Molecular docking studies of some selected gallic acid derivatives against five non-structural proteins of novel coronavirus

Background The World Health Organization has recently declared a new coronavirus disease (COVID-19) a pandemic and a global health emergency. The pressure to produce drugs and vaccines against the ongoing pandemic has resulted in the use of some drugs such as azithromycin, chloroquine (sulfate and phosphate), hydroxychloroquine, dexamethasone, favipiravir, remdesivir, ribavirin, ivermectin, and lopinavir/ritonavir. However, reports from some of the clinical trials with these drugs have proved detrimental on some COVID-19 infected patients with side effects more of which cardiomyopathy, cardiotoxicity, nephrotoxicity, macular retinopathy, and hepatotoxicity have been recently reported. Realizing the need for potent and harmless therapeutic compounds to combat COVID-19, we attempted in this study to find promising therapeutic compounds against the imminent threat of this virus. In this current study, 16 derivatives of gallic acid were docked against five selected non-structural proteins of SARS-COV-2 known to be a good target for finding small molecule inhibitors against the virus, namely, nsp3, nsp5, nsp12, nsp13, and nsp14. All the protein crystal structures and 3D structures of the small molecules (16 gallic acid derivatives and 3 control drugs) were retrieved from the Protein database (PDB) and PubChem server respectively. The compounds with lower binding energy than the control drugs were selected and subjected to pharmacokinetics screening using AdmetSAR server. Results 4-O-(6-galloylglucoside) gave binding energy values of − 8.4, − 6.8, − 8.9, − 9.1, and − 7.5 kcal/mol against Mpro, nsp3, nsp12, nsp13, and nsp15 respectively. Based on the ADMET profile, 4-O-(6-galloylglucoside) was found to be metabolized by the liver and has a very high plasma protein binding. Conclusion The result of this study revealed that 4-O-(6-galloylglucoside) could be a promising inhibitor against these SAR-Cov-2 proteins. However, there is still a need for further molecular dynamic simulation, in vivo and in vitro studies to support these findings.


Background
The World Health Organization has declared the most recent coronavirus (COVID-19) a pandemic and a global emergency that causes a global outbreak since early this year after emerging from Wuhan, Hubei province of China in late December of 2019 [1,2]. This viral disease is caused by severe acute respiratory syndrome 2 (SARS-COV-2); a spherical-shaped, single-stranded, positively sensed RNA virus with a genome size of approximately 30,000 base pairs consisting of about eleven open reading frames (ORFs) that encodes numerous proteins (structural and non-structural) involved in the viral life cycle [2][3][4]. The proteins result from the expression of the viral mRNA and sgRNAs subsequent to its entry into the host cell via the priming of the viral spike glycoprotein to angiotensin-converting enzyme 2 (ACE2). The binding of the viral spike glycoprotein to angiotensinconverting enzyme 2 (ACE2) receptor mediates the viral infectivity, hence, serving as the basis for pathogenicity in its host. Once the virus gains entry into the host cell, it liberates its RNA into the cytoplasm subsequently resulting in the translation of its replicase gene. Within the viral replicase gene are two overlapping open reading frames flagged ORF1a/1b. The translation of the ORFs results into polyprotein1a and 1ab. The two polyproteins are subsequently cleaved by two cysteine proteinases called main protease or 3-chymotrypsin-like protease (Mpro/3CLpro) and papain-like protease (PLpro) releasing 16 non-structural proteins (nsp1 to 16). Among these nsps are the papain-like protease (nsp3), main protease (nsp5), RNA-dependent RNA polymerase (RdRp/ nsp12), helicase (nsp13), and nsp15 (NendoU) which have been viewed to be viable antiviral drug targets [4,5].
Papain-like protease (PLpro) is involved in cleavage at the N-terminus of replicase polyproteins to release nsp1, nsp2, and nsp3, also generating replicase complex to allow viral spread [5,6]. This enzyme is a multifunctional one; aside from polyproteins processing, maturation, and forming replication-transcription complex, it interferes with the host anti-viral immune response by modifying host cellular proteins of immune relevance after their translation [5,7]. Then, the main protease otherwise called nsp5, cleaved itself from the polyproteins and then cleaved the same polyproteins on 11 distinct sites yielding nsp4 to nsp16 [5,8]. Interestingly, this process leads to the maturity of the nsps [5,8]. Also, RNA-dependent RNA polymerase (RdRp/nsp12) is an enzyme of high pertinence that involves in the synthesis of complementary RNA strands with the aid of other nsps such as nsp7 and nsp8 playing a critical role in replication and transcription of the virus [4,5,9]. Helicase (nsp13) as well, unwinds the duplex RNA, and also adds a 5′-terminal cap to the viral RNA, thus allowing its recognition for translation and playing a key role in nuclear export, stability, and splicing of the viral mRNA [4,9]. Finally, Nsp15 (NendoU or EndoU) is an endonuclease that prevents the host immune apparatus to detect the virus via viral RNA cleavage and degradation at the polyuridine sequence region, thereby forming 2′-3′cyclic phosphodiester product [3,4,10].
Due to the worldwide outbreak of COVID-19, pharmaceutical companies, government institutions, private organizations, and biotechnological firms are under burdened to produce drugs and vaccines against the ongoing pandemic. As a result, drugs such as azithromycin, chloroquine (sulfate and phosphate), hydroxychloroquine, dexamethasone, favipiravir, remdesivir, interferon-alpha, ribavirin, sofospuvir, ivermectin, and lopinavir/ritonavir have been compassionately used for treatment [11]. Even though significant data are available from clinical randomized trials that could give clear-cut clinical guidance on their use, dosage, or time for treatment or prophylaxis [12], there were several reports on the side effects of these therapies on COVID-19 patients more of which cardiomyopathy, cardiotoxicity, nephrotoxicity, macular retinopathy, and hepatotoxicity [12][13][14] are the prominent ones. Thus, this has prompted the quest for better and readily available options to thwart this viral malady.
Gallic acid (GA) and its derivatives are one of the abundant plant chemicals with diverse pharmacological aptitudes that can aid in the management of several diseases such as inflammatory, cardiovascular, respiratory, gastrointestinal, and metabolic diseases [15,16]. Our interest in gallic acid derivatives were based on their pharmacological aptitudes, abundance in nature, and most of all, had not been used for any study to evaluate them against the etiological agent of the recent coronavirus outbreak. Although there are numerous studies on gallic acid and its derivatives against different diseases such as antibacterial, antifungal, and antiviral diseases [17,18]; nonetheless, no study has reported its use as a therapeutic compound against COVID-19. The derivatives of gallic acid include propyl gallate, lauryl gallate, octyl gallate, theaflavin, tannic acid, (−)-epigallocatechin gallate (EGCG), (−)-epigallocatechin (EGC), (−)-epicatechin gallate (ECG), and (+)-epicatechin (EC) [18,19]. Realizing the uniqueness of its potentials to treat various diseases, we have considered a subset of its derivatives in this study to understand their mechanistic as inhibitors against the aforementioned viral non-structural proteins via various computational approaches. However, recent studies have revealed the relevance of in silico techniques to find newer and potential therapeutics in drug discovery and development [20][21][22][23]. Thus, an alternative novel evidence based on a molecular docking approach could become dexterous to the advancement of novel drug for the management/treatment of this on-going pandemic. In this study, molecular docking was employed to evaluate some selected gallic acid derivatives as antiviral aptitude against SARS-COV-2. We looked into their binding energies (kcal/mol) and molecular interactions with five key important proteins/enzymes of SARS-COV-2, namely, nsp3, nsp5, nsp12, nsp13, and nsp14. Also, we evaluated the pharmacoactivities of the best hit compounds after the docking experiment in silico.

Materials used for the in silico study
The softwares used to execute this present in silico work are Python Prescription 0.

Selection and optimization of ligands
Gallic acid and fifteen (15) derivatives of gallic acid were selected with three (3) control drugs approved globally to combat COVID-19 (dexamethasone, hydroxychloroquine, and remdesivir). Three-dimensional (3D) structures of these ligands were retrieved from PubChem server (https://pubchem.ncbi.nlm.nih.gov/compound) in simple data format (SDF) and were optimized using Open babel in PyRx (version 0.8) which converted the ligands energetically to the most stable structures using merk molecular force field 94 (MMFF94).

Drug likeness screening of ligands
The drug likeliness of all the ligands was assessed by the Lipinski's rule of five parameters (molecular weight < 500 Da, no more than 5 hydrogen bond donors, no. of hydrogen bond acceptors should be less than 10, and AlogP should not be greater than 5). The Lipinski's rule of five parameters were obtained from the ADMETSar server [24]. Physicochemical properties and toxicity risks assessment prediction was done via the OSIRIS property explorer software (https://www.organic-chemistry.org/ prog/peo/). This was done by uploading the respective compounds' SMILES into the web server and software.

In silico docking protocol validation
Validation of the docking protocol was carried out to substantiate the exactness and dependability of the docking results. The purpose is to correctly reproduce the binding model and the molecular interaction of the cocrystalized ligand of the experimentally crystalized protein structures in this present in silico studies. Accordingly, the native ligands of all the X-ray proteins were separated from the protein and then prepared for docking via UCSF-Chimera [26]. The ligands were subsequently docked back into the receptors' active sites using the Auto Dock Vina module of PyRx. The docked complexes were aligned with the X-rayed resolved crystals of proteins bearing the co-crystalized ligand to produce the root mean square deviation (RMSD) value using PyMOL molecular visualizer and also viewing the molecular interactions that results using LigPlot + [27].

Molecular docking
The molecular docking was executed using a flexible docking protocol as described by Trott and Olson [28] with slight modifications. Accordingly, Python Prescription 0.8, a suite comprising of Auto Dock Vina, was utilized for the molecular docking analysis of the selected ligands with our target proteins. The protein data bank, partial charge, and atom type (PDBQT) files of the proteins were generated (using their previously created PDB files as inputs). All other parameters were kept as default except for the grid box which was adjusted base on the active sites of each protein molecule (see Table 1), and all bonds contained in ligand were allowed to rotate freely, making the receptor rigid [28]. Once the molecular docking experiments were completed and 10 configurations for each protein-ligand complex were generated for all the phytocompounds, text files of scoring results were also produced for the purpose of manual comparative analysis. The lowest binding energy (BE, kcal/mol) and root mean square deviation (RMSD) conformation was considered as the most suitable docking pose. Throughout this in silico investigation, an exhaustiveness of 10 was used for docking, and the number of modes set to 10 so as to achieve more accurate and reliable results. The interaction between ligands and proteins was then prepared, visualized, and analyzed using PyMOL and Discovery Studio 2016 respectively [1,30].

ADMET prediction
ADMET (Adsorption, Distribution, Metabolism, Excretion, and Toxicity) is important to analyze the pharmacodynamics of the proposed molecule which could be used as a drug. ADMETSar and SWISSADME servers were used to predict the ADMET properties of the compounds with the best hits after molecular docking analysis [24,31,32]. SMILES of the ligands from PubChem

Results and discussion
Druglikeness, physicochemical properties, and toxicity risk assessments The results of the druglikeness of the ligands selected for this work are presented in Table 2. The molecular weight of the compounds was found to be within the acceptable range except remdesivir with MW 602.59. None of the compounds had a lipophilicity value above 5. Five molecules exceed the number of hydrogen bond acceptors required. In the same vein, six compounds exceeded the acceptable limit for the number of hydrogen bond donors. Finally, two compounds violate the rule while five compounds violated two parameters.
In addition, the physicochemical and toxicity risk assessments were carried out using OSIRIS property explorer. The results are presented in Table 3 with parameters such as solubility, topological surface area (TPSA), druglikeness, drug score, mutagenicity, tumorigenicity, reproductive effect, and irritation. All compounds showed good solubility between −0.63 and − 4.99. Eight compounds were predicted to have poor druglikeness properties; whereas four showed very low drug scores (below 0.20). The toxicity risk assessment shows that gallic acid, propyl gallate, pyrogallol, resorcinol, and remdesivir pose a toxicity risk.

Molecular docking analysis
Prior to docking, our docking protocol was validated for all target receptors as explained in the "Method" section. This crucial step was carried out in other to provide guarantees of the molecular docking protocols and softwares to accurately deliver correct binding signatures with quality molecular interactions between target receptors and the compounds investigated in this in silico study. The outcome of this exercise is depicted in Figs. 2a, 3, 4, 5, and 6a. This exercise was adjudged to be successful since the docked complexes reproduced the original poses as the native ligands precisely (Figs. 2a, 3, 4, 5, and 6a) with RMSD values of 0.000 Å, 0.533 Å, 0.605 Å, 2.298 Å, and 0.208 Å for Mpro, PLpro, RdRp, helicase, and NendoU respectively. In this present docking work, sixteen ligands (thirteen gallic acid derivatives and three     [28]. The binding energies (BE) that results after the docking experiment are provided in Table 4. After the docking was completed, the least binding energy (BE, kcal/mol) and root mean square deviation (RMSD) conformation was considered as the most suitable docking pose of the compounds as presented in Table 4.
3D crystal structure of nsp12 was retrieved from protein data bank with PDB code of 6 M71, resolution of 2.9 Å, fused together with nsp8 and nsp7 as cofactors, chain A of the complex and sequence length of 942 amino acids [25]. The active site of nsp12 is conserved in the polymerase motifs A-G (Fig. 1), which comprises of Asp618, Ser759, Asp760, and Asp761 [25]. Total of seventeen gallic acid derivatives was screened for identifying the potential drug against RdRp using molecular docking. All the compounds were evaluated on the basis of their maximum scoring function, and the top ranked compound was selected and analyzed for the  Table 4). The docked conformation of 4-O-(6-galloylglucoside) with their interacting residues is shown in Fig. 4   The 3D crystal structure was together with RdRp, nsp8, and nsp7 (6XEZ), with resolution of 3.5 Å, sequence length of 605 amino acids and homodimeric [9]. The amino acids of relevance in the binding cavity of nsp13 are Lys288, Ser289, Asp374, Glu375, Gln404, and Arg567 [9] (Fig. 1). Gallic acid derivatives were screened as potential inhibitors of helicase using molecular docking. At the end of the docking study, 4O6G had the best binding affinity of −9.1 kcal/mol. Also, dexamethasone, one of the control drugs in this study fared better than other control drugs with binding affinity of −8.0 kcal/ mol (Table 4). 4O6G and dexamethasone occupied similar area in the active portion of helicase (Fig. 5b). The molecular interaction in 2D show the binding signatures maintained by both molecules (Fig. 5b). 4O6G was able to create hydrogen bond with seven amino acids such as Gly287, Lys288, Arg443, Arg567, Lys320, Ala316, and Ser289 at distance of 3.18 Å, 3.40 Å, 3.15 Å, 6.80 Å, 5.69 Å, 6.26 Å, 5.71 Å, 5.53 Å, 4.18 Å, 3.86 Å, 3.14 Å, and 4.37 Å respectively. Also, pi-cation bond was formed with Lys288 and Lys320 at distance of 5.46 Å and 4.63 Å respectively. However, Gly538 interacted with 4O6G via a pi-sigma bond. In addition, van der Waals force form the link between 4O6G and eight amino acids which are Pro283, Gly285, Thr286, Pro284, Gln404, Gly400, Gln537, and Glu319 then Glu375 established a carbonhydrogen bond with 4O6G at a distance of 3.47 Å. Dexamethasone, interacted with Thr286, Lys288, Arg443, and Gly285 at distance of 4.00 Å, 4.58 Å, 6.22 Å, and 4.20 Å respectively via hydrogen bond. Although, alkyl interaction occurred with Lys288 and Ala316 at distance of 4.15 and 5.57 respectively but Ala316 formed another interaction via pi-alky bond at a distance of 5.25 Å. However, ten amino acids interacted with dexamethasone by van der Waals force. They include Glu540, Ser289, His290, Gly287, Lys323, Glu219, Gly538, Lys320, Pro284, and Pro283.
A 2.20 Å X-ray resolved protein was used in this current work with sequence length of 342 amino acids and hexameric structure [10]. The active cavity of Nen-doU is made up of His235, His250, Lys290, Thr341, Thr343, and Ser294 [3,10] (Fig. 1). Therefore, it is of pertinence to evaluate the selected gallic acid derivatives against NendoU using molecular docking technique. 4O6G (−7.5 kcal/mol) and dexamethasone (−7.1 kcal/ mol) were found to have the utmost binding affinity among the gallic acid derivatives and the control drugs respectively. Both compounds occupied the same site on the catalytic region of the target protein (Fig. 6b). The

ADMET prediction
3-O-(6-Galloylglucoside), 4-O-(6-galloylglucoside), and epicatechin gallate were screened for their absorption, distribution, metabolism, and toxicity (ADMET) through a web tool called AdmetSAR. Also, dexamethasone and remdesivir were also screened. This in silico toxicity screening of our hit compounds cannot be over emphasized as it will ultimately predict the pharmacokinetics and pharmacodynamics of these compounds in relation to the selected control drugs. As we know, the control drugs are part of the treatment regimen in the fight against this global malady, but there are safety concerns. The results are depicted in Table 4.
In the absorption class, caco-2 permeability, human intestinal absorption, human oral availability, pglycoprotein inhibition and substrates were predicted. Out of the five molecules, only dexamethasone was predicted to permeant caco-2, epicatechin gallate, dexamethasone, and remdesivir were predicted to be absorbed by the human intestine. None of them were predicted to be orally available but dexamethasone and remdesivir might serve as substrate to p-glycoprotein. Although remdesivir was predicted to inhibit pglycoprotein. The distribution of these molecules were predicted via two properties (PPB and BBB). All the compounds indicate that they might possibly reach their target site in high to moderate dose as their ability to bind to plasma proteins was predicted to be high. Dexamethasone and remdesivir might be able to reach the central nervous system (CNS) as they were predicted to permeant the blood-brain-barrier (BBB). All the compounds were predicted to be localized in the mitochondria except remdesivir which was predicted to be localized in the lysosomes. The metabolism of these molecules by the human liver was predicted to ascertain whether they can cause harm or not. More also, none of these molecules were predicted to inhibit cytochrome P450 1A2 and 3A4. 3-O-(6-galloylglucoside) and 4-O-(6-galloylglucoside) were predicted to be non-substrate to Cytochrome P450 3A4. None of the compounds were predicted to be substrates or inhibitors of Cytochrome P450 2C9, 2C19, and 2D6. All the compounds were catalyzed by UGT except epicatechin gallate and remdesivir as predicted by the web server. The toxicity was evaluated based on acute oral toxicity, AMES mutagenesis, carcinogenicity, and human hepatotoxicity. None of the compound was predicted to be AMES mutagenic and carcinogens; 4-O-(6-galloylglucoside) and dexamethasone were predicted to be non-toxic to the liver cells.

Discussion
Gallic acid (GA) and its derivatives are one of many phytocompounds with diverse pharmacological propensities that can assist in the management of several diseases such as inflammatory, cardiovascular, respiratory, gastrointestinal, and metabolic diseases caused by bacteria, fungi, and viruses. It is pertinent to note in this present in silico study, compounds were selected based on their pharmacological roles, large quantity in nature, and most significantly, had not been used for any study to evaluate them against the etiological agent of the recent coronavirus outbreak be it in silico, in vitro, nor in vivo. However, there are numerous studies on gallic acid and its derivatives against different diseases such as antibacterial, antifungal, and antiviral diseases [17,18]; nonetheless, no study have reported its use as a therapeutic compound against the on-going pandemic. Base on resent studies [20][21][22][23], computational approaches have been deployed to discover novel and potential small molecules in drug discovery and development. The compounds and control drugs were screened for their oral druggability via Lipinski's rule; a rule of thumb that states that for a molecule to be orally active as drug, the molecular weight must not exceed 500 Da, lipophilicity must be below 5, number of hydrogen bond acceptors and donors must not exceed 10 and 5 respectively. The consequence of a molecule exceeding these landmarks might affect its adsorption, metabolism, excretion, and toxicity in the body, making it a poor drug. The physicochemical and toxicity assessment was carried out in silico to fish out some compounds that were perceived to be toxicant; this step buttressed some of the findings of the druglikeness screening using Lipinski's rule.
The significance of our selected SARS-CoV-2 proteins had earlier been discussed. Main protease (Mpro) is a vital enzyme in SARS COV 2 that acts on polyproteins to give rise to nsp4 to nsp16. Thus, without this action, viral replication and transcription might not take place. The binding site of Mpro is surrounded by 25 amino acids such as Thr25, Cys44, Thr26, His41, Met49, Tyr54, Phe140, Leu141, Gly143, Cys145, Asn142, His163, His164, Met165, Ser144, Glu166, Pro168, His172, Val186, Asp187, Arg188, Gln189, Phe185, Thr190, and Gln192. Also, the binding site shows a CysHis (Cys145 and His41) catalytic dyad [29,33]. The BE of 3O6G and ECG noticed with Mpro might be as a result of the interactions that were established with the two key amino acid residues that formed the catalytic triad in its substrate binding domain ( Fig. 2 and Table 4). Another vital protein in viral life cycle with an additional function of innate immune antagonism by cleaving ubiquitin and ISG15 from interferon-α (IFN-α) is the papain-like protease (PLpro) [6]. The substrate binding domain is made up of a canonical cysteine protease catalytic triad (Cys111, His272, and Asp286) (Fig. 1) found at the interface between the thumb and palm domains; other amino acids in these domain are Trp106, Gly266, Gly271, His73, Arg140, and Asn109 [7]. It is noteworthy that none of the hit compounds had interactions with the catalytic triad (Cys111, His272, and Asp286) and might be the reason for the characteristics BE values exhibited by them ( Fig. 3 and Table 4).
RNA-dependent RNA polymerase (RdRp/nsp12) is one of the crucial molecular target for developing promising leads against coronavirus since is the central piece of the replication-transcription machinery [9,25,34]. The active site of nsp12 is conserved in the polymerase motifs A-G (Fig. 1), comprising of Asp618, Ser759, Asp760, and Asp761 [25]. The BE exhibited by 4O6G and dexamethasone might be ascribed to the interactions that results with key residues that are pertinent to activity of the enzyme. However, helicase, nsp13 as a member of the functional complex with RdRp called replication-transcription complex (RTC) is also crucial in the life cycle of SARS COV 2 [9]. The amino acids of relevance in the binding cavity are Lys288, Ser289, Asp374, Glu375, Gln404, and Arg567 [9] (Fig. 1). The characteristics BE portrayed by 4O6G and dexamethasone might be attributed to their abilities to interact with three key amino acid residues that contributes to the catalytic activity of helicase ( Fig. 5 and Table 4). NendoU (nsp15) is key to SARS COV 2 since it is known to interfere with the host immune system by cleaving the double and single stranded RNA at the uridylate region releasing 2′-3′cyclic phosphate end [10]. The active cleft of NendoU is made up of His235, His250, Lys290, Thr341, Thr343, and Ser294 [3,10] (Fig. 1). The BE exhibited after docking by 4O6G and dexamethasone against NendoU might be linked to the interactions formed with key amino acid residues that are significant to the catalytic activity of the enzyme (Fig. 6 and Table  4).
The interactions reported in this present in silico study, viz., hydrogen bond, hydrophobic interaction (alkyl and pi-typed bonds), and electrostatic interactions (pi-cation and anion), had been reported [27,35,36] earlier to provide stability to the protein-ligand complexes and also influence the binding energy values of the hit compounds in complex with all the target proteins. Thus, the available facts from the pool of investigated active compounds related to gallic acid, suggest that 3O6G, 4O6G, and ECG may possess the most in silico inhibitory effect against SARS-CoV-2 through a molecular docking technique. However, these outcomes need to be validated using molecular dynamic simulation, in vitro and in vivo studies. The studies from in silico toxicity profiling (Table 5) revealed that 4-O-(6galloylglucoside) is better than the rest of the hit compounds but it will require chemosimilar or pharmacophoric modeling that will improve some key parameters relating to absorption and distribution which might be linked to the earlier screening for druglikeness using the Lipinski's rule of five where it violated the parameters: hydrogen bond donor and acceptor. This is also the same for the remaining two hit compounds. Thus, they could be repurposed as a defense line against this viral malady.

Conclusion
At present, the greatest threat to global human health is the recent coronavirus outbreak caused by the novel SARS-CoV-2 coronavirus. We performed a molecular docking assay with some selected gallic acid derivatives against five key proteins of SARS-CoV-2 that are found to be vital throughout the viral life cycle and proliferation. These proteins are Nsp3, Nsp5, Nsp12, Nsp13, and Nsp15. From our present in silico study, we observed that three of the selected compounds, namely, 3-O-(6galloylglucoside), 4-O-(6-galloylglucoside), and epicatechin gallate could be promising inhibitors of the selected SARS-CoV-2 non-structural proteins, and a possible treatment option against the current COVID-19 pandemic. The result, from our present in silico study, also revealed that 4-O-(6-galloylglucoside) had the best binding energies (BE) against Nsp3, Nsp12, Nsp13, and Nsp15 with BE of −6.8, −8.9, −9.1, and −7.5 kcal/mol respectively. However, 3-O-(6-galloylglucoside) and epicatechin gallate show better BE against Nsp5 of −8.4 kcal/ mol each. Although the studies from in silico toxicity profiling revealed that 4-O-(6-galloylglucoside) is better than the rest of the hit compounds, thus, could be repurposed as a defense line against this viral malady. However, since this study had been accomplished through molecular docking methods, there would be need for molecular dynamics simulation of at least 100 ns to validate the outcome of our present in silico study. More so, an actual experiment of the identified compounds in this study for both in-vitro and in-vivo studies with appropriate models is also recommended to further authenticate the findings from this study.