Discovery of Bioactive Compounds From Medicinal Plants: Insights Into Wrightia Tinctoria as a Potential Antistaphylococcal Agent Targeting

Discovery of Bioactive Compounds From Medicinal Plants: Insights Into Wrightia Tinctoria as a Potential Antistaphylococcal Agent Targeting | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Discovery of Bioactive Compounds From Medicinal Plants: Insights Into Wrightia Tinctoria as a Potential Antistaphylococcal Agent Targeting Vidya S L, Anantha Bhairavi, R Sathishkumar This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6232250/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 04 Feb, 2026 Read the published version in In Silico Pharmacology → Version 1 posted 13 You are reading this latest preprint version Abstract Staphylococcus aureus ,a prominent pathogen demonstrate robust survival capabilities both within and outside host cells.The alarming rise of antibiotic resistance strains poses substantial challenge in modern medicine.Bioactive compounds from medicinal plants could be an effective alternative due to their presence of diverse secondary metabolites. The present study aim to conduct insilico docking and dynamic simulations to identifypromising bioactive compound from medicinal plants against virulence protein Clumping factor A of Staphylococcus aureus through bioinformatic approach. Initial ADME screening of phytocompounds from the plants Breynia retusa , Hemigraphis alternata , Imperata cylindrica , Oldenlandia corymbosa , Sida rhombifolia , Scoparia dulcis , Tephrosia purpurea and Wrightia tinctoria R were conducted to comprehend their pharmacokinetic profile, followed by docking and dynamic simulations.As a result, indirubin showed effecient binding interaction with target protein, offering remarkable G score value of -8.82 Kcal/Mol. In addition, dynamic stimulations validated the top docked complex with significant RMSD and RG stability besides desirable binding free energy in contrast to the standard drug neomycin sulphate. To validate these results, the antibacterial potential of the fresh and dry leaf extract of Wrightia tinctoria was tested, showing strong inhibitory effects against Staphylococcus aureus with a maximum zone of inhibition of 26.33 ± 0.33 mm. A detailed analysis of the ethyl acetate extract using GC-MS revealed the presence of 50 bioactive compounds, underscoring the plant's potential as a natural antimicrobial source. These outcomes indicate that W. tinctoria holds promise as a therapeutic option for managing staphylococcal infections and highlights the need for further research to explore its clinical applications. Staphylococcus aureus Drug resistantance Phytocompounds Wrightia tictoria Molecular Docking Dynamic simulations Antibaterial analysis Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Figure 8 Figure 9 Figure 10 Figure 11 Figure 12 INTRODUCTION Staphylococcus aureus , a Gram-positive bacterium, is one of the most prevalent bacterial species identified in chronic wounds and various other bacterial infections. The pathogen is implicated in both community-based and hospital-acquired infections; however, the treatment is exceedingly challenging due to the prevalence of multidrug-resistant strains (Roy et al ., 2020; Hasan et al., 2016 ). The World Health Organization (WHO) considers antimicrobial resistance as a prime threat to global health and enlists the bacterial priority pathogens and strategies to prevent the resistance (WHO, 2024). Reports unveiled that around 50% of infectious bacteria like E. coli , K. pneumoniae , S. aureus , and P. aeruginosa exhibit resistance to some of the most effective antibiotics, including third-generation cephalosporins (Tillotson and Zinner 2017 ; Terreniet al., 2021 ). Studies have been considered to reduce the rate of microbial infections and promote appropriate usage of active antimicrobial drugs. Several virulence factors, including enterotoxins, toxins, hemolysins (alpha, beta, gamma, and delta), metalloprotease, and hyaluronidase as well as biofilm formation renders a major role in the multidrug resistance mechanisms. The coaggregation of free-floating microbes into multiple layers in biofilm is often facilitated by adhesion via cell wall proteins(He et al., 2024 ). Clumping factor A (ClfA) is a cell-wall anchored protein of S. aureus actively involved in adhesion facilitated by existing sheer mechanical tension through fibrinogen binding(Herman-Bausieret al., 2018 ). This virulence protein initiates infections by inhibiting phagocytosis with enhanced efficacy in the presence of fibrinogen, highlighting its role in S. aureus immune evasion (Higgins et al., 2006 ). Altogether these strategies contribute to the persistent susceptibility to S. aureus infections throughout an individual's lifetime (Bhattacharya and Horswill, 2024 ). Therefore, the present study focuses on utilizing this crucial virulence factor of S. aureus as a target for drug designing purposes. Phytomedicines have played a crucial role in traditional healthcare systems worldwide for millennia entailed with fewer adverse side effects (Yuan et al., 2016 ). Despite the extensive literature available on therapeutic properties, standardized procedures for their identification, phytochemical analysis and pharmacological evaluation are scarce (Patel et al., 2012 ; Thakur et al., 2020 ).Recent studies desperately highlighted the propitious antimicrobial activity of phytocompounds in averting the resistance mechanisms of ESKAPE pathogens which includes Enterococcus faecium, Staphylococcus aureus, Klebsiella pneumoniae, Acinetobacter baumannii, Pseudomonas aeruginosa , and Enterobacter species (Bhatia et al., 2021 ; Ashhar et al. , 2024).With this perspective, the secondary metabolites of the following plants Breynia retusa , Hemigraphis alternata, Imperata cylindrica, Oldentia corymbosa , Sida rhombifolia , Scoparia dulcis, Tephrosia purpurea , and Wrightia tinctoria were chosen to study its binding efficiency with clumping factor ClfA of S. aureus . In the exploration of promising plant derived antibiotics, bioinformatics plays an indispensable role by facilitating the identification and validation of potential drug targets through high throughput screening. Thus, allows bioactive compounds from plants to be analyzed for their underlying molecular mechanism with the target protein (Szymański et al. , 2012; Babar et al. ,2017; Mitra et al., 2022 ). The present study utilizes molecular docking, ADME (Adsorption, Distribution, Metabolism, Excretion) profiling, biological activity prediction and molecular dynamics approaches to identify the most promising drug candidate from the assorted medicinal plants. Additionally, antibacterial screening was conducted to validate in silico findings, followed by GC-MS analysis to identify and characterize the bioactive compounds present in the most effective extract. This extensive approach highlights viable options for preventing staphylococcal infections by bridging the gap between computational predictions and experimental confirmation. MATERIALS AND METHODS PROTEIN RETRIEVAL AND ACTIVE SITE PREDICTION Protein data bank was used to retrieve the 3-dimensional structure of target protein clumping factor A (ClfA) PDB ID: 2VR3) from RCSB Protein Data Bank( www.rcsb.org ) (Prlićet al., 2016 ). Followed by active site prediction using SCFBio online web tool ( http://www.scfbio-iitd.res.in/dock/ActiveSite.jsp ) pocket (Moulishankar and Sundarrajan 2024 ). LIGAND RETRIEVAL AND PREPARATION The 2D structure of literature proven secondary from the following assorted plants Breynia retusa , Hemigraphis alternata, Imperata cylindrica, Oldentia corymbosa , Sida rhombifolia , Scoparia dulcis, Tephrosia purpurea , and Wrightia tinctoria , were retrieved from the PubChem database together with their respective PubChem ID ( https://pubchem.ncbi.nlm.nih.gov/ ) (Sasikumar et al. , 2022). Lig prep module of Maestro Schrodinger suite was utilized to prepare the ligands. Which includes enumeration of their all possible protonation and ionization states using Ionizer at a pH of 7.4 by addition or removal of protons from the ligand(Sahayarayan et al., 2021 ). ADME PREDICTION Around 105 assorted phytocompounds were screened for Adsorption, Distribution, Metabolism and Excretion (ADME) profiling using QikProp module of Schrodinger software to comprehend their pharmacokinetic properties in compliance with Lipinski's rule of five. Enlisting parameters like drug likeliness of small molecules with rotatable bonds, molecular weight, dipole moment, hydrogen bond donor and blood barrier coefficient being the prime parameters (Bhairavi et al., 2023 ; Patidar et al., 2019 ). BIOLOGICAL ACTIVITY PREDICTION The only compounds, obeys Lipinski’s rule of five are solely considered for their biological activity prediction using an online tool PASS (Prediction of Activity Spectra for Substances) way2drug ( http://www.way2drug.com/passonline/predict.php ) (Yusofand Segall 2013; Filimonovet al., 2014 ) MOLECULAR DOCKING ADME cleared compounds were used to perform docking analysis. GLIDE module in maestro Schrödinger software http://www.schrodinger.com/ was widely used to perform docking analysis (Bhairavi et al., 2023 ). It identifies potential areas of interaction between ligands and proteins by using a hierarchical series of filters that identifies the precise site of the ligand within the active site of the protein (Sahayarayanet al., 2021 ).The interactions of each compound with significant Glide score were visualized using the software PyMol (Mishra et al., 2021 ). MOLECULAR DYNAMIC SIMULATIONS To explore the protein-ligand interaction, the best docked compound was subjected to MD simulations along with standard compound Neomycin sulphate. Gromacs 2019.4 software suite was employed to analyze its interactions at atomic level (Cardoza et al., 2024 ). The energy-minimized protein-ligand complex was placed in a vacuum and solvated using the SPC water box and then equilibrated under NPT conditions. A 100 ns production run at constant temperature and pressure followed this step (Gangadharappa et al., 2020 ). RMSD, RMSF, Rg, SASA, and hydrogen bond interactions were evaluated with trajectory analysis by Prasanth et al . Binding free energy, ΔG binding, was calculated using the MM-PBSA method with the g_mmpbsa tool, and energy decomposition for the last 50 ns of the trajectory gave insights into binding stability and affinity (Wang et al., 2019 ; Prasanth et al., 2021 ). SAMPLE COLLECTION AND EXTRACTION The sample Wrightia tinctoria was collected and authenticated (BSI/SRC/5/23/2023-24/Tech/529) from The Botanical Survey of India (BSI) Coimbatore, Tamilnadu.The parts including leaf, bark, and seeds were washed, shade-dried, and finely powdered. Fifty grams of sample (fresh and dried) used for extraction via maceration in 1:10 ratio for seven days, following Haile et al. (2022). The extracts were filtered with Whatman No. 1 paper, and the solvents (n-hexane, ethyl acetate and methanol) were evaporated under reduced pressure using a rotary evaporator. The resulting extracts were stored for subsequent analyses. ANTIBACTERIAL SCREENING Agar well diffusion method was carried out to evaluate the antibacterial efficacy of W. tinctoria (Jegadeeshwari et al., 2023 ). Extracts from fresh and dry plant materials in n-hexane, ethyl acetate, and methanol were studied against Staphylococcus aureus (MTCC-96). An overnight culture of S. aureus , with optical density was adjusted to 0.1, was inoculated on Muller Hinton Agar plates.6 mm wells were prepared and charged with 100µl of plant extracts at a concentration of 1ml/ml. Neomycin sulphate was considered as positive control and organic solvents were used as negative control. Further plates were incubated at 37°C for 24 hrs. ANTIBIOTIC SUSCEPTIBILITY STUDIES Kirby-Bauer disc diffusion method was used to analyse antibiotic susceptibility (Kirby and Bauer, 1966). Muller Hinton agar plates were evenly swabbed with S. aureus that was adjusted to 0.5 McFarland standard. Antibiotic disks (Himedia) including Azithromycin (AZM15), Methicillin (MET5), Streptomycin (S10), Vancomycin (VA30), Tetracycline (TE30), Colistin (CL10), Imipenem (IP8), and Piperacillin (PI100), were used with four discs placed on each plate. For neomycin sulphate, antibiotic susceptibility was assessed using the agar well diffusion method, with 50 µg of neomycin sulphate per well. subsequently plates were then incubated overnight at 37°C, and the results were recorded. GC-MS ANALYSIS Phytochemical profiling of the ethyl acetate extract of Wrightia tinctoria was conducted using GC-MS analysis (GC-MS-QP 2010, Shimadzu, Tokyo, Japan). The analysis utilized an electron ionization energy of 70eV. A standard, non-polar 60 M TRX 5-MS column with dimensions of 30 m in length and a film thickness of 0.25 µm was employed. The instrument's oven temperature was programmed to increase from 100°C to 260°C at a rate of 10°C per minute. Helium was used as the carrier gas, and 2 µl of the sample was injected initially at a steady flow rate of 2 ml per minute. With peak normalisation, the GC-MS analysis proceeded for thirty-five minutes. The identification of several components was made easier by this study. The National Institute of Standards and Technology (NIST) Mass Spectral Library version 2.0 (2005) mass spectra database was used in order to interpret the results (Khan et al., 2021 ). RESULTS ACTIVE SITE PREDICTION 2D image of target protein ClfA was shown in Fig. 1. The chosen X-ray crystallographic structure is characterized with chain A and B comprises of a sequence length of 329 residues. The Among the several cavities, the active site was predicted in cavity 1 that represents the following amino acid residues were predicted as their active site binding pocket: 16 SER, 17 GLY, 18 THR, 19 THR, 21 TYR, 22 PRO, 24 GLN, 25 ALA, 26 GLY, 27 TYR, 28 VAL, 29 LYS, 58 GLY, 59 VAL, 106 TYR, 107 ILE, 109 PRO, 115 THR, 134 LEU, 136 ASP, 137 TYR, 138 GLU, 139 LYS, 140 TYR, 141 GLY, 142 LYS, 150 GLY, 153 ASP, 164 GLN, 166 ILE, 167 TYR, 184 LEU, 185 LYS, 186 PRO, 187 ASN, 188 THR, 190 SER, 20 VAL, 225 GLY, 215 SER, 251 PRO, 252 HIS, 254 ALA, 258 ALA, 264 GLY, 265 ASP, 267 ALA, 269 ARG, 271 THR, 284 MET, 286 ASN, 287 GLY, 288 VAL, 289 GLU, 291 ALA, 292 PHE, 293 LYS, 295 LEU, 316 THR, 317 ASN, 318 VAL, 319 THR, 320 VAL, 321 GLY, 322 ILE, 323 ASP, 324 SER, 326 THR, 338 TYR. 339 ILE, 339 ASP, 357 THR, 384 ILE, 395 ARG, 430 THR, 432 ALA, 433 THR, 434 GLY, 435 ILE, 436 GLY, 437 SER, 438 THR, 439 THR, 440 ALA, 441 ASN, 442 LYS, 443 THR, 444 VAL, 525 ASN, 526 GLU, 532 GLY622 LEU, 623 GLY, 625 ALA, 624 GLY, 626 LYS, 627 GLN. PREDICTION OF ADME PROPERTIES AND PHARMACOLOGICAL ACTIVITIES The secondary metabolites of the plants Breynia retusa, Hemigraphis alternata, Imperata cylindrica, Oldentia corymbosa , Sida rhombifolia , Scoparia dulcis, Tephrosia purpurea , and Wrightia tinctoria , are collected and analyzed for ADME properties. Out of 78 compounds only 19 compounds cleared Lipinski’s rule of five along with molecular weight, hydrogen donor and acceptor, logP value, various other factors like permeability for blood/brain barrier and skin, dipolar property, polar surface area, ionization potential, polarizability, electron affinity, number of rotatable bonds (Table 1). Further, pharmacological activity of these 19 compounds were predicted through PASS online Way2Drug web tool where the importance was given to antibacterial activity. Almost all the compounds listed had antibacterial activity, except 17beta-Hydroxy-28-norolean-12-ene-3,16-dione. However, this compound was predicted to possess wound healing properties. Since each compound is predicted with significant and expected antibacterial activity, all the compounds were considered for molecular docking analysis. DOCKING ANALYSIS Molecular docking interactions for all the 19 compounds were conducted, however, 15 of the compounds had significant interactions with the amino acid residues of ClfA. The substantial Glide score (Kcal/mol), interacting residues, number of hydrogen bonds along with their bond length (Å) are tabulated (Table 2). The top docked compound Indirubin, had highest glide score of -8.82Kcal/mol forming four hydrogen bonds with active site residues GLY 225 and ILE339. The standard drug used was neomycin sulfate, which exhibited a G score of -8.45 kcal/mol. The second-best docked compound Indigo had a G score of -7.19 indicating strong binding interactions with key amino acid residues in active site pocket. The remaining compounds had a glide score ranging from − 6.77 to -2.93Kcal/mol. Amino acid interaction of all these compounds along with standard drug was tabulated in table and their interactions with clumping factor A (ClfA: 2VR3) are visualized using PyMol software (Fig. 1). Table 2 Protein- Ligand Interaction Profile for the selected phytocompounds against target protein Clumping factor ClfA S.No Name of the Ligand (PubChem ID) Residues Interaction Bond Length (Å) No. of Hydrogen Bonds G-Score (Kcal/Mol) Wrightia tinctoria 1 Indirubin (10177) GLY 225 (O-O) ILE 339 (O-H) ILE 339 (O-H) ILE 339 (O-H) 2.9 2.6 1.9 2.3 4 -8.82 2 Indigo (10215) PRO 251 (H-O) ASN 525 (O-H) ILE 384 (O-H) ILE 384 (H-O) 2.2 2.3 1.9 1.9 4 -7.19 3 Palmitic acid (985) ILE 384 (O-H) ASN 525 (O-H) GLU 526 (O-H) 1.8 1.6 2.6 3 -6.77 4 Isatin (7054) GLY 255 (H-O) VAL 288 (O-H) 1.9 2.1 2 -5.95 5 Phytol (5280435) GLY 287 (H-O) 1.8 1 -5.67 6 Diethyl phthalate (6781) HIS 252 (O-H) 2.1 1 -4.95 7 Urs-12-en-3beta-ol (225688) GLY 287 (H-O) 2.0 1 -3.39 8 Ursolic acid (64945) ARG 395 (O-H) 2.1 1 -2.93 Sida rhombifolia 9 Vasicinol (442934) ALA 258 (H-O) ILE 339 (H-O) 2.4 1.9 2 -5.25 Tephrosia purpurea 10 Purpurin (6683) ILE 384 (H-O) ILE 339 (H-O) 2.1 2.0 2 -4.92 11 Semiglabrin (156341) ILE 384 (O-H) ALA 254 (O-H) ILE 339 (O-O) 2.4 2.3 2.8 3 -4.86 Scoparia dulcis 12 5- Benzyloxypyrimidine (561874) ILE 384 (N-H) ILE 384 (O-H) ASN525 (O-H) ASN 525 (O-H) 2.6 2.2 2.3 2.0 4 -4.84 13 Benzofuran 2,3 dihydro (20209882) TYR 338 (O-H) LEU 295 (H-O) LYS 293 (H-O) GLY 532 (O-H) 2.2 2.4 2.6 2.3 4 -4.66 Breynia retusa 14 Epicatechin (72276) ILE 339 (H-O) PRO 251 (H-O) GLU 526 (H-O) GLU 526 (O-H) TRP 523 (O-H) 2.2 1.9 1.8 1.9 2.5 5 -4.75 Standard Antibiotic 15 Neomycin sulphate (8378) ASN 286 (O-H) ASP 385 (H-O) GLU 342 (H-O) GLU 342 (H-O) ASP 340 (H-O) LEU 283 (H-O) 2.0 1.8 1.8 1.8 2.1 2.2 6 -8. 45 MOLECULAR DYNAMICS SIMULATION Molecular dynamics simulation analysis is performed for the best interaction recorded in the docking analysis together with the standard drug interaction for 100 ns of. The changes in the protein-ligand complexes are accounted with the parameters such as root mean square deviation (RMSD), root mean square fluctuation (RMSF), the radius of gyration (Rg), hydrogen bond (H-bond), solvent accessible surface area (SASA), and further binding free energy (ΔGbind) was computed using the 'g_mmpbsa tool'. RMSD AND RMSF The calculation of RMSD describes the dynamic stability of a protein's backbone framework over the course of the simulation. The RMSD value and the trajectories were observed for 100 ns as shown in the figure (Fig. 2, Fig. 3). The receptor-ligand complexes: ClfA-indirubin and ClfA-neomycin sulphate as well as the ligands separately are simulated. The RMSD values for a period of 100 ns for the Indirubin and Neomycin Sulphate protein complexes were measured and found to be 0.18 +/- 0.035 nm and 0.29 +/- 0.069 nm, respectively. These results suggest minimal structural changes in the protein complexes during the simulation, with RMSD values that did not significantly differ from those of the unbound protein. These findings indicate the compounds' relative stability throughout the simulation. Overall, the protein complexes remained relatively stable throughout the simulation, with only minor differences between them, suggesting their similarity in terms of stability. During a 100 ns simulation, the RMSF (root mean square fluctuation) was calculated to understand how the protein's vibrations change over time. This analysis provides insight into the movements of amino acids throughout the MD run and can determine how the absence or presence of a ligand affects protein stability. Figure 4 displays the RMSF results for the Indirubin and Neomycin Sulphate complexes. Based on these results, it appears that there were no significant structural changes throughout the simulation. RADIUS OF GYRATION: The radius of gyration (Rg) measuresthe compact a protein's structure (overall shape and folding changes) via the Rg plot. Both the Indirubin and Neomycin Sulphate complexes had similar Rg values. The average Rg value for the Indirubin and Neomycin Sulphate protein complexes from 0 to 100 ns were 2.36+/-0.014 nm and 2.45+/-0.019 nm respectively. Figure 5shows the Rg plot for both indirubin (black) and neomycin sulphate (red), which remained stable throughout the simulation, indicating a high level of compactness in the protein. SASA AND H-BOND: The SASA was measured to analyze the compactness of the hydrophobic core, where the changes in SASA over time for the Indirubin and Neomycin Sulphate protein complex was studied and shown in Fig. 6. The average SASA value from 0 to 100 ns for the Indirubin and Neomycin Sulphate protein complex proteins were 161.01+/-4.45 nm and 160.46+/-3.51 nm, respectively. The results suggest that there were no changes in the structural level of the proteins throughout the simulation. Figure 7displays the hydrogen bond analysis results for the complexes involving Indirubin and Neomycin Sulphate. The plot clearly shows that both ligands possess a significant number of hydrogens indicating stable protein ligand complex. MM-PBSA MM-PBSA analysis revealed that indirubin has more stable and favorable binding affinity when compared to neomycin sulphate. Indirubins strong van der Waals interaction and lower energy fluctuations indicates its superior binding stability. Thus, indicates indirubin is more efficient ligand for target protein. Table 3 shows a comparison of their binding strength as inhibitors, calculated using the MM-PBSA method. Table 3 Binding free energy calculation of Indirubin and Neomycin sulphate System Van der Waal energy Electrostatic energy Polar solvation energy Binding energy Indirubin -148.937 +/- 5.789 kJ/mol -32.248 +/- 5.754 kJ/mol 122.777 +/- 12.153 kJ/mol -15.699 +/- 0.769 kJ/mol Neomycin Sulphate -44.975 +/- 20.212 kJ/mol -24.216 +/- 91.152 kJ/mol 10.839 +/- 50.370 kJ/mol -52.177 +/- 79.517 kJ/mol ANTIBACTERIAL EFFICIENCY OF PLANT EXTRACTS Antibacterial analysis of fresh and dry leaf extract of Wrightia tinctoria was evaluated against multi drug resistant S. aureus . Among all the tested extracts, the dry leaf ethyl acetate extract demonstrated the highest antibacterial activity, as evidenced by the largest zone of inhibition (ZI). Thus indicates that the active compounds responsible for antibacterial activity may be more concentrated or more stable in the dry form compared to the fresh extract or the other parts of the plant. In contrast, extracts from other parts of W. tinctoria , showed comparatively lower antibacterial effects, with smaller zones of inhibition. The antibacterial activity of W. tinctoria was most pronounced in the dry leaf ethyl acetate, which, at a concentration of 100 µg, demonstrated the highest zone of inhibition measuring 26.33 ± 0.33 mm. This was followed by the fresh leaf ethyl acetate extract, which showed considerable activity with a zone of inhibition of 12.00 ± 0.57 mm. In contrast, extracts from the bark and seeds of W. tinctoria exhibited only low to moderate antibacterial activity. Graphical representations of the antibacterial activity of the dry and fresh leaf extracts of W. tinctoria were generated using GraphPad Prism 5 (Fig. 8 and Fig. 9) ANTIBIOTIC SUSCEPTIBILITY ANALYSIS The multi-drug resistance profile of S. aureus was assessed using the disc diffusion method, involving nine different antibiotics at various concentrations. The results were categorized as sensitive (S), intermediate resistant (I), and resistant (R) in accordance with the Clinical and Laboratory Standards Institute (CLSI) guidelines. Among the antibiotics tested, S. aureus exhibited 100% resistance to methicillin, streptomycin, vancomycin, colistin, and piperacillin. Intermediate resistance was observed with azithromycin and imipenem. Notably, S. aureus showed significant susceptibility to neomycin sulphate, which produced the highest zone of inhibition, followed by tetracycline. The antibiogram results clearly indicate that approximately 70% of the antibiotics tested were ineffective against S. aureus (Fig. 10). This highlights the strain's extensive resistance and underscores the need for alternative treatment options or the development of new antibacterial agents. Note AZM denotes azithromycin, CL- colistin, IP – imipenem, NS- Neomycin Sulphate, MET- Methicillin, PI- Piperacillin, S- streptomycin, TE- Tetracycline, VA- vancomycin. The error bar, with a significance level of P ˂ 0.05, represent the standard error of the mean value. GC-MS PROFILING The GC-MS analysis of the EALE of W. tinctoria revealed the presence of 50 phytocompounds. Among these, the compound with the highest peak area was indirubin, an alkaloid, which constituted 6.59% of the total extract. Other significant compounds identified as 9,19-cyclolanost-24-en-3-ol, (3.beta.) with a peak area of 17.36%, β-sitosterol (11.11%), stigmast-5-en-3-ol, (3.beta.) (8.69%), norolean-12-ene (5.37%), phytol (4.45%), palmitic acid (2.72%), urs-12-en-3beta-ol (2.01%) and indigo (1.39%). The detailed identification of these compound along with their retention time and compound ID was provides (Table 4) as well as the chromatogram of W. tinctoria EALE, illustrating these peaks (Fig. 11). This analysis highlights the complex phytochemical composition of W. tinctoria , with several compounds of potential pharmacological interest. Table 4 GC MS profiling of ethyl acetate leaf extract of Wrightia tinctoria S. No Compound Name Compound ID Retention Time Peak Area % 1 Tricyclo[4.4.0.0(2,7)]dec-3-ene, 1,3-dimethyl-8-(1-methylethyl) 6432119 14.734 0.20 2 Bicyclo[7.2.0]undec-4-ene, 4,11,11-trimethyl-8-methylene 5281515 15.977 0.38 3 1,4,8-cycloundecatriene, 2,6,6,9-tetramethyl 5281520 16.943 0.25 4 Ethanone, 1-(2-methyl-1-cyclopenten-1-yl) 137847 21.095 0.16 5 Sulfurous acid, 2-ethylhexyl isohexyl ester 6420722 21.180 0.33 6 Acetic acid, 1,7,7-trimethyl-bicyclo[2.2.1]hept-2-yl ester 6448 21.332 0.46 7 Hexadecanal 984 23.411 0.32 8 Octahydro-1,8a(1h)-naphthalenediol 574054 24.795 0.71 9 Diethyl Phthalate 6781 26.524 1.91 10 3-Eicosyne 549159 27.000 0.22 11 2H-Pyran, 2-(2-heptadecynyloxy)tetrahydro 544115 27.073 0.29 12 3,7,11,15-Tetramethyl-2-hexadecen-1-ol 5366244 27.379 0.56 13 Palmitic acid 985 28.364 2.72 14 Phytol 5280435 31.602 4.45 15 9,12,15-Octadecatrienoic acid 5280934 31.884 0.89 16 Oleic Acid 445639 31.985 0.33 17 Oleanolic acid 10494 32.317 0.24 18 Octadecanoic acid 5281 32.445 0.33 19 2-biphenylethylamine, 5-methoxy-, hydrochloride 25946 33.443 0.49 20 24-Methylenecycloartanol 94204 35.108 0.31 21 Squalene 638072 36.739 0.28 22 Copaene 12303902 38.306 0.23 23 Tritriacontane 12411 39.819 0.16 24 Hexatriacontane 12412 41.279 0.20 25 Indirubin 638072 42.875 6.59 26 Isatin 7054 44.368 0.87 27 2,6,10,14-Hexadecatetraen-1-ol, 3,7,11,15-tetramethyl 86591503 44.466 0.34 28 17beta-Hydroxyl-28-norolean-12-ene-3,16-dione 102067273 44.539 1.35 29 2,2-dimethyl-3-(3,7,16,20-tetramethyl-heneicosa-3,7,11,15,19-pentaenyl)-oxirane 5367592 44.696 0.37 30 Solanesol 5477212 46.030 0.19 31 Ergost-5-en-3-ol 18660356 46.336 0.88 32 2h-1-benzopyran-6-ol, 3,4-dihydro-2,7,8-trimethyl-2-(4,8,12-trimethyltridecyl) 14986 46.628 1.00 33 β-caryophyllene oxide 1742210 46.778 0.24 34 Ursolic acid 64945 47.270 0.88 35 2-propyldecane-1,3,8,10- tetrol 88829318 47.759 0.52 36 17-Pentatriacontene 5365022 48.270 0.35 37 2,5,7,8-tetramethyl-2-(4,8,12-trimethyltridecyl)-3,4-dihydro-2h-chromen-6-yl hexofuranoside 597057 48.537 0.43 38 Tetrapentacontane 521846 48.733 1.03 39 Undecane 14257 48.840 0.90 40 Urs-12-en-3beta-ol 73170 49.490 2.01 41 2,3,3-Trimethyl-2-(methyl pentanoyl)-cyclopentanone 573846 50.840 2.42 42 Indigo 10215 51.820 1.39 43 9,19-cyclolanostan-3-ol, 24-methylene-, (3.beta) 94204 52.434 1.28 44 Stigmast-5-en-3-ol, (3.beta) 222284 53.800 8.69 45 β- Tocopherol 6857447 54.195 1.02 46 6-Ethyl-2-methyldecane 43923 54.465 5.37 47 Neophytadiene 10446 54.882 12.66 48 β- Sitosterol 225688 55.977 11.11 49 9,19-Cyclolanost-24-en-3-ol, (3.beta) 92110 56.249 17.36 50 Lanosterol 246983 56.505 1.33 DISCUSSION Staphylococcus aureus being a pervasive pathogen found in both hospital and community settings, poses significant public health risks. The misuse and overuse of antibiotics have exacerbated drug resistance issues in treating S. aureus infections (Ahmed et al., 2024 ). According to Mancuso et al. ,(2021), diseases caused by multidrug-resistant bacteria result in approximately 700,000 deaths annually. The presence of multiple virulence mechanisms is particularly concerning due to limited treatment options. These mechanisms enable the bacteria to evade the host's immune response and include colonization of host tissues, leading to conditions such as bloodstream infections, skin infections, necrotizing pneumonia, and other severe tissue infections (Mlynarczyk-Bonikowska et al., 2022 ). Medicinal plants offer a rich source of bioactive compounds that, when combined, can potentially enhance therapeutic efficacy through synergistic effects against a wide range of multidrug-resistant pathogens (Huang et al., 2022 ). Ejaz et al. 2014 demonstrated that plant-based strategies work significantly in inhibiting multidrug resistant bacteria notable S. aureus . In a similar vein a research by Panda et al. 2020 also confirmed this, both of them investigated the anti-staphylococcal activity of many traditional medicinal herbs from Pakistan and India. Thus, the study offers a broad perspective on the bactericidal characteristics of medicinal plants in combating against antibiotic resistance by focusing on various geographic regions and plant species. Similarly, our research focuses on evaluating the potential of phytocompounds from eight medicinal plants known for their remarkable biological properties. Researches have demonstrated that cell wall-anchored proteins are pivotal in facilitating bacterial infections by enabling attachment to specific host tissues. Clumping factor A, for instance, exemplifies such a protein by promoting bacterial adhesion to host tissue fibrinogen, thereby initiating infection (Foster 2019 ; Speziale and Pietrocola, 2020 ; Viljoen et al., 2021 ).In the current study, initially ADME profiling was conducted thereby explored the drug-likeness tests to mitigate the risk of late-stage drug failure rates specifically during clinical trials (Panigrahiet al., 2020 ). Where 19 compounds found to have drugs like properties meeting all necessary parameters and demonstrating favorable pharmacokinetic properties including absorption, distribution, and metabolic stability. Subsequently, Glide module of Schrodinger suite utilizes a grid-based approach to predict ligand binding sites within the target protein and employed an extra precision scoring function during the analysis (Sankar and Engels 2018). As a result,15bioactive compounds demonstrated robust binding interactions with the target protein, achieving an average Glide score ranging from − 8 to -2 kcal/mol. These compounds effectively interacted with specific amino acid residues such as ILE (isoleucine), GLY (glycine), ASN (asparagine), and GLU (glutamic acid), by forming hydrogen bonds with residues including ILE 339, 384, ASN 525, 286, GLU 526, and residue 342. The presence of these amino acid residues was confirmed through active site prediction tool. The phytocompounds with their least G scores were detailed in Table 8. Indirubin derived from W. tinctoria demonstrated the highest binding affinity with the target protein ClfA, forming hydrogen bonds with glycine and isoleucine residues. In a similar vein, following ADME profiling, Ragi et al . (2021) used AutoDock 4.20 to dock a variety of virulence proteins, including ClfA, with its sniff base chemical. Potent binding capacities with the target protein and interactions with the amino acid residues valine, lysine, isoleucine, and glycine are characteristics of their study that are consistent with our findings. In another investigation by Wadapurkar et al. ( 2018 ), out of 86 plant-derived anti-staphylococcal compounds, 46 were deemed suitable for docking analysis. Among which the acetylated abietane quinone derived from mint leaves exhibited a good binding affinity with a G score of -7.52 kcal/mol, while in our study, the top-performing compound indirubin showed a maximum binding energy of -8.82 kcal/mol. Traditionally, Indirubin, extracted from W. tinctoria , were used for treating skin infections and has been documented for its potent antibacterial activity against both Gram-positive and Gram-negative bacteria. Reports also unfold their superior efficacy against S. aureus comparatively with standard antibiotics like ciprofloxacin and streptomycin (Kale et al., 2021 ; Ponnusamy et al., 2010 ). Additionally, Tanaka et al., ( 2019 ) studied the wound-healing properties of indirubin and found that it accelerates the healing process. Indigo, the second-best docked compound, exhibits diverse biological properties including anti-pyretic and anti-inflammatory activities (Xu et al. , 2021).Recently studies by yokote et al. ( 2023 ) suggests that compound indirubin and indigo are capable in treating ferroptosis, a cell death induced by oxidative stress, highlighting its role as a promising drug target for ulcerative colitis. The results of MD simulations showed that, indirubin-receptor complex was found to be stable over the duration of100 ns. The RMSD analysis of thereceptor-indirubin and the receptor -neomycin sulphate complexes shows that the two structures differed minimally in their conformations. The neomycin sulphate complex had a marginally higher RMSD value of 0.2 nm while the indirubin complex maintained a lower RMSD value of 0.18 nm meaning that it had better structural stability. Furthermore, there was minimal fluctuations of the amino acids residues within both the complexes as revealed by RMSF analysis. Hydrogen bonding interactions, which are one of the essential factors in assessing the stability of protein-ligand complexes (Joshi et al., 2022 ) were investigated in detail. Both the docked complexes displayed several stable hydrogen bonds, as demonstrated in Fig. 7 , indicating strong binding interactions. Table 5 consolidated free energy computations that induced the effects of polar solvation, electrostatics, Van der Waals forces, and binding energy. In comparison, Tiwari et al. ( 2022 ) Performed docking and dynamics simulations for compounds isolated from Sisymbriumirio , suggesting that a 70 ns simulation resulted in minimal structural deviation without loss of receptor-ligand complex stability. In line with this, our study based on these simulations demonstrates that indirubin does have the ability to stabilize the compactness and structural integrity of the receptor. By targeting ClfA, phytocompounds from the plant W. tinctoria were shown to have improved staphylococcal activity, according to these in silico investigations. We performed an invitro antibacterial investigation of this plant and then GC-MS phytochemical profiling to confirm these findings. According to preliminary antibacterial screening results, the ethyl acetate extract of dried W. tinctoria leaves exhibited a more significant growth inhibition against S. aureus than other extracts These results are in accordance with previous studies from Srivastava ( 2014 ), who revealed that methanolic extracts of W. tinctoria bark and leaves showed potent inhibition activity against S. aureus . In fact, the leaf extracts exhibited more significant antimicrobial potential than bark and seed extracts. This would likely be due to differences in the phytochemical composition driven by geographical loci, environmental conditions, and extraction methods among others (Liuet al., 2018 ). In addition to the antibacterial screening, antibiotic susceptibility testing was conducted on S. aureus . The findings revealed that the bacterium exhibited resistance to 70% of the antibiotics tested. This aligns with a study by (Akanbiet al., 2017 ), which reported that S. aureus isolated from beef samples showed resistance to 83% of antibiotics tested, including amoxicillin, chloramphenicol, ciprofloxacin, ceftriaxone, gentamicin, and trimethoprim. Moreover, demonstrated intermediate resistance to tetracycline and azithromycin. Similarly, in our study, azithromycin and imipenem showed intermediate resistance, with zones of inhibition measuring 10 mm and 8 mm, respectively. The small zones of inhibition observed suggest a heightened risk of developing antibiotic resistance. Following GC-MS profiling, the ethyl acetate extract of the dried leaves of W. tinctoria revealed a wide range of bioactive compounds, including ursolic acid, diethyl phthalate, lupeol, stigmasterol, lupeol, squalene, isatin, β-sitosterol, indirubin, and 41 others. Different phytochemical compositions of W. tinctoria extracts have been reported in other similar research. Sheela et al. ( 2021 ) reported 12 compounds from ethyl acetate extracts, whereas Khan et al. ( 2021 ) reported 28 compounds, including phytol, stigmast-5-en-3-ol, and β-caryophyllene. These variations rely on the plant's maturity, harvesting conditions, and extraction method (Liu et al., 2018 ). Even while the antibacterial properties of different sections of W. tinctoria have been studied in great detail, nothing is known about the precise mechanisms by which these effects are achieved. Using in silico methods could successfully solve this knowledge gap. Therefore, our study's findings indicate that W. tinctoria that contains indirubin has a lot of potential as an anti-staphylococcal agent. CONCLUSION The current studyunderline the therapeutic promise of W. tinctoria and its bioactive constituents in combating bacterial infections, including those due to drug-resistant strains. Highlighting In silico approaches by bridging the gap and provide useful insight into the molecular interactions of its bioactive compounds. Our combined in silico and in vitro studies highlight that bioactive compounds specificcally W. tinctoria extracts is capable of being a promising anti-staphylococcal agent with stability in binding to the receptors, and also it is highly active as an antibacterial. Further therapeutic studies have to be conducted on indirubin, as well as other bioactive molecules in W. tinctoria , against the particular Staphylococcus aureus and drug-resistant bacterial strains. Thus, the results from our studies strongly suggest that indirubin holds considerable promise as an anti-staphylococcal agent. Abbreviations ClfA- Clumping factor A, WHO- World Health Organization, ADME- Absorption, Distribution, Metabolism, and Excretion, PDB- Protein Data Bank, RMSD-Root Mean Square Deviation, RMSF- Root Mean Square Fluctuation, RG-Radius of Gyration, SASA- Solvent accessible surface area, H-Bond- Hydrogen Bond, MM-PBSA- Molecular Mechanics Poisson-Boltzmann surface area, LIG-Ligand. Declarations ACKNOWLEDGEMENT The authors are grateful to the Kongunadu Arts and Science College administration for supplying all software required to complete this project. FUNDING No funding was received for conducting this study. ETHICAL APPROVAL This article does not contain any studies involving animals and human participants by any of the authors. DISCLOSURE STATEMENT The authors declared that they have no conflict of interest in the studies. DATA AVAILABILITY STATEMENT. The authors confirm that the Data used and/or analyzed in the present study are available within the article. AUTHOR CONTRIBUTION Vidya S. L: Carried out the studies, Data curation, and Drafted the manuscript and software. Anantha Bhairavi V: Formal analysis and Validation. R. Sathishkumar: Designed the study and Supervision. The final manuscript was read and approved by all authors. References Ahmed, S. K., Hussein, S., Qurbani, K., Ibrahim, R. H., Fareeq, A., Mahmood, K. A., & Mohamed, M. G. (2024). Antimicrobial resistance: Impacts, challenges, and future prospects. Journal of Medicine, Surgery, and Public Health, 2, 100081. https://doi.org/10.1016/j.glmedi.2024.100081 Akanbi, O. E., Njom, H. A., Fri, J., Otigbu, A. C., & Clarke, A. M. (2017). Antimicrobial Susceptibility of Staphylococcus aureus Isolated from Recreational Waters and Beach Sand in Eastern Cape Province of South Africa. International Journal of Environmental Research and Public Health, 14(9), 1001. https://doi.org/10.3390/ijerph14091001 Babar, M. M., Zaidi, N.-S. S., Pothineni, V. R., Ali, Z., Faisal, S., Hakeem, K. R., & Gul, A. (2017). Application of Bioinformatics and System Biology in Medicinal Plant Studies. Plant Bioinformatics, 375–393. https://doi.org/10.1007/978-3-319-67156-7_15 Bauer, A. W., Kirby, W. M. M., Sherris, J. C., &Turck, M. (1966). Antibiotic Susceptibility Testing by a Standardized Single Disk Method. American Journal of Clinical Pathology, 45(4_ts), 493–496. https://doi.org/10.1093/ajcp/45.4_ts.493 Bhairavi, V. A., Vidya, S. L., & Sathishkumar, R. (2023). Identification of effective plant extracts against candidiasis: an in silico and in vitro approach. Future Journal of Pharmaceutical Sciences, 9(1). https://doi.org/10.1186/s43094-023-00489-x Bhatia, P., Sharma, A., George, A. J., Anvitha, D., Kumar, P., Dwivedi, V. P., & Chandra, N. S. (2021). Antibacterial activity of medicinal plants against ESKAPE: An update. Heliyon, 7(2), e06310. https://doi.org/10.1016/j.heliyon.2021.e06310 Bhattacharya, M., &Horswill, A. R. (2024). The role of human extracellular matrix proteins in defining Staphylococcus aureus biofilm infections. FEMS Microbiology Reviews, 48(1). https://doi.org/10.1093/femsre/fuae002 Cardoza, S., Singh, A., Sur, S., Singh, M., Dubey, K. D., Samanta, S. K., Mandal, A., & Tandon, V. (2024). Computational investigation of novel synthetic analogs of C-1′β substituted remdesivir against RNA-dependent RNA-polymerase of SARS-CoV-2. Heliyon, 10(17), e36786. https://doi.org/10.1016/j.heliyon.2024.e36786 Ejaz, R., Ashfaq, U. A., & Idrees, S. (2014). Antimicrobial potential of Pakistani medicinal plants against multi-drug resistance Staphylococcus aureus. J Coast Life Med , 2 (9), 714-20.https://doi.org/10.12980/jclm.2.201414b1 Filimonov, D. A., Lagunin, A. A., Gloriozova, T. A., Rudik, A. V., Druzhilovskii, D. S., Pogodin, P. V., &Poroikov, V. V. (2014). Prediction of the Biological Activity Spectra of Organic Compounds Using the Pass Online Web Resource. Chemistry of Heterocyclic Compounds, 50(3), 444–457. https://doi.org/10.1007/s10593-014-1496-1 Foster, T. J. (2019). The MSCRAMM Family of Cell-Wall-Anchored Surface Proteins of Gram-Positive Cocci. Trends in Microbiology, 27(11), 927–941. https://doi.org/10.1016/j.tim.2019.06.007 Gangadharappa, B. S., Rajashekarappa, S., & Sathe, G. (2020). Proteomic profiling of Serratia marcescens by high-resolution mass spectrometry. BioImpacts, 10(2), 123–135. https://doi.org/10.34172/bi.2020.15 Hasan, R., Acharjee, M., & Noor, R. (2016). Prevalence of vancomycin resistant Staphylococcus aureus (VRSA) in methicillin resistant S. aureus (MRSA) strains isolated from burn wound infections. Tzu Chi Medical Journal, 28(2), 49–53. https://doi.org/10.1016/j.tcmj.2016.03.002 He, W., Liu, H., Wang, Z., Tay, F. R., & Shen, Y. (2024). The dynamics of bacterial proliferation, viability, and extracellular polymeric substances in oral biofilm development. Journal of Dentistry, 143, 104882. https://doi.org/10.1016/j.jdent.2024.104882 Herman-Bausier, P., Labate, C., Towell, A. M., Derclaye, S., Geoghegan, J. A., &Dufrêne, Y. F. (2018). clumping factor A is a force-sensitive molecular switch that activates bacterial adhesion. Proceedings of the National Academy of Sciences of the United States of America, 115(21), 5564–5569. https://doi.org/10.1073/pnas.1718104115 Higgins, J., Loughman, A., van Kessel, K. P. M., van Strijp, J. A. G., & Foster, T. J. (2006). Clumping factor A ofStaphylococcusaureusinhibits phagocytosis by human polymorphonuclear leucocytes. FEMS Microbiology Letters, 258(2), 290–296. https://doi.org/10.1111/j.1574-6968.2006.00229.x Huang, W., Wang, Y., Tian, W., Cui, X., Tu, P., Li, J., Shi, S., & Liu, X. (2022). Biosynthesis Investigations of Terpenoid, Alkaloid, and Flavonoid Antimicrobial Agents Derived from Medicinal Plants. Antibiotics, 11(10), 1380. https://doi.org/10.3390/antibiotics11101380 Jegadeeshwari, A., Seelam, N. R., & Myneni, V. R. (2023). Evaluation of Antibacterial and Anticancer Characteristics of Silver Nanoparticles Synthesized from Plant Extracts of Wrightia tinctoria and Acacia chundra. International Journal of Analytical Chemistry, 2023, 1–14. https://doi.org/10.1155/2023/6352503 Joshi, C., Chaudhari, A., Joshi, C., Joshi, M., &Bagatharia, S. (2022). Repurposing of the herbal formulations: molecular docking and molecular dynamics simulation studies to validate the efficacy of phytocompounds against SARS-CoV-2 proteins. Journal of biomolecular structure & dynamics , 40 (18), 8405–8419. https://doi.org/10.1080/07391102.2021.1922095 K, R., Kakkassery, J. T., Raphael, V. P., Johnson, R., & K, V. T. (2021). In vitro antibacterial and in silico docking studies of two Schiff bases on Staphylococcus aureus and its target proteins. Future Journal of Pharmaceutical Sciences, 7(1). https://doi.org/10.1186/s43094-021-00225-3 Kale, N., Rathod, S., More, S., & Shinde, N. (2021). Phyto-Pharmacological Profile of Wrightia tinctoria. Asian Journal of Research in Pharmaceutical Sciences, 301–308. Internet Archive. https://doi.org/10.52711/2231-5659.2021.00047 Khan, N., Ali, A., Qadir, A., Ali, A., Warsi, M. H., Tahir, A., & Ali, A. (2021). GC-MS Analysis and Antioxidant Activity ofWrightia tinctoriaR.Br. Leaf Extract. Journal of AOAC INTERNATIONAL, 104(5), 1415–1419. https://doi.org/10.1093/jaoacint/qsab054 Liu, Y., Chen, P., Zhou, M., Wang, T., Fang, S., Shang, X., & Fu, X. (2018). Geographic Variation in the Chemical Composition and Antioxidant Properties of Phenolic Compounds from Cyclocaryapaliurus (Batal) Iljinskaja Leaves. Molecules, 23(10), 2440. https://doi.org/10.3390/molecules23102440 Mancuso, G., Midiri, A., Gerace, E., & Biondo, C. (2021). Bacterial Antibiotic Resistance: The Most Critical Pathogens. Pathogens, 10(10), 1310. https://doi.org/10.3390/pathogens10101310 Mishra, G. P., Bhadane, R. N., Panigrahi, D., Amawi, H. A., Asbhy, C. R., & Tiwari, A. K. (2021). The interaction of the bioflavonoids with five SARS-CoV-2 proteins targets: An in silico study. Computers in Biology and Medicine, 134, 104464. https://doi.org/10.1016/j.compbiomed.2021.104464 Mitra, D., Mitra, D., Sabri Bensaad, M., Sinha, S., Pant, K., Pant, M., Priyadarshini, A., Singh, P., Dassamiour, S., Hambaba, L., Panneerselvam, P., & Das Mohapatra, P. K. (2022). Evolution of bioinformatics and its impact on modern bio-science in the twenty-first century: Special attention to pharmacology, plant science and drug discovery. Computational Toxicology, 24, 100248. https://doi.org/10.1016/j.comtox.2022.100248 Mlynarczyk-Bonikowska, B., Kowalewski, C., Krolak-Ulinska, A., &Marusza, W. (2022). Molecular Mechanisms of Drug Resistance in Staphylococcus aureus. International Journal of Molecular Sciences, 23(15), 8088. https://doi.org/10.3390/ijms23158088 Moulishankar, A., &Sundarrajan, T. (2024). Pharmacophore, QSAR, molecular docking, molecular dynamics and ADMET study of trisubstituted benzimidazole derivatives as potent anti-tubercular agents. Chemical Physics Impact, 8, 100512. https://doi.org/10.1016/j.chphi.2024.100512 Panda, S. K., Das, R., Lavigne, R., &Luyten, W. (2020). Indian medicinal plant extracts to control multidrug-resistant S. aureus, including in biofilms. South African Journal of Botany, 128, 283–291. https://doi.org/10.1016/j.sajb.2019.11.019 Panigrahi, D., Mishra, A., &Sahu, S. K. (2020). Pharmacophore modelling, QSAR study, molecular docking and insilico ADME prediction of 1,2,3-triazole and pyrazolopyridones as DprE1 inhibitor antitubercular agents. SN Applied Sciences, 2(5). https://doi.org/10.1007/s42452-020-2638-y Patel, D. K., Patel, K., Duraiswamy, B., &Dhanabal, S. P. (2012). Phytochemical analysis and standardization of Strychnosnux-vomica extract through HPTLC techniques. Asian Pacific Journal of Tropical Disease, 2, S56–S60. https://doi.org/10.1016/s2222-1808(12)60124-8 Patidar, K., Panwar, U., Vuree, S., Sweta, J., Sandhu, M. K., Nayarisseri, A., & Singh, S. K. (2019). An In silico Approach to Identify High Affinity Small Molecule Targeting m-TOR Inhibitors for the Clinical Treatment of Breast Cancer. Asian Pacific Journal of Cancer Prevention, 20(4), 1229–1241. https://doi.org/10.31557/apjcp.2019.20.4.1229 Ponnusamy, K., Ramasamy, M., Savarimuthu, I., & Gabriel Paulraj, M. (2010). Indirubin potentiates ciprofloxacin activity in the NorA efflux pump of Staphylococcus aureus . Scandinavian Journal of Infectious Diseases. https://doi.org/10.3109/00365541003713630 Prasanth, D. S. N. B. K., Murahari, M., Chandramohan, V., Panda, S. P., Atmakuri, L. R., &Guntupalli, C. (2021). identification of potential inhibitors from against main protease and spike glycoprotein of SARS CoV-2. Journal of Biomolecular Structure & Dynamics, 39(13), 4618–4632. Prlić, A., Kalro, T., Bhattacharya, R., Christie, C., Burley, S. K., & Rose, P. W. (2016). Integrating genomic information with protein sequence and 3D atomic level structure at the RCSB protein data bank. Bioinformatics, 32(24), 3833–3835. https://doi.org/10.1093/bioinformatics/btw547 Roy, S., Santra, S., Das, A., Dixith, S., Sinha, M., Ghatak, S., Ghosh, N., Banerjee, P., Khanna, S., Mathew-Steiner, S., Ghatak, P. D., Blackstone, B. N., Powell, H. M., Bergdall, V. K., Wozniak, D. J., & Sen, C. K. (2019). Staphylococcus aureus Biofilm Infection Compromises Wound Healing by Causing Deficiencies in Granulation Tissue Collagen. Annals of Surgery, 271(6), 1174–1185. https://doi.org/10.1097/sla.0000000000003053 Sahayarayan, J. J., Rajan, K. S., Vidhyavathi, R., Nachiappan, M., Prabhu, D., Alfarraj, S., Arokiyaraj, S., & Daniel, A. N. (2021). In-silico protein-ligand docking studies against the estrogen protein of breast cancer using pharmacophore based virtual screening approaches. Saudi Journal of Biological Sciences, 28(1), 400–407. https://doi.org/10.1016/j.sjbs.2020.10.023 Sankar, V., & Maida Engels, S. E. (2018). Synthesis, biological evaluation, molecular docking and in silico ADME studies of phenacyl esters of N-Phthaloyl amino acids as pancreatic lipase inhibitors. Future Journal of Pharmaceutical Sciences, 4(2), 276–283. https://doi.org/10.1016/j.fjps.2018.10.004 Sasikumar, A. P., Ramaswamy, S., & Sudhir, S. (2021). A scientific pharmacognosy on Gaucher’s disease: an in silico analysis. Environmental Science and Pollution Research, 29(17), 25308–25317. https://doi.org/10.1007/s11356-021-17534-y Sheela, D., Abdul Rahim, M., Arunagirinadhan, N., Ramya, B., & Indra, V. (2021). GC-MS profiling and antibacterial activity of Wrightia tinctoria and Lantana camara leaves extract. Intern. J. Zool. Invest , 7 (2), 920-938. https://doi.org/10.33745/ijzi.2021.v07i02.084 Speziale, P., &Pietrocola, G. (2020). The Multivalent Role of Fibronectin-Binding Proteins A and B (FnBPA and FnBPB) of Staphylococcus aureus in Host Infections. Frontiers in Microbiology, 11. https://doi.org/10.3389/fmicb.2020.02054 Srivastava, R. (2014). A review on phytochemical, pharmacological, and pharmacognostical profile of Wrightia tinctoria: Adulterant of kurchi. Pharmacognosy Reviews, 8(15), 36. https://doi.org/10.4103/0973-7847.125528 Szymański, P., Markowicz, M., &Mikiciuk-Olasik, E. (2011). Adaptation of High-Throughput Screening in Drug Discovery—Toxicological Screening Tests. International Journal of Molecular Sciences, 13(1), 427–452. https://doi.org/10.3390/ijms13010427 Tanaka, Y., Uchi, H., Ito, T., &Furue, M. (2019). Indirubin-pregnane X receptor-JNK axis accelerates skin wound healing. Scientific Reports, 9(1). https://doi.org/10.1038/s41598-019-54754-2 Terreni, M., Taccani, M., &Pregnolato, M. (2021). New Antibiotics for Multidrug-Resistant Bacterial Strains: Latest Research Developments and Future Perspectives. Molecules, 26(9), 2671. https://doi.org/10.3390/molecules26092671 Thakur, M., Singh, K., &Khedkar, R. (2020). Phytochemicals. Functional and Preservative Properties of Phytochemicals, 341–361. https://doi.org/10.1016/b978-0-12-818593-3.00011-7 Tillotson, G. S., &Zinner, S. H. (2017). Burden of antimicrobial resistance in an era of decreasing susceptibility. Expert Review of Anti-Infective Therapy, 15(7), 663–676. https://doi.org/10.1080/14787210.2017.1337508 Tiwari, M., Gupta, S., & Bhargava, P. (2022). Virtual screening and molecular dynamics simulation studies to predict the binding of Sisymbriumirio L. derived phytochemicals against Staphylococcus aureus dihydrofolate reductase (DHFR). Journal of Applied and Natural Science, 14(4), 1297–1307. https://doi.org/10.31018/jans.v14i4.3641. Viljoen, A., Viela, F., Mathelié-Guinlet, M., Missiakas, D., Pietrocola, G., Speziale, P., &Dufrêne, Y. F. (2021). Staphylococcus aureus vWF-binding protein triggers a strong interaction between clumping factor A and host vWF. Communications Biology, 4(1). https://doi.org/10.1038/s42003-021-01986-6 Wadapurkar, R. M., Shilpa, M. D., Katti, A. K. S., &Sulochana, M. B. (2018). In silico drug design for Staphylococcus aureus and development of host-pathogen interaction network. Informatics in Medicine Unlocked, 10, 58–70. https://doi.org/10.1016/j.imu.2017.11.002 Wang, E., Sun, H., Wang, J., Wang, Z., Liu, H., Zhang, J. Z. H., & Hou, T. (2019). End-Point Binding Free Energy Calculation with MM/PBSA and MM/GBSA: Strategies and Applications in Drug Design. Chemical Reviews, 119(16), 9478–9508. https://doi.org/10.1021/acs.chemrev.9b00055 Xu, xiaorong, Huang, S., Luo, C., Xu, R., Wang, F., He, Y., Yang, M., Lin, J., Han, L., & Zhang, D. (2023). Study on the antipyretic activity and potential mechanism of Indigo Naturalis on lipopolysaccharide-induced fever rat model. https://doi.org/10.21203/rs.3.rs-3174972/v1 Yokote, A., Imazu, N., Umeno, J., Kawasaki, K., Fujioka, S., Fuyuno, Y., Matsuno, Y., Moriyama, T., Miyawaki, K., Akashi, K., Kitazono, T., &Torisu, T. (2023). Ferroptosis in the colon epithelial cells as a therapeutic target for ulcerative colitis. Journal of Gastroenterology, 58(9), 868–882. https://doi.org/10.1007/s00535-023-02016-4 Yuan, H., Ma, Q., Ye, L., & Piao, G. (2016). The Traditional Medicine and Modern Medicine from Natural Products. Molecules, 21(5), 559. https://doi.org/10.3390/molecules21050559 Yusof, I., &Segall, M. D. (2013). Considering the impact drug-like properties have on the chance of success. Drug Discovery Today, 18(13–14), 659–666. https://doi.org/10.1016/j.drudis.2013.02.008 Website Links https://www.who.int/publications/i/item/9789240093461 https://www.who.int/publications/i/item/9789240093461 https://www.rcsb.org/ https://pubchem.ncbi.nlm.nih.gov/ http://www.way2drug.com/passonline/predict.php Table 1 Tables 1 is available in the Supplementary Files section. Additional Declarations No competing interests reported. Supplementary Files Supplementaryfile.docx Table1.docx Cite Share Download PDF Status: Published Journal Publication published 04 Feb, 2026 Read the published version in In Silico Pharmacology → Version 1 posted Editorial decision: Revision requested 25 Sep, 2025 Reviews received at journal 21 Sep, 2025 Reviews received at journal 20 Sep, 2025 Reviewers agreed at journal 17 Sep, 2025 Reviewers agreed at journal 16 Sep, 2025 Reviewers agreed at journal 14 Sep, 2025 Reviewers agreed at journal 14 Sep, 2025 Reviews received at journal 06 May, 2025 Reviewers agreed at journal 04 May, 2025 Reviewers invited by journal 02 May, 2025 Editor assigned by journal 17 Mar, 2025 Submission checks completed at journal 17 Mar, 2025 First submitted to journal 15 Mar, 2025 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-6232250","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":452384544,"identity":"3e684448-563a-427d-b1d9-cb7cb3ac481d","order_by":0,"name":"Vidya S L","email":"","orcid":"","institution":"Kongunadu Arts and Science College","correspondingAuthor":false,"prefix":"","firstName":"Vidya","middleName":"S","lastName":"L","suffix":""},{"id":452384545,"identity":"12720ee3-20dd-4883-9227-758aea70b7aa","order_by":1,"name":"Anantha Bhairavi","email":"","orcid":"","institution":"Kongunadu Arts and Science College","correspondingAuthor":false,"prefix":"","firstName":"Anantha","middleName":"","lastName":"Bhairavi","suffix":""},{"id":452384546,"identity":"b8775c4c-1a53-42aa-8416-e0d5a5301ea8","order_by":2,"name":"R Sathishkumar","email":"data:image/png;base64,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","orcid":"","institution":"Kongunadu Arts and Science College","correspondingAuthor":true,"prefix":"","firstName":"R","middleName":"","lastName":"Sathishkumar","suffix":""}],"badges":[],"createdAt":"2025-03-15 10:38:17","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6232250/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6232250/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1007/s40203-025-00545-9","type":"published","date":"2026-02-04T15:59:31+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":82174806,"identity":"5bd30754-1143-4e79-8731-ad46ddb1215b","added_by":"auto","created_at":"2025-05-07 11:04:25","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":83319,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003e3D structure of the protein clumping factor A (ClFA) from \u003c/strong\u003e\u003cem\u003e\u003cstrong\u003eStaphylococcus aureus \u003c/strong\u003e\u003c/em\u003e\u003cstrong\u003e(PDB ID: 2VR3)\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"floatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-6232250/v1/a1d3d60d6e8dd8f59a22ec48.png"},{"id":82174809,"identity":"a4f2f0ea-7a91-468f-aaa1-283080ddf2dd","added_by":"auto","created_at":"2025-05-07 11:04:25","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":447726,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eFigure 1. Hydrogen bond interaction with target protein Clumping factor A (ClfA: PDB-ID- 2VR3)\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNote: The compounds are listed based on their G.score value (A) Indirubin; (B) Neomycin sulphate; the figure represents the hydrogen bond interaction of compound with the target protein along with Protein-ligand complex in space filling representation\u003c/p\u003e","description":"","filename":"floatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-6232250/v1/e303cc9774901875839a5dfb.png"},{"id":82174812,"identity":"6fae2c52-f7e2-43a1-a650-c164e7e073ec","added_by":"auto","created_at":"2025-05-07 11:04:25","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":166044,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eFigure 2. Root means square deviation of backbone atoms of Indirubin and Neomycin Sulphate\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"floatimage3.png","url":"https://assets-eu.researchsquare.com/files/rs-6232250/v1/ecd4c0728eff0e816c670f71.png"},{"id":82176086,"identity":"a419c27c-438d-4b80-a05e-ae1ef6d2308b","added_by":"auto","created_at":"2025-05-07 11:12:25","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":149815,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eFigure 3. Ligand-Root means square deviation of backbone atoms of Indirubin and Neomycin Sulphate.\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"floatimage4.png","url":"https://assets-eu.researchsquare.com/files/rs-6232250/v1/0e912e9c03a52ad4c1b71864.png"},{"id":82174818,"identity":"5c553458-4ba9-4d0a-8193-937b54d81c3d","added_by":"auto","created_at":"2025-05-07 11:04:25","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":160604,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eFigure 4. Root means square fluctuation of c-alpha atoms of Indirubin and Neomycin Sulphate\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"floatimage5.png","url":"https://assets-eu.researchsquare.com/files/rs-6232250/v1/ec56c1184bf7ddedcd805868.png"},{"id":82176087,"identity":"6c276643-7c84-4ebe-832e-9580a6489b0b","added_by":"auto","created_at":"2025-05-07 11:12:25","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":158414,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eFigure 5. RG of backbone atoms with Indirubin and Neomycin Sulphate\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"floatimage6.png","url":"https://assets-eu.researchsquare.com/files/rs-6232250/v1/eaea90ecff2f3bf8522d38b0.png"},{"id":82174814,"identity":"ec6a87a0-b25e-4736-b009-08d8776b5d07","added_by":"auto","created_at":"2025-05-07 11:04:25","extension":"png","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":138599,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eFigure 6. SASA of backbone atoms with Indirubin and Neomycin Sulphate\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"floatimage7.png","url":"https://assets-eu.researchsquare.com/files/rs-6232250/v1/ca97783647c592009c7b4726.png"},{"id":82176085,"identity":"aa02d71a-12d5-4644-8464-6fb7081c82c6","added_by":"auto","created_at":"2025-05-07 11:12:25","extension":"png","order_by":8,"title":"Figure 8","display":"","copyAsset":false,"role":"figure","size":404565,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eFig. 7: H-Bond of Indirubin and Neomycin Sulphate\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"floatimage8.png","url":"https://assets-eu.researchsquare.com/files/rs-6232250/v1/1522e67645064e00c9d2f9a1.png"},{"id":82174821,"identity":"b6c0d61b-5038-47f0-8298-d108b5f48740","added_by":"auto","created_at":"2025-05-07 11:04:25","extension":"png","order_by":9,"title":"Figure 9","display":"","copyAsset":false,"role":"figure","size":130000,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eFigure 8. Graphical representation of antibacterial activity of \u003c/strong\u003e\u003cem\u003e\u003cstrong\u003eWrightia tinctoria\u003c/strong\u003e\u003c/em\u003e\u003cstrong\u003e (dry extract: leaf, bark, seed) on \u003c/strong\u003e\u003cem\u003e\u003cstrong\u003eStaphylococcus aureus\u003c/strong\u003e\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eNote: The error bar, denoting a significance level of (P ˂ 0.05), represent the standard error of the mean value. M denotes methanol, E- Ethyl acetate, H- n-Hexane and NS- neomycin sulphate\u003c/p\u003e","description":"","filename":"floatimage9.png","url":"https://assets-eu.researchsquare.com/files/rs-6232250/v1/d98568d16a685c7741cd2c98.png"},{"id":82176095,"identity":"bc0e8192-9fca-41e5-a94f-b95211025f0e","added_by":"auto","created_at":"2025-05-07 11:12:26","extension":"png","order_by":10,"title":"Figure 10","display":"","copyAsset":false,"role":"figure","size":90488,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eFigure 9. Graphical representation of antibacterial activity of \u003c/strong\u003e\u003cem\u003e\u003cstrong\u003eWrightia tinctoria\u003c/strong\u003e\u003c/em\u003e\u003cstrong\u003e (fresh extract: leaf, bark, seed) on \u003c/strong\u003e\u003cem\u003e\u003cstrong\u003eStaphylococcus aureus\u003c/strong\u003e\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eNote: The error bar, denoting a significance level of (P ˂ 0.05), represent the standard error of the mean value. M denotes methanol, E- Ethyl acetate, H- n-Hexane and NS- neomycin sulphate\u003c/p\u003e","description":"","filename":"floatimage10.png","url":"https://assets-eu.researchsquare.com/files/rs-6232250/v1/ecbe338273b89c2e882bb54a.png"},{"id":82177289,"identity":"920c004d-d089-4af1-9a52-40b613afadf5","added_by":"auto","created_at":"2025-05-07 11:20:25","extension":"png","order_by":11,"title":"Figure 11","display":"","copyAsset":false,"role":"figure","size":97280,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eFigure 10. Antibiotic susceptibility Profile on \u003c/strong\u003e\u003cem\u003e\u003cstrong\u003eStaphylococcus aureus\u003c/strong\u003e\u003c/em\u003e\u003cstrong\u003e to a range of commercially available antibiotics\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNote: AZM denotes azithromycin, CL- colistin, IP – imipenem, NS- Neomycin Sulphate, MET- Methicillin, PI- Piperacillin, S- streptomycin, TE- Tetracycline, VA- vancomycin. The error bar, with a significance level of P ˂ 0.05, represent the standard error of the mean value.\u003c/p\u003e","description":"","filename":"floatimage11.png","url":"https://assets-eu.researchsquare.com/files/rs-6232250/v1/1c1acd05ee80b7de2f53fbdf.png"},{"id":82176089,"identity":"b403ff47-c227-423d-94ea-74e1947d2cc2","added_by":"auto","created_at":"2025-05-07 11:12:25","extension":"png","order_by":12,"title":"Figure 12","display":"","copyAsset":false,"role":"figure","size":24015,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eFigure 11. GC MS Chromatogram of ethyl acetate leaf extract of \u003c/strong\u003e\u003cem\u003e\u003cstrong\u003eWrightia tinctoria\u003c/strong\u003e\u003c/em\u003e\u003c/p\u003e","description":"","filename":"floatimage12.png","url":"https://assets-eu.researchsquare.com/files/rs-6232250/v1/2ba0e643791bad32914e7598.png"},{"id":102234266,"identity":"bfb4ddbe-f714-4a1d-a279-f3a2ac09c19f","added_by":"auto","created_at":"2026-02-09 16:08:46","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":3647727,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6232250/v1/2c6d5c79-b6ef-49b9-9043-a3c0191fe214.pdf"},{"id":82174817,"identity":"331cbfc9-2cd5-4d00-b1f8-fb7f803be969","added_by":"auto","created_at":"2025-05-07 11:04:25","extension":"docx","order_by":0,"title":"","display":"","copyAsset":false,"role":"supplement","size":3539539,"visible":true,"origin":"","legend":"","description":"","filename":"Supplementaryfile.docx","url":"https://assets-eu.researchsquare.com/files/rs-6232250/v1/06109e879a33c48f28af9eb2.docx"},{"id":82174805,"identity":"931d1f66-ad7e-47c8-bf7b-673c670b17e8","added_by":"auto","created_at":"2025-05-07 11:04:25","extension":"docx","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":28849,"visible":true,"origin":"","legend":"","description":"","filename":"Table1.docx","url":"https://assets-eu.researchsquare.com/files/rs-6232250/v1/010269a23487a463fefb00b9.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"\u003cp\u003eDiscovery of Bioactive Compounds From Medicinal Plants: Insights Into \u003cem\u003eWrightia Tinctoria \u003c/em\u003eas a Potential Antistaphylococcal Agent Targeting\u003c/p\u003e","fulltext":[{"header":"INTRODUCTION","content":"\u003cp\u003e \u003cem\u003eStaphylococcus aureus\u003c/em\u003e, a Gram-positive bacterium, is one of the most prevalent bacterial species identified in chronic wounds and various other bacterial infections. The pathogen is implicated in both community-based and hospital-acquired infections; however, the treatment is exceedingly challenging due to the prevalence of multidrug-resistant strains (Roy \u003cem\u003eet al\u003c/em\u003e., 2020; Hasan et al., \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). The World Health Organization (WHO) considers antimicrobial resistance as a prime threat to global health and enlists the bacterial priority pathogens and strategies to prevent the resistance (WHO, 2024). Reports unveiled that around 50% of infectious bacteria like \u003cem\u003eE. coli\u003c/em\u003e, \u003cem\u003eK. pneumoniae\u003c/em\u003e, \u003cem\u003eS. aureus\u003c/em\u003e, and \u003cem\u003eP. aeruginosa\u003c/em\u003e exhibit resistance to some of the most effective antibiotics, including third-generation cephalosporins (Tillotson and Zinner \u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Terreniet al., \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Studies have been considered to reduce the rate of microbial infections and promote appropriate usage of active antimicrobial drugs. Several virulence factors, including enterotoxins, toxins, hemolysins (alpha, beta, gamma, and delta), metalloprotease, and hyaluronidase as well as biofilm formation renders a major role in the multidrug resistance mechanisms. The coaggregation of free-floating microbes into multiple layers in biofilm is often facilitated by adhesion via cell wall proteins(He et al., \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Clumping factor A (ClfA) is a cell-wall anchored protein of \u003cem\u003eS. aureus\u003c/em\u003e actively involved in adhesion facilitated by existing sheer mechanical tension through fibrinogen binding(Herman-Bausieret al., \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). This virulence protein initiates infections by inhibiting phagocytosis with enhanced efficacy in the presence of fibrinogen, highlighting its role in \u003cem\u003eS. aureus\u003c/em\u003e immune evasion (Higgins et al., \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2006\u003c/span\u003e). Altogether these strategies contribute to the persistent susceptibility to \u003cem\u003eS. aureus\u003c/em\u003e infections throughout an individual's lifetime (Bhattacharya and Horswill, \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Therefore, the present study focuses on utilizing this crucial virulence factor of \u003cem\u003eS. aureus\u003c/em\u003e as a target for drug designing purposes.\u003c/p\u003e \u003cp\u003ePhytomedicines have played a crucial role in traditional healthcare systems worldwide for millennia entailed with fewer adverse side effects (Yuan et al., \u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). Despite the extensive literature available on therapeutic properties, standardized procedures for their identification, phytochemical analysis and pharmacological evaluation are scarce (Patel et al., \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2012\u003c/span\u003e; Thakur et al., \u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e2020\u003c/span\u003e).Recent studies desperately highlighted the propitious antimicrobial activity of phytocompounds in averting the resistance mechanisms of ESKAPE pathogens which includes\u003cem\u003eEnterococcus faecium, Staphylococcus aureus, Klebsiella pneumoniae, Acinetobacter baumannii, Pseudomonas aeruginosa\u003c/em\u003e, and \u003cem\u003eEnterobacter species\u003c/em\u003e(Bhatia et al., \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Ashhar \u003cem\u003eet al.\u003c/em\u003e, 2024).With this perspective, the secondary metabolites of the following plants \u003cem\u003eBreynia retusa\u003c/em\u003e, \u003cem\u003eHemigraphis alternata, Imperata cylindrica, Oldentia corymbosa\u003c/em\u003e, \u003cem\u003eSida rhombifolia\u003c/em\u003e, \u003cem\u003eScoparia dulcis, Tephrosia purpurea\u003c/em\u003e, and \u003cem\u003eWrightia tinctoria\u003c/em\u003e were chosen to study its binding efficiency with clumping factor ClfA of \u003cem\u003eS. aureus\u003c/em\u003e.\u003c/p\u003e \u003cp\u003eIn the exploration of promising plant derived antibiotics, bioinformatics plays an indispensable role by facilitating the identification and validation of potential drug targets through high throughput screening. Thus, allows bioactive compounds from plants to be analyzed for their underlying molecular mechanism with the target protein (Szymański\u003cem\u003eet al.\u003c/em\u003e, 2012; Babar \u003cem\u003eet al.\u003c/em\u003e,2017; Mitra et al., \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). The present study utilizes molecular docking, ADME (Adsorption, Distribution, Metabolism, Excretion) profiling, biological activity prediction and molecular dynamics approaches to identify the most promising drug candidate from the assorted medicinal plants. Additionally, antibacterial screening was conducted to validate in silico findings, followed by GC-MS analysis to identify and characterize the bioactive compounds present in the most effective extract. This extensive approach highlights viable options for preventing staphylococcal infections by bridging the gap between computational predictions and experimental confirmation.\u003c/p\u003e"},{"header":"MATERIALS AND METHODS","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\n \u003ch2\u003ePROTEIN RETRIEVAL AND ACTIVE SITE PREDICTION\u003c/h2\u003e\n \u003cp\u003eProtein data bank was used to retrieve the 3-dimensional structure of target protein clumping factor A (ClfA) PDB ID: 2VR3) from RCSB Protein Data Bank(\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ewww.rcsb.org\u003c/span\u003e\u003c/span\u003e) (Prlićet al., \u003cspan class=\"CitationRef\"\u003e2016\u003c/span\u003e). Followed by active site prediction using SCFBio online web tool (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://www.scfbio-iitd.res.in/dock/ActiveSite.jsp\u003c/span\u003e\u003c/span\u003e) pocket (Moulishankar and Sundarrajan \u003cspan class=\"CitationRef\"\u003e2024\u003c/span\u003e).\u003c/p\u003e\n\u003c/div\u003e\n\u003ch3\u003eLIGAND RETRIEVAL AND PREPARATION\u003c/h3\u003e\n\u003cp\u003eThe 2D structure of literature proven secondary from the following assorted plants \u003cem\u003eBreynia retusa\u003c/em\u003e, \u003cem\u003eHemigraphis alternata, Imperata cylindrica, Oldentia corymbosa\u003c/em\u003e, \u003cem\u003eSida rhombifolia\u003c/em\u003e, \u003cem\u003eScoparia dulcis, Tephrosia purpurea\u003c/em\u003e, and \u003cem\u003eWrightia tinctoria\u003c/em\u003e, were retrieved from the PubChem database together with their respective PubChem ID (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://pubchem.ncbi.nlm.nih.gov/\u003c/span\u003e\u003c/span\u003e) (Sasikumar \u003cem\u003eet al.\u003c/em\u003e, 2022). Lig prep module of Maestro Schrodinger suite was utilized to prepare the ligands. Which includes enumeration of their all possible protonation and ionization states using Ionizer at a pH of 7.4 by addition or removal of protons from the ligand(Sahayarayan et al., \u003cspan class=\"CitationRef\"\u003e2021\u003c/span\u003e).\u003c/p\u003e\n\u003ch3\u003eADME PREDICTION\u003c/h3\u003e\n\u003cp\u003eAround 105 assorted phytocompounds were screened for Adsorption, Distribution, Metabolism and Excretion (ADME) profiling using QikProp module of Schrodinger software to comprehend their pharmacokinetic properties in compliance with Lipinski\u0026apos;s rule of five. Enlisting parameters like drug likeliness of small molecules with rotatable bonds, molecular weight, dipole moment, hydrogen bond donor and blood barrier coefficient being the prime parameters (Bhairavi et al., \u003cspan class=\"CitationRef\"\u003e2023\u003c/span\u003e; Patidar et al., \u003cspan class=\"CitationRef\"\u003e2019\u003c/span\u003e).\u003c/p\u003e\n\u003ch3\u003eBIOLOGICAL ACTIVITY PREDICTION\u003c/h3\u003e\n\u003cp\u003eThe only compounds, obeys Lipinski\u0026rsquo;s rule of five are solely considered for their biological activity prediction using an online tool PASS (Prediction of Activity Spectra for Substances) way2drug (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://www.way2drug.com/passonline/predict.php\u003c/span\u003e\u003c/span\u003e) (Yusofand Segall 2013; Filimonovet al., \u003cspan class=\"CitationRef\"\u003e2014\u003c/span\u003e)\u003c/p\u003e\n\u003ch3\u003eMOLECULAR DOCKING\u003c/h3\u003e\n\u003cp\u003eADME cleared compounds were used to perform docking analysis. GLIDE module in maestro Schr\u0026ouml;dinger software \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://www.schrodinger.com/\u003c/span\u003e\u003c/span\u003e was widely used to perform docking analysis (Bhairavi et al., \u003cspan class=\"CitationRef\"\u003e2023\u003c/span\u003e). It identifies potential areas of interaction between ligands and proteins by using a hierarchical series of filters that identifies the precise site of the ligand within the active site of the protein (Sahayarayanet al., \u003cspan class=\"CitationRef\"\u003e2021\u003c/span\u003e).The interactions of each compound with significant Glide score were visualized using the software PyMol (Mishra et al., \u003cspan class=\"CitationRef\"\u003e2021\u003c/span\u003e).\u003c/p\u003e\n\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e\n \u003ch2\u003eMOLECULAR DYNAMIC SIMULATIONS\u003c/h2\u003e\n \u003cp\u003eTo explore the protein-ligand interaction, the best docked compound was subjected to MD simulations along with standard compound Neomycin sulphate. Gromacs 2019.4 software suite was employed to analyze its interactions at atomic level (Cardoza et al., \u003cspan class=\"CitationRef\"\u003e2024\u003c/span\u003e). The energy-minimized protein-ligand complex was placed in a vacuum and solvated using the SPC water box and then equilibrated under NPT conditions. A 100 ns production run at constant temperature and pressure followed this step (Gangadharappa et al., \u003cspan class=\"CitationRef\"\u003e2020\u003c/span\u003e). RMSD, RMSF, Rg, SASA, and hydrogen bond interactions were evaluated with trajectory analysis by Prasanth \u003cem\u003eet al\u003c/em\u003e. Binding free energy, \u0026Delta;G binding, was calculated using the MM-PBSA method with the g_mmpbsa tool, and energy decomposition for the last 50 ns of the trajectory gave insights into binding stability and affinity (Wang et al., \u003cspan class=\"CitationRef\"\u003e2019\u003c/span\u003e; Prasanth et al., \u003cspan class=\"CitationRef\"\u003e2021\u003c/span\u003e).\u003c/p\u003e\n\u003c/div\u003e\n\u003ch3\u003eSAMPLE COLLECTION AND EXTRACTION\u003c/h3\u003e\n\u003cp\u003eThe sample \u003cem\u003eWrightia tinctoria\u003c/em\u003e was collected and authenticated \u003cem\u003e(BSI/SRC/5/23/2023-24/Tech/529)\u003c/em\u003e from The Botanical Survey of India (BSI) Coimbatore, Tamilnadu.The parts including leaf, bark, and seeds were washed, shade-dried, and finely powdered. Fifty grams of sample (fresh and dried) used for extraction via maceration in 1:10 ratio for seven days, following Haile \u003cem\u003eet al.\u003c/em\u003e (2022). The extracts were filtered with Whatman No. 1 paper, and the solvents (n-hexane, ethyl acetate and methanol) were evaporated under reduced pressure using a rotary evaporator. The resulting extracts were stored for subsequent analyses.\u003c/p\u003e\n\u003ch3\u003eANTIBACTERIAL SCREENING\u003c/h3\u003e\n\u003cp\u003eAgar well diffusion method was carried out to evaluate the antibacterial efficacy of \u003cem\u003eW. tinctoria\u003c/em\u003e (Jegadeeshwari et al., \u003cspan class=\"CitationRef\"\u003e2023\u003c/span\u003e). Extracts from fresh and dry plant materials in n-hexane, ethyl acetate, and methanol were studied against \u003cem\u003eStaphylococcus aureus\u003c/em\u003e (MTCC-96). An overnight culture of \u003cem\u003eS. aureus\u003c/em\u003e, with optical density was adjusted to 0.1, was inoculated on Muller Hinton Agar plates.6 mm wells were prepared and charged with 100\u0026micro;l of plant extracts at a concentration of 1ml/ml. Neomycin sulphate was considered as positive control and organic solvents were used as negative control. Further plates were incubated at 37\u0026deg;C for 24 hrs.\u003c/p\u003e\n\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e\n \u003ch2\u003eANTIBIOTIC SUSCEPTIBILITY STUDIES\u003c/h2\u003e\n \u003cp\u003eKirby-Bauer disc diffusion method was used to analyse antibiotic susceptibility (Kirby and Bauer, 1966). Muller Hinton agar plates were evenly swabbed with S. aureus that was adjusted to 0.5 McFarland standard. Antibiotic disks (Himedia) including Azithromycin (AZM15), Methicillin (MET5), Streptomycin (S10), Vancomycin (VA30), Tetracycline (TE30), Colistin (CL10), Imipenem (IP8), and Piperacillin (PI100), were used with four discs placed on each plate.\u003c/p\u003e\n \u003cp\u003eFor neomycin sulphate, antibiotic susceptibility was assessed using the agar well diffusion method, with 50 \u0026micro;g of neomycin sulphate per well. subsequently plates were then incubated overnight at 37\u0026deg;C, and the results were recorded.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec12\" class=\"Section2\"\u003e\n \u003ch2\u003eGC-MS ANALYSIS\u003c/h2\u003e\n \u003cp\u003ePhytochemical profiling of the ethyl acetate extract of \u003cem\u003eWrightia tinctoria\u003c/em\u003e was conducted using GC-MS analysis (GC-MS-QP 2010, Shimadzu, Tokyo, Japan). The analysis utilized an electron ionization energy of 70eV. A standard, non-polar 60 M TRX 5-MS column with dimensions of 30 m in length and a film thickness of 0.25 \u0026micro;m was employed. The instrument\u0026apos;s oven temperature was programmed to increase from 100\u0026deg;C to 260\u0026deg;C at a rate of 10\u0026deg;C per minute. Helium was used as the carrier gas, and 2 \u0026micro;l of the sample was injected initially at a steady flow rate of 2 ml per minute. With peak normalisation, the GC-MS analysis proceeded for thirty-five minutes. The identification of several components was made easier by this study. The National Institute of Standards and Technology (NIST) Mass Spectral Library version 2.0 (2005) mass spectra database was used in order to interpret the results (Khan et al., \u003cspan class=\"CitationRef\"\u003e2021\u003c/span\u003e).\u003c/p\u003e\n\u003c/div\u003e"},{"header":"RESULTS","content":"\u003cdiv id=\"Sec14\"\u003e\n \u003ch2\u003eACTIVE SITE PREDICTION\u003c/h2\u003e\n \u003cp\u003e2D image of target protein ClfA was shown in Fig. 1. The chosen X-ray crystallographic structure is characterized with chain A and B comprises of a sequence length of 329 residues. The Among the several cavities, the active site was predicted in cavity 1 that represents the following amino acid residues were predicted as their active site binding pocket: 16 SER, 17 GLY, 18 THR, 19 THR, 21 TYR, 22 PRO, 24 GLN, 25 ALA, 26 GLY, 27 TYR, 28 VAL, 29 LYS, 58 GLY, 59 VAL, 106 TYR, 107 ILE, 109 PRO, 115 THR, 134 LEU, 136 ASP, 137 TYR, 138 GLU, 139 LYS, 140 TYR, 141 GLY, 142 LYS, 150 GLY, 153 ASP, 164 GLN, 166 ILE, 167 TYR, 184 LEU, 185 LYS, 186 PRO, 187 ASN, 188 THR, 190 SER, 20 VAL, 225 GLY, 215 SER, 251 PRO, 252 HIS, 254 ALA, 258 ALA, 264 GLY, 265 ASP, 267 ALA, 269 ARG, 271 THR, 284 MET, 286 ASN, 287 GLY, 288 VAL, 289 GLU, 291 ALA, 292 PHE, 293 LYS, 295 LEU, 316 THR, 317 ASN, 318 VAL, 319 THR, 320 VAL, 321 GLY, 322 ILE, 323 ASP, 324 SER, 326 THR, 338 TYR. 339 ILE, 339 ASP, 357 THR, 384 ILE, 395 ARG, 430 THR, 432 ALA, 433 THR, 434 GLY, 435 ILE, 436 GLY, 437 SER, 438 THR, 439 THR, 440 ALA, 441 ASN, 442 LYS, 443 THR, 444 VAL, 525 ASN, 526 GLU, 532 GLY622 LEU, 623 GLY, 625 ALA, 624 GLY, 626 LYS, 627 GLN.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec15\"\u003e\n \u003ch2\u003ePREDICTION OF ADME PROPERTIES AND PHARMACOLOGICAL ACTIVITIES\u003c/h2\u003e\n \u003cp\u003eThe secondary metabolites of the plants \u003cem\u003eBreynia retusa, Hemigraphis alternata, Imperata cylindrica, Oldentia corymbosa\u003c/em\u003e, \u003cem\u003eSida rhombifolia\u003c/em\u003e, \u003cem\u003eScoparia dulcis, Tephrosia purpurea\u003c/em\u003e, and \u003cem\u003eWrightia tinctoria\u003c/em\u003e, are collected and analyzed for ADME properties. Out of 78 compounds only 19 compounds cleared Lipinski’s rule of five along with molecular weight, hydrogen donor and acceptor, logP value, various other factors like permeability for blood/brain barrier and skin, dipolar property, polar surface area, ionization potential, polarizability, electron affinity, number of rotatable bonds (Table\u0026nbsp;1).\u003c/p\u003e\n \u003cp\u003eFurther, pharmacological activity of these 19 compounds were predicted through PASS online Way2Drug web tool where the importance was given to antibacterial activity. Almost all the compounds listed had antibacterial activity, except 17beta-Hydroxy-28-norolean-12-ene-3,16-dione. However, this compound was predicted to possess wound healing properties. Since each compound is predicted with significant and expected antibacterial activity, all the compounds were considered for molecular docking analysis.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec16\"\u003e\n \u003ch2\u003eDOCKING ANALYSIS\u003c/h2\u003e\n \u003cp\u003eMolecular docking interactions for all the 19 compounds were conducted, however, 15 of the compounds had significant interactions with the amino acid residues of ClfA. The substantial Glide score (Kcal/mol), interacting residues, number of hydrogen bonds along with their bond length (Å) are tabulated (Table\u0026nbsp;2). The top docked compound Indirubin, had highest glide score of -8.82Kcal/mol forming four hydrogen bonds with active site residues GLY 225 and ILE339. The standard drug used was neomycin sulfate, which exhibited a G score of -8.45 kcal/mol. The second-best docked compound Indigo had a G score of -7.19 indicating strong binding interactions with key amino acid residues in active site pocket. The remaining compounds had a glide score ranging from − 6.77 to -2.93Kcal/mol. Amino acid interaction of all these compounds along with standard drug was tabulated in table and their interactions with clumping factor A (ClfA: 2VR3) are visualized using PyMol software (Fig.\u0026nbsp;1).\u003c/p\u003e\n \u003cdiv\u003e\u0026nbsp;\u003ctable id=\"Tab2\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv\u003eTable 2\u003c/div\u003e\n \u003cdiv\u003e\n \u003cp\u003eProtein- Ligand Interaction Profile for the selected phytocompounds against target protein Clumping factor ClfA\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eS.No\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eName of the Ligand\u003c/p\u003e\n \u003cp\u003e(PubChem ID)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eResidues Interaction\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eBond Length\u003c/p\u003e\n \u003cp\u003e(Å)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eNo. of Hydrogen Bonds\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eG-Score\u003c/p\u003e\n \u003cp\u003e(Kcal/Mol)\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003cth align=\"left\" colspan=\"6\"\u003e\n \u003cp\u003e\u003cem\u003eWrightia tinctoria\u003c/em\u003e\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eIndirubin (10177)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGLY 225 (O-O)\u003c/p\u003e\n \u003cp\u003eILE 339 (O-H)\u003c/p\u003e\n \u003cp\u003eILE 339 (O-H)\u003c/p\u003e\n \u003cp\u003eILE 339 (O-H)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.9\u003c/p\u003e\n \u003cp\u003e2.6\u003c/p\u003e\n \u003cp\u003e1.9\u003c/p\u003e\n \u003cp\u003e2.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-8.82\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eIndigo (10215)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePRO 251 (H-O)\u003c/p\u003e\n \u003cp\u003eASN 525 (O-H)\u003c/p\u003e\n \u003cp\u003eILE 384 (O-H)\u003c/p\u003e\n \u003cp\u003eILE 384 (H-O)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.2\u003c/p\u003e\n \u003cp\u003e2.3\u003c/p\u003e\n \u003cp\u003e1.9\u003c/p\u003e\n \u003cp\u003e1.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-7.19\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePalmitic acid (985)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eILE 384 (O-H)\u003c/p\u003e\n \u003cp\u003eASN 525 (O-H)\u003c/p\u003e\n \u003cp\u003eGLU 526 (O-H)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.8\u003c/p\u003e\n \u003cp\u003e1.6\u003c/p\u003e\n \u003cp\u003e2.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-6.77\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eIsatin (7054)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGLY 255 (H-O)\u003c/p\u003e\n \u003cp\u003eVAL 288 (O-H)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.9\u003c/p\u003e\n \u003cp\u003e2.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-5.95\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePhytol (5280435)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGLY 287 (H-O)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-5.67\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDiethyl phthalate (6781)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHIS 252 (O-H)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-4.95\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eUrs-12-en-3beta-ol (225688)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGLY 287 (H-O)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-3.39\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eUrsolic acid (64945)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eARG 395 (O-H)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-2.93\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"6\"\u003e\n \u003cp\u003e\u003cstrong\u003eSida rhombifolia\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e9\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eVasicinol (442934)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eALA 258 (H-O)\u003c/p\u003e\n \u003cp\u003eILE 339 (H-O)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.4\u003c/p\u003e\n \u003cp\u003e1.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-5.25\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"6\"\u003e\n \u003cp\u003e\u003cstrong\u003eTephrosia purpurea\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePurpurin (6683)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eILE 384 (H-O)\u003c/p\u003e\n \u003cp\u003eILE 339 (H-O)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.1\u003c/p\u003e\n \u003cp\u003e2.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-4.92\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSemiglabrin (156341)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eILE 384 (O-H)\u003c/p\u003e\n \u003cp\u003eALA 254 (O-H)\u003c/p\u003e\n \u003cp\u003eILE 339 (O-O)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.4\u003c/p\u003e\n \u003cp\u003e2.3\u003c/p\u003e\n \u003cp\u003e2.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-4.86\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"6\"\u003e\n \u003cp\u003e\u003cstrong\u003eScoparia dulcis\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5- Benzyloxypyrimidine (561874)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eILE 384 (N-H)\u003c/p\u003e\n \u003cp\u003eILE 384 (O-H)\u003c/p\u003e\n \u003cp\u003eASN525 (O-H)\u003c/p\u003e\n \u003cp\u003eASN 525 (O-H)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.6\u003c/p\u003e\n \u003cp\u003e2.2\u003c/p\u003e\n \u003cp\u003e2.3\u003c/p\u003e\n \u003cp\u003e2.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-4.84\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eBenzofuran 2,3 dihydro (20209882)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTYR 338 (O-H)\u003c/p\u003e\n \u003cp\u003eLEU 295 (H-O)\u003c/p\u003e\n \u003cp\u003eLYS 293 (H-O)\u003c/p\u003e\n \u003cp\u003eGLY 532 (O-H)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.2\u003c/p\u003e\n \u003cp\u003e2.4\u003c/p\u003e\n \u003cp\u003e2.6\u003c/p\u003e\n \u003cp\u003e2.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-4.66\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"6\"\u003e\n \u003cp\u003e\u003cstrong\u003eBreynia retusa\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eEpicatechin (72276)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eILE 339 (H-O)\u003c/p\u003e\n \u003cp\u003ePRO 251 (H-O)\u003c/p\u003e\n \u003cp\u003eGLU 526 (H-O)\u003c/p\u003e\n \u003cp\u003eGLU 526 (O-H)\u003c/p\u003e\n \u003cp\u003eTRP 523 (O-H)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.2\u003c/p\u003e\n \u003cp\u003e1.9\u003c/p\u003e\n \u003cp\u003e1.8\u003c/p\u003e\n \u003cp\u003e1.9\u003c/p\u003e\n \u003cp\u003e2.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-4.75\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"6\"\u003e\n \u003cp\u003e\u003cstrong\u003eStandard Antibiotic\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNeomycin sulphate\u003c/p\u003e\n \u003cp\u003e(8378)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eASN 286 (O-H)\u003c/p\u003e\n \u003cp\u003eASP 385 (H-O)\u003c/p\u003e\n \u003cp\u003eGLU 342 (H-O)\u003c/p\u003e\n \u003cp\u003eGLU 342 (H-O)\u003c/p\u003e\n \u003cp\u003eASP 340 (H-O)\u003c/p\u003e\n \u003cp\u003eLEU 283 (H-O)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.0\u003c/p\u003e\n \u003cp\u003e1.8\u003c/p\u003e\n \u003cp\u003e1.8\u003c/p\u003e\n \u003cp\u003e1.8\u003c/p\u003e\n \u003cp\u003e2.1\u003c/p\u003e\n \u003cp\u003e2.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-8. 45\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec17\"\u003e\n \u003ch2\u003eMOLECULAR DYNAMICS SIMULATION\u003c/h2\u003e\n \u003cp\u003eMolecular dynamics simulation analysis is performed for the best interaction recorded in the docking analysis together with the standard drug interaction for 100 ns of. The changes in the protein-ligand complexes are accounted with the parameters such as root mean square deviation (RMSD), root mean square fluctuation (RMSF), the radius of gyration (Rg), hydrogen bond (H-bond), solvent accessible surface area (SASA), and further binding free energy (ΔGbind) was computed using the 'g_mmpbsa tool'.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec18\"\u003e\n \u003ch2\u003eRMSD AND RMSF\u003c/h2\u003e\n \u003cp\u003eThe calculation of RMSD describes the dynamic stability of a protein's backbone framework over the course of the simulation. The RMSD value and the trajectories were observed for 100 ns as shown in the figure (Fig.\u0026nbsp;2, Fig.\u0026nbsp;3). The receptor-ligand complexes: ClfA-indirubin and ClfA-neomycin sulphate as well as the ligands separately are simulated. The RMSD values for a period of 100 ns for the Indirubin and Neomycin Sulphate protein complexes were measured and found to be 0.18 +/- 0.035 nm and 0.29 +/- 0.069 nm, respectively. These results suggest minimal structural changes in the protein complexes during the simulation, with RMSD values that did not significantly differ from those of the unbound protein. These findings indicate the compounds' relative stability throughout the simulation. Overall, the protein complexes remained relatively stable throughout the simulation, with only minor differences between them, suggesting their similarity in terms of stability.\u003c/p\u003e\n \u003cp\u003eDuring a 100 ns simulation, the RMSF (root mean square fluctuation) was calculated to understand how the protein's vibrations change over time. This analysis provides insight into the movements of amino acids throughout the MD run and can determine how the absence or presence of a ligand affects protein stability. Figure\u0026nbsp;4 displays the RMSF results for the Indirubin and Neomycin Sulphate complexes. Based on these results, it appears that there were no significant structural changes throughout the simulation.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec19\"\u003e\n \u003ch2\u003eRADIUS OF GYRATION:\u003c/h2\u003e\n \u003cp\u003eThe radius of gyration (Rg) measuresthe compact a protein's structure (overall shape and folding changes) via the Rg plot. Both the Indirubin and Neomycin Sulphate complexes had similar Rg values. The average Rg value for the Indirubin and Neomycin Sulphate protein complexes from 0 to 100 ns were 2.36+/-0.014 nm and 2.45+/-0.019 nm respectively. Figure\u0026nbsp;5shows the Rg plot for both indirubin (black) and neomycin sulphate (red), which remained stable throughout the simulation, indicating a high level of compactness in the protein.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec20\"\u003e\n \u003ch2\u003eSASA AND H-BOND:\u003c/h2\u003e\n \u003cp\u003eThe SASA was measured to analyze the compactness of the hydrophobic core, where the changes in SASA over time for the Indirubin and Neomycin Sulphate protein complex was studied and shown in Fig.\u0026nbsp;6. The average SASA value from 0 to 100 ns for the Indirubin and Neomycin Sulphate protein complex proteins were 161.01+/-4.45 nm and 160.46+/-3.51 nm, respectively. The results suggest that there were no changes in the structural level of the proteins throughout the simulation. Figure\u0026nbsp;7displays the hydrogen bond analysis results for the complexes involving Indirubin and Neomycin Sulphate. The plot clearly shows that both ligands possess a significant number of hydrogens indicating stable protein ligand complex.\u003c/p\u003e\n \u003cdiv id=\"Sec21\"\u003e\n \u003ch2\u003eMM-PBSA\u003c/h2\u003e\n \u003cp\u003eMM-PBSA analysis revealed that indirubin has more stable and favorable binding affinity when compared to neomycin sulphate. Indirubins strong van der Waals interaction and lower energy fluctuations indicates its superior binding stability. Thus, indicates indirubin is more efficient ligand for target protein. Table\u0026nbsp;3 shows a comparison of their binding strength as inhibitors, calculated using the MM-PBSA method.\u003c/p\u003e\n \u003cdiv\u003e\u0026nbsp;\u003ctable id=\"Tab3\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv\u003eTable 3\u003c/div\u003e\n \u003cdiv\u003e\n \u003cp\u003eBinding free energy calculation of Indirubin and Neomycin sulphate\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eSystem\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eVan der Waal energy\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eElectrostatic energy\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003ePolar solvation energy\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eBinding energy\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eIndirubin\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-148.937 +/- 5.789 kJ/mol\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-32.248 +/- 5.754 kJ/mol\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e122.777 +/- 12.153 kJ/mol\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-15.699 +/- 0.769 kJ/mol\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNeomycin Sulphate\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-44.975 +/- 20.212 kJ/mol\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-24.216 +/- 91.152 kJ/mol\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e10.839 +/- 50.370 kJ/mol\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-52.177 +/- 79.517 kJ/mol\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n \u003c/div\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec22\"\u003e\n \u003ch2\u003eANTIBACTERIAL EFFICIENCY OF PLANT EXTRACTS\u003c/h2\u003e\n \u003cp\u003eAntibacterial analysis of fresh and dry leaf extract of \u003cem\u003eWrightia tinctoria\u003c/em\u003e was evaluated against multi drug resistant \u003cem\u003eS. aureus\u003c/em\u003e. Among all the tested extracts, the dry leaf ethyl acetate extract demonstrated the highest antibacterial activity, as evidenced by the largest zone of inhibition (ZI). Thus indicates that the active compounds responsible for antibacterial activity may be more concentrated or more stable in the dry form compared to the fresh extract or the other parts of the plant. In contrast, extracts from other parts of \u003cem\u003eW. tinctoria\u003c/em\u003e, showed comparatively lower antibacterial effects, with smaller zones of inhibition.\u003c/p\u003e\n \u003cp\u003eThe antibacterial activity of \u003cem\u003eW. tinctoria\u003c/em\u003e was most pronounced in the dry leaf ethyl acetate, which, at a concentration of 100 µg, demonstrated the highest zone of inhibition measuring 26.33 ± 0.33 mm. This was followed by the fresh leaf ethyl acetate extract, which showed considerable activity with a zone of inhibition of 12.00 ± 0.57 mm. In contrast, extracts from the bark and seeds of \u003cem\u003eW. tinctoria\u003c/em\u003e exhibited only low to moderate antibacterial activity. Graphical representations of the antibacterial activity of the dry and fresh leaf extracts of \u003cem\u003eW. tinctoria\u003c/em\u003e were generated using GraphPad Prism 5 (Fig. 8 and Fig. 9)\u003c/p\u003e\n \u003cdiv id=\"Sec23\"\u003e\n \u003ch2\u003eANTIBIOTIC SUSCEPTIBILITY ANALYSIS\u003c/h2\u003e\n \u003cp\u003eThe multi-drug resistance profile of \u003cem\u003eS. aureus\u003c/em\u003e was assessed using the disc diffusion method, involving nine different antibiotics at various concentrations. The results were categorized as sensitive (S), intermediate resistant (I), and resistant (R) in accordance with the Clinical and Laboratory Standards Institute (CLSI) guidelines. Among the antibiotics tested, \u003cem\u003eS. aureus\u003c/em\u003e exhibited 100% resistance to methicillin, streptomycin, vancomycin, colistin, and piperacillin. Intermediate resistance was observed with azithromycin and imipenem. Notably, \u003cem\u003eS. aureus\u003c/em\u003e showed significant susceptibility to neomycin sulphate, which produced the highest zone of inhibition, followed by tetracycline. The antibiogram results clearly indicate that approximately 70% of the antibiotics tested were ineffective against \u003cem\u003eS. aureus\u003c/em\u003e (Fig. 10). This highlights the strain's extensive resistance and underscores the need for alternative treatment options or the development of new antibacterial agents.\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eNote\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003eAZM denotes azithromycin, CL- colistin, IP – imipenem, NS- Neomycin Sulphate, MET- Methicillin, PI- Piperacillin, S- streptomycin, TE- Tetracycline, VA- vancomycin. The error bar, with a significance level of P ˂ 0.05, represent the standard error of the mean value.\u003c/p\u003e\n \u003c/div\u003e\n \u003cdiv id=\"Sec24\"\u003e\n \u003ch2\u003eGC-MS PROFILING\u003c/h2\u003e\n \u003cp\u003eThe GC-MS analysis of the EALE of \u003cem\u003eW. tinctoria\u003c/em\u003e revealed the presence of 50 phytocompounds. Among these, the compound with the highest peak area was indirubin, an alkaloid, which constituted 6.59% of the total extract. Other significant compounds identified as 9,19-cyclolanost-24-en-3-ol, (3.beta.) with a peak area of 17.36%, β-sitosterol (11.11%), stigmast-5-en-3-ol, (3.beta.) (8.69%), norolean-12-ene (5.37%), phytol (4.45%), palmitic acid (2.72%), urs-12-en-3beta-ol (2.01%) and indigo (1.39%). The detailed identification of these compound along with their retention time and compound ID was provides (Table 4) as well as the chromatogram of \u003cem\u003eW. tinctoria\u003c/em\u003e EALE, illustrating these peaks (Fig. 11). This analysis highlights the complex phytochemical composition of \u003cem\u003eW. tinctoria\u003c/em\u003e, with several compounds of potential pharmacological interest.\u003c/p\u003e\n \u003cdiv\u003e\u0026nbsp;\u003ctable id=\"Tab4\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv\u003eTable 4\u003c/div\u003e\n \u003cdiv\u003e\n \u003cp\u003eGC MS profiling of ethyl acetate leaf extract of \u003cem\u003eWrightia tinctoria\u003c/em\u003e\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eS. No\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eCompound Name\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eCompound ID\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eRetention Time\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003ePeak Area %\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTricyclo[4.4.0.0(2,7)]dec-3-ene, 1,3-dimethyl-8-(1-methylethyl)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e6432119\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e14.734\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.20\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eBicyclo[7.2.0]undec-4-ene, 4,11,11-trimethyl-8-methylene\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e5281515\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e15.977\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.38\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1,4,8-cycloundecatriene, 2,6,6,9-tetramethyl\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e5281520\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e16.943\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.25\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eEthanone, 1-(2-methyl-1-cyclopenten-1-yl)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e137847\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e21.095\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.16\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSulfurous acid, 2-ethylhexyl isohexyl ester\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e6420722\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e21.180\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.33\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAcetic acid, 1,7,7-trimethyl-bicyclo[2.2.1]hept-2-yl ester\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e6448\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e21.332\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.46\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHexadecanal\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e984\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e23.411\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.32\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eOctahydro-1,8a(1h)-naphthalenediol\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e574054\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e24.795\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.71\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDiethyl Phthalate\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e6781\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e26.524\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.91\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3-Eicosyne\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e549159\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e27.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.22\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2H-Pyran, 2-(2-heptadecynyloxy)tetrahydro\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e544115\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e27.073\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.29\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3,7,11,15-Tetramethyl-2-hexadecen-1-ol\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e5366244\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e27.379\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.56\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePalmitic acid\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e985\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e28.364\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2.72\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePhytol\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e5280435\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e31.602\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e4.45\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e9,12,15-Octadecatrienoic acid\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e5280934\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e31.884\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.89\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e16\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eOleic Acid\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e445639\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e31.985\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.33\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e17\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eOleanolic acid\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e10494\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e32.317\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.24\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e18\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eOctadecanoic acid\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e5281\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e32.445\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.33\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e19\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2-biphenylethylamine, 5-methoxy-, hydrochloride\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e25946\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e33.443\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.49\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e20\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e24-Methylenecycloartanol\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e94204\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e35.108\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.31\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e21\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSqualene\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e638072\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e36.739\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.28\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e22\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCopaene\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e12303902\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e38.306\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.23\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e23\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTritriacontane\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e12411\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e39.819\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.16\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e24\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHexatriacontane\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e12412\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e41.279\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.20\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e25\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eIndirubin\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e638072\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e42.875\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e6.59\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e26\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eIsatin\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e7054\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e44.368\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.87\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e27\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2,6,10,14-Hexadecatetraen-1-ol, 3,7,11,15-tetramethyl\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e86591503\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e44.466\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.34\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e28\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e17beta-Hydroxyl-28-norolean-12-ene-3,16-dione\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e102067273\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e44.539\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.35\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e29\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2,2-dimethyl-3-(3,7,16,20-tetramethyl-heneicosa-3,7,11,15,19-pentaenyl)-oxirane\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e5367592\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e44.696\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.37\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e30\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSolanesol\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e5477212\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e46.030\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.19\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e31\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eErgost-5-en-3-ol\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e18660356\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e46.336\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.88\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e32\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2h-1-benzopyran-6-ol, 3,4-dihydro-2,7,8-trimethyl-2-(4,8,12-trimethyltridecyl)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e14986\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e46.628\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.00\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e33\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eβ-caryophyllene oxide\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1742210\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e46.778\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.24\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e34\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eUrsolic acid\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e64945\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e47.270\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.88\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e35\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2-propyldecane-1,3,8,10- tetrol\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e88829318\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e47.759\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.52\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e36\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e17-Pentatriacontene\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e5365022\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e48.270\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.35\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e37\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2,5,7,8-tetramethyl-2-(4,8,12-trimethyltridecyl)-3,4-dihydro-2h-chromen-6-yl hexofuranoside\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e597057\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e48.537\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.43\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e38\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTetrapentacontane\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e521846\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e48.733\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.03\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e39\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eUndecane\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e14257\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e48.840\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.90\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e40\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eUrs-12-en-3beta-ol\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e73170\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e49.490\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2.01\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e41\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2,3,3-Trimethyl-2-(methyl pentanoyl)-cyclopentanone\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e573846\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e50.840\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2.42\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e42\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eIndigo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e10215\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e51.820\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.39\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e43\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e9,19-cyclolanostan-3-ol, 24-methylene-, (3.beta)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e94204\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e52.434\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.28\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e44\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eStigmast-5-en-3-ol, (3.beta)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e222284\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e53.800\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e8.69\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e45\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eβ- Tocopherol\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e6857447\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e54.195\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.02\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e46\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e6-Ethyl-2-methyldecane\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e43923\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e54.465\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e5.37\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e47\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNeophytadiene\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e10446\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e54.882\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e12.66\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e48\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eβ- Sitosterol\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e225688\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e55.977\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e11.11\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e49\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e9,19-Cyclolanost-24-en-3-ol, (3.beta)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e92110\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e56.249\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e17.36\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e50\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLanosterol\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e246983\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e56.505\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.33\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n \u003c/div\u003e\n\u003c/div\u003e"},{"header":"DISCUSSION","content":"\u003cp\u003e\u003cem\u003eStaphylococcus aureus\u003c/em\u003e being a pervasive pathogen found in both hospital and community settings, poses significant public health risks. The misuse and overuse of antibiotics have exacerbated drug resistance issues in treating \u003cem\u003eS. aureus\u003c/em\u003e infections (Ahmed et al., \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). According to Mancuso \u003cem\u003eet al.\u003c/em\u003e,(2021), diseases caused by multidrug-resistant bacteria result in approximately 700,000 deaths annually. The presence of multiple virulence mechanisms is particularly concerning due to limited treatment options. These mechanisms enable the bacteria to evade the host's immune response and include colonization of host tissues, leading to conditions such as bloodstream infections, skin infections, necrotizing pneumonia, and other severe tissue infections (Mlynarczyk-Bonikowska et al., \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Medicinal plants offer a rich source of bioactive compounds that, when combined, can potentially enhance therapeutic efficacy through synergistic effects against a wide range of multidrug-resistant pathogens (Huang et al., \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Ejaz et al. \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2014\u003c/span\u003e demonstrated that plant-based strategies work significantly in inhibiting multidrug resistant bacteria notable \u003cem\u003eS. aureus\u003c/em\u003e. In a similar vein a research by Panda et al. \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2020\u003c/span\u003ealso confirmed this, both of them investigated the anti-staphylococcal activity of many traditional medicinal herbs from Pakistan and India. Thus, the study offers a broad perspective on the bactericidal characteristics of medicinal plants in combating against antibiotic resistance by focusing on various geographic regions and plant species. Similarly, our research focuses on evaluating the potential of phytocompounds from eight medicinal plants known for their remarkable biological properties. Researches have demonstrated that cell wall-anchored proteins are pivotal in facilitating bacterial infections by enabling attachment to specific host tissues. Clumping factor A, for instance, exemplifies such a protein by promoting bacterial adhesion to host tissue fibrinogen, thereby initiating infection (Foster \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Speziale and Pietrocola, \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Viljoen et al., \u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e2021\u003c/span\u003e).In the current study, initially ADME profiling was conducted thereby explored the drug-likeness tests to mitigate the risk of late-stage drug failure rates specifically during clinical trials (Panigrahiet al., \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Where 19 compounds found to have drugs like properties meeting all necessary parameters and demonstrating favorable pharmacokinetic properties including absorption, distribution, and metabolic stability. Subsequently, Glide module of Schrodinger suite utilizes a grid-based approach to predict ligand binding sites within the target protein and employed an extra precision scoring function during the analysis (Sankar and Engels 2018). As a result,15bioactive compounds demonstrated robust binding interactions with the target protein, achieving an average Glide score ranging from \u0026minus;\u0026thinsp;8 to -2 kcal/mol. These compounds effectively interacted with specific amino acid residues such as ILE (isoleucine), GLY (glycine), ASN (asparagine), and GLU (glutamic acid), by forming hydrogen bonds with residues including ILE 339, 384, ASN 525, 286, GLU 526, and residue 342. The presence of these amino acid residues was confirmed through active site prediction tool. The phytocompounds with their least G scores were detailed in Table\u0026nbsp;8. Indirubin derived from \u003cem\u003eW. tinctoria\u003c/em\u003e demonstrated the highest binding affinity with the target protein ClfA, forming hydrogen bonds with glycine and isoleucine residues. In a similar vein, following ADME profiling, Ragi \u003cem\u003eet al\u003c/em\u003e. (2021) used AutoDock 4.20 to dock a variety of virulence proteins, including ClfA, with its sniff base chemical. Potent binding capacities with the target protein and interactions with the amino acid residues valine, lysine, isoleucine, and glycine are characteristics of their study that are consistent with our findings. In another investigation by Wadapurkar et al. (\u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e2018\u003c/span\u003e), out of 86 plant-derived anti-staphylococcal compounds, 46 were deemed suitable for docking analysis. Among which the acetylated abietane quinone derived from mint leaves exhibited a good binding affinity with a G score of -7.52 kcal/mol, while in our study, the top-performing compound indirubin showed a maximum binding energy of -8.82 kcal/mol. Traditionally, Indirubin, extracted from \u003cem\u003eW. tinctoria\u003c/em\u003e, were used for treating skin infections and has been documented for its potent antibacterial activity against both Gram-positive and Gram-negative bacteria. Reports also unfold their superior efficacy against \u003cem\u003eS. aureus\u003c/em\u003e comparatively with standard antibiotics like ciprofloxacin and streptomycin (Kale et al., \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Ponnusamy et al., \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2010\u003c/span\u003e). Additionally, Tanaka et al., (\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e2019\u003c/span\u003e) studied the wound-healing properties of indirubin and found that it accelerates the healing process. Indigo, the second-best docked compound, exhibits diverse biological properties including anti-pyretic and anti-inflammatory activities (Xu\u003cem\u003eet al.\u003c/em\u003e, 2021).Recently studies by yokote et al. (\u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e2023\u003c/span\u003e) suggests that compound indirubin and indigo are capable in treating ferroptosis, a cell death induced by oxidative stress, highlighting its role as a promising drug target for ulcerative colitis. The results of MD simulations showed that, indirubin-receptor complex was found to be stable over the duration of100 ns. The RMSD analysis of thereceptor-indirubin and the receptor -neomycin sulphate complexes shows that the two structures differed minimally in their conformations. The neomycin sulphate complex had a marginally higher RMSD value of 0.2 nm while the indirubin complex maintained a lower RMSD value of 0.18 nm meaning that it had better structural stability. Furthermore, there was minimal fluctuations of the amino acids residues within both the complexes as revealed by RMSF analysis. Hydrogen bonding interactions, which are one of the essential factors in assessing the stability of protein-ligand complexes (Joshi et al., \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2022\u003c/span\u003e) were investigated in detail. Both the docked complexes displayed several stable hydrogen bonds, as demonstrated in Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e7\u003c/span\u003e, indicating strong binding interactions. Table\u0026nbsp;5 consolidated free energy computations that induced the effects of polar solvation, electrostatics, Van der Waals forces, and binding energy. In comparison, Tiwari et al. (\u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e2022\u003c/span\u003e) Performed docking and dynamics simulations for compounds isolated from \u003cem\u003eSisymbriumirio\u003c/em\u003e, suggesting that a 70 ns simulation resulted in minimal structural deviation without loss of receptor-ligand complex stability. In line with this, our study based on these simulations demonstrates that indirubin does have the ability to stabilize the compactness and structural integrity of the receptor. By targeting ClfA, phytocompounds from the plant \u003cem\u003eW. tinctoria\u003c/em\u003e were shown to have improved staphylococcal activity, according to these in silico investigations. We performed an invitro antibacterial investigation of this plant and then GC-MS phytochemical profiling to confirm these findings.\u003c/p\u003e \u003cp\u003eAccording to preliminary antibacterial screening results, the ethyl acetate extract of dried \u003cem\u003eW. tinctoria\u003c/em\u003e leaves exhibited a more significant growth inhibition against \u003cem\u003eS. aureus\u003c/em\u003e than other extracts These results are in accordance with previous studies from Srivastava (\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e2014\u003c/span\u003e), who revealed that methanolic extracts of \u003cem\u003eW. tinctoria\u003c/em\u003e bark and leaves showed potent inhibition activity against \u003cem\u003eS. aureus\u003c/em\u003e. In fact, the leaf extracts exhibited more significant antimicrobial potential than bark and seed extracts. This would likely be due to differences in the phytochemical composition driven by geographical loci, environmental conditions, and extraction methods among others (Liuet al., \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). In addition to the antibacterial screening, antibiotic susceptibility testing was conducted on \u003cem\u003eS. aureus\u003c/em\u003e. The findings revealed that the bacterium exhibited resistance to 70% of the antibiotics tested. This aligns with a study by (Akanbiet al., \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2017\u003c/span\u003e), which reported that \u003cem\u003eS. aureus\u003c/em\u003e isolated from beef samples showed resistance to 83% of antibiotics tested, including amoxicillin, chloramphenicol, ciprofloxacin, ceftriaxone, gentamicin, and trimethoprim. Moreover, demonstrated intermediate resistance to tetracycline and azithromycin. Similarly, in our study, azithromycin and imipenem showed intermediate resistance, with zones of inhibition measuring 10 mm and 8 mm, respectively. The small zones of inhibition observed suggest a heightened risk of developing antibiotic resistance. Following GC-MS profiling, the ethyl acetate extract of the dried leaves of \u003cem\u003eW. tinctoria\u003c/em\u003e revealed a wide range of bioactive compounds, including ursolic acid, diethyl phthalate, lupeol, stigmasterol, lupeol, squalene, isatin, β-sitosterol, indirubin, and 41 others. Different phytochemical compositions of \u003cem\u003eW. tinctoria\u003c/em\u003e extracts have been reported in other similar research. Sheela et al. (\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e2021\u003c/span\u003e) reported 12 compounds from ethyl acetate extracts, whereas Khan et al. (\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2021\u003c/span\u003e) reported 28 compounds, including phytol, stigmast-5-en-3-ol, and β-caryophyllene. These variations rely on the plant's maturity, harvesting conditions, and extraction method (Liu et al., \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). Even while the antibacterial properties of different sections of \u003cem\u003eW. tinctoria\u003c/em\u003e have been studied in great detail, nothing is known about the precise mechanisms by which these effects are achieved. Using in silico methods could successfully solve this knowledge gap. Therefore, our study's findings indicate that \u003cem\u003eW. tinctoria\u003c/em\u003e that contains indirubin has a lot of potential as an anti-staphylococcal agent.\u003c/p\u003e"},{"header":"CONCLUSION","content":"\u003cp\u003eThe current studyunderline the therapeutic promise of \u003cem\u003eW. tinctoria\u003c/em\u003e and its bioactive constituents in combating bacterial infections, including those due to drug-resistant strains. Highlighting In silico approaches by bridging the gap and provide useful insight into the molecular interactions of its bioactive compounds. Our combined in silico and in vitro studies highlight that bioactive compounds specificcally \u003cem\u003eW. tinctoria\u003c/em\u003e extracts is capable of being a promising anti-staphylococcal agent with stability in binding to the receptors, and also it is highly active as an antibacterial. Further therapeutic studies have to be conducted on indirubin, as well as other bioactive molecules in \u003cem\u003eW. tinctoria\u003c/em\u003e, against the particular \u003cem\u003eStaphylococcus aureus\u003c/em\u003e and drug-resistant bacterial strains. Thus, the results from our studies strongly suggest that indirubin holds considerable promise as an anti-staphylococcal agent.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cp\u003eClfA- Clumping factor A, WHO- World Health Organization, ADME- Absorption, Distribution, Metabolism, and Excretion, PDB- Protein Data Bank, RMSD-Root Mean Square Deviation, RMSF- Root Mean Square Fluctuation, RG-Radius of Gyration, SASA- Solvent accessible surface area, H-Bond- Hydrogen Bond, MM-PBSA- Molecular Mechanics Poisson-Boltzmann surface area, LIG-Ligand.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eACKNOWLEDGEMENT\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors are grateful to the Kongunadu Arts and Science College administration for supplying all software required to complete this project.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFUNDING\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNo funding was received for conducting this study.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eETHICAL APPROVAL\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis article does not contain any studies involving animals and human participants by any of the authors.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eDISCLOSURE STATEMENT\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;The authors declared that they have no conflict of interest in the studies.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eDATA AVAILABILITY STATEMENT.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors confirm that the Data used and/or analyzed in the present study are available within the article.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAUTHOR CONTRIBUTION\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eVidya S. L: Carried out the studies, Data curation, and Drafted the manuscript and software. \u0026nbsp;Anantha Bhairavi V: Formal analysis and Validation. R. Sathishkumar: Designed the study and Supervision.\u003c/p\u003e\n\u003cp\u003eThe final manuscript was read and approved by all authors.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eAhmed, S. K., Hussein, S., Qurbani, K., Ibrahim, R. H., Fareeq, A., Mahmood, K. A., \u0026amp; Mohamed, M. G. (2024). Antimicrobial resistance: Impacts, challenges, and future prospects. Journal of Medicine, Surgery, and Public Health, 2, 100081. https://doi.org/10.1016/j.glmedi.2024.100081\u003c/li\u003e\n\u003cli\u003eAkanbi, O. E., Njom, H. A., Fri, J., Otigbu, A. C., \u0026amp; Clarke, A. M. (2017). Antimicrobial Susceptibility of Staphylococcus aureus Isolated from Recreational Waters and Beach Sand in Eastern Cape Province of South Africa. International Journal of Environmental Research and Public Health, 14(9), 1001. https://doi.org/10.3390/ijerph14091001\u003c/li\u003e\n\u003cli\u003eBabar, M. M., Zaidi, N.-S. S., Pothineni, V. R., Ali, Z., Faisal, S., Hakeem, K. R., \u0026amp; Gul, A. (2017). Application of Bioinformatics and System Biology in Medicinal Plant Studies. Plant Bioinformatics, 375\u0026ndash;393. https://doi.org/10.1007/978-3-319-67156-7_15\u003c/li\u003e\n\u003cli\u003eBauer, A. W., Kirby, W. M. M., Sherris, J. C., \u0026amp;Turck, M. (1966). Antibiotic Susceptibility Testing by a Standardized Single Disk Method. American Journal of Clinical Pathology, 45(4_ts), 493\u0026ndash;496. https://doi.org/10.1093/ajcp/45.4_ts.493\u003c/li\u003e\n\u003cli\u003eBhairavi, V. A., Vidya, S. L., \u0026amp; Sathishkumar, R. (2023). Identification of effective plant extracts against candidiasis: an in silico and in vitro approach. Future Journal of Pharmaceutical Sciences, 9(1). https://doi.org/10.1186/s43094-023-00489-x\u003c/li\u003e\n\u003cli\u003eBhatia, P., Sharma, A., George, A. J., Anvitha, D., Kumar, P., Dwivedi, V. P., \u0026amp; Chandra, N. S. (2021). Antibacterial activity of medicinal plants against ESKAPE: An update. Heliyon, 7(2), e06310. https://doi.org/10.1016/j.heliyon.2021.e06310\u003c/li\u003e\n\u003cli\u003eBhattacharya, M., \u0026amp;Horswill, A. R. (2024). The role of human extracellular matrix proteins in defining Staphylococcus aureus biofilm infections. FEMS Microbiology Reviews, 48(1). https://doi.org/10.1093/femsre/fuae002\u003c/li\u003e\n\u003cli\u003eCardoza, S., Singh, A., Sur, S., Singh, M., Dubey, K. D., Samanta, S. K., Mandal, A., \u0026amp; Tandon, V. (2024). Computational investigation of novel synthetic analogs of C-1\u0026prime;\u0026beta; substituted remdesivir against RNA-dependent RNA-polymerase of SARS-CoV-2. Heliyon, 10(17), e36786. https://doi.org/10.1016/j.heliyon.2024.e36786\u003c/li\u003e\n\u003cli\u003eEjaz, R., Ashfaq, U. A., \u0026amp; Idrees, S. (2014). Antimicrobial potential of Pakistani medicinal plants against multi-drug resistance Staphylococcus aureus. \u003cem\u003eJ Coast Life Med\u003c/em\u003e, \u003cem\u003e2\u003c/em\u003e(9), 714-20.https://doi.org/10.12980/jclm.2.201414b1\u003c/li\u003e\n\u003cli\u003eFilimonov, D. A., Lagunin, A. A., Gloriozova, T. A., Rudik, A. V., Druzhilovskii, D. S., Pogodin, P. V., \u0026amp;Poroikov, V. V. (2014). Prediction of the Biological Activity Spectra of Organic Compounds Using the Pass Online Web Resource. Chemistry of Heterocyclic Compounds, 50(3), 444\u0026ndash;457. https://doi.org/10.1007/s10593-014-1496-1\u003c/li\u003e\n\u003cli\u003eFoster, T. J. (2019). The MSCRAMM Family of Cell-Wall-Anchored Surface Proteins of Gram-Positive Cocci. Trends in Microbiology, 27(11), 927\u0026ndash;941. https://doi.org/10.1016/j.tim.2019.06.007\u003c/li\u003e\n\u003cli\u003eGangadharappa, B. S., Rajashekarappa, S., \u0026amp; Sathe, G. (2020). Proteomic profiling of Serratia marcescens by high-resolution mass spectrometry. BioImpacts, 10(2), 123\u0026ndash;135. https://doi.org/10.34172/bi.2020.15\u003c/li\u003e\n\u003cli\u003eHasan, R., Acharjee, M., \u0026amp; Noor, R. (2016). Prevalence of vancomycin resistant Staphylococcus aureus (VRSA) in methicillin resistant S. aureus (MRSA) strains isolated from burn wound infections. Tzu Chi Medical Journal, 28(2), 49\u0026ndash;53. https://doi.org/10.1016/j.tcmj.2016.03.002\u003c/li\u003e\n\u003cli\u003eHe, W., Liu, H., Wang, Z., Tay, F. R., \u0026amp; Shen, Y. (2024). The dynamics of bacterial proliferation, viability, and extracellular polymeric substances in oral biofilm development. Journal of Dentistry, 143, 104882. https://doi.org/10.1016/j.jdent.2024.104882\u003c/li\u003e\n\u003cli\u003eHerman-Bausier, P., Labate, C., Towell, A. M., Derclaye, S., Geoghegan, J. A., \u0026amp;Dufr\u0026ecirc;ne, Y. F. (2018). clumping factor A is a force-sensitive molecular switch that activates bacterial adhesion. Proceedings of the National Academy of Sciences of the United States of America, 115(21), 5564\u0026ndash;5569. https://doi.org/10.1073/pnas.1718104115\u003c/li\u003e\n\u003cli\u003eHiggins, J., Loughman, A., van Kessel, K. P. M., van Strijp, J. A. G., \u0026amp; Foster, T. J. (2006). Clumping factor A ofStaphylococcusaureusinhibits phagocytosis by human polymorphonuclear leucocytes. FEMS Microbiology Letters, 258(2), 290\u0026ndash;296. https://doi.org/10.1111/j.1574-6968.2006.00229.x\u003c/li\u003e\n\u003cli\u003eHuang, W., Wang, Y., Tian, W., Cui, X., Tu, P., Li, J., Shi, S., \u0026amp; Liu, X. (2022). Biosynthesis Investigations of Terpenoid, Alkaloid, and Flavonoid Antimicrobial Agents Derived from Medicinal Plants. Antibiotics, 11(10), 1380. https://doi.org/10.3390/antibiotics11101380\u003c/li\u003e\n\u003cli\u003eJegadeeshwari, A., Seelam, N. R., \u0026amp; Myneni, V. R. (2023). Evaluation of Antibacterial and Anticancer Characteristics of Silver Nanoparticles Synthesized from Plant Extracts of Wrightia tinctoria and Acacia chundra. International Journal of Analytical Chemistry, 2023, 1\u0026ndash;14. https://doi.org/10.1155/2023/6352503\u003c/li\u003e\n\u003cli\u003eJoshi, C., Chaudhari, A., Joshi, C., Joshi, M., \u0026amp;Bagatharia, S. (2022). Repurposing of the herbal formulations: molecular docking and molecular dynamics simulation studies to validate the efficacy of phytocompounds against SARS-CoV-2 proteins. \u003cem\u003eJournal of biomolecular structure \u0026amp; dynamics\u003c/em\u003e, \u003cem\u003e40\u003c/em\u003e(18), 8405\u0026ndash;8419. https://doi.org/10.1080/07391102.2021.1922095\u003c/li\u003e\n\u003cli\u003eK, R., Kakkassery, J. T., Raphael, V. P., Johnson, R., \u0026amp; K, V. T. (2021). In vitro antibacterial and in silico docking studies of two Schiff bases on Staphylococcus aureus and its target proteins. Future Journal of Pharmaceutical Sciences, 7(1). https://doi.org/10.1186/s43094-021-00225-3\u003c/li\u003e\n\u003cli\u003eKale, N., Rathod, S., More, S., \u0026amp; Shinde, N. (2021). Phyto-Pharmacological Profile of Wrightia tinctoria. Asian Journal of Research in Pharmaceutical Sciences, 301\u0026ndash;308. Internet Archive. https://doi.org/10.52711/2231-5659.2021.00047\u003c/li\u003e\n\u003cli\u003eKhan, N., Ali, A., Qadir, A., Ali, A., Warsi, M. H., Tahir, A., \u0026amp; Ali, A. (2021). GC-MS Analysis and Antioxidant Activity ofWrightia tinctoriaR.Br. Leaf Extract. Journal of AOAC INTERNATIONAL, 104(5), 1415\u0026ndash;1419. https://doi.org/10.1093/jaoacint/qsab054\u003c/li\u003e\n\u003cli\u003eLiu, Y., Chen, P., Zhou, M., Wang, T., Fang, S., Shang, X., \u0026amp; Fu, X. (2018). Geographic Variation in the Chemical Composition and Antioxidant Properties of Phenolic Compounds from Cyclocaryapaliurus (Batal) Iljinskaja Leaves. Molecules, 23(10), 2440. https://doi.org/10.3390/molecules23102440\u003c/li\u003e\n\u003cli\u003eMancuso, G., Midiri, A., Gerace, E., \u0026amp; Biondo, C. (2021). Bacterial Antibiotic Resistance: The Most Critical Pathogens. Pathogens, 10(10), 1310. https://doi.org/10.3390/pathogens10101310\u003c/li\u003e\n\u003cli\u003eMishra, G. P., Bhadane, R. N., Panigrahi, D., Amawi, H. A., Asbhy, C. R., \u0026amp; Tiwari, A. K. (2021). The interaction of the bioflavonoids with five SARS-CoV-2 proteins targets: An in silico study. Computers in Biology and Medicine, 134, 104464. https://doi.org/10.1016/j.compbiomed.2021.104464\u003c/li\u003e\n\u003cli\u003eMitra, D., Mitra, D., Sabri Bensaad, M., Sinha, S., Pant, K., Pant, M., Priyadarshini, A., Singh, P., Dassamiour, S., Hambaba, L., Panneerselvam, P., \u0026amp; Das Mohapatra, P. K. (2022). Evolution of bioinformatics and its impact on modern bio-science in the twenty-first century: Special attention to pharmacology, plant science and drug discovery. Computational Toxicology, 24, 100248. https://doi.org/10.1016/j.comtox.2022.100248\u003c/li\u003e\n\u003cli\u003eMlynarczyk-Bonikowska, B., Kowalewski, C., Krolak-Ulinska, A., \u0026amp;Marusza, W. (2022). Molecular Mechanisms of Drug Resistance in Staphylococcus aureus. International Journal of Molecular Sciences, 23(15), 8088. https://doi.org/10.3390/ijms23158088\u003c/li\u003e\n\u003cli\u003eMoulishankar, A., \u0026amp;Sundarrajan, T. (2024). Pharmacophore, QSAR, molecular docking, molecular dynamics and ADMET study of trisubstituted benzimidazole derivatives as potent anti-tubercular agents. Chemical Physics Impact, 8, 100512. https://doi.org/10.1016/j.chphi.2024.100512\u003c/li\u003e\n\u003cli\u003ePanda, S. K., Das, R., Lavigne, R., \u0026amp;Luyten, W. (2020). Indian medicinal plant extracts to control multidrug-resistant S. aureus, including in biofilms. South African Journal of Botany, 128, 283\u0026ndash;291. https://doi.org/10.1016/j.sajb.2019.11.019\u003c/li\u003e\n\u003cli\u003ePanigrahi, D., Mishra, A., \u0026amp;Sahu, S. K. (2020). Pharmacophore modelling, QSAR study, molecular docking and insilico ADME prediction of 1,2,3-triazole and pyrazolopyridones as DprE1 inhibitor antitubercular agents. SN Applied Sciences, 2(5). https://doi.org/10.1007/s42452-020-2638-y\u003c/li\u003e\n\u003cli\u003ePatel, D. K., Patel, K., Duraiswamy, B., \u0026amp;Dhanabal, S. P. (2012). Phytochemical analysis and standardization of Strychnosnux-vomica extract through HPTLC techniques. Asian Pacific Journal of Tropical Disease, 2, S56\u0026ndash;S60. https://doi.org/10.1016/s2222-1808(12)60124-8\u003c/li\u003e\n\u003cli\u003ePatidar, K., Panwar, U., Vuree, S., Sweta, J., Sandhu, M. K., Nayarisseri, A., \u0026amp; Singh, S. K. (2019). An In silico Approach to Identify High Affinity Small Molecule Targeting m-TOR Inhibitors for the Clinical Treatment of Breast Cancer. Asian Pacific Journal of Cancer Prevention, 20(4), 1229\u0026ndash;1241. https://doi.org/10.31557/apjcp.2019.20.4.1229\u003c/li\u003e\n\u003cli\u003ePonnusamy, K., Ramasamy, M., Savarimuthu, I., \u0026amp; Gabriel Paulraj, M. (2010). Indirubin potentiates ciprofloxacin activity in the NorA efflux pump of \u003cem\u003eStaphylococcus aureus\u003c/em\u003e. Scandinavian Journal of Infectious Diseases. https://doi.org/10.3109/00365541003713630\u003c/li\u003e\n\u003cli\u003ePrasanth, D. S. N. B. K., Murahari, M., Chandramohan, V., Panda, S. P., Atmakuri, L. R., \u0026amp;Guntupalli, C. (2021). identification of potential inhibitors from against main protease and spike glycoprotein of SARS CoV-2. Journal of Biomolecular Structure \u0026amp; Dynamics, 39(13), 4618\u0026ndash;4632.\u003c/li\u003e\n\u003cli\u003ePrlić, A., Kalro, T., Bhattacharya, R., Christie, C., Burley, S. K., \u0026amp; Rose, P. W. (2016). Integrating genomic information with protein sequence and 3D atomic level structure at the RCSB protein data bank. Bioinformatics, 32(24), 3833\u0026ndash;3835. https://doi.org/10.1093/bioinformatics/btw547\u003c/li\u003e\n\u003cli\u003eRoy, S., Santra, S., Das, A., Dixith, S., Sinha, M., Ghatak, S., Ghosh, N., Banerjee, P., Khanna, S., Mathew-Steiner, S., Ghatak, P. D., Blackstone, B. N., Powell, H. M., Bergdall, V. K., Wozniak, D. J., \u0026amp; Sen, C. K. (2019). Staphylococcus aureus Biofilm Infection Compromises Wound Healing by Causing Deficiencies in Granulation Tissue Collagen. Annals of Surgery, 271(6), 1174\u0026ndash;1185. https://doi.org/10.1097/sla.0000000000003053\u003c/li\u003e\n\u003cli\u003eSahayarayan, J. J., Rajan, K. S., Vidhyavathi, R., Nachiappan, M., Prabhu, D., Alfarraj, S., Arokiyaraj, S., \u0026amp; Daniel, A. N. (2021). In-silico protein-ligand docking studies against the estrogen protein of breast cancer using pharmacophore based virtual screening approaches. Saudi Journal of Biological Sciences, 28(1), 400\u0026ndash;407. https://doi.org/10.1016/j.sjbs.2020.10.023\u003c/li\u003e\n\u003cli\u003eSankar, V., \u0026amp; Maida Engels, S. E. (2018). Synthesis, biological evaluation, molecular docking and in silico ADME studies of phenacyl esters of N-Phthaloyl amino acids as pancreatic lipase inhibitors. Future Journal of Pharmaceutical Sciences, 4(2), 276\u0026ndash;283. https://doi.org/10.1016/j.fjps.2018.10.004\u003c/li\u003e\n\u003cli\u003eSasikumar, A. P., Ramaswamy, S., \u0026amp; Sudhir, S. (2021). A scientific pharmacognosy on Gaucher\u0026rsquo;s disease: an in silico analysis. Environmental Science and Pollution Research, 29(17), 25308\u0026ndash;25317. https://doi.org/10.1007/s11356-021-17534-y\u003c/li\u003e\n\u003cli\u003eSheela, D., Abdul Rahim, M., Arunagirinadhan, N., Ramya, B., \u0026amp; Indra, V. (2021). GC-MS profiling and antibacterial activity of Wrightia tinctoria and Lantana camara leaves extract. \u003cem\u003eIntern. J. Zool. Invest\u003c/em\u003e, \u003cem\u003e7\u003c/em\u003e(2), 920-938. https://doi.org/10.33745/ijzi.2021.v07i02.084\u003c/li\u003e\n\u003cli\u003eSpeziale, P., \u0026amp;Pietrocola, G. (2020). The Multivalent Role of Fibronectin-Binding Proteins A and B (FnBPA and FnBPB) of Staphylococcus aureus in Host Infections. Frontiers in Microbiology, 11. https://doi.org/10.3389/fmicb.2020.02054\u003c/li\u003e\n\u003cli\u003eSrivastava, R. (2014). A review on phytochemical, pharmacological, and pharmacognostical profile of Wrightia tinctoria: Adulterant of kurchi. Pharmacognosy Reviews, 8(15), 36. https://doi.org/10.4103/0973-7847.125528\u003c/li\u003e\n\u003cli\u003eSzymański, P., Markowicz, M., \u0026amp;Mikiciuk-Olasik, E. (2011). Adaptation of High-Throughput Screening in Drug Discovery\u0026mdash;Toxicological Screening Tests. International Journal of Molecular Sciences, 13(1), 427\u0026ndash;452. https://doi.org/10.3390/ijms13010427\u003c/li\u003e\n\u003cli\u003eTanaka, Y., Uchi, H., Ito, T., \u0026amp;Furue, M. (2019). Indirubin-pregnane X receptor-JNK axis accelerates skin wound healing. Scientific Reports, 9(1). https://doi.org/10.1038/s41598-019-54754-2\u003c/li\u003e\n\u003cli\u003eTerreni, M., Taccani, M., \u0026amp;Pregnolato, M. (2021). New Antibiotics for Multidrug-Resistant Bacterial Strains: Latest Research Developments and Future Perspectives. Molecules, 26(9), 2671. https://doi.org/10.3390/molecules26092671\u003c/li\u003e\n\u003cli\u003eThakur, M., Singh, K., \u0026amp;Khedkar, R. (2020). Phytochemicals. Functional and Preservative Properties of Phytochemicals, 341\u0026ndash;361. https://doi.org/10.1016/b978-0-12-818593-3.00011-7\u003c/li\u003e\n\u003cli\u003eTillotson, G. S., \u0026amp;Zinner, S. H. (2017). Burden of antimicrobial resistance in an era of decreasing susceptibility. Expert Review of Anti-Infective Therapy, 15(7), 663\u0026ndash;676. https://doi.org/10.1080/14787210.2017.1337508\u003c/li\u003e\n\u003cli\u003eTiwari, M., Gupta, S., \u0026amp; Bhargava, P. (2022). Virtual screening and molecular dynamics simulation studies to predict the binding of Sisymbriumirio L. derived phytochemicals against Staphylococcus aureus dihydrofolate reductase (DHFR). Journal of Applied and Natural Science, 14(4), 1297\u0026ndash;1307. https://doi.org/10.31018/jans.v14i4.3641.\u003c/li\u003e\n\u003cli\u003eViljoen, A., Viela, F., Matheli\u0026eacute;-Guinlet, M., Missiakas, D., Pietrocola, G., Speziale, P., \u0026amp;Dufr\u0026ecirc;ne, Y. F. (2021). Staphylococcus aureus vWF-binding protein triggers a strong interaction between clumping factor A and host vWF. Communications Biology, 4(1). https://doi.org/10.1038/s42003-021-01986-6\u003c/li\u003e\n\u003cli\u003eWadapurkar, R. M., Shilpa, M. D., Katti, A. K. S., \u0026amp;Sulochana, M. B. (2018). In silico drug design for Staphylococcus aureus and development of host-pathogen interaction network. Informatics in Medicine Unlocked, 10, 58\u0026ndash;70. https://doi.org/10.1016/j.imu.2017.11.002\u003c/li\u003e\n\u003cli\u003eWang, E., Sun, H., Wang, J., Wang, Z., Liu, H., Zhang, J. Z. H., \u0026amp; Hou, T. (2019). End-Point Binding Free Energy Calculation with MM/PBSA and MM/GBSA: Strategies and Applications in Drug Design. Chemical Reviews, 119(16), 9478\u0026ndash;9508. https://doi.org/10.1021/acs.chemrev.9b00055\u003c/li\u003e\n\u003cli\u003eXu, xiaorong, Huang, S., Luo, C., Xu, R., Wang, F., He, Y., Yang, M., Lin, J., Han, L., \u0026amp; Zhang, D. (2023). Study on the antipyretic activity and potential mechanism of Indigo Naturalis on lipopolysaccharide-induced fever rat model. https://doi.org/10.21203/rs.3.rs-3174972/v1\u003c/li\u003e\n\u003cli\u003eYokote, A., Imazu, N., Umeno, J., Kawasaki, K., Fujioka, S., Fuyuno, Y., Matsuno, Y., Moriyama, T., Miyawaki, K., Akashi, K., Kitazono, T., \u0026amp;Torisu, T. (2023). Ferroptosis in the colon epithelial cells as a therapeutic target for ulcerative colitis. Journal of Gastroenterology, 58(9), 868\u0026ndash;882. https://doi.org/10.1007/s00535-023-02016-4\u003c/li\u003e\n\u003cli\u003eYuan, H., Ma, Q., Ye, L., \u0026amp; Piao, G. (2016). The Traditional Medicine and Modern Medicine from Natural Products. Molecules, 21(5), 559. https://doi.org/10.3390/molecules21050559\u003c/li\u003e\n\u003cli\u003eYusof, I., \u0026amp;Segall, M. D. (2013). Considering the impact drug-like properties have on the chance of success. Drug Discovery Today, 18(13\u0026ndash;14), 659\u0026ndash;666. https://doi.org/10.1016/j.drudis.2013.02.008 \u003c/li\u003e\n\u003c/ol\u003e\n\u003cp\u003eWebsite Links\u003c/p\u003e\n\u003cp\u003ehttps://www.who.int/publications/i/item/9789240093461\u003c/p\u003e\n\u003cp\u003ehttps://www.who.int/publications/i/item/9789240093461\u003c/p\u003e\n\u003cp\u003ehttps://www.rcsb.org/\u003c/p\u003e\n\u003cp\u003ehttps://pubchem.ncbi.nlm.nih.gov/\u003c/p\u003e\n\u003cp\u003ehttp://www.way2drug.com/passonline/predict.php \u003c/p\u003e"},{"header":"Table 1","content":"\u003cp\u003eTables 1 is available in the Supplementary Files section.\u003c/p\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"in-silico-pharmacology","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"insp","sideBox":"Learn more about [In Silico Pharmacology](https://link.springer.com/journal/40203)","snPcode":"40203","submissionUrl":"https://submission.nature.com/new-submission/40203/3","title":"In Silico Pharmacology","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"Staphylococcus aureus, Drug resistantance, Phytocompounds, Wrightia tictoria, Molecular Docking, Dynamic simulations, Antibaterial analysis","lastPublishedDoi":"10.21203/rs.3.rs-6232250/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6232250/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e \u003cem\u003eStaphylococcus aureus\u003c/em\u003e,a prominent pathogen demonstrate robust survival capabilities both within and outside host cells.The alarming rise of antibiotic resistance strains poses substantial challenge in modern medicine.Bioactive compounds from medicinal plants could be an effective alternative due to their presence of diverse secondary metabolites. The present study aim to conduct insilico docking and dynamic simulations to identifypromising bioactive compound from medicinal plants against virulence protein Clumping factor A of \u003cem\u003eStaphylococcus aureus\u003c/em\u003e through bioinformatic approach. Initial ADME screening of phytocompounds from the plants \u003cem\u003eBreynia retusa\u003c/em\u003e, \u003cem\u003eHemigraphis alternata\u003c/em\u003e, \u003cem\u003eImperata cylindrica\u003c/em\u003e, \u003cem\u003eOldenlandia corymbosa\u003c/em\u003e, \u003cem\u003eSida rhombifolia\u003c/em\u003e, \u003cem\u003eScoparia dulcis\u003c/em\u003e, \u003cem\u003eTephrosia purpurea\u003c/em\u003e and \u003cem\u003eWrightia tinctoria\u003c/em\u003e R were conducted to comprehend their pharmacokinetic profile, followed by docking and dynamic simulations.As a result, indirubin showed effecient binding interaction with target protein, offering remarkable G score value of -8.82 Kcal/Mol. In addition, dynamic stimulations validated the top docked complex with significant RMSD and RG stability besides desirable binding free energy in contrast to the standard drug neomycin sulphate. To validate these results, the antibacterial potential of the fresh and dry leaf extract of \u003cem\u003eWrightia tinctoria\u003c/em\u003e was tested, showing strong inhibitory effects against \u003cem\u003eStaphylococcus aureus\u003c/em\u003e with a maximum zone of inhibition of 26.33\u0026thinsp;\u0026plusmn;\u0026thinsp;0.33 mm. A detailed analysis of the ethyl acetate extract using GC-MS revealed the presence of 50 bioactive compounds, underscoring the plant's potential as a natural antimicrobial source. These outcomes indicate that \u003cem\u003eW. tinctoria\u003c/em\u003e holds promise as a therapeutic option for managing staphylococcal infections and highlights the need for further research to explore its clinical applications.\u003c/p\u003e","manuscriptTitle":"Discovery of Bioactive Compounds From Medicinal Plants: Insights Into Wrightia Tinctoria as a Potential Antistaphylococcal Agent Targeting","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-05-07 11:04:20","doi":"10.21203/rs.3.rs-6232250/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2025-09-25T19:02:21+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-09-21T10:13:46+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-09-20T09:05:22+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"103156558486086510223033595740601198419","date":"2025-09-17T06:08:15+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"123650039164046523598091237069364513913","date":"2025-09-16T22:40:14+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"210284237240852590900263582057870811047","date":"2025-09-15T02:14:14+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"333413022995603721959179515916122962219","date":"2025-09-15T01:29:52+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-05-06T04:17:27+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"214853002970325732887889434839971569648","date":"2025-05-04T15:18:53+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-05-02T15:13:42+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-03-17T11:21:55+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-03-17T11:20:44+00:00","index":"","fulltext":""},{"type":"submitted","content":"In Silico Pharmacology","date":"2025-03-15T10:28:16+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"in-silico-pharmacology","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"insp","sideBox":"Learn more about [In Silico Pharmacology](https://link.springer.com/journal/40203)","snPcode":"40203","submissionUrl":"https://submission.nature.com/new-submission/40203/3","title":"In Silico Pharmacology","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"b29b0172-6c1a-4d21-94d2-29106a3c1179","owner":[],"postedDate":"May 7th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2026-02-09T16:06:04+00:00","versionOfRecord":{"articleIdentity":"rs-6232250","link":"https://doi.org/10.1007/s40203-025-00545-9","journal":{"identity":"in-silico-pharmacology","isVorOnly":false,"title":"In Silico Pharmacology"},"publishedOn":"2026-02-04 15:59:31","publishedOnDateReadable":"February 4th, 2026"},"versionCreatedAt":"2025-05-07 11:04:20","video":"","vorDoi":"10.1007/s40203-025-00545-9","vorDoiUrl":"https://doi.org/10.1007/s40203-025-00545-9","workflowStages":[]},"version":"v1","identity":"rs-6232250","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6232250","identity":"rs-6232250","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}