Electrostatic Energy*

The obtained RMSD values signified minimal fluctuations for Withanolide A-6M0J and Withanolide B-5WRG complexes, revealing that the complexes remained stable throughout the simulations. In addition, hydrogen bonds were found to be the main stabilizing factors during the 100ns simulation run. In the light of the present study, Withanolides A and Bare hereby proposed as potential natural anti-SARS-SoV-2prophylactic and therapeutic drug candidates and warrant further evaluation in vitro and in vivo.

The present study demonstrated that Withanolides A and B from WS interact with and hence, block the binding of SARS-CoV-2 spike protein with hACE2 receptor, thus, preventing the entry of the virus into the host lung epithelia. In this aspect, Withanolides A and B can be viewed as effective prophylactic agents against COVID-19, MERS and SARS. On the other hand, since Withanolides A and B also displayed potent binding to the active sites of SARS-CoV-2 main protease and papain-like protease which are crucial for the perpetuation of the viral life cycle and, therefore, can also be viewed as potential therapeutic agents against the above-mentioned diseases. To the best of knowledge of the authors, very few studies have focussed on the prophylactic and therapeutic potential of WS together. Additionally, docking results revealed potent binding of both WS lactones to all SARS-CoV-2 variants. Stable interaction(s) were observed between Withanolide A-SARS-CoV-2 spike RBD and Withanolide B-SARS-CoV spike glycoprotein. Thus, Withanolides A and B have the potential to be tested further in vitro and in vivo as prophylactic and therapeutic agents against all emerging SARS-CoV-2 variants.

Acknowledgements

The authors gratefully acknowledge Dr. Meraj Ahmad, Professor, Dept. of Sociology, University of Lucknow, Lucknow, India, for being the inspiration and motivation behind the present work in view of his exceptional work and contribution in the area of social sciences, social work, humanities and public health.

Conflict of Interest

The authors report no conflicts of interest. The authors alone are responsible for the content and writing of this article.

Author Contributions

AS drafted the manuscript whereas RA planned, conceptualized and supervised the study as well as co-drafted, edited and revised the manuscript. AS, SS, AT, IH and SU carried out the computational analyses viz. molecular docking and molecular chemoinformatics, calculations, data analysis and interpretation; TA carried out molecular dynamics simulation studies using Schrödinger Release 2020-4 package; AS and SSD carried out HPLC analysis of the WSME whereas AS carried out the in vitro biological activity experiments. KY and IAW provided technical support whereas KY co-supervised the study and critically reviewed the manuscript.

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No competing interests reported.

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Steroidal lactones from Withania somnifera effectively target Beta, Gamma, Delta and Omicron variants of SARS-CoV-2 and reveal a decreased susceptibility to viral infection and perpetuation: A Polypharmacology Approach

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Abstract

Figures

1. Introduction

2. Materials And Methods

2.1. Reagents and chemicals

2.2. Preparation and HPLC characterization of WSME

2.2.1. Preparation of the standard for HPLC Analysis

2.2.2. Preparation of WSME for HPLC Analysis

2.2.3. Procedure

2.3. Culture of human breast cancer cell line

2.4. 3-(4,5-dimethylthiazol-2-yl)-2,5-diphenyltetrazolium bromide (MTT) assay on MDA-MB-231 cells

2.5. Homology Modeling

2.6. Molecular Docking

2.7. Molecular Dynamics (MD) Simulation

2.8. Statistical Analysis

3. Results

3.1. Phytochemical Characterization of WSME

3.2. Molprobity Scoring of Modeled structure of Omicron Variant of SARS-CoV-2

3.3 Comparative Assessment of Binding Energies of Withanolide A and B with Spike Glycoprotein of SARS-CoV-2 Beta, Delta, Gamma and Omicron Variants

3.4. MD simulation studies

3.5. Protein-Ligand Contact Analysis

3.6. Ligand Properties

3.7. Ligand Torsion profile

Discussion

Conclusion

Declarations

References

Additional Declarations

Supplementary Files

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