Daucosterol and Beta-Sitosterol – the Future-Ready phytochemicals from Terminalia chebula to combat SARS-CoV-2

Daucosterol and Beta-Sitosterol – the Future-Ready phytochemicals from Terminalia chebula to combat SARS-CoV-2 | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Daucosterol and Beta-Sitosterol – the Future-Ready phytochemicals from Terminalia chebula to combat SARS-CoV-2 Sayak Dey, Mriganka Sekhar Das, Dror Tobi, Debasmita Paul, Boudhayan Bandyopadhyay This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7194350/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract The emergence of variants of SARS-CoV-2 over time raised the global concern that the strain might develop and modify again into new unknown variants in the future in an unprecedented manner, and the disease can be relapsed at any time, although it is declared as endemic. In this scenario, it becomes deemed necessary to identify, design, and formulate a future-ready drug that will be effective against the existing as well as new variants. In order to find out such types of drugs, we performed in silico screening of three phytochemicals, i.e.1,3,6-tri-O-galloyl-beta-D-glucose, Beta-Sitosterol and Daucosterol of Terminalia chebula , which were proved to be effective against SARS-CoV-2 Wild-type proteins in our previous report. In this study, we performed molecular docking experiments with those three phytochemicals against the fifteen variants of the spike protein of SARS-CoV-2 to find out the most effective candidate, which possesses the potential to inhibit the ACE2 receptor binding activities of the spike protein of the most of the variants. Our study showed that Beta-Sitosterol and Daucosterol exhibited the potential for strong binding to spike proteins of almost all variants through mainly hydrophobic interaction. Our results were further validated by MM-GBSA binding free energy calculations. This finding suggests that Beta-Sitosterol and Daucosterol can serve as potential drugs against most of the variants of SARS-CoV-2 and may be effective against the newly emerged variant. We believe that our finding, along with the validation by wet-lab experiments, can help the scientific and healthcare communities to prepare themselves against SARS-CoV-2 in the future. SARS-CoV-2 Phytochemicals Protein modeling Molecular docking MM-GBSA calculation Future-ready drug Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Figure 8 Figure 9 Figure 10 Figure 11 Figure 12 Figure 13 Figure 14 Introduction The outbreak of SARS-CoV-2 shook the entire world and started a global pandemic after it emerged during the later stages of 2019 and started causing unimaginable damage to humanity. As of 7th March 2023, 6,866,434 deaths due to SARS-CoV-2 have been reported, according to WHO (World Health Organization) [ 1 ]. In the past couple of years, we have witnessed the rise of several SARS-CoV-2 variants, with varying transmissibility, increased risk of reinfection, and decrement in vaccine efficacy. Several other variants with similar mutations and biological features are being identified. The mutations amongst the variants are propelling the spread of the virus in spite of improved population immunity [ 2 ]. The spread and development of such variants require special attention and research from the scientific community in order to discover drugs that should have the potential to combat all types of variants and the upcoming variants in the near future. Although it is currently listed as endemic, the chances of the pandemic relapsing still remain due to the high mutating tendencies of the SARS-CoV-2. For this particular purpose, we need a highly versatile and effective inhibitor that would be conducive against multiple variants of this virus, and hence, in case of any relapse of the pandemic, that inhibitor will stand a good chance to be effective against the newer variants. This global need led us to identify such types of potential inhibitors that can serve the purpose of future-ready drugs against the new unknown variant of SARS-CoV-2 in the future. SARS-CoV-2 is a single-stranded RNA virus belonging to the Coronaviridae family, genus Betacoronavirus, and is a relative of MERS-CoV and SARS-CoV [ 3 ]. It consists of 4 structural proteins, namely Spike (S) protein, Envelope protein (E), Membrane protein (M), and Nucleocapsid protein (N), along with 16 non-structural proteins and nine accessory proteins [ 4 ]. The S protein is responsible for the viral entry into the host cells with the S protein binding to the receptor angiotensin-converting enzyme 2 (ACE2), and after that, membrane fusion takes place, subsequently leading to viral replications [ 5 ]. Inhibiting the interaction between S protein and ACE2 can prevent viral entry, which is the main focus of this study. This study is the continuation of our previously published paper [ 6 ], where a library of 15 phytochemicals from the ayurvedic medicinal plant Terminalia chebula was created, and eight target proteins were selected from the SARS-CoV-2, and blind molecular docking was performed for screening purposes. The target proteins included nucleocapsid protein, N-terminal RNA binding domain, NSP15 Endoribonuclease, Nsp9 RNA binding protein, Papain-like protease, Nonstructural protein 10 (NSP10), NSP13 helicase, main protease and RNA-dependent RNA polymerase (RdRp). Out of 15 phytochemicals, it was found that 1,3,6-tri-O-galloyl-beta-D-glucose, Beta-Sitosterol, and Daucosterol possessed the most promising inhibitory effect against all eight SARS-CoV-2 proteins [ 6 ]. In this study, we wanted to elucidate the efficacy of those three reported phytochemicals against fifteen variants reported to date. So far, to our knowledge, no study with all these variants has yet been done with these phytochemicals of T. chebula as a potential future-ready drug, and hence, we felt the need to perform such research, which would better prepare us for any relapse of the COVID 19 pandemic. In this study, we evaluated the inhibitory efficacy of those three screened phytochemicals, from our previous study, against the S protein of 15 different variants of SARS-CoV-2 using blind docking as well. We found that Daucosterol showed promising inhibitory tendencies against 14 out of those 15 variants we worked with, while Beta-Sitosterol showed inhibitory potential against 13 of them, and 1,3,6-tri-O-galloyl-beta-D-glucose was effective against 10 of them. The stability of those complexes was further validated by MM-GBSA calculations. Hence, we inferred that Daucosterol and Beta-Sitosterol can be the most effective against all possible mutated versions of spike proteins of the virus, and we think this might be effective against future variants as well. We believe that our report will help the scientific community to develop such type of future-ready drug with further validation by wet-lab experiments in the future. Methods Database For the purpose of molecular modeling, the sequences of spike protein of SARS-CoV-2 variants i.e., Wild-type, Alpha, Beta, Delta, Epsilon, Eta, Gamma, Iota, Kappa, Lambda, Omicron BA.1, Omicron BA.2.12.1, Omicron BA.2.75, Omicron BA.4, Omicron BA.5 were retrieved from ViralZone Expasy [ 7 ]. The PDB file (7ZDQ) of the Cryo-EM structure of Human ACE2 (Angiotensin-converting enzyme 2 receptor protein) bound to the spike protein of SARS-CoV-2 mutant was obtained from the RCSB Protein Data Bank ( https://www.rcsb.org/ ). Protein Modeling Since the crystal structures of the receptor-binding-domain (RBD) of S protein of all fifteen variants were not available to date, it was necessary to model these proteins for our present study. For this purpose, an online protein structure prediction server named Robetta ( https://robetta.bakerlab.org/ ) was used to construct the molecular model of the RBD. Upon submitting protein sequences, predicted structures were obtained. The RBD sequences of proteins, i.e., from 319 to 541 [ 8 ] were obtained from the spike protein sequences of the variants with the use of the sequence alignment software Clustal Omega. After submitting the sequences to Robetta , five models were predicted for each protein. Amongst the five predicted models, the best model was chosen based on two parameters on the given graph for the respective models, namely, Angstroms Error Estimates and frequency of the Angstroms Error Estimates fluctuations. The one that has the lowest error estimate value in its highest peak was chosen. If more than one model for the same protein was found with a very similar error estimate value, then the frequency of fluctuations of the graph was considered. Molecule preparation prior to docking After modeling the proteins, in order to optimize the geometry of the molecule and to remove unfavourable contacts between atoms, energy minimization was performed. Chosen protein RBD models from Robetta were loaded to the YASARA (Yet Another Scientific Artificial Reality Application) server [ 9 ] and the energy minimization of each macromolecule was performed. YASARA server uses the YASARA force field, where parameters are preset to perform the energy minimization. Those energy-minimized PDB structures were loaded in PyMOL ( https://pymol.org/2/ ), and the water molecules were removed from the file. Thus, the proteins were ready for molecular docking. Blind Molecular Docking The ligands 1,3,6-tri-O-galloyl-beta-D-glucose (PubChem: 452707), Beta-Sitosterol (PubChem: 222284), Daucosterol (PubChem: 5742590) were obtained from NCBI PubChem database ( https://pubchem.ncbi.nlm.nih.gov/ ). The ligands were in SDF format and were converted into PDB format in PyRx ( https://pyrx.sourceforge.io/ ) during docking. The 15 energy-minimized RBD models were docked against those three above-mentioned ligands using the PyRx molecular docking tool ( https://pyrx.sourceforge.io/ ). A blind docking approach was chosen instead of local docking (where, the ligand is forced to bind to the specific grooves) to ensure that all the interactions were unbiased. The energy minimization of the ligand molecules was performed using PyRx. For each ligand docking with each protein, nine models were generated along with the data of binding affinity, Upper Bound and Lower Bound RMSD values. Selection of Best Fit Models For nine predicted poses, the docked complex possessed almost similar values of binding affinity, Upper Bound and Lower Bound RMSD values, hence, the best model was chosen as follows. For example, in the case of molecular docking between RBD of the alpha variant of the SARS-CoV-2 (macro-molecule) and 1,3,6-tri-O-galloyl-beta-D-glucose (ligand), nine models of the docked complex were generated, and the best model had to be selected. The reported Cryo-EM structure (PDB: 7ZDQ) of Human ACE2 bound to the spike protein of SARS-CoV-2 mutant was used as a template of interaction to select the best model through 3-dimensional structure comparison. This PDB file (Green coloured, Fig. 1 ) was opened with PyMOL. Then, the nine models (Cyan coloured, Fig. 1 ) of all the docked complexes were superimposed individually with the PDB file to judge which one was the best fit. When the two structures were superimposed, the spike protein part (Fig. 1 ) got superimposed leaving behind the ACE2 receptor and the ligand completely visible. In Fig. 1 , it is visible that the ligand molecule is binding almost exactly in the region where the spike protein of the SARS-CoV-2 virus binds with the ACE2 receptor. This type of superimposed model is the best example of a representation of competitive inhibition, where the ligand is binding in the region of the active site of the protein. This docking pose was considered the best-fit model in this case. Likewise, the best-fit model for other docked complexes was selected accordingly. MM/GBSA binding free energy calculations To further validate our findings, MM-GBSA binding free energy calculations were performed for those complexes where the ligands were found to interact at the binding interface between RBM and ACE2. All calculations were done using the Schrödinger Maestro software package ( https://www.schrodinger.com/platform/products/maestro/ ). Protein-ligand complexes were prepared using the protein preparation module. MM-GBSA ∆G calculations were conducted using OPLS4 forcefield [ 10 ] and an implicit solvation model in Prime [ 11 , 12 ]. Binding energy was calculated as: MMGBSA ∆G Bind = E(Complex) – E(Receptor) – E(Ligand free). Amino acids within 5Å of the ligand were allowed flexibility, and the protein-ligand interactions were optimized using the hierarchical sampling technique [ 13 ], thus accounting for possible induced fit between the protein and the ligand upon binding. RESULTS Daucosterol – the future-ready phytochemical To evaluate the efficacy of Daucosterol, it was docked against S proteins of fifteen variants of SARS-CoV-2. In the case of the Wild-type S protein, binding energy was – 6.9 kcal/mol, which determines the spontaneity of the interaction of Daucosterol with the S protein-ACE2 complex. The docking pose revealed that Daucosterol can interact at the binding interface between RBM and ACE2, as is visible in Fig. 2.1 .(A) (left panel), indicating competitive inhibition by the ligand. The corresponding ligplot (Fig. 2.1 .(A), right panel) shows that Daucosterol is involved in hydrophobic interactions with Leu134, Leu174, and Phe172 residues of beta sheets and one hydrogen bonding with Gly167 residue. In the case of the alpha variant S protein, the binding energy (– 7 kcal/mol) is similar to that of Wild-type. According to the docking pose, the Daucosterol acts as a competitive inhibitor interacting at the binding interface between RBM and ACE2 (Fig. 2.1 .(B), left panel). The corresponding ligplot (Fig. 2.1 .(B), right panel) reveals that the interaction between Daucosterol and RBM is mainly hydrophobic in nature, where Leu134, Ile150, and Phe172 residues are involved. Apart from that Gln175 and Phe172 are involved in hydrogen bonding with the ligand. In the case of the beta variant S protein, the binding energy was found to be – 6.7 kcal/mol. As visible from Fig. 2.1 .(C), (left panel) the Daucosterol can act as a competitive inhibitor binding at the interface between RBM and ACE2. In this case, Tyr177, Tyr183, and Tyr187 residues of RBM are involved in hydrophobic interaction with steroid moiety of Daucosterol. Gln175 and Ser176 are involved in hydrogen bonding with the hydroxyl groups of the glucopyranosyl residue (Fig. 2.1 .(C), right panel). In the case of the delta variant S protein, Daucosterol interacts with a favourable binding energy of – 6.7 kcal/mol. As is evident from Fig. 2.1 .(D), (left panel) Daucosterol is capable of acting as a competitive inhibitor against this variant as well. The binding took place through hydrophobic interactions between steroid moiety of Daucosterol and Tyr103, Tyr155 residues of S protein, the hydrogen bonding between Lys140 and the oxygen atom of glucopyranosyl residue (Fig. 2.1 .(D), right panel). In the case of the S protein of the epsilon variant, Daucosterol interacts with a binding energy of – 6.5 kcal/mol. Figure 2.2 .(E), (left panel) shows that Daucosterol fits between the RBM and ACE2, indicating competitive inhibition against the S protein-ACE2 complex. The corresponding ligplot Fig. 2.2 .(E), (right panel) reveals that Daucosterol binds with RBM through hydrophobic interaction with Tyr177 and Tyr187, and through hydrogen bonding with Glu166 and Arg134 residues. In the case of the S protein of the eta variant, Daucosterol interacts near the binding interface between RBM and ACE2 with a binding energy of – 7.1kcal/mol indicating favourable competitive inhibition Fig. 2.2 .(F), (left panel). In this case, the interaction is completely hydrophobic, as is evident from the ligplot on the right panel. Tyr103, Tyr155, Phe138, and Cys162 are found to be involved in hydrophobic interactions with steroid moiety of Daucosterol. When Daucosterol was docked with the S protein of the gamma variant, it was found to be placed between the binding interface of RBM and ACE2 (Fig. 2.2 .(G), left panel), suggesting competitive inhibition. The binding energy was – 7.1kcal/mol. It was observed in the corresponding ligplot (Fig. 2.2 .(G), right panel) that Daucosterol binds with RDB through hydrophobic interaction between steroid moiety of the ligand and Tyr131, Tyr183, and Tyr187 residues. It is also involved in hydrogen bonding between hydroxyl groups of its glucopyranosyl residue and Gly186 and Asp87. In the case of the Iota variant S protein, Daucosterol was found to be bound at the binding interface of RBM and ACE2 with a binding energy of – 6.2 kcal/mol. The docking pose in Fig. 2.2 .(H), (left panel) suggests that the inhibition is competitive in nature. As is evident from the corresponding ligplot (Fig. 2.2 .(H), right panel) the interaction between the ligand and the protein is completely hydrophobic in nature, where Phe172 residue interacts with the steroid moiety of Daucosterol through pi-alkyl interaction. In the case of the S protein of the Kappa variant, Daucosterol binds near the interface between RBM and ACE2 with a binding energy of – 6.9 kcal/mol. Although it could not fit perfectly at the binding interface, since its binding site is very close to the RBM-ACE2 interaction site (Fig. 2.3 .(I), left panel) the inhibition can be competitive in nature. In this case, Daucosterol binds with RBM through hydrophobic interaction with Ala54 and Val185; and hydrogen bonding with Gly86 and Gly186 (Fig. 2.3 .(I), right panel). In the case of the S protein of the lambda variant, Daucosterol was found to bind at the binding interface between RBM and ACE2, indicating competitive inhibition favoured by the binding energy of – 7.0 kcal/mol (Fig. 2.3 .(J), left panel). It was observed from the corresponding right panel that the binding of Daucosterol is happening through hydrophobic interactions with Ile150, Ala34, and Ala30; and hydrogen bonding with Leu174, Tyr131 (Fig. 2.3 .(J), right panel). The docking of Daucosterol was performed against the S protein of five variants of Omicron i.e. BA.1, BA.2.12.1, BA.2.75, BA.4, and BA.5 (Fig. 2.3 .(K), 2.3.(L), 2.4.(M), 2.4.(N), 2.4.(O) respectively). Except for BA.4, in all other four cases, Daucosterol was capable of binding at the interface of the RBM-ACE2 complex. The binding energy was also favourable in these cases (Table 1 ). The binding of Daucosterol took place through both hydrophobic interactions and hydrogen bonding, as is evident from corresponding ligplots. Table 1 Binding energies of docked complexes. Unit is in kcal/mol S protein variant Binding energy (kcal/mol) Daucosterol Beta-Sitosterol 1,3,6-tri-O-galloyl-beta-D-glucose Wild-type -6.9 -6.3 -7.5 Alpha -7 -7.1 -7.2 Beta -6.7 -6.7 -7.7 Delta -6.7 -7 -6.8 Epsilon -6.5 -6.6 -6.7 Eta -7.1 -6.8 -6.3 Gamma -7.1 -6.8 -6.5 Iota -6.2 -6.7 -7.6 Kappa -6.9 -6.8 -7 Lambda -7 -6.1 -6.6 Omicron_BA.1 -7.7 -7.3 -6.3 Omicron_BA.2.12.1 -7.9 -7.4 -7.2 Omicron_BA.2.75 -7.4 -6.3 -6.5 Omicron_BA.4 -7.3 -6.3 -7.3 Omicron_BA.5 -6.2 -6.5 -6.3 In the case of the Omicron_BA.4, Daucosterol failed to bind at the binding interface of RBM and ACE2. It bounds far from the active site of RBM with a binding energy of – 7.3 kcal/mol (Fig. 2.4 .(N), left panel). This indicates that in the case of Omicron_BA.4, the inhibition is not competitive in nature. Beta-Sitosterol – the promising potential Beta-Sitosterol was also docked against S proteins of fifteen variants of SARS-CoV-2 (Table 1 ). In the case of the Wild-type S protein, Beta-Sitosterol binds at the interface of the RBM-ACE2 complex with a binding energy of – 6.3 kcal/mol (Fig. 3.1 .(A), left panel). The ligand can act as a competitive inhibitor in this case. As is evident from the corresponding ligplot (Fig. 3.1 .(A), right panel) the binding of Beta-Sitosterol with RBM occurred through hydrophobic interaction of its steroid moiety with Lys140, Tyr155, Phe138 residues and hydrogen bonding with Tyr 103. In the case of the S protein of the alpha variant, Beta-Sitosterol was found to bind in between RBM and ACE2 indicating a competitive type of inhibition (Fig. 3.1 .(B), left panel). The binding energy was – 7.1 kcal/mol. The corresponding ligplot at the right panel showed that the interaction is completely hydrophobic in nature, which involves the participation of Val89, Val115, and Val185 residues (Fig. 3.1 .(B), right panel). In the case of the S protein of the beta variant, Beta-Sitosterol perfectly fit in the binding interface of RBM and ACE2, indicating competitive inhibition (Fig. 3.1 .(C), left panel). The binding energy was – 6.7 kcal/mol. It was visible from the corresponding ligplot (Fig. 3.1 .(C), right panel) that the ligand binds to RBM through hydrophobic interaction with Tyr177, Tyr183, and Tyr187 residues. In the case of the S protein of the delta variant, Beta-Sitosterol binds at the interface between RBM and ACE2 with a binding energy of – 7 kcal/mol (Fig. 3.1 .(D), left panel). This docking pose indicates that this inhibition is competitive in nature. As evident from Fig. 3.1 .(D), right panel, Beta-Sitosterol binds through hydrophobic interaction with Leu137, Tyr135, and hydrogen bonding with Asn104. In the case of the S protein of the epsilon variant, Beta-Sitosterol binds at the interaction interface between RBM and ACE2 with a binding energy of – 6.6 kcal/mol. The docking pose (Fig. 3.2 .(E), left panel) suggests that the inhibition is competitive in nature. The corresponding ligplot (Fig. 3.2 .(E), right panel) confirms that the interaction is highly hydrophobic which involves the participation of Phe138, Tyr103, Tyr155, and Lys140 residues. Thr160 was found to be involved in hydrogen bonding. In the case of the S protein of the eta variant, Beta-Sitosterol was found to bind at the active site of RBM, indicating competitive inhibition with a binding energy of – 6.8 kcal/mol (Fig. 3.2 .(F), left panel). The corresponding ligplot (Fig. 3.2 .(F), right panel) suggests that the interaction is purely hydrophobic in nature. In the case of the S protein of the gamma variant, Beta-Sitosterol perfectly fits into the binding interface between RBM and ACE2 (Fig. 3.2 .(G), left panel) indicating competitive inhibition. The binding energy involved in this case is – 6.8 kcal/mol. From the corresponding ligplot (Fig. 3.2 .(G), right panel) it is visible that the ligand-protein binding is completely hydrophobic in nature, involving Tyr177, Tyr183, and Tyr187. For S proteins of Iota and Kappa variants, Beta-Sitosterol was not capable of binding at the interface between RBM and ACE2 (Fig. 3.2 .(H) and 3.3.(I)). These docking poses suggest that Beta-Sitosterol cannot act as competitive inhibitor, since it can’t bind to the active site of the proteins. In the case of the S protein of the lambda variant, Beta-Sitosterol binds at the interface between RBM and ACE2 (Fig. 3.3 .(J), left panel) suggesting competitive inhibition. The corresponding binding energy is – 6.1 kcal/mol. The ligplot (Fig. 3.3 .(J), right panel) shows that the interaction is completely hydrophobic in nature, involving Tyr131, Tyr177, and Tyr187. For S proteins of the Omicron variants, Beta-Sitosterol binds at the interface between RBM and ACE2 (Fig. 3.3 .(K) – 3.4.(O), left panels) with favourable binding energy indicating competitive inhibition. The corresponding ligplots suggest that all the interactions are mainly hydrophobic in nature involving non-polar residues. 1,3,6-tri-O-galloyl-beta-D-glucose – not so efficient against all variants For Wild-type S protein, 1,3,6-tri-O-galloyl-beta-D-glucose binds far from the interface between RBM and ACE2 with the favourable binding energy of – 7.5 kcal/mol (Fig. 4.1 .(A), left panel). This suggests that this ligand cannot act as a competitive inhibitor in this case. In the case of the S protein of the alpha variant, 1,3,6-tri-O-galloyl-beta-D-glucose binds at the interface of RBM and ACE2 with a favourable binding energy of – 7.2 kcal/mol (Fig. 4.1 .(B), left panel). The docking pose suggested that the ligand acts as a competitive inhibitor. The corresponding ligplot showed that the interaction is taking place mainly through hydrogen bonding. Polar residues of RBM such as Ser176, Arg85, and Glu166 are taking part in hydrogen bonding with hydroxyl functional groups of the galloyl group of the ligand (Fig. 4.1 .(B), right panel). For the S protein of the beta variant, 1,3,6-tri-O-galloyl-beta-D-glucose could not fit at the binding interface between RBM and ACE2, although the binding energy is highly favourable (– 7.7 kcal/mol). In this case, the ligand was found to interact far from the binding interface (Fig. 4.1 .(C), left panel). This suggests that the activity of S protein may not be influenced by this ligand, in this case. In the case of the S protein of the delta variant, 1,3,6-tri-O-galloyl-beta-D-glucose binds near the interface between RBM and ACE2 with a binding energy of – 6.8 kcal/mol (Fig. 4.1 .(D), left panel). Here in this region, this ligand may act as a competitive inhibitor as is visible from the docking pose. In the corresponding ligplot (Fig. 4.1 .(D), right panel) it was observed that the ligand interacts with protein through hydrogen bonding. The hydroxyl groups of the galloyl group of the ligand form hydrogen bonding with Glu153, Gln156, Ser159, Lys160, and Cys162 residues. In the case of the S protein of the Epsilon variant, 1,3,6-tri-O-galloyl-beta-D-glucose binds at the interface between RBM and ACE2 with binding energy of – 6.7 kcal/mol (Fig. 4.2 .(E), left panel), suggesting that the ligand is acting as a competitive inhibitor. The corresponding ligplot (Fig. 4.2 .(E), right panel) indicates that the interaction is hydrophobic in nature. For the S protein of the Eta variant, 1,3,6-tri-O-galloyl-beta-D-glucose binds at the interface between RBM and ACE2 with a binding energy of – 6.3 kcal/mol (Fig. 4.2 .(F), left panel). The docking pose indicates that the ligand-protein interaction is competitive in nature. In the corresponding ligplot, it was observed that the galloyl group of the ligand is interacting with RBM through hydrogen bonding with Ser57, Asn119, Asn121, and Asn183 (Fig. 4.2 .(F), right panel). However, some unfavourable interactions were visible in this case. In the case of the S protein of the gamma variant, it was observed from the docking pose that 1,3,6-tri-O-galloyl-beta-D-glucose binds in between the interaction interface of RBM and ACE2 with a binding energy of – 6.5 kcal/mol (Fig. 4.2 .(G), left panel) suggesting competitive inhibition. The corresponding ligplot showed that the galloyl group of 1,3,6-tri-O-galloyl-beta-D-glucose interacts with RBM through hydrogen bonding with polar amino acid residues of RBM (Fig. 4.2 .(G), right panel). In the case of the S protein of the Iota variant, 1,3,6-tri-O-galloyl-beta-D-glucose was found to bind far from the active site of the protein (Fig. 4.2 .(H), left panel) with the binding energy of – 7.6 kcal/mol. For the S protein of the Kappa variant, 1,3,6-tri-O-galloyl-beta-D-glucose binds at the interaction interface between RBM and ACE2 with a binding energy of – 7.0 kcal/mol (Fig. 4.3 .(I), left panel) suggesting that the inhibition is competitive in nature. The corresponding ligplot (Fig. 4.3 .(I), right panel) showed that 1,3,6-tri-O-galloyl-beta-D-glucose interacts with RBM through hydrogen bonding with Gln91, Arg85, Tyr135, Tyr177, Ser176, and Asn183 residues of RBM and pi-pi interaction between phenyl ring of the ligand and Tyr187 residue of RBM. However, two unfavourable interactions are visible in this case. In the case of the S protein of the lambda variant, 1,3,6-tri-O-galloyl-beta-D-glucose interacts at the binding interface between RBM and ACE2 with a binding energy of – 6.6 kcal/mol (Fig. 4.3 .(J), left panel) indicating competitive inhibitory action of this phytochemical. The corresponding ligplot (Fig. 4.3 .(J), right panel) showed that interaction is happening through mainly hydrogen bonding between the galloyl group and the polar residues of RBM. In the case of the S protein of the Omicron variants, 1,3,6-tri-O-galloyl-beta-D-glucose interacts with three variants, i.e., Omicron_BA.1, Omicron_BA.2.12.1 and Omicron_BA.5, as a competitive inhibitor, as is visible from the Fig. 4.3 .(K), 4.3.(L) and 4.4.(O), (left panels) with a favourable binding energy of – 6.3, – 7.2 and – 6.3 kcal/mol, respectively. In the case of the Omicron_BA.2.75 and Omicron_BA.4 variants, 1,3,6-tri-O-galloyl-beta-D-glucose binds far away from the active site (Fig. 4.4 .(M) and 4.4.(N), left panel). Validation of binding energy data Further validation was required to confirm our observations. Prime MM/GBSA calculations were conducted to assess the stability of those complexes where the ligands were found to be docked at the binding interface of RBD and ACE2. All ligands showed significant negative binding energy, demonstrating their ability to bind to the different variants of the Spike protein. The average binding energy of Daucosterol to the different variants was – 56.27 kcal/mol (Table 2 ). Similarly, Beta-Sitosterol and 1,3,6-Tri-O-galloyl-beta-D-glucose showed average binding energies of – 48.12 and – 59.74 kcal/mol, respectively, to the different variants (Table 2 ). These binding energy values are similar in range to those reported by Dasmahapatra et al upon developing PI3K inhibitors [ 14 ]. Table 2 MM-GBSA free Binding energies of docked complexes. Unit is in kcal/mol. S protein variant MMGBSA_dG_Binding energy (kcal/mol) Daucosterol Beta-Sitosterol 1,3,6-tri-O-galloyl-beta-D-glucose Wild-type -33.60 -53.04 - Alpha -62.40 -43.51 -50.69 Beta -52.18 -41.37 - Delta -62.20 -38.61 -66.33 Epsilon -51.94 -52.19 -71.83 Eta -74.52 -55.65 -41.60 Gamma -61.43 -51.08 -68.03 Iota -59.43 - - Kappa -42.91 - -54.85 Lambda -55.98 -41.87 -71.41 Omicron_BA.1 -61.67 -59.08 -63.91 Omicron_BA.2.12.1 -59.67 -56.72 -43.17 Omicron_BA.2.75 -60.01 -36.24 - Omicron_BA.4 -* -47.31 - Omicron_BA.5 -49.86 -48.85 -65.56 * ‘-’ represents the cases where the ligand binds far away from the RBM and ACE2 binding interface. MM-GBSA free Binding energy calculation was not performed for those cases. Daucosterol binds to the Iota variant with ∆G binding of – 59.43 kcal/mol, which was close to the average binding energy. The interactions between Daucosterol and this variant are depicted in Fig. 5 . The surface of the Iota binding site is colored according to the electrostatic potential (Fig. 5 A). Negatively and positively charged surfaces are colored red and blue, while hydrophobic regions are colored grey. From this figure, it is clearly visible that most of the binding site is hydrophobic. Daucosterol is presented using wire representation and colored green (carbon atoms) and red (oxygen atoms). Detailed interactions between Daucosterol and the protein are depicted in Fig. 5 B. The ligand is involved in hydrophobic interactions with L135, L150, L174, and F172 amino acids. One of the hydroxyl groups of Daucosterol forms two hydrogen bonds, one with the backbone nitrogen of F172 and another with the side chain nitrogen of Q175 amino acids. DISCUSSION From the onset of emergence till date, the SARS-CoV-2 has continuously mutated itself, leading to the development of new variants [ 5 ]. Although several vaccines ( https://www.who.int/publications/m/item/draft-landscape-of-covid-19-candidate-vaccines ) have been developed to date, the vaccine effectiveness has been compromised against the new variants of SARS-CoV-2 [ 15 ]. Apart from vaccines, no effective drugs have been developed to combat against SARS-CoV-2 to date. Therefore, there is an urgent need for the development of a potential drug that can be effective against the newly emerged variant of SARS-CoV-2 in the near future. In our previous study, we screened fifteen phytochemicals of Terminalia chebula against eight SARS-CoV-2 proteins and found 1,3,6-tri-O-galloyl-beta-D-glucose, Beta-Sitosterol and Daucosterol as potential inhibitors against those protein activities [ 6 ]. In this study, we wanted to elucidate the efficacy of these three phytochemicals against the spike proteins of fifteen well-known variants of SARS-CoV-2, to find out the future-ready phytochemical that can combat any type of newly emerged variants of the virus. For this purpose, first we modeled the 3-dimensional structures of S proteins of all fifteen variants and then performed blind docking between each phytochemical molecule and the S protein of each variant. The blind docking was chosen over local docking to avoid the active site biases and to check for the spontaneity of binding of phytochemicals to the protein target, based on binding free energy. Here, in this study, we targeted the S protein, since it is responsible for the viral entry through the binding to cell receptor ACE2. The S protein binds to ACE2 mainly through the receptor binding motif (RBM) and leads the viral entry into the host. If we can find that any of those phytochemicals bind to this interaction interface, we can confirm that that compound may act as a competitive inhibitor against this interaction. To screen for such type of candidates out of those three above-mentioned phytochemicals, first we performed molecular docking between the phytochemical and S protein model of each variant, and then we superimposed the docking pose on the reported crystal structure of the RBM-ACE2 complex. We selected the docking pose based on the binding position of the phytochemical and the binding energy. Since the binding energy was on an average – 7 kcal/mol, which supports the spontaneity of the interaction, we set the docking pose of the phytochemicals as the main criteria to choose the best possible candidate. If the phytochemical was found to bind to the interaction interface between RBM and ACE2, that phytochemical was considered a competitive inhibitor. If the phytochemical bound far away from RBM, it might not inhibit effectively or act as a non-competitive inhibitor. Our molecular docking results showed that 1,3,6-tri-O-galloyl-beta-D-glucose was unable to bind to the interaction interface of RBM and ACE2 in the case of the S proteins of Wild-type, Beta, Iota, Omicron_BA.2.75 and Omicron_BA.4 variants (Table 1 ). Beta-Sitosterol was found to bind far away from that interaction interface for those of Iota and Kappa variants. Daucosterol failed to bind at the above-mentioned active site of RBM in the case of the Omicron_BA.4 variant. In these cases, the phytochemicals may not act as competitive inhibitors or can be non-competitive inhibitors. These results suggest that 1,3,6-tri-O-galloyl-beta-D-glucose may not be effective against all fifteen variants of SARS-CoV-2, whereas Daucosterol and Beta-Sitosterol will be effective against most of these variants. To further validate the dynamic behaviour and stability of the complexes, MM/GBSA binding free energy calculations were performed. From the calculated average binding free energy, it was confirmed that the ligands will bind effectively with the variants of spike proteins and should be stable over time. 1,3,6-tri-O-galloyl-beta-D-glucose is composed of D-glucose flanked by three galloyl groups. Because of the presence of these galloyl groups, it is capable of forming hydrogen bonds with the target protein. From the binding poses, it seems that may be due to the bigger structure of the molecule, 1,3,6-tri-O-galloyl-beta-D-glucose was not able to bind to the active site of the protein, for five variants. Beta-Sitosterol and Daucosterol belong to the steroid category of molecules. In both cases, the molecules interact with the S protein variants mainly through hydrophobic interactions because of the presence of hydrophobic steroid moieties. Our finding indicates that Beta-Sitosterol and Daucosterol can be used as potential inhibitors against all these fifteen variants and may also be effective against the new possible variants in the future. In a recent study [ 16 ] four phytochemicals, i.e. withanolide F, serotobenine, orobanchol, and gibberellin A51 are reported as a strong binder to RBD of native and five variants of concern (alpha, beta, gamma, delta, and omicron) of SARS-CoV-2 based on in silico screening, molecular docking and molecular dynamics simulation. Several research groups have performed phytochemical screening experiments on SARS-CoV-2 Wild-type and other variants such as Omicron [ 17 – 19 ], delta variant [ 20 ] to identify the potential drug candidates. Vimalanathan et. al. investigated the effectiveness of Echinacea purpurea (Echinaforce® extract, EF) against alpha, beta, gamma, delta, Scottish, eta, omicron variants; S protein-pseudotyped viral particles; reference strain OC43 and Wild-type of SARS-CoV-2 [ 21 ]. The EF extract was found to be effective against all the variants and SARS-CoV-2 pseudoparticles. The molecular dynamics simulation studies of interaction between Echinacea 's phytochemical markers and the S protein of SARS-CoV-2 revealed that alkylamides, caftaric acid, and feruloyl-tartaric acid exhibited constant binding affinities towards the S protein. In the case of the alkylamides, a similar type of hydrophobic interaction with RBD of S protein was observed like Beta-Sitosterol and Daucosterol [ 21 ]. Ahmed et. al. performed in silico screening of 46 phytochemicals against S proteins of Alpha, Beta, Delta, Gamma, and Omicron variants and found liquiritigenin as a broad-spectrum inhibitor against SARS-CoV-2 [ 22 ]. Liquiritigenin belongs to the flavonoid category which can interact through both hydrophobic and hydrogen bonding with S protein. In a recent demographic data analysis of a group of Polish men and women [ 23 ], reduced risk of COVID-19 was found among the people who intake phytosterols such as stigmasterol and Beta-Sitosterol. In a bioinformatics study by Sing et. al. [ 24 ], Beta-Sitosterol was found to act as an anti-inflammatory agent against COVID-19 cytokine storm. There are several other reports, which support our finding that Beta-Sitosterol can serve as a future-ready drug to combat unknown newly emerged SARS-CoV-2 variant(s). However, there are no reports on Daucosterol, except the molecular dynamics simulation study by Ghosh et. al. [ 25 ], which showed that among seven phytochemicals of Terminalia Chebula , Daucosterol exhibited the strongest binding with the main protease (M pro ) of the SARS-CoV-2. To compare the effectiveness of these phytochemicals with that of existing drugs, we performed a literature survey where both wet- and dry-lab-based experiments were conducted to identify the potential drugs against SARS-CoV-2. We found some repurposed drugs such as Benzimidazole [ 26 ]; Artemisinin, Dihydroartemisinin, chloroquine [ 27 ]; Tolvaptan [ 28 ], which were reportedly inhibiting the interaction between RBD and ACE2 at micromolar range in vitro; and in silico interact at the binding interface of RBD and ACE2 with a binding energy of – 6.24, – 7 and – 8.8 kcal/mol respectively. The binding energy of these molecular docking studies matches with the data of our study which further suggests that 1,3,6-tri-O-galloyl-beta-D-glucose, Daucosterol and Beta-Sitosterol may inhibit the interaction at the micromolar range, which may be subjected to further validation by wet-lab experiments. CONCLUSION When a pathogenic strain continuously develops itself with time, it becomes deemed necessary to identify a drug that will be effective towards those developing strains. In the case of SARS-CoV-2, it is continuously changing itself with time although COVID-19 has been declared as endemic, and there will always be a chance of relapsing of this disease. Keeping this scenario in mind, we started looking for the drug from previously screened phytochemicals i.e., 1,3,6-tri-O-galloyl-beta-D-glucose, Daucosterol and Beta-Sitosterol, which possess the future-ready potential. We found that Daucosterol and Beta-Sitosterol are highly effective and can act as competitive inhibitors against most of the variants of SARS-CoV-2 reported to date. Since they were found to bind effectively at the active site of the S protein in most of the variants, it was expected that they would also be effective towards the newly emerged unknown variants. ADME/T studies were already performed for these phytochemicals in the previous report and it was found that 1,3,6-tri-O-galloyl-beta-D-glucose does not meet the criteria of drug-like properties. Although further wet-lab based experiments are required in future to validate our report, our finding has provided a solid foundation towards the development of a future-ready anti-viral drug. Declarations ACKNOWLEDGEMENT BB is thankful to the Department of Science and Technology (DST), Govt. of India, for providing funding through grant no: SRG/2022/001543 Ethical Approval: Not applicable Funding: This work was supported by DST – SERB, Govt. of India (Grant number: SRG/2022/001543). Data Availability: The datasets generated during and/or analysed during the current study are available from the corresponding author on reasonable request. Competing Interests: The authors have no relevant financial or non-financial interests to disclose. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-7194350","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":492606911,"identity":"1d8ee77d-ac45-4759-9e1a-251a7dd0f7e3","order_by":0,"name":"Sayak Dey","email":"","orcid":"","institution":"VIT Bhopal University","correspondingAuthor":false,"prefix":"","firstName":"Sayak","middleName":"","lastName":"Dey","suffix":""},{"id":492606912,"identity":"50091cfe-439a-4287-babd-dbbb414946e9","order_by":1,"name":"Mriganka Sekhar Das","email":"","orcid":"","institution":"VIT Bhopal University","correspondingAuthor":false,"prefix":"","firstName":"Mriganka","middleName":"Sekhar","lastName":"Das","suffix":""},{"id":492606913,"identity":"be19c021-a725-4ac9-b790-0e4ce501ceb8","order_by":2,"name":"Dror Tobi","email":"","orcid":"","institution":"Ariel University","correspondingAuthor":false,"prefix":"","firstName":"Dror","middleName":"","lastName":"Tobi","suffix":""},{"id":492606914,"identity":"7e3bd903-56a2-4723-8e89-c30ddd9d8866","order_by":3,"name":"Debasmita Paul","email":"","orcid":"","institution":"Adamas University","correspondingAuthor":false,"prefix":"","firstName":"Debasmita","middleName":"","lastName":"Paul","suffix":""},{"id":492606915,"identity":"0e09ba5d-7863-46cd-8fbd-85906590cdb4","order_by":4,"name":"Boudhayan Bandyopadhyay","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA3UlEQVRIiWNgGAWjYBACAyCWBrOYmQ8ASQkZErSwsyWAtPCQoIWfB8RmIKzFnL3H8HZBzWF5c2aez69u1FjwMLAfProBnxbLnjPG1jOOHTbc2cy7zTrnGNBhPGlpN/A67EaOmTQP22HGDYd5txnnsAG1SPCYEaHl32H7DYd5nhnn/CNWC2/b4USgFubHuW1EaLHsOVZszduXnrzhMJsZc26fBA8bIb+YszdvvM3zzdp2w/nDjz/nfKuT42c/fAyvFihoBhFsEmCSCOUgUAcimD8QqXoUjIJRMApGGAAAN2pEoykNTd0AAAAASUVORK5CYII=","orcid":"","institution":"Adamas University","correspondingAuthor":true,"prefix":"","firstName":"Boudhayan","middleName":"","lastName":"Bandyopadhyay","suffix":""}],"badges":[],"createdAt":"2025-07-23 09:08:11","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-7194350/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-7194350/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":88007589,"identity":"eaef16d1-4771-4e1c-afa0-6d38210d5214","added_by":"auto","created_at":"2025-07-31 11:17:08","extension":"jpg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":259281,"visible":true,"origin":"","legend":"\u003cp\u003eSuperimposed structures of the docked S protein-ligand (1,3,6-tri-O-galloyl-beta-D-glucose) complex (cyan coloured) upon the reported Cryo-EM structure (Green coloured) (PDB: 7ZDQ) of Human ACE2 bound to the spike protein of SARS-CoV-2, showing that the ligand interferes the binding of human ACE2 and S protein of SARS-CoV-2.\u003c/p\u003e","description":"","filename":"fig1.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7194350/v1/8bf83e01daffdb84765ab2ae.jpg"},{"id":88007588,"identity":"99c36891-664b-4ee9-81b0-a4b1c7c33d06","added_by":"auto","created_at":"2025-07-31 11:17:08","extension":"jpg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":484024,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eFig 2.1: \u003c/strong\u003eInteraction between Daucosterol and S protein of SARS-CoV-2 of following variants i.e., (A) Wild-type, (B) Alpha, (C) Beta, (D) Delta. The left panel represents the docking pose of the S protein– phytochemical complex, superimposed with the reported Cryo-EM structure of human ACE2 bound to S protein (PDB: 7ZDQ). The right panel represents a 2D interaction plot of the corresponding docking complex.\u003c/p\u003e","description":"","filename":"fig2.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7194350/v1/988d1e10c2bc7cc40ddacb10.jpg"},{"id":88007590,"identity":"ce577b6d-0b19-4978-a381-d93c2e00e7ae","added_by":"auto","created_at":"2025-07-31 11:17:09","extension":"jpg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":487009,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eFig 2.2: \u003c/strong\u003eInteraction between Daucosterol and S protein of SARS-CoV-2 of following variants i.e., (E) Epsilon, (F) Eta, (G) Gamma, (H) Iota. The left panel represents the docking pose of the S protein- phytochemical complex, superimposed with the reported Cryo-EM structure of human ACE2 bound to the \u0026nbsp;S protein (PDB: 7ZDQ). The right panel represents a 2D interaction plot of the corresponding docking complex.\u003c/p\u003e","description":"","filename":"fig3.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7194350/v1/c453cd01e07bd31a51d71bf5.jpg"},{"id":88008674,"identity":"86329ddf-9b8f-48dd-bbbf-62d75e9972f8","added_by":"auto","created_at":"2025-07-31 11:25:09","extension":"jpg","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":510486,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eFig 2.3: \u003c/strong\u003eInteraction between Daucosterol and S protein of SARS-CoV-2 of following variants i.e., (I) Kappa, (J) Lambda, (K) Omicron BA.1, (L) Omicron BA.2.12.1. Left panel represents the docking pose of S protein-phytochemical complex, superimposed with the reported Cryo-EM structure of human ACE2 bound to S protein (PDB: 7ZDQ). The right panel represents a 2D interaction plot of the corresponding docking complex.\u003c/p\u003e","description":"","filename":"fig4.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7194350/v1/b9ada80449292220be2d9aa1.jpg"},{"id":88007593,"identity":"9afa92c3-041d-4d64-b383-515d5df95e86","added_by":"auto","created_at":"2025-07-31 11:17:09","extension":"jpg","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":422355,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eFig 2.4: \u003c/strong\u003eInteraction between Daucosterol and S protein of SARS-CoV-2 of following variants i.e., (M) Omicron BA.2.75, (N) Omicron BA.4, (O) Omicron BA.5. Left panel represents the docking pose of S protein-phytochemical complex, superimposed with the reported Cryo-EM structure of human ACE2 bound to S protein (PDB: 7ZDQ). The right panel represents a 2D interaction plot of corresponding docking complex.\u003c/p\u003e","description":"","filename":"fig5.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7194350/v1/04c599b0cd34ab5093b18c50.jpg"},{"id":88008949,"identity":"11a371f1-4866-4cbd-b009-5f33c2fdf43f","added_by":"auto","created_at":"2025-07-31 11:33:09","extension":"jpg","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":480629,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eFig 3.1: \u003c/strong\u003eInteraction between Beta-Sitosterol and S protein of SARS-CoV-2 of following variants i.e., (A) Wild-type, (B) Alpha, (C) Beta, (D) Delta. The left panel represents the docking pose of the S protein–phytochemical complex, superimposed with the reported Cryo-EM structure of human ACE2 bound to the S protein (PDB: 7ZDQ). The right panel represents 2D interaction plot of the corresponding docking complex.\u003c/p\u003e","description":"","filename":"fig6.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7194350/v1/be424e9f88e8a845373cbd4a.jpg"},{"id":88008677,"identity":"53fc150e-8939-408d-8d1b-362ce9b37cd7","added_by":"auto","created_at":"2025-07-31 11:25:09","extension":"jpg","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":487514,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eFig 3.2: \u003c/strong\u003eInteraction between Beta-Sitosterol and S protein of SARS-CoV-2 of following variants i.e., (E) Epsilon, (F) Eta, (G) Gamma, (H) Iota. The left panel represents the docking pose of S protein–phytochemical complex, superimposed with the reported Cryo-EM structure of human ACE2 bound to S protein (PDB: 7ZDQ). The right panel represents a 2D interaction plot of corresponding docking complex.\u003c/p\u003e","description":"","filename":"fig7.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7194350/v1/e6941114bde29d6b58748839.jpg"},{"id":88007608,"identity":"d16c9eca-4754-4cfe-8096-c41eaede9646","added_by":"auto","created_at":"2025-07-31 11:17:09","extension":"jpg","order_by":8,"title":"Figure 8","display":"","copyAsset":false,"role":"figure","size":468129,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eFig 3.3: \u003c/strong\u003eInteraction between Beta-Sitosterol and S protein of SARS-CoV-2 of following variants i.e., (I) Kappa, (J) Lambda, (K) Omicron BA.1, (L) Omicron BA.2.12.1. Left panel represents the docking pose of S protein–phytochemical complex, superimposed with the reported Cryo-EM structure of human ACE2 bound to S protein (PDB: 7ZDQ). The right panel represents 2D interaction plot of the corresponding docking complex.\u003c/p\u003e","description":"","filename":"fig8.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7194350/v1/93cc2cd23ed94566b0d1a1df.jpg"},{"id":88007601,"identity":"5b61c593-4dd1-4400-ae77-f04bf958fb06","added_by":"auto","created_at":"2025-07-31 11:17:09","extension":"jpg","order_by":9,"title":"Figure 9","display":"","copyAsset":false,"role":"figure","size":365753,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eFig 3.4: \u003c/strong\u003eInteraction between Beta-Sitosterol and S protein of SARS-CoV-2 of following variants i.e., (M) Omicron BA.2.75, (N) Omicron BA.4, (O) Omicron BA.5. The left panel represents the docking pose of S protein–phytochemical complex, superimposed with the reported Cryo-EM structure of human ACE2 bound to S protein (PDB: 7ZDQ). The right panel represents 2D interaction plot of the corresponding docking complex.\u003c/p\u003e","description":"","filename":"fig9.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7194350/v1/fdf58b0acf4cf6832b7f63d0.jpg"},{"id":88008947,"identity":"2a615efa-48c9-43f3-9555-08876b7c6cad","added_by":"auto","created_at":"2025-07-31 11:33:09","extension":"jpg","order_by":10,"title":"Figure 10","display":"","copyAsset":false,"role":"figure","size":488341,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eFig 4.1: \u003c/strong\u003eInteraction between 1,3,6-tri-O-galloyl-beta-D-glucose and S protein of SARS-CoV-2 of following variants i.e., (A) Wild-type, (B) Alpha, (C) Beta, (D) Delta. The left panel represents the docking pose of the S protein–phytochemical complex, superimposed with the reported Cryo-EM structure of human ACE2 bound to the S protein (PDB: 7ZDQ). The right panel represents 2D interaction plot of the corresponding docking complex.\u003c/p\u003e","description":"","filename":"fig10.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7194350/v1/72c3caae9ace370a27906f67.jpg"},{"id":88008685,"identity":"b6ea3e7e-4ede-44e1-8fff-f684d1c5a0ef","added_by":"auto","created_at":"2025-07-31 11:25:09","extension":"jpg","order_by":11,"title":"Figure 11","display":"","copyAsset":false,"role":"figure","size":512742,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eFig 4.2: \u003c/strong\u003eInteraction between 1,3,6-tri-O-galloyl-beta-D-glucose and S protein of SARS-CoV-2 of following variants i.e., (E) Epsilon, (F) Eta, (G) Gamma, (H) Iota. The left panel represents the docking pose of the S protein–phytochemical complex, superimposed with the reported Cryo-EM structure of human ACE2 bound to the S protein (PDB: 7ZDQ). The right panel represents a 2D interaction plot of the corresponding docking complex.\u003c/p\u003e","description":"","filename":"fig11.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7194350/v1/91b3c33b671293574c294962.jpg"},{"id":88007634,"identity":"a5e1bb6d-6162-49a2-866d-3407ed17b574","added_by":"auto","created_at":"2025-07-31 11:17:10","extension":"jpg","order_by":12,"title":"Figure 12","display":"","copyAsset":false,"role":"figure","size":506425,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eFig 4.3: \u003c/strong\u003eInteraction between 1,3,6-tri-O-galloyl-beta-D-glucose and S protein of SARS-CoV-2 of following variants i.e., (I) Kappa, (J) Lambda, (K) Omicron BA.1, (L) Omicron BA.2.12.1. Left panel represents the docking pose of S protein–phytochemical complex, superimposed with the reported Cryo-EM structure of human ACE2 bound to S protein (PDB: 7ZDQ). The right panel represents a 2D interaction plot of the corresponding docking complex.\u003c/p\u003e","description":"","filename":"fig12.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7194350/v1/515ceccb39ec9e9c995e3724.jpg"},{"id":88007603,"identity":"9a262dcb-a3b2-4c5a-94d6-f7ab10886771","added_by":"auto","created_at":"2025-07-31 11:17:09","extension":"jpg","order_by":13,"title":"Figure 13","display":"","copyAsset":false,"role":"figure","size":356191,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eFig 4.4: \u003c/strong\u003eInteraction between 1,3,6-tri-O-galloyl-beta-D-glucose and S protein of SARS-CoV-2 of following variants i.e., (M) Omicron BA.2.75, (N) Omicron BA.4, (O) Omicron BA.5. Left panel represents the docking pose of S protein–phytochemical complex, superimposed with the reported Cryo-EM structure of human ACE2 bound to S protein (PDB: 7ZDQ). The right panel represents a 2D interaction plot of corresponding docking complex.\u003c/p\u003e","description":"","filename":"fig13.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7194350/v1/e19ead72c7fc67a3ed3bec11.jpg"},{"id":88008684,"identity":"490d5024-1757-48f5-a70d-26de7dc5282e","added_by":"auto","created_at":"2025-07-31 11:25:09","extension":"jpg","order_by":14,"title":"Figure 14","display":"","copyAsset":false,"role":"figure","size":188717,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eFig 5: \u003c/strong\u003eSpike protein of Iota variant-Daucosterol Docked Complex interactions. (A) Surface representation of the Spike iota variant binding site. Negatively and positively charged are colored red and blue, while hydrophobic regions are colored grey. Daucosterol is shown using wire representation and colored green (carbon atoms) and red (oxygen atoms). (B) Licorice representation of the iota variant binding site. H-bonds are shown using a yellow dashed line. The figures were created using the Schrödinger Maestro software package.\u003c/p\u003e","description":"","filename":"fig14.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7194350/v1/453dc28db624a395d6fb5244.jpg"},{"id":88425452,"identity":"26faa9a6-bfd2-41d5-9e97-e1d14762003f","added_by":"auto","created_at":"2025-08-06 09:53:58","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":6925415,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7194350/v1/38cffb74-8c8b-46de-a0d1-9647b9ef4ad2.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Daucosterol and Beta-Sitosterol – the Future-Ready phytochemicals from Terminalia chebula to combat SARS-CoV-2","fulltext":[{"header":"Introduction","content":"\u003cp\u003eThe outbreak of SARS-CoV-2 shook the entire world and started a global pandemic after it emerged during the later stages of 2019 and started causing unimaginable damage to humanity. As of 7th March 2023, 6,866,434 deaths due to SARS-CoV-2 have been reported, according to WHO (World Health Organization) [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. In the past couple of years, we have witnessed the rise of several SARS-CoV-2 variants, with varying transmissibility, increased risk of reinfection, and decrement in vaccine efficacy. Several other variants with similar mutations and biological features are being identified. The mutations amongst the variants are propelling the spread of the virus in spite of improved population immunity [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. The spread and development of such variants require special attention and research from the scientific community in order to discover drugs that should have the potential to combat all types of variants and the upcoming variants in the near future. Although it is currently listed as endemic, the chances of the pandemic relapsing still remain due to the high mutating tendencies of the SARS-CoV-2. For this particular purpose, we need a highly versatile and effective inhibitor that would be conducive against multiple variants of this virus, and hence, in case of any relapse of the pandemic, that inhibitor will stand a good chance to be effective against the newer variants. This global need led us to identify such types of potential inhibitors that can serve the purpose of future-ready drugs against the new unknown variant of SARS-CoV-2 in the future.\u003c/p\u003e\u003cp\u003eSARS-CoV-2 is a single-stranded RNA virus belonging to the Coronaviridae family, genus Betacoronavirus, and is a relative of MERS-CoV and SARS-CoV [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. It consists of 4 structural proteins, namely Spike (S) protein, Envelope protein (E), Membrane protein (M), and Nucleocapsid protein (N), along with 16 non-structural proteins and nine accessory proteins [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. The S protein is responsible for the viral entry into the host cells with the S protein binding to the receptor angiotensin-converting enzyme 2 (ACE2), and after that, membrane fusion takes place, subsequently leading to viral replications [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. Inhibiting the interaction between S protein and ACE2 can prevent viral entry, which is the main focus of this study.\u003c/p\u003e\u003cp\u003eThis study is the continuation of our previously published paper [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e], where a library of 15 phytochemicals from the ayurvedic medicinal plant \u003cem\u003eTerminalia chebula\u003c/em\u003e was created, and eight target proteins were selected from the SARS-CoV-2, and blind molecular docking was performed for screening purposes. The target proteins included nucleocapsid protein, N-terminal RNA binding domain, NSP15 Endoribonuclease, Nsp9 RNA binding protein, Papain-like protease, Nonstructural protein 10 (NSP10), NSP13 helicase, main protease and RNA-dependent RNA polymerase (RdRp). Out of 15 phytochemicals, it was found that 1,3,6-tri-O-galloyl-beta-D-glucose, Beta-Sitosterol, and Daucosterol possessed the most promising inhibitory effect against all eight SARS-CoV-2 proteins [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. In this study, we wanted to elucidate the efficacy of those three reported phytochemicals against fifteen variants reported to date. So far, to our knowledge, no study with all these variants has yet been done with these phytochemicals of \u003cem\u003eT. chebula\u003c/em\u003e as a potential future-ready drug, and hence, we felt the need to perform such research, which would better prepare us for any relapse of the COVID 19 pandemic.\u003c/p\u003e\u003cp\u003eIn this study, we evaluated the inhibitory efficacy of those three screened phytochemicals, from our previous study, against the S protein of 15 different variants of SARS-CoV-2 using blind docking as well. We found that Daucosterol showed promising inhibitory tendencies against 14 out of those 15 variants we worked with, while Beta-Sitosterol showed inhibitory potential against 13 of them, and 1,3,6-tri-O-galloyl-beta-D-glucose was effective against 10 of them. The stability of those complexes was further validated by MM-GBSA calculations. Hence, we inferred that Daucosterol and Beta-Sitosterol can be the most effective against all possible mutated versions of spike proteins of the virus, and we think this might be effective against future variants as well.\u003c/p\u003e\u003cp\u003eWe believe that our report will help the scientific community to develop such type of future-ready drug with further validation by wet-lab experiments in the future.\u003c/p\u003e"},{"header":"Methods","content":"\u003cp\u003e\u003cb\u003eDatabase\u003c/b\u003e\u003c/p\u003e\u003cp\u003eFor the purpose of molecular modeling, the sequences of spike protein of SARS-CoV-2 variants i.e., Wild-type, Alpha, Beta, Delta, Epsilon, Eta, Gamma, Iota, Kappa, Lambda, Omicron BA.1, Omicron BA.2.12.1, Omicron BA.2.75, Omicron BA.4, Omicron BA.5 were retrieved from ViralZone Expasy [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. The PDB file (7ZDQ) of the Cryo-EM structure of Human ACE2 (Angiotensin-converting enzyme 2 receptor protein) bound to the spike protein of SARS-CoV-2 mutant was obtained from the RCSB Protein Data Bank (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.rcsb.org/\u003c/span\u003e\u003cspan address=\"https://www.rcsb.org/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e).\u003c/p\u003e\u003cp\u003e\u003cb\u003eProtein Modeling\u003c/b\u003e\u003c/p\u003e\u003cp\u003eSince the crystal structures of the receptor-binding-domain (RBD) of S protein of all fifteen variants were not available to date, it was necessary to model these proteins for our present study. For this purpose, an online protein structure prediction server named \u003cem\u003eRobetta\u003c/em\u003e (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://robetta.bakerlab.org/\u003c/span\u003e\u003cspan address=\"https://robetta.bakerlab.org/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) was used to construct the molecular model of the RBD. Upon submitting protein sequences, predicted structures were obtained. The RBD sequences of proteins, i.e., from 319 to 541 [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e] were obtained from the spike protein sequences of the variants with the use of the sequence alignment software Clustal Omega. After submitting the sequences to \u003cem\u003eRobetta\u003c/em\u003e, five models were predicted for each protein. Amongst the five predicted models, the best model was chosen based on two parameters on the given graph for the respective models, namely, Angstroms Error Estimates and frequency of the Angstroms Error Estimates fluctuations. The one that has the lowest error estimate value in its highest peak was chosen. If more than one model for the same protein was found with a very similar error estimate value, then the frequency of fluctuations of the graph was considered.\u003c/p\u003e\u003cp\u003e\u003cb\u003eMolecule preparation prior to docking\u003c/b\u003e\u003c/p\u003e\u003cp\u003eAfter modeling the proteins, in order to optimize the geometry of the molecule and to remove unfavourable contacts between atoms, energy minimization was performed. Chosen protein RBD models from \u003cem\u003eRobetta\u003c/em\u003e were loaded to the YASARA (Yet Another Scientific Artificial Reality Application) server [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e] and the energy minimization of each macromolecule was performed. YASARA server uses the YASARA force field, where parameters are preset to perform the energy minimization.\u003c/p\u003e\u003cp\u003eThose energy-minimized PDB structures were loaded in PyMOL (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://pymol.org/2/\u003c/span\u003e\u003cspan address=\"https://pymol.org/2/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e), and the water molecules were removed from the file. Thus, the proteins were ready for molecular docking.\u003c/p\u003e\u003cp\u003e\u003cb\u003eBlind Molecular Docking\u003c/b\u003e\u003c/p\u003e\u003cp\u003eThe ligands 1,3,6-tri-O-galloyl-beta-D-glucose (PubChem: 452707), Beta-Sitosterol (PubChem: 222284), Daucosterol (PubChem: 5742590) were obtained from NCBI PubChem database (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://pubchem.ncbi.nlm.nih.gov/\u003c/span\u003e\u003cspan address=\"https://pubchem.ncbi.nlm.nih.gov/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e). The ligands were in SDF format and were converted into PDB format in PyRx (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://pyrx.sourceforge.io/\u003c/span\u003e\u003cspan address=\"https://pyrx.sourceforge.io/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) during docking.\u003c/p\u003e\u003cp\u003eThe 15 energy-minimized RBD models were docked against those three above-mentioned ligands using the PyRx molecular docking tool (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://pyrx.sourceforge.io/\u003c/span\u003e\u003cspan address=\"https://pyrx.sourceforge.io/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e). A blind docking approach was chosen instead of local docking (where, the ligand is forced to bind to the specific grooves) to ensure that all the interactions were unbiased.\u003c/p\u003e\u003cp\u003eThe energy minimization of the ligand molecules was performed using PyRx. For each ligand docking with each protein, nine models were generated along with the data of binding affinity, Upper Bound and Lower Bound RMSD values.\u003c/p\u003e\u003cp\u003e\u003cb\u003eSelection of Best Fit Models\u003c/b\u003e\u003c/p\u003e\u003cp\u003eFor nine predicted poses, the docked complex possessed almost similar values of binding affinity, Upper Bound and Lower Bound RMSD values, hence, the best model was chosen as follows. For example, in the case of molecular docking between RBD of the alpha variant of the SARS-CoV-2 (macro-molecule) and 1,3,6-tri-O-galloyl-beta-D-glucose (ligand), nine models of the docked complex were generated, and the best model had to be selected. The reported Cryo-EM structure (PDB: 7ZDQ) of Human ACE2 bound to the spike protein of SARS-CoV-2 mutant was used as a template of interaction to select the best model through 3-dimensional structure comparison. This PDB file (Green coloured, Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e1\u003c/span\u003e) was opened with PyMOL. Then, the nine models (Cyan coloured, Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e1\u003c/span\u003e) of all the docked complexes were superimposed individually with the PDB file to judge which one was the best fit. When the two structures were superimposed, the spike protein part (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e1\u003c/span\u003e) got superimposed leaving behind the ACE2 receptor and the ligand completely visible. In Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e1\u003c/span\u003e, it is visible that the ligand molecule is binding almost exactly in the region where the spike protein of the SARS-CoV-2 virus binds with the ACE2 receptor. This type of superimposed model is the best example of a representation of competitive inhibition, where the ligand is binding in the region of the active site of the protein. This docking pose was considered the best-fit model in this case. Likewise, the best-fit model for other docked complexes was selected accordingly.\u003c/p\u003e\u003cp\u003e\u003cb\u003eMM/GBSA binding free energy calculations\u003c/b\u003e\u003c/p\u003e\u003cp\u003eTo further validate our findings, MM-GBSA binding free energy calculations were performed for those complexes where the ligands were found to interact at the binding interface between RBM and ACE2. All calculations were done using the Schrödinger Maestro software package (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.schrodinger.com/platform/products/maestro/\u003c/span\u003e\u003cspan address=\"https://www.schrodinger.com/platform/products/maestro/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e). Protein-ligand complexes were prepared using the protein preparation module.\u003c/p\u003e\u003cp\u003eMM-GBSA ∆G calculations were conducted using OPLS4 forcefield [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e] and an implicit solvation model in Prime [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e, \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. Binding energy was calculated as: MMGBSA ∆G Bind = E(Complex) – E(Receptor) – E(Ligand free). Amino acids within 5Å of the ligand were allowed flexibility, and the protein-ligand interactions were optimized using the hierarchical sampling technique [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e], thus accounting for possible induced fit between the protein and the ligand upon binding.\u003c/p\u003e"},{"header":"RESULTS","content":"\u003cp\u003e\u003cb\u003eDaucosterol \u0026ndash; the future-ready phytochemical\u003c/b\u003e\u003c/p\u003e\u003cp\u003eTo evaluate the efficacy of Daucosterol, it was docked against S proteins of fifteen variants of SARS-CoV-2. In the case of the Wild-type S protein, binding energy was \u0026ndash; 6.9 kcal/mol, which determines the spontaneity of the interaction of Daucosterol with the S protein-ACE2 complex. The docking pose revealed that Daucosterol can interact at the binding interface between RBM and ACE2, as is visible in Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e2.1\u003c/span\u003e.(A) (left panel), indicating competitive inhibition by the ligand. The corresponding ligplot (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e2.1\u003c/span\u003e.(A), right panel) shows that Daucosterol is involved in hydrophobic interactions with Leu134, Leu174, and Phe172 residues of beta sheets and one hydrogen bonding with Gly167 residue.\u003c/p\u003e\u003cp\u003eIn the case of the alpha variant S protein, the binding energy (\u0026ndash; 7 kcal/mol) is similar to that of Wild-type. According to the docking pose, the Daucosterol acts as a competitive inhibitor interacting at the binding interface between RBM and ACE2 (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e2.1\u003c/span\u003e.(B), left panel). The corresponding ligplot (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e2.1\u003c/span\u003e.(B), right panel) reveals that the interaction between Daucosterol and RBM is mainly hydrophobic in nature, where Leu134, Ile150, and Phe172 residues are involved. Apart from that Gln175 and Phe172 are involved in hydrogen bonding with the ligand.\u003c/p\u003e\u003cp\u003eIn the case of the beta variant S protein, the binding energy was found to be \u0026ndash; 6.7 kcal/mol. As visible from Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e2.1\u003c/span\u003e.(C), (left panel) the Daucosterol can act as a competitive inhibitor binding at the interface between RBM and ACE2. In this case, Tyr177, Tyr183, and Tyr187 residues of RBM are involved in hydrophobic interaction with steroid moiety of Daucosterol. Gln175 and Ser176 are involved in hydrogen bonding with the hydroxyl groups of the glucopyranosyl residue (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e2.1\u003c/span\u003e.(C), right panel).\u003c/p\u003e\u003cp\u003eIn the case of the delta variant S protein, Daucosterol interacts with a favourable binding energy of \u0026ndash; 6.7 kcal/mol. As is evident from Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e2.1\u003c/span\u003e.(D), (left panel) Daucosterol is capable of acting as a competitive inhibitor against this variant as well. The binding took place through hydrophobic interactions between steroid moiety of Daucosterol and Tyr103, Tyr155 residues of S protein, the hydrogen bonding between Lys140 and the oxygen atom of glucopyranosyl residue (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e2.1\u003c/span\u003e.(D), right panel).\u003c/p\u003e\u003cp\u003eIn the case of the S protein of the epsilon variant, Daucosterol interacts with a binding energy of \u0026ndash; 6.5 kcal/mol. Figure\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e2.2\u003c/span\u003e.(E), (left panel) shows that Daucosterol fits between the RBM and ACE2, indicating competitive inhibition against the S protein-ACE2 complex. The corresponding ligplot Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e2.2\u003c/span\u003e.(E), (right panel) reveals that Daucosterol binds with RBM through hydrophobic interaction with Tyr177 and Tyr187, and through hydrogen bonding with Glu166 and Arg134 residues.\u003c/p\u003e\u003cp\u003eIn the case of the S protein of the eta variant, Daucosterol interacts near the binding interface between RBM and ACE2 with a binding energy of \u0026ndash; 7.1kcal/mol indicating favourable competitive inhibition Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e2.2\u003c/span\u003e.(F), (left panel). In this case, the interaction is completely hydrophobic, as is evident from the ligplot on the right panel. Tyr103, Tyr155, Phe138, and Cys162 are found to be involved in hydrophobic interactions with steroid moiety of Daucosterol.\u003c/p\u003e\u003cp\u003eWhen Daucosterol was docked with the S protein of the gamma variant, it was found to be placed between the binding interface of RBM and ACE2 (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e2.2\u003c/span\u003e.(G), left panel), suggesting competitive inhibition. The binding energy was \u0026ndash; 7.1kcal/mol. It was observed in the corresponding ligplot (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e2.2\u003c/span\u003e.(G), right panel) that Daucosterol binds with RDB through hydrophobic interaction between steroid moiety of the ligand and Tyr131, Tyr183, and Tyr187 residues. It is also involved in hydrogen bonding between hydroxyl groups of its glucopyranosyl residue and Gly186 and Asp87.\u003c/p\u003e\u003cp\u003eIn the case of the Iota variant S protein, Daucosterol was found to be bound at the binding interface of RBM and ACE2 with a binding energy of \u0026ndash; 6.2 kcal/mol. The docking pose in Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e2.2\u003c/span\u003e.(H), (left panel) suggests that the inhibition is competitive in nature. As is evident from the corresponding ligplot (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e2.2\u003c/span\u003e.(H), right panel) the interaction between the ligand and the protein is completely hydrophobic in nature, where Phe172 residue interacts with the steroid moiety of Daucosterol through pi-alkyl interaction.\u003c/p\u003e\u003cp\u003eIn the case of the S protein of the Kappa variant, Daucosterol binds near the interface between RBM and ACE2 with a binding energy of \u0026ndash; 6.9 kcal/mol. Although it could not fit perfectly at the binding interface, since its binding site is very close to the RBM-ACE2 interaction site (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e2.3\u003c/span\u003e.(I), left panel) the inhibition can be competitive in nature. In this case, Daucosterol binds with RBM through hydrophobic interaction with Ala54 and Val185; and hydrogen bonding with Gly86 and Gly186 (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e2.3\u003c/span\u003e.(I), right panel).\u003c/p\u003e\u003cp\u003eIn the case of the S protein of the lambda variant, Daucosterol was found to bind at the binding interface between RBM and ACE2, indicating competitive inhibition favoured by the binding energy of \u0026ndash; 7.0 kcal/mol (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e2.3\u003c/span\u003e.(J), left panel). It was observed from the corresponding right panel that the binding of Daucosterol is happening through hydrophobic interactions with Ile150, Ala34, and Ala30; and hydrogen bonding with Leu174, Tyr131 (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e2.3\u003c/span\u003e.(J), right panel).\u003c/p\u003e\u003cp\u003eThe docking of Daucosterol was performed against the S protein of five variants of Omicron i.e. BA.1, BA.2.12.1, BA.2.75, BA.4, and BA.5 (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e2.3\u003c/span\u003e.(K), 2.3.(L), 2.4.(M), 2.4.(N), 2.4.(O) respectively). Except for BA.4, in all other four cases, Daucosterol was capable of binding at the interface of the RBM-ACE2 complex. The binding energy was also favourable in these cases (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). The binding of Daucosterol took place through both hydrophobic interactions and hydrogen bonding, as is evident from corresponding ligplots.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eBinding energies of docked complexes. Unit is in kcal/mol\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"4\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003eS protein variant\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"3\" nameend=\"c4\" namest=\"c2\"\u003e\u003cp\u003eBinding energy (kcal/mol)\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eDaucosterol\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eBeta-Sitosterol\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1,3,6-tri-O-galloyl-beta-D-glucose\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eWild-type\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-6.9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-6.3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-7.5\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAlpha\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-7.1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-7.2\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eBeta\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-6.7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-6.7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-7.7\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDelta\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-6.7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-6.8\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eEpsilon\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-6.5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-6.6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-6.7\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eEta\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-7.1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-6.8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-6.3\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eGamma\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-7.1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-6.8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-6.5\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eIota\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-6.2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-6.7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-7.6\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eKappa\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-6.9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-6.8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-7\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLambda\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-6.1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-6.6\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eOmicron_BA.1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-7.7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-7.3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-6.3\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eOmicron_BA.2.12.1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-7.9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-7.4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-7.2\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eOmicron_BA.2.75\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-7.4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-6.3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-6.5\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eOmicron_BA.4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-7.3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-6.3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-7.3\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eOmicron_BA.5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-6.2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-6.5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-6.3\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eIn the case of the Omicron_BA.4, Daucosterol failed to bind at the binding interface of RBM and ACE2. It bounds far from the active site of RBM with a binding energy of \u0026ndash; 7.3 kcal/mol (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e2.4\u003c/span\u003e.(N), left panel). This indicates that in the case of Omicron_BA.4, the inhibition is not competitive in nature.\u003c/p\u003e\u003cp\u003e\u003cb\u003eBeta-Sitosterol \u0026ndash; the promising potential\u003c/b\u003e\u003c/p\u003e\u003cp\u003eBeta-Sitosterol was also docked against S proteins of fifteen variants of SARS-CoV-2 (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). In the case of the Wild-type S protein, Beta-Sitosterol binds at the interface of the RBM-ACE2 complex with a binding energy of \u0026ndash; 6.3 kcal/mol (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e3.1\u003c/span\u003e.(A), left panel). The ligand can act as a competitive inhibitor in this case. As is evident from the corresponding ligplot (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e3.1\u003c/span\u003e.(A), right panel) the binding of Beta-Sitosterol with RBM occurred through hydrophobic interaction of its steroid moiety with Lys140, Tyr155, Phe138 residues and hydrogen bonding with Tyr 103.\u003c/p\u003e\u003cp\u003eIn the case of the S protein of the alpha variant, Beta-Sitosterol was found to bind in between RBM and ACE2 indicating a competitive type of inhibition (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e3.1\u003c/span\u003e.(B), left panel). The binding energy was \u0026ndash; 7.1 kcal/mol. The corresponding ligplot at the right panel showed that the interaction is completely hydrophobic in nature, which involves the participation of Val89, Val115, and Val185 residues (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e3.1\u003c/span\u003e.(B), right panel).\u003c/p\u003e\u003cp\u003eIn the case of the S protein of the beta variant, Beta-Sitosterol perfectly fit in the binding interface of RBM and ACE2, indicating competitive inhibition (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e3.1\u003c/span\u003e.(C), left panel). The binding energy was \u0026ndash; 6.7 kcal/mol. It was visible from the corresponding ligplot (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e3.1\u003c/span\u003e.(C), right panel) that the ligand binds to RBM through hydrophobic interaction with Tyr177, Tyr183, and Tyr187 residues.\u003c/p\u003e\u003cp\u003eIn the case of the S protein of the delta variant, Beta-Sitosterol binds at the interface between RBM and ACE2 with a binding energy of \u0026ndash; 7 kcal/mol (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e3.1\u003c/span\u003e.(D), left panel). This docking pose indicates that this inhibition is competitive in nature. As evident from Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e3.1\u003c/span\u003e.(D), right panel, Beta-Sitosterol binds through hydrophobic interaction with Leu137, Tyr135, and hydrogen bonding with Asn104.\u003c/p\u003e\u003cp\u003eIn the case of the S protein of the epsilon variant, Beta-Sitosterol binds at the interaction interface between RBM and ACE2 with a binding energy of \u0026ndash; 6.6 kcal/mol. The docking pose (Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e3.2\u003c/span\u003e.(E), left panel) suggests that the inhibition is competitive in nature. The corresponding ligplot (Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e3.2\u003c/span\u003e.(E), right panel) confirms that the interaction is highly hydrophobic which involves the participation of Phe138, Tyr103, Tyr155, and Lys140 residues. Thr160 was found to be involved in hydrogen bonding.\u003c/p\u003e\u003cp\u003eIn the case of the S protein of the eta variant, Beta-Sitosterol was found to bind at the active site of RBM, indicating competitive inhibition with a binding energy of \u0026ndash; 6.8 kcal/mol (Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e3.2\u003c/span\u003e.(F), left panel). The corresponding ligplot (Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e3.2\u003c/span\u003e.(F), right panel) suggests that the interaction is purely hydrophobic in nature.\u003c/p\u003e\u003cp\u003eIn the case of the S protein of the gamma variant, Beta-Sitosterol perfectly fits into the binding interface between RBM and ACE2 (Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e3.2\u003c/span\u003e.(G), left panel) indicating competitive inhibition. The binding energy involved in this case is \u0026ndash; 6.8 kcal/mol. From the corresponding ligplot (Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e3.2\u003c/span\u003e.(G), right panel) it is visible that the ligand-protein binding is completely hydrophobic in nature, involving Tyr177, Tyr183, and Tyr187.\u003c/p\u003e\u003cp\u003eFor S proteins of Iota and Kappa variants, Beta-Sitosterol was not capable of binding at the interface between RBM and ACE2 (Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e3.2\u003c/span\u003e.(H) and 3.3.(I)). These docking poses suggest that Beta-Sitosterol cannot act as competitive inhibitor, since it can\u0026rsquo;t bind to the active site of the proteins.\u003c/p\u003e\u003cp\u003eIn the case of the S protein of the lambda variant, Beta-Sitosterol binds at the interface between RBM and ACE2 (Fig.\u0026nbsp;\u003cspan refid=\"Fig9\" class=\"InternalRef\"\u003e3.3\u003c/span\u003e.(J), left panel) suggesting competitive inhibition. The corresponding binding energy is \u0026ndash; 6.1 kcal/mol. The ligplot (Fig.\u0026nbsp;\u003cspan refid=\"Fig9\" class=\"InternalRef\"\u003e3.3\u003c/span\u003e.(J), right panel) shows that the interaction is completely hydrophobic in nature, involving Tyr131, Tyr177, and Tyr187.\u003c/p\u003e\u003cp\u003eFor S proteins of the Omicron variants, Beta-Sitosterol binds at the interface between RBM and ACE2 (Fig.\u0026nbsp;\u003cspan refid=\"Fig9\" class=\"InternalRef\"\u003e3.3\u003c/span\u003e.(K) \u0026ndash; 3.4.(O), left panels) with favourable binding energy indicating competitive inhibition. The corresponding ligplots suggest that all the interactions are mainly hydrophobic in nature involving non-polar residues.\u003c/p\u003e\u003cp\u003e\u003cb\u003e1,3,6-tri-O-galloyl-beta-D-glucose \u0026ndash; not so efficient against all variants\u003c/b\u003e\u003c/p\u003e\u003cp\u003eFor Wild-type S protein, 1,3,6-tri-O-galloyl-beta-D-glucose binds far from the interface between RBM and ACE2 with the favourable binding energy of \u0026ndash; 7.5 kcal/mol (Fig.\u0026nbsp;\u003cspan refid=\"Fig11\" class=\"InternalRef\"\u003e4.1\u003c/span\u003e.(A), left panel). This suggests that this ligand cannot act as a competitive inhibitor in this case.\u003c/p\u003e\u003cp\u003eIn the case of the S protein of the alpha variant, 1,3,6-tri-O-galloyl-beta-D-glucose binds at the interface of RBM and ACE2 with a favourable binding energy of \u0026ndash; 7.2 kcal/mol (Fig.\u0026nbsp;\u003cspan refid=\"Fig11\" class=\"InternalRef\"\u003e4.1\u003c/span\u003e.(B), left panel). The docking pose suggested that the ligand acts as a competitive inhibitor. The corresponding ligplot showed that the interaction is taking place mainly through hydrogen bonding. Polar residues of RBM such as Ser176, Arg85, and Glu166 are taking part in hydrogen bonding with hydroxyl functional groups of the galloyl group of the ligand (Fig.\u0026nbsp;\u003cspan refid=\"Fig11\" class=\"InternalRef\"\u003e4.1\u003c/span\u003e.(B), right panel).\u003c/p\u003e\u003cp\u003eFor the S protein of the beta variant, 1,3,6-tri-O-galloyl-beta-D-glucose could not fit at the binding interface between RBM and ACE2, although the binding energy is highly favourable (\u0026ndash; 7.7 kcal/mol). In this case, the ligand was found to interact far from the binding interface (Fig.\u0026nbsp;\u003cspan refid=\"Fig11\" class=\"InternalRef\"\u003e4.1\u003c/span\u003e.(C), left panel). This suggests that the activity of S protein may not be influenced by this ligand, in this case.\u003c/p\u003e\u003cp\u003eIn the case of the S protein of the delta variant, 1,3,6-tri-O-galloyl-beta-D-glucose binds near the interface between RBM and ACE2 with a binding energy of \u0026ndash; 6.8 kcal/mol (Fig.\u0026nbsp;\u003cspan refid=\"Fig11\" class=\"InternalRef\"\u003e4.1\u003c/span\u003e.(D), left panel). Here in this region, this ligand may act as a competitive inhibitor as is visible from the docking pose. In the corresponding ligplot (Fig.\u0026nbsp;\u003cspan refid=\"Fig11\" class=\"InternalRef\"\u003e4.1\u003c/span\u003e.(D), right panel) it was observed that the ligand interacts with protein through hydrogen bonding. The hydroxyl groups of the galloyl group of the ligand form hydrogen bonding with Glu153, Gln156, Ser159, Lys160, and Cys162 residues.\u003c/p\u003e\u003cp\u003eIn the case of the S protein of the Epsilon variant, 1,3,6-tri-O-galloyl-beta-D-glucose binds at the interface between RBM and ACE2 with binding energy of \u0026ndash; 6.7 kcal/mol (Fig.\u0026nbsp;\u003cspan refid=\"Fig12\" class=\"InternalRef\"\u003e4.2\u003c/span\u003e.(E), left panel), suggesting that the ligand is acting as a competitive inhibitor. The corresponding ligplot (Fig.\u0026nbsp;\u003cspan refid=\"Fig12\" class=\"InternalRef\"\u003e4.2\u003c/span\u003e.(E), right panel) indicates that the interaction is hydrophobic in nature.\u003c/p\u003e\u003cp\u003eFor the S protein of the Eta variant, 1,3,6-tri-O-galloyl-beta-D-glucose binds at the interface between RBM and ACE2 with a binding energy of \u0026ndash; 6.3 kcal/mol (Fig.\u0026nbsp;\u003cspan refid=\"Fig12\" class=\"InternalRef\"\u003e4.2\u003c/span\u003e.(F), left panel). The docking pose indicates that the ligand-protein interaction is competitive in nature. In the corresponding ligplot, it was observed that the galloyl group of the ligand is interacting with RBM through hydrogen bonding with Ser57, Asn119, Asn121, and Asn183 (Fig.\u0026nbsp;\u003cspan refid=\"Fig12\" class=\"InternalRef\"\u003e4.2\u003c/span\u003e.(F), right panel). However, some unfavourable interactions were visible in this case.\u003c/p\u003e\u003cp\u003eIn the case of the S protein of the gamma variant, it was observed from the docking pose that 1,3,6-tri-O-galloyl-beta-D-glucose binds in between the interaction interface of RBM and ACE2 with a binding energy of \u0026ndash; 6.5 kcal/mol (Fig.\u0026nbsp;\u003cspan refid=\"Fig12\" class=\"InternalRef\"\u003e4.2\u003c/span\u003e.(G), left panel) suggesting competitive inhibition. The corresponding ligplot showed that the galloyl group of 1,3,6-tri-O-galloyl-beta-D-glucose interacts with RBM through hydrogen bonding with polar amino acid residues of RBM (Fig.\u0026nbsp;\u003cspan refid=\"Fig12\" class=\"InternalRef\"\u003e4.2\u003c/span\u003e.(G), right panel).\u003c/p\u003e\u003cp\u003eIn the case of the S protein of the Iota variant, 1,3,6-tri-O-galloyl-beta-D-glucose was found to bind far from the active site of the protein (Fig.\u0026nbsp;\u003cspan refid=\"Fig12\" class=\"InternalRef\"\u003e4.2\u003c/span\u003e.(H), left panel) with the binding energy of \u0026ndash; 7.6 kcal/mol.\u003c/p\u003e\u003cp\u003eFor the S protein of the Kappa variant, 1,3,6-tri-O-galloyl-beta-D-glucose binds at the interaction interface between RBM and ACE2 with a binding energy of \u0026ndash; 7.0 kcal/mol (Fig.\u0026nbsp;\u003cspan refid=\"Fig13\" class=\"InternalRef\"\u003e4.3\u003c/span\u003e.(I), left panel) suggesting that the inhibition is competitive in nature. The corresponding ligplot (Fig.\u0026nbsp;\u003cspan refid=\"Fig13\" class=\"InternalRef\"\u003e4.3\u003c/span\u003e.(I), right panel) showed that 1,3,6-tri-O-galloyl-beta-D-glucose interacts with RBM through hydrogen bonding with Gln91, Arg85, Tyr135, Tyr177, Ser176, and Asn183 residues of RBM and pi-pi interaction between phenyl ring of the ligand and Tyr187 residue of RBM. However, two unfavourable interactions are visible in this case.\u003c/p\u003e\u003cp\u003eIn the case of the S protein of the lambda variant, 1,3,6-tri-O-galloyl-beta-D-glucose interacts at the binding interface between RBM and ACE2 with a binding energy of \u0026ndash; 6.6 kcal/mol (Fig.\u0026nbsp;\u003cspan refid=\"Fig13\" class=\"InternalRef\"\u003e4.3\u003c/span\u003e.(J), left panel) indicating competitive inhibitory action of this phytochemical. The corresponding ligplot (Fig.\u0026nbsp;\u003cspan refid=\"Fig13\" class=\"InternalRef\"\u003e4.3\u003c/span\u003e.(J), right panel) showed that interaction is happening through mainly hydrogen bonding between the galloyl group and the polar residues of RBM.\u003c/p\u003e\u003cp\u003eIn the case of the S protein of the Omicron variants, 1,3,6-tri-O-galloyl-beta-D-glucose interacts with three variants, i.e., Omicron_BA.1, Omicron_BA.2.12.1 and Omicron_BA.5, as a competitive inhibitor, as is visible from the Fig.\u0026nbsp;\u003cspan refid=\"Fig13\" class=\"InternalRef\"\u003e4.3\u003c/span\u003e.(K), 4.3.(L) and 4.4.(O), (left panels) with a favourable binding energy of \u0026ndash; 6.3, \u0026ndash; 7.2 and \u0026ndash; 6.3 kcal/mol, respectively. In the case of the Omicron_BA.2.75 and Omicron_BA.4 variants, 1,3,6-tri-O-galloyl-beta-D-glucose binds far away from the active site (Fig.\u0026nbsp;\u003cspan refid=\"Fig14\" class=\"InternalRef\"\u003e4.4\u003c/span\u003e.(M) and 4.4.(N), left panel).\u003c/p\u003e\u003cp\u003e\u003cb\u003eValidation of binding energy data\u003c/b\u003e\u003c/p\u003e\u003cp\u003eFurther validation was required to confirm our observations. Prime MM/GBSA calculations were conducted to assess the stability of those complexes where the ligands were found to be docked at the binding interface of RBD and ACE2. All ligands showed significant negative binding energy, demonstrating their ability to bind to the different variants of the Spike protein. The average binding energy of Daucosterol to the different variants was \u0026ndash; 56.27 kcal/mol (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). Similarly, Beta-Sitosterol and 1,3,6-Tri-O-galloyl-beta-D-glucose showed average binding energies of \u0026ndash; 48.12 and \u0026ndash; 59.74 kcal/mol, respectively, to the different variants (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). These binding energy values are similar in range to those reported by Dasmahapatra \u003cem\u003eet al\u003c/em\u003e upon developing PI3K inhibitors [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e].\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eMM-GBSA free Binding energies of docked complexes. Unit is in kcal/mol.\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"4\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003eS protein variant\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"3\" nameend=\"c4\" namest=\"c2\"\u003e\u003cp\u003eMMGBSA_dG_Binding energy (kcal/mol)\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eDaucosterol\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eBeta-Sitosterol\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1,3,6-tri-O-galloyl-beta-D-glucose\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eWild-type\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-33.60\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-53.04\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAlpha\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-62.40\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-43.51\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-50.69\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eBeta\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-52.18\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-41.37\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDelta\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-62.20\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-38.61\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-66.33\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eEpsilon\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-51.94\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-52.19\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-71.83\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eEta\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-74.52\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-55.65\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-41.60\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eGamma\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-61.43\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-51.08\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-68.03\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eIota\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-59.43\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eKappa\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-42.91\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-54.85\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLambda\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-55.98\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-41.87\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-71.41\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eOmicron_BA.1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-61.67\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-59.08\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-63.91\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eOmicron_BA.2.12.1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-59.67\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-56.72\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-43.17\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eOmicron_BA.2.75\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-60.01\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-36.24\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eOmicron_BA.4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-*\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-47.31\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eOmicron_BA.5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-49.86\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-48.85\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-65.56\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003ctfoot\u003e\u003ctr\u003e\u003ctd colspan=\"4\"\u003e* \u0026lsquo;-\u0026rsquo; represents the cases where the ligand binds far away from the RBM and ACE2 binding interface. MM-GBSA free Binding energy calculation was not performed for those cases.\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eDaucosterol binds to the Iota variant with ∆G binding of \u0026ndash; 59.43 kcal/mol, which was close to the average binding energy. The interactions between Daucosterol and this variant are depicted in Fig.\u0026nbsp;\u003cspan refid=\"Fig15\" class=\"InternalRef\"\u003e5\u003c/span\u003e. The surface of the Iota binding site is colored according to the electrostatic potential (Fig.\u0026nbsp;\u003cspan refid=\"Fig15\" class=\"InternalRef\"\u003e5\u003c/span\u003eA). Negatively and positively charged surfaces are colored red and blue, while hydrophobic regions are colored grey. From this figure, it is clearly visible that most of the binding site is hydrophobic. Daucosterol is presented using wire representation and colored green (carbon atoms) and red (oxygen atoms). Detailed interactions between Daucosterol and the protein are depicted in Fig.\u0026nbsp;\u003cspan refid=\"Fig15\" class=\"InternalRef\"\u003e5\u003c/span\u003eB. The ligand is involved in hydrophobic interactions with L135, L150, L174, and F172 amino acids. One of the hydroxyl groups of Daucosterol forms two hydrogen bonds, one with the backbone nitrogen of F172 and another with the side chain nitrogen of Q175 amino acids.\u003c/p\u003e"},{"header":"DISCUSSION","content":"\u003cp\u003eFrom the onset of emergence till date, the SARS-CoV-2 has continuously mutated itself, leading to the development of new variants [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. Although several vaccines (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.who.int/publications/m/item/draft-landscape-of-covid-19-candidate-vaccines\u003c/span\u003e\u003cspan address=\"https://www.who.int/publications/m/item/draft-landscape-of-covid-19-candidate-vaccines\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) have been developed to date, the vaccine effectiveness has been compromised against the new variants of SARS-CoV-2 [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. Apart from vaccines, no effective drugs have been developed to combat against SARS-CoV-2 to date. Therefore, there is an urgent need for the development of a potential drug that can be effective against the newly emerged variant of SARS-CoV-2 in the near future. In our previous study, we screened fifteen phytochemicals of \u003cem\u003eTerminalia chebula\u003c/em\u003e against eight SARS-CoV-2 proteins and found 1,3,6-tri-O-galloyl-beta-D-glucose, Beta-Sitosterol and Daucosterol as potential inhibitors against those protein activities [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. In this study, we wanted to elucidate the efficacy of these three phytochemicals against the spike proteins of fifteen well-known variants of SARS-CoV-2, to find out the future-ready phytochemical that can combat any type of newly emerged variants of the virus. For this purpose, first we modeled the 3-dimensional structures of S proteins of all fifteen variants and then performed \u003cem\u003eblind\u003c/em\u003e docking between each phytochemical molecule and the S protein of each variant. The \u003cem\u003eblind\u003c/em\u003e docking was chosen over \u003cem\u003elocal\u003c/em\u003e docking to avoid the active site biases and to check for the spontaneity of binding of phytochemicals to the protein target, based on binding free energy.\u003c/p\u003e\u003cp\u003eHere, in this study, we targeted the S protein, since it is responsible for the viral entry through the binding to cell receptor ACE2. The S protein binds to ACE2 mainly through the receptor binding motif (RBM) and leads the viral entry into the host. If we can find that any of those phytochemicals bind to this interaction interface, we can confirm that that compound may act as a competitive inhibitor against this interaction. To screen for such type of candidates out of those three above-mentioned phytochemicals, first we performed molecular docking between the phytochemical and S protein model of each variant, and then we superimposed the docking pose on the reported crystal structure of the RBM-ACE2 complex. We selected the docking pose based on the binding position of the phytochemical and the binding energy. Since the binding energy was on an average \u0026ndash; 7 kcal/mol, which supports the spontaneity of the interaction, we set the docking pose of the phytochemicals as the main criteria to choose the best possible candidate. If the phytochemical was found to bind to the interaction interface between RBM and ACE2, that phytochemical was considered a competitive inhibitor. If the phytochemical bound far away from RBM, it might not inhibit effectively or act as a non-competitive inhibitor.\u003c/p\u003e\u003cp\u003eOur molecular docking results showed that 1,3,6-tri-O-galloyl-beta-D-glucose was unable to bind to the interaction interface of RBM and ACE2 in the case of the S proteins of Wild-type, Beta, Iota, Omicron_BA.2.75 and Omicron_BA.4 variants (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). Beta-Sitosterol was found to bind far away from that interaction interface for those of Iota and Kappa variants. Daucosterol failed to bind at the above-mentioned active site of RBM in the case of the Omicron_BA.4 variant. In these cases, the phytochemicals may not act as competitive inhibitors or can be non-competitive inhibitors. These results suggest that 1,3,6-tri-O-galloyl-beta-D-glucose may not be effective against all fifteen variants of SARS-CoV-2, whereas Daucosterol and Beta-Sitosterol will be effective against most of these variants. To further validate the dynamic behaviour and stability of the complexes, MM/GBSA binding free energy calculations were performed. From the calculated average binding free energy, it was confirmed that the ligands will bind effectively with the variants of spike proteins and should be stable over time.\u003c/p\u003e\u003cp\u003e1,3,6-tri-O-galloyl-beta-D-glucose is composed of D-glucose flanked by three galloyl groups. Because of the presence of these galloyl groups, it is capable of forming hydrogen bonds with the target protein. From the binding poses, it seems that may be due to the bigger structure of the molecule, 1,3,6-tri-O-galloyl-beta-D-glucose was not able to bind to the active site of the protein, for five variants. Beta-Sitosterol and Daucosterol belong to the steroid category of molecules. In both cases, the molecules interact with the S protein variants mainly through hydrophobic interactions because of the presence of hydrophobic steroid moieties. Our finding indicates that Beta-Sitosterol and Daucosterol can be used as potential inhibitors against all these fifteen variants and may also be effective against the new possible variants in the future.\u003c/p\u003e\u003cp\u003eIn a recent study [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e] four phytochemicals, i.e. withanolide F, serotobenine, orobanchol, and gibberellin A51 are reported as a strong binder to RBD of native and five variants of concern (alpha, beta, gamma, delta, and omicron) of SARS-CoV-2 based on \u003cem\u003ein silico\u003c/em\u003e screening, molecular docking and molecular dynamics simulation.\u003c/p\u003e\u003cp\u003eSeveral research groups have performed phytochemical screening experiments on SARS-CoV-2 Wild-type and other variants such as Omicron [\u003cspan additionalcitationids=\"CR18\" citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e], delta variant [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e] to identify the potential drug candidates.\u003c/p\u003e\u003cp\u003eVimalanathan \u003cem\u003eet. al.\u003c/em\u003e investigated the effectiveness of \u003cem\u003eEchinacea purpurea\u003c/em\u003e (Echinaforce\u0026reg; extract, EF) against alpha, beta, gamma, delta, Scottish, eta, omicron variants; S protein-pseudotyped viral particles; reference strain OC43 and Wild-type of SARS-CoV-2 [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]. The EF extract was found to be effective against all the variants and SARS-CoV-2 pseudoparticles. The molecular dynamics simulation studies of interaction between \u003cem\u003eEchinacea\u003c/em\u003e's phytochemical markers and the S protein of SARS-CoV-2 revealed that alkylamides, caftaric acid, and feruloyl-tartaric acid exhibited constant binding affinities towards the S protein. In the case of the alkylamides, a similar type of hydrophobic interaction with RBD of S protein was observed like Beta-Sitosterol and Daucosterol [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eAhmed \u003cem\u003eet. al.\u003c/em\u003e performed \u003cem\u003ein silico\u003c/em\u003e screening of 46 phytochemicals against S proteins of Alpha, Beta, Delta, Gamma, and Omicron variants and found liquiritigenin as a broad-spectrum inhibitor against SARS-CoV-2 [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]. Liquiritigenin belongs to the flavonoid category which can interact through both hydrophobic and hydrogen bonding with S protein.\u003c/p\u003e\u003cp\u003eIn a recent demographic data analysis of a group of Polish men and women [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e], reduced risk of COVID-19 was found among the people who intake phytosterols such as stigmasterol and Beta-Sitosterol. In a bioinformatics study by Sing \u003cem\u003eet. al.\u003c/em\u003e [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e], Beta-Sitosterol was found to act as an anti-inflammatory agent against COVID-19 cytokine storm. There are several other reports, which support our finding that Beta-Sitosterol can serve as a future-ready drug to combat unknown newly emerged SARS-CoV-2 variant(s). However, there are no reports on Daucosterol, except the molecular dynamics simulation study by Ghosh \u003cem\u003eet. al.\u003c/em\u003e [\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e], which showed that among seven phytochemicals of \u003cem\u003eTerminalia Chebula\u003c/em\u003e, Daucosterol exhibited the strongest binding with the main protease (M\u003csup\u003epro\u003c/sup\u003e) of the SARS-CoV-2.\u003c/p\u003e\u003cp\u003eTo compare the effectiveness of these phytochemicals with that of existing drugs, we performed a literature survey where both wet- and dry-lab-based experiments were conducted to identify the potential drugs against SARS-CoV-2. We found some repurposed drugs such as Benzimidazole [\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]; Artemisinin, Dihydroartemisinin, chloroquine [\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e]; Tolvaptan [\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e], which were reportedly inhibiting the interaction between RBD and ACE2 at micromolar range \u003cem\u003ein vitro;\u003c/em\u003e and \u003cem\u003ein silico\u003c/em\u003e interact at the binding interface of RBD and ACE2 with a binding energy of \u0026ndash; 6.24, \u0026ndash; 7 and \u0026ndash; 8.8 kcal/mol respectively. The binding energy of these molecular docking studies matches with the data of our study which further suggests that 1,3,6-tri-O-galloyl-beta-D-glucose, Daucosterol and Beta-Sitosterol may inhibit the interaction at the micromolar range, which may be subjected to further validation by wet-lab experiments.\u003c/p\u003e"},{"header":"CONCLUSION","content":"\u003cp\u003eWhen a pathogenic strain continuously develops itself with time, it becomes deemed necessary to identify a drug that will be effective towards those developing strains. In the case of SARS-CoV-2, it is continuously changing itself with time although COVID-19 has been declared as endemic, and there will always be a chance of relapsing of this disease. Keeping this scenario in mind, we started looking for the drug from previously screened phytochemicals i.e., 1,3,6-tri-O-galloyl-beta-D-glucose, Daucosterol and Beta-Sitosterol, which possess the future-ready potential. We found that Daucosterol and Beta-Sitosterol are highly effective and can act as competitive inhibitors against most of the variants of SARS-CoV-2 reported to date. Since they were found to bind effectively at the active site of the S protein in most of the variants, it was expected that they would also be effective towards the newly emerged unknown variants. ADME/T studies were already performed for these phytochemicals in the previous report and it was found that 1,3,6-tri-O-galloyl-beta-D-glucose does not meet the criteria of drug-like properties. Although further wet-lab based experiments are required in future to validate our report, our finding has provided a solid foundation towards the development of a future-ready anti-viral drug.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eACKNOWLEDGEMENT\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eBB is thankful to the Department of Science and Technology (DST), Govt. of India, for providing funding through grant no: SRG/2022/001543\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthical Approval:\u0026nbsp;\u003c/strong\u003eNot applicable\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding:\u0026nbsp;\u003c/strong\u003eThis work was supported by DST \u0026ndash; SERB, Govt. of India (Grant number: SRG/2022/001543).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData Availability:\u0026nbsp;\u003c/strong\u003eThe datasets generated during and/or analysed during the current study are available from the corresponding author on reasonable request.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting Interests:\u0026nbsp;\u003c/strong\u003eThe authors have no relevant financial or non-financial interests to disclose.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors\u0026apos; contributions:\u0026nbsp;\u003c/strong\u003eSayak Dey and Mriganka Sekhar Das contributed equally. All authors contributed to the study conception and design. Molecule preparation, data collection and analysis were performed by Sayak Dey, Mriganka Sekhar Das, Dror Tobi and Boudhayan Bandyopadhyay. The first draft of the manuscript was written by Sayak Dey, Dror Tobi and Boudhayan Bandyopadhyay. All authors commented on previous versions of the manuscript. All authors read and approved the final manuscript.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eWHO. \u003cem\u003eWHO Coronavirus (COVID-19) Dashboard\u003c/em\u003e. 2023 [cited 2023 07/03/2023]; Available from: https://covid19.who.int/.\u003c/li\u003e\n\u003cli\u003eTao, K., P.L. Tzou, J. Nouhin, R.K. Gupta, T. de Oliveira, S.L. Kosakovsky Pond, D. Fera, and R.W. 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S, \u003cem\u003eStructure-based virtual screening and molecular docking of drugs against the SARS-CoV-2 spike protein-ACE2 receptor complex.\u003c/em\u003e Pak J Pharm Sci, 2022. \u003cstrong\u003e35\u003c/strong\u003e(6): p. 1531-1538.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"SARS-CoV-2, Phytochemicals, Protein modeling, Molecular docking, MM-GBSA calculation, Future-ready drug","lastPublishedDoi":"10.21203/rs.3.rs-7194350/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7194350/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThe emergence of variants of SARS-CoV-2 over time raised the global concern that the strain might develop and modify again into new unknown variants in the future in an unprecedented manner, and the disease can be relapsed at any time, although it is declared as endemic. In this scenario, it becomes deemed necessary to identify, design, and formulate a future-ready drug that will be effective against the existing as well as new variants. In order to find out such types of drugs, we performed \u003cem\u003ein silico\u003c/em\u003e screening of three phytochemicals, i.e.1,3,6-tri-O-galloyl-beta-D-glucose, Beta-Sitosterol and Daucosterol of \u003cem\u003eTerminalia chebula\u003c/em\u003e, which were proved to be effective against SARS-CoV-2 Wild-type proteins in our previous report. In this study, we performed molecular docking experiments with those three phytochemicals against the fifteen variants of the spike protein of SARS-CoV-2 to find out the most effective candidate, which possesses the potential to inhibit the ACE2 receptor binding activities of the spike protein of the most of the variants. Our study showed that Beta-Sitosterol and Daucosterol exhibited the potential for strong binding to spike proteins of almost all variants through mainly hydrophobic interaction. Our results were further validated by MM-GBSA binding free energy calculations. This finding suggests that Beta-Sitosterol and Daucosterol can serve as potential drugs against most of the variants of SARS-CoV-2 and may be effective against the newly emerged variant. We believe that our finding, along with the validation by wet-lab experiments, can help the scientific and healthcare communities to prepare themselves against SARS-CoV-2 in the future.\u003c/p\u003e","manuscriptTitle":"Daucosterol and Beta-Sitosterol – the Future-Ready phytochemicals from Terminalia chebula to combat SARS-CoV-2","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-07-31 11:17:04","doi":"10.21203/rs.3.rs-7194350/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"fdf82ff9-dfd9-4735-b3a7-22bcf7b59ab6","owner":[],"postedDate":"July 31st, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2025-08-06T09:53:18+00:00","versionOfRecord":[],"versionCreatedAt":"2025-07-31 11:17:04","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7194350","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7194350","identity":"rs-7194350","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}