Breaking the Biofilm Barrier: An in-silico therapeutic approach against Vibrio cholerae using metabolites of Erigeron breviscapus

Breaking the Biofilm Barrier: An in-silico therapeutic approach against Vibrio cholerae using metabolites of Erigeron breviscapus | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Breaking the Biofilm Barrier: An in-silico therapeutic approach against Vibrio cholerae using metabolites of Erigeron breviscapus Udisha Singh, Praveen Nagella, Aathika Nizam, Vasantha Veerappa Lakshmaiah This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6767051/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 The biomolecules present in Erigeron breviscapus are bestowed with many properties. The current study explores the antibiofilm activity of these bioactive compounds against Vibrio cholerae by examining their binding affinities with three significant biofilm-forming proteins, i.e., sugar-binding proteins Bap1 (Biofilm-Associated Protein 1) and RbmC, and adhesion protein RbmA. The structures of these receptor proteins and 31 phytochemicals were derived from open-source databases, prepared and then subjected to molecular docking analysis. Their binding energies were examined, and the phytochemicals showing lesser energy than resveratrol, the positive control, were selected for ADMET (absorption, distribution, metabolism, excretion, and toxicity) studies. The analysis of their interaction profiles followed this. The short-listed phytochemicals included apigenin, eriodictyol, and naringenin. Finally, the stability and dynamic behaviour of the protein-ligand complexes were confirmed by employing molecular dynamics (MD) simulation studies. The results suggested that the plant Erigeron breviscapus can be a potential antibiofilm agent in controlling the infections of Vibrio cholerae , and can be explored to a greater extent in the field of phytopharmacology. Anti-biofilm computer-aided drug discovery phytochemicals resistance Vibrio cholerae Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 INTRODUCTION Many bacteria form biofilms helping them escape immune response and withstand a wide range of antibiotics and adverse environmental stress (Ramalingam et al. 2019 ). These may be helpful or harmful to people depending on the microbial species and their location. It is suggested that biofilms contribute to over 75% of human infections (Miquel et al. 2016 ). To grow in a complex community with one or more species, bacteria use various polymeric extracellular materials to form biofilms on both biotic and abiotic surfaces (Gutiérrez et al. 2016 ). These materials protect the community by maintaining its structure and enabling an exchange of materials (Flemming and Wingender 2010 ). Biofilm formation allows the bacteria to establish and survive in varied ecological niches, which has become the most favoured lifestyle for microorganisms (Hall-Stoodley et al. 2012 ). However, it can negatively affect humans since these biofilms are not easily eliminated. Biofilms created by pathogenic bacteria can cause chronic infections as they possess resistance to the host immune system (Gutiérrez et al. 2016 ). Antimicrobials have no effect since they cannot penetrate the biofilm (Singh et al. 2010 ). In addition to affecting health, biofilms also negatively influence industrial activities (Whitehead and Verran 2015 ). Vibrio cholerae , the cholera-causing bacterium, employs biofilms to increase its antibiotic tolerance and become hyper-infectious. The biofilm matrix comprises VPS ( Vibrio polysaccharides), nucleic acids, and proteins such as RbmA, RbmC, and Bap1. These proteins are crucial for cell adhesion, surface attachment, and biofilm architecture (Giglio et al. 2013 ; Kaus et al. 2019 ). The role of biofilm development in V. cholerae pathogenesis is well understood. It is the main reason that cholera continues to be a life-threatening disease, especially in resource-poor countries (Kierek and Watnick 2003 ). The resistant qualities of biofilm cells and their genetic and phenotypic versatility encouraged researchers to develop biofilm-specific medicines. The phrase “anti-biofilm” first appeared in the literature in the 1990s. It is a natural or induced process that alters bacterial biomass's cellular structure, integrity, adhesion, and extracellular components (Miquel et al. 2016 ). Thus, understanding biofilm formation mechanisms and strategies to destroy them is the core area of research. As evidenced by many recent articles, anti-biofilm methods are promising approaches for medical innovation. Still, they should be cautiously used, as it could alter the composition of established ecosystems and harm beneficial bacteria. Bioinformatics can be a useful tool in identifying potential targets against biofilms (Miquel et al. 2016 ). The shift from experimental to computational analysis can be attributed to the plethora of bioactive molecules, their availability in limited amounts, and to do away with animal testing (Daina and Zoete 2016 ). Computational methods can estimate the properties of such agents rapidly and accurately, allow the easy prediction of molecular interactions and their associated binding energy, and consume minimal time for screening numerous bioactive molecules. Therefore, it finds many applications in the pharmaceutical industry (Forli et al. 2016 ; Hariprasath et al. 2022 ). The present study aims to investigate the potential of phytochemicals from Erigeron breviscapus as novel anti-biofilm agents against V. cholerae . We used computational methods to evaluate the antibiofilm efficacy by studying the inhibition of biofilm formation in comparison to resveratrol, which has been proven to inhibit biofilms in V. cholerae , with the possibility of developing resistance to it being minuscule (Augustine et al. 2014 ). Resveratrol demonstrates several therapeutic properties (Baur and Sinclair 2006 ). Its merits are its quick clearance from the circulatory system and its high efficacy despite minimal bioavailability. In contrast, its demerits include toxicity at higher concentrations and high costs. These disadvantages can be overcome by blocking its metabolism, generating analogues having higher bioavailability, or discovering new and more potent alternatives (Baur and Sinclair 2006 ). Hence, we have investigated phytochemicals from E. breviscapus as potential alternatives to resveratrol. This daisy-like flowering plant species belonging to the Compositae family, are native to China (He et al. 2021 ). It is an important medicinal plant whose multi-effect character can be attributed to its mixed herbal components. These bioactive molecules possess various properties and hence can act on diseases by binding to various sites to produce complicated and synergistic therapeutic effects (Ramalingam et al. 2019 ; He et al. 2021 ). These molecules were put through different studies like docking, ADMET and MD simulation to establish the potential of E. breviscapus as an anti-biofilm agent for controlling infections of V. cholerae . METHODS Ligand acquisition and preparation The canonical SMILES (simplified molecular input line entry system) of the ligands were obtained from the PubChem database (Kim et al. 2022 ) and converted into 3D structures by employing the CORINA Classic Demo online tool (Molecular Networks GmbH, Germany and Altamira, LLC, USA; Sadowski et al. 2002 ). These pdb structures were energy-optimised using Avogadro software (Hanwell et al. 2012 ), and further prepared with AutoDock Tools 1.5.6 (Sanner 1999 ). Following structural optimisation and protonation, the bioactive were saved in pdbqt format. Receptor acquisition and preparation The receptors, i.e., Bap1 (6MLT), RbmA (5G50), and RbmC (5V6C), were downloaded from the RCSB PDB (Research Collaboratory for Structural Bioinformatics Protein Data Bank) database (Berman et al. 2000 ). The receptors were initially prepared on PyMOL (Schrödinger and DeLano 2020 ) by eliminating the associated heteroatoms and water molecules. AutoDock Tools 1.5.6 was utilised for further preparation, and structures were saved in pdbqt format. Binding site prediction A stable complex comprises a ligand, with the best conformation, in the receptor’s active site to carry out its expected activity (Azam and Abbasi 2013 ). BIOVIA Discovery Studio (DS) Visualiser was employed to determine the active site of the protein. This software has been utilised for predicting binding sites in previous works as well (Singh et al. 2016 ; Musfiroh et al. 2020 ; Prasanth et al. 2021b ). The active site coordinates were maintained in a text file and utilised for docking. Molecular docking Docking was performed using AutoDock Vina (Trott and Olson 2010 ), a highly accurate, easily accessible and user–friendly tool (Hariprasath et al. 2022 ). The combined energies of all the intermolecular interactions occurring in the receptor-ligand complex are represented as docking energies, calculated by AutoDock Vina (Augustine et al. 2014 ; Eberhardt et al. 2021 ). Thirty phytochemicals from E. breviscapus and resveratrol were docked against Bap1, RbmA and RbmC, to find suitable lead molecules for clinical applications. The docking proceeded as described by Tarachand et al. ( 2023 ). Assessment of ADMET properties Subsequently, suitable phytochemicals were selected based on their ADMET profiles. This was evaluated using the SwissADME (Daina et al. 2017 ) and pkCSM (Pires et al. 2015 ) online tools. After uploading the canonical SMILES to these sites, the selected ligands were evaluated based on different parameters. The phytochemicals meeting all the requirements were further studied. Visualisation BIOVIA DS Visualiser and PyMOL were employed to analyse the complex's interactions and the amino acids participating in them (Nag and Chowdhury 2020 ). MD simulation MD simulations were conducted using GROMACS (GROningen MAchine for Chemical Simulations) software (Páll et al. 2015 ; Abraham et al. 2015 ; Malathi and Ramaiah 2016 ) to evaluate the stability of the selected complexes, following the protocol mentioned by Tarachand et al. ( 2023 ). Binding free energy (G binding ) was determined by utilising the MMPBSA (Molecular Mechanics Poisson-Boltzmann Surface Area) method (Tarachand et al. 2023 ), and was calculated using the following formula: ∆G binding = G complex – (G receptor + G ligand ) where ∆G binding is the total binding free energy, and G complex , G receptor and G ligand are the binding energies of the protein-ligand complex, the unbound receptor and the unbound ligand, respectively (Bhardwaj and Purohit 2020 ). RESULTS Binding site prediction The active site was generated in the form of XYZ coordinates. The grid box centre values for Bap1 were determined to be x = 7.363433, y = 12.227564, and z = 93.993667. The coordinates for RbmA were kept as x= -18.279229, y= -5.319617, z = 11.417807, whereas RbmC showed values of x= -29.232892, y= -13.900945, z= -23.041052. The active sites of the three proteins are shown in Online Resource 1 . Molecular docking analysis The docking step revealed that among all the phytochemicals, the least binding energy of a ligand with Bap1 was − 8.2 kcal/mol for Naringenin-7-O-glucuronide compared to -7.1 kcal/mol for resveratrol, with the other ligands ranging between − 8.1 and − 3.9 kcal/mol. Similarly, for RbmA, Kaempferol-3-O-glucoside and Naringenin-7-O-glucuronide had the least binding energy of -9.9 kcal/mol compared to -7.6 kcal/mol for resveratrol, while the remaining bioactive molecules showed affinities between − 9.8 and − 4.0 kcal/mol. For RbmC, a binding energy of -10.4 kcal/mol for 3,4-Di-O-caffeoylquinic acid and 4,5-Di-O-caffeoylquinic acid was the least compared to -7.4 kcal/mol for resveratrol, with the rest of the phytochemicals scoring between − 10.1 and − 4.9 kcal/mol. Additionally, 17, 19 and 21 ligands showed more affinity towards Bap1, RbmA, and RbmC than resveratrol, respectively. The PubChem CID (compound identifier) and binding affinities of the phytochemicals to the receptors are highlighted in Online Resource 2. Evaluation of drug-likeness The top-scoring ligands were screened, and the druggability factor for three phytochemicals showed zero violations- apigenin, eriodictyol, and naringenin. These phytochemicals possessed significant drug-likeness, highlighted in Table 1 and Online Resource 3 , along with the properties of resveratrol. Table 1 ADME profile of resveratrol and the selected ligands PROPERTY PARAMETERS LIGANDS Resveratrol Apigenin Eriodictyol Naringenin Physicochemical TPSA (Å 2 ) 60.69 90.90 107.22 86.99 Lipophilicity Consensus Log P o/w 2.48 2.11 1.45 1.84 Water Solubility Log S (ESOL) -3.62 -3.94 -3.26 -3.49 Solubility Class Soluble Soluble Soluble Soluble Pharmacokinetics GI absorption High High High High BBB permeant No No No No P-gp substrate No No Yes Yes Druglikeness Lipinski Yes Yes Yes Yes Lipinski violations 0 0 0 0 Bioavailability score 0.55 0.55 0.55 0.55 The pharmacokinetic analysis studied properties such as gastrointestinal absorption, access to the brain and skin, and efflux regulated by P-glycoprotein (permeability glycoprotein). By considering their lipophilicity and polarity, these can be predicted simultaneously with the help of BOILED-Egg (Brain Or IntestinaL EstimateD permeation method). The BOILED-Egg output for our chosen phytochemicals, along with resveratrol, are shown in Fig. 1 . Toxicity The toxicity profiles of the top3 chosen phytochemicals and resveratrol were analysed, and the results are detailed in Table 2 . Table 2 Toxicity evaluation of resveratrol and the selected ligands PROPERTIES LIGANDS Resveratrol Apigenin Eriodictyol Naringenin AMES toxicity Yes No No No Max. tolerated dose (human; log mg/kg/day) 0.331 0.328 0.014 -0.176 hERG Ⅰ inhibitor No No No No hERG Ⅱ inhibitor No No No No Oral Rat Acute Toxicity (LD50; mol/kg) 2.529 2.45 2.03 1.791 Oral Rat Chronic Toxicity (LOAEL; log mg/kg_bw/day) 1.533 2.298 2.475 1.944 Hepatotoxicity No No No No Skin Sensitisation No No No No T.Pyriformis toxicity (log ug/L) 0.746 0.38 0.332 0.369 Minnow toxicity (log mM) 1.522 2.432 2.972 2.136 Visualisation The interactions of the potent ligands are listed in Table 3 . The 3D illustration of these molecules docked in the active site of the receptors along with the 2D image of their interactions are given in Online Resource 4 , 5 and 6 . Table 3 Interacting residues in the active site LIGANDS INTERACTING RESIDUES Bap1 RbmA RbmC Resveratrol Thr648, Gln672 - Tyr899(A), Lys926(A), Ile825(B) Apigenin Thr648, Ser659, Thr677 Leu158, Glu263 Lys926(A), Gly822(B) Eriodictyol Arg649, Ser659, Arg687 Lys128, Asp154, Ser157, Leu158 Gly827(A), Tyr899(A), Asn924(A) Naringenin Arg649, His678, Arg687 Thr130, Arg261, Gln264, Ser265 Lys926(A) It can be observed that the ligands demonstrated significant interactions like conventional hydrogen, carbon-hydrogen, pi-based, van der Waals and hydrophobic bonding among others, with the receptors. In the case of Bap1, apigenin formed three H-bonds with Thr648, Ser659 and Thr677; eriodictyol formed four H-bond interactions with Arg649, Ser659 and Arg687; and naringenin formed three H-bonds with Arg649, His678 and Arg687. The standard drug, resveratrol, showed two H-bonds with Thr648 and Gln672. Similarly, in the active site of receptor RbmA, apigenin formed two H-bonds with Leu158 and Glu263; eriodictyol formed four H-bonds with Lys128, Asp154, Ser157 and Leu158; naringenin also made four H-bonds with Thr130, Arg261, Gln264 and Ser265; while resveratrol didn’t form any H-bonds with this receptor. Apigenin, eriodictyol and naringenin formed two, three and one H-bond with RbmC receptor, respectively. Apigenin interacted with Lys926 of chain A and Gly822 of chain B; eriodictyol was bound to Gly827, Tyr899 and Asn924 of chain A; whereas naringenin formed bonds with Lys926 of chain A. Resveratrol interacted with RbmC through three H-bonds formed by Tyr899 and Lys926 of chain A, and Ile825 of chain B. MD simulation RMSD Figure 2 Highlights the RMSD values for the complexes. Apigenin and naringenin stabilised the Bap1 complex after 50 ns, with average deviations of 0.40 nm and 0.20 nm, respectively, whereas eriodictyol stabilised the complex after 70 ns, with an average RMSD of 0.40 nm. For RbmA, interaction with all three ligands stabilised the complex post 80 ns with average deviations around 0.30 nm, 0.30 nm and 0.17 nm for apigenin, eriodictyol and naringenin, respectively. With RbmC, apigenin and eriodictyol maintained a constant RMSD of 0.13 nm throughout the simulation while naringenin caused a significantly higher RMSD of 2.13 nm for most of the simulation time. RMSF Figure 3 illustrates the RMSF for the complexes studied. Bap1 showed significant fluctuations between 300 and 500 residue positions with most regions showing stability and RMSF value below 0.60 nm for all three phytochemicals. There were minimal fluctuations in RbmA on interaction with the three ligands, and the RMSF was less than 0.20 nm. For RbmC, the fluctuations were very high while interacting with naringenin. Apigenin and eriodictyol caused decreased fluctuations with values below 0.20 nm. Rg The Rg values for the complexes are illustrated in Fig. 4 . Deviations in the Bap1 complexes relaxed post 50000 ns with a high Rg value, over 2.80 nm, for the three ligands. In the case of RbmA, the Rg values remained under 2.40 nm with naringenin fluctuating more compared to apigenin and eriodictyol. The Rg values were lower for the RbmC complexes, remaining below 2.0 nm, with few deviations for naringenin. H-bond analysis Figure 5 highlights the number of H-bonds formed by the complexes. Apigenin formed a maximum of 6, 5 and 5 H-bonds with Bap1, RbmA and RbmC, respectively. The highest number of H-bonds formed by eriodictyol with Bap1, RbmA and RbmC were 6, 6 and 7. Similarly, naringenin formed a maximum of 5, 7 and 5 H-bonds with Bap1, RbmA and RbmC, respectively. MM-PBSA The binding energies of apigenin, eriodictyol and naringenin with Bap1, RbmA and RbmC are summarised in Table 4 . Table 4 MM-PBSA calculations of binding free energy for the selected complexes ENERGY COMPONENT LIGANDS Apigenin Eriodictyol Naringenin RECEPTOR- Bap1 van der Waal energy -84.908 +/- 15.965 kJ/mol -82.597 +/- 10.033 kJ/mol -2.807 +/- 10.645 kJ/mol Electrostatic energy -44.215 +/- 25.407 kJ/mol -56.715 +/- 20.750 kJ/mol -1.606 +/- 6.574 kJ/mol Polar solvation energy 103.744 +/- 35.686 kJ/mol 92.468 +/- 21.917 kJ/mol -22.259 +/- 75.103 kJ/mol SASA energy -10.960 +/- 1.439 kJ/mol -10.099 +/- 0.978 kJ/mol 0.072 +/- 2.720 kJ/mol SAV energy 0.000 +/- 0.000 kJ/mol 0.000 +/- 0.000 kJ/mol 0.000 +/- 0.000 kJ/mol WCA energy 0.000 +/- 0.000 kJ/mol 0.000 +/- 0.000 kJ/mol 0.000 +/- 0.000 kJ/mol Binding energy -36.339 +/- 13.642 kJ/mol -56.944 +/- 11.408 kJ/mol -26.599 +/- 71.495 kJ/mol RECEPTOR- RbmA van der Waal energy -119.375 +/- 19.108 kJ/mol -128.636 +/- 14.789 kJ/mol -110.938 +/- 13.601 kJ/mol Electrostatic energy -23.209 +/- 18.788 kJ/mol -34.221 +/- 18.547 kJ/mol -68.382 +/- 18.366 kJ/mol Polar solvation energy 106.337 +/- 25.932 kJ/mol 145.928 +/- 23.001 kJ/mol 168.889 +/- 27.036 kJ/mol SASA energy -14.104 +/- 1.704 kJ/mol -15.402 +/- 1.034 kJ/mol -14.479 +/- 1.299 kJ/mol SAV energy 0.000 +/- 0.000 kJ/mol 0.000 +/- 0.000 kJ/mol 0.000 +/- 0.000 kJ/mol WCA energy 0.000 +/- 0.000 kJ/mol 0.000 +/- 0.000 kJ/mol 0.000 +/- 0.000 kJ/mol Binding energy -50.351 +/- 10.460 kJ/mol -32.331 +/- 16.766 kJ/mol -24.911 +/- 13.596 kJ/mol RECEPTOR- RbmC van der Waal energy -142.794 +/- 9.431 kJ/mol -140.924 +/- 10.906 kJ/mol -51.150 +/- 23.142 kJ/mol Electrostatic energy -13.571 +/- 10.460 kJ/mol -39.962 +/- 9.588 kJ/mol -15.860 +/- 15.663 kJ/mol Polar solvation energy 91.622 +/- 10.944 kJ/mol 144.004 +/- 21.929 kJ/mol 43.524 +/- 42.996 kJ/mol SASA energy -13.541 +/- 0.688 kJ/mol -15.721 +/- 0.894 kJ/mol -6.852 +/- 2.871 kJ/mol SAV energy 0.000 +/- 0.000 kJ/mol 0.000 +/- 0.000 kJ/mol 0.000 +/- 0.000 kJ/mol WCA energy 0.000 +/- 0.000 kJ/mol 0.000 +/- 0.000 kJ/mol 0.000 +/- 0.000 kJ/mol Binding energy -78.284 +/- 14.555 kJ/mol -52.603 +/- 20.189 kJ/mol -30.337 +/- 19.461 kJ/mol DISCUSSION Molecular docking analysis Lower binding energy makes a compound manifest more binding affinity towards the proteins. Hence, the selected molecules must possess high negative interaction energy responsible for their dynamic nature and help them function as potential inhibitors of biofilms (Madeswaran et al. 2012 ; Bhardwaj et al. 2021 ). The docking analysis exhibited that most of the bioactive molecules exhibited lesser binding energy than resveratrol, and hence have an excellent tendency to bind with the receptor targets. Therefore, these molecules can be potential alternatives to existing anti-biofilm agents. However, docking the ligand in the receptor’s active site is inadequate to prove its applicability as a drug. Hence, drug-likeness and toxicity studies are carried out to filter out potential drug molecules from unsuitable ones. The ligands that showed higher negative binding energy than resveratrol were further evaluated for drug-like properties. Evaluation of drug-likeness A bioactive molecule, to be commercially available as a pharmaceutical drug, should possess desirable pharmacological, biopharmaceutical, and physicochemical characteristics (Walters and Murcko 2002 ). This ensures the molecule is available to the target in adequate amounts and for sufficient time to carry out its expected activity (Daina et al. 2017 ). Hence, an essential step in drug discovery is using in silico methods to evaluate these properties and drug-likeness of chemical entities. Drug-likeness qualitatively estimates a chemical’s suitability as a drug in terms of its bioavailability and pharmacokinetics (Vistoli et al. 2008 ; Daina et al. 2017 ; Ilieva 2018 ). Predicting drug-likeness early in the drug development process minimises late-stage failures allowing an efficient entry of drug candidates into the global market (Jorgensen 2004 ; Daina and Zoete 2016 ). Amongst physicochemical properties, the web tool calculated polarity as TPSA (topological polar surface area), a measure of molecular bioabsorption (Ilieva 2018 ). Several ADME parameters, like absorption and brain access, are predicted using this. The TPSA values were 90.90 Å 2 for apigenin, 107.22 Å 2 for eriodictyol, and 86.99 Å 2 for naringenin. Drug candidates should be able to permeate through the cell membranes easily and must have a TPSA between 20 and 130 Å 2 , whereas a TPSA not in this range indicates poor permeation (Daina and Zoete 2016 ; Ibrahim et al. 2020 ). The phytochemicals showed values within the reference range. Hence, they can penetrate the cell membranes easily and be delivered to their targets after administration. The behaviour of drug candidates can be affected by lipophilicity. It is an essential property (Arnott and Planey 2012 ; Daina et al. 2014 ) and was determined to be 2.11 for apigenin, 1.45 for eriodictyol, and 1.84 for naringenin. The optimum range of lipophilicity is when consensus log P lies between 0 and 3 (Arnott and Planey 2012 ). All three ligands were identified as lipophilic, having values within this range. The low consensus values indicate that these phytochemicals will have good bioavailability if administered orally. The solubility class is assigned to the molecule under investigation, based on the log S scale. Three models were adopted to assess water solubility at 25°C, a significant parameter controlling absorption (Ottaviani et al. 2010 ; Daina et al. 2014 , 2017 ). The solubility values for apigenin, eriodictyol and naringenin were found to be -3.94, -3.26, and − 3.49, respectively. Our study revealed them to be soluble, having log S values between − 4 and − 2. Therefore, these ligands can be easily absorbed and ease the handling and formulation of drugs during the development process (Savjani et al. 2012 ; Ritchie et al. 2013 ). The BOILED-Egg visually represents a molecule’s absorption and brain penetration potential (Daina and Zoete 2016 ). Molecules undergoing passive gastrointestinal absorption occur in the white region, molecules showing passive BBB permeation occur in the yellow (yolk) region and molecules possessing both low absorption and BBB impermeability occur outside the BOILED-Egg. Similarly, red dots represent molecules that are not P-glycoprotein substrates. The opposite is true for molecules represented by blue dots (Daina and Zoete 2016 ; Floresta et al. 2019 ). The BOILED-Egg output shows that our chosen ligands occur in the white region and hence can undergo passive gastrointestinal absorption easily. Therefore, they can be administered orally, the preferred route for drug administration, making them suitable drug candidates. Our brain is secured by the BBB (Wilhelm and Krizbai 2014 ; Erickson and Banks 2018 ; Pimentel et al. 2020 ). It prevents the entry of chemicals that can harm the brain and lead to neurodegeneration. Our target compounds are not BBB permeants since they do not occur in the yolk region of the BOILED-Egg. This is advantageous as the risk of toxicity and side effects to the CNS is reduced. P-glycoprotein is an active efflux transporter that restricts the substrates from entering into the systemic circulation after absorption. This influences the drug pharmacokinetics by affecting drug absorption, distribution and elimination (Finch and Pillans 2014 ; Ilieva 2018 ). Additionally, compounds that induce P-glycoprotein reduce the bioavailability of other drugs, whereas those that inhibit them increase the bioavailability of susceptible drugs (König et al. 2013 ). Apigenin is not a P-glycoprotein substrate, whereas eriodictyol and naringenin are. Co-administering eriodictyol and naringenin with P-glycoprotein inhibitors can help overcome this disadvantage and increase their plasma concentrations (Kim 2002 ). Lipinski’s rules state the range of properties required for exhibiting optimum pharmacokinetic activity on oral administration (Kiran et al. 2020 ). Notably, Pfizer employs Lipinski’s rules during its drug discovery process (Lipinski 2016 ). Should any molecule lie outside the physicochemical range set by these rules, its probability of becoming an oral drug will also be reduced (Daina and Zoete 2016 ). Our selected compounds adhered to all the rules without any violations. They also obeyed other drug candidate filters like Ghose, Veber, Egan and Muegge (Muegge et al. 2001 ; Veber et al. 2002 ; Floresta et al. 2019 ), without any violations. The Abbott Bioavailability score ascertains whether the molecule has at least 10% oral bioavailability in rats or measurable Caco-2 permeability (Daina et al. 2017 ). It is the rate and extent to which it enters the blood circulatory system to reach its target site. The three phytochemicals revealed good bioavailability scores of 0.55 hence, their concentration in the plasma reaches the required level before they are metabolised. This study revealed that the phytochemicals of E. breviscapus , especially apigenin, eriodictyol and naringenin, showed favourable drug-likeness profiles except for a few disadvantages that can be overcome through chemical alterations during drug development or with the aid of drug carriers (Chan et al. 2015 ; Landersdorfer et al. 2015 ). Toxicity New drug molecules can sometimes be toxic and affect the human body negatively, leading to drug failure during clinical trials (van de Waterbeemd and Gifford 2003 ). Not only is this life-threatening, but it also leads to loss of money and resources. Therefore, a toxicity study is paramount to ensure the safety of new molecules to be used as drugs (Subash 2020 ; Hariprasath et al. 2022 ). AMES toxicity is used to ascertain if a ligand can cause mutations in the human body, as mutagenic ligands can also be carcinogenic (Cheng et al. 2013 ). Since our bioactive molecules tested negative for AMES toxicity, they are neither mutagenic nor carcinogenic. Inhibition of hERG Ⅰ and Ⅱ is an essential parameter in toxicity studies and has led to the withdrawal of numerous medications from the market (Pires et al. 2015 ). Ligands inhibiting this gene block the potassium channels encoded by it. This is the source of QT prolongation, leading to lethal ventricular arrhythmia (Cheng et al. 2013 ). None of our ligands are inhibitors of hERG Ⅰ and Ⅱ. The hepatotoxicity parameter reveals ligands that can harm the liver (Cheng et al. 2013 ). Since all three bioactive molecules showed an absence of hepatotoxicity, their administration as drugs will not bring about any pathological or physiological alteration that can impair liver function (Pires et al. 2015 ). Skin sensitisation predicts if a drug candidate can cause adverse reactions after contact with the skin (Pires et al. 2015 ). It is a vital parameter, and this study shows that our selected compounds are not associated with skin sensitisation. Gauging the maximum amount of compound that can be administered, beyond which it will become hazardous, is done by the maximum tolerated dose parameter. This influences the maximum advised starting dose for the drug candidates during clinical trials. A good drug should have a low maximum tolerated dose, within 0.477 (log mg/kg/day) (Pires et al. 2015 ; Tarachand et al. 2023 ). The values for apigenin, eriodictyol and naringenin were found to be 0.328, 0.014, and − 0.176, respectively. These were less than 0.477, indicating their potential to be promising drug candidates. Minnow toxicity is a parameter that estimates the concentration of a compound that can cause 50% of fathead minnows to perish. The Minnow toxicity values were calculated as 2.432 for apigenin, 2.972 for eriodictyol, and 2.136 for naringenin. A compound shows low toxicity when its log mM value exceeds − 0.3 (Pires et al. 2015 ). The screened ligands possessed low minnow toxicity presenting values higher than − 0.3. Our study indicated that the three shortlisted phytochemicals have a low toxicity profile, and are suitable for administration in the appropriate amount. Visualisation The formation of H-bonds in the complex is essential for molecular recognition, quality of docking and strength of interactions by increasing the specificity between the interacting ligands and proteins (Hariprasath et al. 2022 ). Our ligands demonstrated significant interactions with the receptors and were adequately positioned in their binding pocket. They demonstrated a general trend of exhibiting equal or more H-bond interactions with the receptors compared to resveratrol, except for RbmC-apigenin and RbmC-naringenin complexes. It was also observed that certain interacting residues were common among the complexes formed between a particular receptor and the docked ligands. For Bap1, these essential residues consisted of Thr648, Arg649, Ser659 and Arg687 whereas, for RbmA it was Leu158. The common interacting residues for RbmC included Tyr899 and Lys926 of chain A. MD simulation The static poses generated during molecular docking are unable to highlight additional factors that stabilise the complex, like residue flexibility, solvent effects and secondary structural elements (Purohit 2014 ; Pagadala et al. 2017 ; Pantsar and Poso 2018 ). The biological function of a protein is disturbed when there is a change in its conformation, usually due to the protein’s dynamic behaviour (Bhardwaj and Purohit 2020 ). MD studies help visualise these disturbances when present in an actual biological environment. The conformational space in the complexes is also observed (Bhardwaj et al. 2021 ). These studies can improve the credibility of the estimated binding energies (Ibrahim et al. 2020 ) and involve calculating RMSD, RMSF, Rg, H-bonds and MM-PBSA. RMSD One method of determining behaviour, stability, structural changes, equilibration of proteins and validating docking poses is RMSD, which calculates the mean square displacement of atoms (2017; Thirumoorthy et al. 2022 ). This is carried out by calculating the RMSD of Cα atoms present in the backbone of the complex, which is plotted against time (Ragunathan et al. 2018 ). Protein complexes are deemed stable when the RMSD values are low, trajectories show minimal fluctuations, and there is very little difference in the average RMSD values (Pandey et al. 2018 ; Bhardwaj et al. 2021 ). It can be analysed from Fig. 2 that the complexes formed by RbmC protein with apigenin and eriodictyol are considerably more stable owing to their low and constant average RMSD values, and their trajectories highlighting minimal fluctuations. RMSF In addition to calculating the receptor mobility, after binding with the ligand on a time basis, the RMSF of C-atoms enables us to understand the effects of ligand binding on the flexibility of the receptor by recognising the fluctuating protein part during simulations. Therefore, a reduction in RMSF value corresponds to stable residues (Prasanth et al. 2021a ). Figure 3 shows that the complexes formed by RbmA with eriodictyol and naringenin, and RbmC with apigenin and eriodictyol can be deemed stable owing to their low RMSF values and reduced fluctuations for most of the simulation. Additionally, high peaks towards the ends are indicative of the N- and C-terminals of the protein, which are very flexible. Rg Along with RMSD, the stability of the protein and computation of the dimensions can be determined by calculating the Rg value, which is the mass-weighted root mean square distance of each atom from its centroid (Kumar et al. 2014 ). The compactness of a protein can be understood by plotting the Rg value of the backbone Cα atoms against the simulation time. Hence, the Rg value is stable when the protein undergoes secure folding, whereas the Rg value fluctuates during protein unfolding (2017). Similarly, a high Rg value corresponds to amino acids being loosely packed; otherwise, the Rg value is low (Liao et al. 2014 ; Tarachand et al. 2023 ). From Fig. 4 it can be observed that apigenin and eriodictyol showed stable deviations for RbmA and RbmC, indicating secure folding of these proteins in their corresponding complexes. Additionally, the complexes formed by RbmC showed a lower Rg value compared to those formed by RbmA, signifying tight packaging of residues. Hence, the complex RbmC forms with apigenin and eriodictyol are significantly more stable than other complexes. H-bond analysis Intermolecular hydrogen bonds and their corresponding strength in an aqueous environment determine the stability of a complex (2017; Pantsar and Poso 2018 ). H-bonds provide directionality, strength, and lucidity to the interactions, which in turn helps recognise and select specific molecules. H-bonding also changes secondary structures, leading to interactions (Bhardwaj et al. 2021 ; Tarachand et al. 2023 ). In our study, the maximum number of H-bonds formed was 7, found in the RbmA-naringenin and RbmC-eriodictyol complexes. MM-PBSA Binding energy is the energy released when the protein and bioactive molecules interact, leading to bond formation. It combines electrostatic, polar solvation, SASA, SAV, van der Waals and WCA energies. The lesser the binding energy value, the stronger the bond between the receptor and ligand (Bhardwaj et al. 2021 ). In our study, all the complexes demonstrated a low binding free energy, indicating a strong binding between the receptor and ligands. The complexes formed between Bap1 and eriodictyol, RbmA and apigenin, RbmC and apigenin, and RbmC and eriodictyol revealed a more negative binding free energy of -56.944 +/- 11.408 kJ/mol, -50.351 +/- 10.460 kJ/mol, -78.284 +/- 14.555 kJ/mol, and − 52.603 +/- 20.189 kJ/mol, respectively. This indicates a stronger binding affinity between these proteins and bioactive molecules, as compared to the other complexes. CONCLUSION In this study, we have evaluated the anti-biofilm potential of 30 phytochemicals from E. breviscapus by docking them with important biofilm proteins- Bap1, RbmA, and RbmC of V. cholerae . The short-listed phytochemicals were chosen based on the docking scores, ADMET properties, interaction studies and MD simulations. The results suggested that compounds like apigenin, eriodictyol and naringenin can be further explored to formulate new plant-based drugs for tackling the V. cholerae biofilms. However, extensive experimental investigation is further required for developing drugs targeting these biofilms. Declarations ACKNOWLEDGEMENTS: Authors express deepest acknowledgement to Christ(Deemed to be University) for the support provided towards completion of project. CONFLICT OF INTEREST : The authors declare that they have no conflict of interest in the publication. AUTHOR CONTRIBUTIONS: Conceptualization of the work was done by Vasantha Veerappa Lakshmaiah, methodology and original manuscript writing by Udisha Singh, validation of data by Praveen Nagella, Review of manuscript by Aatika Nizam. STATEMENTS AND DECLARATION: There are no competing interests through funding or any other way which could influence the content of this manuscript. Availability of data and material: All necessary data generated or analysed during this study are included in the article. Additional data could be made available from the corresponding author upon request. Ethical approval: This particular aspect of the research involved no human subjects as participants, volunteers or respondents and there was no need to seek consent to participate.No studies with human or animal subjects were conducted for this article. Funding No funding was received to conduct this study. 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11:52:08","extension":"html","order_by":25,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":206922,"visible":true,"origin":"","legend":"","description":"","filename":"earlyproof.html","url":"https://assets-eu.researchsquare.com/files/rs-6767051/v1/1489ce48eae4ea63a05f739b.html"},{"id":93678991,"identity":"9538dda5-7dd8-419a-8880-1717a69b38c8","added_by":"auto","created_at":"2025-10-16 11:52:07","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":161357,"visible":true,"origin":"","legend":"\u003cp\u003eBOILED-Egg output of resveratrol and phytochemicals of \u003cem\u003eE. breviscapus\u003c/em\u003e\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-6767051/v1/2836939717c263b5ae650d51.png"},{"id":93678989,"identity":"dcabd48e-d1ee-4c1c-bf11-5fe9ab83f838","added_by":"auto","created_at":"2025-10-16 11:52:07","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":297697,"visible":true,"origin":"","legend":"\u003cp\u003eRMSD of the backbone atoms of the protein-ligand complexes: (a) Bap1, (b) RbmA, (c) RbmC\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-6767051/v1/e0c5782e860bd88f223cdba5.png"},{"id":93680563,"identity":"9b17a560-5291-44da-bd23-9f7fc81d8719","added_by":"auto","created_at":"2025-10-16 12:08:07","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":283186,"visible":true,"origin":"","legend":"\u003cp\u003eRMSF of the c-α atoms of the proteins: (a) Bap1, (b) RbmA, (c) RbmC\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-6767051/v1/7c82affe6766b7d549b8c6de.png"},{"id":93678992,"identity":"08e6fcaf-a480-4b7a-b939-2441258a50c6","added_by":"auto","created_at":"2025-10-16 11:52:07","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":330815,"visible":true,"origin":"","legend":"\u003cp\u003eRg of the backbone atoms of the protein-ligand complexes: (a) Bap1, (b) RbmA, (c) RbmC\u003c/p\u003e","description":"","filename":"4.png","url":"https://assets-eu.researchsquare.com/files/rs-6767051/v1/8f034e0c45828280391e875b.png"},{"id":93680166,"identity":"c2ec6ad2-81f8-4820-b05f-bbeacb0602ae","added_by":"auto","created_at":"2025-10-16 12:00:07","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":336206,"visible":true,"origin":"","legend":"\u003cp\u003eH-Bonds of the protein-ligand complexes: (a) Bap1, (b) RbmA, (c) RbmC\u003c/p\u003e","description":"","filename":"5.png","url":"https://assets-eu.researchsquare.com/files/rs-6767051/v1/9b4b3dc1d0f8f69a37178de6.png"},{"id":98430690,"identity":"d7cf7d43-a697-4597-b11e-0d55bc760834","added_by":"auto","created_at":"2025-12-17 16:46:03","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2829919,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6767051/v1/d1e57c3d-4954-4313-8b07-099c9fd06a20.pdf"},{"id":93678994,"identity":"8f588ca3-9d3b-40e4-9555-5deb1f7fc4d8","added_by":"auto","created_at":"2025-10-16 11:52:07","extension":"docx","order_by":0,"title":"","display":"","copyAsset":false,"role":"supplement","size":3890195,"visible":true,"origin":"","legend":"","description":"","filename":"Supplementaryfile.docx","url":"https://assets-eu.researchsquare.com/files/rs-6767051/v1/f1b32243cfa09ba02bc77167.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Breaking the Biofilm Barrier: An in-silico therapeutic approach against Vibrio cholerae using metabolites of Erigeron breviscapus","fulltext":[{"header":"INTRODUCTION","content":"\u003cp\u003eMany bacteria form biofilms helping them escape immune response and withstand a wide range of antibiotics and adverse environmental stress (Ramalingam et al. \u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). These may be helpful or harmful to people depending on the microbial species and their location. It is suggested that biofilms contribute to over 75% of human infections (Miquel et al. \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). To grow in a complex community with one or more species, bacteria use various polymeric extracellular materials to form biofilms on both biotic and abiotic surfaces (Guti\u0026eacute;rrez et al. \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). These materials protect the community by maintaining its structure and enabling an exchange of materials (Flemming and Wingender \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2010\u003c/span\u003e). Biofilm formation allows the bacteria to establish and survive in varied ecological niches, which has become the most favoured lifestyle for microorganisms (Hall-Stoodley et al. \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2012\u003c/span\u003e). However, it can negatively affect humans since these biofilms are not easily eliminated. Biofilms created by pathogenic bacteria can cause chronic infections as they possess resistance to the host immune system (Guti\u0026eacute;rrez et al. \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). Antimicrobials have no effect since they cannot penetrate the biofilm (Singh et al. \u003cspan citationid=\"CR66\" class=\"CitationRef\"\u003e2010\u003c/span\u003e). In addition to affecting health, biofilms also negatively influence industrial activities (Whitehead and Verran \u003cspan citationid=\"CR75\" class=\"CitationRef\"\u003e2015\u003c/span\u003e).\u003c/p\u003e\u003cp\u003e\u003cem\u003eVibrio cholerae\u003c/em\u003e, the cholera-causing bacterium, employs biofilms to increase its antibiotic tolerance and become hyper-infectious. The biofilm matrix comprises VPS (\u003cem\u003eVibrio\u003c/em\u003e polysaccharides), nucleic acids, and proteins such as RbmA, RbmC, and Bap1. These proteins are crucial for cell adhesion, surface attachment, and biofilm architecture (Giglio et al. \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2013\u003c/span\u003e; Kaus et al. \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). The role of biofilm development in \u003cem\u003eV. cholerae\u003c/em\u003e pathogenesis is well understood. It is the main reason that cholera continues to be a life-threatening disease, especially in resource-poor countries (Kierek and Watnick \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2003\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eThe resistant qualities of biofilm cells and their genetic and phenotypic versatility encouraged researchers to develop biofilm-specific medicines. The phrase \u0026ldquo;anti-biofilm\u0026rdquo; first appeared in the literature in the 1990s. It is a natural or induced process that alters bacterial biomass's cellular structure, integrity, adhesion, and extracellular components (Miquel et al. \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). Thus, understanding biofilm formation mechanisms and strategies to destroy them is the core area of research. As evidenced by many recent articles, anti-biofilm methods are promising approaches for medical innovation. Still, they should be cautiously used, as it could alter the composition of established ecosystems and harm beneficial bacteria. Bioinformatics can be a useful tool in identifying potential targets against biofilms (Miquel et al. \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). The shift from experimental to computational analysis can be attributed to the plethora of bioactive molecules, their availability in limited amounts, and to do away with animal testing (Daina and Zoete \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). Computational methods can estimate the properties of such agents rapidly and accurately, allow the easy prediction of molecular interactions and their associated binding energy, and consume minimal time for screening numerous bioactive molecules. Therefore, it finds many applications in the pharmaceutical industry (Forli et al. \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; Hariprasath et al. \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2022\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eThe present study aims to investigate the potential of phytochemicals from \u003cem\u003eErigeron breviscapus\u003c/em\u003e as novel anti-biofilm agents against \u003cem\u003eV. cholerae\u003c/em\u003e. We used computational methods to evaluate the antibiofilm efficacy by studying the inhibition of biofilm formation in comparison to resveratrol, which has been proven to inhibit biofilms in \u003cem\u003eV. cholerae\u003c/em\u003e, with the possibility of developing resistance to it being minuscule (Augustine et al. \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2014\u003c/span\u003e). Resveratrol demonstrates several therapeutic properties (Baur and Sinclair \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2006\u003c/span\u003e). Its merits are its quick clearance from the circulatory system and its high efficacy despite minimal bioavailability. In contrast, its demerits include toxicity at higher concentrations and high costs. These disadvantages can be overcome by blocking its metabolism, generating analogues having higher bioavailability, or discovering new and more potent alternatives (Baur and Sinclair \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2006\u003c/span\u003e). Hence, we have investigated phytochemicals from \u003cem\u003eE. breviscapus\u003c/em\u003e as potential alternatives to resveratrol. This daisy-like flowering plant species belonging to the Compositae family, are native to China (He et al. \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). It is an important medicinal plant whose multi-effect character can be attributed to its mixed herbal components. These bioactive molecules possess various properties and hence can act on diseases by binding to various sites to produce complicated and synergistic therapeutic effects (Ramalingam et al. \u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; He et al. \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). These molecules were put through different studies like docking, ADMET and MD simulation to establish the potential of \u003cem\u003eE. breviscapus\u003c/em\u003e as an anti-biofilm agent for controlling infections of \u003cem\u003eV. cholerae\u003c/em\u003e.\u003c/p\u003e"},{"header":"METHODS","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\u003ch2\u003eLigand acquisition and preparation\u003c/h2\u003e\u003cp\u003eThe canonical SMILES (simplified molecular input line entry system) of the ligands were obtained from the PubChem database (Kim et al. \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2022\u003c/span\u003e) and converted into 3D structures by employing the CORINA Classic Demo online tool (Molecular Networks GmbH, Germany and Altamira, LLC, USA; Sadowski et al. \u003cspan citationid=\"CR61\" class=\"CitationRef\"\u003e2002\u003c/span\u003e). These pdb structures were energy-optimised using Avogadro software (Hanwell et al. \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2012\u003c/span\u003e), and further prepared with AutoDock Tools 1.5.6 (Sanner \u003cspan citationid=\"CR62\" class=\"CitationRef\"\u003e1999\u003c/span\u003e). Following structural optimisation and protonation, the bioactive were saved in pdbqt format.\u003c/p\u003e\u003c/div\u003e\n\u003ch3\u003eReceptor acquisition and preparation\u003c/h3\u003e\n\u003cp\u003eThe receptors, i.e., Bap1 (6MLT), RbmA (5G50), and RbmC (5V6C), were downloaded from the RCSB PDB (Research Collaboratory for Structural Bioinformatics Protein Data Bank) database (Berman et al. \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2000\u003c/span\u003e). The receptors were initially prepared on PyMOL (Schr\u0026ouml;dinger and DeLano \u003cspan citationid=\"CR64\" class=\"CitationRef\"\u003e2020\u003c/span\u003e) by eliminating the associated heteroatoms and water molecules. AutoDock Tools 1.5.6 was utilised for further preparation, and structures were saved in pdbqt format.\u003c/p\u003e\n\u003ch3\u003eBinding site prediction\u003c/h3\u003e\n\u003cp\u003eA stable complex comprises a ligand, with the best conformation, in the receptor\u0026rsquo;s active site to carry out its expected activity (Azam and Abbasi \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2013\u003c/span\u003e). BIOVIA Discovery Studio (DS) Visualiser was employed to determine the active site of the protein. This software has been utilised for predicting binding sites in previous works as well (Singh et al. \u003cspan citationid=\"CR65\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; Musfiroh et al. \u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Prasanth et al. \u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e2021b\u003c/span\u003e). The active site coordinates were maintained in a text file and utilised for docking.\u003c/p\u003e\n\u003ch3\u003eMolecular docking\u003c/h3\u003e\n\u003cp\u003eDocking was performed using AutoDock Vina (Trott and Olson \u003cspan citationid=\"CR70\" class=\"CitationRef\"\u003e2010\u003c/span\u003e), a highly accurate, easily accessible and user\u0026ndash;friendly tool (Hariprasath et al. \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). The combined energies of all the intermolecular interactions occurring in the receptor-ligand complex are represented as docking energies, calculated by AutoDock Vina (Augustine et al. \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2014\u003c/span\u003e; Eberhardt et al. \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Thirty phytochemicals from \u003cem\u003eE. breviscapus\u003c/em\u003e and resveratrol were docked against Bap1, RbmA and RbmC, to find suitable lead molecules for clinical applications. The docking proceeded as described by Tarachand et al. (\u003cspan citationid=\"CR68\" class=\"CitationRef\"\u003e2023\u003c/span\u003e).\u003c/p\u003e\n\u003ch3\u003eAssessment of ADMET properties\u003c/h3\u003e\n\u003cp\u003eSubsequently, suitable phytochemicals were selected based on their ADMET profiles. This was evaluated using the SwissADME (Daina et al. \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2017\u003c/span\u003e) and pkCSM (Pires et al. \u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e2015\u003c/span\u003e) online tools. After uploading the canonical SMILES to these sites, the selected ligands were evaluated based on different parameters. The phytochemicals meeting all the requirements were further studied.\u003c/p\u003e\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e\u003ch2\u003eVisualisation\u003c/h2\u003e\u003cp\u003eBIOVIA DS Visualiser and PyMOL were employed to analyse the complex's interactions and the amino acids participating in them (Nag and Chowdhury \u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e2020\u003c/span\u003e).\u003c/p\u003e\u003c/div\u003e\n\u003ch3\u003eMD simulation\u003c/h3\u003e\n\u003cp\u003eMD simulations were conducted using GROMACS (GROningen MAchine for Chemical Simulations) software (P\u0026aacute;ll et al. \u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e2015\u003c/span\u003e; Abraham et al. \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2015\u003c/span\u003e; Malathi and Ramaiah \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e2016\u003c/span\u003e) to evaluate the stability of the selected complexes, following the protocol mentioned by Tarachand et al. (\u003cspan citationid=\"CR68\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Binding free energy (G\u003csub\u003ebinding\u003c/sub\u003e) was determined by utilising the MMPBSA (Molecular Mechanics Poisson-Boltzmann Surface Area) method (Tarachand et al. \u003cspan citationid=\"CR68\" class=\"CitationRef\"\u003e2023\u003c/span\u003e), and was calculated using the following formula:\u003c/p\u003e\u003cp\u003e∆G\u003csub\u003ebinding\u003c/sub\u003e = G\u003csub\u003ecomplex\u003c/sub\u003e \u0026ndash; (G\u003csub\u003ereceptor\u003c/sub\u003e + G\u003csub\u003eligand\u003c/sub\u003e)\u003c/p\u003e\u003cp\u003ewhere ∆G\u003csub\u003ebinding\u003c/sub\u003e is the total binding free energy, and G\u003csub\u003ecomplex\u003c/sub\u003e, G\u003csub\u003ereceptor\u003c/sub\u003e and G\u003csub\u003eligand\u003c/sub\u003e are the binding energies of the protein-ligand complex, the unbound receptor and the unbound ligand, respectively (Bhardwaj and Purohit \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2020\u003c/span\u003e).\u003c/p\u003e"},{"header":"RESULTS","content":"\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e\u003ch2\u003eBinding site prediction\u003c/h2\u003e\u003cp\u003eThe active site was generated in the form of XYZ coordinates. The grid box centre values for Bap1 were determined to be x\u0026thinsp;=\u0026thinsp;7.363433, y\u0026thinsp;=\u0026thinsp;12.227564, and z\u0026thinsp;=\u0026thinsp;93.993667. The coordinates for RbmA were kept as x= -18.279229, y= -5.319617, z\u0026thinsp;=\u0026thinsp;11.417807, whereas RbmC showed values of x= -29.232892, y= -13.900945, z= -23.041052. The active sites of the three proteins are shown in \u003cb\u003eOnline Resource 1\u003c/b\u003e.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec12\" class=\"Section2\"\u003e\u003ch2\u003eMolecular docking analysis\u003c/h2\u003e\u003cp\u003eThe docking step revealed that among all the phytochemicals, the least binding energy of a ligand with Bap1 was \u0026minus;\u0026thinsp;8.2 kcal/mol for Naringenin-7-O-glucuronide compared to -7.1 kcal/mol for resveratrol, with the other ligands ranging between \u0026minus;\u0026thinsp;8.1 and \u0026minus;\u0026thinsp;3.9 kcal/mol. Similarly, for RbmA, Kaempferol-3-O-glucoside and Naringenin-7-O-glucuronide had the least binding energy of -9.9 kcal/mol compared to -7.6 kcal/mol for resveratrol, while the remaining bioactive molecules showed affinities between \u0026minus;\u0026thinsp;9.8 and \u0026minus;\u0026thinsp;4.0 kcal/mol. For RbmC, a binding energy of -10.4 kcal/mol for 3,4-Di-O-caffeoylquinic acid and 4,5-Di-O-caffeoylquinic acid was the least compared to -7.4 kcal/mol for resveratrol, with the rest of the phytochemicals scoring between \u0026minus;\u0026thinsp;10.1 and \u0026minus;\u0026thinsp;4.9 kcal/mol. Additionally, 17, 19 and 21 ligands showed more affinity towards Bap1, RbmA, and RbmC than resveratrol, respectively. The PubChem CID (compound identifier) and binding affinities of the phytochemicals to the receptors are highlighted in Online Resource 2.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec13\" class=\"Section2\"\u003e\u003ch2\u003eEvaluation of drug-likeness\u003c/h2\u003e\u003cp\u003eThe top-scoring ligands were screened, and the druggability factor for three phytochemicals showed zero violations- apigenin, eriodictyol, and naringenin. These phytochemicals possessed significant drug-likeness, highlighted in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e and \u003cb\u003eOnline Resource 3\u003c/b\u003e, along with the properties of resveratrol.\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\u003eADME profile of resveratrol and the selected ligands\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=\"left\" 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=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003ePROPERTY\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003ePARAMETERS\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"4\" nameend=\"c6\" namest=\"c3\"\u003e\u003cp\u003eLIGANDS\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eResveratrol\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eApigenin\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eEriodictyol\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003eNaringenin\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\u003ePhysicochemical\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cb\u003eTPSA (\u0026Aring;\u003c/b\u003e\u003csup\u003e\u003cb\u003e2\u003c/b\u003e\u003c/sup\u003e\u003cb\u003e)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e60.69\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e90.90\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e107.22\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e86.99\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eLipophilicity\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cb\u003eConsensus Log\u003c/b\u003e \u003cb\u003eP\u003c/b\u003e\u003csub\u003e\u003cb\u003eo/w\u003c/b\u003e\u003c/sub\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e2.48\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e2.11\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1.45\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e1.84\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003e\u003cb\u003eWater Solubility\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cb\u003eLog\u003c/b\u003e \u003cb\u003eS\u003c/b\u003e \u003cb\u003e(ESOL)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-3.62\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-3.94\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-3.26\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e-3.49\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cb\u003eSolubility Class\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eSoluble\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eSoluble\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eSoluble\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eSoluble\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e\u003cp\u003e\u003cb\u003ePharmacokinetics\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cb\u003eGI absorption\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eHigh\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eHigh\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eHigh\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eHigh\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cb\u003eBBB permeant\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eNo\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eNo\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eNo\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eNo\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cb\u003eP-gp substrate\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eNo\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eNo\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e\u003cp\u003e\u003cb\u003eDruglikeness\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cb\u003eLipinski\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cb\u003eLipinski violations\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cb\u003eBioavailability score\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.55\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.55\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.55\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.55\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\u003eThe pharmacokinetic analysis studied properties such as gastrointestinal absorption, access to the brain and skin, and efflux regulated by P-glycoprotein (permeability glycoprotein). By considering their lipophilicity and polarity, these can be predicted simultaneously with the help of BOILED-Egg (Brain Or IntestinaL EstimateD permeation method). The BOILED-Egg output for our chosen phytochemicals, along with resveratrol, are shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec14\" class=\"Section2\"\u003e\u003ch2\u003eToxicity\u003c/h2\u003e\u003cp\u003eThe toxicity profiles of the top3 chosen phytochemicals and resveratrol were analysed, and the results are detailed in Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e.\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\u003eToxicity evaluation of resveratrol and the selected ligands\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=\"left\" 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=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003ePROPERTIES\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"4\" nameend=\"c5\" namest=\"c2\"\u003e\u003cp\u003eLIGANDS\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eResveratrol\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eApigenin\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eEriodictyol\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eNaringenin\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\u003eAMES toxicity\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eNo\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eNo\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eNo\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eMax. tolerated dose (human; log mg/kg/day)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.331\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.328\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.014\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-0.176\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003ehERG Ⅰ inhibitor\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eNo\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eNo\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eNo\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eNo\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003ehERG Ⅱ inhibitor\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eNo\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eNo\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eNo\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eNo\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eOral Rat Acute Toxicity (LD50; mol/kg)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e2.529\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e2.45\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e2.03\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1.791\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eOral Rat Chronic Toxicity (LOAEL; log mg/kg_bw/day)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.533\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e2.298\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e2.475\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1.944\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eHepatotoxicity\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eNo\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eNo\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eNo\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eNo\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eSkin Sensitisation\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eNo\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eNo\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eNo\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eNo\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eT.Pyriformis\u003c/b\u003e \u003cb\u003etoxicity (log ug/L)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.746\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.38\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.332\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.369\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eMinnow toxicity (log mM)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.522\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e2.432\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e2.972\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e2.136\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=\"Sec15\" class=\"Section2\"\u003e\u003ch2\u003eVisualisation\u003c/h2\u003e\u003cp\u003eThe interactions of the potent ligands are listed in Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e. The 3D illustration of these molecules docked in the active site of the receptors along with the 2D image of their interactions are given in \u003cb\u003eOnline Resource 4\u003c/b\u003e, \u003cb\u003e5\u003c/b\u003e and \u003cb\u003e6\u003c/b\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\u003eInteracting residues in the active site\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=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003eLIGANDS\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"3\" nameend=\"c4\" namest=\"c2\"\u003e\u003cp\u003eINTERACTING RESIDUES\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eBap1\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eRbmA\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eRbmC\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eResveratrol\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eThr648, Gln672\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eTyr899(A), Lys926(A), Ile825(B)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eApigenin\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eThr648, Ser659, Thr677\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eLeu158, Glu263\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eLys926(A), Gly822(B)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eEriodictyol\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eArg649, Ser659, Arg687\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eLys128, Asp154, Ser157, Leu158\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eGly827(A), Tyr899(A), Asn924(A)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNaringenin\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eArg649, His678, Arg687\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eThr130, Arg261, Gln264, Ser265\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eLys926(A)\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\u003eIt can be observed that the ligands demonstrated significant interactions like conventional hydrogen, carbon-hydrogen, pi-based, van der Waals and hydrophobic bonding among others, with the receptors. In the case of Bap1, apigenin formed three H-bonds with Thr648, Ser659 and Thr677; eriodictyol formed four H-bond interactions with Arg649, Ser659 and Arg687; and naringenin formed three H-bonds with Arg649, His678 and Arg687. The standard drug, resveratrol, showed two H-bonds with Thr648 and Gln672.\u003c/p\u003e\u003cp\u003eSimilarly, in the active site of receptor RbmA, apigenin formed two H-bonds with Leu158 and Glu263; eriodictyol formed four H-bonds with Lys128, Asp154, Ser157 and Leu158; naringenin also made four H-bonds with Thr130, Arg261, Gln264 and Ser265; while resveratrol didn\u0026rsquo;t form any H-bonds with this receptor.\u003c/p\u003e\u003cp\u003eApigenin, eriodictyol and naringenin formed two, three and one H-bond with RbmC receptor, respectively. Apigenin interacted with Lys926 of chain A and Gly822 of chain B; eriodictyol was bound to Gly827, Tyr899 and Asn924 of chain A; whereas naringenin formed bonds with Lys926 of chain A. Resveratrol interacted with RbmC through three H-bonds formed by Tyr899 and Lys926 of chain A, and Ile825 of chain B.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec16\" class=\"Section2\"\u003e\u003ch2\u003eMD simulation\u003c/h2\u003e\u003cdiv id=\"Sec17\" class=\"Section3\"\u003e\u003ch2\u003eRMSD\u003c/h2\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eFigure \u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e Highlights the RMSD values for the complexes. Apigenin and naringenin stabilised the Bap1 complex after 50 ns, with average deviations of 0.40 nm and 0.20 nm, respectively, whereas eriodictyol stabilised the complex after 70 ns, with an average RMSD of 0.40 nm. For RbmA, interaction with all three ligands stabilised the complex post 80 ns with average deviations around 0.30 nm, 0.30 nm and 0.17 nm for apigenin, eriodictyol and naringenin, respectively. With RbmC, apigenin and eriodictyol maintained a constant RMSD of 0.13 nm throughout the simulation while naringenin caused a significantly higher RMSD of 2.13 nm for most of the simulation time.\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv id=\"Sec18\" class=\"Section2\"\u003e\u003ch2\u003eRMSF\u003c/h2\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eFigure \u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e illustrates the RMSF for the complexes studied. Bap1 showed significant fluctuations between 300 and 500 residue positions with most regions showing stability and RMSF value below 0.60 nm for all three phytochemicals. There were minimal fluctuations in RbmA on interaction with the three ligands, and the RMSF was less than 0.20 nm. For RbmC, the fluctuations were very high while interacting with naringenin. Apigenin and eriodictyol caused decreased fluctuations with values below 0.20 nm.\u003c/p\u003e\u003cp\u003eRg\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eThe Rg values for the complexes are illustrated in Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e. Deviations in the Bap1 complexes relaxed post 50000 ns with a high Rg value, over 2.80 nm, for the three ligands. In the case of RbmA, the Rg values remained under 2.40 nm with naringenin fluctuating more compared to apigenin and eriodictyol. The Rg values were lower for the RbmC complexes, remaining below 2.0 nm, with few deviations for naringenin.\u003c/p\u003e\u003cp\u003eH-bond analysis\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eFigure \u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e highlights the number of H-bonds formed by the complexes. Apigenin formed a maximum of 6, 5 and 5 H-bonds with Bap1, RbmA and RbmC, respectively. The highest number of H-bonds formed by eriodictyol with Bap1, RbmA and RbmC were 6, 6 and 7. Similarly, naringenin formed a maximum of 5, 7 and 5 H-bonds with Bap1, RbmA and RbmC, respectively.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec19\" class=\"Section2\"\u003e\u003ch2\u003eMM-PBSA\u003c/h2\u003e\u003cp\u003eThe binding energies of apigenin, eriodictyol and naringenin with Bap1, RbmA and RbmC are summarised in Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\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\u003eMM-PBSA calculations of binding free energy for the selected complexes\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=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003eENERGY COMPONENT\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"3\" nameend=\"c4\" namest=\"c2\"\u003e\u003cp\u003eLIGANDS\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eApigenin\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eEriodictyol\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eNaringenin\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003ctr\u003e\u003cth align=\"left\" colspan=\"4\" nameend=\"c4\" namest=\"c1\"\u003e\u003cp\u003eRECEPTOR- Bap1\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\u003evan der Waal energy\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-84.908 +/- 15.965 kJ/mol\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-82.597 +/- 10.033 kJ/mol\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-2.807 +/- 10.645 kJ/mol\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eElectrostatic energy\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-44.215 +/- 25.407 kJ/mol\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-56.715 +/- 20.750 kJ/mol\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-1.606 +/- 6.574 kJ/mol\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003ePolar solvation energy\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e103.744 +/- 35.686 kJ/mol\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e92.468 +/- 21.917 kJ/mol\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-22.259 +/- 75.103 kJ/mol\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eSASA energy\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-10.960 +/- 1.439 kJ/mol\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-10.099 +/- 0.978 kJ/mol\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.072 +/- 2.720 kJ/mol\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eSAV energy\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.000 +/- 0.000 kJ/mol\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.000 +/- 0.000 kJ/mol\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.000 +/- 0.000 kJ/mol\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eWCA energy\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.000 +/- 0.000 kJ/mol\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.000 +/- 0.000 kJ/mol\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.000 +/- 0.000 kJ/mol\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eBinding energy\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-36.339 +/- 13.642 kJ/mol\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-56.944 +/- 11.408 kJ/mol\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-26.599 +/- 71.495 kJ/mol\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"4\" nameend=\"c4\" namest=\"c1\"\u003e\u003cp\u003e\u003cb\u003eRECEPTOR- RbmA\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003evan der Waal energy\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-119.375 +/- 19.108 kJ/mol\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-128.636 +/- 14.789 kJ/mol\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-110.938 +/- 13.601 kJ/mol\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eElectrostatic energy\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-23.209 +/- 18.788 kJ/mol\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-34.221 +/- 18.547 kJ/mol\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-68.382 +/- 18.366 kJ/mol\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003ePolar solvation energy\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e106.337 +/- 25.932 kJ/mol\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e145.928 +/- 23.001 kJ/mol\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e168.889 +/- 27.036 kJ/mol\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eSASA energy\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-14.104 +/- 1.704 kJ/mol\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-15.402 +/- 1.034 kJ/mol\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-14.479 +/- 1.299 kJ/mol\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eSAV energy\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.000 +/- 0.000 kJ/mol\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.000 +/- 0.000 kJ/mol\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.000 +/- 0.000 kJ/mol\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eWCA energy\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.000 +/- 0.000 kJ/mol\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.000 +/- 0.000 kJ/mol\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.000 +/- 0.000 kJ/mol\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eBinding energy\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-50.351 +/- 10.460 kJ/mol\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-32.331 +/- 16.766 kJ/mol\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-24.911 +/- 13.596 kJ/mol\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"4\" nameend=\"c4\" namest=\"c1\"\u003e\u003cp\u003e\u003cb\u003eRECEPTOR- RbmC\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003evan der Waal energy\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-142.794 +/- 9.431 kJ/mol\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-140.924 +/- 10.906 kJ/mol\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-51.150 +/- 23.142 kJ/mol\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eElectrostatic energy\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-13.571 +/- 10.460 kJ/mol\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-39.962 +/- 9.588 kJ/mol\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-15.860 +/- 15.663 kJ/mol\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003ePolar solvation energy\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e91.622 +/- 10.944 kJ/mol\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e144.004 +/- 21.929 kJ/mol\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e43.524 +/- 42.996 kJ/mol\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eSASA energy\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-13.541 +/- 0.688 kJ/mol\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-15.721 +/- 0.894 kJ/mol\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-6.852 +/- 2.871 kJ/mol\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eSAV energy\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.000 +/- 0.000 kJ/mol\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.000 +/- 0.000 kJ/mol\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.000 +/- 0.000 kJ/mol\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eWCA energy\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.000 +/- 0.000 kJ/mol\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.000 +/- 0.000 kJ/mol\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.000 +/- 0.000 kJ/mol\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eBinding energy\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-78.284 +/- 14.555 kJ/mol\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-52.603 +/- 20.189 kJ/mol\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-30.337 +/- 19.461 kJ/mol\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"},{"header":"DISCUSSION","content":"\u003cdiv id=\"Sec21\" class=\"Section2\"\u003e\u003ch2\u003eMolecular docking analysis\u003c/h2\u003e\u003cp\u003eLower binding energy makes a compound manifest more binding affinity towards the proteins. Hence, the selected molecules must possess high negative interaction energy responsible for their dynamic nature and help them function as potential inhibitors of biofilms (Madeswaran et al. \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e2012\u003c/span\u003e; Bhardwaj et al. \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). The docking analysis exhibited that most of the bioactive molecules exhibited lesser binding energy than resveratrol, and hence have an excellent tendency to bind with the receptor targets. Therefore, these molecules can be potential alternatives to existing anti-biofilm agents. However, docking the ligand in the receptor\u0026rsquo;s active site is inadequate to prove its applicability as a drug. Hence, drug-likeness and toxicity studies are carried out to filter out potential drug molecules from unsuitable ones. The ligands that showed higher negative binding energy than resveratrol were further evaluated for drug-like properties.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec22\" class=\"Section2\"\u003e\u003ch2\u003eEvaluation of drug-likeness\u003c/h2\u003e\u003cp\u003eA bioactive molecule, to be commercially available as a pharmaceutical drug, should possess desirable pharmacological, biopharmaceutical, and physicochemical characteristics (Walters and Murcko \u003cspan citationid=\"CR74\" class=\"CitationRef\"\u003e2002\u003c/span\u003e). This ensures the molecule is available to the target in adequate amounts and for sufficient time to carry out its expected activity (Daina et al. \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). Hence, an essential step in drug discovery is using \u003cem\u003ein silico\u003c/em\u003e methods to evaluate these properties and drug-likeness of chemical entities. Drug-likeness qualitatively estimates a chemical\u0026rsquo;s suitability as a drug in terms of its bioavailability and pharmacokinetics (Vistoli et al. \u003cspan citationid=\"CR73\" class=\"CitationRef\"\u003e2008\u003c/span\u003e; Daina et al. \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Ilieva \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). Predicting drug-likeness early in the drug development process minimises late-stage failures allowing an efficient entry of drug candidates into the global market (Jorgensen \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2004\u003c/span\u003e; Daina and Zoete \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2016\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eAmongst physicochemical properties, the web tool calculated polarity as TPSA (topological polar surface area), a measure of molecular bioabsorption (Ilieva \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). Several ADME parameters, like absorption and brain access, are predicted using this. The TPSA values were 90.90 \u0026Aring;\u003csup\u003e2\u003c/sup\u003e for apigenin, 107.22 \u0026Aring;\u003csup\u003e2\u003c/sup\u003e for eriodictyol, and 86.99 \u0026Aring;\u003csup\u003e2\u003c/sup\u003e for naringenin. Drug candidates should be able to permeate through the cell membranes easily and must have a TPSA between 20 and 130 \u0026Aring;\u003csup\u003e2\u003c/sup\u003e, whereas a TPSA not in this range indicates poor permeation (Daina and Zoete \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; Ibrahim et al. \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). The phytochemicals showed values within the reference range. Hence, they can penetrate the cell membranes easily and be delivered to their targets after administration.\u003c/p\u003e\u003cp\u003eThe behaviour of drug candidates can be affected by lipophilicity. It is an essential property (Arnott and Planey \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2012\u003c/span\u003e; Daina et al. \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2014\u003c/span\u003e) and was determined to be 2.11 for apigenin, 1.45 for eriodictyol, and 1.84 for naringenin. The optimum range of lipophilicity is when consensus log \u003cem\u003eP\u003c/em\u003e lies between 0 and 3 (Arnott and Planey \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2012\u003c/span\u003e). All three ligands were identified as lipophilic, having values within this range. The low consensus values indicate that these phytochemicals will have good bioavailability if administered orally.\u003c/p\u003e\u003cp\u003eThe solubility class is assigned to the molecule under investigation, based on the log \u003cem\u003eS\u003c/em\u003e scale. Three models were adopted to assess water solubility at 25\u0026deg;C, a significant parameter controlling absorption (Ottaviani et al. \u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e2010\u003c/span\u003e; Daina et al. \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2014\u003c/span\u003e, \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). The solubility values for apigenin, eriodictyol and naringenin were found to be -3.94, -3.26, and \u0026minus;\u0026thinsp;3.49, respectively. Our study revealed them to be soluble, having log \u003cem\u003eS\u003c/em\u003e values between \u0026minus;\u0026thinsp;4 and \u0026minus;\u0026thinsp;2. Therefore, these ligands can be easily absorbed and ease the handling and formulation of drugs during the development process (Savjani et al. \u003cspan citationid=\"CR63\" class=\"CitationRef\"\u003e2012\u003c/span\u003e; Ritchie et al. \u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e2013\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eThe BOILED-Egg visually represents a molecule\u0026rsquo;s absorption and brain penetration potential (Daina and Zoete \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). Molecules undergoing passive gastrointestinal absorption occur in the white region, molecules showing passive BBB permeation occur in the yellow (yolk) region and molecules possessing both low absorption and BBB impermeability occur outside the BOILED-Egg. Similarly, red dots represent molecules that are not P-glycoprotein substrates. The opposite is true for molecules represented by blue dots (Daina and Zoete \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; Floresta et al. \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). The BOILED-Egg output shows that our chosen ligands occur in the white region and hence can undergo passive gastrointestinal absorption easily. Therefore, they can be administered orally, the preferred route for drug administration, making them suitable drug candidates. Our brain is secured by the BBB (Wilhelm and Krizbai \u003cspan citationid=\"CR76\" class=\"CitationRef\"\u003e2014\u003c/span\u003e; Erickson and Banks \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Pimentel et al. \u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). It prevents the entry of chemicals that can harm the brain and lead to neurodegeneration. Our target compounds are not BBB permeants since they do not occur in the yolk region of the BOILED-Egg. This is advantageous as the risk of toxicity and side effects to the CNS is reduced. P-glycoprotein is an active efflux transporter that restricts the substrates from entering into the systemic circulation after absorption. This influences the drug pharmacokinetics by affecting drug absorption, distribution and elimination (Finch and Pillans \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2014\u003c/span\u003e; Ilieva \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). Additionally, compounds that induce P-glycoprotein reduce the bioavailability of other drugs, whereas those that inhibit them increase the bioavailability of susceptible drugs (K\u0026ouml;nig et al. \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2013\u003c/span\u003e). Apigenin is not a P-glycoprotein substrate, whereas eriodictyol and naringenin are. Co-administering eriodictyol and naringenin with P-glycoprotein inhibitors can help overcome this disadvantage and increase their plasma concentrations (Kim \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2002\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eLipinski\u0026rsquo;s rules state the range of properties required for exhibiting optimum pharmacokinetic activity on oral administration (Kiran et al. \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Notably, Pfizer employs Lipinski\u0026rsquo;s rules during its drug discovery process (Lipinski \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). Should any molecule lie outside the physicochemical range set by these rules, its probability of becoming an oral drug will also be reduced (Daina and Zoete \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). Our selected compounds adhered to all the rules without any violations. They also obeyed other drug candidate filters like Ghose, Veber, Egan and Muegge (Muegge et al. \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e2001\u003c/span\u003e; Veber et al. \u003cspan citationid=\"CR72\" class=\"CitationRef\"\u003e2002\u003c/span\u003e; Floresta et al. \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2019\u003c/span\u003e), without any violations.\u003c/p\u003e\u003cp\u003eThe Abbott Bioavailability score ascertains whether the molecule has at least 10% oral bioavailability in rats or measurable Caco-2 permeability (Daina et al. \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). It is the rate and extent to which it enters the blood circulatory system to reach its target site. The three phytochemicals revealed good bioavailability scores of 0.55 hence, their concentration in the plasma reaches the required level before they are metabolised.\u003c/p\u003e\u003cp\u003eThis study revealed that the phytochemicals of \u003cem\u003eE. breviscapus\u003c/em\u003e, especially apigenin, eriodictyol and naringenin, showed favourable drug-likeness profiles except for a few disadvantages that can be overcome through chemical alterations during drug development or with the aid of drug carriers (Chan et al. \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2015\u003c/span\u003e; Landersdorfer et al. \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2015\u003c/span\u003e).\u003c/p\u003e\u003cdiv id=\"Sec23\" class=\"Section3\"\u003e\u003ch2\u003eToxicity\u003c/h2\u003e\u003cp\u003eNew drug molecules can sometimes be toxic and affect the human body negatively, leading to drug failure during clinical trials (van de Waterbeemd and Gifford \u003cspan citationid=\"CR71\" class=\"CitationRef\"\u003e2003\u003c/span\u003e). Not only is this life-threatening, but it also leads to loss of money and resources. Therefore, a toxicity study is paramount to ensure the safety of new molecules to be used as drugs (Subash \u003cspan citationid=\"CR67\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Hariprasath et al. \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2022\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eAMES toxicity is used to ascertain if a ligand can cause mutations in the human body, as mutagenic ligands can also be carcinogenic (Cheng et al. \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2013\u003c/span\u003e). Since our bioactive molecules tested negative for AMES toxicity, they are neither mutagenic nor carcinogenic. Inhibition of hERG Ⅰ and Ⅱ is an essential parameter in toxicity studies and has led to the withdrawal of numerous medications from the market (Pires et al. \u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e2015\u003c/span\u003e). Ligands inhibiting this gene block the potassium channels encoded by it. This is the source of QT prolongation, leading to lethal ventricular arrhythmia (Cheng et al. \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2013\u003c/span\u003e). None of our ligands are inhibitors of hERG Ⅰ and Ⅱ. The hepatotoxicity parameter reveals ligands that can harm the liver (Cheng et al. \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2013\u003c/span\u003e). Since all three bioactive molecules showed an absence of hepatotoxicity, their administration as drugs will not bring about any pathological or physiological alteration that can impair liver function (Pires et al. \u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e2015\u003c/span\u003e). Skin sensitisation predicts if a drug candidate can cause adverse reactions after contact with the skin (Pires et al. \u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e2015\u003c/span\u003e). It is a vital parameter, and this study shows that our selected compounds are not associated with skin sensitisation. Gauging the maximum amount of compound that can be administered, beyond which it will become hazardous, is done by the maximum tolerated dose parameter. This influences the maximum advised starting dose for the drug candidates during clinical trials. A good drug should have a low maximum tolerated dose, within 0.477 (log mg/kg/day) (Pires et al. \u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e2015\u003c/span\u003e; Tarachand et al. \u003cspan citationid=\"CR68\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). The values for apigenin, eriodictyol and naringenin were found to be 0.328, 0.014, and \u0026minus;\u0026thinsp;0.176, respectively. These were less than 0.477, indicating their potential to be promising drug candidates. Minnow toxicity is a parameter that estimates the concentration of a compound that can cause 50% of fathead minnows to perish. The Minnow toxicity values were calculated as 2.432 for apigenin, 2.972 for eriodictyol, and 2.136 for naringenin. A compound shows low toxicity when its log mM value exceeds \u0026minus;\u0026thinsp;0.3 (Pires et al. \u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e2015\u003c/span\u003e). The screened ligands possessed low minnow toxicity presenting values higher than \u0026minus;\u0026thinsp;0.3.\u003c/p\u003e\u003cp\u003eOur study indicated that the three shortlisted phytochemicals have a low toxicity profile, and are suitable for administration in the appropriate amount.\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv id=\"Sec24\" class=\"Section2\"\u003e\u003ch2\u003eVisualisation\u003c/h2\u003e\u003cp\u003eThe formation of H-bonds in the complex is essential for molecular recognition, quality of docking and strength of interactions by increasing the specificity between the interacting ligands and proteins (Hariprasath et al. \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Our ligands demonstrated significant interactions with the receptors and were adequately positioned in their binding pocket. They demonstrated a general trend of exhibiting equal or more H-bond interactions with the receptors compared to resveratrol, except for RbmC-apigenin and RbmC-naringenin complexes. It was also observed that certain interacting residues were common among the complexes formed between a particular receptor and the docked ligands. For Bap1, these essential residues consisted of Thr648, Arg649, Ser659 and Arg687 whereas, for RbmA it was Leu158. The common interacting residues for RbmC included Tyr899 and Lys926 of chain A.\u003c/p\u003e\u003cdiv id=\"Sec25\" class=\"Section3\"\u003e\u003ch2\u003eMD simulation\u003c/h2\u003e\u003cp\u003eThe static poses generated during molecular docking are unable to highlight additional factors that stabilise the complex, like residue flexibility, solvent effects and secondary structural elements (Purohit \u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e2014\u003c/span\u003e; Pagadala et al. \u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Pantsar and Poso \u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). The biological function of a protein is disturbed when there is a change in its conformation, usually due to the protein\u0026rsquo;s dynamic behaviour (Bhardwaj and Purohit \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). MD studies help visualise these disturbances when present in an actual biological environment. The conformational space in the complexes is also observed (Bhardwaj et al. \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). These studies can improve the credibility of the estimated binding energies (Ibrahim et al. \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2020\u003c/span\u003e) and involve calculating RMSD, RMSF, Rg, H-bonds and MM-PBSA.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec26\" class=\"Section3\"\u003e\u003ch2\u003eRMSD\u003c/h2\u003e\u003cp\u003eOne method of determining behaviour, stability, structural changes, equilibration of proteins and validating docking poses is RMSD, which calculates the mean square displacement of atoms (2017; Thirumoorthy et al. \u003cspan citationid=\"CR69\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). This is carried out by calculating the RMSD of Cα atoms present in the backbone of the complex, which is plotted against time (Ragunathan et al. \u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). Protein complexes are deemed stable when the RMSD values are low, trajectories show minimal fluctuations, and there is very little difference in the average RMSD values (Pandey et al. \u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Bhardwaj et al. \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). It can be analysed from Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e that the complexes formed by RbmC protein with apigenin and eriodictyol are considerably more stable owing to their low and constant average RMSD values, and their trajectories highlighting minimal fluctuations.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec27\" class=\"Section3\"\u003e\u003ch2\u003eRMSF\u003c/h2\u003e\u003cp\u003eIn addition to calculating the receptor mobility, after binding with the ligand on a time basis, the RMSF of C-atoms enables us to understand the effects of ligand binding on the flexibility of the receptor by recognising the fluctuating protein part during simulations. Therefore, a reduction in RMSF value corresponds to stable residues (Prasanth et al. \u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e2021a\u003c/span\u003e). Figure\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e shows that the complexes formed by RbmA with eriodictyol and naringenin, and RbmC with apigenin and eriodictyol can be deemed stable owing to their low RMSF values and reduced fluctuations for most of the simulation. Additionally, high peaks towards the ends are indicative of the N- and C-terminals of the protein, which are very flexible.\u003c/p\u003e\u003cp\u003eRg\u003c/p\u003e\u003cp\u003eAlong with RMSD, the stability of the protein and computation of the dimensions can be determined by calculating the Rg value, which is the mass-weighted root mean square distance of each atom from its centroid (Kumar et al. \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2014\u003c/span\u003e). The compactness of a protein can be understood by plotting the Rg value of the backbone Cα atoms against the simulation time. Hence, the Rg value is stable when the protein undergoes secure folding, whereas the Rg value fluctuates during protein unfolding (2017). Similarly, a high Rg value corresponds to amino acids being loosely packed; otherwise, the Rg value is low (Liao et al. \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e2014\u003c/span\u003e; Tarachand et al. \u003cspan citationid=\"CR68\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). From Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e it can be observed that apigenin and eriodictyol showed stable deviations for RbmA and RbmC, indicating secure folding of these proteins in their corresponding complexes. Additionally, the complexes formed by RbmC showed a lower Rg value compared to those formed by RbmA, signifying tight packaging of residues. Hence, the complex RbmC forms with apigenin and eriodictyol are significantly more stable than other complexes.\u003c/p\u003e\u003cp\u003eH-bond analysis\u003c/p\u003e\u003cp\u003eIntermolecular hydrogen bonds and their corresponding strength in an aqueous environment determine the stability of a complex (2017; Pantsar and Poso \u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). H-bonds provide directionality, strength, and lucidity to the interactions, which in turn helps recognise and select specific molecules. H-bonding also changes secondary structures, leading to interactions (Bhardwaj et al. \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Tarachand et al. \u003cspan citationid=\"CR68\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). In our study, the maximum number of H-bonds formed was 7, found in the RbmA-naringenin and RbmC-eriodictyol complexes.\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv id=\"Sec28\" class=\"Section2\"\u003e\u003ch2\u003eMM-PBSA\u003c/h2\u003e\u003cp\u003eBinding energy is the energy released when the protein and bioactive molecules interact, leading to bond formation. It combines electrostatic, polar solvation, SASA, SAV, van der Waals and WCA energies. The lesser the binding energy value, the stronger the bond between the receptor and ligand (Bhardwaj et al. \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). In our study, all the complexes demonstrated a low binding free energy, indicating a strong binding between the receptor and ligands. The complexes formed between Bap1 and eriodictyol, RbmA and apigenin, RbmC and apigenin, and RbmC and eriodictyol revealed a more negative binding free energy of -56.944 +/- 11.408 kJ/mol, -50.351 +/- 10.460 kJ/mol, -78.284 +/- 14.555 kJ/mol, and \u0026minus;\u0026thinsp;52.603 +/- 20.189 kJ/mol, respectively. This indicates a stronger binding affinity between these proteins and bioactive molecules, as compared to the other complexes.\u003c/p\u003e\u003c/div\u003e"},{"header":"CONCLUSION","content":"\u003cp\u003eIn this study, we have evaluated the anti-biofilm potential of 30 phytochemicals from \u003cem\u003eE. breviscapus\u003c/em\u003e by docking them with important biofilm proteins- Bap1, RbmA, and RbmC of \u003cem\u003eV. cholerae\u003c/em\u003e. The short-listed phytochemicals were chosen based on the docking scores, ADMET properties, interaction studies and MD simulations. The results suggested that compounds like apigenin, eriodictyol and naringenin can be further explored to formulate new plant-based drugs for tackling the \u003cem\u003eV. cholerae\u003c/em\u003e biofilms. However, extensive experimental investigation is further required for developing drugs targeting these biofilms.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eACKNOWLEDGEMENTS:\u0026nbsp;\u003c/strong\u003eAuthors express deepest acknowledgement to Christ(Deemed to be University) for the support provided towards completion of project.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCONFLICT OF INTEREST\u003c/strong\u003e: The authors declare that they have no conflict of interest in the publication.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAUTHOR CONTRIBUTIONS:\u0026nbsp;\u003c/strong\u003eConceptualization of the work was done by Vasantha Veerappa Lakshmaiah, methodology and original manuscript writing by Udisha Singh, validation of data by Praveen Nagella, Review of manuscript by Aatika Nizam.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eSTATEMENTS AND DECLARATION:\u0026nbsp;\u003c/strong\u003eThere are no competing interests through funding or any other way which could influence the content of this manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and material:\u0026nbsp;\u003c/strong\u003eAll necessary data generated or analysed during this study are included in the article. Additional data could be made available from the corresponding author upon request.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthical approval:\u003c/strong\u003e This particular aspect of the research involved no human subjects as participants, volunteers or respondents and there was no need to seek consent to participate.No studies with human or animal subjects were conducted for this article.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNo funding was received to conduct this study.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eAbraham MJ, Murtola T, Schulz R, et al (2015) GROMACS: High performance molecular simulations through multi-level parallelism from laptops to supercomputers. SoftwareX 1-2:19\u0026ndash;25. https://doi.org/10.1016/j.softx.2015.06.001\u003c/li\u003e\n\u003cli\u003eArnott JA, Planey SL (2012) The influence of lipophilicity in drug discovery and design. 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Comput Biol Chem 67:1\u0026ndash;8. https://doi.org/10.1016/j.compbiolchem.2016.12.001\u003c/li\u003e\n\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":"Anti-biofilm, computer-aided drug discovery, phytochemicals, resistance, Vibrio cholerae","lastPublishedDoi":"10.21203/rs.3.rs-6767051/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6767051/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThe biomolecules present in \u003cem\u003eErigeron breviscapus\u003c/em\u003e are bestowed with many properties. The current study explores the antibiofilm activity of these bioactive compounds against \u003cem\u003eVibrio cholerae\u003c/em\u003e by examining their binding affinities with three significant biofilm-forming proteins, i.e., sugar-binding proteins Bap1 (Biofilm-Associated Protein 1) and RbmC, and adhesion protein RbmA. The structures of these receptor proteins and 31 phytochemicals were derived from open-source databases, prepared and then subjected to molecular docking analysis. Their binding energies were examined, and the phytochemicals showing lesser energy than resveratrol, the positive control, were selected for ADMET (absorption, distribution, metabolism, excretion, and toxicity) studies. The analysis of their interaction profiles followed this. The short-listed phytochemicals included apigenin, eriodictyol, and naringenin. Finally, the stability and dynamic behaviour of the protein-ligand complexes were confirmed by employing molecular dynamics (MD) simulation studies. The results suggested that the plant \u003cem\u003eErigeron breviscapus\u003c/em\u003e can be a potential antibiofilm agent in controlling the infections of \u003cem\u003eVibrio cholerae\u003c/em\u003e, and can be explored to a greater extent in the field of phytopharmacology.\u003c/p\u003e","manuscriptTitle":"Breaking the Biofilm Barrier: An in-silico therapeutic approach against Vibrio cholerae using metabolites of Erigeron breviscapus","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-10-16 11:52:02","doi":"10.21203/rs.3.rs-6767051/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":"d5b73e7a-dd81-449d-8783-fd23528bdab5","owner":[],"postedDate":"October 16th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2025-12-13T09:39:02+00:00","versionOfRecord":[],"versionCreatedAt":"2025-10-16 11:52:02","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-6767051","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6767051","identity":"rs-6767051","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}