Identification of Potential Inhibitors against Epstein−Barr Virus Nuclear Antigen 1 (EBNA1): An Insight from Docking and Molecular Dynamic Simulations

Shweta Jakhmola,§ Nisha Amarnath Jonniya,§ Md Fulbabu Sk, Annu Rani, Parimal Kar,* and Hem Chandra Jha*


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*sı Supporting Information

Epstein−Barr virus (EBV), or human gammaherpesvirus 4, is a dsDNA virus that belongs to the Gammaherpesvirinae subfamily. EBV is widely associated with a myriad of malignancies and nonmalignancies, including neuronal dis- orders. This significant disparity in specific illnesses is
influenced by environmental factors, such as co-infections, social and immune status of an individual, host genetics, and
diverse strains of EBV. The virus is known to infect

and latent membrane proteins (LMP1, LMP2A, and LMP2B). Following latency III, the virus expresses an even more limited set of genes (EBNA1, EBNA-LP, EBERs, LMP1, LMP2A, and LMP2B), which induce differentiation of B cells to memory cells. Besides, latency II is known in oropharyngeal epithelial cells. The virus further restricts its gene expression to EBNA1 and EBERs, which marks the entry into latency I. The resting memory B cells express latency type 0. Notably, EBNA1 is the only viral protein consistently expressed throughout all the

approXimately 95% of the adult human population though most of them remain asymptomatic.1 The virus can infect epithelial cells, neurons, and glial cells.2−6 Albeit, B cells are the primary target of EBV, in which the virus may establish a lifelong stage of latency. EBV displays a peculiar ability to establish distinct latent gene expression patterns, namely, types 0, I, II, and III, in the resting and proliferating B cells. Briefly, the EBV infected transformed B cells express latency III transcription program characterized by the coexpression of
EBV nuclear antigens (EBNA2, EBNA-LP, EBNA1, EBNA3A, EBNA3B, and EBNA3C), EBV encoded small RNA (EBERs),

stages of latencies.7 EBNA1 is a dimeric 641-amino acid protein, which exists either freely in the solution or bound to DNA. The DNA-binding and dimerization domains located at the C-terminal region range from 459 to 607 amino acids.

Table 1. Detailed Description of MS Oral Drugs and Natural Ligand Molecules along with Their Binding Affinities Based on Molecular Docking

sr. no. ligand (molecular wt.) molecular formula PubChem compound identifier (CID) affinity (kcal/mol)
1 Bafiertam (monomethyl fumarate) (130.1 g/mol) C5H6O4 5369209 −4.1
2 Tecfidera (dimethyl fumarate) (144.12 g/mol) C6H8O4 637568 −4.2
3 Vumerity (diroXimel fumarate) (255.22 g/mol) C11H13NO6 73330464 −5.8
4 Aubagio (teriflunomide) (270.21 g/mol) C12H9F3N2O2 54684141 −7.7
5 Avenclad (cladribine) (285.69g/mol) C10H12ClN5O3 20279 −5.9
6 Gilenya (fingolimod) (307.5 g/mol) C19H33NO2 107970 −5.7
7 Zeposia (ozanimod) (404.5 g/mol) C23H24N4O3 52938427 −8.6
8 Mayzent (siponimod) (516.6 g/mol) C29H35F3N2O3 44599207 −7.8
9 angelicin (186.16 g/mol) C11H6O3 10658 −6.7
10 emodin (270.24 g/mol) C15H10O5 3220 −7.3
11 protoapigenone (286.24 g/mol) C15H10O6 11644907 −7.2
12 andrographolide (350.4 g/mol) C20H30O5 5318517 −5.5
13 lignans (414.4 g/mol) C22H22O8 443013 −5.9
14 moronic acid (454.7 g/mol) C30H46O3 489941 −5.1
15 EGCG (458.4 g/mol) C22H18O11 65064 −6.8

EBNA1 can bind to unique EBV genome sequences to initiate the process of DNA synthesis and facilitate an even distribution of the viral episomes to daughter cells during mitosis.8 A study by Jiang et al. evaluated the inhibition of EBV-induced tumors using peptides targeting EBNA1 dimerization.9 Another study showed that disruption of EBNA1 binding to the virus origin of replication site using a synthesized pyrrole−imidazole polyamide−Hoechst 33258 conjugate inhibited virus replication in vivo and in vitro.10 Therefore, EBNA1 is an amicable therapeutic target for the treatment of EBV infection.
Although numerous anti-EBV compounds are predicted and proved to target the EBV lytic replication in vitro, exceptionally little information is available regarding any drug’s ability to target the EBV in the latent stage. To date, no FDA (Food and Drug Administration) or EMA (European Medicines Agency) approved antiviral exists that could be used to treat EBV infection. On the other hand, several anti-EBV natural compounds are known to inhibit EBV progression, although a clear description of the mechanism remains elusive. Reduced toXicity is an added advantage of using natural compounds against pathogenic organisms. In our study, we investigated the interactions between some of the potential anti-EBV natural compounds whose targets in EBV are anonymous or at least have variable EBV targets than EBNA1. Among them, andrographolide isolated from Andrographis paniculata, commonly used in bacterial infection due to its anti- inflammatory property, is known to inhibit EBV by influencing the transcription of BRLF1 and BZLF1 genes.11 Also, epigallocatechin-3-gallate (EGCG), emodin, lignans, and moronic acid inhibit EBV-lytic replication activity. Further- more, angelicin, a furocoumarin molecule, is found to impede the autoactivation of the RTA promoter, resulting in inhibition of the early steps of the replicative lytic cycle in γ- herpesviruses.12 In addition, we evaluated the drug-likeness of these natural compounds by investigating their ADMET properties and other parameters like the Lipinski rule, which could predict the plausible druggability.
Furthermore, multiple sclerosis (MS), a central nervous system disorder wherein the myelin sheath is deteriorated due to the body’s own response, is frequently associated with the EBV infection. The link between EBV and MS is supported by high frequency of appearance of the neurological disorder in
Infectious mononucleosis patients.13 The virus’s association with the disease is further supported by elevated antibodies specific to the virus proteins like EBNA1, VCA, and EA in MS patients.14 A follow-up cohort study on 147 clinically isolated syndrome patients evaluated their immune response to different viruses like EBV, human herpesvirus 6, human cytomegalovirus, and measles. Intriguingly the study found elevated immune responses toward EBNA1 and not to any of the other viruses or other proteins of EBV.15 Though MS has no particular cure or treatment to abrogate the illness completely, several FDA-approved MS drugs alleviate the symptoms. Here in our study, we intend to investigate the influence of orally administered MS drugs on EBNA1. The drugs include fingolimod, ozanimod, and siponimod, known sphingosine-1-phosphate receptor (S1PR) modulators.16 They preferentially sequester the lymphocytes in the lymph nodes, making them unavailable for the purpose of autoimmune activity. Further, we included an immunosuppressive pyrimi- dine synthesis inhibitor, teriflunomide, and dimethyl fumarate (tecfidera) reported to have the most benign side effect profile in our study.17 Other drugs are monomethyl fumarate, similar to tecfidera, diroXimel fumarate that influences various signaling pathways causing beneficial immune and neuro- protective effects, and finally cladribine, which is a purine nucleoside. Overall, our study could screen some potent phytochemical and MS drugs against EBNA1 via molecular dynamics simulations in conjunction with the molecular mechanics generalized Born surface area scheme (MM- GBSA) that may act as potent anti-EBNA1 compounds.

2.1. Molecular Docking. The binding free energies of ligands obtained from molecular docking are recorded in Table
1. These ligands interacted with EBNA1 through several strong (H-bond) and weak (π−alkyl, π−σ, π−cation, unfavorable donor−donor, and halogen) interactions. The docking score was found to lie between −4.1 kcal/mol and −8.6 kcal/mol for all compounds. Among the eight known FDA-approved MS
oral drugs, ozanimod displayed the highest binding affinity (−8.6 kcal/mol). Earlier studies indicated the effectiveness of various proteins against autoimmune disorders. Proteins like the decernotinib, CD27 and M217 antibody complex, and scFv (derived from iXekizumab) reported binding energies as −9.10,


Figure 1. (A) Root-mean-square deviations (RMSDs) of backbone atoms of EBNA1; (B) RMSD of around 5 Å of the inhibitor bound structure;

(C) RMSF of EBNA1; (D) 1D PMF with respect to RMSD of inhibitors.
Table 2. Average Backbone RMSD, Binding Pocket RMSD at 5 Å Radius around Ligand, Radius of Gyration (Rg), and Solvent Accessible Surface Area (SASA) for the Screened Complexesa

system RMSD (Å) RMSD at 5 Å (Å) Rg (Å) SASA (Å2)
Apo 0.89 ± 0.01 18.54 ± 0.01 12216.18 ± 20.52
EBNA1/ozanimod 0.91 ± 0.01 0.70 ± 0.02 18.56 ± 0.01 11916.92 ± 28.77
EBNA1/teriflunomide 0.95 ± 0.01 0.70 ± 0.03 18.57 ± 0.01 11961.77 ± 24.70
EBNA1/siponimod 0.91 ± 0.01 0.60 ± 0.01 18.57 ± 0.01 11841.29 ± 19.04
EBNA1/emodin 0.97 ± 0.03 0.67 ± 0.03 18.61 ± 0.01 12023.52 ± 23.71
EBNA1/protoapigenone 1.21 ± 0.03 0.72 ± 0.02 18.65 ± 0.01 12141.34 ± 24.31
EBNA1/EGCG 0.95 ± 0.02 0.94 ± 0.05 18.60 ± 0.01 11915.15 ± 29.54
aThe data are reported as the average ± standard error of the mean (SEM).

−8.00, and −8.00 kcal/mol, with ozanimod, respectively.18 Ozanimod bound with a better affinity to EBNA1 compared to its binding with two of the above-stated proteins used to treat autoimmune disorders. In addition, siponimod and terifluno- mide displayed a binding free energy of −7.8 and −7.7 kcal/ mol, respectively (Table 1). It is worth noting here that in a quest to identify potential drugs against SARS-CoV-2, docking and binding of siponimod were performed with Nsp14 and papain-like protease (PLpro). The predicted binding energy of siponimod with PLpro was −8.0 kcal/mol.19 Siponimod bound with almost similar affinity with EBNA1. However, teri- flunomide, when docked against dihydroorotate dehydrogen-
ase (DHODH), reported a docking score of −9.26.20 The binding affinity of teriflunomide and EBNA1 was −7.7 kcal/ mol. Out of the seven known anti-EBV phytochemicals, emodin had the highest binding affinity (−7.3 kcal/mol), followed by protoapigenone (−7.2 kcal/mol) and EGCG (−6.8 kcal/mol). A thermodynamic study revealed a similar binding strength between emodin and Helicobacter pylori β- hydroXyacyl−acyl carrier protein dehydratase (HpFabZ).21 Moreover, recently the interaction of EGCG with various
SARS-CoV-2 proteins was analyzed. The docking energies for EGCG with PLpro, 3CLpro, spike RBD, and RdRp were −8.9 kcal/mol, −8.3 kcal/mol, −9.7 kcal/mol, and −5.7 kcal/mol,

respectively. Distinctly the binding energy for EGCG-RdRp was less than that of EGCG-EBNA1.22
2.2. ADMET/Drug-likeness of Natural Compounds. Analysis of the compound’s ADMET properties enables excluding the compounds with ominous characteristics prior to the upscale formulation process. Subsequently, structural refinements could be proposed in the molecules, which could contribute to improved ADMET properties. Here we analyzed the ADMET properties of the seven proposed anti-EBV phytochemicals. Interestingly, all the natural compounds recorded compelling intestinal permeability of >30%, and three (andrographolide, angelicin, and moronic acid) out of seven had high intestinal mucosa permeability represented by values of >0.90 log Papp (Table S1). The top two molecules with the highest binding affinity among all the naturally derived compounds, i.e., emodin and protoapigenone, were P- glycoprotein substrates and negative inhibitors of P-glyco- proteins I and II. Similar results were obtained for EGCG. However, EGCG was a suggested inhibitor of P-glycoprotein
II. Furthermore, protoapigenone and EGCG reported CNS permeability corroborated by log P values of −3.96 and −3.04, respectively, unlike emodin (−2.33) (Table S1). Considering the metabolism of emodin, protoapigenone, and EGCG, the molecules were unlikely substrates of enzyme cytochrome
P450 2D6 (CYP2D6) and CYP3A4. However, emodin and

EGCG were reported inhibitors of CYP1A2 and CYP3A4, respectively (Table S1). Thereafter the renal clearance of the drugs is dependent on the functioning of the organic cation transporter-2 enzyme. Interestingly, no natural compound was reported as a substrate of the enzyme. Also, excluding angelicin, all remaining phytochemicals were nonmutagenic according to the AMES test (Table S1). Moreover, the drug- likeness predicted by the five-step Lipinski rule suggested favorable druggability of the natural molecules (Table S2).
2.3. Molecular Dynamics Simulations. 2.3.1. Structural
Stability and Flexibility Analysis. On the basis of the above docking results (protein−ligand interaction in static con- formations) and ADMET analysis of natural compounds and FDA drugs against EBNA1, the top-siX molecules were scrutinized. To assay the docked complexes’ stabilities within the binding cavity and to get better insights into the docking results, 100 ns molecular dynamics (MD) simulation studies of the siX molecules complex with EBNA1 were carried out. For the qualitative investigation of the simulated systems’ stability and convergence, the root-mean-square deviation (RMSD) of
the protein backbone atoms was analyzed and plotted against time in Figure 1A. Average RMSD values for the last 50 ns simulations were reported in Table 2. It is evident from Figure 1A that the RMSD values for all protein backbone atoms for each system initially increased up to 10 ns; after that, all five systems reached an equilibrium state throughout the entire last
90 ns of the production simulations. The protoapigenone- bound system showed a different phenomena. Initially, 0−40 ns backbone RMSD showed stable fluctuation around 1 Å, and 40−50 ns showed an increased pattern of RMSD and finally reached stable equilibrium during the last 50 ns. The RMSD was calculated concerning its initial structure.
According to Figure 1A and Table 2, the average RMSD of the backbone atoms of EBNA1 varied from 0.89 to 1.21 Å. The apo-EBNA1 system showed a slightly larger deviation after 80 ns. On the other hand, the ligand-bound system got stabilized

fluctuations were observed at chain B active sites after ligand binding, and the average RMSF of these residues was less than 0.5 Å for residues such as K477′, N480′, N519′, I481′, T590′, L582′, T585′, T590′, P587′, and N475′. These results suggested that upon ligand binding, the EBNA1 receptor
protein’s active site got rigid conformations and interacted strongly with ligands. We also explored the inhibitor’s stability and revealed relatively low energy states sampled in MD simulations. The potential of the mean force (PMF) analysis with respect to ligand heavy atoms RMSD was performed. The PMF plot in terms of RMSD of inhibitors is as shown in Figure 1D. PMF is used to explore the low energy state sampled by all the inhibitors. It is evident from Figure 1D that emodin, teriflunomide, and EGCG had a single free minimum found at
∼0.2, ∼1.2, and ∼2.0 Å, respectively. The movement of each
small molecule is dissimilar inside the binding site, as seen in Figure 1D. In ozanimod, siponimod, and protoapigenone, the PMF is characterized by a global minimum located at ∼1.6,
∼2.5, and ∼1.0 Å, respectively, and a secondary minimum at
∼2.1, ∼3.3, and ∼1.7 Å, respectively.
Finally, we estimated the radius of gyration (Rg) and solvent accessible surface area (SASA) of EBNA1 at the condition of without ligand (apo) and with the ligand. The average Rg and SASA values were reported in Table 2, and time evolution plots of these parameters were shown in Supporting Information Figure S1. Rg provides an insight into the overall size and dimensions of the protein complexes. Also, it helps to examine structural drifts in protein complexes. The average fluctuations shown by Rg for apo and ligands ozanimod, siponimod, emodin, protoapigenone, teriflunomide, and EGCG bound proteins were 18.54, 18.56, 18.57, 18.61, 18.65, 18.57, and 18.60 Å, respectively. Overall, the Rg values were more or less the same for all systems, and did not change much after inhibitor binding. Similarly, SASA provided information regarding exposure of protein surfaces to solvent and indicated structural relaxation. The average SASA values
except for the protoapigenone (avg RMSD ∼ 1.21 Å), which

ranged from 11 841.29 to 12 141.34 Å2 for the siX ligand-
showed a similar trend as that of the apo system. The average backbone atoms RMSD were found to be 0.91, 0.95, 0.91, 0.97, and 0.95 Å for EBNA1−ligand complexes of ozanimod, teriflunomide, siponimod, emodin, and EGCG, respectively. Overall, it suggests that the inhibitor binding stabilizes EBNA1. Moreover, the temporal RMSD of the backbone atoms of residues within 5 Å around the ligand in the binding pocket relative to the respective initial conformation is shown in Figure 1B. The last 80 ns trajectory average values are listed in Table 2. It is clear from Table 2 that average RMSD
fluctuations are within 1 Å throughout the last 80 ns simulations for each system. Initially, in all the ligand-bound systems, RMSD values fluctuated up to 50 ns. However, in the last 50 ns, these residues were relatively stable Figure 1B. The higher overall RMSDs of the ligand complexes are due to the presence of flexible loops in the binding pocket.
Furthermore, to illustrate ligand-bound EBNA1 protein backbone Cα atom flexibility, the root mean square fluctuations bound complexes, and the SASA value of the apo EBNA1 was 12 216.18 Å2, which was relatively higher than bound systems.
2.3.2. Binding Pocket Stability. To further explore the binding stability of each inhibitor obtained from the static conformation of the docked EBNA1−inhibitor complex, the binding pocket of the EBNA1−inhibitor complex was analyzed. For this, the center-of-mass distance between the
binding pocket of EBNA1 and inhibitor throughout the 100 ns of MD simulations was calculated and plotted in Figure 2. The average distance from the last 80 ns trajectory ranged from 3 to
6 Å for emodin, teriflunomide, ozanimod, siponimod, and EGCG, while for protoapigenone the average distance was found to be 22.7 Å. It is evident that except for the protoapigenone, the remaining five inhibitors remained in the binding pocket of EBNA1 in the whole trajectory of MD simulations. However, protoapigenone moved out of the binding pocket, as shown in Figure 2 after 40 ns. It suggested that although the EBNA1−protoapigenone complex showed a

complexes along with apo protein were

good docking scoring, the stability of the docked complex can

monitored; see Figure 1C. The RMSF analyses highlighted probable protein motions involving all residues for all systems, and both chains’ atomic fluctuations are quite dissimilar. The higher RMSF values were found in the loop region of the EBNA1 protein for all cases. The loop region near residue 555/555′ showed high fluctuation in chain A in the case of protoapigenone bound EBNA1 compared to chain B. Less

be evaluated by performing the dynamic simulations, which revealed whether the inhibitor remained bound to the target or not. Overall, our results predicted that protoapigenone was not a good inhibitor of EBNA1.
2.3.3. Predicted Inhibitory Efficiency. The susceptibility of the screened FDA drugs and phytochemicals to EBNA1 was estimated using ΔGbind calculations based on the MMGBS

Figure 2. Center-of-mass distance between the binding pocket of EBNA1 and inhibitor for each complex from the trajectory of 100 ns MD simulations.

method. It provided different components to the total binding energy (ΔGbind), such as van der Waals interactions (ΔEvdW), electrostatic interactions (ΔEele), polar solvation energy (ΔGpol), and nonpolar solvation free energy (ΔGnp). As shown in Table 3 and Supporting Information Figure S2, the van der Waals interaction (ΔEvdW) was the main force inducing the protein−ligand complexation except for proto- apigenone and was approXimately 2- to 5-fold stronger than ΔEele, while for the protoapigenone−EBNA1 complex, there was a slight increase in ΔEele compared to ΔEvdW that enhanced its complexations. Among them, FDA-approved
drugs exhibited higher affinity than those of phytochemicals. The highest binding affinity to EBNA1 was predicted for siponimod (ΔGbind = −34.72 kcal/mol), followed by ozanimod (ΔGbind = −28.96 kcal/mol). Interestingly the IC50 determined in a study for siponimod against sphingosine-1 phosphate receptor-1 (S1P1) was 1.67 × 10−10 M resulting in ΔG =
−13.37 kcal/mol which is comparably higher than the binding
energy obtained in our study for siponimod and EBNA1.23 The phytochemical EGCG and the FDA-approved drug teriflunomide showed a comparable binding affinity with

ΔGbind of −20.08 kcal/mol and −19.87 kcal/mol. Although for teriflunomide ΔG = −8.92 kcal/mol according to a case study. Also, ΔG was found to be −9.65 kcal/mol as per a study conducted in a rat model (Table S3).24 Moreover, ΔG obtained from an in vitro study conducted on CHO cells for EGCG was −6.93 kcal/mol.25 The emodin−EBNA1 complex displayed ΔGbind of −18.14 kcal/mol. An in vitro study conducted on CCRF-CEM enabled us to determine ΔG as
−6.08 kcal/mol, which was almost 3 times less negative than the obtained ΔG for the emodin−EBNA1 complex (Table S3).26 However, for the protoapigenone−EBNA1 complex, the lowest binding affinity was observed (ΔGbind = −8.51 kcal/ mol). This result of MMGBSA also agreed with the above
findings of the binding pocket stability where we showed that the protoapigenone was displaced out of EBNA1. Notably, the total favorable components of the molecular complexation (ΔEvdW + ΔGnp) also favored the most for siponimod (−48.32
kcal/mol), followed by ozanimod (−43.27 kcal/mol) among
the FDA approved drugs. Besides, among the phytochemicals, EGCG exhibited the most favorable interactions (−33.81 kcal/ mol). Overall, on the basis of this evidence, siponimod, ozanimod, and EGCG are suggested to be used as an inhibitor to combat the EBV infection.
2.3.4. Hotspot Residues. To investigate the crucial amino acid residues at the active site of EBNA1 with the associated FDA and phytochemicals, each residual contribution to the binding was calculated based on the MM/GBSA method. The total energy contributions from each residue involved in
binding the inhibitor−EBNA1 complex are plotted in Figure 3. The negative and positive ΔGbind values denoted energy stabilization and destabilization, respectively. It should be noted that residues exhibiting the energy stabilization of ≤1.0 kcal/mol were mentioned in Figure 3. Other residues contributing energy up to ≤0.5 kcal/mol are also given for each complex in Table 4. The obtained results demonstrated that the main residues were associated with the binding for siponimod (I481′, K477′, K586′, L582′, and P486′), ozanimod

Table 3. Binding Free Energy Components Calculated for EBNA1−Inhibitor Complexes from the MM-GBSA Scheme in kcal/ mola

component ozanimod teriflunomide siponimod emodin protapigenone EGCG
ΔEelec −10.08 −15.41 −15.62 −5.91 −12.58 −19.54
(0.14) (0.23) (0.13) (0.11) (0.26) (0.21)
ΔEvdW −38.32 −24.62 −42.62 −26.96 −11.63 −28.96
(0.07) (0.06) (0.07) (0.05) (0.14) (0.10)
ΔGnp −4.95 −3.97 −5.70 −3.85 −1.83 −4.85
(0.01) (0.01) (0.01) (0.01) (0.02) (0.02)
ΔGpol 24.39 24.14 29.23 18.58 17.53 33.27
(0.12) (0.20) (0.09) (0.09) (0.25) (0.18)
ΔG b 19.44 20.16 23.53 14.73 15.70 28.42
(0.12) (0.20) (0.09) (0.09) (0.23) (0.17)
ΔGpol, elecc 14.31 8.73 13.61 12.67 4.95 13.73
(0.14) (0.27) (0.14) (0.12) (0.32) (0.24)

(0.14) (0.23) (0.13) (0.11) (0.28) (0.22)

ΔG′ f −28.96 −19.87 −34.72 −18.14 −8.51 −20.08
(0.06) (0.06) (0.08) (0.05) (0.11) (0.09)aStandard error of the mean is provided in the parentheses. bΔGsolv = ΔGnp + ΔGpol. cΔGpol,elec = ΔGpol + ΔEelec. dΔGvdW,np = ΔEvdW + ΔGnp.

eΔEMM = EvdW + ΔEelec. fΔG′ = ΔEMM + ΔGsolv.

 Figure 3. Per-residue decomposition of binding free energy in kcal/mol for each complex.

(I481′, K477′, L582′), EGCG (I481′, K477′, K586′, P587′,
T590′), teriflunomide (I481′, K477′, L582′), and emodin (I481′). However, the protoapigenone−EBNA1 complex clearly reflects that it is not bound to the EBNA1, with no residues contributing to its binding. Overall, the hotspot residues from the main complex suggested that I481′, K477′,
L582′, and K586′ played a significant role in the ligand
binding.Our findings are consistent with previously reported studies, which showed the significance of K477 that made two crucial interactions in the major groove of the DNA and facilitated EBNA1 and DNA interaction.27 Similarly, I481 mediated DNA binding and dimerization, and K586 being a part of the core- domain was involved in dimerization. In an extended study by Messick et al., saturation transfer difference (STD)-NMR and surface plasmon resonance (SPR) were used to demonstrate that the coumarin molecule, AC37287, had a plausibility to act as an inhibitor and may engage EBNA1 residues K477, N519, K586, and T590 that made sequence-specific contacts with DNA.28 Also, the pocket consisted of key hydrophobic residues, namely, I481, L485, L520, and L582.
2.3.5. Computational Alanine Scanning (CAS). The influence of a particular mutation in the binding of an inhibitor toward EBNA1 can be studied via the CAS method. After replacing the residues of wild-type with the alanine, we computed the changes in the binding free energy as given in eq
4 (methods section). The more the positive value of the
ΔΔGbind, the higher is the impact of those mutations in

protein−inhibitor interactions. It could be noted from MD simulations that protoapigenone is not bound to EBNA1. Therefore, EBNA1 with ozanimod, siponimod, teriflunomide, emodin, and EGCG was taken into calculations for CAS. Only those residues that exhibited high contribution in the decomposition free energy as given in Table 4 were considered
in the CAS analysis. Thus, the binding free energy components for residues K477′, I481′, and K586′ were considered and compared with the wild-type and listed in Table 5. For each complex, the value of ΔGbind of mutants is less than the wild- type. It suggested that mutation decreased the binding affinity of the inhibitors toward EBNA1. Overall, the vdW interactions contributed more than electrostatic interactions for each
complex. The higher value of ΔΔGbind was found for I481′. Thus, I481′ played a crucial role in binding with the inhibitors. Finally, the CAS analysis suggested that increasing the hydrophobic contact of inhibitors with Ile481′ of EBNA1 improved the binding associations.
Notably, the H-bond interactions observed for EBNA1− EGCG were comparatively more than other complexes, as given in Table S4. Residues T590′ and S516′ showed comparatively strong H-bond. It agreed with binding energy results that exhibited the highest electrostatic interaction in the EBNA1−EGCG complex than others. Earlier studies have also demonstrated similar results indicating the engagement of T590 and S516; one such report suggested that the side chains of T590 and K586 served as additional hydrogen donors to ligands.29 The hydrogen bond analysis revealed that N480,

Table 4. Decomposition of the Binding Free Energy as Per-Residue Energy in kcal/mol between the EBNA1 and Different Ligand Molecules

residue TvdW Tele Tpol Tnp Tback Tside Ttotal
Ile481′ −2.74 −0.04 0.20 −0.30 −0.40 −2.48 −2.88
Lys477′ −3.33 −1.06 2.32 −0.46 −0.52 −2.02 −2.54
Lys586′ −2.69 −5.35 6.17 −0.55 −0.02 −2.40 −2.43
Leu582′ −1.79 −0.24 0.62 −0.14 −0.25 −1.30 −1.55
Pro476′ −1.16 −0.22 0.52 −0.18 −0.28 −0.75 −1.04
Thr585′ −1.16 −0.12 0.71 −0.19 −0.18 −0.57 −0.75
Asn480′ −2.36 −0.45 2.51 −0.42 −0.27 −0.45 −0.72
Ile481′ −2.26 −0.11 0.15 −0.22 −0.54 −1.90 −2.44
Leu582′ −1.32 −0.24 0.41 −0.10 −0.22 −1.04 −1.26
Lys477′ −1.64 −0.47 1.13 −0.25 −0.34 −0.90 −1.24
Gly484′ −0.83 −0.34 0.29 −0.12 −0.71 −0.29 −0.10
Asn480′ −1.79 −0.37 1.48 −0.29 −0.23 −0.73 −0.96
Lys586′ −2.13 −0.73 2.30 −0.39 −0.01 −0.94 −0.95
Thr515′ −1.06 −0.19 0.54 −0.18 −0.26 −0.62 −0.89
Asn519′ −1.21 −0.30 0.84 −0.11 −0.11 −0.67 −0.79
Ser516′ −0.87 −0.20 0.38 −0.06 −0.41 −0.34 −0.75
Ile481′ −1.64 −0.11 0.15 −0.21 −0.25 −1.56 −1.81
Lys586′ −1.50 −4.12 4.74 −0.30 −0.01 −1.16 −1.17
Leu582′ −1.03 −0.23 0.29 −0.08 −0.22 −0.83 −1.05
Asn519′ −1.27 −0.16 0.50 −0.12 −0.26 −0.78 −1.04
Leu520′ −0.80 −0.01 0.06 −0.05 −0.21 −0.59 −0.80
Thr590′ −0.85 −0.12 0.38 −0.13 −0.10 −0.61 −0.72
Ile481′ −1.83 −0.16 0.19 −0.22 −0.23 −1.79 −2.02
Pro587′ −1.41 −0.02 0.10 −0.33 −0.19 −1.46 −1.65
Lys586′ −2.28 −6.21 7.46 −0.34 −0.12 −1.25 −1.37
Lys477′ −1.61 −1.33 1.91 −0.33 −0.43 −0.94 −1.36
Thr590′ −0.74 −1.01 0.73 −0.14 −0.11 −1.05 −1.16
Asn519′ −0.92 −0.72 0.91 −0.15 −0.20 −0.69 −0.89
Ser516′ −0.43 −0.60 0.35 −0.04 −0.38 −0.34 −0.72
Leu582′ −0.61 0.07 −0.12 −0.03 −0.20 −0.50 −0.70
Ile481′ −2.55 −0.06 0.20 −0.26 −0.37 −2.31 −2.67
Leu582′ −0.98 −0.15 0.25 −0.08 −0.20 −0.76 −0.96
Lys586′ −1.92 −0.74 2.11 −0.36 −0.03 −0.94 −0.91
Asn519′ −1.02 −0.36 0.59 −0.11 −0.20 −0.70 −0.90
Asn480′ −1.37 −0.03 0.96 −0.27 −0.16 −0.55 −0.71
Arg496 −0.77 −0.17 0.62 −0.10 −0.02 −0.45 −0.42
Glu495 0.032 −2.56 2.22 −0.05 −0.02 −0.34 −0.36

S516, N519, K586, and T590 at the recognition heliX contributed to binding.29 In yet another study, EBNA1 T590 and N519 interacted with the acidic moiety of most ligand molecules through hydrogen bonds.28
Further, hydrophobic and H-bond interactions were analyzed from the last stable conformations of the MD trajectory and shown in Figure S3. The Ligplot interactions plot depicting hydrophobic interactions were favored in the ozanimod and siponimod complex. The main-hydrophobic interacting residues in both the complex included N519′,I481′, L582′, K586′, K477′, and N480′. Similarly, the
EBNA1−EGCG complex showed hydrophobic interacting residues, such as K586′, I481′, and K477′ along with that H- bonding of N519′ and N475′. The EBNA1−teriflunomide showed hydrophobic interactions involving residues L582′,

T590′, S516′, and T515′, along with the H-bonding of K477′. However, the emodin complex showed lesser hydrophobic interactions involving I481′, L582′, and K586′. Similar studies that involved the binding of various small molecules with EBNA1 revealed that N519 and K477 residues of EBNA1 contributed to hydrogen bonding most frequently.29 Also, a coumarin molecule, AC37287, bound to EBNA1 and inhibited the DNA binding ability. Hydrogen bonding was evident between N519 and AC37287.30
Altogether, this evidence suggested that hydrophobic
interactions dramatically increased the binding toward EBNA1 as shown in the case of FDA drugs such as siponimod and ozanimod. Among phytochemicals, ECGC showed promising binding with the EBNA1 due to its electrostatic interactions.

Table 5. Energetic Components (ΔGbind) between EBNA1 and Ligands along with the Computational Alanine Scanning (CAS) Mutagenesis Results of the Complexes, in kcal/mola
system ΔEvdW ΔEelec ΔGpol ΔGnp ΔGbind ΔΔGa

Figure 4. Degenerative effects of EBNA1 in an infected host cell. EBNA1 (PDB code 6NPP) can induce apoptosis upon engagement of USP7 and destabilization of p53 and MDM2. The process of programmed cell death results from inhibition of TRIAP1 tran- scription as the prevalence of p53 is reduced. Apoptosis is also influenced by the destruction of PML proteins after phosphorylation induced by the recruitment of CK2 kinase by EBNA1. EBNA1 can also cause ROS generation by induction of NOX2, NOX1, and NOX2

oXidase enzymes. The viral protein is also known to influence
processes prevalent in cancer like phosphorylation of p65 kinase and IKK α/β and inhibition of Smad complexes leading to reduced TGF-β transcripts.phosphorylated PML proteins.38 Nonetheless, the viral proteinis also known to induce oXidative reactions through anNADPH oXidase.39,40 Enhanced expression of these genes can
2.4. Binding of Predicted Natural Compounds or MS Drugs May Render EBNA1 Ineffective To Induce Degenerative Effects on the Host Cells. As stated earlier, a humongous amount of clinical data is available that signify
the presence of EBNA1 in the brains of patients with neurological modalities, particularly MS. However, there is a paucity of in vitro or in vivo molecular studies that explain the role of EBNA1 in cellular degeneration. Figure 4 represents the possible contribution of EBNA1 toward degenerative effects in a cell like induction of apoptosis, reactive oXygen species (ROS) generation, inhibition of NF-κB, etc. Although the critical role of EBNA1 in a cell is to aid in the process of carcinogenesis by promoting stabilization of p53, the viral protein can also promote apoptosis. In the absence of EBNA1, i.e., in an uninfected cell USP7, a deubiquitylating enzyme can bind to p53 and MDM2, an E3 ubiquitin ligase for p53, thus preventing the degradation of these molecules through ubiquitination.31,32 EBNA1, p53, and MDM2 compete for a similar binding site on USP7; however, EBNA1 shows preferential binding to the active site of the receptor.33 Engagement of USP7 by EBNA1 results in polyubiquitination of p53 and MDM2, therefore lowering the amounts of transcription factor p53.33 This results in inhibition of TRIAP1 transcription leading to increased apoptosis.34 Also, EBNA1 disrupts promyelocytic leukemia (PML) nuclear bodies released as a part of innate antiviral response.35 Its destruction can result in altered DNA repair activity, acetylation of p53, and abilities to repair DNA damage, thus initiating apoptosis.36,37 EBNA1 can bind to CK2 kinase resulting in phosphorylation of PML proteins causing demolition of
increase the levels of ROS. ROS can further be converted to hydrogen peroXide by the superoXide dismutase enzyme (SOD).40 These processes increase the oXidative stress in a cell leading to damage. Nonetheless, EBNA1 can inhibit p65 kinase and IKK α/β phosphorylation affecting the NF-κB signaling.41 EBNA1 can also cause a reduction in TGF-β transcripts via downregulation of the smad2 complex.42 Thus, binding the predicted anti-EBNA1 candidates to the viral protein and the proteins mentioned above can show cumulative beneficial effects in a host. These compounds with reduced toXicity apart from alleviating the disease symptoms may also eliminate EBNA1 induced molecular alterations.
The probable lack of availability of the anti-EBV drug is partly because of the difficulty in diagnosis of EBV-induced infectious mononucleosis and also due to the inability of the drugs to achieve high concentrations in the oropharynx where EBV is released in high titers.12 Also, increased involvement of B cells in MS guides toward the plausibility of EBV being a pathogenic agent in exacerbating the neurological condition. A study had quantified EBNA1 gene expression in the cerebrospinal fluid (CSF) and peripheral blood of MS patients, using a real-time PCR assay.43
In the current study, molecular docking and simulation of EBNA1 with various phytochemicals and FDA-approved MS drugs into the defined pocket revealed the involvement of critical amino acids in the interactions of the small molecules with EBNA1. All the ligands were bound stably to the EBNA1

Figure 5. Structural representation of EBNA1 (PDB code 6NPP) with binding pocket information. EBNA1 consists of a flanking domain composed of the extended loop, α heliX, and connector region and a core domain. The ligand-binding pocket amino acids are shown in dark blue.
after 50 ns of minor fluctuations throughout 100 ns of simulation. Smaller deviations imply greater stability of the protein−ligand complex structures.44 Also, analysis of ADMET properties revealed that the natural compounds had appro- priate intestinal absorption, and the Lipinski′s rule predicted favorable druggability of the phytochemicals. The binding pocket included amino acids from the flanking region (composed of α heliX and an extended chain 461−503)
critical in dimerization and the core domain (504−604) crucial
for contact with the DNA. Mutations in the core and flanking domain influence binding to the DNA. Under native conditions, the protein always exists as a dimer, and the monomers have never been noticed. Also, energetically the folding of the EBNA1 monomer is also very unlikely. It is noteworthy to mention that dimerization of the protein is absolutely essential for EBNA1 binding to the DNA. Mutations leading to destabilized dimerization of EBNA1 may affect DNA binding. Intriguingly the role of flanking domain in DNA recognition is demonstrated where the absence of the flanking domain introduced weak binding of EBNA1 to the DNA. Further, a mutation in the core domain revealed that DNA recognition is mediated by the core domain. Although a high docking score was observed for protoapigenone, our simulation results showed that it did not form a stable complex with the EBNA1. The remaining compounds exhibited a stable interaction with the EBNA1. Among the FDA-approved drugs siponimod and ozanimod bound with the highest affinities with EBNA1. Mainly the van der Waals interaction favored the binding of siponimod and ozanimod to EBNA1. Moreover, EGCG was strongly bound to EBNA1. Thus, it can be used as a suitable inhibitor of EBV. Further
analysis revealed that I481′, K477′, L582′, and K586′ are

signal transduction activator of transcription 1 (STAT1) and influenced cytokines and growth factors related pathway.46 Also, the targets of the FDA-approved MS drugs are known in humans. Siponimod and ozanimod, are known S1PR modulators. Conclusively, EGCG, siponimod, and ozanimod, the possible anti-EBNA1 candidates if experimentally proven to show inhibitory effects against EBNA1 and their influence on host proteins, could also aid in alleviating cancer or MS disease symptoms as many clinical studies speculate the role of EBNA1 in these disorders.
Although limited studies are conducted that investigated the neurodegenerative effects of EBNA1, the viral protein can potentially alter processes in the cells, which could contribute to the degenerative impacts. Nevertheless, EBNA1 is consistently reported in the autopsied brains of MS patients, which is suggestive of the role of EBV as a possible etiological agent. Our study catered to the need for anti-EBV compounds by targeting EBNA1 with natural compounds and MS oral drugs. However, further experimental evaluation is needed to affirm an inactivated EBNA1 formed on viral protein binding with the investigated phytochemicals and MS drugs. Analyses of competitive binding or binding of the phytochemicals and MS drugs with the viral antigen reversibly or irreversibly need to be conducted to understand the authenticity of predicted anti-EBNA1 agents.

4.1. Protein and Ligand Structure Retrieval and Prepara- tion. A high resolution (1.35 Å) crystal structure of EBNA1 nuclear protein (PDB code 6NPP) was obtained from the protein database (RCSB PDB) (https://www.rcsb.org/structure/6NPP).28 In addi- tion, the structure-data files of various ligand molecules, namely,

critical in the binding of ligands to EBNA1. K477′ at the amino terminal of EBNA1 established interaction with two bases at the major groove of the DNA. We have found that siponimod is bound to EBNA1 engaging K477′, thus making it unavailable for DNA interaction. Further, the CAS results showed that the I481′ residue played a significant role in the binding between an inhibitor and EBNA1, as revealed from the decreased binding affinity. Overall, the drugs and phytochem- icals under investigation have promising results to be used as potent anti-EBNA1 compounds.
Additionally, it is noteworthy to mention that EGCG has been widely investigated as a potent anticancer agent. It can bind directly with several host proteins like Pin1, TGFR-II, and metalloproteinases 2 and 9, inhibit NF-κB, epithelial- mesenchymal transition (EMT) and cellular invasion.45 Furthermore, surface plasmon resonance, molecular modeling, and site-directed mutagenesis revealed that EGCG bound to

cladribine (mavenclad), dimethyl fumarate (tecfidera), diroXimel fumarate (vumerity), fingolimod (gilenya), monomethyl fumarate (bafierta), ozanimod (zeposia), siponimod (mayzent), teriflunomide (aubagio), andrographolide, angelicin, EGCG, emodin, lignans, moronic acid, and protoapigenone were obtained from PubChem (https://pubchem.ncbi.nlm.nih.gov/).47 The details of the ligand molecules are mentioned in Table 1. Further, the ligands were converted to PDB format using Chimera for future docking studies. For molecular docking, protein and ligand preparation was carried out using AutoDock Tools 1.5.6 (ADT). Protein preparation involved the removal of ligand molecules attached to the crystal structure, addition of polar hydrogens, united atom Kollman charges, and solvation parameters (removal of water molecules), while ligand preparation included the addition of Gasteiger charges, merging of nonpolar hydrogens, identification of aromatic carbons, and rotatable bonds along with the setting of the torsion tree.
4.2. Active Site Prediction and Molecular Docking. Ligand binding site on the protein was identified using the Computed Atlas of Surface Topography of proteins (CASTp) program sts.bioe.uic.edu/castp/calculation.html).48,49 CASTp involves the identification of protein−ligand interaction sites based on weighted Delaunay triangulation and the α complex for shape measurements. The program can easily provide information regarding the accessible surface cavities of the protein along with the hidden interior cavities. The binding pockets predicted by the software are ranked according to the pocket’s areas and volume. We selected the top-ranked pocket
ΔGsolv = ΔGpol + ΔGnp (3)
The polar part of the solvation energy ΔGpol was calculated by using the GB equation,73 while the ΔGnp was derived from the SASA calculation as shown in eq 4, where γ and b are the experimental
solvation parameters equal to 0.005 42 kcal/(mol·Å2 ) and 0.92 kcal/ mol, respectively.74 with a predicted area of 123.486 Å2 and volume of 53.259 Å3 by the
ΔG = γ(SASA) + bsoftware. Following identification of plausible active sites, the grid dimensions in ADT 1.5.6. was set as 19 × 21 × 22 XYZ points with a grid spacing of 0.375 Å, and the grid center was designated at dimensions (x, y, and z): (−8.7150, −32.268, and −14.1290) for molecular docking. The grid was set to enclose the following amino acids: K477′, N480′, I481′, G484′, S516′, N519′, L520′, L582′,
V583′, T585′, K586′, T590′, I593′ in the predicted pocket (Figure 5).
The docking program was run on AutoDock/Vina, which employs iterated local search global optimizer.50,51 During the rigid docking procedure, the exhaustiveness was set at 8 to obtain various poses of ligand docked into the predicted protein cavity. The binding affinities (kcal/mol) of the top binding pose with zero RMSD value are presented in Table 1. Further, the interacting amino acids involved in binding with the ligand along with the pocket amino acids were visualized using Discovery studio (Figure 5).
4.3. MD Simulation. Using molecular dynamic simulations (MD), we can explore the atomic-level details of protein−ligand interactions. Here, the top hit compounds obtained via molecular docking were subjected to MD simulations for studying their stability and dynamic behavior. The MD simulations of all systems were performed by using the pmemd.cuda module of AMBER18 (Case et al., 2018),52 and analyses were performed using the Cpptraj module.53 AMBER ff14SB force field54 and the generalized Amber force field (GAFF2)55 were used in the simulations for assigning parameters to proteins and small molecules, respectively. Appropriate counterions were added to neutralize the system. The TIP3P water model56 was used for solvating the systems. The particle-mesh Ewald (PME)57 scheme was employed to treat the charge−charge interactions, and the nonbonded cutoff was taken as 10 Å. The SHAKE algorithm58 was used to constrain all covalent bonds involving hydrogen atoms.
Initially, each system was subjected to minimizations for 1000 steps, followed by gradual heating from 0 to 300 K with an application of harmonic constraint of 2.0 kcal mol−1 Å−2 at the NVT ensemble. Subsequently, 50 ps MD simulations with a restraint force constant of
2.0 kcal mol−1 Å−2 were performed at a constant pressure of 1 atm and 300 K. Each complex was equilibrated for 1 ns. Finally, MD simulations with a time step of 2 fs were performed under the NPT
ensemble (300 K and 1 atm) until reaching 100 ns. The Langevin thermostat59 and the Berendsen barostat60 were used for controlling the temperature at 300 K and pressure at 1 bar, respectively. Each simulation was performed under the periodic boundary condition with the isothermal−isobaric (NPT) ensemble.
4.4. Binding Free Energy Using the MM/PBSA Scheme. The binding free energy (ΔGbind) and per-residue decomposition free energy were calculated using the molecular mechanics/generalized Born surface area (MM/GBSA)61−65 on the last 60 ns of the production simulation. It was carried out to examine the binding affinity between different lead molecules with EBNA1. The estimation of the binding free energy (ΔGbind) was done using the MMPBSA.py script of the AMBER package. The details of the MM/(PB/GB)SA scheme were discussed in our previous papers.66−72 The same protocol has been followed in the present study. ΔGbind consists of molecular mechanics energy (ΔEMM) in the gas phase, solvation
energy (ΔGsolv), and entropy term (TΔS) as given in eq 1.np (4)
Overall, 6000 structural frames from the last 60 ns were used for the calculation of the MM/GBSA. Due to computational complexity in the calculation of entropy, we avoided it.
Moreover, CAS was performed for some of the essential residues. This method employs the energy difference between the wild type and mutant (alanine) variants.
ΔΔGbind = ΔGmutant − ΔGwild (5)
In the CAS method, the residue is substituted with the alanine residue that affects only the side-chain beyond Cβ and not in the main-chain. Mutational alanine scanning helps to find the hotspot residues in protein−protein interfaces. CAS is an excellent alternative approach to that of in vitro site-directed mutagenesis or in vitro experimental alanine scanning.75,76 Hence, here we employed a CAS method to illustrate the hotspot residues and their impact on the ligand binding toward EBNA1.
4.5. ADMET/Drug-likeness Properties. The pharmacokinetic properties, such as absorption, distribution, metabolism, excretion, and toXicity collectively known as ADMET of the natural ligand molecules, were calculated on the pk-CSM pharmacokinetics web server (http://biosig.unimelb.edu.au/pkcsm/prediction) (Table S1). Further, the drug-likeness of all naturally acquired compounds was evaluated employing Lipinski’s rule using SwissADME (http://www. swissadme.ch/). Lipinski’s rule predicted the possible druggability of all the natural ligand molecules (Table S2).

*sı Supporting Information
The Supporting Information is available free of charge at


ADMET properties and drug-likeness of the phytochem- icals; IC50 and EC50 values of the drugs and phytochemicals; hydrogen bond interactions between EBNA1 and ligands; radius of gyration (Rg) and solvent- accessible surface area (SASA) of ligand protein complexes, including Apo throughout the simulations; different components of ΔGbind for EBNA1−inhibitor complexes obtained from the MMGBSA scheme; Ligplot showing the protein−ligand interactions (PDF)
Corresponding Authors
Parimal Kar − Computational Biophysics Group, Department of Biosciences and Biomedical Engineering, Indian Institute of Technology Indore, Indore, Madhya Pradesh 453552, India;
orcid.org/0000-0001-8451-9739; Email: parimal@ iiti.ac.in
Hem Chandra Jha − Infection Bioengineering Group,
artment of Biosciences and Biomedical Engineering,

bind MM solv
It was obtained by combining the electrostatic (ΔEelec) and van der Waals interaction energies (ΔEvdW) between protein and ligand. On the other hand, ΔGsolv, which consists of polar and nonpolar solvation
energy, was calculated according to eq 2 and eq 3, respectively.
Indian Institute of Technology Indore, Indore, Madhya Pradesh 453552, India; orcid.org/0000-0002-9698- 4547; Email: [email protected]
AuthorsΔEMM = ΔEcovalent + ΔEelec + ΔEvdW(2)Shweta Jakhmola − Infection Bioengineering Group, Department of Biosciences and Biomedical Engineering,

Indian Institute of Technology Indore, Indore, Madhya Pradesh 453552, India

Nisha Amarnath Jonniya − Computational Biophysics Group, Department of Biosciences and Biomedical Engineering, Indian Institute of Technology Indore, Indore, Madhya
Pradesh 453552, India; orcid.org/0000-0001-8022-3113
Md Fulbabu Sk − Computational Biophysics Group, Department of Biosciences and Biomedical Engineering, Indian Institute of Technology Indore, Indore, Madhya Pradesh 453552, India; orcid.org/0000-0003-4341-8370
Annu Rani − Infection Bioengineering Group, Department of Biosciences and Biomedical Engineering, Indian Institute of Technology Indore, Indore, Madhya Pradesh 453552, IndiaAuthor ContributionsConceptualization was by S.J., N.A.J, P.K., and H.C.J. Methodology was by S.J., N.A.J., M.F.S., A.R., P.K., and

H.C.J. Software handling was by S.J., N.A.J., M.F.S., A.R., P.K., and H.C.J. Validation was by S.J., P.K., and H.C.J. Formal analysis was by S.J., N.A.J., M.F.S., P.K., and H.C.J. Investigation was conducted by S.J., N.A.J., M.F.S., A.R., P.K., and H.C.J. Resources were handled by P.K. and H.C.J. Data curation was done by S.J., N.A.J., P.K., and H.C.J. Writing was done by S.J., A.R., N.A.J., P.K., and H.C.J. Writing review and editing were by S.J., N.A.J., P.K., and H.C.J. Visualization was by S.J., N.A.J., P.K., and H.C.J. Supervision was RPC1063 conducted by P.K. and H.C.J. Project administration was by H.C.J. Funding acquisition was by P.K. and H.C.J. All authors have read and agreed to the published version of the manuscript.Author Contributions§S.J. and N.A.J. are equal contributors.
The authors declare no competing financial interest.
Data availability statement: The data that support the findings of this study are available from the corresponding author P.K. upon reasonable request. Ethics statement: There is no human or animal experiment in this study.
We are grateful to the Ministry of Human Resource for the
scholarship to S.J. This project was supported by the Council of Scientific and Industrial Research Grant 37(1693)/17/ EMR-II and Department of Science and Technology as Ramanujan Fellowship Grants SB/S2/RJN-132/20/5 and DST-EMR: EMR/2017/001637. This work was partially supported by the Department of Science and Technology, Government of India (Grant DST/NSM/R&D_HPC_Appli- cations/2021/03.18). The funding organization has not played any role in designing the study and manuscript preparation. We acknowledge the insightful advice and suggestions of our lab colleagues. We are thankful to the Indian Institute of Technology Indore for providing facilities.
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