Pseudomonas aeruginosa virulence reduction through phytochemical inhibition of Quorum Sensing activity: A Molecular Docking, Molecular Dynamics Simulation study

Pseudomonas aeruginosa virulence reduction through phytochemical inhibition of Quorum Sensing activity: A Molecular Docking, Molecular Dynamics Simulation study | 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 Article Pseudomonas aeruginosa virulence reduction through phytochemical inhibition of Quorum Sensing activity: A Molecular Docking, Molecular Dynamics Simulation study Arnav Padhi, Pabitra Mohan Behera, Soumyadip Ghosh, Sudha Ramaiah, and 2 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7664093/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Pseudomonas aeruginosa is an opportunistic pathogen which employs quorum sensing (QS) to regulate virulence and biofilm formation, leading to the emergence of multidrug resistance (MDR) necessitating novel therapeutic strategies. This study aimed to identify phytocompounds from Cistus munbyi essential oil as potential inhibitors of the LasR QS receptor in P. aeruginosa . A library of 44 phytocompounds was screened through molecular docking studies targeting LasR and its natural variants (LasR-Var1: R144I, LasR-Var2: R180W). Cuminaldehyde and Sabinyl acetate emerged as top candidates, exhibiting strong binding affinities comparable to the native ligand, N-3-Oxo-Dodecanoyl-L-Homoserine. Molecular dynamics (MD) simulations over 100 ns confirmed stable interactions with key conserved residues, with Cuminaldehyde demonstrating superior stability in LasR-Var2 (RMSD: ~0.6-0.8 nm). Density Functional Theory (DFT) analysis revealed favourable chemical reactivity for Cuminaldehyde (energy gap: 5.071 eV) and Sabinyl acetate (energy gap: 6.162 eV), supporting their potential as QS inhibitors. Parameters like RMSD, RMSF, radius of gyration, and solvent accessible surface area validated the structural stability of these complexes, while principal component analysis highlighted distinct conformational dynamics. These findings underscore the potential of Cuminaldehyde and Sabinyl acetate as anti-QS agents to mitigate P. aeruginosa virulence and combat MDR. The study advocates for further in vitro validation to translate these in silico findings into novel phytochemical-based therapeutics, offering promising prospects for addressing antimicrobial resistance Biological sciences/Biochemistry Physical sciences/Chemistry Biological sciences/Computational biology and bioinformatics Biological sciences/Drug discovery Biological sciences/Microbiology Pseudomonas aeruginosa Quorum sensing LasR Multidrug Resistance Cistus munbyi Molecular docking Molecular dynamic Simulations and Density Functional Theory 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 Figure 13 Introduction Pseudomonas aeruginosa is a Gram negative opportunistic pathogen responsible for severe hospital-acquired infections 1 , 2 . It can survive in extreme environmental conditions and causes pathogenicity in different hosts, including humans, animals, and plants 2 . Over the years, bacteria have developed resistance to different antibiotics, leading to the emergence of multidrug-resistant (MDR) strains that contribute to elevated morbidity and mortality rates 2 . Owing to its growing resistance to antibiotics and higher dissemination rates, P. aeruginosa has been classified as a critical priority pathogen by the World Health Organisation (WHO), underscoring the urgent need for alternative therapeutic strategies 3 . Moreover, P. aeruginosa forms biofilms on medical devices such as implants and catheters by forming microbial aggregates through quorum sensing. These biofilm forming strains are responsible for causing ventilator associated pneumonia, catheter associated Urinary Tract Infections (UTI) and other nosocomial infections 4 . Quorum sensing (QS) is a mechanism by which P. aeruginosa manifests virulence through cell to cell communication, facilitated by the LasR, LasI, RhlR, RhlI, and Pseudomonas Quinolone Signal (PQS) genes 5 , 6 . The LasI gene synthesises the signalling molecule N-(3-oxododecanoyl)-L-homoserine lactones (3-oxo-C12-HSL) and RhlI synthesises N-butanoyl-L-homoserine lactone (C4-HSL), which is recognised by RhIR. Additionally,PQS regulates the release of extracellular DNA (eDNA) during biofilm formation 6 , 7 . eDNA helps P. aeruginosa in adhering to cell surfaces and serves as a nutrition source during the early stages of biofilm development 7 . LasR acts as the master regulator of quorum sensing, catalysing the production of virulence factors upon binding to its autoinducer, 3-oxo-C12-HSL 8 . The lasR gene plays a critical role in the regulation of quorum sensing, virulence and pathogenesis in P. aeruginosa , contributes to respiratory tract infections, pneumonia, and bacteremia 7 , 9 , 10 . Targeting the lasR gene and inhibiting its function can effectively prevent the expression of virulence factors in P. aeruginosa and reduce its pathogenicity. The activation of the QS signalling molecules in P. aeruginosa triggers biofilm formation, rendering antibiotics ineffective 11 . Disrupting these QS mechanisms is a highly recommended strategy for treating infections caused by P. aeruginosa and combating antimicrobial resistance(AMR) 12 . Previous reports have shown that phytoextracts from plants such as Piper bogotense, Syzygium jambos, Dioonspinulosum , and Psidium guajava exhibit excellent anti-quorum sensing and antibiofilm activities against P. aeruginosa 13 – 16 . In recent years, phytomolecules derived from essential oils have increasingly gained attention as antiquorum sensing and antibacterial agents due to their lower toxicity and high medicinal properties 17 , 18 . Essential oils (EO) are volatile phytochemicals which are extracted from the roots, stems, flowers and leaves of the plants, showing a wide range of antioxidant, antibacterial, and antifungal properties 19 , 20 . Several essential oils have been shown to have good antibacterial activity against nosocomial pathogens such as E. coli , S. aureus , P. aeruginosa as well as different food borne pathogens such as Salmonella typhi and Bacillus cereus 21 – 24 . Cistus munbyi is a medicinal shrub belonging to the Cistaceae family, commonly found in the Mediterranean region and in alkaline soils of Algeria 25 – 27 . This plant has been traditionally used to treat pulmonary infections 27 . In addition to its antimicrobial properties against both gram positive and gram negative bacteria, extracts from C. munbyi contain polyphenols, which contribute to its strong antioxidant properties and enhance its therapeutic value 28 . In this work, we compiled a dataset of phytocompounds derived from C. munbyi essential oil and analysed their anti-quorum sensing activity against the LasR proteins of P. aeruginosa using molecular docking techniques. The most promising compounds were evaluated for their drug likeness, pharmacokinetic properties, toxicity profiles and were further subjected to molecular dynamic (MD) simulations and density functional theory (DFT) analysis to identify promising inhibitors of quorum sensing protein LasR, aimed at combating infections caused by P. aeruginosa. Materials and Methods Selection of LasR canonical and natural variants of P. aeruginosa The UniProtKB database was searched with the keyword “LasR” and the results were customized to review sequences only 29 . The result was further customized with the selection of species-specific sequences from which the entry with UniProtKB accession number P25084 was selected representing the LasR sequence and variants of P. aeruginosa. The canonical and two natural variant sequences were downloaded in fasta format for further analysis. Selection of potential phytochemicals from Selection of potential phytochemicals from Cistus munbyi The essential oil of C. munbyi was selected from literature as it is reported to possess antibacterial activities against P. aeruginosa which contains 44 phytochemicals in it 27 . All the phytochemical structures were downloaded from the PubChem database ( https://pubchem.ncbi.nlm.nih.gov/ ) in 3D SDF format. The compound having only a 2D structure was converted to 3D using Avogadro v1.2.0 30 and saved as a MOL2 file. The physicochemical properties of these 44 phytochemicals are summarised in (Supplementary Table S1 ). Molecular modelling of LasR canonical and natural variant proteins of Molecular modelling of LasR canonical and natural variant proteins of P. aeruginosa Although there were many crystal structures of LasR reported in the PDB database, we performed the molecular modelling of all three (One canonical and two natural variants) protein sequences by querying them against the PDB database. The BlastP program used for the cause was customized with the parameters reading as maximum target sequences as 10, expected threshold of 0.05, word size of 5, scoring matrix as BLOSUM62 and gap costs of existence of 10 and extension as 1 31 . The suitable template (PDB ID: 6MWZ) selected after the similarity searching was used for alignment with three LasR protein sequences and then predicting their models with the use of Modeller v10.6 program 32 . About ten models were predicted for each protein sequence from which the best models were selected with the lowest readings of DOPE scores and evaluated with prediction of Ramachandran plots with the PROCHECK program of SAVES V6.1 server ( https://saves.mbi.ucla.edu/ ) 33 . In order to remove some redundancy in the alignment of three models with the template, the AlphaFold generated model was used as template for predicting the models once again with Modeller v10.6. The best models were selected with suitable DOPE scores, evaluated by predicting their Ramachandran plots and then aligned with the AlphaFold model, predicting suitable values of RMSD calculations. The model quality of the protein was predicted through ProSA-web and ProQ web server 34 , 35 . Molecular docking of selected phytochemicals against LasR All three LasR receptor models were first prepared by importing them one by one in AutoDockTools v1.5.7. They were added with polar hydrogen, assigned Kollmann charges and finally exported in PDBQT format. Once the receptor models are prepared, the gridboxes for each model were generated by selecting the residues lining the ligand binding site. The ligand binding sites for each receptor model were fetched by the alignment of the receptor models with their template and selection of residues lying within 5Å area of the co-crystal or Native ligand (3-oxo-C12-HSL) which is used as the reference compound. All three grid boxes generated for three receptor models were characterized with grid center parameters as x = 4.65, y = -3.74, z = -7.21 and grid volume dimensions as 25(x) × 25(y) × 25(z). At last, all forty-four selected phytochemicals along with the Native ligand were prepared by importing them individually in AutoDockTools v1.5.7. They were added with polar hydrogens with calculations of Gasteiger charges, assignment of suitable values of TORSDOF predicting their flexibility and exported in PDBQT format. The molecular docking studies of selected phytochemicals along with the Native ligand on three LasR receptor models was done with Auto Dock Vina v1.2.3 36 . About 135 such docking studies were done by generating individual configuration files. The receptor models were kept static and the phytochemicals were kept flexible to provide at least nine docking conformation of each phytochemical. The docking results were saved for further analysis. Drug likeness and Pharmacokinetic analysis of the lead compounds Drug likeness and Pharmacokinetic properties were determined through the Pubchem database and SwissADME tool ( http://www.swissadme.ch/ ) by generating the BOILED-Egg model for the lead compounds by feeding the SMILES(simplified molecular input line entry system) notations of the compounds retrieved from pubchem database. Toxicity profiling The toxicity profiles of the top compounds were computed through the ProTox-3 web server ( https://tox.charite.de/protox3/ ) 37 . Different parameters were evaluated such as, Respiratory toxicity, Cytotoxicity, Carcinogenicity, Immunotoxicity and Mutagenicity. Additionally, STopTox (Systemic and Topical chemical Toxicity webserver ( https://stoptox.mml.unc.edu/ ) 38 was used to predict the acute toxicity on the basis of 6 pack assays which includes three systematic (acute inhalation toxicity, acute oral toxicity, acute dermal toxicity and three topical (eye irritation and corrosion, skin sensitization and skin irritation and corrosion) toxicity end points. The SMILES structural notions of cuminaldehyde and sabinyl acetate was used as inputs to operate the system. Molecular Dynamic (MD) Simulations MD analysis was conducted on the selected compounds. Here, MD simulations were run for 100 ns using GROMACS 2022 39–42 . The protein was minimized using AMBERff99SB force field. The protein was prepared by solvation and charges neutralized with appropriate amounts of Na + and Cl-. The entire system was energy minimized using the steepest descent method for 50000 steps. All the systems were prepared to the canonical ensemble (NVT) for 1 ns and then switched to isothermal-isobaric ensemble (NPT) for 1 ns. Both systems were finally simulated for 100 ns. After the simulations were executed, trajectories were subjected to extraction, and all analyses were checked pertaining to the MD’s post analyses, which involved looking into the stability and conformational shifts of the protein systems. Different parameters such as root-mean-square-deviation (RMSD), root-mean-square-fluctuation (RMSF), radius of gyration (Rg), solvent accessible surface area (SASA), and principal component analyses (PCA) were calculated. Density Functional Theory (DFT) calculations In this study, DFT was utilized to determine the electronic properties and reactive states of selected phytocompounds, along with their controls, using the Gaussian 16 software 43 . The geometry optimization of the compounds was conducted utilizing the DFT at the B3LYP/6–311 G(d, p) basis level set in the gas phase. The B3LYP functional is a valuable approach for analyzing the vibrational frequencies of small-to medium-sized molecules 44 . Use of B3LYP method along with the 6-311G (d, p) basis provides more robust understanding of the phytochemicals in the context of pharmacodynamics and ensures high precision and accuracy 45 . Highest occupied molecular orbital (HOMO) is the ability of a compound to donate electrons and lowest unoccupied molecular orbital (LUMO) is the ability of a compound to receive electrons. Understanding the value of these orbitals are crucial for understanding their structural properties and their significance in biological systems 46 . Additionally, based on the energies of the HOMO and LUMO, other reactivity parameters, such as the energy gap, dipole moment, ionizing potential (IP), electron affinity (EA), electronegativity (χ), electrochemical potential (µ), hardness (η), softness (S), and electrophilicity index (ψ) were computed 47 , 48 Gauss View v6.1.1 was employed to visualize the charge distribution within the selected compounds through Molecular Electrostatic Potential (MEP) analysis 49 . Results and discussion Properties of P. aeruginosa LasR protein and its variants The LasR protein, identified by the UniProt accession number P25084, consists of 239 amino acids and has a molecular mass of 26,619 Da. The two reported natural variants with point mutation have been identified:LasR-Var1 (R144I) and LasR-Var2 (R180W). The lasR-Var1 has been reported in strain IFO 3455 and PA103. Strain possessing the mutation M144I in IFO 3455 is responsible for elastase production in P. aeruginosa 50 which is a key virulence factor responsible for quorum sensing. LasR-Var2 has been exclusively found in PA103 which is a non-elastase producing strain 51 . Bacterial isolates showing ventilator associated pneumonia are reported to have shown elastase activity. Nearly one third of them showed reduced elastase activity than PA103 52 . Since elastase is positively regulated by the LasR quorum sensing system, such findings highlight the clinical relevance of LasR mutants. Homology modelling For the homology modelling, the best structural models for the canonical LasR and its two variants were selected based on the lowest DOPE scores, which indicate structural quality. Lower DOPE generally implies more reliable and energetically favourable models. The model having the lowest DOPE score was selected for further analysis, including ABL% from Ramachandran plots evaluations and root-mean-square deviation(RMSD) alignment scores compared to their template models. On using the PDB ID-6MWZ as template, the mutation sites of LasR-Var2(R180W) was observed in the loop region as shown in (Supplementary Fig. 1). Loop regions exhibit limited noncovalent interactions and hence they are prone to unfolding. This can affect protein stability and structural integrity and therefore needs refinement. The AlphaFold model was used to refine the three LasR models, and their corresponding ABL% and alignment scores are summarised in (Table 1 ). Improvement was observed in ABL% scores, rising from ~ 90.0% for template based models to ~ 95.0% for AlphaFold based model. Hence, AlphaFold generated models were selected for further analysis. The 3D view of the aligned template; PDB ID-6MWZ and the AlphaFold generated models of LasR-Can, LasR-Var1, LasR-Var2 are shown in (Fig. 1 ). Table 1 Comparative analysis of ABL% and alignment scores for template based and AlphaFold based models. Sl. No Protein model ABL % with 6MWZ as template ABL % with alphafold as template Alignment RMSD with 6MWZ as template Alignment RMSD with AlphaFold as Template 1 LasR-Can 89.6 96.7 0.260 Å 0.673 Å 2 LasR-Var1 90.0 95.3 0.223 Å 1.607 Å 3 LasR-Var2 90.0 94.8 0.293 Å 0.741 Å The modelled 3D structures, Ramachandran plots and ProSA-web server evaluations of the best LasR models derived from the alpha fold template are depicted in (Supplementary Fig. 2). The Ramachandran plot shows that all the three proteins had more than 94% of the residues in the favourable regions, which align with the previous studies that classify good quality model with ABL % above 90 12 . Notably, no residues occupied in the disallowed regions in LasR-Can and LasR-Var2 suggesting that the models have good stereo chemical properties and are reliable. Modelled proteins having no residues in the disallowed regions have previously been reported 12 , 53 . The Z scores for LasR-Can, LasR-Var1 and LasR-Var2 were − 7.18, -7.16, -and 7.24 respectively and the corresponding LG scores were 11.197, 11.022, and 11.134 respectively. The Z scores indicate that the modelled proteins fall within the range of NMR solved protein structures, confirming their high quality, as proteins tend to show better quality with more negative Z scores 54 . The LG scores, essential in determining the model quality of the protein and measure the structural accuracy of the predicted proteins were assessed using the ProQ web server. As seen from our study, the LG score of all the three protein models are very high (> 11) suggesting that the modelled proteins closely aligns with the native structure and will provide stronger insights into drug target interactions 35 . Furthermore, multiple sequence alignment of the three proteins showed highly conserved amino acid residues. The mutation sites for LasR-Var1 and LasR-Var2 are located at M144I and R180W sites, respectively (Supplementary Fig. 3). Post docking analysis of phytochemicals on three LasR receptor models The docking studies involving 44 selected phytochemicals from C. munbyi along with the Native ligand were analysed using PyMOL visualization software. A total of 135 docking studies, yielding different docking poses, were analysed, showing docking scores between − 5 to -8 kcal/mol (Supplementary Table S5 ). For each docking study the corresponding receptor file was imported first, followed by sequential import of all phytochemical docking poses. Amino acid residues within 5Å area of each docked poses were selected to predict the number of H-bond interactions with the receptor residues. Phytochemicals lacking H-bond interactions with the amino acid residues from the three receptors were excluded from the analysis, resulting in 28, 26 and 27 phytochemicals remaining for LasR canonical, variant 1 and variant 2, respectively. Compounds forming H bonds with proteins are strongly anchored and oriented across the binding pocket regions thus indicate precise binding and can accurately interact with the proteins active site due to its stability. Compounds which do not form H bonds more likely bind loosely to the active site residues with less specificity henceforth affecting their biological significance. The detailed information of the number of docked conformations for these phytochemicals is provided in (Supplementary Tables S2, S3 and S4). While suitable docking scores highlight the potential binding, they do not fully explain binding efficacy. Therefore, the post-docking analysis was refined by including the number of docked conformations and number of H-bond interactions with the conserved residues predicted from the multiple sequence alignment of LasR sequences (Supplementary Fig. 3).Then the phytochemicals were ranked with the descending order of the number of docked conformation (Supplementary Table S5 ) and top three compounds were selected for docking analysis on three receptor models as well as further simulation studies. The leading compounds identified from this study includes the Native ligand; N-3-Oxo-Dodecanoyl-L-Homoserine, Cuminaldehyde and Sabinyl acetate. Cuminaldehyde and Sabinyl acetate account for 0.29% and 0.31% of the total proportion of essential oils of C. munbyi respectively 27 . Though other phytocompounds account for higher proportions of the total oil volume, these two compounds identified as hit molecules in our study show significant good docking scores and favourable binding interactions with the QS receptor LasR and its two variants. Although present in trace amounts, these two molecules precisely accommodate in the proteins active site owning to the presence of specific functional groups which can have stronger affinity to interact with the protein molecules. Prioritizing these compounds as potential lead molecules highlights the significance of low proportion phytocompounds as lead drug candidates. It is observed that prior to the Native ligand, Cuminaldehyde showed the highest number of docked confirmations (20) showing H bond interactions across LasR-Can, LasR-Var1 and LasR-Var2 followed by Sabinyl acetate with 19 docked confirmations (Supplementary Table S5 ). The two compounds showed significant H bond interactions with conserved amino acids across different docked confirmations of the three LasR proteins suggesting that they have more stable and robust binding patterns beyond just the docking scores. The docking confirmation having the best docked scores of Cuminaldehyde, Sabinyl acetate and the Native ligand with LasR and its two variants were tabulated as shown in (Table 2 ). It was observed from the table that the docking scores of Cuminaldehyde and Sabinyl acetate were reasonable (-6.98 to -7.35 kcal/mol) and they were proceeded for further analysis. Table 2 Comparative best docking scores of the lead compounds Cuminaldehyde, Sabinyl acetate and the Native ligand with the proteins: LasR-Can, LasR-Var1, LasR-Var2. Compound name LasR-Can LasR-Var1 LasR-Var2 Cuminaldehyde -6.98 -7.25 -7.35 Sabinyl acetate -7.09 -7.22 -7.21 N-3-Oxo-Dodecanoyl-L-Homoserine(Native Ligand) -8.25 -8.3 -8.02 C. munbyi phytochemicals suitably accommodate in the LasR receptor models The molecular docking results for selected phytochemicals of C. munbyi indicate potential docking scores (Supplementary Table S2 , S3, S4). The top three entities, like N-3-Oxo-Dodecanoyl-L-Homoserine, Cuminaldehyde and Sabinyl acetate, were selected to describe their suitable accommodation in the ligand binding sites of three LasR receptor models. In the LasR canonical model, all three selected entities were docked within the ligand-binding domain (Fig. 2 A). The Native ligand formed H-bond interactions with the residues W60, D73, T75, W88, Y93 and S129, where W60, T75, W88, Y93 and S129 were highly conserved. The best docked pose of the Native ligand has interactions with Y93, W60 and S129 (Fig. 2 B). Cuminaldehyde forms an H-bond interaction with all highly conserved residues R61, Y64, T75, Y93 and S129 and its best docked pose has interactions with Y93 (Fig. 2 C). Cuminaldehyde has been reported to show antibacterial and antibiofilm activity against E. coli and S. aureus 55 . The second potential phytochemical, Sabinyl acetate, formed H-bond interaction with the conserved residues R61, T75 and S129, with the best docked pose exhibiting interactions with S129 (Fig. 2 D). High concentration of Sabinyl acetate in essential oil have been reported to exhibit antibacterial activity against E. coli , S. aureus and P. aeruginosa 56 . A similar study showed that natural compounds produced by fungi growing in termite habitats exhibit good activity against the quorum-sensing protein LasR 57 . Their study highlighted that the compound FridamycinA formed H bonds with residues W54, R55, Y87, L104, L119, and S123 in the LasR protein active site and showed a gold score of 75.46. Another study revealed that the compounds 6-Gingerol and Curcumin act as a LasR inhibitors in P. aeruginosa 58 . Our lead compounds Cuminaldehyde (-6.98 kcal/mol) and Sabinyl acetate (-7.09 kcal/mol) exhibited stronger binding affinities than previously reported LasR inhibitors including Methyl dihydrojasmonate(-5.92 kcal/mol), Methyl benzoate(-5.81 kcal/mol) and 4a-Methyl-4,4a,5,6,7,8-hexahydro-2(3H)- (-5.47 kcal/mol) derived from extracts of Cassia occidentalis L 6 and PBA 27 (-5.10 kcal/mol) from marine derived metabolites 59 . The LasR variant 1, characterized by a point mutation at M144I, was modelled with the same template that applied to the LasR canonical model and further refined using the AlphaFold-generated model. It was observed that most of the docked conformation of all three selected entities was within the ligand binding site (Fig. 3 A). Native ligand formed H-bond interaction with all highly conserved residues, including E48, W60, R61, A70, T75, W88, Y93, T115, A127 and S129. The optimal docked pose for the Native ligand exhibited H-bond interactions with S129, Y93 and W60 (Fig. 3 B). Cuminaldehyde formed H-bond interactions with all highly conserved residues, such as W60, R61, D73, T75, Y93, L110, A127 and S129, with its best docked pose demonstrating interactions with R61 and W60 (Fig. 3 C). Sabinyl acetate similarly formed H-bond interactions with residues W60, R61, Y93, A127 and S129, with its optimal pose showing interactions with S129 (Fig. 3 D). The LasR variant 2, characterized by a point mutation at R180W, was modelled with the same template as the LasR canonical model and refined by the AlphaFold model. It was observed that most of the docked conformation of all three selected entities was within the ligand binding site (Fig. 4 A). The Native ligand formed H-bond interactions with all highly conserved residues like T75, W60, S129, R61, A127, Y64, and Y56. The best docked pose of the Native ligand showed interactions with T75 (Fig. 4 B). Cuminaldehyde formed H-bond interaction with key residues Y93, R61, S129, T75, W60, D65, W88, T115 and presented its best docked pose having interaction with Y93 (Fig. 4 C). The second potential phytochemical Sabinyl acetate formed H-bond interaction with conserved residues S129, R61, T75, W60, A127, with the best docked pose exhibiting interactions with T75 (Fig. 4 D). Overall, our docking studies confirm that Cuminaldehyde and Sabinyl acetate effectively bind to both LasR variants and are harmonical with its docking scores with LasR. This suggests that the two compounds can be effectively used to inhibit LasR mutant proteins. Drug likeness and pharmacokinetic properties of lead compounds The two lead compounds, Cuminaldehyde, and Sabinyl acetate, were evaluated to analyse their drug likeness properties and described in (Supplementary Table S1 ). Cuminaldehyde has a low molecular weight of 148.20g/mol, XLOGP3 value of 2.7, no hydrogen bond donors (HBD), one hydrogen bond acceptor (HBA) and 2 rotatable bonds. Sabinyl acetate has a molecular weight of 194.27g/mol, XLOGP3 of 2.4, no HBD, 2 HBA and 3 rotatable bonds. From the results, it was observed that the molecular weight of the compounds were less than 500g/mol, H bond donors were less than 5, and H bond acceptors were less than 10, indicating a balance between solubility, permeability and metabolic stability 60 . Both the compounds showed less (< 10) rotatable bonds, indicating that they have good oral absorption and high bioavailability 61 . Analysis of the pharmacokinetic properties of the 2 lead compounds reveals that these compounds show characteristics which are favourable for the drug development process and are appropriate for toxicity evaluation. Prediction of Gastrointestinal Absorption and Brain Penetration using BOILED-Egg Model Boiled egg model provides insights into the absorption and distribution properties of the lead compounds. The egg shaped plot is divided into three parts. The white region represents the human area of high intestinal absorption (HIA), the yellow region represents the area of high blood-brain barrier (BBB) penetration 62 . Blue colour indicates status for P glycoprotein substrate (PGP+) and the red colour suggests compounds which are not P glycoprotein substrates(PGP-). Figure 5 shows the boiled egg model of Cuminaldehyde and Sabinyl acetate, which are located in the yellow region, indicating good blood-brain barrier (BBB) penetration. Both the compounds were found not to be P Glycoprotein substrates and do not efflux suggesting that they have good bioavailability and can get accumulated in the targeted site. Toxicity analysis Computational toxicity profiling is a crucial step in the field of drug discovery, as it determines the safety profiles of potential drug candidates for therapeutic use. Protox-3 webserver was employed to compute the toxicity profiles of the lead compounds. It predicts the 2D similarity analysis between the molecules under examination 63 . This provides insights into the toxicity risks of the compounds by analysing their chemical and structural features. The compounds Cuminaldehyde and Sabinyl acetate displayed no evidence of hepatotoxicity, carcinogenicity, cytotoxicity, mutagenicity or respiratory toxicity. Both compounds were classified within toxicity class 4 and 5 which underscores their safety profile and thereby indicates low toxicity potential. The probability of non-toxicity of the compounds across the different parameters is shown in (Fig. 6 ). Cuminaldehyde exhibited a high probability of non-toxicity (> 70%) with regard to respiratory toxicity, mutagenicity, cytotoxicity and hepatotoxicity. Sabinyl acetate also showed promising results with > 60% probability of non-toxicity for hepatoxicity and mutagenicity, and > 70% probability of non-toxicity for cytotoxicity and respiratory toxicity. Cuminaldehyde showed inactive respiratory toxicity with a probability of 95% suggesting low risk to the respiratory system. It is inactive for mutagenicity with a probability of 97%, indicating its very low likelihood of cell mutations and inactive for cytotoxicity with a probability of 89%, indicating very low chances to cause cell toxicity. It showed inactive hepatotoxicity with a probability of 71% which implies a low risk of damage to liver cells. Sabinyl acetate shows inactive respiratory toxicity with a probability of 70%, indicating a low risk of respiratory damage, and inactive mutagenicity with a probability of 65%, suggesting a low likelihood to be mutagenic. Cytotoxicity is inactive with a probability of 77% suggesting a low risk of cell damage. Hepatotoxicity is inactive with a probability of 62% indicating its low likelihood to harm liver cells. In comparison, carcinogenicity analysis revealed that Cuminaldehyde and Sabinyl acetate have 52% and 59% probability of not being carcinogenic agents, which moderately show low potential to cause cancer. Overall, both compounds were predicted to have low toxicity and, therefore, could be promising candidates for QS and biofilm inhibition. Many earlier reports have revealed the toxicity analysis of phytocompounds towards developing antibacterial therapies using the Protox webserver 64 – 66 . Acute toxicity profiling The acute toxicity analysis of Cuminaldehyde and Sabinyl acetate was investigated using the STopTox online web tool. It is a machine learning based webserver which gives information about toxicity based on 6-pack assays ( acute oral toxicity, acute dermal toxicity, acute inhalation toxicity, skin irritation and corrosion, eye irritation and corrosion, and skin sensitization). The test results of the compounds are shown in Table 3 . Both Cuminaldehyde and Sabinyl acetate showed negative results for acute dermal toxicity, skin irritation and corrosion. Cuminaldehyde showed prediction probabilities as 51% for acute dermal toxicity and 50% for skin irritation and corrosion, which fall into the non-toxic category. Sabinyl acetate showed probabilities of 59% and 60%, indicating a lack of acute dermal toxicity and skin irritation. Cuminaldehyde showed negative results for acute inhalation toxicity, acute oral toxicity, eye irritation and corrosion with 91%, 61% and 63% prediction probabilities. Regarding skin sensitization, Cuminaldehyde showed toxicity with a prediction probability of 60% whereas Sabinyl acetate was non-toxic, showing a prediction probability of 60%. STopTox classified Sabinyl acetate as toxic with regard to acute inhalation toxicity, acute oral toxicity and eye irritation and corrosion with prediction probabilities of 59%, 61% and 56%. Overall, the toxicity profiles of Cuminaldehyde and Sabinyl acetate are favourable. The predicted fragment contribution of both compounds are shown in (Fig. 7 ) and (Fig. 8 ). The green areas in the contour maps of functional groups indicate no toxicity, and red areas indicate toxic properties. Many earlier studies have reported the acute toxicity of drug candidates through the STopTox server to evaluate their safety 67 , 68 . This six-pack acute toxicity analysis is a valuable approach towards rational drug discovery, which can minimise the need for animal testing 67 . Table 3 Acute toxicity of the selected phytocompounds through STopTox web server. Parameter Cuminaldehyde (Prediction) Cuminaldehyde (Confidence Score in %) Sabinyl acetate (Prediction) Sabinyl acetate (Confidence Score in %) Acute inhalation toxicity Negative 91 Positive 59 Acute oral toxicity Negative 61 Positive 61 Acute dermal toxicity Negative 51 Negative 59 Eye irritation and corrosion Negative 63 Positive 56 Skin sensitization Positive 60 Negative 60 Skin irritation and corrosion Negative 50 Negative 60 Molecular Dynamic simulations MD simulation is a crucial computational tool to understand the dynamic behaviour of protein ligand interactions under specific conditions over a given period of time. It is a well-established structure‐based approach in computer aided drug design (CADD) to understand the atomic‐level interactions of protein–ligand complexes through analysis of deviation, fluctuation, protein folding and interaction 46 . To access the stability of the two best hit compounds; Cuminaldehyde and Sabinyl acetate, MD simulations were conducted on the protein ligand complexes. The analysis considered parameters such as Root Mean Square Deviation (RMSD), RMSF, Radius of Gyration (Rg), Solvent Accessible Surface Area (SASA) and Principle Component Analysis (PCA) to understand the behaviour of interactions between the LasR proteins; LasR-Can, LasR-Var1, LasR-Var2 and the selected phytocompounds. RMSD analysis Root mean square deviation (RMSD) analysis was conducted to evaluate the overall structural flexibility of the Cα backbone atoms of LasR-Can, LasR-Var1 and LasR-Var2 when bound to the Native ligand, Sabinyl acetate and Cuminaldehyde during a 100ns MD simulation (Fig. 9 A-C). In the LasR-Can system (Fig. 9 A), the complex with the Native ligand exhibited the most stable trajectory, maintaining a relatively consistent RMSD of approximately 0.9 nm throughout the simulation, indicating a well-formed and rigid protein-ligand complex. In contrast, the complexes formed with Sabiny acetate and Cuminaldehyde exhibited elevated and more fluctuating RMSD profiles, stabilizing around 1.4–1.5 nm and 1.5–1.6 nm, respectively. For LasR-Can, the Cuminaldehyde complex exhibited an average RMSD of 1.214 nm, which is slightly lower than that of Sabinyl acetate (1.228 nm), indicating enhanced binding and stability. These findings are in close proximity with the results from a previously published study on LasR inhibitors 59 . Our study suggests that while the Native ligand promotes a compact and stable conformation with LasR-Can, both phytocompounds induce conformational variability but achieved stability over time upon binding with LasR-Can. In the LasR-Var1 system (Fig. 9 B), the RMSD trajectories for the Native ligand and Sabinyl acetate exhibited similar patterns, but with significant fluctuations ranging from 0.4 to 1.6 nm. The RMSD profile of the LasR-Var1-Sabinyl acetate complex indicated early stabilization after 20 ns, with only minor deviations, suggesting structural convergence over time. In contrast, Cuminaldehyde demonstrated a more favourable dynamic profile, initially increasing gradually from ~ 0.3 nm and stabilizing around 0.4 to 0.5 nm in the latter half of the simulation. The comparatively lower amplitude of fluctuations for Cuminaldehyde suggests more consistent binding interactions and improved conformational maintenance in LasR-Var1. Interestingly, in the LasR-Var2 system (Fig. 9 C), both the Native ligand and Sabinyl acetate displayed broad and persistent RMSD fluctuations ranging from ~ 0.4 to 1.4 nm over the course of the simulations. Such variability indicates considerable structural rearrangements. In contrast, Cuminaldehyde demonstrated a highly stable binding behaviour with RMSD values consistently confined to the ~ 0.6 to 0.8 nm range. This narrow deviation band suggests strong and stable interactions with LasR-Var2, potentially favouring its bioactivity and inhibitory potential compared to the other ligands. Our findings demonstrated higher RMSD values than those reported from previous studies involving protein variants 69 , 70 . These discrepancies may stem from variations in simulation parameters, protein structure, and ligand flexibility. RMSF analysis To complement the RMSD findings and gain insights into local flexibility patterns, RMSF analysis were performed for the Cα atoms of LasR-Can, LasR-Var1, and LasR-Var2 in complex with the Native ligand, Sabinyl acetate and Cuminaldehyde (Fig. 9 D–F). In the LasR-Can system (Fig. 9 D), the Native ligand-bound complex displayed a stable residue fluctuation profile, with most RMSF values confined between 0.1 and 0.35 nm, suggesting a compact and conformationally stable structure. Sabinyl acetate induced higher residue mobility, with RMSF peaks reaching upto ~ 1.0 nm mainly in the loop and surface exposed regions. Cuminaldehyde showed the highest degree of flexibility, with certain residues showing fluctuations as high as ~ 1.4 nm. As anticipated, higher peaks in the RMSF plot correspond to regions of increased atomic motion. In contrast, the LasR-Var1 system (Fig. 9 E) demonstrated comparable RMSF profiles across all three ligands, with no significant differences in fluctuation magnitude or pattern. Residue fluctuation for the Native ligand, Sabinyl acetate and Cuminaldehyde mostly remained in the range of ~ 0.1–0.5 nm, showing consistent peaks in the flexible loop regions and terminal domains. This indicates that the ligand identity has a relatively minor influence on residue-level dynamics. A distinct trend was observed in the LasR-Var2 (Fig. 9 F). Among the three ligands, Cuminaldehyde induced the lowest overall RMSF with fluctuations remaining mostly below ~ 0.2 nm across the structure, indicating a highly stable and rigid complex. The Sabinyl acetate bound complex showed moderately higher fluctuation, with peaks around ~ 0.1–0.9 nm. Both Sabinyl acetate and Native ligand bound complex showed higher flexibility, following each other's fluctuation patterns, with values rising to ~ 0.9 nm, especially in the loop and terminal regions. These results highlight Cuminaldehyde’s potential for effective binding with LasR-Var2 at the residue level, corroborating its consistent RMSD profile and suggesting tight and favourable binding interactions. Rg analysis The compactness of a protein upon interacting with the compounds were investigated through the radius of gyration (Rg). Figure 10 (A-C) shows the Rg plots of the protein ligand complexes. The average Rg values for Native ligand, Sabinyl acetate and Cuminaldehyde in complex with LasR-Can are 2.113 nm, 2.163 nm and 2.131 nm. The average Rg values for all the complexes are very similar, indicating minimal differences among the complexes. The Rg plot of the Native ligand in complex with LasR-Can showed minimum fluctuations and remained stable throughout the simulation period. Rg plots of Sabinyl acetate and Cuminaldehyde bound to LasR-Can initially showed some fluctuations, but ultimately attained stability over time. The measured Rg values of the top compounds from our findings are lower than previous reported values for phytochemicals acting as inhibitors against K . pneumonia 71 , MRSA 72 and L . Monocytogenes 73 . This suggests that the compounds in complex with LasR-Can are more compact and properly folded throughout the simulation period. The average Rg values for Native ligand, Sabinyl acetate and Cuminaldehyde with LasR-Var1 are 2.166 nm, 2.170 nm and 2.060 nm. The compounds showed similar values as compared to the native ligand and initially showed some fluctuations, but with time, they attained stability till the end of the simulation period. The Rg plot of LasR-Var2 in complex with Native ligand and Sabinyl acetate showed an average Rg values of 2.135 nm and 2.190 nm, which are consistent. The Rg plot of Cuminaldehyde with LasR-Var2 showed an average Rg value of 1.929 nm which is lower that Native ligand which implies that the complex is more compact and stable. The Rg results of the two variants in complex with the top lead compounds are marginally similar to those reported in a study examining litchen derived compound, Barbatoli cacid and Orcinyl lecanorate as inhibitors of S. aureus mutant 70 . Overall, findings from our present study suggest that all protein ligand complexes exhibited similar compactness behaviour and were properly folded throughout the simulations. SASA analysis The SASA of the compounds bound to the LasR proteins provides insights into the surface area of the protein exposed to solvent, as well as the solvent like characteristics of a protein-ligand complexes. Figure. 10(D-F) displays the SASA plots for all the protein ligand complexes. The average SASA value for Native ligand, Sabinyl acetate and Cuminaldehyde bound with LasR-Can are 133.273 nm 2 , 132.652 nm 2 and 133.829 nm 2 , respectively, indicating that they are very similar. Throughout the 100ns simulation, minimum variations in the SASA values were observed for the complexes. For LasR-Var1, the average SASA values exhibited a very narrow range, with 131.540 nm 2 for Native ligand, 133.232 nm 2 for Sabinyl acetate and 132.953 nm 2 for Cuminaldehyde exhibiting only minimum fluctuations. The SASA plots of LasR-Var2 in complex with Native ligand, Sabinyl acetate and Cumindehyde showed average SASA values of 133.226 nm 2 , 133.608 nm 2 and 132.237 nm 2 , respectively. These findings suggest that upon binding of Cuminaldehyde and Sabinyl acetate, the LasR-Var2 protein experienced comparatively greater exposure to the solvents due to distinct hydrophobic and hydrophilic interactions. Notably, Cuminaldehyde, when bound to LasR-Var1 and Las-uVar2, showed slightly lower SASA values as compared to Sabinyl acetate, suggesting that the two LasR variants had reduced exposure to solvent when bound to Cuminaldehyde, implying better stability and higher binding interface area. The SASA findings from our complexes align with a previous study, which identified kaempferol as an inhibitor of QS regulator protein in P. aeruginiosa 74 . Our SASA values observed for the top compounds were lower than earlier reported studies involving compounds as inhibitors against A. baumanni 75 , 76 , K. pneumonia 77 , 71 and E. faecalis 78 . Thereby, our findings strongly imply that the protein structures are less exposed to solvent and water molecules upon binding with Cuminaldehyde and Sabinyl acetate, making them a more compact and firmly bound complex. Principle Component Analysis The principle component analysis (PCA) is an important tool for identifying fluctuations in a protein structure when bound to ligands. It is widely used to study atomic simulations of proteins and to understand correlated movements. In this study, PCA was performed to analyse the motion of the LasR protein and its two variants when complexed with Native ligand, Sabinyl acetate and Cuminaldehyde under both apo and ligand-bound conditions (Fig. 11 A-C). Cartesian coordinates reflecting atomic displacements along the MD trajectories were used to construct covariance matrices that represent the proteins’ accessible degrees of freedom (DOF). Decomposition of these covariance matrices into orthogonal eigenvectors allowed characterization of collective motions, with the associated eigenvalue indicating the magnitude of variance. Larger eigenvalues correspond to motions occurring over larger spatial scales. Figure. 11(A-C) shows two-dimensional projections along the first two principal components (PC1 and PC2) for LasR-Can, LasR-Var1, and LasR-Var2 when bound to the native ligand, under apo conditions. Upon ligand binding with Sabinyl Acetate and Cuminaldehyde, these projections reveal altered conformational sampling. Notably, the variants explored broader or distinct conformational subspaces compared to LasR-Can, indicating differences in flexibility and dynamic behaviour. Across simulations, LasR-Var1 occupied a larger conformational subspace, suggesting increased atomic mobility and structural plasticity (Fig. 11 B), whereas LasR-Var2 demonstrated comparatively restricted motion in the presence of Cuminaldehyde, implying stabilization effect (Fig. 11 C). Density Functional Theory (DFT) calculations DFT is a highly effective theoretical framework with numerous applications, including the determination of the kinetic and thermodynamic stability of compounds, structural calculations, molecular interaction analysis, and assessments of the optical and electronic properties of atoms and molecules 47 . The geometry optimised structures of selected phytochemicals did not show any imaginary frequencies, suggesting that they reached their lowest energy gradient. The HOMO and LUMO values of the compounds, along with their energy gaps, are depicted in (Fig. 12 ). The colour coding area is represented as green, which shows the regions having a high probability of finding electrons and red shows the regions having a low probability of finding electrons. The stability of the compounds is influenced by several factors, such as dipole moment, HOMO, LUMO and atomic properties. The energetic parameters based on DFT analysis are shown in (Table 4 ). The compound Cuminaldehyde exhibits a lower energy gap (∆E) of 5.071 eV, which is less than that of the Native ligand (5.925eV) and Sabinyl acetate (6.162eV), suggesting that it has higher chemical reactivity and may demonstrate better biological activity (Fig. 12 A, C). In the present study, the energy gaps (ΔE) of Cuminaldehyde and Sabinyl acetate were lower than T-muurolol, Valencene which have previously been reported as effective inhibitors against MDR S. aureus 79 and E. coli , respectively 80 . This implies that our lead phytocompounds have more potential to bind to the target proteins and likely exhibit enhanced antibacterial activity. The ionising potential (IP) of Cuminaldehyde and Sabinyl acetate are similar, implying that both compounds show good reactivity, whereas Cuminaldehyde showed enhanced activity owing to its higher IP value. Cuminaldehyde also exhibits the highest electronegativity (χ) and electrophilicity index (ψ), indicating its strong ability to attract electrons and form stronger bonds with molecules. The electrophilicity index (ψ) for all investigated compounds are more than 1.5eV indicating that they are strong electrophiles 47 ,values ranging from 1.886 eV to 4.005eV. Electron affinity (EA) of Cuminaldehyde, Sabinyl acetate and Native ligand are 1.971 eV, 0.328 eV and 1.184 eV, respectively. Cuminaldehyde shows a high dipole moment value of 4.2707 Debye (Table 4 ) suggesting that it forms stronger interactions with the proteins through electrostatic attractions and is more suitable for biological activity. Hardness (η) and softness (σ) are the parameters used to determine the stability of a compound in a chemical reaction. Cuminaldehyde demonstrates a lower hardness value (2.535 eV) than Sabinyl acetate (3.081 eV) and shows a higher softness value (0.394 eV) than Sabinyl acetate (0.324 eV). Compounds showing lower hardness exhibit a narrow energy gap, and those with higher hardness show a wider energy gap 46 . The findings of our study show a close resemblance with the values of quantum chemical parameters for the phytocompounds 3-Hydroxyisoagatholactone, β-Sitsterol from Cyphostemma cyphopetalum 81 and those of Asperuloside, Asperulosidic acid, and Deacetylasperulosidic acid from Morinda citrifolia 82 . Our findings are consistent, which shows that Cuminaldehyde is confirmed to be more reactive than Sabinyl acetate. The electrochemical potential (µ), (Table 4 ), shows negative values for all the analysed compounds, which indicates good stability in interaction with the proteins. All the compounds, including the Native ligand, show negative values, suggesting a strong interaction with the LasR receptors of P. aeruginosa . Table 4 DFT based Energetic parameters based of the selected phytocompounds with the reference compound3-oxo-C12-HSL. Parameter Compound Cuminaldehyde Compound Sabinyl acetate Native ligand (3-oxo-C12-HSL) Dipole moment (Debye) 4.2707 1.9495 3.7921 Ionising Potential IP (eV) 7.042 6.490 7.109 Electron affinity EA (eV) 1.971 0.328 1.184 Electronegativity χ (eV) 4.506 3.409 4.146 Electrochemical potential µ (eV) -4.506 -3.409 -4.146 Hardness η (eV) 2.535 3.081 2.962 Softness S (eV) 0.394 0.324 0.338 Electrophilicity Index ψ(eV) 4.005 1.886 3.247 Molecular electrostatic potential (MEP) analysis The MEP surface analysis provides insight into the potential reactive sites of the compounds with electrophiles and nucleophiles 83 . It visualizes the positive, negative and neutral electrostatic potential of a surface, represented in different colours (Fig. 13 ). In these maps, red indicates a high density of electrons, and acts as a site for electrophilic attack. Yellow indicates moderate values of MEP, while green denotes areas with zero potential and no electron density. Blue indicates low electron density, and acts as a site of nucleophilic attacks. The increasing order of electrostatic potential is seen as red < yellow < green < blue. For the compound Cuminaldehyde (Fig. 13 A), the O1 atom is located in the yellow region, indicating a high negative charge. In contrast, the atoms C11, C10, C8, C6, C3, C5, H23, H12, H19 were located in the blue regions, indicating lower electron density and positive charge. In the case of Sabinyl acetate, the MEP map reveals that the atoms O1 and O2 are associated with the yellow and red regions, indicating a high negative charge. Conversely, C11, H26, H25, H27 show high positive charges in the blue regions (Fig. 13 B). The MEP map of the Native ligand reveals the presence of atoms O2, O3, and O4 in the red regions, surrounding areas of high negative charge. High positive charges are shown by atoms H39, C15, C14, C11 and N5 (Fig. 13 C). Our results correlate with the MEP maps of compounds derived from Millettia dielsiana 84 and the metabolite BagremycinA 85 , indicating a similar electronic distribution of atoms. The oxygen and hydrogen atoms of our compounds are involved in forming H bonds with the LasR proteins, as confirmed by the docking studies. Conclusions The research identified promising phytochemicals from C. munbyi against the quorum sensing protein LasR and its two variants through computational methods. Molecular docking analysis provided valuable insights into the binding interaction of phytochemicals with the LasR protein and its variants. Notably, Cuminaldehyde and Sabinyl acetate emerged as the top hit compounds, demonstrating favourable drug likeness, toxicological properties and binding affinities comparable to those of the native ligand. MD simulation over 100 ns confirmed the ability of these compounds to accurately accommodate in the ligand binding regions of the LasR proteins, revealing stable ligand binding interactions throughout the simulations. Additionally, DFT computational study suggests that Cuminaldehyde and Sabinyl acetate could serve as promising anti-QS agents against LasR proteins and its variants. The key research findings from this study highlight that the identified phytocompounds can be used as anti-quorum sensing agents against MDR P. aeruginosa. The promising results from these in silico findings pave the way for developing new therapeutic strategies targeting multidrug resistant bacterial infections. However, further in vitro validation is required to confirm these findings, which could ultimately lead to a novel phytochemical-based treatment option for quorum sensing inhibition in P. aeruginosa and contribute to the fight against antimicrobial resistance. Declarations Data availability All the data generated and analysed during this study are included in the published article. Other data which support the findings of the study are available in the paper and its supplementary information files. Acknowledgements The authors express their sincere gratitude to the Centre for Biotechnology, Siksha 'O′ Anusandhan Deemed to be University, Bhubaneshwar, India for providing infrastructure and facility for carrying out the work Author contributions Arnav Padhi: Conceptualization, methodology, data curation, software, in silico study design, analysis, writing original draft. Pabitra Mohan Behera: Conceptualization, methodology, in silico analysis. writing original draft. Soumyadip Ghosh: Methodology, software, in silico analysis, Sudha Ramaiah: Investigation, manuscript review. Surajit De Mandal: Supervision, manuscript review, editing and proofreading, Enketeswara Subudhi: Conceptualization, supervision, manuscript review, editing and proofreading. All authors read and approved the final manuscript. Funding No funding was received for conducting this study. Competing interests The authors declare no competing interests. Additional information Supplementary Information The online version contains supplementary material available at Correspondence and requests for materials should be addressed to E.S References Ren, Y. et al. Quercetin: a promising virulence inhibitor of Pseudomonas aeruginosa LasB in vitro. Appl. Microbiol. Biotechnol. 108 , 57 (2024). Elfadadny, A. et al. Antimicrobial resistance of Pseudomonas aeruginosa: navigating clinical impacts, current resistance trends, and innovations in breaking therapies. Front. Microbiol. 15 , 1374466 (2024). Hosu, M. C., Vasaikar, S. D., Okuthe, G. E. & Apalata, T. 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17:59:12","extension":"png","order_by":33,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":64676,"visible":true,"origin":"","legend":"","description":"","filename":"Onlinefloatimage9.png","url":"https://assets-eu.researchsquare.com/files/rs-7664093/v1/f07775588970d97197a75888.png"},{"id":94962934,"identity":"bf5ef7d2-fa48-4666-960d-89518e08546c","added_by":"auto","created_at":"2025-11-02 17:59:11","extension":"xml","order_by":34,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":198758,"visible":true,"origin":"","legend":"","description":"","filename":"f004ab99e80740068e5075e44c3d62731structuring.xml","url":"https://assets-eu.researchsquare.com/files/rs-7664093/v1/30d92f00dcaaefca52122490.xml"},{"id":94962959,"identity":"33ada7f3-35f4-4f4a-a141-99fcc8a8ec75","added_by":"auto","created_at":"2025-11-02 17:59:13","extension":"html","order_by":35,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":216748,"visible":true,"origin":"","legend":"","description":"","filename":"earlyproof.html","url":"https://assets-eu.researchsquare.com/files/rs-7664093/v1/b7fd65ac95ad262f6f77472b.html"},{"id":94962915,"identity":"89796c73-f46c-4d19-af04-449b9b5986fe","added_by":"auto","created_at":"2025-11-02 17:59:10","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":1299369,"visible":true,"origin":"","legend":"\u003cp\u003eAlignment of LasR-Can, LasR-Var1 and LasR-Var2 sequences with the template (PDB ID-6MWZ) (A,B,C) and crystal structure of LasR predicted from alphafold of \u003cem\u003eP. aeruginosa \u003c/em\u003e(D, E, F).\u003c/p\u003e","description":"","filename":"image1.png","url":"https://assets-eu.researchsquare.com/files/rs-7664093/v1/08caa6afcea75e975147bb69.png"},{"id":94989077,"identity":"ba0e4bd4-316c-4c51-b23a-0b7fd2e56d85","added_by":"auto","created_at":"2025-11-03 07:12:00","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":972148,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cem\u003eLasR canonical model showing docking of three entities : The surface view of the canonical model with the highlighted ligand binding site showing accommodation of all three entities (A), Ligand interaction diagram of Native Ligand (B), Ligand interaction diagram of Cuminaldehyde (C), Ligand interaction diagram of Sabinyl acetate (D).\u003c/em\u003e\u003c/p\u003e","description":"","filename":"image2.png","url":"https://assets-eu.researchsquare.com/files/rs-7664093/v1/335afda468fd0f3a826552eb.png"},{"id":94962937,"identity":"0a573e7b-f24f-4074-a5ac-5abf4c45f165","added_by":"auto","created_at":"2025-11-02 17:59:11","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":1006484,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cem\u003eLasR variant 1 model showing docking of three entities : The surface view of the variant 1 model with the highlighted ligand binding site showing accommodation of all three entities (A), Ligand interaction diagram of Native Ligand (B), Ligand interaction diagram of Cuminaldehyde (C), Ligand interaction diagram of Sabinyl acetate (D).\u003c/em\u003e\u003c/p\u003e","description":"","filename":"image3.png","url":"https://assets-eu.researchsquare.com/files/rs-7664093/v1/e3436bea4033e5a82c2ae3ff.png"},{"id":94962940,"identity":"eb14d80f-3019-4497-96b0-881941b25912","added_by":"auto","created_at":"2025-11-02 17:59:12","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":979299,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cem\u003eLasR variant 2 model showing docking of three entities : The surface view of the variant 2 model with the highlighted ligand binding site showing accommodation of all three entities (A), Ligand interaction diagram of Native Ligand (B), Ligand interaction diagram of Cuminaldehyde (C), Ligand interaction diagram of Sabinyl acetate (D).\u003c/em\u003e\u003c/p\u003e","description":"","filename":"image4.png","url":"https://assets-eu.researchsquare.com/files/rs-7664093/v1/c193abcc3b952e417c867915.png"},{"id":94988077,"identity":"3ac5d966-93ac-43a6-a01f-fe94c987846f","added_by":"auto","created_at":"2025-11-03 07:04:28","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":216963,"visible":true,"origin":"","legend":"\u003cp\u003eBoiled egg model diagram of compounds, Cuminaldehyde (A) and Sabinyl acetate (B) from the swissADME server.\u003c/p\u003e","description":"","filename":"image5.png","url":"https://assets-eu.researchsquare.com/files/rs-7664093/v1/4efbc3db0934cb7959b66ec8.png"},{"id":94962953,"identity":"b21d4bb0-5937-4f58-b053-1d34b56b3728","added_by":"auto","created_at":"2025-11-02 17:59:12","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":136042,"visible":true,"origin":"","legend":"\u003cp\u003eHeat map representation of the non-probability toxicity profiles of Cuminaldehyde and Sabinyl acetate.\u003c/p\u003e","description":"","filename":"image6.png","url":"https://assets-eu.researchsquare.com/files/rs-7664093/v1/5bd05ee8b00c2e3aa1d9ab73.png"},{"id":94962924,"identity":"526c47d6-6358-4f58-b113-1c0677bc21a6","added_by":"auto","created_at":"2025-11-02 17:59:11","extension":"png","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":945299,"visible":true,"origin":"","legend":"\u003cp\u003ePredicted fragment contribution of Cuminaldehyde for : Acute inhalation toxicity (A),Acute oral toxicity (B), Acute dermal toxicity (C), Eye irritation and corrosion (D), Skin sensitization (E), Skin irritation and corrosion (F).\u003c/p\u003e","description":"","filename":"image7.png","url":"https://assets-eu.researchsquare.com/files/rs-7664093/v1/af4a881d9ec085162b94f2ef.png"},{"id":94962920,"identity":"41402d30-e0d4-4942-a99f-d8d5444f8e26","added_by":"auto","created_at":"2025-11-02 17:59:11","extension":"png","order_by":8,"title":"Figure 8","display":"","copyAsset":false,"role":"figure","size":699422,"visible":true,"origin":"","legend":"\u003cp\u003ePredicted fragment contribution of Sabinyl acetate for : Acute inhalation toxicity (A),Acute oral toxicity (B), Acute dermal toxicity (C), Eye irritation and corrosion (D), Skin sensitization (E), Skin irritation and corrosion (F).\u003c/p\u003e","description":"","filename":"image8.png","url":"https://assets-eu.researchsquare.com/files/rs-7664093/v1/7e355cd3befd5157864c7ee0.png"},{"id":94962941,"identity":"305f1195-d2d5-4d49-95cd-88b34d5bba3c","added_by":"auto","created_at":"2025-11-02 17:59:12","extension":"png","order_by":9,"title":"Figure 9","display":"","copyAsset":false,"role":"figure","size":1044554,"visible":true,"origin":"","legend":"\u003cp\u003eRMSD(A,B,C) and RMSF(D,E,F) of LasR-Can, LasR-Var1, LasR-Var2 Cα atoms in complex with Native ligand, Cuminaldehyde, and Sabinyl acetate during 100ns.\u003c/p\u003e","description":"","filename":"image9.png","url":"https://assets-eu.researchsquare.com/files/rs-7664093/v1/a9214c43869cbeee71e5767b.png"},{"id":94962932,"identity":"0365d52a-53aa-42af-b9d9-a514bb93f1be","added_by":"auto","created_at":"2025-11-02 17:59:11","extension":"png","order_by":10,"title":"Figure 10","display":"","copyAsset":false,"role":"figure","size":1189135,"visible":true,"origin":"","legend":"\u003cp\u003eRg(A,B,C) and SASA(D,E,F) of LasR-Can, LasR-Var1, LasR-Var2 Cα atoms in complex with Native ligand, Cuminaldehyde and Sabinyl acetate during 100ns.\u003c/p\u003e","description":"","filename":"image10.png","url":"https://assets-eu.researchsquare.com/files/rs-7664093/v1/49a13574e2f244249a590b29.png"},{"id":94962954,"identity":"632689c3-6160-4140-a23b-a81061c0a296","added_by":"auto","created_at":"2025-11-02 17:59:12","extension":"png","order_by":11,"title":"Figure 11","display":"","copyAsset":false,"role":"figure","size":776657,"visible":true,"origin":"","legend":"\u003cp\u003ePCA of the trajectory motions of Native ligand, Sabinyl acetate and Cuminaldehyde in complex with LasR-Can (A), LasR-Var1 (B) and LasR-Var2 (C)\u003c/p\u003e","description":"","filename":"image11.png","url":"https://assets-eu.researchsquare.com/files/rs-7664093/v1/922525e32205f13892040da8.png"},{"id":94962944,"identity":"50b99a48-e52f-4ea0-a5ff-c985e3de50f6","added_by":"auto","created_at":"2025-11-02 17:59:12","extension":"png","order_by":12,"title":"Figure 12","display":"","copyAsset":false,"role":"figure","size":804634,"visible":true,"origin":"","legend":"\u003cp\u003ePCA of the trajectory motions of Native ligand, Sabinyl acetate and Cuminaldehyde in complex with LasR-Can (A), LasR-Var1 (B) and LasR-Var2 (C)\u003c/p\u003e","description":"","filename":"image12.png","url":"https://assets-eu.researchsquare.com/files/rs-7664093/v1/c96c1c13a5e67190b129a782.png"},{"id":94989292,"identity":"5a079aa0-fcbc-4312-b0b5-5eb506c392b9","added_by":"auto","created_at":"2025-11-03 07:12:34","extension":"png","order_by":13,"title":"Figure 13","display":"","copyAsset":false,"role":"figure","size":1178772,"visible":true,"origin":"","legend":"\u003cp\u003eMolecular electrostatic potential (MEP) surface of the phytocompounds Cuminaldehyde (A), Sabinyl acetate (B) and Native ligand (C).\u003c/p\u003e","description":"","filename":"image13.png","url":"https://assets-eu.researchsquare.com/files/rs-7664093/v1/91d9a806320c3a81168a859f.png"},{"id":98779231,"identity":"4611f5c0-02ab-458b-828d-b656c0d2cdbe","added_by":"auto","created_at":"2025-12-22 12:30:05","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":11985117,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7664093/v1/c2f79e61-1c49-4d46-ac76-96fce57e3194.pdf"},{"id":94962916,"identity":"fb675a65-4e8e-49f4-8129-199c14036784","added_by":"auto","created_at":"2025-11-02 17:59:10","extension":"xlsx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":17377,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementarytableS2.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-7664093/v1/642765e4a7fe5c19521914c1.xlsx"},{"id":94962922,"identity":"7825c5c7-441e-47f6-bd5e-d9fe95507356","added_by":"auto","created_at":"2025-11-02 17:59:11","extension":"xlsx","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":17912,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementarytableS3.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-7664093/v1/9593fd596b5864cef2a329db.xlsx"},{"id":94962927,"identity":"2571a8ad-b391-4f90-a483-3c4a0c65f9ab","added_by":"auto","created_at":"2025-11-02 17:59:11","extension":"xlsx","order_by":3,"title":"","display":"","copyAsset":false,"role":"supplement","size":16485,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementarytableS4.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-7664093/v1/2619b869d28b158dccb92064.xlsx"},{"id":94962926,"identity":"dbc43382-0407-4b67-b7fc-92a0aabcae55","added_by":"auto","created_at":"2025-11-02 17:59:11","extension":"xlsx","order_by":4,"title":"","display":"","copyAsset":false,"role":"supplement","size":11670,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementarytableS5.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-7664093/v1/2e9c82de5cf15c31921d737f.xlsx"},{"id":94962933,"identity":"d38f6cd1-bb3d-4b2d-96a8-5d527c943ea6","added_by":"auto","created_at":"2025-11-02 17:59:11","extension":"docx","order_by":5,"title":"","display":"","copyAsset":false,"role":"supplement","size":1491433,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryFile.docx","url":"https://assets-eu.researchsquare.com/files/rs-7664093/v1/08846a45f2d5b43114148fa8.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Pseudomonas aeruginosa virulence reduction through phytochemical inhibition of Quorum Sensing activity: A Molecular Docking, Molecular Dynamics Simulation study","fulltext":[{"header":"Introduction","content":"\u003cp\u003e\u003cem\u003ePseudomonas aeruginosa\u003c/em\u003e is a Gram negative opportunistic pathogen responsible for severe hospital-acquired infections\u003csup\u003e\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e,\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u003c/sup\u003e. It can survive in extreme environmental conditions and causes pathogenicity in different hosts, including humans, animals, and plants\u003csup\u003e\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u003c/sup\u003e. Over the years, bacteria have developed resistance to different antibiotics, leading to the emergence of multidrug-resistant (MDR) strains that contribute to elevated morbidity and mortality rates\u003csup\u003e\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u003c/sup\u003e. Owing to its growing resistance to antibiotics and higher dissemination rates, \u003cem\u003eP. aeruginosa\u003c/em\u003e has been classified as a critical priority pathogen by the World Health Organisation (WHO), underscoring the urgent need for alternative therapeutic strategies \u003csup\u003e\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e\u003c/sup\u003e. Moreover, \u003cem\u003eP. aeruginosa\u003c/em\u003e forms biofilms on medical devices such as implants and catheters by forming microbial aggregates through quorum sensing. These biofilm forming strains are responsible for causing ventilator associated pneumonia, catheter associated Urinary Tract Infections (UTI) and other nosocomial infections\u003csup\u003e\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003eQuorum sensing (QS) is a mechanism by which \u003cem\u003eP. aeruginosa\u003c/em\u003e manifests virulence through cell to cell communication, facilitated by the LasR, LasI, RhlR, RhlI, and Pseudomonas Quinolone Signal (PQS) genes\u003csup\u003e\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e,\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e\u003c/sup\u003e. The LasI gene synthesises the signalling molecule N-(3-oxododecanoyl)-L-homoserine lactones (3-oxo-C12-HSL) and RhlI synthesises N-butanoyl-L-homoserine lactone (C4-HSL), which is recognised by RhIR. Additionally,PQS regulates the release of extracellular DNA (eDNA) during biofilm formation\u003csup\u003e\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e,\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e\u003c/sup\u003e. eDNA helps \u003cem\u003eP. aeruginosa\u003c/em\u003e in adhering to cell surfaces and serves as a nutrition source during the early stages of biofilm development\u003csup\u003e\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e\u003c/sup\u003e. LasR acts as the master regulator of quorum sensing, catalysing the production of virulence factors upon binding to its autoinducer, 3-oxo-C12-HSL\u003csup\u003e\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e\u003c/sup\u003e. The lasR gene plays a critical role in the regulation of quorum sensing, virulence and pathogenesis in \u003cem\u003eP. aeruginosa\u003c/em\u003e, contributes to respiratory tract infections, pneumonia, and bacteremia\u003csup\u003e\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e,\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e,\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003eTargeting the lasR gene and inhibiting its function can effectively prevent the expression of virulence factors in \u003cem\u003eP. aeruginosa\u003c/em\u003e and reduce its pathogenicity. The activation of the QS signalling molecules in \u003cem\u003eP. aeruginosa\u003c/em\u003e triggers biofilm formation, rendering antibiotics ineffective\u003csup\u003e\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e\u003c/sup\u003e. Disrupting these QS mechanisms is a highly recommended strategy for treating infections caused by \u003cem\u003eP. aeruginosa\u003c/em\u003e and combating antimicrobial resistance(AMR)\u003csup\u003e\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e\u003c/sup\u003e. Previous reports have shown that phytoextracts from plants such as \u003cem\u003ePiper bogotense, Syzygium jambos, Dioonspinulosum\u003c/em\u003e, and \u003cem\u003ePsidium guajava\u003c/em\u003e exhibit excellent anti-quorum sensing and antibiofilm activities against \u003cem\u003eP. aeruginosa\u003c/em\u003e\u003csup\u003e\u003cspan additionalcitationids=\"CR14 CR15\" citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e\u003c/sup\u003e. In recent years, phytomolecules derived from essential oils have increasingly gained attention as antiquorum sensing and antibacterial agents due to their lower toxicity and high medicinal properties\u003csup\u003e\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e,\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e\u003c/sup\u003e. Essential oils (EO) are volatile phytochemicals which are extracted from the roots, stems, flowers and leaves of the plants, showing a wide range of antioxidant, antibacterial, and antifungal properties\u003csup\u003e\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e,\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e\u003c/sup\u003e. Several essential oils have been shown to have good antibacterial activity against nosocomial pathogens such as \u003cem\u003eE. coli\u003c/em\u003e, \u003cem\u003eS. aureus\u003c/em\u003e, \u003cem\u003eP. aeruginosa\u003c/em\u003e as well as different food borne pathogens such as \u003cem\u003eSalmonella typhi\u003c/em\u003e and \u003cem\u003eBacillus cereus\u003c/em\u003e\u003csup\u003e\u003cspan additionalcitationids=\"CR22 CR23\" citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003e\u003cem\u003eCistus munbyi\u003c/em\u003e is a medicinal shrub belonging to the Cistaceae family, commonly found in the Mediterranean region and in alkaline soils of Algeria\u003csup\u003e\u003cspan additionalcitationids=\"CR26\" citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e\u003c/sup\u003e. This plant has been traditionally used to treat pulmonary infections \u003csup\u003e\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e\u003c/sup\u003e. In addition to its antimicrobial properties against both gram positive and gram negative bacteria, extracts from \u003cem\u003eC. munbyi\u003c/em\u003e contain polyphenols, which contribute to its strong antioxidant properties and enhance its therapeutic value\u003csup\u003e\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e\u003c/sup\u003e. In this work, we compiled a dataset of phytocompounds derived from \u003cem\u003eC. munbyi\u003c/em\u003e essential oil and analysed their anti-quorum sensing activity against the LasR proteins of \u003cem\u003eP. aeruginosa\u003c/em\u003e using molecular docking techniques. The most promising compounds were evaluated for their drug likeness, pharmacokinetic properties, toxicity profiles and were further subjected to molecular dynamic (MD) simulations and density functional theory (DFT) analysis to identify promising inhibitors of quorum sensing protein LasR, aimed at combating infections caused by \u003cem\u003eP. aeruginosa.\u003c/em\u003e\u003c/p\u003e"},{"header":"Materials and Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\u003ch2\u003eSelection of LasR canonical and natural variants of \u003cem\u003eP. aeruginosa\u003c/em\u003e\u003c/h2\u003e\u003cp\u003eThe UniProtKB database was searched with the keyword \u0026ldquo;LasR\u0026rdquo; and the results were customized to review sequences only\u003csup\u003e\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e\u003c/sup\u003e. The result was further customized with the selection of species-specific sequences from which the entry with UniProtKB accession number P25084 was selected representing the LasR sequence and variants of \u003cem\u003eP. aeruginosa.\u003c/em\u003e The canonical and two natural variant sequences were downloaded in fasta format for further analysis.\u003c/p\u003e\u003c/div\u003e\n\u003ch3\u003eSelection of potential phytochemicals from \u003c/h3\u003e\n\u003cdiv class=\"Heading\"\u003eSelection of potential phytochemicals from \u003cem\u003eCistus munbyi\u003c/em\u003e\u003c/div\u003e\u003cp\u003eThe essential oil of \u003cem\u003eC. munbyi\u003c/em\u003e was selected from literature as it is reported to possess antibacterial activities against \u003cem\u003eP. aeruginosa\u003c/em\u003e which contains 44 phytochemicals in it\u003csup\u003e27\u003c/sup\u003e. All the phytochemical structures were downloaded from the PubChem database (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://pubchem.ncbi.nlm.nih.gov/\u003c/span\u003e\u003cspan address=\"https://pubchem.ncbi.nlm.nih.gov/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) in 3D SDF format. The compound having only a 2D structure was converted to 3D using Avogadro v1.2.0\u003csup\u003e30\u003c/sup\u003e and saved as a MOL2 file. The physicochemical properties of these 44 phytochemicals are summarised in (Supplementary Table \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e).\u003c/p\u003e\n\u003ch3\u003eMolecular modelling of LasR canonical and natural variant proteins of \u003c/h3\u003e\n\u003cdiv class=\"Heading\"\u003eMolecular modelling of LasR canonical and natural variant proteins of \u003cem\u003eP. aeruginosa\u003c/em\u003e\u003c/div\u003e\u003cp\u003eAlthough there were many crystal structures of LasR reported in the PDB database, we performed the molecular modelling of all three (One canonical and two natural variants) protein sequences by querying them against the PDB database. The BlastP program used for the cause was customized with the parameters reading as maximum target sequences as 10, expected threshold of 0.05, word size of 5, scoring matrix as BLOSUM62 and gap costs of existence of 10 and extension as 1\u003csup\u003e31\u003c/sup\u003e. The suitable template (PDB ID: 6MWZ) selected after the similarity searching was used for alignment with three LasR protein sequences and then predicting their models with the use of Modeller v10.6 program\u003csup\u003e\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e\u003c/sup\u003e. About ten models were predicted for each protein sequence from which the best models were selected with the lowest readings of DOPE scores and evaluated with prediction of Ramachandran plots with the PROCHECK program of SAVES V6.1 server (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://saves.mbi.ucla.edu/\u003c/span\u003e\u003cspan address=\"https://saves.mbi.ucla.edu/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e)\u003csup\u003e33\u003c/sup\u003e. In order to remove some redundancy in the alignment of three models with the template, the AlphaFold generated model was used as template for predicting the models once again with Modeller v10.6. The best models were selected with suitable DOPE scores, evaluated by predicting their Ramachandran plots and then aligned with the AlphaFold model, predicting suitable values of RMSD calculations. The model quality of the protein was predicted through ProSA-web and ProQ web server\u003csup\u003e\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e,\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e\n\u003ch3\u003eMolecular docking of selected phytochemicals against LasR\u003c/h3\u003e\n\u003cp\u003eAll three LasR receptor models were first prepared by importing them one by one in AutoDockTools v1.5.7. They were added with polar hydrogen, assigned Kollmann charges and finally exported in PDBQT format. Once the receptor models are prepared, the gridboxes for each model were generated by selecting the residues lining the ligand binding site. The ligand binding sites for each receptor model were fetched by the alignment of the receptor models with their template and selection of residues lying within 5\u0026Aring; area of the co-crystal or Native ligand (3-oxo-C12-HSL) which is used as the reference compound. All three grid boxes generated for three receptor models were characterized with grid center parameters as x\u0026thinsp;=\u0026thinsp;4.65, y = -3.74, z = -7.21 and grid volume dimensions as 25(x) \u0026times; 25(y) \u0026times; 25(z). At last, all forty-four selected phytochemicals along with the Native ligand were prepared by importing them individually in AutoDockTools v1.5.7. They were added with polar hydrogens with calculations of Gasteiger charges, assignment of suitable values of TORSDOF predicting their flexibility and exported in PDBQT format. The molecular docking studies of selected phytochemicals along with the Native ligand on three LasR receptor models was done with Auto Dock Vina v1.2.3\u003csup\u003e36\u003c/sup\u003e. About 135 such docking studies were done by generating individual configuration files. The receptor models were kept static and the phytochemicals were kept flexible to provide at least nine docking conformation of each phytochemical. The docking results were saved for further analysis.\u003c/p\u003e\n\u003ch3\u003eDrug likeness and Pharmacokinetic analysis of the lead compounds\u003c/h3\u003e\n\u003cp\u003eDrug likeness and Pharmacokinetic properties were determined through the Pubchem database and SwissADME tool \u003cem\u003e(\u003c/em\u003e\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://www.swissadme.ch/\u003c/span\u003e\u003cspan address=\"http://www.swissadme.ch/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) by generating the BOILED-Egg model for the lead compounds by feeding the SMILES(simplified molecular input line entry system) notations of the compounds retrieved from pubchem database.\u003c/p\u003e\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e\u003ch2\u003eToxicity profiling\u003c/h2\u003e\u003cp\u003eThe toxicity profiles of the top compounds were computed through the ProTox-3 web server (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://tox.charite.de/protox3/\u003c/span\u003e\u003cspan address=\"https://tox.charite.de/protox3/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003cem\u003e)\u003c/em\u003e\u003csup\u003e37\u003c/sup\u003e. Different parameters were evaluated such as, Respiratory toxicity, Cytotoxicity, Carcinogenicity, Immunotoxicity and Mutagenicity. Additionally, STopTox (Systemic and Topical chemical Toxicity webserver (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://stoptox.mml.unc.edu/\u003c/span\u003e\u003cspan address=\"https://stoptox.mml.unc.edu/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e)\u003csup\u003e38\u003c/sup\u003e was used to predict the acute toxicity on the basis of 6 pack assays which includes three systematic (acute inhalation toxicity, acute oral toxicity, acute dermal toxicity and three topical (eye irritation and corrosion, skin sensitization and skin irritation and corrosion) toxicity end points. The SMILES structural notions of cuminaldehyde and sabinyl acetate was used as inputs to operate the system.\u003c/p\u003e\u003c/div\u003e\n\u003ch3\u003eMolecular Dynamic (MD) Simulations\u003c/h3\u003e\n\u003cp\u003eMD analysis was conducted on the selected compounds. Here, MD simulations were run for 100 ns using GROMACS 2022\u003csup\u003e39\u0026ndash;42\u003c/sup\u003e. The protein was minimized using AMBERff99SB force field. The protein was prepared by solvation and charges neutralized with appropriate amounts of Na\u0026thinsp;+\u0026thinsp;and Cl-. The entire system was energy minimized using the steepest descent method for 50000 steps. All the systems were prepared to the canonical ensemble (NVT) for 1 ns and then switched to isothermal-isobaric ensemble (NPT) for 1 ns. Both systems were finally simulated for 100 ns. After the simulations were executed, trajectories were subjected to extraction, and all analyses were checked pertaining to the MD\u0026rsquo;s post analyses, which involved looking into the stability and conformational shifts of the protein systems. Different parameters such as root-mean-square-deviation (RMSD), root-mean-square-fluctuation (RMSF), radius of gyration (Rg), solvent accessible surface area (SASA), and principal component analyses (PCA) were calculated.\u003c/p\u003e\n\u003ch3\u003eDensity Functional Theory (DFT) calculations\u003c/h3\u003e\n\u003cp\u003eIn this study, DFT was utilized to determine the electronic properties and reactive states of selected phytocompounds, along with their controls, using the Gaussian 16 software\u003csup\u003e\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e\u003c/sup\u003e. The geometry optimization of the compounds was conducted utilizing the DFT at the B3LYP/6\u0026ndash;311 G(d, p) basis level set in the gas phase. The B3LYP functional is a valuable approach for analyzing the vibrational frequencies of small-to medium-sized molecules\u003csup\u003e\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e\u003c/sup\u003e. Use of B3LYP method along with the 6-311G (d, p) basis provides more robust understanding of the phytochemicals in the context of pharmacodynamics and ensures high precision and accuracy\u003csup\u003e\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e\u003c/sup\u003e. Highest occupied molecular orbital (HOMO) is the ability of a compound to donate electrons and lowest unoccupied molecular orbital (LUMO) is the ability of a compound to receive electrons. Understanding the value of these orbitals are crucial for understanding their structural properties and their significance in biological systems\u003csup\u003e\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e\u003c/sup\u003e. Additionally, based on the energies of the HOMO and LUMO, other reactivity parameters, such as the energy gap, dipole moment, ionizing potential (IP), electron affinity (EA), electronegativity (χ), electrochemical potential (\u0026micro;), hardness (η), softness (S), and electrophilicity index (ψ) were computed\u003csup\u003e\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e,\u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e\u003c/sup\u003e Gauss View v6.1.1 was employed to visualize the charge distribution within the selected compounds through Molecular Electrostatic Potential (MEP) analysis\u003csup\u003e\u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e"},{"header":"Results and discussion","content":"\u003cp\u003e\u003cb\u003eProperties of\u003c/b\u003e \u003cb\u003eP. aeruginosa\u003c/b\u003e \u003cb\u003eLasR protein and its variants\u003c/b\u003e\u003c/p\u003e\u003cp\u003eThe LasR protein, identified by the UniProt accession number P25084, consists of 239 amino acids and has a molecular mass of 26,619 Da. The two reported natural variants with point mutation have been identified:LasR-Var1 (R144I) and LasR-Var2 (R180W). The lasR-Var1 has been reported in strain IFO 3455 and PA103. Strain possessing the mutation M144I in IFO 3455 is responsible for elastase production in \u003cem\u003eP. aeruginosa\u003c/em\u003e\u003csup\u003e\u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e\u003c/sup\u003e which is a key virulence factor responsible for quorum sensing. LasR-Var2 has been exclusively found in PA103 which is a non-elastase producing strain\u003csup\u003e\u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e51\u003c/span\u003e\u003c/sup\u003e. Bacterial isolates showing ventilator associated pneumonia are reported to have shown elastase activity. Nearly one third of them showed reduced elastase activity than PA103\u003csup\u003e52\u003c/sup\u003e. Since elastase is positively regulated by the LasR quorum sensing system, such findings highlight the clinical relevance of LasR mutants.\u003c/p\u003e\u003cdiv id=\"Sec12\" class=\"Section2\"\u003e\u003ch2\u003eHomology modelling\u003c/h2\u003e\u003cp\u003eFor the homology modelling, the best structural models for the canonical LasR and its two variants were selected based on the lowest DOPE scores, which indicate structural quality. Lower DOPE generally implies more reliable and energetically favourable models. The model having the lowest DOPE score was selected for further analysis, including ABL% from Ramachandran plots evaluations and root-mean-square deviation(RMSD) alignment scores compared to their template models. On using the PDB ID-6MWZ as template, the mutation sites of LasR-Var2(R180W) was observed in the loop region as shown in (Supplementary Fig.\u0026nbsp;1). Loop regions exhibit limited noncovalent interactions and hence they are prone to unfolding. This can affect protein stability and structural integrity and therefore needs refinement. The AlphaFold model was used to refine the three LasR models, and their corresponding ABL% and alignment scores are summarised in (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). Improvement was observed in ABL% scores, rising from ~\u0026thinsp;90.0% for template based models to ~\u0026thinsp;95.0% for AlphaFold based model. Hence, AlphaFold generated models were selected for further analysis.\u003c/p\u003e\u003cp\u003eThe 3D view of the aligned template; PDB ID-6MWZ and the AlphaFold generated models of LasR-Can, LasR-Var1, LasR-Var2 are shown in (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eComparative analysis of ABL% and alignment scores for template based and AlphaFold based models.\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"6\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSl. No\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eProtein model\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eABL % with 6MWZ as template\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eABL % with alphafold as template\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eAlignment RMSD\u003c/p\u003e\u003cp\u003ewith 6MWZ as template\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003eAlignment RMSD\u003c/p\u003e\u003cp\u003ewith AlphaFold as Template\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eLasR-Can\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e89.6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e96.7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.260 \u0026Aring;\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.673 \u0026Aring;\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eLasR-Var1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e90.0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e95.3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.223 \u0026Aring;\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e1.607 \u0026Aring;\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eLasR-Var2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e90.0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e94.8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.293 \u0026Aring;\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.741 \u0026Aring;\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eThe modelled 3D structures, Ramachandran plots and ProSA-web server evaluations of the best LasR models derived from the alpha fold template are depicted in (Supplementary Fig.\u0026nbsp;2). The Ramachandran plot shows that all the three proteins had more than 94% of the residues in the favourable regions, which align with the previous studies that classify good quality model with ABL % above 90\u003csup\u003e12\u003c/sup\u003e. Notably, no residues occupied in the disallowed regions in LasR-Can and LasR-Var2 suggesting that the models have good stereo chemical properties and are reliable. Modelled proteins having no residues in the disallowed regions have previously been reported\u003csup\u003e\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e,\u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e53\u003c/span\u003e\u003c/sup\u003e. The Z scores for LasR-Can, LasR-Var1 and LasR-Var2 were \u0026minus;\u0026thinsp;7.18, -7.16, -and 7.24 respectively and the corresponding LG scores were 11.197, 11.022, and 11.134 respectively. The Z scores indicate that the modelled proteins fall within the range of NMR solved protein structures, confirming their high quality, as proteins tend to show better quality with more negative Z scores\u003csup\u003e\u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e54\u003c/span\u003e\u003c/sup\u003e. The LG scores, essential in determining the model quality of the protein and measure the structural accuracy of the predicted proteins were assessed using the ProQ web server. As seen from our study, the LG score of all the three protein models are very high (\u0026gt;\u0026thinsp;11) suggesting that the modelled proteins closely aligns with the native structure and will provide stronger insights into drug target interactions\u003csup\u003e\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e\u003c/sup\u003e. Furthermore, multiple sequence alignment of the three proteins showed highly conserved amino acid residues. The mutation sites for LasR-Var1 and LasR-Var2 are located at M144I and R180W sites, respectively (Supplementary Fig.\u0026nbsp;3).\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec13\" class=\"Section2\"\u003e\u003ch2\u003ePost docking analysis of phytochemicals on three LasR receptor models\u003c/h2\u003e\u003cp\u003eThe docking studies involving 44 selected phytochemicals from \u003cem\u003eC. munbyi\u003c/em\u003e along with the Native ligand were analysed using PyMOL visualization software. A total of 135 docking studies, yielding different docking poses, were analysed, showing docking scores between \u0026minus;\u0026thinsp;5 to -8 kcal/mol (Supplementary Table \u003cspan refid=\"MOESM5\" class=\"InternalRef\"\u003eS5\u003c/span\u003e). For each docking study the corresponding receptor file was imported first, followed by sequential import of all phytochemical docking poses. Amino acid residues within 5\u0026Aring; area of each docked poses were selected to predict the number of H-bond interactions with the receptor residues. Phytochemicals lacking H-bond interactions with the amino acid residues from the three receptors were excluded from the analysis, resulting in 28, 26 and 27 phytochemicals remaining for LasR canonical, variant 1 and variant 2, respectively. Compounds forming H bonds with proteins are strongly anchored and oriented across the binding pocket regions thus indicate precise binding and can accurately interact with the proteins active site due to its stability. Compounds which do not form H bonds more likely bind loosely to the active site residues with less specificity henceforth affecting their biological significance. The detailed information of the number of docked conformations for these phytochemicals is provided in (Supplementary Tables S2, S3 and S4).\u003c/p\u003e\u003cp\u003eWhile suitable docking scores highlight the potential binding, they do not fully explain binding efficacy. Therefore, the post-docking analysis was refined by including the number of docked conformations and number of H-bond interactions with the conserved residues predicted from the multiple sequence alignment of LasR sequences (Supplementary Fig.\u0026nbsp;3).Then the phytochemicals were ranked with the descending order of the number of docked conformation (Supplementary Table \u003cspan refid=\"MOESM5\" class=\"InternalRef\"\u003eS5\u003c/span\u003e) and top three compounds were selected for docking analysis on three receptor models as well as further simulation studies. The leading compounds identified from this study includes the Native ligand; N-3-Oxo-Dodecanoyl-L-Homoserine, Cuminaldehyde and Sabinyl acetate. Cuminaldehyde and Sabinyl acetate account for 0.29% and 0.31% of the total proportion of essential oils of \u003cem\u003eC. munbyi\u003c/em\u003e respectively\u003csup\u003e\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e\u003c/sup\u003e. Though other phytocompounds account for higher proportions of the total oil volume, these two compounds identified as hit molecules in our study show significant good docking scores and favourable binding interactions with the QS receptor LasR and its two variants. Although present in trace amounts, these two molecules precisely accommodate in the proteins active site owning to the presence of specific functional groups which can have stronger affinity to interact with the protein molecules. Prioritizing these compounds as potential lead molecules highlights the significance of low proportion phytocompounds as lead drug candidates. It is observed that prior to the Native ligand, Cuminaldehyde showed the highest number of docked confirmations (20) showing H bond interactions across LasR-Can, LasR-Var1 and LasR-Var2 followed by Sabinyl acetate with 19 docked confirmations (Supplementary Table \u003cspan refid=\"MOESM5\" class=\"InternalRef\"\u003eS5\u003c/span\u003e). The two compounds showed significant H bond interactions with conserved amino acids across different docked confirmations of the three LasR proteins suggesting that they have more stable and robust binding patterns beyond just the docking scores. The docking confirmation having the best docked scores of Cuminaldehyde, Sabinyl acetate and the Native ligand with LasR and its two variants were tabulated as shown in (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). It was observed from the table that the docking scores of Cuminaldehyde and Sabinyl acetate were reasonable (-6.98 to -7.35 kcal/mol) and they were proceeded for further analysis.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eComparative best docking scores of the lead compounds Cuminaldehyde, Sabinyl acetate and the Native ligand with the proteins: LasR-Can, LasR-Var1, LasR-Var2.\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"4\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCompound name\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eLasR-Can\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eLasR-Var1\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eLasR-Var2\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCuminaldehyde\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e-6.98\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e-7.25\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e-7.35\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSabinyl acetate\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e-7.09\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e-7.22\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e-7.21\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eN-3-Oxo-Dodecanoyl-L-Homoserine(Native Ligand)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e-8.25\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e-8.3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e-8.02\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec14\" class=\"Section2\"\u003e\u003ch2\u003e\u003cem\u003eC. munbyi\u003c/em\u003e phytochemicals suitably accommodate in the LasR receptor models\u003c/h2\u003e\u003cp\u003eThe molecular docking results for selected phytochemicals of \u003cem\u003eC. munbyi\u003c/em\u003e indicate potential docking scores (Supplementary Table \u003cspan refid=\"MOESM2\" class=\"InternalRef\"\u003eS2\u003c/span\u003e, S3, S4). The top three entities, like N-3-Oxo-Dodecanoyl-L-Homoserine, Cuminaldehyde and Sabinyl acetate, were selected to describe their suitable accommodation in the ligand binding sites of three LasR receptor models. In the LasR canonical model, all three selected entities were docked within the ligand-binding domain (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eA). The Native ligand formed H-bond interactions with the residues W60, D73, T75, W88, Y93 and S129, where W60, T75, W88, Y93 and S129 were highly conserved. The best docked pose of the Native ligand has interactions with Y93, W60 and S129 (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eB). Cuminaldehyde forms an H-bond interaction with all highly conserved residues R61, Y64, T75, Y93 and S129 and its best docked pose has interactions with Y93 (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eC). Cuminaldehyde has been reported to show antibacterial and antibiofilm activity against \u003cem\u003eE. coli\u003c/em\u003e and \u003cem\u003eS. aureus\u003c/em\u003e\u003csup\u003e\u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e55\u003c/span\u003e\u003c/sup\u003e. The second potential phytochemical, Sabinyl acetate, formed H-bond interaction with the conserved residues R61, T75 and S129, with the best docked pose exhibiting interactions with S129 (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eD). High concentration of Sabinyl acetate in essential oil have been reported to exhibit antibacterial activity against \u003cem\u003eE. coli\u003c/em\u003e, \u003cem\u003eS. aureus\u003c/em\u003e and \u003cem\u003eP. aeruginosa\u003c/em\u003e\u003csup\u003e\u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e56\u003c/span\u003e\u003c/sup\u003e. A similar study showed that natural compounds produced by fungi growing in termite habitats exhibit good activity against the quorum-sensing protein LasR\u003csup\u003e\u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e57\u003c/span\u003e\u003c/sup\u003e. Their study highlighted that the compound FridamycinA formed H bonds with residues W54, R55, Y87, L104, L119, and S123 in the LasR protein active site and showed a gold score of 75.46. Another study revealed that the compounds 6-Gingerol and Curcumin act as a LasR inhibitors in \u003cem\u003eP. aeruginosa\u003c/em\u003e\u003csup\u003e\u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e58\u003c/span\u003e\u003c/sup\u003e. Our lead compounds Cuminaldehyde (-6.98 kcal/mol) and Sabinyl acetate (-7.09 kcal/mol) exhibited stronger binding affinities than previously reported LasR inhibitors including Methyl dihydrojasmonate(-5.92 kcal/mol), Methyl benzoate(-5.81 kcal/mol) and 4a-Methyl-4,4a,5,6,7,8-hexahydro-2(3H)- (-5.47 kcal/mol) derived from extracts of \u003cem\u003eCassia occidentalis\u003c/em\u003e L\u003csup\u003e6\u003c/sup\u003e and PBA 27 (-5.10 kcal/mol) from marine derived metabolites\u003csup\u003e\u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e59\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003eThe LasR variant 1, characterized by a point mutation at M144I, was modelled with the same template that applied to the LasR canonical model and further refined using the AlphaFold-generated model. It was observed that most of the docked conformation of all three selected entities was within the ligand binding site (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eA). Native ligand formed H-bond interaction with all highly conserved residues, including E48, W60, R61, A70, T75, W88, Y93, T115, A127 and S129. The optimal docked pose for the Native ligand exhibited H-bond interactions with S129, Y93 and W60 (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eB). Cuminaldehyde formed H-bond interactions with all highly conserved residues, such as W60, R61, D73, T75, Y93, L110, A127 and S129, with its best docked pose demonstrating interactions with R61 and W60 (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eC). Sabinyl acetate similarly formed H-bond interactions with residues W60, R61, Y93, A127 and S129, with its optimal pose showing interactions with S129 (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eD).\u003c/p\u003e\u003cp\u003eThe LasR variant 2, characterized by a point mutation at R180W, was modelled with the same template as the LasR canonical model and refined by the AlphaFold model. It was observed that most of the docked conformation of all three selected entities was within the ligand binding site (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eA). The Native ligand formed H-bond interactions with all highly conserved residues like T75, W60, S129, R61, A127, Y64, and Y56. The best docked pose of the Native ligand showed interactions with T75 (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eB). Cuminaldehyde formed H-bond interaction with key residues Y93, R61, S129, T75, W60, D65, W88, T115 and presented its best docked pose having interaction with Y93 (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eC). The second potential phytochemical Sabinyl acetate formed H-bond interaction with conserved residues S129, R61, T75, W60, A127, with the best docked pose exhibiting interactions with T75 (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eD). Overall, our docking studies confirm that Cuminaldehyde and Sabinyl acetate effectively bind to both LasR variants and are harmonical with its docking scores with LasR. This suggests that the two compounds can be effectively used to inhibit LasR mutant proteins.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec15\" class=\"Section2\"\u003e\u003ch2\u003eDrug likeness and pharmacokinetic properties of lead compounds\u003c/h2\u003e\u003cp\u003eThe two lead compounds, Cuminaldehyde, and Sabinyl acetate, were evaluated to analyse their drug likeness properties and described in (Supplementary Table \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e). Cuminaldehyde has a low molecular weight of 148.20g/mol, XLOGP3 value of 2.7, no hydrogen bond donors (HBD), one hydrogen bond acceptor (HBA) and 2 rotatable bonds. Sabinyl acetate has a molecular weight of 194.27g/mol, XLOGP3 of 2.4, no HBD, 2 HBA and 3 rotatable bonds. From the results, it was observed that the molecular weight of the compounds were less than 500g/mol, H bond donors were less than 5, and H bond acceptors were less than 10, indicating a balance between solubility, permeability and metabolic stability\u003csup\u003e\u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e60\u003c/span\u003e\u003c/sup\u003e. Both the compounds showed less (\u0026lt;\u0026thinsp;10) rotatable bonds, indicating that they have good oral absorption and high bioavailability\u003csup\u003e\u003cspan citationid=\"CR61\" class=\"CitationRef\"\u003e61\u003c/span\u003e\u003c/sup\u003e. Analysis of the pharmacokinetic properties of the 2 lead compounds reveals that these compounds show characteristics which are favourable for the drug development process and are appropriate for toxicity evaluation.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec16\" class=\"Section2\"\u003e\u003ch2\u003ePrediction of Gastrointestinal Absorption and Brain Penetration using BOILED-Egg Model\u003c/h2\u003e\u003cp\u003eBoiled egg model provides insights into the absorption and distribution properties of the lead compounds. The egg shaped plot is divided into three parts. The white region represents the human area of high intestinal absorption (HIA), the yellow region represents the area of high blood-brain barrier (BBB) penetration\u003csup\u003e\u003cspan citationid=\"CR62\" class=\"CitationRef\"\u003e62\u003c/span\u003e\u003c/sup\u003e. Blue colour indicates status for P glycoprotein substrate (PGP+) and the red colour suggests compounds which are not P glycoprotein substrates(PGP-). Figure\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e shows the boiled egg model of Cuminaldehyde and Sabinyl acetate, which are located in the yellow region, indicating good blood-brain barrier (BBB) penetration. Both the compounds were found not to be P Glycoprotein substrates and do not efflux suggesting that they have good bioavailability and can get accumulated in the targeted site.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec17\" class=\"Section2\"\u003e\u003ch2\u003eToxicity analysis\u003c/h2\u003e\u003cp\u003eComputational toxicity profiling is a crucial step in the field of drug discovery, as it determines the safety profiles of potential drug candidates for therapeutic use. Protox-3 webserver was employed to compute the toxicity profiles of the lead compounds. It predicts the 2D similarity analysis between the molecules under examination\u003csup\u003e\u003cspan citationid=\"CR63\" class=\"CitationRef\"\u003e63\u003c/span\u003e\u003c/sup\u003e. This provides insights into the toxicity risks of the compounds by analysing their chemical and structural features. The compounds Cuminaldehyde and Sabinyl acetate displayed no evidence of hepatotoxicity, carcinogenicity, cytotoxicity, mutagenicity or respiratory toxicity. Both compounds were classified within toxicity class 4 and 5 which underscores their safety profile and thereby indicates low toxicity potential. The probability of non-toxicity of the compounds across the different parameters is shown in (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003e). Cuminaldehyde exhibited a high probability of non-toxicity (\u0026gt;\u0026thinsp;70%) with regard to respiratory toxicity, mutagenicity, cytotoxicity and hepatotoxicity. Sabinyl acetate also showed promising results with \u0026gt;\u0026thinsp;60% probability of non-toxicity for hepatoxicity and mutagenicity, and \u0026gt;\u0026thinsp;70% probability of non-toxicity for cytotoxicity and respiratory toxicity. Cuminaldehyde showed inactive respiratory toxicity with a probability of 95% suggesting low risk to the respiratory system. It is inactive for mutagenicity with a probability of 97%, indicating its very low likelihood of cell mutations and inactive for cytotoxicity with a probability of 89%, indicating very low chances to cause cell toxicity. It showed inactive hepatotoxicity with a probability of 71% which implies a low risk of damage to liver cells. Sabinyl acetate shows inactive respiratory toxicity with a probability of 70%, indicating a low risk of respiratory damage, and inactive mutagenicity with a probability of 65%, suggesting a low likelihood to be mutagenic. Cytotoxicity is inactive with a probability of 77% suggesting a low risk of cell damage. Hepatotoxicity is inactive with a probability of 62% indicating its low likelihood to harm liver cells. In comparison, carcinogenicity analysis revealed that Cuminaldehyde and Sabinyl acetate have 52% and 59% probability of not being carcinogenic agents, which moderately show low potential to cause cancer. Overall, both compounds were predicted to have low toxicity and, therefore, could be promising candidates for QS and biofilm inhibition. Many earlier reports have revealed the toxicity analysis of phytocompounds towards developing antibacterial therapies using the Protox webserver \u003csup\u003e\u003cspan additionalcitationids=\"CR65\" citationid=\"CR64\" class=\"CitationRef\"\u003e64\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR66\" class=\"CitationRef\"\u003e66\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec18\" class=\"Section2\"\u003e\u003ch2\u003eAcute toxicity profiling\u003c/h2\u003e\u003cp\u003eThe acute toxicity analysis of Cuminaldehyde and Sabinyl acetate was investigated using the STopTox online web tool. It is a machine learning based webserver which gives information about toxicity based on 6-pack assays \u003cb\u003e(\u003c/b\u003eacute oral toxicity, acute dermal toxicity, acute inhalation toxicity, skin irritation and corrosion, eye irritation and corrosion, and skin sensitization). The test results of the compounds are shown in Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e. Both Cuminaldehyde and Sabinyl acetate showed negative results for acute dermal toxicity, skin irritation and corrosion. Cuminaldehyde showed prediction probabilities as 51% for acute dermal toxicity and 50% for skin irritation and corrosion, which fall into the non-toxic category. Sabinyl acetate showed probabilities of 59% and 60%, indicating a lack of acute dermal toxicity and skin irritation. Cuminaldehyde showed negative results for acute inhalation toxicity, acute oral toxicity, eye irritation and corrosion with 91%, 61% and 63% prediction probabilities. Regarding skin sensitization, Cuminaldehyde showed toxicity with a prediction probability of 60% whereas Sabinyl acetate was non-toxic, showing a prediction probability of 60%. STopTox classified Sabinyl acetate as toxic with regard to acute inhalation toxicity, acute oral toxicity and eye irritation and corrosion with prediction probabilities of 59%, 61% and 56%. Overall, the toxicity profiles of Cuminaldehyde and Sabinyl acetate are favourable. The predicted fragment contribution of both compounds are shown in (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003e) and (Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003e). The green areas in the contour maps of functional groups indicate no toxicity, and red areas indicate toxic properties. Many earlier studies have reported the acute toxicity of drug candidates through the STopTox server to evaluate their safety\u003csup\u003e\u003cspan citationid=\"CR67\" class=\"CitationRef\"\u003e67\u003c/span\u003e,\u003cspan citationid=\"CR68\" class=\"CitationRef\"\u003e68\u003c/span\u003e\u003c/sup\u003e. This six-pack acute toxicity analysis is a valuable approach towards rational drug discovery, which can minimise the need for animal testing\u003csup\u003e\u003cspan citationid=\"CR67\" class=\"CitationRef\"\u003e67\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eAcute toxicity of the selected phytocompounds through STopTox web server.\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"5\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eParameter\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eCuminaldehyde (Prediction)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eCuminaldehyde (Confidence Score in %)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eSabinyl acetate (Prediction)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eSabinyl acetate (Confidence Score in %)\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAcute inhalation toxicity\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eNegative\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e91\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003ePositive\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e59\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAcute oral toxicity\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eNegative\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e61\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003ePositive\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e61\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAcute dermal toxicity\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eNegative\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e51\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eNegative\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e59\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eEye irritation and corrosion\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eNegative\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e63\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003ePositive\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e56\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSkin sensitization\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003ePositive\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e60\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eNegative\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e60\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSkin irritation and corrosion\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eNegative\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e50\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eNegative\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e60\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec19\" class=\"Section2\"\u003e\u003ch2\u003eMolecular Dynamic simulations\u003c/h2\u003e\u003cp\u003eMD simulation is a crucial computational tool to understand the dynamic behaviour of protein ligand interactions under specific conditions over a given period of time. It is a well-established structure‐based approach in computer aided drug design (CADD) to understand the atomic‐level interactions of protein\u0026ndash;ligand complexes through analysis of deviation, fluctuation, protein folding and interaction\u003csup\u003e\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e\u003c/sup\u003e. To access the stability of the two best hit compounds; Cuminaldehyde and Sabinyl acetate, MD simulations were conducted on the protein ligand complexes. The analysis considered parameters such as Root Mean Square Deviation (RMSD), RMSF, Radius of Gyration (Rg), Solvent Accessible Surface Area (SASA) and Principle Component Analysis (PCA) to understand the behaviour of interactions between the LasR proteins; LasR-Can, LasR-Var1, LasR-Var2 and the selected phytocompounds.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec20\" class=\"Section2\"\u003e\u003ch2\u003eRMSD analysis\u003c/h2\u003e\u003cp\u003eRoot mean square deviation (RMSD) analysis was conducted to evaluate the overall structural flexibility of the Cα backbone atoms of LasR-Can, LasR-Var1 and LasR-Var2 when bound to the Native ligand, Sabinyl acetate and Cuminaldehyde during a 100ns MD simulation (Fig.\u0026nbsp;\u003cspan refid=\"Fig9\" class=\"InternalRef\"\u003e9\u003c/span\u003eA-C). In the LasR-Can system (Fig.\u0026nbsp;\u003cspan refid=\"Fig9\" class=\"InternalRef\"\u003e9\u003c/span\u003eA), the complex with the Native ligand exhibited the most stable trajectory, maintaining a relatively consistent RMSD of approximately 0.9 nm throughout the simulation, indicating a well-formed and rigid protein-ligand complex. In contrast, the complexes formed with Sabiny acetate and Cuminaldehyde exhibited elevated and more fluctuating RMSD profiles, stabilizing around 1.4\u0026ndash;1.5 nm and 1.5\u0026ndash;1.6 nm, respectively. For LasR-Can, the Cuminaldehyde complex exhibited an average RMSD of 1.214 nm, which is slightly lower than that of Sabinyl acetate (1.228 nm), indicating enhanced binding and stability. These findings are in close proximity with the results from a previously published study on LasR inhibitors\u003csup\u003e\u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e59\u003c/span\u003e\u003c/sup\u003e. Our study suggests that while the Native ligand promotes a compact and stable conformation with LasR-Can, both phytocompounds induce conformational variability but achieved stability over time upon binding with LasR-Can. In the LasR-Var1 system (Fig.\u0026nbsp;\u003cspan refid=\"Fig9\" class=\"InternalRef\"\u003e9\u003c/span\u003eB), the RMSD trajectories for the Native ligand and Sabinyl acetate exhibited similar patterns, but with significant fluctuations ranging from 0.4 to 1.6 nm. The RMSD profile of the LasR-Var1-Sabinyl acetate complex indicated early stabilization after 20 ns, with only minor deviations, suggesting structural convergence over time. In contrast, Cuminaldehyde demonstrated a more favourable dynamic profile, initially increasing gradually from ~\u0026thinsp;0.3 nm and stabilizing around 0.4 to 0.5 nm in the latter half of the simulation. The comparatively lower amplitude of fluctuations for Cuminaldehyde suggests more consistent binding interactions and improved conformational maintenance in LasR-Var1. Interestingly, in the LasR-Var2 system (Fig.\u0026nbsp;\u003cspan refid=\"Fig9\" class=\"InternalRef\"\u003e9\u003c/span\u003eC), both the Native ligand and Sabinyl acetate displayed broad and persistent RMSD fluctuations ranging from ~\u0026thinsp;0.4 to 1.4 nm over the course of the simulations. Such variability indicates considerable structural rearrangements. In contrast, Cuminaldehyde demonstrated a highly stable binding behaviour with RMSD values consistently confined to the ~\u0026thinsp;0.6 to 0.8 nm range. This narrow deviation band suggests strong and stable interactions with LasR-Var2, potentially favouring its bioactivity and inhibitory potential compared to the other ligands. Our findings demonstrated higher RMSD values than those reported from previous studies involving protein variants\u003csup\u003e\u003cspan citationid=\"CR69\" class=\"CitationRef\"\u003e69\u003c/span\u003e,\u003cspan citationid=\"CR70\" class=\"CitationRef\"\u003e70\u003c/span\u003e\u003c/sup\u003e. These discrepancies may stem from variations in simulation parameters, protein structure, and ligand flexibility.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec21\" class=\"Section2\"\u003e\u003ch2\u003eRMSF analysis\u003c/h2\u003e\u003cp\u003eTo complement the RMSD findings and gain insights into local flexibility patterns, RMSF analysis were performed for the Cα atoms of LasR-Can, LasR-Var1, and LasR-Var2 in complex with the Native ligand, Sabinyl acetate and Cuminaldehyde (Fig.\u0026nbsp;\u003cspan refid=\"Fig9\" class=\"InternalRef\"\u003e9\u003c/span\u003eD\u0026ndash;F). In the LasR-Can system (Fig.\u0026nbsp;\u003cspan refid=\"Fig9\" class=\"InternalRef\"\u003e9\u003c/span\u003eD), the Native ligand-bound complex displayed a stable residue fluctuation profile, with most RMSF values confined between 0.1 and 0.35 nm, suggesting a compact and conformationally stable structure. Sabinyl acetate induced higher residue mobility, with RMSF peaks reaching upto\u0026thinsp;~\u0026thinsp;1.0 nm mainly in the loop and surface exposed regions. Cuminaldehyde showed the highest degree of flexibility, with certain residues showing fluctuations as high as ~\u0026thinsp;1.4 nm. As anticipated, higher peaks in the RMSF plot correspond to regions of increased atomic motion. In contrast, the LasR-Var1 system (Fig.\u0026nbsp;\u003cspan refid=\"Fig9\" class=\"InternalRef\"\u003e9\u003c/span\u003eE) demonstrated comparable RMSF profiles across all three ligands, with no significant differences in fluctuation magnitude or pattern. Residue fluctuation for the Native ligand, Sabinyl acetate and Cuminaldehyde mostly remained in the range of ~\u0026thinsp;0.1\u0026ndash;0.5 nm, showing consistent peaks in the flexible loop regions and terminal domains. This indicates that the ligand identity has a relatively minor influence on residue-level dynamics. A distinct trend was observed in the LasR-Var2 (Fig.\u0026nbsp;\u003cspan refid=\"Fig9\" class=\"InternalRef\"\u003e9\u003c/span\u003eF). Among the three ligands, Cuminaldehyde induced the lowest overall RMSF with fluctuations remaining mostly below ~\u0026thinsp;0.2 nm across the structure, indicating a highly stable and rigid complex. The Sabinyl acetate bound complex showed moderately higher fluctuation, with peaks around ~\u0026thinsp;0.1\u0026ndash;0.9 nm. Both Sabinyl acetate and Native ligand bound complex showed higher flexibility, following each other's fluctuation patterns, with values rising to ~\u0026thinsp;0.9 nm, especially in the loop and terminal regions. These results highlight Cuminaldehyde\u0026rsquo;s potential for effective binding with LasR-Var2 at the residue level, corroborating its consistent RMSD profile and suggesting tight and favourable binding interactions.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec22\" class=\"Section2\"\u003e\u003ch2\u003eRg analysis\u003c/h2\u003e\u003cp\u003eThe compactness of a protein upon interacting with the compounds were investigated through the radius of gyration (Rg). Figure\u0026nbsp;\u003cspan refid=\"Fig10\" class=\"InternalRef\"\u003e10\u003c/span\u003e(A-C) shows the Rg plots of the protein ligand complexes. The average Rg values for Native ligand, Sabinyl acetate and Cuminaldehyde in complex with LasR-Can are 2.113 nm, 2.163 nm and 2.131 nm. The average Rg values for all the complexes are very similar, indicating minimal differences among the complexes. The Rg plot of the Native ligand in complex with LasR-Can showed minimum fluctuations and remained stable throughout the simulation period. Rg plots of Sabinyl acetate and Cuminaldehyde bound to LasR-Can initially showed some fluctuations, but ultimately attained stability over time. The measured Rg values of the top compounds from our findings are lower than previous reported values for phytochemicals acting as inhibitors against \u003cem\u003eK\u003c/em\u003e. \u003cem\u003epneumonia\u003c/em\u003e\u003csup\u003e\u003cspan citationid=\"CR71\" class=\"CitationRef\"\u003e71\u003c/span\u003e\u003c/sup\u003e, MRSA\u003csup\u003e\u003cspan citationid=\"CR72\" class=\"CitationRef\"\u003e72\u003c/span\u003e\u003c/sup\u003e and \u003cem\u003eL\u003c/em\u003e. \u003cem\u003eMonocytogenes\u003c/em\u003e\u003csup\u003e\u003cspan citationid=\"CR73\" class=\"CitationRef\"\u003e73\u003c/span\u003e\u003c/sup\u003e. This suggests that the compounds in complex with LasR-Can are more compact and properly folded throughout the simulation period. The average Rg values for Native ligand, Sabinyl acetate and Cuminaldehyde with LasR-Var1 are 2.166 nm, 2.170 nm and 2.060 nm. The compounds showed similar values as compared to the native ligand and initially showed some fluctuations, but with time, they attained stability till the end of the simulation period. The Rg plot of LasR-Var2 in complex with Native ligand and Sabinyl acetate showed an average Rg values of 2.135 nm and 2.190 nm, which are consistent. The Rg plot of Cuminaldehyde with LasR-Var2 showed an average Rg value of 1.929 nm which is lower that Native ligand which implies that the complex is more compact and stable. The Rg results of the two variants in complex with the top lead compounds are marginally similar to those reported in a study examining litchen derived compound, Barbatoli cacid and Orcinyl lecanorate as inhibitors of \u003cem\u003eS. aureus\u003c/em\u003e mutant\u003csup\u003e\u003cspan citationid=\"CR70\" class=\"CitationRef\"\u003e70\u003c/span\u003e\u003c/sup\u003e. Overall, findings from our present study suggest that all protein ligand complexes exhibited similar compactness behaviour and were properly folded throughout the simulations.\u003c/p\u003e\u003cdiv id=\"Sec23\" class=\"Section3\"\u003e\u003ch2\u003eSASA analysis\u003c/h2\u003e\u003cp\u003eThe SASA of the compounds bound to the LasR proteins provides insights into the surface area of the protein exposed to solvent, as well as the solvent like characteristics of a protein-ligand complexes. Figure. 10(D-F) displays the SASA plots for all the protein ligand complexes. The average SASA value for Native ligand, Sabinyl acetate and Cuminaldehyde bound with LasR-Can are 133.273 nm\u003csup\u003e2\u003c/sup\u003e, 132.652 nm\u003csup\u003e2\u003c/sup\u003e and 133.829 nm\u003csup\u003e2\u003c/sup\u003e, respectively, indicating that they are very similar. Throughout the 100ns simulation, minimum variations in the SASA values were observed for the complexes. For LasR-Var1, the average SASA values exhibited a very narrow range, with 131.540 nm\u003csup\u003e2\u003c/sup\u003e for Native ligand, 133.232 nm\u003csup\u003e2\u003c/sup\u003e for Sabinyl acetate and 132.953 nm\u003csup\u003e2\u003c/sup\u003e for Cuminaldehyde exhibiting only minimum fluctuations. The SASA plots of LasR-Var2 in complex with Native ligand, Sabinyl acetate and Cumindehyde showed average SASA values of 133.226 nm\u003csup\u003e2\u003c/sup\u003e, 133.608 nm\u003csup\u003e2\u003c/sup\u003e and 132.237 nm\u003csup\u003e2\u003c/sup\u003e, respectively. These findings suggest that upon binding of Cuminaldehyde and Sabinyl acetate, the LasR-Var2 protein experienced comparatively greater exposure to the solvents due to distinct hydrophobic and hydrophilic interactions. Notably, Cuminaldehyde, when bound to LasR-Var1 and Las-uVar2, showed slightly lower SASA values as compared to Sabinyl acetate, suggesting that the two LasR variants had reduced exposure to solvent when bound to Cuminaldehyde, implying better stability and higher binding interface area. The SASA findings from our complexes align with a previous study, which identified kaempferol as an inhibitor of QS regulator protein in \u003cem\u003eP. aeruginiosa\u003c/em\u003e\u003csup\u003e\u003cspan citationid=\"CR74\" class=\"CitationRef\"\u003e74\u003c/span\u003e\u003c/sup\u003e. Our SASA values observed for the top compounds were lower than earlier reported studies involving compounds as inhibitors against \u003cem\u003eA. baumanni\u003c/em\u003e\u003csup\u003e\u003cspan citationid=\"CR75\" class=\"CitationRef\"\u003e75\u003c/span\u003e,\u003cspan citationid=\"CR76\" class=\"CitationRef\"\u003e76\u003c/span\u003e\u003c/sup\u003e, \u003cem\u003eK. pneumonia\u003c/em\u003e\u003csup\u003e\u003cspan citationid=\"CR77\" class=\"CitationRef\"\u003e77\u003c/span\u003e,\u003cspan citationid=\"CR71\" class=\"CitationRef\"\u003e71\u003c/span\u003e\u003c/sup\u003e and \u003cem\u003eE. faecalis\u003c/em\u003e\u003csup\u003e\u003cspan citationid=\"CR78\" class=\"CitationRef\"\u003e78\u003c/span\u003e\u003c/sup\u003e. Thereby, our findings strongly imply that the protein structures are less exposed to solvent and water molecules upon binding with Cuminaldehyde and Sabinyl acetate, making them a more compact and firmly bound complex.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv id=\"Sec24\" class=\"Section2\"\u003e\u003ch2\u003ePrinciple Component Analysis\u003c/h2\u003e\u003cp\u003eThe principle component analysis (PCA) is an important tool for identifying fluctuations in a protein structure when bound to ligands. It is widely used to study atomic simulations of proteins and to understand correlated movements. In this study, PCA was performed to analyse the motion of the LasR protein and its two variants when complexed with Native ligand, Sabinyl acetate and Cuminaldehyde under both apo and ligand-bound conditions (Fig.\u0026nbsp;\u003cspan refid=\"Fig11\" class=\"InternalRef\"\u003e11\u003c/span\u003eA-C). Cartesian coordinates reflecting atomic displacements along the MD trajectories were used to construct covariance matrices that represent the proteins\u0026rsquo; accessible degrees of freedom (DOF). Decomposition of these covariance matrices into orthogonal eigenvectors allowed characterization of collective motions, with the associated eigenvalue indicating the magnitude of variance. Larger eigenvalues correspond to motions occurring over larger spatial scales. Figure. 11(A-C) shows two-dimensional projections along the first two principal components (PC1 and PC2) for LasR-Can, LasR-Var1, and LasR-Var2 when bound to the native ligand, under apo conditions. Upon ligand binding with Sabinyl Acetate and Cuminaldehyde, these projections reveal altered conformational sampling. Notably, the variants explored broader or distinct conformational subspaces compared to LasR-Can, indicating differences in flexibility and dynamic behaviour. Across simulations, LasR-Var1 occupied a larger conformational subspace, suggesting increased atomic mobility and structural plasticity (Fig.\u0026nbsp;\u003cspan refid=\"Fig11\" class=\"InternalRef\"\u003e11\u003c/span\u003eB), whereas LasR-Var2 demonstrated comparatively restricted motion in the presence of Cuminaldehyde, implying stabilization effect (Fig.\u0026nbsp;\u003cspan refid=\"Fig11\" class=\"InternalRef\"\u003e11\u003c/span\u003eC).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cdiv id=\"Sec25\" class=\"Section3\"\u003e\u003ch2\u003eDensity Functional Theory (DFT) calculations\u003c/h2\u003e\u003cp\u003eDFT is a highly effective theoretical framework with numerous applications, including the determination of the kinetic and thermodynamic stability of compounds, structural calculations, molecular interaction analysis, and assessments of the optical and electronic properties of atoms and molecules\u003csup\u003e\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e\u003c/sup\u003e. The geometry optimised structures of selected phytochemicals did not show any imaginary frequencies, suggesting that they reached their lowest energy gradient. The HOMO and LUMO values of the compounds, along with their energy gaps, are depicted in (Fig.\u0026nbsp;\u003cspan refid=\"Fig12\" class=\"InternalRef\"\u003e12\u003c/span\u003e). The colour coding area is represented as green, which shows the regions having a high probability of finding electrons and red shows the regions having a low probability of finding electrons. The stability of the compounds is influenced by several factors, such as dipole moment, HOMO, LUMO and atomic properties. The energetic parameters based on DFT analysis are shown in (Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e). The compound Cuminaldehyde exhibits a lower energy gap (∆E) of 5.071 eV, which is less than that of the Native ligand (5.925eV) and Sabinyl acetate (6.162eV), suggesting that it has higher chemical reactivity and may demonstrate better biological activity (Fig.\u0026nbsp;\u003cspan refid=\"Fig12\" class=\"InternalRef\"\u003e12\u003c/span\u003eA, C). In the present study, the energy gaps (ΔE) of Cuminaldehyde and Sabinyl acetate were lower than T-muurolol, Valencene which have previously been reported as effective inhibitors against MDR \u003cem\u003eS. aureus\u003c/em\u003e\u003csup\u003e\u003cspan citationid=\"CR79\" class=\"CitationRef\"\u003e79\u003c/span\u003e\u003c/sup\u003e and \u003cem\u003eE. coli\u003c/em\u003e, respectively\u003csup\u003e\u003cspan citationid=\"CR80\" class=\"CitationRef\"\u003e80\u003c/span\u003e\u003c/sup\u003e. This implies that our lead phytocompounds have more potential to bind to the target proteins and likely exhibit enhanced antibacterial activity. The ionising potential (IP) of Cuminaldehyde and Sabinyl acetate are similar, implying that both compounds show good reactivity, whereas Cuminaldehyde showed enhanced activity owing to its higher IP value. Cuminaldehyde also exhibits the highest electronegativity (χ) and electrophilicity index (ψ), indicating its strong ability to attract electrons and form stronger bonds with molecules. The electrophilicity index (ψ) for all investigated compounds are more than 1.5eV indicating that they are strong electrophiles\u003csup\u003e\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e\u003c/sup\u003e,values ranging from 1.886 eV to 4.005eV. Electron affinity (EA) of Cuminaldehyde, Sabinyl acetate and Native ligand are 1.971 eV, 0.328 eV and 1.184 eV, respectively.\u003c/p\u003e\u003cp\u003eCuminaldehyde shows a high dipole moment value of 4.2707 Debye (Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e) suggesting that it forms stronger interactions with the proteins through electrostatic attractions and is more suitable for biological activity. Hardness (η) and softness (σ) are the parameters used to determine the stability of a compound in a chemical reaction. Cuminaldehyde demonstrates a lower hardness value (2.535 eV) than Sabinyl acetate (3.081 eV) and shows a higher softness value (0.394 eV) than Sabinyl acetate (0.324 eV). Compounds showing lower hardness exhibit a narrow energy gap, and those with higher hardness show a wider energy gap\u003csup\u003e\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e\u003c/sup\u003e. The findings of our study show a close resemblance with the values of quantum chemical parameters for the phytocompounds 3-Hydroxyisoagatholactone, β-Sitsterol from \u003cem\u003eCyphostemma cyphopetalum\u003c/em\u003e\u003csup\u003e\u003cspan citationid=\"CR81\" class=\"CitationRef\"\u003e81\u003c/span\u003e\u003c/sup\u003e and those of Asperuloside, Asperulosidic acid, and Deacetylasperulosidic acid from \u003cem\u003eMorinda citrifolia\u003c/em\u003e\u003csup\u003e\u003cspan citationid=\"CR82\" class=\"CitationRef\"\u003e82\u003c/span\u003e\u003c/sup\u003e. Our findings are consistent, which shows that Cuminaldehyde is confirmed to be more reactive than Sabinyl acetate. The electrochemical potential (\u0026micro;), (Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e), shows negative values for all the analysed compounds, which indicates good stability in interaction with the proteins. All the compounds, including the Native ligand, show negative values, suggesting a strong interaction with the LasR receptors of \u003cem\u003eP. aeruginosa\u003c/em\u003e.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eDFT based Energetic parameters based of the selected phytocompounds with the reference compound3-oxo-C12-HSL.\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"4\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eParameter\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eCompound Cuminaldehyde\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eCompound\u003c/p\u003e\u003cp\u003eSabinyl acetate\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eNative ligand\u003c/p\u003e\u003cp\u003e(3-oxo-C12-HSL)\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eDipole moment (Debye)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e4.2707\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1.9495\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e3.7921\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eIonising Potential IP (eV)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e7.042\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e6.490\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e7.109\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eElectron affinity EA (eV)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e1.971\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.328\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e1.184\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eElectronegativity χ (eV)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e4.506\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e3.409\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e4.146\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eElectrochemical potential \u0026micro; (eV)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e-4.506\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e-3.409\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e-4.146\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eHardness η (eV)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e2.535\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e3.081\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e2.962\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eSoftness S (eV)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.394\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.324\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.338\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eElectrophilicity Index ψ(eV)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e4.005\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1.886\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e3.247\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec26\" class=\"Section3\"\u003e\u003ch2\u003eMolecular electrostatic potential (MEP) analysis\u003c/h2\u003e\u003cp\u003eThe MEP surface analysis provides insight into the potential reactive sites of the compounds with electrophiles and nucleophiles\u003csup\u003e\u003cspan citationid=\"CR83\" class=\"CitationRef\"\u003e83\u003c/span\u003e\u003c/sup\u003e. It visualizes the positive, negative and neutral electrostatic potential of a surface, represented in different colours (Fig.\u0026nbsp;\u003cspan refid=\"Fig13\" class=\"InternalRef\"\u003e13\u003c/span\u003e). In these maps, red indicates a high density of electrons, and acts as a site for electrophilic attack. Yellow indicates moderate values of MEP, while green denotes areas with zero potential and no electron density. Blue indicates low electron density, and acts as a site of nucleophilic attacks. The increasing order of electrostatic potential is seen as red\u0026thinsp;\u0026lt;\u0026thinsp;yellow\u0026thinsp;\u0026lt;\u0026thinsp;green\u0026thinsp;\u0026lt;\u0026thinsp;blue. For the compound Cuminaldehyde (Fig.\u0026nbsp;\u003cspan refid=\"Fig13\" class=\"InternalRef\"\u003e13\u003c/span\u003eA), the O1 atom is located in the yellow region, indicating a high negative charge. In contrast, the atoms C11, C10, C8, C6, C3, C5, H23, H12, H19 were located in the blue regions, indicating lower electron density and positive charge. In the case of Sabinyl acetate, the MEP map reveals that the atoms O1 and O2 are associated with the yellow and red regions, indicating a high negative charge. Conversely, C11, H26, H25, H27 show high positive charges in the blue regions (Fig.\u0026nbsp;\u003cspan refid=\"Fig13\" class=\"InternalRef\"\u003e13\u003c/span\u003eB). The MEP map of the Native ligand reveals the presence of atoms O2, O3, and O4 in the red regions, surrounding areas of high negative charge. High positive charges are shown by atoms H39, C15, C14, C11 and N5 (Fig.\u0026nbsp;\u003cspan refid=\"Fig13\" class=\"InternalRef\"\u003e13\u003c/span\u003eC). Our results correlate with the MEP maps of compounds derived from \u003cem\u003eMillettia dielsiana\u003c/em\u003e\u003csup\u003e\u003cspan citationid=\"CR84\" class=\"CitationRef\"\u003e84\u003c/span\u003e\u003c/sup\u003e and the metabolite BagremycinA\u003csup\u003e\u003cspan citationid=\"CR85\" class=\"CitationRef\"\u003e85\u003c/span\u003e\u003c/sup\u003e, indicating a similar electronic distribution of atoms. The oxygen and hydrogen atoms of our compounds are involved in forming H bonds with the LasR proteins, as confirmed by the docking studies.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e"},{"header":"Conclusions","content":"\u003cp\u003eThe research identified promising phytochemicals from \u003cem\u003eC. munbyi against the quorum sensing protein LasR and its two variants through computational methods.\u0026nbsp;\u003c/em\u003eMolecular docking analysis provided valuable insights into the binding interaction of phytochemicals with the LasR protein and its variants. Notably, Cuminaldehyde and Sabinyl acetate emerged as the top hit compounds, demonstrating favourable drug likeness, toxicological properties and binding affinities comparable to those of the native ligand. MD simulation over 100 ns confirmed the ability of these compounds to accurately accommodate in the ligand binding regions of the\u0026nbsp;LasR\u0026nbsp;proteins, revealing stable ligand binding interactions throughout the simulations.\u0026nbsp;Additionally, DFT computational study suggests that Cuminaldehyde and Sabinyl acetate could serve as promising anti-QS agents against LasR proteins and its variants. The key research findings from this study highlight that the identified phytocompounds can be used as anti-quorum sensing agents against MDR \u003cem\u003eP. aeruginosa.\u0026nbsp;\u003c/em\u003eThe promising results from these \u003cem\u003ein silico\u003c/em\u003e findings\u0026nbsp;pave the way for\u0026nbsp;developing new therapeutic strategies\u0026nbsp;targeting\u0026nbsp;multidrug resistant bacterial infections.\u0026nbsp;However,\u0026nbsp;further \u003cem\u003ein vitro\u003c/em\u003e validation is required to confirm these findings, which could ultimately lead to a novel phytochemical-based treatment option for quorum sensing inhibition in \u003cem\u003eP. aeruginosa\u003c/em\u003e and contribute to the fight against antimicrobial resistance.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eData availability\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll the data generated and analysed during this study are included in the published article. Other data which support the findings of the study are available in the paper and its supplementary information files.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors express their sincere gratitude to the Centre for Biotechnology, Siksha \u0026apos;O\u0026prime; Anusandhan Deemed to be University, Bhubaneshwar, India for providing infrastructure and facility for carrying out the work\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eArnav Padhi: Conceptualization, methodology, data curation, software, in silico study design, analysis, writing original draft. Pabitra Mohan Behera: Conceptualization, methodology, in silico analysis. writing original draft. Soumyadip Ghosh: Methodology, software, in silico analysis, Sudha Ramaiah: Investigation, manuscript review. Surajit De Mandal: Supervision, manuscript review, editing and proofreading, Enketeswara Subudhi: Conceptualization, supervision, manuscript review, editing and proofreading. All authors read and approved the final manuscript.\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\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare no competing interests.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAdditional information\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eSupplementary Information\u0026nbsp;\u003c/strong\u003eThe online version contains supplementary material available\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eat\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCorrespondence\u0026nbsp;\u003c/strong\u003eand requests for materials should be addressed to E.S\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eRen, Y. et al. 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Rep.\u003c/em\u003e \u003cb\u003e14\u003c/b\u003e, 10976 (2024).\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Pseudomonas aeruginosa, Quorum sensing, LasR, Multidrug Resistance, Cistus munbyi, Molecular docking, Molecular dynamic Simulations and Density Functional Theory","lastPublishedDoi":"10.21203/rs.3.rs-7664093/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7664093/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cem\u003ePseudomonas aeruginosa\u003c/em\u003e is an opportunistic pathogen which employs quorum sensing (QS) to regulate virulence and biofilm formation, leading to the emergence of multidrug resistance (MDR) necessitating novel therapeutic strategies. This study aimed to identify phytocompounds from \u003cem\u003eCistus munbyi\u003c/em\u003e essential oil as potential inhibitors of the LasR QS receptor in \u003cem\u003eP. aeruginosa\u003c/em\u003e. A library of 44 phytocompounds was screened through molecular docking studies targeting LasR and its natural variants (LasR-Var1: R144I, LasR-Var2: R180W). Cuminaldehyde and Sabinyl acetate emerged as top candidates, exhibiting strong binding affinities comparable to the native ligand, N-3-Oxo-Dodecanoyl-L-Homoserine. Molecular dynamics (MD) simulations over 100 ns confirmed stable interactions with key conserved residues, with Cuminaldehyde demonstrating superior stability in LasR-Var2 (RMSD: ~0.6-0.8 nm). Density Functional Theory (DFT) analysis revealed favourable chemical reactivity for Cuminaldehyde (energy gap: 5.071 eV) and Sabinyl acetate (energy gap: 6.162 eV), supporting their potential as QS inhibitors. Parameters like RMSD, RMSF, radius of gyration, and solvent accessible surface area validated the structural stability of these complexes, while principal component analysis highlighted distinct conformational dynamics. These findings underscore the potential of Cuminaldehyde and Sabinyl acetate as anti-QS agents to mitigate \u003cem\u003eP. aeruginosa\u003c/em\u003e virulence and combat MDR. The study advocates for further \u003cem\u003ein vitro\u003c/em\u003evalidation to translate these \u003cem\u003ein silico\u003c/em\u003efindings into novel phytochemical-based therapeutics, offering promising prospects for addressing antimicrobial resistance\u003c/p\u003e","manuscriptTitle":"Pseudomonas aeruginosa virulence reduction through phytochemical inhibition of Quorum Sensing activity: A Molecular Docking, Molecular Dynamics Simulation study","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-11-02 17:59:05","doi":"10.21203/rs.3.rs-7664093/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"b71b5044-05e0-4ec8-ac18-503d797b54e6","owner":[],"postedDate":"November 2nd, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":56674756,"name":"Biological sciences/Biochemistry"},{"id":56674757,"name":"Physical sciences/Chemistry"},{"id":56674758,"name":"Biological sciences/Computational biology and bioinformatics"},{"id":56674759,"name":"Biological sciences/Drug discovery"},{"id":56674760,"name":"Biological sciences/Microbiology"}],"tags":[],"updatedAt":"2025-12-22T05:24:30+00:00","versionOfRecord":[],"versionCreatedAt":"2025-11-02 17:59:05","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7664093","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7664093","identity":"rs-7664093","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}