Persistent synergistic antiproliferative and apoptosis induction effects of PGV-1 and tamoxifen or 4-hydroxytamoxifen on ER-positive breast cancer cells

Protein–protein interaction (PPI) string analysis

Using the biology repository and web tool, STRING server has been used for the pre-computed gene or protein, and the interaction between the various interconnected proteins is predicted. The tool asses the functional enrichment for the given lists of proteins or genes using functional classification systems such as GO, Pfam, and KEGG. Protein–protein interactions are annotated with “scores,” or confidence marks. The scale runs from 0 to 1, where 1 signifies the highest degree of certainty [8].

Gene interaction network construction

Network-based methods overlay genes or proteins that appear insignificant in a large-scale network environment to provide an analytical framework and a comprehensive presentation of genomic data from high-throughput studies. By using network modules, the number of dimensions in the data is reduced and statistical techniques are more successful. A variety of supplemental data sources are used to construct the functional interface network, and human-curated network databases are used to extract the network’s functional links. Cytoscape is the most useful tool for studying and visualizing biological networks. This has made it possible to quickly and cheaply adopt cutting-edge network biology techniques, such as dialect libraries, carefully customized studies, Cytoscape process integrations, and best-in-class tools [9].

Virtual screening of phytochemicals

Determining the concentration of different bioactive components in plants is made easier with the use of the GC-MS method, which was employed for the analysis of the produced plant extracts. Medicinal plants, which form the foundation of traditional medicine, have been the subject of much contemporary pharmacological research. This has been made feasible by the realization of the medicinal plants’ significance as sources of novel therapeutic compounds and lead molecules for pharmaceutical development [10].

The GC spectrum study indicates that the extracts of E. hirta, V. negundo, and S. nigrum had the highest GC values for various compounds in their GC fraction. The lead compounds retrieved from the GCMS analysis are displayed in Table 1. The M/Z ratio, molecular formula, and retention time in minutes of the retrieved compounds are given in Table 2. Additionally, each detected compound’s mass spectrum and chromatogram graph are included in the supplemental file separately.

Table 1. Libraries retrieved from the GCMS analysis of extracts of E. hirta, V. negundo, and S. nigrum. [Click here to view]

Table 2. Indicating the name of the compounds, M/Z ratio, molecular formula, and retention time, of E. hirta, V. negundo, and S. nigrum extracts as shown by the GC- MS analysis. [Click here to view]

Molecular docking

Molecular docking is being extensively explored as a lead-discovery method as the structures of an increasing number of proteins and nucleic acids are known. As more experimentally described protein structures are found, the number of proteins that can be docked against homology-modeled targets rises. As more docking experiments are carried out, the degree of accuracy and “drug-likeness” of docking hits are being examined. With upgrades to relevant search algorithms and enhancements to the scoring function, the molecular docking approach will become a reliable drug-design tool that incorporates significant volumes of biological data [11]. Autodock Vina (MGLtools 1.5.7) was used for active site identification, ligand preparation, molecular docking studies, and energy minimization performed by NAMD software. Protein DNA GyrB, derived from the PDB website with PDB ID 5D7D, was docked against the lead compounds for further studies.

Grid parameters

To ascertain whether the ligand was successfully bound to the protein’s active site, the active site residues identified using pymol visualization software with the help of protein sequence from PDB (PDB ID: 5D7D) mentioned in Table 3 were applied. Assigning the grid parameters, which direct the ligand to the protein’s required location, is the most important stage in the molecular docking process. The area of the protein where docking will take place is indicated by the grid box position. During docking, no area outside the box will be investigated. The docking’s perimeter was established using the grid box. The grid box should encompass the entire macromolecule if you want to examine every surface possible without knowing the protein’s active site. The grid box’s center and measurements were selected so as to enable the protein–ligand complex to fit inside of it. Grid spacing was set to 0.647 Å by default. The values of x = 22.757, y = 4.436, and z = 33.904 were fixed for the center grid box, with offset values of −8.743, −36.064, and 6.154, respectively. The dimensions of x, y, and z were assigned 84 × 108 × 74 grid points. The corresponding maps had a total of 67,1533 grid points. Compounds are docked to binding sites with known structures using genetic algorithms. During the docking procedure, the genetic algorithm technique is used to decrease the energy of intermolecular interactions. The docking run of the genetic algorithm was 10. These characteristics surrounded the protein’s whole three-dimensional active site. The output was stored in the grid parameter file (GPF) format by the grid parameter.

Table 3. Determination of active site of the protein. [Click here to view]

Protein–ligand interaction profiler (PLIP)

Software called the PLIP aids in confirming both the kind and degree of interaction between a protein and ligand. The amino acid and ligand bond lengths as well as the residues are extracted from the PLIP. The precise interaction types and bond lengths of the docked complex are provided when the PDB file of the protein–ligand docked complex is used as the input file.

In Silico ADME analysis and pharmacokinetic study

This study presents the free Swiss ADME server, a web-based tool to assess the ADME properties of phytoconstituents. Understanding the biological and pharmacological characteristics of the plant can be aided by this information. Swiss ADME uses an analytical method based on the Lipinski rule of 5 to assess the bioavailability, drug-like characteristics, and biopharmaceutical compatibility of the bioactive compounds that are collected. The study of a drug’s distribution, metabolism, removal, and absorption is known as pharmacokinetics. Drug distribution, biotransformation, excretion, and absorption are the main times when drug–drug interactions happen. The number of binding proteins has a basic influence on distribution, and many medications alter protein binding. The medication is transformed into active or inactive metabolites as it moves through the liver. This process is called biotransformation, and it has a big impact on how well medications work [12].

Molecular dynamics

Molecular biology and drug discovery domains have seen a notable surge in the application of molecular dynamics (MD) simulations in the past few years. These simulations faithfully capture the atomic-level behavior of proteins and other biomolecules at incredibly fine temporal resolutions. Simulation computations have been useful in the investigation of protein–biomolecule interactions, in the synthesis of novel proteins, peptides, and small molecules, and in determining the structural causes of disease [13]. A comparable molecular dynamics program called NAMD version 2.14 is intended for efficient modeling of large biomolecular systems. The file formats, future CHARMM functionalities, and limits all function well with NAMD. When combined with NAMD, the molecular graphics program visual molecular dynamics (VMD) version 1.9.4 offers a thorough modeling environment. Because of its other features, VMD can provide functionality that NAMD cannot. VMD provides a unique environment for examining data from NAMD simulations [14].

NAMD simulations of MD require a number of consecutive stages. First, the biomolecular system is configured, and formatting and parameterization are appropriately applied. The force field that most closely resembles the properties of the system is chosen. It is necessary to build input files in order to supply simulation parameters such as pressure, temperature, and boundary conditions. These files include coordinate, parameter, topology, and configuration files. The next stage in addressing steric conflicts and maintaining the structure is energy minimization. After that, the system is heated and maintained at the target temperature for the duration of equilibration, after which any positional restrictions are progressively lifted. Production begins for the specified amount of time, and MD simulation begins and integrates dynamics using chosen interaction algorithms and periodic boundary conditions. Understanding the generated trajectory data’s dynamic behavior and structural features comes via analysis. Data are refined for analysis and visualization through postprocessing, which results in methodology, results reporting, and documentation. These steps enable a full investigation of biomolecular systems using NAMD, leading to significant new insights into their interactions and behavior [15].

MD simulations are used in the MMPBSA method via Ambeer Tools (version 23) to find the binding free energy in molecular systems. Next, snapshots are taken in order to estimate the solvation-free energy and conformational entropy. By offering perspectives on how molecules interact using a comparison of the total free energy, binding energy, van der Waals energy, electrostatic energy, and polar solvation of complex, receptor, and ligand states, it aids in ligand selection and therapeutic focus [14].

String analysis of protein–protein interaction

Predicting interactions between proteins for the pre-calculated gene or protein is done using the biology database and the web application STRING server. It uses functional classification systems such as GO, Pfam, and KEGG to evaluate the functional enrichment of the given lists of proteins or genes. Figure 1’s protein–protein interaction is marked with “scores” or confidence markers. All ratings are on a scale from 0 to 1, where 1 is the highest level of confidence. The program provides a group of genes known as nodes based on the greatest confidence level (0.9) after string interactive network analysis. The nodes in this string network indicate that the gyrB protein has a greater interaction rate than all other proteins in Table 4, which illustrates how the protein interacts with other proteins in the database. Furthermore, the molecular interaction network on these nodes is constructed using Cytoscape.

Figure 1. Network of protein–protein interaction from string database for the proteins retrieved from NCBI. [Click here to view]

Table 4. Nodes provided by the string interactive network analysis. [Click here to view]

Pathway analysis—gene ontology enrichment

The computer representation of current discoveries on the functions of both coding and noncoding genes is called gene ontology. Gene ontologies, or functional enrichment of genes reconstructed as nodes from string databases, are shown in Table 5. Each ontology explains a particular feature of the functionality of a gene or gene product, in addition to the relationships between the ideas. A gene that is linked to a term implies that it is linked to all of the term’s parents as well. Further focusing the search on genes that interact within an organism or are comparable between two organisms may also be helpful.

Table 5. The gene ontologies (functional enrichment) of genes retrieved from the string database. [Click here to view]

Gene network construction

Gene network interaction was calculated to identify the functional relationship among the genes, which is calculated in the form of rank. In this study, we have calculated the MMC, degree method, closeness, and betweenness method. The GyrB gene has the highest rank of 1 in all the four methods, as shown in Tables 6–9.

Table 6. Ranking of genes by maximal clique centrality (MCC) method. [Click here to view]

Table 7. Ranking of genes by a degree method. [Click here to view]

Table 8. Ranking of genes by the closeness method. [Click here to view]

Table 9. Ranking of genes by the betweenness method. [Click here to view]

As shown in Figures 2–5, the topological algorithms are used in this study to combine the global techniques such as maximum clique centrality (MCC), degree, and closeness with local techniques like betweenness.

Figure 2. Network construction by the maximal clique centrality (MCC) method. [Click here to view]

Figure 3. Network construction by the degree method. [Click here to view]

Figure 4. Network construction by the closeness method. [Click here to view]

Figure 5. Network construction by the betweenness method. [Click here to view]

Molecular docking

The affinity between a protein and its ligand increases with negative binding energy. Docking forecasts the optimal configuration of one molecule for another when a target (ligand) and a protein create a stable complex together. Once the optimal orientation has been established, factors, such as binding energy, link stability, or binding affinity, among the two molecules can be used to forecast which ligand will be chosen for simulation experiments. The binding energies of ligands against the DNA gyrB gene are shown in Table 10.

Table 10. Binding energies and residue interactions between DNA gyrB gene and ligands. [Click here to view]

Commercial drugs such as novobiocin are used for managing bacterial infections, especially those caused by Gram-positive bacteria. It is primarily used to treat S. aureus-caused staphylococcal infections. The significant interacting amino acids are Ala61, Arg84, Ile86, Ile175, Ile51, Asn54, Asp53, Ile56, Asp57, Glu58, Ser55, Ile102, Val131, Val99, Leu138, Ser129, Leu103, and Glu50 residues with a binding energy of −8.3 kcal/mol when novobiocin is docked against the DNA gyrB gene as shown in Figure 6. Novobiocin was selected as the reference compound for our investigation to find a treatment employing bioactive components generated from naturally produced plant metabolites since it is a synthetic derivative and to determine the alternate route for a naturally derived source.

Figure 6. 2D and 3D interaction of novobiocin (reference ligand) with DNA gyrB gene (−8.3kcal/mol) and 3D visualization of the binding pocket (dot surface view). [Click here to view]

Using Autodock, molecular docking was used to anticipate the interaction between the binding site of a target receptor and the target ligand. It determines which orientations of a ligand within a protein’s active site are most energetically advantageous by using sampling algorithms and scoring systems. The goal of this specific active site, which was derived via complex docking and visualized utilizing Discovery Studio, is to comprehend how ligands adhere to proteins, which aids in lead optimization and drug creation.

Figure 7 shows the 2D and 3D interaction of lead 1 with DNA gyrB gene with a binding energy of −6.23 kcal/mol, which is lesser than the binding energy of novobiocin, since it is a plant-based derived compound. The interactions between the protein and the ligand are found to be Asp81, Ser55, Glu58, Asn54, Thr173, Ile51, Val79, Ile175, Leu103, Ile86, Ile102, and Gly85. Out of these 12 residues, 7 residues are nonpolar amino acids, namely, Ile, Val, Leu, and Glu are involved in this interaction, which aid in improved binding toward the protein and the ligand.

Figure 7. 2D and 3D interaction of lead 1 (Bicyclo [7.2.0] Undec-4-Ene,4,11,11-Trimethyl-8-Methylene-, [1r-(1r*,4z,9s*)]-) with DNA gyrB gene. (−6.23kcal/mol) and 3D visualization of the binding pocket (dot surface view). [Click here to view]

Figure 8 depicts the interaction between (4-(2,2,6trimethyl-bicyclo [4.1.0] hept-1-yl)-butan-2-one) with DNA gyrB protein and the binding energy was found to be −5.70 kcal/mol. The 2D and 3D interactions are shown in Figure 8. The protein interacted with the ligand with the amino acids’ residues Gly85, Gly83, Gly172, Glu58, Ser55, Thr173, Asp81, Ile51, Asn54, Ile175, Ile102, Arg84, Ile86, and Pro87. Among these, Gly, Thr, Ile, Arg, and Pro are present in 10 out of 14 residues, which helps in the effective binding of ligand to the protein.

Figure 8. 2D and 3D interaction of lead 2 (4-(2,2,6- Trimethyl-Bicyclo [4.1.0] Hept-1-Yl)-Butan-2-One) with DNA gyrB gene. (−5.70kcal/mol) and 3D visualization of the binding pocket (dot surface view). [Click here to view]

The docking and interactions (2D&3D) of the compound Lead 3 with the protein were docked and the binding energy was found to be −6.01 kcal/mol as represented in Figure 9. According to our findings, the active sites can be quite versatile in their use of different protein domain positions and highly varied in their conservation of amino acids. Since one of the primary biochemical roles of proteins is ligand binding, identifying ligands and their binding sites is the first step toward establishing a protein’s function. Important locations, such as those directly connected to protein function, are frequently linked to amino acid sequence conservation.

Figure 9. 2D and 3D interaction of lead 3 (1-Hexyl-2-Nitrocyclohexane) with DNA gyrB gene. (−6.01kcal/mol) and 3Dvisualization of the binding pocket (dot surface view). [Click here to view]

When novobiocin is docked against the DNA gyrB protein as shown in Figure 6, the amino acid residues are found to be Ala61, Arg84, Ile86, Ile175, Ile51, Asn54, Asp53, Ile56, Asp57, Glu58, Ser55, Ile102, Val131, Val99, Leu138, Ser129, Leu103, and Glu50 with the binding energy of −8.3kcal/mol. When 7-propyl-3-[2-(pyridin-3-yl)-1,3-thiazol-5-yl]-1,7-dihydro-6h-pyrazolo[3,4-b] pyridin-6-one (Reference ligand) was docked against DNA GyrB protein, the ligand interacted at the protein residues Asn54, Glu58, Ser55, Thr173, Gly85, Arg84, Ile86, Pro87, Asp81, and Arg144, as depicted in Figure 7. The binding energy of the complex was found to be −6.33 kcal/mol. The nonpolaramino acids surrounding the active site of the protein help in effective binding toward the protein. Though the complex has nonpolar interactions, this chemical complex has the lowest binding affinity among the compounds that were taken into consideration for docking. When Lead 2 is docked with the gyrase protein, the binding energy was found to be −5.70 kcal/mol. The protein interacted with the ligand at the following amino acid residues Gly85, Gly83, Gly172, Glu58, Ser55, Thr173, Asp81, Ile51, Asn54, Ile175, Ile102, Arg84, Ile86, and Pro87. Among these, Gly, Thr, Ile, Arg, and Pro are present in 10 out of 14 residues, which aid in improved binding.

When docking of the compound Lead 3 with DNA gyrB protein with the binding energy of −6.01kcal/mol as represented in Figure 9, these lead compounds have higher binding energy when docked against the DNA gyrB protein, which can significantly inhibit the protein in the Staphylococcal infection out of these seven leads, and lead 1 has 12 active sites of amino residues; out of these 12, 7 amino acids (Ile51, Val79, Ile175, Leu103, Ile86, Ile102, and Gly85) are nonpolar, which increases the binding stability of the complex.

Upon closer examination, lead 1 has the highest stacking interaction with the protein compared to all the other chemicals studied, proving that this compound is more effective against Staphylococcal infection than the chemical-based drugs. This study includes seven leads for the docking studies, and out of that lead 1 is chosen for the simulation process based on the binding affinity, protein–ligand interaction, and highest binding energy. The reference compound is taken from the PDB, which is shown as the reference compound for the protein. Thus, the other compounds that showed less binding energy compared to lead 1 are not further processed for the simulation studies.

Protein–ligand interaction profiler

Protein–ligand interaction of the complex of DNA GyrB protein with reference, lead 1, lead 2, and lead 3 compounds are demonstrated using PLIP software, and the types of ligand interaction, bond length, and distance are listed. The diagrammatic representation, including Figures 10–13, is the protein–ligand interaction of the docked protein and ligand complex. The docked complex of protein DNA GyrB with the ligand novobiocin has hydrophobic interaction, hydrogen bond, and π-cation interactions. The bond length and amino acid interactions are shown in Table 11. The docked complex of protein DNA GyrB with the ligand lead 1 has hydrophobic interaction, and the bond length and amino acid interactions are shown in Table 12. The docked complex of protein DNA GyrB with the ligand lead 1 has hydrophobic interaction. The bond length and amino acid interactions are shown in Table 13. The docked complex of protein DNA GyrB with the ligand lead 1 has a hydrogen bond. The bond length and amino acid interactions are shown in Table 14.

Figure 10. Protein–ligand interaction of DNA GyrB with reference ligand. [Click here to view]

Figure 11. Protein–ligand interaction of DNA GyrB with lead 1 ligand. [Click here to view]

Figure 12. Protein–ligand interaction of DNA GyrB with lead 2 ligand. [Click here to view]

Figure 13. Protein–ligand interaction of DNA GyrB with lead 3 ligand. [Click here to view]

Table 11. Interaction between ligands and amino acid residues with bond length of the DNA GyrB with reference. [Click here to view]

Table 12. Interaction between ligands and amino acid residues with bond length of the DNA GyrB with lead 1. [Click here to view]

Table 13. Interaction between ligands and amino acid residues with bond length of the DNA GyrB with lead 2. [Click here to view]

Table 14. Interaction between ligands and amino acid residues with bond length of the DNA GyrB with lead 3. [Click here to view]

Class and biological properties of the lead compounds

Table 15 depicts the class and biological properties of lead compounds. Most of the lead compounds show antibacterial activity, anti-inflammatory activity, and analgesic properties.

Table 15. Class and biological properties of lead compounds. [Click here to view]

Pharmacokinetic properties in silico analysis

Table 16 describes the virtual screening of phytochemicals using the Swiss-ADME platform that follows Lipinski’s rule of five. The seven phytochemicals lead 1, lead 2, lead 3, lead 4, lead 5, lead 6, and lead 7, which followed Lipinski’s rule of 5, are further laid open to molecular docking against the protein (DNA gyrB).

Table 16. Virtual screening of phytochemicals using Swiss-ADME. [Click here to view]

Pharmacokinetic prediction

The pharmacokinetic characteristics of putative bioactive substances were predicted, researched, and recorded in Tables 17 and 18. Every compound breached the blood–brain barrier (BBB) except the novobiocin.

Table 17. Pharmacokinetic properties of phytochemicals. [Click here to view]

Table 18. Toxicity properties of phytochemicals. [Click here to view]

BBB indicates whether the compound can cross the blood–brain barrier. “BBB+” denotes that the compound can cross the blood–brain barrier, while “BBB-” indicates that it cannot. Human GI absorption (HIA) indicates whether the compound is absorbed in the human gastrointestinal (GI) tract. “HIA+” means that the compound is absorbed, while “HIA-” means that it is not. Caco-2 permeability indicates the permeability of the compound through Caco-2 cell monolayers, which are commonly used to study intestinal absorption and drug transport. “Caco2+” means that the compound is permeable, while “Caco2-” means that it is not. P-glycoprotein substrate indicates whether the compound is a substrate for P-glycoprotein, a drug efflux transporter involved in multidrug resistance. “Yes” means that it is a substrate, while “No” means that it is not. P-glycoprotein inhibitor indicates whether the compound inhibits P-glycoprotein activity. “Yes” means that it inhibits, while “No” means that it does not. Subcellular localization indicates the location within the cell where the compound is localized. Examples include nucleus, mitochondria, lysosome, and plasma membrane. CYP450 1A2 inhibitor indicates whether the compound inhibits the activity of cytochrome P450 1A2 enzyme. “Yes” means that it inhibits, while “No” means that it does not. CYP450 2C9 inhibitor indicates whether the compound inhibits the activity of cytochrome P450 2C9 enzyme. “Yes” means that it inhibits, while “No” means that it does not.

AMES toxicity indicates whether the compound shows toxicity in the AMES test, which is a bacterial reverse mutation assay used to assess the mutagenic potential of chemicals. “Yes” indicates toxicity, while “No” indicates no toxicity. Acute oral toxicity represents the acute toxicity of the compound when administered orally. It is measured in LD₅₀, which is the lethal dose required to kill 50% of the test population. It is expressed in mol/kg. Carcinogenicity indicates whether the compound is carcinogenic. “Yes” indicates that it is carcinogenic, while “No” indicates that it is not. Tetrahymena pyriformis toxicity (pIGC₅₀, μg/l) is a type of ciliated protozoan often used as a model organism in toxicity testing. This column provides the toxicity of the compound against t. pyriformis, expressed as pIGC₅₀ (half maximal inhibitory concentration) in micrograms per liter (μg/l). Rat acute toxicity (LD₅₀, mol/kg) represents the acute toxicity of the compound when administered to rats. It is measured in LD₅₀, which is the lethal dose required to kill 50% of the test population. It is expressed in mol/kg. Biodegradation indicates whether the compound undergoes biodegradation, meaning that it can be broken down by biological processes into simpler compounds. “Yes” indicates that it undergoes biodegradation, while “No” indicates that it does not. All the compounds are noncarcinogenicity except lead 3, lead 4, and lead 7.

Molecular dynamics

The firmness and dynamics of the free protein (PDB ID: 5D7D) were modeled for 100 ns using a ligand with high affinity and low binding energy to examine the RMSD and RMSF. The stability of protein increases with an increase in RMSF and decreases in RMSD. Around one lakh distinct frames were generated using the simulation study for the single protein–ligand complex. The molecular structure at that precise moment was captured in a single frame. The target ligand, lead 1, and the reference ligand (novobiocin) fluctuations against the target protein (DNA gyrB) are depicted in Figures 14 and 15.

Figure 14. RMSD of target and reference ligand. [Click here to view]

Figure 15. RMSF fluctuation of target and reference ligand. [Click here to view]

Hydrogen bonding is one of the most important interactions for maintaining the stability of the protein–ligand complex. Figure 16 shows how DNA gyrB bound to lead 1 and the reference chemical (novobiocin) showed a higher number of average hydrogen bonds, indicating the efficacy of these molecules as DNA gyrB inhibitors. As the reference molecule (novobiocin) is a commercially available prescription chemical medication that is used to treat patients with staphylococcal infection, a bioactive ingredient lead 1 can be a helpful herbal formulation substitute.

Figure 16. Number of hydrogen bond interactions of target and reference ligand. [Click here to view]

Figure 17 compares the target protein (DNA gyrB) to the solvent-accessible surface area (SASA) of the target ligand lead 1 (Bicyclo [7.2.0]Undec-4-Ene,4,11,11-Trimethyl-8-Methylene-,[1r-(1r*,4z,9s*)]-) and co-crystal reference ligand (7-Propyl-3-[2-(Pyridin-3-Yl)-1,3-Thiazol-5-Yl]-1,7-Dihydro-6h-Pyrazolo[3,4-B]pyridin-6-One). The region of a protein that is sufficiently exposed to contact with nearby solvent molecules is referred to as SASA. SASA is a deciding factor in studies on protein stability and folding. The more points we receive, the more of the molecule is sticking out into the water. Low scores imply that the molecule is more thoroughly incorporated into the protein. Lower scores are given to the target protein (DNA gyrB) compared to the target ligand lead 1. The ideal molecule to investigate should typically be the one with the lowest score for therapeutic applications.

Figure 17. Solvent accessible surface area (SASA) of both target ligand and reference ligand versus target protein. [Click here to view]

The dynamic behavior of the screened compounds is examined for low energy profiles employing MM/PBSA, which is utilized for docked structure postprocessing in addition to the dependability of inside the flexible binding pocket, complex binding. The lead molecule and the standard drug appear to be fit precisely in the binding site and have a low energy profile, based on the 100-ns protein–ligand complex simulation and the MM-PBSA binding free energy. The literature that is currently available indicates that the compounds in MM/PBSA with lower energy profiles would make good candidates for additional experimental investigation. Table 19 and Figures 18 and 19 show the MM/PBSA calculation of reference and lead 1 ligand.

Table 19. MMPBSA energy calculation of reference and target ligand. [Click here to view]

Figure 18. MMPBSA energy calculation for lead 1. [Click here to view]

Figure 19. MMPBSA energy calculation for reference. [Click here to view]

This research focuses on the discovery of new plant-based drug to treat staphylococcal infection, which seems like a promising endeavor. Using molecular simulation studies, system biological tools and docking analysis proves that the lead 1 (Bicyclo [7.2.0] Undec-4-Ene,4,11,11-Trimethyl-8-Methylene-, [1r-(1r*,4z,9s*)]-) being extracted from the traditional medicinal plants may be used as novel therapeutic target lead toward the staphylococcal infections.