Virtual Screening, Molecular Dynamics Simulations, and Antiviral Evaluation of Ocimum basilicum Phytoconstituents Against Japanese Encephalitis Virus

doi:10.21203/rs.3.rs-4888640/v1

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Virtual Screening, Molecular Dynamics Simulations, and Antiviral Evaluation of Ocimum basilicum Phytoconstituents Against Japanese Encephalitis Virus

https://doi.org/10.21203/rs.3.rs-4888640/v1

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In conventional medicinal systems, Ocimum basilicum (OB) is known to be effective against viral infections. A thorough screening of OB's phytoconstituents against the Japanese encephalitis virus (JEV) in an in-silico model has not been documented. Therefore, we used the Schrodinger software to do a virtual screening and molecular dynamics simulation (MDS) (100 ns) on 265 phytocompounds of OB against the envelope (E) protein (PDB ID: 3P54) of JEV. Chicoric acid (CA), rutin, and salvianolic acid A (SA) complex of E-protein showed outstanding docking scores (Kcal/mol) of -9.136, -9.135, and − 11.838, which were all higher than the reference mycophenolate (-4.481). The MDS analysis revealed that these hit compounds, especially CA and rutin, showed comparatively strong stability on the binding pocket of the protein. Besides this, CA and rutin exhibited lower free binding energy with this protein than the standard. Moreover, the principal component and free energy landscape analysis highlighted the antiviral potential of these hit compounds against JEV. The in vitro study further supported the antiviral potential of CA and rutin at the early stage of the virus’s lifecycle. Consequently, this study provided insight into the therapeutic potential of the topmost hit compounds, suggesting their development as novel anti-JEV agents. However, further detailed study is required to validate the mechanism of anti-JEV activity of these compounds.

Envelope protein

Japanese encephalitis

virtual screening

phytocompounds

Ocimum basilicum

Japanese encephalitis (JE) is a severe neurological condition caused by the Japanese encephalitis virus (JEV), which results in significant inflammation of the central nervous system. This inflammation can lead to severe, enduring neurological damage with a mortality rate of up to 30% [1]. An estimated 68,000 cases of JE occur worldwide each year, resulting in between 13,600 and 20,400 deaths fatalities across 24 countries in Southeast Asia and the Western Pacific. Over three billion individuals are at risk of infection as a result, with China and India having the highest rates of JE in 2019 [2, 3]. A growing number of individuals are at risk of contracting JEV infection due to increased travel to JE-endemic areas globally, creating a serious threat to public health globally [4].

The entry of JEV into host cells is a complex multi-step process involving the recognition of host cell surface receptors such as attachment or entry receptors, endocytosis, and membrane fusion followed by uncoating to release the genetic material into the host cells’ cytoplasm for the genetic material translation and transcription (Fig. 1) [5]. Each of these steps plays a critical role in the virus's ability to infect and cause disease in the host [6]. Among three structural proteins, the envelope (E) protein of JEV functions as the host cell receptor-binding protein for viral attachment and membrane fusion [7]. The ectodomain of envelope protein exits as a homodimer folded into three distinct domains connected by flexible hinge regions: domain I (DI)—a central β-barrel region, domain II (DII)—an elongated finger-like dimerization region that incorporates a fusion loop, and domain III (DIII) that is exposed on the viral surface and contains cellular receptor-binding motifs (Fig. 2) [8]. Because of the availability of high-resolution crystal structure [8], and its role in the early life cycle of the virus, the E-protein serves as an excellent target for structure-based antiviral drug screening.

At present, there is no approved antiviral drug for JE, despite numerous attempts to develop effective treatment and prevention strategies [2]. Several compounds in the preclinical study pipeline act in an early stage of JEV infection by inhibiting virus attachment, include carbon quantum dots (Cur-CQDs) [9], cyanohydrazones [10], griffithsin [11], and mycophenolate [12], or binding to putative cell surface receptors, for example, apoptozole [13], or decreasing low-density lipoprotein (LDL), like berbamine [14]. Moreover, chlorpromazine [15], manidipine [16], chloroquine [16], and pyrimidines [17] block the early lifecycle of the virus via inhibition of endocytosis, cellular trafficking, low pH of the endosome, and membrane fusion, respectively, as shown in Fig. 1. Currently, none of these compounds are in clinical use as anti-JEV drugs. Although four distinct vaccine types are in clinical use for JE prevention, their efficacy has been compromised due to factors such as insufficient universal immunization coverage [18] and the lack of cross-protection against re-emerging genotype V [19]. Consequently, the discovery of novel, virus-specific drugs must be prioritized in the fight against JEV infection.

Plant-derived compounds are gaining popularity as antiviral therapy due to the lack of effective antiviral drugs and the emergence and re-emergence of different viral pathogens [20]. Numerous herbs, herbal extracts, and plant-derived compounds have been identified as antiviral agents against various viral pathogens owing to their immunostimulatory and antiviral nature [21, 22]. Ocimum basilicum, also known as sweet/holy basil, is one such herb known for its nutritional value and antiviral activities attributed to its pharmacologically active secondary metabolites [23]. Although individual bioactive phytocompounds of OB, such as rosmarinic acid [24], quercetin [25], and rutin [26], were reported to have anti-JEV activity in different experimental models, there is no scientific report regarding comprehensive screening of phytoconstituents of OB in an in-silico and in vitro model of JEV. Thus, in this study, we have screened the antiviral potential of 265 phytocompounds of OB against the E-protein of JEV using molecular docking, molecular dynamics simulations (MDS), and free energy calculations, and evaluated the two topmost hit compounds in an in-vitro study.

The Schrödinger software (Maestro 2021-2 suite) was employed to perform docking and molecular dynamics simulation studies. The LIGPREP module, Protein Preparation wizard, and GLIDE module of the software were utilized for ligand preparation, protein preparation, and the extra-precision (XP) docking studies, respectively. The Desmond module was also used for executing the molecular dynamics simulation studies.

2.1. Docking study

2.1.1. Protein preparation and binding site detection

The 3D structure of the Envelope (E) protein of JEV (PDB ID: 3P54) was obtained from the Protein Data Bank (PDB) (https://www.rcsb.org/) [27], and this target was subsequently refined, optimized, and minimized using the protein preparation wizard of the Schrödinger software 2021-2 suite, which involved adding missing hydrogen atoms, assigning bond orders, removing water molecules, and treating for metals. Because of the lack of a co-crystallized ligand with this protein, site mapping was performed to identify the ligand-binding pocket of the protein.

2.1.2. Ligand Preparations

Two hundred sixty-five phytocompounds, reported from different parts of OB, were compiled from published articles in PubMed, Scopus, Web of Science, and Google Scholar. The 2D structures of these compounds were retrieved from PubChem [28], and subsequently drawn using Chem-Bio-Draw 15.0 (PerkinElmer Informatics, Waltham, MA, USA). These structures were then prepared using the LIGPREP tool of the Schrödinger software, which generated all possible tautomers at a pH range of 7.0 ± 2.0 utilizing an OPLS4 force field. Among the generated isomers, the best one was selected based on the docking score against the optimized protein target.

2.1.3. Molecular Docking

Using the GLIDE module of the Schrödinger software, flexible ligand docking was executed in extra precision (XP) mode between the prepared phytocompounds and the E-protein of JEV [29]. The OPLS4 force field of the module was used to calculate the docking scores, which were employed to guide the selection of stable ligand poses within the receptor binding site [30]. The hit compounds showing the most favorable docking scores against the JEV’s E-protein target were ranked based on minimal binding energy values to prioritize the best binders. The ligand-protein interactions during docking were characterized by hydrophobic, ionic, hydrogen bond, and water-bridge force of interactions.

2.1.4. Molecular Dynamics Studies

The protein-ligand complexes, sourced from docking output files, were subjected to molecular dynamics simulations using the "Desmond V5.9 package" in the Schrödinger software [31]. The System Builder tool of the package was used to prepare the protein-ligand complexes for further simulation study. Following the preparation of the ligand-protein complex, a transferable intermolecular interaction potential 3 points (TIP3P) water molecules solvent model, along with an orthorhombic simulation box, was selected. This solvated system was neutralized using Na+/Cl − counter ions with 0.15 molar concentration of salt to imitate the physiological conditions [32]. The solvated and neutralized ligand-protein complex was then subjected to energy minimization using OPLS-2005 force field and equilibration before running the actual MD simulation to study the complex stability and dynamics over time. Subsequently, the simulation was executed under an isothermal and isobaric ensemble (NPT) by maintaining the temperature, pressure, and thermostat relaxation time at 300 K, 1 atm, and 100 ps, respectively. The temperature and pressure during the molecular dynamics simulations (MDS) were maintained at constant levels using the Nose–Hoover thermostat and the Martyne–Tobias–Klein barostat methods [33]. The simulation trend was recorded at 10 ps intervals by initiating the NPT ensemble after the initial simulation phase, running for a production period of 100 ns. The resulting trajectories were analyzed to assess fluctuations in protein conformation by gathering frames and employing simulation interaction diagrams. The stability of the protein-ligand complex was dynamically visualized through various statistical parameters, including Root Mean Square Deviation (RMSD), Root Mean Square Fluctuation (RMSF), Radius of Gyration (rGyr), Solvent-Accessible Surface Area (SASA), and the principal component and free energy landscape analysis.

2.2. Post-Simulation Analysis

2.2.1. Estimation of Binding Free Energy

Gibbs free energy (ΔG) derived from the molecular mechanics-generalized Born surface area (MM-GBSA) analysis was utilized to establish a correlation between experimental and predicted binding affinities. The OPLS force field-based molecular mechanics energies, incorporating the variable dielectric generalized Born (VSGB) 2.0 solvation model to account for residue-specific effects, were applied for the polar solvation term. The nonpolar solvation terms, including solvent-accessible surface area (SASA) and Van der Waals interactions, were integrated using the Prime MM-GBSA method [35] to estimate the binding free energy (ΔG_binding) of the final docked complex. The binding free energies of the target protein-ligand complex were computed using the Prime MM-GBSA module in the Schrödinger 2021-2 suite with the OPLS4 force field [34]. The binding free energy represents the free energy difference between the complex and the sum of the individual free energies of the protein and ligand.

ΔG_binding = ΔG_(complex) - ΔG_(protein) - ΔG_(ligand)

where the binding free energy is denoted by ΔG_binding and the free energy of complex, protein, and ligand was denoted by ΔG_complex, ΔG_protein, and ΔG_ligand respectively.

2.2.2. Principal Component Analysis

Principal component analysis (PCA) was performed using the Schrödinger implemented python script run trj_essential_dynamics.py [35] to investigate collective motions in proteins with and without docked ligands. Snapshots recorded every 10 ps over a 100 ns simulation were analyzed to identify significant conformations, reflecting major global movements. Covariance matrices of the Cα atoms captured essential movements, with diagonalization yielding eigenvectors and eigenvalues; larger eigenvalues indicated greater dynamic relevance. The first two principal components (PC1 and PC2) were used to create a 2D PCA plot.

2.2.3. Free energy Landscape Analysis

The Free Energy Landscape (FEL) analysis was performed using the Schrödinger Python script (http://pca_vs_pca_v1.py/) [35] to assess energy minima and protein stability. During MD simulations, numerous energetically distinct conformational clusters were generated. Ligand interactions can redistribute these conformations and create new ones. RMSD values of the Cα atoms were used to identify energetically favorable and unfavorable backbone conformations. Gibbs free energy of the Cα atoms was calculated based on the first two principal components (PC1 and PC2) [36], with different color codes representing favored and unfavored conformations.

2.3. In vitro study

2.3.1. MTT Assay of Antiviral Activity

Cell viability assay was performed using MTT to evaluate the antiviral activity of the hits in SH-SY5Y cells, as described in an earlier study [36]. Briefly, the cells were seeded at a density of 10⁴ cells/well in 96-well plates for 24 h. The antiviral mode of action of CA (1, 4, and 16 µM) and rutin (3.12, 12.5, and 50 µM) was evaluated using prior, simultaneous treatment approaches, adding the drugs onto the cells 1 h before or simultaneously with JEV (MOI = 5) inoculation, respectively. The monolayer of the cells was washed with PBS before incubation of the plate at 370C, 5% CO2 for 24 h. The monolayer of the cells was incubated with 20µl of 5mg/ml MTT solution at 37°C, 5% CO2 for 2 h. The formation of formazan crystals within the cells were checked under a microscope, and then dissolved by adding 130µl of DMSO. Finally, absorbance was measured at 570 nm using Spark® Multimodal Microplate Reader (Tecan Trading AG, Männedorf, Switzerland).

2.3.2. Plaque Yield Reduction Assay

The anti-JEV activity of CA (0.5–16 µM) and rutin (3.12–100 µM) in different treatment approaches was evaluated using plaque assay as described elsewhere [37]. Briefly, SH-SY5Y cells were cultured on a 24-well cell culture plate at a density of 1×10⁵ cells/well and incubated at 37°C with 5% CO₂ for 24 h. To assess the potential antiviral mode of action of the test compounds, prior and simultaneous treatment experiments were performed. In the pre-treatment assay, the drug-pretreated/mock-treated cells with CA or rutin for 1 h were infected with JEV (100 pfu/well) and incubated for another one hour. The cells’ monolayer was then washed with PBS to remove the test compound and the unbound virus. The monolayer of the cells was then overlaid with 1ml of maintenance medium containing 0.3% agarose before incubating the plate at 37⁰C, 5% CO₂ for 72 h. The number of plaques per well was counted after fixing and staining with 10% formalin and 0.1% crystal violet, respectively.

In the case of simultaneous treatment assay, the monolayer of the cells was given the treatment and the infection concurrently, followed by removing the unbound virus and the test compound using PBS wash before incubating the cells for the plaque assay. The monolayer of the cells was then washed with PBS, and a new maintenance medium containing 0.3% agarose was added before incubating the plate at 37°C, 5% CO₂ for 72 h. The virus titer was determined by plaque assay.

The ability of the test compounds to block virus entry into SH-SY5Y cells was evaluated using the virus attachment assay. The mixture of JEV (adjusted to 100 pfu/well) with different concentrations of CA or rutin was immediately added to the cell in the 24-well plates and incubated for 1 h at 4⁰C. After removing the mixture and washing it with cold PBS, the cell monolayer was overlaid with a maintenance medium containing 0.3% agarose. Upon completion of a three-day incubation at 37⁰C with 5% CO₂, the number of plaques per well was enumerated as described in the pre-treatment protocol.

The average number of plaques per well was counted to determine the virus titer (pfu/ml) using the following formula: Titer (pfu/ml) = number of plaques/ (volume of the diluted virus added to the well × dilution factor of the virus used to infect the well in which the plaques were enumerated). The median inhibitory concentration (IC₅₀) of the hit comounds in different treatment approaches was determined using nonlinear regression in GraphPad Prism 8.0.1 software (GraphPad Software Inc., San Diego California, USA, 2018) after calculating the percentage inhibition in plaque yield as given below.

Inhibition (%) of JEV = [(Mean number of plaques in the virus control group –number of plaques in the drug-treated group)/ (Mean number of plaques in the virus control group)] * 100

3.1. Molecular Docking and Contact Analysis

Among the 265 phytocompounds of OB, chicoric acid (CA), rutin, and salvianolic acid A (SA) were screened as the top-hit compounds. Chicoric acid (-9.136 kcal/mol), rutin (-9.135 kcal/mol), and SA (-11.838 kcal/mol) demonstrated superior docking scores against the E-protein compared to the standard, mycophenolate (-4.481 kcal/mol). Molecular docking is a computational approach utilized to predict the binding affinity between a small molecule and a target protein [30]. Generally, higher docking scores imply stronger binding interactions [38, 39].

The interaction between the ligand and the target protein encompasses the establishment of hydrogen bonds, ionic interactions, salt bridges, and hydrophobic interactions, including pi-cation interactions. These interactions are crucial for the binding affinity and stability of the ligand within the protein's binding site [40]. Consequently, hydrogen bonds and hydrophobic interactions were employed to assess the binding affinity of the identified compounds to the target protein. The majority of the amino acid residues within the binding pocket of the target protein participated in hydrogen bond formation with hit compounds than the standard (Fig. 3). Moreover, at Lys336, CA depicted salt-bridge interaction, whereas rutin had pi-cation interaction on the same amino acid residue (Fig. 3B, C). These interactions indicate the ligands' closer and stronger binding affinity to the target protein [39, 41].

3.2. Molecular Dynamics (MD) Analysis

In a conventional molecular dynamics (MD) simulation, atoms and molecules are permitted to move over a brief period, with the interactions between them determined by force field parameters. These parameters typically depict the temporal evolution of bond lengths, angles, torsions, non-bonding van der Waals interactions, and electrostatic forces among atoms [42]. In this study, MD simulations were evaluated using RMSD, RMSF, protein secondary structure elements (SSE), protein-ligand contacts, the radius of gyration (rGyr), and solvent-accessible surface area (SASA) for each protein-ligand complex to determine their stability [43]. The binding mechanism and stability of the reference compound within the target protein complex served as a benchmark for evaluating the hit molecules. While all the hit compounds exhibited relatively better or comparable stability within the binding pocket of the target protein, only CA and rutin consistently maintained stability across various MD simulation parameters as indicated in the MD analysis results.

3.2.1. RMSD, RMSF, and SSE Analysis

Protein backbone RMSD

The protein Root Means Square Deviation (RMSD) analysis was performed on protein-ligand complexes to assess the envelope protein backbone's overall stability and conformational changes during the simulation. The RMSD values of the protein's backbone Cα atoms demonstrated greater stability with CA and rutin complexes, as these complexes exhibited relatively lower and less fluctuating RMSD values, which suggests that the binding of the hit compounds to domain III of envelope protein without causing instability in the protein structure (Fig. 4A). The protein complexed with CA displayed an average RMSD value of 3.021 Å, peaking at 12.7 ns (5.511 Å), and then became stable after 20 ns in the entire simulation time. Similarly, the protein complexed with rutin exhibited stable RMSD values between 1.672 and 5.950 Å, averaging 3.774 Å, though some fluctuations were observed after 50 ns. However, the protein complex exhibited more significant fluctuations with SA and mycophenolate throughout the entire simulation period. The average (and range of) RMSD values were 4.411 Å (2.067 to 6.38 Å) for SA and 5.351 Å (1.209 to 7.814 Å) for mycophenolate complex. These results suggest that the backbone Cα atoms of the E-protein achieve greater stability when complexed with CA and rutin than the standard.

Ligand RMSD

The RMSD values of CA, rutin, SA, and mycophenolate displayed diverse stability profiles within the protein's binding pocket, as shown in Fig. 4B. The protein complexed with CA displayed an average RMSD value of 3.892 Å in a range of 1.06 to 8.717 Å, with minor fluctuations observed between 60 and 75 ns. Similarly, the protein complexed with rutin exhibited stable RMSD values ranging between 1.357 and 9.579 Å, averaging 4.413 Å, though some fluctuations were observed after 95 ns. However, the SA, in the binding pocket of the target protein, experienced significant scattering with an average RMSD value of 6.196 Å within a range of 1.12 to 12.307 Å. The average RMSD value of mycophenolate was 5.351 Å within the range of 1.12 to 12.307 Å. Overall, the lower and less fluctuating RMSD values of CA and rutin suggested strong and stable binding of these hits to the protein than SA and the standard.

RMSF and SSE Analysis

The root means square fluctuation (RMSF) of protein residues is instrumental in identifying flexible regions within the protein structure by mapping local variations in amino acid residue movement along the protein sequence [44], while secondary structural elements (SSE) provide insights into the overall folding and stability of the protein [45]. RMSF measures the flexibility of individual residues in the protein, indicating which regions are more dynamic. The E-protein in the complex with CA, rutin, and SA showed less fluctuation and lower peaks than mycophenolate. The average (and range of) ligand RMSF value of CA, rutin, SA, and mycophenolate complex was 1.707 Å (0.577 to 9.608 Å), 1.896 Å (0.626 to 9.92 Å), 1.880 Å (0.725 to 9.682 Å), and 2.173 Å (0.233 to 8.501 Å), respectively. These findings showed that CA and rutin were associated with the protein without causing much changes to the overall conformational of the protein.

The protein-hit compound complexes showed major peaks and fluctuations in naturally flexible regions, such as the loop regions and at the protein's terminal position residues, although the binding was stable at the binding pocket of the protein. In the case of the protein-CA complex, major peaks were observed at loop position 101 (Trp101; RMSF, 3.842 Å), 106 (Gly106; RMSF, 4.173 Å), and 107 (Phe107; RMSF, 4.142 Å); and at the terminal residue 402 (Thr402; RMSF, 7.066 Å), 403 (Leu403; RMSF, 8.501 Å), and 404 (Gly404; RMSF, 9.608 Å). A similar RMSF fluctuation of amino acid residues was noticed for the protein's complex of rutin, although the amplitude of the RMSF value fluctuation was highest for SA and the standard (Fig. 4C). The SSE analysis of the protein’s complex of reported hit compounds similarly demonstrated the overall stability of the protein-ligand complexes. The percentile composition of the α-helices and β-strands was 4.36% and 41.54% for mycophenolate, 4.47% and 41.68% for CA, 4.72% and 41.90% for rutin, and 4.57% and 42.00% for SA, respectively (Supp. Fig.S1). These findings from RMSD, RMSF, and SSE analysis revealed that CA and rutin were more firmly bound than the other two ligands, with minimal changes in the conformation of the target protein [30]. An investigational drug that binds strongly to domain III (DIII) of the viral envelope protein was reported to effectively inhibit virus entry and the subsequent infection of target host cells [46]. The loop regions of the envelope protein, especially at positions ranging from 105 to 107, are crucial for conformational changes of the protein required during fusion, attachment, and the subsequent infection of host cells [8]. The stable binding of the hit compounds at DIII along with increased flexibility of amino acid residues in the loop region due to binding of the hit compounds could block the virus entry into the host cells.

3.2.3. Radius of gyration and Solvent-Accessible Surface Area Analysis

The radius of gyration (rGyr) represents the average distance of atoms from their axis of rotation, calculated as the root mean square distance. It is also the structural parameter that measures the protein’s compactness and extendedness upon ligand binding and is equivalent to the protein’s principal moment of inertia [45]. The MD simulation analysis findings of the rGyr value for the E proteins’ backbone atoms in 100 ns simulation time are shown in Fig. 5A.

The complex of CA, rutin, and SA with E-protein was examined to analyze the influence of the ligand binding on the overall structural compactness of the protein. The average (and range of) rGyr value for CA, rutin, SA, and mycophenolate was 6.459 Å (5.805 to 6.804 Å), 4.600 Å (4.324 to 5.207 Å), 5.082 Å (4.231 to 6.119 Å), and 4.017 Å (3.733 to 4.304 Å) respectively. The rutin complex showed a similar rGyr value to the reference compound, with slight jerks at 22–26 ns, 43–46 ns, and 84–89 ns. However, the CA complex of the protein showed a higher but stable rGyr pattern, while the SA complex experienced lower and less fluctuating rGyr till 46.20 ns before it became more fluctuating with higher rGyr value throughout the simulation time (Fig. 5A). The less fluctuating but higher rGyr values of CA and rutin protein complex suggest the increased extendedness of the protein’s structure to accommodate the bound hit compounds and the ligands' stability on the target protein's binding pocket. This increased extendedness of the protein structure allows for better fitting and stabilization of the ligands within the binding pocket [49].

The solvent-accessible surface area (SASA) of the target proteins complexed with the hit and reference compounds was analyzed to trace changes in the surface area of the proteins accessible to water molecules to evaluate the protein’s unfolding and folding in the presence of ligands [43]. As depicted in Fig. 5B, the target proteins appeared to have varying values of SASA depending on the specific ligand interacting with the target protein. In the case of CA, rutin, SA, and mycophenolate complexed with E-protein, the average SASA value of the protein was 261.358 Å², 285.239 Å², 347.225 Å², and 228.493 Å², respectively, with noticeable fluctuation at 10ns and 83ns for rutin, from 60 to 70 ns for CA, throughout the simulation time after 43 ns for SA complex. The protein complex showed a similar pattern of SASA fluctuation for the reference compound with that of CA and rutin (Fig. 5B). The relatively lower SASA value of the protein complexed with chicoric aid and rutin indicates that hydrophobic residues of the protein were less exposed to the solvent system, and the more stable and folded protein conformation was maintained during ligand binding [50]. However, the SA complex of the protein showed more fluctuating and higher SASA value than the standard, suggesting less stability of the protein-ligand complex.

3.2.5. Protein-Ligand Contact Analysis

As depicted in Fig. 6, most of the amino acid residues of the E-protein formed water bridge and hydrogen bond interactions with hit compounds over the simulation period. The protein–ligand contact analysis of E-protein when complexed with the reference compound mycophenolate, showed that Asn358 (~ 40%), and Leu354 (> 70%) residues were involved in hydrogen bond formation during the whole simulation period. A hydrophobic interaction was formed by Pro350 and Leu354 residues for about 10% of the total interaction time. Asp37, Thr40, Lys336, and Lal335 residues engaged in a water bridge interaction for more than 50%, 30%, 20%, and 30% of the total running time respectively (Fig. 6A). The protein-CA complex formed hydrogen bond for about 50% of running time at Met34, Lys336, and Asn358 residues, while Met303, Val340, and Val340 residues showed hydrophobic interaction for approximately 25% of analysis running time; Asp37, Lys336, Ile337, Ile339, and Asn358 residues contributed to water bridges interaction for about 50% of the experimentation time with Asp37, Lys336, and Asn358 displaying significant hydrogen-bond interaction as well. Furthermore, the ionic bond was formed at Lys336 for about 10% of the simulation time with CA (Fig. 6B). Leu354 residues of the protein in the rutin–E-protein complex showed hydrogen bond interaction for greater than 50% of total interaction time; Asp37 and Asn358 residues were also noted to form water bridge interaction for at most 75% of simulation time. Val340 and Pro350 residues formed hydrophobic bonds for approximately 25% of the interaction time (Fig. 6C). The protein complex of SA formed hydrogen bonds at Asp37, Lys38, and Leu354; water bridge at Asp37, Lys336, and Asn358; hydrophobic interaction at Leu296 and Val357; and ionic interaction at Lys249 and Lys336 for more than 50%, 10%, 25% and 5% of the simulation time respectively (Fig. 6D). Overall, the majority of the amino acid residues of the protein partook in hydrogen bond and water bridge interactions with the hit compounds, which indicates the stable binding of the ligands to the binding target of the proteins.

3.2.6. Binding Free Energy Calculation

All MD simulation trajectories, generated at 100 ns intervals, were subjected to free binding energy calculations against complexes using the MM/GBSA method [51]. The average binding energy, coulomb energy, covalent binding energy, hydrogen-bonding correction, pi-pi packing correction, lipophilic energy, generalized born electrostatic solvation energy, and van der Waals energy were computed for CA, rutin, SA, and mycophenolate complex of E-protein. A more negative free energy value indicates a stronger binding affinity of the ligand to the target protein [26].

Chicoric acid, rutin, and SA demonstrated lower net binding free energy (ΔG_bind) against E-protein than mycophenolate (Table 1). Salvianolic acid A scored the lowest net binding free energy (ΔG_bind) than CA and rutin against the target protein. Interestingly, ∆G_{Bind_Coulomb}, ∆G_{Bind_Lipo}, and ∆G_{Bind_vdW} contributed substantially towards ΔG_bind of the protein-ligand complexes, whereas ∆G_{Bind_Covalent} showed unfavorable energy to the ΔG_bind for all the complexes. Although ∆G_{Bind_Solv_GB} favored net binding free energy for the complex of mycophenolate with E-protein, it unfavored the net binding free energy across the hit compounds.

Table 1

Binding free energy calculation for E protein-ligand complex using prime/MM-GBSA approach
Energy Contribution (kcal/mol)	Hit phytocompounds
Energy Contribution (kcal/mol)	Chicoric acid	Rutin	Salvianolic acid A	Mycophenolate
ΔG_bind	-57.421	-50.265	-61.599	-39.857
ΔG_{bind_colummb}	-15.504	-38.244	-23.221	30.787
ΔG_{bind_covalent}	6.086	6.237	-0.950	0.580
ΔG_{bind_Hbond}	-3.977	-4.749	-4.464	-2.062
ΔG_{bind_Lipo}	-16.390	-10.319	-13.898	-12.915
ΔG_{bind_Packing}	-0.001	0.000	-0.016	0.000
ΔG_{bind_Solv_GB}	20.309	32.909	17.657	-24.313
ΔG_{bind_vdW}	-47.945	-36.098	-36.707	-31.934

3.2.7. Principal Component Analysis (PCA)

Principal component analysis (PCA) was done to get insight about the significant motion of the protein owning to ligand binding through eigenfractions derived from a covariance matrix. The first ten eigen modules were taken for PCA, as the majority of functionally relevant dynamics of the proteins are accounted for by the top 10 to 20 eigen models [52]. Consequently, the target protein complexes with CA, rutin, SA, and mycophenolate showed simulated motions between 52.664% and 65.786% from the first two eigenvectors. The first two principal components (PC1: black cluster, and PC2: red cluster) were selected for the projection of the major dynamics of the protein. In a 2D plot of PCA (Fig. 7), the complex occupying minimal phase space with a condensed conformational distribution is more stable, while a complex that takes a wider space and has a dispersed distribution indicates a noteworthy alteration in the protein structure and less stability of the protein-ligand complex [53]. PCA helps to identify significant conformational alteration in the protein structure, which could lead to loss of the protein function, as the result of ligand binding [34].

The complex system involving the E-protein and mycophenolate exhibited the highest level of correlated motion, with percentages of 43.679% for PC1 and 22.107% for PC2. Following this, the apo E-protein (PC1: 39.614%; PC2: 23.498%), SA (PC1: 37.92%; PC2: 14.779%), CA (PC1: 35.547%; PC2: 22.896%), and rutin (PC1: 34.943%; PC2: 17.702%) complexes of the protein demonstrated progressively lower levels of motion. These findings indicated that the hit compounds' binding stabilized the E-protein compared to the standard. These high PCA values could be linked to the increased flexibility of the amino acid residues in the binding site, as was noticed from the RMSF values of the mycophenolate-protein complex. The E-protein's CA and rutin complexes occupied relatively less phase space with a condensed cluster because of their stable complex systems (Fig. 7C-E). These stable complexes of CA and rutin suggest that they could inhibit the virus attachment to host cells thereby blocking subsequent virus entry into the host cells.

3.2.8. Free Energy Landscape Analysis

The Free Energy Landscape (FEL) helps to pinpoint the structure of the ligand-protein complex with the lowest energy (0 Kcal/mol) from all potential conformations generated during MD simulation [54]. The 2D free energy profile in the left panel of Fig. 8, displaying the lowest energy coordinates of PC1 and PC2, was utilized to extract the frame corresponding to the energy minima. Subsequently, the docked complex was superimposed onto the structure indicative of the lowest free energy minima, allowing for the examination of changes in the ligands' binding poses, as shown in the right panel of Fig. 8. In the left panel, the energy states of the structural conformations are depicted, with the black color representing the lowest stable state. The right panel illustrates the ligand's lowest energy conformation post-MD simulation in green, while the blue color indicates the conformations of the docked ligand before the MD simulation.

The RMSD values for local minima structure of the complex of E-protein with mycophenolate (PC1: 17.562; PC2: -10.774), CA (PC1: 12.78; PC2: 7.764), rutin (PC1: -30.454; PC2: -9.945), SA (PC1: -10.989; PC2: -5.769), and superimposed with the corresponding docked pose, were 0.9523 Å, 1.5950 Å, 2.0368 Å, and 2.6439 Å, respectively, (Fig. 8A-D). Moreover, CA, rutin, and SA formed one hydrogen bond interaction at the most stable conformation via Asn358, Asn358, and Lys38, respectively. In addition to salt-bridge interaction at Lys360 with CA and at Lys336 with rutin, the E-protein formed pi-cation interaction with the later hit compound at Lys336 (Supp. Fig. S2). These observations suggest that CA and rutin formed a more stable complex with E-protein, which could result in the inhibition of virus attachment and entry into host cells.

3.3. Chicoric acid and rutin increased viability of JEV-challenged cells

The antiviral effect of CA and rutin was evaluated using prior and simultaneous treatment approaches using MTT assay in SHSY-5Y cells challenged with JEV. In the prior treatment assay, CA demonstrated significant antiviral activity at 4 µM and 16 µM with 79.73% and 84.49% cell viability, respectively. Similarly, rutin showed a dose-dependent significant antiviral activity (Fig. 9A). Moreover, both CA and rutin significantly increased cell viability in a dose-dependent fashion when the cells were infected and treated concurrently (Fig. 9B). In the simultaneous treatment mode, the percentage viability of JEV-infected cells treated with 1, 4, and 16 µM of CA was 73.28%, 84.42%, and 94.18%, respectively. In this experiment, when the infected cells were treated with 3.12, 12.5, and 50 µM of rutin, the survival rates of the cells were 80.83%, 92.32%, and 96.41%, respectively, which were comparably higher than that of CA at all the tested doses.

3.3. Chicoric acid and rutin reduced plaque yield in vitro

The antiviral effect of CA and rutin was further evaluated by plaque assay across various treatment modalities (Fig. 10). The findings from these experiments showed that both CA (0.5–16 µM) and rutin (3.12–100 µM) significantly reduced plaque yield in all treatment modalities at all dose levels except at 0.5 µM for CA and 3.12 µM for rutin (Fig. 10A, B). As illustrated in Table 2, CA and rutin demonstrated a range of median inhibitory concentration (IC₅₀) across various treatment paradigms in SH-SY5Y cells challenged with JEV. During pretreatment, CA and rutin showed IC₅₀ values of 11.64 µM and 26.31 µM, respectively, indicating their effectiveness in reducing viral plaque titer by 50% when cells were pre-exposed to these compounds before JEV infection. In simultaneous treatment, the IC₅₀ values were 11.22 µM for CA and 19.6 µM for rutin, suggesting a slight increase in rutin's potency. Virus attachment inhibition showed IC₅₀ values of 10.53 µM for CA and 17.16 µM for rutin, indicating both compounds effectively hinder viral attachment. The virus attachment inhibition effect of CA and rutin might be due to their ability to compete with JEV for binding to cellular receptors by forming a stable complex with the virus’s E-protein [55, 56]. It was also reported that compounds with higher molecular weight possess superior antiviral activity at the early stage of the virus cycle by preventing virion binding to host receptors [36]. Furthermore, the antiviral activity of such polyphenolic compounds at the virus adsorption and internalization stage could also be because of their direct virus inactivation effect as a result of their higher molecular weight, especially in the case of rutin as this compound showed improved potency in a simultaneous treatment approach [56]. However, further detailed study is needed to validate the molecular mechanism of action the hit compounds in in vitro in vivo experimental models.

Table 2

Plaque reduction effect (IC₅₀) of CA and rutin in JEV-infected SH-SY5Y cells
S.No.	Treatment Approaches	IC₅₀ (µM) of CA	IC₅₀ (µM) of rutin
1.	Pretreatment	11.64	26.31
2.	Simultaneous treatment	11.22	19.6
4.	Attachment inhibition	10.53	17.16

In conclusion, the virtual screening and molecular dynamics analysis of 265 phytocompounds from Ocimum basilicum unveiled CA, rutin, and SA as the topmost hit compounds. Notably, CA, rutin, and SA displayed excellent docking scores, outperforming the reference, mycophenolate. Moreover, the 100 ns molecular dynamics simulations indicated that the hit compounds, especially CA and rutin, demonstrated better stability on the target protein's binding pocket. The lower binding free energy of the hits further underlines their excellent binding affinity to the envelope protein of JEV. Further post-MD simulation investigations, such as PCA and FEL analysis, unveiled the antiviral potential of these two hit compounds against JEV. On top of these, the in vitro study further supported the antiviral potential of the hit compounds at the early stage of the virus lifecycle. Hence, this comprehensive exploration uncovered the therapeutic potential of CA and rutin as antiviral agents against JEV. However, further study is required to validate their antiviral mechanism of action in in-vitro and in-vivo experimental models.

Conflicts of interest

The author declares that there is no conflict of interest.

Acknowledgments

SKA is thankful to the Indian Council for Cultural Relations (ICCR) and the Central University of Punjab, Bathinda for the financial assistantship. SKA and DG are also thankful to Mr. Yogesh Singh and Dr. Suresh Thareja, Department of Pharmaceutical Sciences and Natural Products, Central University of Punjab, Bathinda, India for their expert opinion in in-silico studies.

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Virtual Screening, Molecular Dynamics Simulations, and Antiviral Evaluation of Ocimum basilicum Phytoconstituents Against Japanese Encephalitis Virus

Status:

Version 1

Abstract

Figures

1. INTRODUCTION

2. MATERIALS AND METHODS

2.1. Docking study

2.1.1. Protein preparation and binding site detection

2.1.2. Ligand Preparations

2.1.3. Molecular Docking

2.1.4. Molecular Dynamics Studies

2.2. Post-Simulation Analysis

2.2.1. Estimation of Binding Free Energy

2.2.2. Principal Component Analysis

2.2.3. Free energy Landscape Analysis

2.3. In vitro study

2.3.1. MTT Assay of Antiviral Activity

2.3.2. Plaque Yield Reduction Assay

3. RESULTS AND DISCUSSIONS

3.1. Molecular Docking and Contact Analysis

3.2. Molecular Dynamics (MD) Analysis

3.2.1. RMSD, RMSF, and SSE Analysis

3.2.3. Radius of gyration and Solvent-Accessible Surface Area Analysis

3.2.5. Protein-Ligand Contact Analysis

3.2.6. Binding Free Energy Calculation

3.2.7. Principal Component Analysis (PCA)

3.2.8. Free Energy Landscape Analysis

3.3. Chicoric acid and rutin increased viability of JEV-challenged cells

3.3. Chicoric acid and rutin reduced plaque yield in vitro

4. Conclusion

Declarations

Conflicts of interest

Acknowledgments

References

Supplementary Files

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