Computational Insight into Biofilm Inhibitory Activity of Ketidocillinone B and C against Pseudomonas aeruginosa: A Computational Study

Computational Insight into Biofilm Inhibitory Activity of Ketidocillinone B and C against Pseudomonas aeruginosa: A Computational Study | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Computational Insight into Biofilm Inhibitory Activity of Ketidocillinone B and C against Pseudomonas aeruginosa: A Computational Study Prince Manu, Prisca Baah Nketia, Priscilla Osei-Poku, Alexander Kwarteng This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-5690135/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 6 You are reading this latest preprint version Abstract Lymphatic filariasis (LF) remains a significant public health challenge, particularly in endemic regions where secondary bacterial infections exacerbate the morbidity associated with chronic lymphedema. Among these infections, Pseudomonas aeruginosa stands out due to its biofilm-forming ability and resistance to conventional antibiotics. This study underscores the importance of targeting P. aeruginosa in LF patients, as biofilm-associated infections are prevalent in chronic wounds, complicating treatment and increasing healthcare burdens. Leveraging molecular docking and molecular dynamics simulations, we screened 100 fungal polyketides against LasR and PqsR, quorum-sensing proteins critical to P. aeruginosa biofilm formation. Ketidocillinone B (Ket B) and Ketidocillinone C (Ket C) emerged as promising candidates with notable binding affinities of -9.3 kcal/mol and − 9.5 kcal/mol to LasR, and − 7.9 kcal/mol and − 8.8 kcal/mol to PqsR, respectively. Molecular dynamics simulations revealed sustained stability of both compounds within the active sites, with binding energies of -82.559 kJ/mol (Ket B) and − 68.680 kJ/mol (Ket C) for LasR, and − 86.855 kJ/mol (Ket B) and − 90.342 kJ/mol (Ket C) for PqsR. Pharmacokinetic evaluations indicated high gastrointestinal absorption, solubility, and favorable metabolic profiles, with Ket B exhibiting a clearance rate of 16.306 mL/min/kg and Ket C 14.881 mL/min/kg. These findings highlight the potential of Ket B and Ket C as therapeutic agents against P. aeruginosa infections in LF patients, through computational investigation. Future experimental validation could help by offering a novel approach to mitigate complications associated with this neglected tropical disease using KetB and Ket C as starting scaffold. Lymphatic Filariasis (LF) Molecular Docking Molecular Dynamics Simulations Biofilm Formation Polyketides Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Figure 8 Figure 9 Figure 10 Figure 11 Figure 12 Figure 13 Introduction Lymphatic filariasis (LF) stands as the second most significant cause of long-term and irreversible disability worldwide, disproportionately affecting populations in Africa, Asia, and South America. Currently, it is estimated that approximately 657 million individuals are at risk of contracting this debilitating disease from 39 countries worldwide, with over 51 million already living with the infection since 2019 [ 1 ]. Among the three types of parasitic filarial worms responsible for LF, Wuchereria bancrofti is the primary culprit, accounting for 90% of all reported cases [ 2 ]. The clinical outcomes of W. bancrofti infections can be severe and varied, manifesting as lymphedema, genital disorders such as hydrocele, and acute recurrent bacterial infections. These complications not only inflict a substantial burden on the healthcare systems in endemic regions but also severely impair the quality of life of those affected. The current global strategy to combat LF predominantly focuses on mass drug administration (MDA) programs, which are designed to interrupt the transmission cycle by targeting the microfilariae, the larval stage of the parasites circulating in the bloodstream. While these programs have demonstrated progress in curbing the spread of the infection, they are largely ineffective in addressing the more severe, late-stage clinical manifestations of the disease, such as chronic lymphedema and hydrocele. As a result, a significant proportion of patients with advanced LF remain in need of alternative therapeutic interventions and effective morbidity management strategies, despite the strides made by the Global Programme to Eliminate Lymphatic Filariasis (GPELF) [ 3 ]. One of the most pressing challenges facing the GPELF is the alarming rise in drug resistance, which threatens to undermine the gains achieved through mass treatment initiatives [ 4 ]. In recent years, the global health community has become increasingly alarmed by the rise of antimicrobial resistance (AMR), which complicates the management of LF-related bacterial infections. The emergence of drug-resistant pathogens has made treating these secondary infections more challenging. Patients suffering from co-infections particularly those with both bacterial infections and filarial disease are at a significantly higher risk of poor clinical outcomes. Studies have shown that individuals with polymicrobial infections are four times more likely to experience fatal complications compared to those with isolated bacterial infections [ 5 ]. Recently our lab has shown that among the bacteria isolated from the wound of an infected LF patients, Pseudomonas aeruginosa remains the most significant multidrug resistant gram-negative pathogen [ 6 ]. This pathogen is a problematic nosocomial pathogen that seriously threatens critically ill and immunocompromised patients. The resistance of P. aeruginosa and other microbes is significantly heightened due to their biofilm-forming ability. There is therefore a strong demand for effective treatment strategies to eradicate biofilms associated with P. aeruginosa . The production of biofilms and the control of virulence factors in P. aeruginosa result from cell-to-cell communication among bacteria, a process known as quorum sensing (QS). This bacterial communication is facilitated by diffusible chemical signaling molecules called autoinducers (AIs) [ 7 ]. Bacterial population growth causes signaling molecules to build up in the environment, amplifying a feedback mechanism that promotes gene expression for biofilm formation and the regulation of other population-dependent behaviors. Two interconnected QS regulatory systems are crucial for the development of biofilms, pathogenicity, and a host of other features like antibiotic, detergent, and biocide tolerance. Signaling molecules build up in the environment around bacteria as their population rises, amplifying a feedback mechanism that encourages the expression of genes that lead to biofilm development and control. The acylhomoserine lactone synthase (lasI) and the transcriptional activator (lasR) form the first system (Las) [ 8 ]. A transcriptional activator (rhlR) and an acyl homoserine lactone synthase (rhlI) make up the second system (Rhl). Pseudomonas Quinolone Signal (PQS), which employs quinolone as the signaling chemical, and Integrated Quorum System (IQS) are two other QS systems that have just recently been identified. LasR and PqsR systems produce 100 folds of virulence factors [ 7 ], [ 8 ], [ 9 ], [ 10 ]. This emphasizes LasR's and PqsR’s significance as a potential target for suppressing quorum sensing and thereby regulating biofilm development. The current treatments for lymphatic filariasis, which include diethylcarbamazine (DEC), ivermectin, and albendazole, have made significant strides in reducing the disease's burden. However, these treatments have notable limitations. One major drawback is their predominant action against microfilariae, the immature larvae, while leaving adult worms largely unaffected [ 11 ]. As a result, adult worms can continue producing microfilariae for years, perpetuating the infection. Additionally, concerns about drug resistance have emerged due to prolonged mass drug administration (MDA) programs, particularly in regions with overlapping parasitic infections. For instance, resistance to ivermectin in Onchocerca volvulus infections raises alarms about the potential for similar challenges in lymphatic filariasis [ 11 ]. Another limitation involves the adverse side effects associated with these medications. DEC can cause severe allergic reactions, especially in individuals with high microfilarial loads, while ivermectin is known to trigger Mazzotti-like reactions in those co-infected with Onchocerca [ 11 ], [ 12 ], [ 13 ]. Geographical coverage presents yet another hurdle; many endemic regions face logistical difficulties due to socio-economic challenges, weak healthcare infrastructure, and community non-compliance with MDA programs. Moreover, the current drugs exhibit limited efficacy against Wolbachia, a bacterial endosymbiont essential for the parasite's survival and reproduction. Although doxycycline has shown promise in targeting Wolbachia, its use is restricted in children and pregnant women, limiting its broader application [ 11 ], [ 12 ], [ 13 ], [ 14 ]. Again, the increasing resistance to routinely used antiparasitic and antimicrobial agents is particularly concerning in Sub-Saharan Africa, where high levels of resistance have been observed [ 15 ]. For instance, resistance to ivermectin, one of the key drugs used in LF treatment, has already been documented in Ghana. Additionally, widespread resistance to benzimidazole compounds, including albendazole, has been linked to mutations in the β-tubulin gene, which are responsible for conferring resistance to these drugs [ 16 ]. This resistance compromises the efficacy of treatment regimens and poses a significant barrier to achieving the long-term eradication of LF [ 17 ]. Further complicating the situation is the suboptimal efficacy of diethylcarbamazine (DEC), another cornerstone of LF treatment [ 6 ], [ 18 ]. Research has demonstrated that DEC does not provide complete protection or cure in all cases, further highlighting the need for new treatment approaches and a deeper understanding of the disease's progression [ 6 ]. Moreover, our field observations have indicated that patients with filarial lymphedema are more susceptible to secondary bacterial infections, likely due to their compromised lymphatic systems [ 18 ]. These secondary infections can be recurrent and often exacerbate the severity of the lymphedema, leading to further complications and increased morbidity [ 17 ]. Given these limitations, natural product-derived therapies offer a promising alternative. Natural compounds provide an extensive repertoire of bioactive molecules with structural diversity that can target multiple stages of the parasite's lifecycle. Plant-derived alkaloids, flavonoids, terpenoids, and polyphenols have demonstrated significant antifilarial activity in various studies. In addition to their antiparasitic properties, many natural products have antibacterial effects that could disrupt Wolbachia symbiosis, thereby weakening the parasite. Examples such as, extracts from Azadirachta indica (neem) and Clerodendrum species have shown encouraging antifilarial potential with fewer side effects than synthetic drugs [ 19 ]. The chance to identify novel medications from natural products is always present, and this is also true with fungi-derived compounds. According to reports, marine fungi generate polyketides as one of their main secondary metabolites as a form of protection. Additionally, various experimental findings have been published that discuss several polyketides' antibiofilm and antimicrobial properties [ 20 ], [ 21 ], [ 22 ], [ 23 ], [ 24 ]. In this study, we present an in-silico investigation of compounds derived from marine sources, targeting the biofilm and quorum sensing proteins LasR and PqsR through molecular docking and molecular dynamics simulations. Virtual screening of over 100 compounds identified two promising candidates Ketidocillinone B and Ketidocillinone C from the polyketide category as exhibiting higher binding affinities against LasR and PqsR compared to their natural autoinducers and other compounds from different categories which some of the showed no affinity for the active sites of these proteins or reported binding affinities less than that of the autoinducers for both proteins. While computational method such as virtual screening and molecular docking can rapidly screen large datasets, they often lack the ability to fully capture the complexity of biological systems, such as protein dynamics. As a result of this failure, molecular dynamics simulation was employed to further examine protein dynamics to some extent and also assess the stability of these compounds within the ligand-binding domains of LasR and PqsR, demonstrating interactions that could potentially block the autoinducer binding and disrupt signal transduction. As a result, the production of virulence factors would be halted. Based on these findings, we propose that Ketidocillinone B and C hold potential as alternative leads for treating secondary infections caused by resistant pathogenic strains in filarial lymphedema patients, particularly in LF-hyperendemic regions. Methods Protein selection and preparation Pseudomonas aeruginosa utilizes the LasR and PqsR receptors as key regulators for biofilm formation. Given their critical roles, this study focuses on these two receptors. Inhibiting LasR and PqsR leads to the disruption of biofilm formation, a process known as biofilm quenching. For this study, the 3D structures of LasR and PqsR proteins from P. aeruginosa complexed with their respective autoinducers (OHN for LasR and NNQ for PqsR) were retrieved from the Protein Data Bank (PDB IDs: 6V7X and 4JVD). Chain B of LasR was selected and prepared by removing water molecules, heteroatoms, the anti-activator (Aqs1), and the autoinducer (OHN). Similarly, Chain A of PqsR was chosen, with water molecules, heteroatoms, and the autoinducer (NNQ) removed [ 25 ]. Major groups were removed because, these groups such as water molecules are not need for the biological activities of both proteins. Also, autoinducers and Aqs1 were moved before the apo state of the protein in currently not available at the PDB database. The backbone structures of the proteins were then energy minimized using the AMBERff4SB force field, with Gasteiger charges computed via Antechamber in Chimera to further refine the models for subsequent molecular dynamics simulations. Ligand preparation Ligand preparation in this study involved using Spartan ’14 software to model and optimize selected compounds. Structural optimizations and energy minimizations were performed with density functional theory (DFT) using the B3LYP/6-31G* basis set to ensure accurate molecular geometries and electronic properties [ 26 ]. DFT is a powerful tool for ligand energy minimization but has limitations. It struggles with accurately capturing dispersion forces, solvent effects, and self-interaction errors, which can influence binding affinity predictions hence the docking was replicated. The compounds were saved in .pdb and .sdf formats for further computational analysis. These optimized ligands, identified as potential inhibitors of quorum sensing and biofilm formation in Pseudomonas aeruginosa , were evaluated for their interactions with the LasR and PqsR receptors. Figure 1 highlights the hit compounds selected for their promising binding properties and stability, forming the basis for further therapeutic exploration. Molecular docking Virtual Screening and Precision Docking The docking process involved the use of both global and precision docking approaches to evaluate the interaction between modeled ligands and prepared proteins. Initially, a global search of over 100 marine-derived compounds, known for their antibiofilm activity in Pseudomonas aeruginosa , was conducted using AutoDock Vina embedded in PyRx [ 27 ]. The search grid was set to cover the entire protein and allow the compounds to search for their preferred binding sites. Virtual screening was done for more than 100 compounds from marine source with antibiofilm activity in Pseudomonas aeruginosa with reported IC 50 values from experimental researches. Compounds with reported IC 50 values ≤ 10 µg/mL and have shown antibiofilm activity were selected. Selected compounds were grouped into alkaloids, steroids, flavones, macrolids, and polyketides. Since the molecular docking approach considered proteins as rigid and does not account for solvent effect, the docking was done in replicates (3times) and the average binding scores were calculated. The x, y and z dimensions of the box were approximately 50. 9317, 44. 2871 and 57. 7354 Å, respectively, while the x, y and centers were; 22. 7209, 4. 0930 and − 13. 2669 respectively for LasR. For the PqsR, the x, y and z dimensions of the box were approximately 48. 2587, 44. 8693 and 48. 6144 Å, respectively, while the x, y and centers were; -27. 1696, 64. 3398 and 9. 8905. Precision docking was then performed using AutoDock Vina in UCSF Chimera, focusing on compounds that preferentially bound to the ligand-binding domain of the LasR and PqsR proteins. The OHN, and NNQ were redocked in the ligand binding domain of LasR and PqsR to validate the docking protocol in precision docking using the x, y and z dimensions of the box that fit the active site. For the LasR, the x, y and z dimension used in the study was; 22. 9078, -3. 37362 and − 4. 80686 Å and radius of 19. 2 Å. For PqsR, the x, y and z dimension used in the study was; -33. 3486, 57. 8889 and 9. 39797 Å and radius of 15. 8 Å. The active site residues were obtained from the PDBSUM entry for 6V7X and 4JVD with binding site residues. For 6V7X, the identified active site residues are; Trp60, Tyr93, Ser129, Asp73, Ile52, Ala50, Ala127, Leu125, Leu40, Val76, and Cys79. For 4JVD, the identified active site residues were; Ala 168, Ile149, Leu207, Ile263, Leu189, Ile186, Ile236, Ala102, Val170, and Tyr258. The accuracy of the docking protocol was validated by ensuring a low root-mean-square deviation (RMSD) of less than 2 Å between the redocked ligands and their original co-crystallized orientations. Additionally, the interactions observed in the PDB structures were successfully replicated, further affirming the reliability of the docking procedure. This comprehensive docking strategy provided insights into the binding affinities and interactions of the selected compounds with the LasR and PqsR proteins, highlighting their potential as biofilm inhibitors. Molecular dynamics simulation study All-atom molecular dynamics simulations were conducted for hit molecules, NNQ, and OHN for 200 ns using GROMACS V.2018.6 [ 28 ] on the Lengau cluster (Centre for High-Performance Computing, Cape Town). While 200 ns is sufficient for many systems, some protein-ligand interactions may require longer simulations to fully capture conformational changes. Ligand poses with the lowest energy conformations from docking was saved in the mol2 format and were prepared with hydrogens added and corresponding charges using CHARMM force fields. Ligand topologies were generated using the CGenFF force field server [ 29 ], while protein topologies were generated with the pdb2gmx tool with the CHARMM36 force field. CHARMM force field was used in this study since this forcefield accurately give proper accounts for atoms in small molecules and macromolecules. TIP3P water model was employed in the solvation process in a dodecahedron box maintaining a margin of 1.0 nm. Ionization was achieved by adding Na + and Cl − ions where necessary, replacing water molecules until neutrality was achieved. In a dodecahedron box, the system was solvated with the TIP3P water model. Energy minimization was performed for 50,000 steps, followed by NVT and NPT equilibration at 300 K and 1 bar for 100 ps. Compounds were then subjected to MD simulation utilizing PME for long-range electrostatics with cut-offs of 1.2 nm for both Coulomb and Van der Waals interactions, and a time step of 2 fs.During the production run, coordinate trajectories were recorded every 10 ps, applying three-dimensional periodic boundary conditions (PBC) throughout the 200 ns production run [ 25 ].The Molecular Mechanics Poisson-Boltzmann Surface Area (MM-PBSA) method implemented in GROMACS was employed to calculate binding energies. The Molecular Mechanics Poisson-Boltzmann Surface Area (MM-PBSA) method implemented in GROMACS was used to calculate binding energies [ 30 ]. Post-MD analysis The stabilities of all complexes were investigated using simulation output files obtained after MD productions. Parameters such as the Root mean square deviation (RMSD), root mean square fluctuation (RMSF), radius of gyration (RoG), and the principal component analysis (PCA) values were generated. Output files were graphed using XMGRACE. By extracting frames at 200 ns, trajectory analysis of the complexes was undertaken. All hydrogen bonds were estimated using a bond angle and bond length of 180° and 3 Å, respectively as previously described [ 31 ], [ 32 ]. Further analysis helped retrieve the number of hydrogen bonding interactions and their occupancies during the 200 ns MD simulation. For stable complexes, energy calculations were computed using the last 50 ns of the simulation period. G_mmpbsa was used to calculate the binding energies of the complexes [ 33 ]. The MM-PBSA method estimated the binding energy based on the energetic terms that correspond to the change in the potential energy in vacuum. These include bond angle, torsional energies, van der Waals and electrostatic interactions, desolvation of different species, which includes the polar and non-polar solvation energy using the implicit solvation model, and the configurational entropy associated with the complex formation. Molecular mechanical potential energy (EMM), polar (Gpol), and apolar (Gapol) solvation parameters were included in the estimated binding energies. The MM-PBSA method calculated the binding energy by examining energetic terms related to changes in potential energy in a vacuum. These terms include bond angles, torsional energies, van der Waals interactions, electrostatic interactions, and the desolvation of various species, which comprises both polar and non-polar solvation energy using a deducible solvation model. The configurational entropy associated with complex formation was also considered. The estimated binding energies also included molecular mechanical potential energy (EMM), as well as polar (Gpol) and apolar (Gapol) solvation parameters. ADME-Tox evaluation The drug-likeness and pharmacokinetic properties of the selected compounds were evaluated using ADMETLab2.0 ( https://admetmesh.scbdd.com/ ) and SwissADME ( www.swissadme.ch ) web servers [ 25 ], [ 34 ], [ 35 ], [ 36 ], [ 37 ]. The 3D structures of the ligands were downloaded from PubChem and converted to SMILES strings for analysis. ADMETLab2.0 assessed drug-likeness and various ADME (Absorption, Distribution, Metabolism, Excretion) parameters, including potential bioavailability, tissue distribution, metabolic pathways, excretion routes, and toxicity risks. SwissADME further analyzed lipophilicity, polarity, drug-likeness, and pharmacokinetics to determine the compounds' suitability as therapeutic agents. Even though theses webservers are in silico predictions, it provides initial evaluation and screening parameter for compounds used in this study. Some associated limitations of in silico ADME-T predictions are that, they are susceptible to experimental evaluation and validation. This comprehensive evaluation provided insights into the compounds' potential efficacy and safety. Results and Discussion Molecular docking Virtual screening Results from the virtual screening, suggests that two polyketide compounds ketidocillinone B and C, (Ket B and Ket C) had the most binding poses with high affinity for the active site of both proteins under study (Fig. 2 ). Ketidocillinone B (Ket B) and Ketidocillinone C (Ket C) emerged as promising candidates based on stringent selection criteria during virtual screening, including high binding affinity, stability within the binding pocket. Comparing the binding affinities recorded by other compounds from various categories, Ket B and Ket C had better binding affinity when compared among compounds of the same category and other categories as both compounds exhibit better binding affinity when compound to co-crystalized ligands (OHN and NNQ). These compounds were then subjected to precision docking. Binding affinity does not account for pharmacokinetic properties such as absorption, distribution, metabolism, and excretion (ADME), which are critical for drug development, hence further ADME-T profiling shows that both compounds are excellent candidates. Validation of the molecular docking protocol used in this study In the quest to validate the molecular docking protocol employed in this study, we performed a re-docking experiment using co-crystalized ligands, to the receptors. By comparing the re-docked conformation of co-crystallized ligands with its experimental alignment, the validation of the docking was evaluated. Using the LasR receptor, N-3-oxo-dodecanoyl-L-homoserine lactone (OHN), and that for the PqsR receptor, 2-nonylquinolin-4(1H)-one (NNQ) was used. OHN was bound at the active site of the LasR receptor with a binding energy of -8.3 kcal/mol and the RMSD (Root Mean Square Deviation) of 1.199 Å (Fig. 3 a). For the PqsR receptor, NNQ had a binding energy of -6.9 kcal/mol and a RMSD value of 0.919 Å (Fig. 3 b). An RMSD value < 2 Å clearly indicates that the docking protocol employed is validated [ 31 ]. Precision docking LasR-ligand complexes From the analysis, good docking scores (kcal/mol) were recorded for both compounds (Table 1 ). Analysis from the LasR-ligand complexes showed a binding affinity of -9.3 kcal/mol and − 9.5 kcal/mol, for Ket B and Ket C, respectively. The interactions observed between LasR and Ket B were mostly hydrogen bond and hydrophobic interactions. Amino acid residues; Tyr 43, Ala 66, and Thr 111 formed hydrogen bond interactions. Hydrophobic interactions with Asp 69, Trp 84, Ala 101, and Phe 97. Hydrophobic interactions were predominant in the LasR-Ket B complex (Fig. 4 (a) left). For the LasR-Ket C complex, hydrophobic interaction was predominant. The hydrophobic interactions established were two amino acid residues: Trp 84 and Asp 69. The Pi-Pi T-shaped interaction was established between the benzyl moiety, Trp 84, and Pi-anion interaction with Asp 69 (Fig. 4 (a) right). From a study conducted, the native ligand acyl homoserine lactone (AHL) binds to the LasR active site with a definite pose [ 38 ], [ 39 ], [ 40 ]. In this pose, AHL establishes both hydrophobic and hydrogen bond interactions are observed. The LasR-AHL complex's hydrophobic interactions sequester additional non-polar residues into a beneficial hydrophobic environment while protecting the ligand binding pocket from bulk solvent[ 25 ]. These interactions must be precise and vital for LasR to be activated. After activation, LasR dimers and binds to the promoter gene to initiate transcription of the proteins required for the synthesis of virulence factors. In the absence of AHL, the loss of these helpful interactions causes loop flexibility, this then exposes the buried hydrophobic residues in the pocket to bulk solvent and causes protein aggregation [ 7 ]. On this account, an antagonist is therefore recommended not to mimic the aforementioned interactions. From the visual interaction analysis, both Ket B and Ket C did not mimic the interactions and can be proposed as potential inhibitors of the LasR receptor. In-depth was drawn from the molecular dynamics simulations as both ligands were bound at the active site throughout the simulation period. Table 1 Docking score with established hydrogen bond and hydrophobic bond interactions by the polyketides against the LasR protein evaluated from molecular docking with specific amino acid residues Compounds Docking score (kcal/mol) Most established interactions 1st docking 2nd docking 3rd docking Average Hydrogen bond interactions Hydrophobic interactions Ket B -9.3 -9.4 -9.4 -9.4 Ala66, Tyr43, Thr111 Asp69, Trp84, Phe97, Ala101 Ket C -9.5 -9.6 -9.5 -9.5 Trp84, Asp69, Tyr60, Ala66, Phe97, Ala101 OHN -8.3 -8.3 -8.3 -8.3 Trp60, Ser129, Asp73 Ala127, Ala50, Ile52, Leu40, Val76, Leu125, Cys79 Table 2 Docking score with established hydrogen bond and hydrophobic bond interactions by the polyketides against the PqsR protein evaluated from molecular docking with specific amino acid residues Compounds Docking score (kcal/mol) Most established interactions 1st docking 2nd docking 3rd docking Average Hydrogen bond interactions Hydrophobic interactions Ket B -7.9 -7.8 -7.9 -7.9 Gln101, Ile143, Ser103, Leu 115 Ala75, Ile56 Ket C -8.8 -8.9 -8.9 -8.9 Ile143, Leu104, Ser103, Gln101, Ile102 Ala9, Ala75, Ile56 NNQ -6.9 -6.9 -6.9 -6.9 Ala168, Val170, Ala102, Ile149, Ile263, Ile236, Leu207 PqsR-ligand complexes The docking score for both Ket B and Ket C was − 7.9 kcal/mol and − 8.8 kcal/mol, respectively (Table 2 ). Hydrophobic and hydrogen bond interactions were predominant in both complexes. For the PqsR-Ket B system, the hydrogen bond interactions were between amino acids such as; Ile 143, Gln 101, Ser 103, and Leu 115. Hydrogen bonds were seen between Ala 75 and Ile 56 amino acid residues (Fig. 4 (b) left). Similar observations were made for the PqsR-Ket C system. Both hydrogen and hydrophobic bond interactions were predominant. Amino acids such as; Ile 143, Leu 104, Ile 102, Ser 103, and Gln 101 were involved in hydrogen bonding while Ala 9, Ala 75, and Ile 56 were involved in hydrophobic bond interaction (Fig. 4 (b) right). The ligand binding site in PqsR is a hydrophobic cavity, as demonstrated by crystal soaking tests conducted with NNQ. There are no electrostatic interactions involved; hydrophobic interactions are what stabilize the PqsR-NNQ complex. Nevertheless, an obvious switch involving two isosteres switches a quinazolinone (QZN) agonist into an antagonist, which also affects the activation of the synthesis of bacterial virulence factors[ 41 ]. Hence the inactivation of PqsR requires a strong interaction between the proposed antagonist and the receptor since the earlier research clearly shows the involvement of the exchanged isosteres. Therefore, with the different structural properties of Ket B and Ket C as compared to the agonist (NNQ), the ligands under study can be potential inhibitors of the receptor. Molecular Dynamics Simulations Analyzing internal motions through MD simulations opens up avenues to explore diverse biological functions, including the synthesis of new molecules with specific conformations and structural characteristics. MD simulations in an explicit solvent were conducted to explore the potential inhibition of LasR and PqsR protein targets by Ket B and Ket C. The stability of ligand interactions with the selected protein targets was evaluated using molecular dynamics simulations, taking into accounts aspects such as protein structure, solvation, ionic effects, and entropic contributions. In assessing stability, various factors were considered, such as the RMSD of the protein backbone and ligands, RMSF of protein side chains, Rg (Radius of Gyration), and Principal Component analysis of the protein. While RMSD is a useful measure of protein stability, it does not provide information about local conformational changes that could affect ligand binding. Similarly, RMSF and Rg provide insights into protein flexibility but do not capture specific interactions between the ligand and protein residues. Molecular dynamics simulations offer a comprehensive understanding of ligand-protein interaction stability by considering characteristics beyond those addressed by molecular docking alone. These simulations account for dynamic aspects, providing a deeper insight into the stability of such interactions. LasR-Ket B and Ket C complexes Both Ket B and Ket C exhibited stability and effectively bound to the active site of the LasR protein. Examination of the LasR-Ket B complex indicated stable binding of Ket B, Ket C, and the native ligand (OHN) within the active site, as illustrated in Fig. 5 (a). The RMSD value of Ket B was 0.6 nm, which is less than 2 nm hence shows that the compound is stable [ 25 ], [ 31 ], [ 41 ]. Also, the RMSD value of Ket C was 0.3 nm. Comparing the stability of the compounds clearly shows that Ket C had the least stability, and this can be explained by the rigidity of the compound. Both hydrogen and hydrophobic interactions were observed in both ligand complexes. Hydrophobic interactions were predominant as also seen from the docking. This is because of the hydrophobic nature of the active site. Even though hydrophobic interactions play a critical role in the mechanism of action of drug-like molecules against protein targets, hydrogen bonds are also needed in enzymatic activity inhibition. With this, hydrogen bond occupancy for the ligands (Ket C and Ket C), was evaluated. Ket C had the most hydrogen bond occupancy with a hydrogen bond count of 9 while Ket C had the least hydrogen bond occupancy with a count of 3 (Fig. 5 (b)). This also explains the extent of stability of both ligands. The stability of the ligands was analyzed based on their RMSD. The analysis also included assessing the RMSD of the protein backbone. The Apo protein exhibited notable deviation around ~ 195–200 ns. In contrast, the LasR-AQS1 bound protein backbone showed no significant deviation. A similar trend was observed for the LasR-Ket B bound protein, although a slight deviation occurred around ~ 15 ns. However, considerable protein backbone deviation persisted throughout the simulation period for the LasR-Ket C bound protein. Figure 5 (c) illustrates the trajectory of the RMSD plot for both bound and unbound protein systems. Similar observation of the LasR protein was made elsewhere, where LasR exhibited a low RMSD value of ∼0.3 Å upon favorable binding with hydrophorones [ 25 ]. The Rg analysis revealed that the flexibility of the unbound protein (Apo protein) and the LasR-Ahl systems was comparable, as depicted in Fig. 5 (d). This suggests that the native ligand had minimal influence on the protein's structural features. Consequently, the protein maintained similar structural integrity whether in the unbound state or when bound to the native ligand. This observation is logical considering that the native ligand serves as a natural substrate for the protein, necessitating consistent structural integrity for effective signal transduction upon binding. In contrast, both Ket B and Ket C bound systems exhibited reduced protein flexibility, indicating a noticeable impact on the protein structure by these ligands. This is evidenced by the increased compactness of the protein in these bound states. Figure 5 (d) illustrates the Rg plot for both bound and unbound systems. Furthermore, analysis of RMS fluctuation of amino acid residues showed no significant deviations between the bound and unbound systems, indicating stability in the protein's amino acid residues regardless of the ligand binding state. Figure 6 illustrates the protein's structural stability while Ket B and Ket C remain bound to the active site throughout the simulation duration. Nonetheless, notable discrepancies among all systems primarily concern amino acid residues situated in the loop regions of the protein, as depicted in Fig. 5 (e). [ 42 ] discovered a similar pattern where similar fluctuations were observed when hydrophorones were bonded to LasR. The PCA analysis of the Cα-atoms was conducted to examine the slow and functional motions exhibited by these atoms across the amino acid residues. This assessment involved scrutinizing the vigorous motion of the Cα-atom through eigenvectors, which represent the overall direction of atom motion, and eigenvalues, indicating the atomic contribution to motion. Upon reviewing the PCA plots of both bound and unbound systems, it became evident that the bound protein systems (LasR-Ket B and LasR-Ket C) displayed reduced collective motion of the Cα-atoms compared to the unbound system, as depicted in Fig. 5 (f). These findings correspond to the results obtained from the Rg analysis, which indicated higher flexibility in the unbound protein compared to the bound protein. Consequently, this analysis suggests that the motion of amino acid residues was constrained when the ligands were bound to the protein. PqsR-Ket B and Ket C complexes Both Ket B and Ket C demonstrated stability as they securely bound to the active site of the PqsR protein. Upon analyzing the PqsR-Ket B complex, a slight deviation was observed at approximately 49 ns, followed by another deviation around 70 ns. Despite these deviations, the RMSD of Ket B remained below 2 nm, ranging from approximately 0.45 to 1.5 nm, as illustrated in Fig. 7 (a). This deviation primarily stemmed from the flipping motion of the rotatable bonds, which persisted throughout the simulation. Notably, both hydrogen and hydrophobic interactions played significant roles in maintaining Ket B within the active site, with observable bond distances. Conversely, the binding poses of Ket C remained stable throughout the simulation period, with no deviations observed, as depicted in Fig. 7 (a). Hydrophobic interactions were crucial in anchoring Ket C within the active site pocket, alongside established hydrogen bond interactions. The RMSD of Ket C was approximately 0.25 nm. Moreover, both compounds exhibited a higher frequency of hydrogen bond interactions, with Ket B and Ket C recording 9 and 7 hydrogen bond counts, respectively, as shown in Fig. 7 (b). The analysis of RMSD was conducted for both bound and unbound proteins. The RMSD for the unbound protein exhibited a consistent range between 0.3 and 0.4 nm. When Ket B was bound to the protein, a slight increase in RMSD occurred around 50 ns, reaching approximately 0.35 nm for the remainder of the simulation period, as shown in Fig. 7 (c). Similarly, Ket C bound protein displayed slight deviations throughout the simulation duration, as depicted in Fig. 7 (c). These findings indicate the overall stability of the protein over the simulation period. Assessment of the RMSF of amino acid residues revealed significant fluctuations between bound and unbound proteins, particularly in the loop regions of the protein, as illustrated in Fig. 7 (e) and Fig. 8 . Additionally, the PCA analysis indicated more collective motions of the Cα-atoms in the presence of Ket B compared to the unbound protein, while for PqsR-Ket C, collective motions of the Cα-atoms were less pronounced, as depicted in Fig. 7 (f). In both Ket B and Ket C bound systems, the protein's compactness was evident as protein flexibility decreased. This observation aligns closely with the results obtained from the Rg analysis, as illustrated in Fig. 7 (d). Furthermore, Fig. 9 illustrates the protein's structural stability when Ket B and Ket C were bound to the active region during the simulation. Binding Energy Calculations MMPBSA Although molecular docking offers the lowest energy and the ligand-bound protein's binding conformation, it does not take into account the protein's natural conformational changes, solvation and ionic effects, or the entropic contributions to the total binding free energies [ 31 ], [ 32 ]. The stability and corresponding energy of ligands while bound to their targets are estimated via molecular dynamics, which also takes into consideration these docking restrictions. Whiles MMPBSA calculation is important, its reliance on a single trajectory and its inability to fully capture entropic contributions to binding. Calculated binding free energies were used to assess the compounds' affinities for the pocket residues of LasR and PqsR. Van der Waals, electrostatic, polar, and non-polar solvation energies are only a few of the energy components that contribute to the overall binding energy. Van der Waals, electrostatic, and non-polar energies all significantly contribute to the binding of the complexes, according to analyses of the complexes. However, the polar solvation energy had a negative impact on how well all the complexes bound. From the binding energy calculations, Ket B bound to the LasR protein had a total binding energy of -82.559 kJ/mol with Van der Waals and electrostatic contributions of -166.041 kJ/mol and − 18.632 kJ/mol respectively. Non-polar solvation energy of -18.212 kJ/mol and polar solvation energy of 120.355 kJ/mol. For Ket C bound to LasR, a total binding energy of -68.680 kJ/mol, Van der Waals contribution was − 131.480 kJ/mol, electrostatic contribution was − 42.134 kJ/mol, and polar solvation of 119.901 kJ/mol with a non-polar solvation energy of -14.967 kJ/mol. The results clearly indicate that, the recorded binding free energies of Ket B and Ket C was favorable and this was due to the positive effect establish by Van der Waals and electrostatic contributions which is accompanied by the non-polar solvation energy. In a study by [ 25 ], the AHL is unable to interact favorably with the L3 loop to create a favorable hydrophobic environment. This mechanism highlights the major fluctuations seen in this loop region. In our study, the positive impact by Van der Waals and electrostatic contributions indicates that the compounds were not able to interact with the amino acid at the loop region during the simulation. This may lead to changes in the L3 loop, exposing the pocket to the surrounding solvent. Disruption of interactions between the compounds and the pocket residues results from the availability of solvent molecules in the binding pocket. As a result, this could contribute to the observed decline in Van der Waals energy, nonpolar energy, and electrostatic energy between the compounds and the pocket residues, similar to findings reported in other studies [ 43 ]. With this scenario [ 44 ], suggested that exposing buried hydrophobic residues to the surrounding solvent leads to protein assemblage. Calculated binding energies revealed that even in the presence of solvent molecules, all compounds spontaneously bound to the protein during the simulation indicating the compounds’ stability within the binding domain. Therefore, we propose that the compounds bind to LasR for a sufficiently long duration which causes the bulk solvent to accumulate in the ligand binding domain leading to protein aggregation. The energy calculations of Ket B and Ket C against LasR protein are summarized in Table 3 a. Table 3 a . MM-PBSA calculations of binding energy for Ket B and Ket C against the LasR receptor. Complex ΔE vdW ΔE elect ΔG PB ΔG SASA ΔG bind LasR-Ket B -166.041 ± 9.590 -18.632 ± 9.972 120.355 ± 17.953 -18.212 ± 0.777 -82.529 ± 15.153 LasR-Ket C -131.480 ± 10.425 -42.134 ± 12.105 119.901 ± 10.459 -14.967 ± 0.726 -68.680 ± 11.185 LasR-OHN -182.9 ± 12.10 -131.8 ± 13.50 -258.7 ± 16.60 -20.3 ± 0.7 -76.3 ± 14.6 Considering the compounds against the PqsR, the total binding energies recorded are 86.855 kJ/mol and − 90.342 kJ/mol for Ket B and Ket C respectively. For Ket B, Van der Waals contribution was − 129.798 kJ/mol, electrostatic contribution was − 22.608 kJ/mol, and polar solvation of 82.155 kJ/mol with a non-polar solvation energy of -16.605 kJ/mol. Also, in the case of Ket C, -150.552 kJ/mol was contributed by van der Waals, -35.803 kJ/mol by electrostatics, and 112.041 kJ/mol was contributed by polar solvation with a non-polar solvation energy of -16.028 kJ/mol. All of the compounds spontaneously bound to the protein throughout the simulation in the presence of solvent molecules, demonstrating the compounds' stability in the pocket, according to the predicted binding energies. As a result, we propose that the compounds bind to PqsR for a long enough time to result in the bulk solvent gathering in the ligand binding domain, which subsequently results in protein clumping. Table 3 b provides a summary of the energy estimations for Ket B and Ket C against the PqsR protein. Table 3 b . MM-PBSA calculations of binding energy for Ket B and Ket C against the PqsR receptor. Complex ΔE vdW ΔE elect ΔG PB ΔG SASA ΔG bind PqsR-Ket B -129.798 ± 12.994 -22.608 ± 9.138 82.155 ± 13.422 -16.605 ± 1.126 -86.855 ± 12.012 PqsR-Ket C -150.552 ± 8.983 -35.803 ± 4.634 112.041 ± 5.446 -16.028 ± 0.624 -90.342 ± 8.418 PqsR-NNQ -147.5 ± 11.6 -12.7 ± 6.6 76.7 ± 9.6 -18.5.3 ± 1.0 -102.0 ± 11.5 Polyketides' antibacterial activity has also been emphasized in earlier in vitro research. According to [ 22 ] Ket B and Ket C have been tested for their ability to inhibit the growth of Pseudomonas aeruginosa . According to the study, the compounds are highly effective at inhibiting the growth of biofilms and P. aeruginosa strains. Therefore, this work offers a mechanistic explanation for the pharmacological actions observed in in vitro experiments. Ligand Orientations, Interactions and Atom Fluctuations LasR-ligand systems Throughout the MD simulations, AHL, Ket B and Ket C established various orientations to enhance stability at the active site. For AHL, hydrophobic interactions were established with amino acid residues; Val76, Leu36, Ile52, Ala127, Ala50, Leu40, and Tyr47. Hydrogen bond interactions were established with amino acids such as; Trp60, Arg61 and Ser129. Ket B established only hydrophobic interactions with amino acids such as; Trp60, Tyr64, Arg61, Leu36, Ile52, Ala50, Leu125, and Cys79. Hydrophobic interactions observed between Ket C and LasR were with amino acids such as; Phe101, Trp60, Ala105, Trp88, Tyr64, Leu36, Ile52 and Arg61. Ket C establish some hydrogen bonding with Asp73, Tyr56 and Leu110. In general, all compounds (AHL, Ket B and Ket C), establish common interactions with residues such as; Trp60, Arg61, Leu36, Ile52 and Ala50. From the study by [ 45 ] highlighted that, residues play crucial role in maintaining autoinducer at the active site. Some of the roles played by these residues include; (i) Tyr56-OH with the amide 1-oxo (2.65 Å), (ii) likewise Ser129-OG with the 1-oxo (2.70 Å), (iii) Trp60-NE with the lactone carbonyl (3.01 Å), (iv) Asp73-OD2 with the amide NH (2.76 Å), (v) Thr75-OG1 likewise (3.39 Å), and (vi) an indirect H-bond between the 3-oxo group and a water molecule (2.85 Å) and then Arg61-NE1 (2.90 Å) and NH1 (2.84 Å). Also, 12-carbon AHL is housed in a large hydrophobic pocket formed by residues; Leu36, Gly38, Leu39, Leu40, Tyr47, Glu48, Ala50, Ile52, Tyr56, Trp60, Arg61, Tyr64, Asp65, Gly68, Tyr69, Ala70, Asp73, Pro74, Thr75, Val76, Cys79, Thr80, Trp88, Tyr93, Phe101, Phe102, Ala105, Leu110, Thr115, Leu125, Gly126, Ala127, and Ser129. These interactions were similarly observed in this study. From this, Ket B and Ket C interacting with similar residues help in maintaining these compounds at the active throughout the simulations as shown in Fig. 10 . Other researchers demonstrated that potential inhibitors such as furanones[ 46 ] interacts with these residues to cause inhibition and this is same in the case of other marine metabolites that have shown inhibitory activity against the LasR protein through a computational study[ 47 ]. [ 48 ]also, highlighted that allosteric binding at the interface between the ligand-binding domain and DNA-binding domain, caused displacement of most hydrophobic interactions between AHL and active site residues. Also, fungi secondary metabolites have demonstrated that biofilm inhibition activity by establishing strong interactions at the active site of LasR[ 49 ], causing significant structural changes which is also observed in this study. Understanding the fluctuations of moieties in Ket B and Ket C will helped to explain how these compounds are interacting with active site residues during the MD run. Hence atomic-wise fluctuation analysis was done for Ket B and Ket C respectively. From this it was clearly observed that, methyl side-groups of both Ket B and Ket C played an important role in establishing hydrophobic interactions with active site residues during the simulations. [ 45 ] research showed that, the long 12C chain of AHL is stabilized by interacting amino acids at the hydrophobic pocket. Similarly, the methyl group of both Ket B (C 12 ) and Ket C (C 11 ) interacted with residues such as Ala50, Ile52, Tyr56, Trp60, Arg61, Val76, Cys79, Thr80, Trp88, Ala105, Leu110, Ala127, and Ser129 as shown in Fig. 11 . Also, major fluctuation was observed for in C 15 Ket C, this is accompanied by opposite fluctuation of C 12 − 15 . This was different in Ket B because of the alkene functionality between C 10 − 11 as seen Fig. 11 . PqsR-ligand systems For PqsR-ligand systems, notable interactions observed between NNQ and the receptor were only hydrophobic interactions. Hydrophobic (Pi-sigma, Pi-alkyl, and alkyl) interactions between NHQ with Ala168, Ile149, Leu207, Ile263, Ile236, Ala102, Leu189, Val170, Ile186, and Tyr258 were observed. Ket B established hydrogen bond interaction with Thr265 and hydrophobic interactions with residues such as; Gln194, Leu208, Ile149, Phe221, Ala109, Pro126, Pro238, Ala168, Tyr258, and Leu207. For Ket C, hydrogen bond interactions were established with Ser196, Leu197, Tyr258, and Thr265. Hydrophobic interactions were established with Leu207, Leu208, Ile236, Gln194, and Ala168 (Fig. 12 ). Over the years, a lot of studies has demonstrated the importance of inhibiting the PqsR receptor to curb biofilm formation. Furanones from marine sources have been demonstrated as potential inhibitors of PqsR[ 46 ], and similarly, other marine secondary metabolites have also been reported to inhibit P. aeruginosa PqsR receptor[ 47 ]. [ 50 ]reported a competitive PqsR inhibitor (M64), which established interactions with Tyr258 and Gln194. These interactions are crucial for inhibition as reported by [ 50 ]. Another study by [ 51 ] demonstrated the importance of interactions with some residues such as; Gln194, Ile236 Leu208, and Tyr258. From these findings, it is evident that an inhibitor should establish strong interactions with these residues evaluated by other studies and from this study. Having established the fact that some interactions are important to cause inhibition, understanding the fluctuations of moieties used for these interactions are of great importance. Hence this study considered the fluctuations observed in various moieties of Ket B and Ket C. As observed in LasR, the methyl group (C 12 ) on Ket B had major fluctuations during the MD run. Also, it was observed that, the methyl group (C 17 ) from the ester had major fluctuations in Ket B when bound at the active site of the PqsR protein. Slight fluctuations were observed at CH 2 (C 13 ) (Fig. 13 a). For Ket C, fluctuations were observed for the methyl group (C 11 ), oxygen at the carbonyl functionality and methyl group (C 15 ) (Fig. 13 b). These fluctuations helped to established strong hydrophobic and hydrogen bond interactions at the active site throughout the simulations. ADME-T Prediction The ADME-T characteristics of the two primary compounds identified through molecular docking and molecular dynamics were investigated using the ADMETlab 2.0 and SwissADME web servers. Various criteria such as Lipinski's rule of five, oral bioavailability, solubility class, gastrointestinal absorption, P-glycoprotein substrate status, blood-brain barrier (BBB) penetration, metabolism analysis, excretion, and toxicity predictions were assessed (Table 4 ). These attributes play a pivotal role in identifying potential drug candidates for disease treatment. Ket B, in this research, demonstrated compliance with Lipinski's rules and displayed high water solubility. Moreover, it exhibited high gastrointestinal absorption and BBB permeability, while also lacking P-gp substrate status. Ket C also showed excellent ADME-T profiling such as, high gastrointestinal absorption and BBB permeability, while also lacking P-gp substrate status. Since drugs with high molecular weight and logP values may have poor solubility or membrane permeability, which could limit their effectiveness in vivo. The molecular weight of both Ket B and Ket C have lower molecular weight and hence better clog P (lipophilicity) values, and this support their T 1/2 ; Half-life respectively (Table 4 ) . Table 4 ADME-T properties of hit compounds from the study. These properties highlight the importance of ADME-T predictions for future perspective of these compounds for novel therapeutics against P. aeruginosa receptors. Compound Physicochemical Medicinal Absorption Excretion Solubility MW n HA n HD n Rot TPSA clog P Lipinski Pfizer GI P-gp sub Cl T 1/2 ESOL log Class Ket B 261.14 4 2 6 66.76 3.271 Accepted Accepted High No 16.306 0.918 -3.35 Soluble Ket C 238.46 4 2 5 66.76 2.251 Accepted Accepted High No 14.881 0.888 -3.35 Soluble MW; Molecular weight, nHA; number of hydrogen bond acceptors, nHD; number of hydrogen bond donor, nRot; number of rotatable bonds, cLog P; lipophilicity, TPSA; Topological Polar Surface Area, GI; gastrointestinal absorption, Cl; clearance, T 1/2 ; Half-life, H. soluble; highly soluble Metabolism analysis revealed that Ket B did not inhibit the majority of cytochrome P450 enzymes. Its predicted clearance rate was notably high at 16.306 mL/min/kg, yielding a bioavailability score of 0.55, indicating favorable drug-like characteristics. Similarly, Ket C adhered to Lipinski's rules, displayed high water solubility, and exhibited robust gastrointestinal absorption while not being a P-gp substrate. Metabolism analysis indicated no inhibition of cytochrome P450 enzymes by Ket C. Its clearance rate was moderate at 14.881 mL/min/kg, resulting in a bioavailability score of 0.55. In summary, both polyketides investigated in this study demonstrated outstanding ADME-T properties and minimal toxicity. Consequently, these compounds offer promise as an alternative for addressing antimicrobial resistance in patients with filarial lymphedema. While the findings regarding the ADME-T properties of Ket B and Ket C are promising for their potential as drug candidates for filarial lymphedema, it is important to acknowledge some limitations of this study: the study primarily relied on computational tools and in silico predictions to assess ADME-T characteristics. Experimental validation through in vitro and in vivo studies is necessary to confirm the predicted properties and safety of these compounds. Correlation between in silico and experimental studies highlights that, Ket B and Ket B antibiofilm activity is evident from the experimental research done by [ 22 ]. However, future assays like enzymatic assay can be explored to further probe into the inhibition mechanism observed through the in silico study conducted. Conclusion To elucidate the molecular basis of the anti-biofilm and anti-quorum sensing activities of fungal polyketides, this study integrates molecular docking and molecular dynamics simulations. Virtual screening of 100 compounds identified two with substantial affinity for LasR and PqsR, marking them as promising candidates for further computational and experimental investigations. Molecular dynamics simulations confirmed the stability of these compounds within the binding pocket, even in the presence of solvent molecules. Notably, their interactions with LasR induced the formation of bulk solvents in the binding pocket, a phenomenon that may contribute to protein aggregation, similar to what was observed with PqsR. This study provides computational evidence supporting the antibiofilm potential of these polyketides by analyzing their stability and interactions. The absence of autoinducer-like activity further reinforces their antibiofilm effects, aligning with experimental findings. Additionally, these compounds exhibit favorable drug-like properties and ADMET profiles, highlighting their therapeutic potential. Ketidocillinones B and C emerge as promising anti-biofilm agents that could aid in combating antibiotic resistance, particularly in patients with filarial lymphedema in Ghana and the world at large. Their broader applicability in addressing antibiotic resistance in neglected tropical diseases underscores their significance as potential therapeutic agents. Declarations Acknowledgments We acknowledge the Borquaye Research Group (www.borquayelab.com) for their support for this work. Author contributions PM, PBN, and AK conceived the study. All experiments were designed by PM, PBN, POP, and AK. Computations were done by PM, and PBN. Data analysis was done by PM, PBN and POP. The initial manuscript draft was prepared by PM, PBN, POP, and AK. All authors read and approved of the final manuscript. Funding No funding is available for this study. Data Availability All data generated or analyzed during this study are included in this published article. Ethics approval Not applicable Consent to participate Not applicable Consent to Publish Not Applicable Clinical trial number Not applicable. Conflict of interests All the authors report no conflicts of interest. Competing interests The authors declare no competing interests. References C. Abbafati et al. , “Global burden of 369 diseases and injuries in 204 countries and territories, 1990–2019: a systematic analysis for the Global Burden of Disease Study 2019,” Lancet, vol. 396, no. 10258, pp. 1204–1222, 2020, doi: 10.1016/S0140-6736(20)30925-9 . K. M. Pfarr, A. Y. Debrah, S. Specht, and A. Hoerauf, “Filariasis and lymphoedema,” Parasite Immunol., vol. 31, no. 11, pp. 664–672, 2009, doi: 10.1111/j.1365-3024.2009.01133.x . P. E. Simonsen, E. M. Pedersen, R. T. Rwegoshora, M. N. Malecela, Y. A. Derua, and S. M. Magesa, “Lymphatic filariasis control in Tanzania: Effect of repeated mass drug administration with ivermectin and albendazole on infection and transmission,” PLoS Negl. Trop. Dis., vol. 4, no. 6, 2010, doi: 10.1371/journal.pntd.0000696 . H. F. McGarry, G. L. Egerton, and M. J. Taylor, “Population dynamics of Wolbachia bacterial endosymbionts in Brugia malayi,” Mol. Biochem. Parasitol., vol. 135, no. 1, pp. 57–67, 2004, doi: 10.1016/j.molbiopara.2004.01.006 . F. Corona and J. L. Martinez, “Phenotypic resistance to antibiotics,” Antibiotics, vol. 2, no. 2, pp. 237–255, 2013, doi: 10.3390/antibiotics2020237 . S. Wireko et al. , “Prevalence of Methicillin-Resistant Staphylococcus Species Among Filarial Lymphedema Patients in Ahanta West District of Ghana,” Front. Trop. Dis. , vol. 2, no. November, pp. 1–8, 2021, doi: 10.3389/fitd.2021.786378 . L. Zhou, Y. Zhang, Y. Ge, X. Zhu, and J. Pan, “Regulatory Mechanisms and Promising Applications of Quorum Sensing-Inhibiting Agents in Control of Bacterial Biofilm Formation,” Front. Microbiol. , vol. 11, no. October, pp. 1–11, 2020, doi: 10.3389/fmicb.2020.589640 . J. Lee and L. Zhang, “The hierarchy quorum sensing network in Pseudomonas aeruginosa,” Protein Cell, vol. 6, no. 1, pp. 26–41, 2015, doi: 10.1007/s13238-014-0100-x . S. Wang, G. F. Payne, and W. E. Bentley, “Quorum Sensing Communication: Molecularly Connecting Cells, Their Neighbors, and even Devices,” Annu. Rev. Chem. Biomol. Eng., vol. 11, pp. 447–468, 2020, doi: 10.1146/annurev-chembioeng-101519-124728 . P. K. Taylor, A. T. Y. Yeung, and R. E. W. Hancock, “Antibiotic resistance in Pseudomonas aeruginosa biofilms: Towards the development of novel anti-biofilm therapies,” J. Biotechnol., vol. 191, pp. 121–130, 2014, doi: 10.1016/j.jbiotec.2014.09.003 . M. Y. Osei-Atweneboana, K. Awadzi, S. K. Attah, D. A. Boakye, J. O. Gyapong, and R. K. Prichard, “Phenotypic evidence of emerging ivermectin resistance in Onchocerca volvulus,” PLoS Negl. Trop. Dis., vol. 5, no. 3, pp. 1–11, 2011, doi: 10.1371/journal.pntd.0000998 . S. Mand et al. , “Doxycycline improves filarial lymphedema independent of active filarial infection: A randomized controlled trial,” Clin. Infect. Dis., vol. 55, no. 5, pp. 621–630, 2012, doi: 10.1093/cid/cis486 . M. J. Taylor, W. H. Makunde, H. F. McGarry, J. D. Turner, S. Mand, and A. Hoerauf, “Macrofilaricidal activity after doxycycline treatment of Wuchereria bancrofti: A double-blind, randomised placebo-controlled trial,” Lancet, vol. 365, no. 9477, pp. 2116–2121, 2005, doi: 10.1016/S0140-6736(05)66591-9 . M. J. Taylor, A. Hoerauf, and M. Bockarie, “Lymphatic filariasis and onchocerciasis,” Lancet, vol. 376, no. 9747, pp. 1175–1185, 2010, doi: 10.1016/S0140-6736(10)60586-7 . S. J. Leopold, F. van Leth, H. Tarekegn, and C. Schultsz, “Antimicrobial drug resistance among clinically relevant bacterial isolates in sub-Saharan Africa: A systematic review,” J. Antimicrob. Chemother., vol. 69, no. 9, pp. 2337–2353, 2014, doi: 10.1093/jac/dku176 . D. W. Bailey, D. C. Anderson, and D. E. Doornbos, “Comparison of Ivermectin and Conventional External Parasite Control in Young Beef Cows,” Prof. Anim. Sci., vol. 13, no. 3, pp. 129–132, 1997, doi: 10.15232/S1080-7446(15)31865-9 . F. Cobo, “Determinants of parasite drug resistance in human lymphatic filariasis,” Rev. Esp. Quimioter., vol. 29, no. 6, pp. 288–295, 2016. A. Kwarteng et al. , “Shift in the skin microbiome among individuals presenting with filarial lymphedema compared to non-filarial healthy individuals in Ghana,” Sci. African, vol. 16, p. e01237, 2022, doi: 10.1016/j.sciaf.2022.e01237 . F. A. Fordjour et al. , “Use of medicinal plants as a remedy against lymphatic filariasis: Current status and future prospect,” Heal. Sci. Reports, vol. 6, no. 5, pp. 1–8, 2023, doi: 10.1002/hsr2.1295 . T. Oja et al. , “Effective antibiofilm polyketides against Staphylococcus aureus from the pyranonaphthoquinone biosynthetic pathways of Streptomyces species,” Antimicrob. Agents Chemother., vol. 59, no. 10, pp. 6046–6052, 2015, doi: 10.1128/AAC.00991-15 . M. V. Arasu, V. Duraipandiyan, and S. Ignacimuthu, “Antibacterial and antifungal activities of polyketide metabolite from marine Streptomyces sp. AP-123 and its cytotoxic effect,” Chemosphere, vol. 90, no. 2, pp. 479–487, 2013, doi: 10.1016/j.chemosphere.2012.08.006 . M. Shah et al. , “Antibacterial polyketides from Antarctica sponge-derived fungus Penicillium sp. HDN151272,” Mar. Drugs, vol. 18, no. 2, pp. 1–9, 2020, doi: 10.3390/md18020071 . M. A. Almalki, “Isolation and characterization of polyketide drug molecule from Streptomyces species with antimicrobial activity against clinical pathogens,” J. Infect. Public Health, vol. 13, no. 1, pp. 125–130, 2020, doi: 10.1016/j.jiph.2019.07.002 . W. Mozirandi, D. Tagwireyi, and S. Mukanganyama, “Evaluation of antimicrobial activity of chondrillasterol isolated from Vernonia adoensis (Asteraceae),” BMC Complement. Altern. Med., vol. 19, no. 1, pp. 1–11, 2019, doi: 10.1186/s12906-019-2657-7 . J. O. Mensah et al. , “Computational Studies Provide a Molecular Basis for the Quorum Sensing Inhibitory Action of Compounds from Dioon spinulosum Dyer Ex Eichler,” ChemistrySelect, vol. 8, no. 1, 2023, doi: 10.1002/slct.202203773 . J. M. Martell and H. Hengtai, “Molecular decompositions of acetaldehyde and formamide: Theoretical studies using Hartree-Fock, Moller-Plesset and density functional theories,” Mol. Phys., vol. 92, no. 3, pp. 497–502, 1997, doi: 10.1080/002689797170248 . A. J. O. O. Trott, “Software News and Updates Gabedit — A Graphical User Interface for Computational Chemistry Softwares,” J. Comput. Chem., vol. 32, pp. 174–182, 2010, doi: 10.1002/jcc . C. Kutzner, S. Páll, M. Fechner, A. Esztermann, B. L. de Groot, and H. Grubmüller, “More bang for your buck: Improved use of GPU nodes for GROMACS 2018,” J. Comput. Chem., vol. 40, no. 27, pp. 2418–2431, 2019, doi: 10.1002/jcc.26011 . K. Vanommeslaeghe et al. , “CHARMM general force field: A force field for drug-like molecules compatible with the CHARMM all‐atom additive biological force fields,” J. Comput. Chem., vol. 31, no. 4, pp. 671–690, 2010. M. T. Che Omar, “Data analysis of molecular dynamics simulation trajectories of β-sitosterol, sonidegib and cholesterol in smoothened protein with the CHARMM36 force field,” Data Br. , vol. 33, no. 2020, p. 106350, 2020, doi: 10.1016/j.dib.2020.106350 . L. K. Kyei, E. N. Gasu, G. B. Ampomah, J. O. Mensah, and L. S. Borquaye, “An In Silico Study of the Interactions of Alkaloids from Cryptolepis sanguinolenta with Plasmodium falciparum Dihydrofolate Reductase and Dihydroorotate Dehydrogenase,” J. Chem. , vol. 2022, pp. 1–26, 2022, doi: 10.1155/2022/5314179 . L. S. Borquaye et al. , “Alkaloids from Cryptolepis sanguinolenta as Potential Inhibitors of SARS-CoV-2 Viral Proteins: An in Silico Study,” Biomed Res. Int. , vol. 2020, 2020, doi: 10.1155/2020/5324560 . R. Kumari, R. Kumar, and A. Lynn, “g-mmpbsa -A GROMACS tool for high-throughput MM-PBSA calculations,” J. Chem. Inf. Model., vol. 54, no. 7, pp. 1951–1962, 2014, doi: 10.1021/ci500020m . P. Manu, J. O. Mensah, E. N. Gasu, and L. S. Borquaye, “The Amaryllidaceae alkaloid, montanine, is a potential inhibitor of the Trypanosoma cruzi trans-sialidase enzyme,” J. Biomol. Struct. Dyn., 2023, doi: 10.1080/07391102.2023.2272750 . D. Amon et al. , “Antimicrobial resistance modifying effects and molecular docking studies of Affinine, derived from Tabernaemontana crassa,” Sci. African , vol. 26, no. July, p. e02382, 2024, doi: 10.1016/j.sciaf.2024.e02382 . P. Manu, P. B. Nketia, P. Osei-poku, and A. Kwarteng, “Computational Mutagenesis and Inhibition of Staphylococcus aureus AgrA LytTR Domain Using Phenazine Scaffolds: Insight From a Biophysical Study,” vol. 2024, 2024, doi: 10.1155/2024/8843954 . P. B. Nketia, E. N. Gasu, J. O. Mensah, and L. S. Borquaye, “In silico identification of α-bisabolol and letestuianin C as potential inhibitors of Trypanosoma brucei trypanothione reductase,” J. Biomol. Struct. Dyn., vol. 0, no. 0, pp. 1–13, 2023, doi: 10.1080/07391102.2023.2247084 . A. R. McCready, J. E. Paczkowski, B. R. Henke, and B. L. Bassler, “Structural determinants driving homoserine lactone ligand selection in the Pseudomonas aeruginosa LasR quorum-sensing receptor,” Proc. Natl. Acad. Sci. U. S. A. , vol. 116, no. 1, pp. 245–254, 2019, doi: 10.1073/pnas.1817239116 . S. Fernandes, A. Borges, I. B. Gomes, S. F. Sousa, and M. Simões, “Curcumin and 10-undecenoic acid as natural quorum sensing inhibitors of LuxS/AI-2 of Bacillus subtilis and LasI/LasR of Pseudomonas aeruginosa,” Food Res. Int. , vol. 165, no. October 2022, 2023, doi: 10.1016/j.foodres.2023.112519 . G. D. Geske, J. C. O. Neill, and H. E. Blackwell, “Antagonize or Superagonize Quorum Sensing in Vibrio fischeri,” vol. 2, no. 5, pp. 315–319, 2009, doi: 10.1021/cb700036x.N . A. Ilangovan et al. , “Structural Basis for Native Agonist and Synthetic Inhibitor Recognition by the Pseudomonas aeruginosa Quorum Sensing Regulator PqsR (MvfR),” PLoS Pathog., vol. 9, no. 7, 2013, doi: 10.1371/journal.ppat.1003508 . A. Dalal et al. , “Computational investigations on the potential role of hygrophorones as quorum sensing inhibitors against LasR protein of Pseudomonas aeruginosa,” J. Biomol. Struct. Dyn., vol. 41, no. 6, pp. 2249–2259, 2023, doi: 10.1080/07391102.2022.2029570 . M. Götte, “Free energy calculations of protein-ligand complexes with computational molecular dynamics,” p. 116, 2008, [Online]. Available: http://webdoc.sub.gwdg.de/diss/2008/goette/ Y. Zou and S. K. Nair, “Molecular Basis for the Recognition of Structurally Distinct Autoinducer Mimics by the Pseudomonas aeruginosa LasR Quorum-Sensing Signaling Receptor,” Chem. Biol., vol. 16, no. 9, pp. 961–970, 2009, doi: 10.1016/j.chembiol.2009.09.001 . M. J. Bottomley, E. Muraglia, R. Bazzo, and A. Carfì, “Molecular insights into quorum sensing in the human pathogen Pseudomonas aeruginosa from the structure of the virulence regulator LasR bound to its autoinducer,” J. Biol. Chem., vol. 282, no. 18, pp. 13592–13600, 2007, doi: 10.1074/jbc.M700556200 . A. Boakye, M. P. Seidu, A. Adomako, M. K. Laryea, and L. S. Borquaye, “Marine-Derived Furanones Targeting Quorum-Sensing Receptors in Pseudomonas aeruginosa: Molecular Insights and Potential Mechanisms of Inhibition,” Bioinform. Biol. Insights, vol. 18, 2024, doi: 10.1177/11779322241275843 . M. P. Seidu, A. Adomako, A. Boakye, M. K. Laryea, and L. S. Borquaye, “Targeting Quorum Sensing in Pseudomonas aeruginosa Using Marine-Derived Metabolites — An In Silico Approach,” vol. 2024, 2024, doi: 10.1155/joch/2172452 . P. B. Nketia, P. Manu, P. Osei-poku, and A. Kwarteng, “Phenazine Scaffolds as a Potential Allosteric Inhibitor of LasR Protein in Pseudomonas aeruginosa,” 2025, doi: 10.1177/11779322251319594 . P. Manu, A. Abakah, P. Kwesi, A. Priscilla, O. Poku, and A. Kwarteng, “Disrupting quorum sensing by exploring conformational dynamics and active site flexibility of LasR protein in Pseudomonas aeruginosa,” Discov. Chem., 2025, doi: 10.1007/s44371-025-00121-2 . T. Kitao et al. , “Molecular insights into function and competitive inhibition of pseudomonas aeruginosa multiple virulence factor regulator,” MBio, vol. 9, no. 1, 2018, doi: 10.1128/mBio.02158-17 . H. Xiao et al. , “Multidimensional Criteria for Virtual Screening of PqsR Inhibitors Based on Pharmacophore, Docking, and Molecular Dynamics,” Int. J. Mol. Sci., vol. 25, no. 3, 2024, doi: 10.3390/ijms25031869 . Additional Declarations No competing interests reported. Cite Share Download PDF Status: Under Review Version 1 posted Editor assigned by journal 09 Apr, 2025 Reviews received at journal 03 Apr, 2025 Reviewers agreed at journal 03 Apr, 2025 Reviewers invited by journal 03 Apr, 2025 Submission checks completed at journal 02 Apr, 2025 First submitted to journal 30 Mar, 2025 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-5690135","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":438001667,"identity":"505a11be-b517-4d79-ae47-06cee55517e5","order_by":0,"name":"Prince Manu","email":"","orcid":"","institution":"Kwame Nkrumah University of Science and Technology","correspondingAuthor":false,"prefix":"","firstName":"Prince","middleName":"","lastName":"Manu","suffix":""},{"id":438001668,"identity":"4fcb7361-6182-4d99-8456-a6dc7b0b3a03","order_by":1,"name":"Prisca Baah Nketia","email":"","orcid":"","institution":"Kwame Nkrumah University of Science and Technology","correspondingAuthor":false,"prefix":"","firstName":"Prisca","middleName":"Baah","lastName":"Nketia","suffix":""},{"id":438001669,"identity":"7848f659-41ce-4c3b-b82d-9d6a79789394","order_by":2,"name":"Priscilla Osei-Poku","email":"","orcid":"","institution":"Kwame Nkrumah University of Science and Technology","correspondingAuthor":false,"prefix":"","firstName":"Priscilla","middleName":"","lastName":"Osei-Poku","suffix":""},{"id":438001670,"identity":"f3eb0dbc-9c16-4066-b6d9-7a598c2b5676","order_by":3,"name":"Alexander Kwarteng","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA+ElEQVRIiWNgGAWjYHACZhiD8QEDwwEYJ4GgFgkQw4BkLWwSRGmRb+89bMxTc6+OX7r9WjVPzR05fgbmhw8Y29JwajE4cy45medYsYTknDNlt3mOPTOWbGAzNmBsy8GtRSLH+HAOW4KEwY2ctNs8bIcTNxzgYZNgbKvA7bD5b4Ba/iVI2AO1FPP8I0ILww0e4+TcNqAtEunHmHnb4FrwOOxMjrHx374EyRk3cpgl5/YdNpZsBvol4Rxu78u3nzGWnPEtgZ9/RvrDD2++HZbjZ29++OBDWTJuhyEAjwETD4gGxVMCMRoYGNgfMP4gTuUoGAWjYBSMMAAAk0FRq/no65kAAAAASUVORK5CYII=","orcid":"","institution":"Kwame Nkrumah University of Science and Technology","correspondingAuthor":true,"prefix":"","firstName":"Alexander","middleName":"","lastName":"Kwarteng","suffix":""}],"badges":[],"createdAt":"2024-12-21 14:23:16","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-5690135/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-5690135/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":79907305,"identity":"59241b48-de49-406d-98db-4ebe0b4a4cc5","added_by":"auto","created_at":"2025-04-04 11:10:43","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":110870,"visible":true,"origin":"","legend":"\u003cp\u003eHit compounds identified in the study.\u003c/p\u003e","description":"","filename":"image1.png","url":"https://assets-eu.researchsquare.com/files/rs-5690135/v1/cc25ea35bd08fccf3b1baa01.png"},{"id":79909870,"identity":"03e19ee5-fc05-4913-b940-423236a95b9d","added_by":"auto","created_at":"2025-04-04 11:26:44","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":212072,"visible":true,"origin":"","legend":"\u003cp\u003ePotential binding poses of hit compounds from virtual screening, (a) binding poses of Ket B and Ket C against the LasR protein and (b) binding poses of Ket B and Ket C against PqsR protein.\u003c/p\u003e","description":"","filename":"image2.png","url":"https://assets-eu.researchsquare.com/files/rs-5690135/v1/4863071cbed3a303feabb7ff.png"},{"id":79909867,"identity":"9cea1e20-7885-4e43-9ccb-c3e18643508f","added_by":"auto","created_at":"2025-04-04 11:26:44","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":96859,"visible":true,"origin":"","legend":"\u003cp\u003eValidation poses for co-crystalized ligands with interacting residues, (a) PDB pose of OHN (cyan) against redocked pose (magenta) and (b) PDB pose of NNQ (cyan) against redocked pose (magenta).\u003c/p\u003e","description":"","filename":"image3.png","url":"https://assets-eu.researchsquare.com/files/rs-5690135/v1/954efc6ef0ebc488b994f19c.png"},{"id":79907311,"identity":"57573811-c995-4c53-8781-76247a4baf93","added_by":"auto","created_at":"2025-04-04 11:10:44","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":114126,"visible":true,"origin":"","legend":"\u003cp\u003e2D representation of established interactions between compounds and proteins active site residues, (a) LasR-Ket B interactions (left), LasR-Ket C interactions (right) and (b) PqsR-Ket B interactions (left), PqsR-Ket C interactions (right).\u003c/p\u003e","description":"","filename":"image4.png","url":"https://assets-eu.researchsquare.com/files/rs-5690135/v1/5500a03f53266f4f7de20a69.png"},{"id":79907308,"identity":"922497b2-9771-4fb7-baf7-00d06284cb9b","added_by":"auto","created_at":"2025-04-04 11:10:44","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":169978,"visible":true,"origin":"","legend":"\u003cp\u003ePost MD trajectories for determining ligand stability (a) RMSD of ligands (Ket B, Ket C and Ahl), (b) Hydrogen bond occupancy for ligands (Ket B and Ket C), (c) RMSD of bound and unbound protein backbone (d) Rg of bound and unbound protein, (e) RMS Fluctuations of bound and unbound protein, (f) PCA of bound and unbound protein.\u003c/p\u003e","description":"","filename":"image5.png","url":"https://assets-eu.researchsquare.com/files/rs-5690135/v1/fe1e0f068f3479470d4eb52f.png"},{"id":79908355,"identity":"601b9391-336a-4ea7-ae59-6e6e8afb91a8","added_by":"auto","created_at":"2025-04-04 11:18:44","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":232217,"visible":true,"origin":"","legend":"\u003cp\u003eStructural conformations of the LasR protein observed when ligands (Ket B and Ket C) where bound at the active site.\u003c/p\u003e","description":"","filename":"image6.png","url":"https://assets-eu.researchsquare.com/files/rs-5690135/v1/98d3f74d8e68d19b6684117a.png"},{"id":79907314,"identity":"6183528c-7c4b-4ad3-800d-93fc2964bea1","added_by":"auto","created_at":"2025-04-04 11:10:44","extension":"png","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":170638,"visible":true,"origin":"","legend":"\u003cp\u003ePost MD trajectories for determining ligand stability (a) RMSD of ligands (Ket B, Ket C and Nnq), (b) Hydrogen bond occupancy for ligands (Ket B and Ket C), (c) RMSD of bound and unbound protein backbone (d) Rg of bound and unbound protein, (e) RMS Fluctuations of bound and unbound protein, (f) PCA of bound and unbound protein.\u003c/p\u003e","description":"","filename":"image7.png","url":"https://assets-eu.researchsquare.com/files/rs-5690135/v1/1e2304e4f5451491c495e744.png"},{"id":79907309,"identity":"71a72396-df2b-420a-a704-911752edc727","added_by":"auto","created_at":"2025-04-04 11:10:44","extension":"png","order_by":8,"title":"Figure 8","display":"","copyAsset":false,"role":"figure","size":124840,"visible":true,"origin":"","legend":"\u003cp\u003eMajor loops responsible for deviations of the indicated fluctuations during simulation. White color representing before and black representing after.\u003c/p\u003e","description":"","filename":"8.png","url":"https://assets-eu.researchsquare.com/files/rs-5690135/v1/62f2ca43b6f39fcf6d7e89ff.png"},{"id":79908373,"identity":"16622651-f557-4856-8830-a7740eacea79","added_by":"auto","created_at":"2025-04-04 11:18:44","extension":"png","order_by":9,"title":"Figure 9","display":"","copyAsset":false,"role":"figure","size":243023,"visible":true,"origin":"","legend":"\u003cp\u003eStructural conformations of the PqsR protein observed when ligands (Ket B and Ket C) where bound at the active site.\u003c/p\u003e","description":"","filename":"image9.png","url":"https://assets-eu.researchsquare.com/files/rs-5690135/v1/e4d90811e78507bbc22cca1d.png"},{"id":79907332,"identity":"b7f32d2c-523a-4971-b891-c55b4c68f3ba","added_by":"auto","created_at":"2025-04-04 11:10:45","extension":"png","order_by":10,"title":"Figure 10","display":"","copyAsset":false,"role":"figure","size":366940,"visible":true,"origin":"","legend":"\u003cp\u003eLigand orientations at the active site of LasR protein during MD simulation. (a) Various orientations of AHL bound to the LasR active site at time; 50ns, 100ns, 150, and 200ns; (b) Various orientations of Ket B bound to the LasR active site at time; 50ns, 100ns, 150, and 200ns; (c) Various orientations of Ket C bound to the LasR active site at time; 50ns, 100ns, 150, and 200ns.\u003c/p\u003e","description":"","filename":"image10.png","url":"https://assets-eu.researchsquare.com/files/rs-5690135/v1/159132df41c622cdd6b78585.png"},{"id":79908383,"identity":"fc2f3e20-8d88-4b33-a460-4484ac870ced","added_by":"auto","created_at":"2025-04-04 11:18:45","extension":"png","order_by":11,"title":"Figure 11","display":"","copyAsset":false,"role":"figure","size":148879,"visible":true,"origin":"","legend":"\u003cp\u003eAtomistic RMSF analysis showing the fluctuations of different moieties in the compounds considered for this study. (a) Fluctuations of heavy atoms of Ket B during the simulation; (b) Fluctuations of heavy atoms of Ket C during the simulation.\u003c/p\u003e","description":"","filename":"image11.png","url":"https://assets-eu.researchsquare.com/files/rs-5690135/v1/76e3fa5a46b37630db3c989e.png"},{"id":79907329,"identity":"3564fcee-0fa3-4c31-aaf7-2afcf21b7e3b","added_by":"auto","created_at":"2025-04-04 11:10:45","extension":"png","order_by":12,"title":"Figure 12","display":"","copyAsset":false,"role":"figure","size":331886,"visible":true,"origin":"","legend":"\u003cp\u003eLigand orientations at the active site of LasR protein during MD simulation. (a) Various orientations of NNQ bound to the LasR active site at time; 50ns, 100ns, 150, and 200ns; (b) Various orientations of Ket B bound to the LasR active site at time; 50ns, 100ns, 150, and 200ns; (c) Various orientations of Ket C bound to the LasR active site at time; 50ns, 100ns, 150, and 200ns.\u003c/p\u003e","description":"","filename":"image12.png","url":"https://assets-eu.researchsquare.com/files/rs-5690135/v1/a93a3ce97bea28e4bc09b4c8.png"},{"id":79907340,"identity":"b61dd84b-e0a6-4744-b698-9b8fba5cb249","added_by":"auto","created_at":"2025-04-04 11:10:45","extension":"png","order_by":13,"title":"Figure 13","display":"","copyAsset":false,"role":"figure","size":148611,"visible":true,"origin":"","legend":"\u003cp\u003eAtomistic RMSF analysis showing the fluctuations of different moieties in the compounds considered for this study. (a) Fluctuations of heavy atoms of Ket B during the simulation; (b) Fluctuations of heavy atoms of Ket C during the simulation.\u003c/p\u003e","description":"","filename":"image13.png","url":"https://assets-eu.researchsquare.com/files/rs-5690135/v1/950a4cc32a073c51608e0e4e.png"},{"id":79910757,"identity":"caeeea42-de4a-40b3-861f-a6a67ef78dda","added_by":"auto","created_at":"2025-04-04 11:34:45","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":3715752,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-5690135/v1/78645e35-718e-4e19-aaa3-5d2d1076468b.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Computational Insight into Biofilm Inhibitory Activity of Ketidocillinone B and C against Pseudomonas aeruginosa: A Computational Study","fulltext":[{"header":"Introduction","content":"\u003cp\u003eLymphatic filariasis (LF) stands as the second most significant cause of long-term and irreversible disability worldwide, disproportionately affecting populations in Africa, Asia, and South America. Currently, it is estimated that approximately 657\u0026nbsp;million individuals are at risk of contracting this debilitating disease from 39 countries worldwide, with over 51\u0026nbsp;million already living with the infection since 2019 [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. Among the three types of parasitic filarial worms responsible for LF, \u003cem\u003eWuchereria bancrofti\u003c/em\u003e is the primary culprit, accounting for 90% of all reported cases [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. The clinical outcomes of \u003cem\u003eW. bancrofti\u003c/em\u003e infections can be severe and varied, manifesting as lymphedema, genital disorders such as hydrocele, and acute recurrent bacterial infections. These complications not only inflict a substantial burden on the healthcare systems in endemic regions but also severely impair the quality of life of those affected. The current global strategy to combat LF predominantly focuses on mass drug administration (MDA) programs, which are designed to interrupt the transmission cycle by targeting the microfilariae, the larval stage of the parasites circulating in the bloodstream. While these programs have demonstrated progress in curbing the spread of the infection, they are largely ineffective in addressing the more severe, late-stage clinical manifestations of the disease, such as chronic lymphedema and hydrocele. As a result, a significant proportion of patients with advanced LF remain in need of alternative therapeutic interventions and effective morbidity management strategies, despite the strides made by the Global Programme to Eliminate Lymphatic Filariasis (GPELF) [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eOne of the most pressing challenges facing the GPELF is the alarming rise in drug resistance, which threatens to undermine the gains achieved through mass treatment initiatives [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. In recent years, the global health community has become increasingly alarmed by the rise of antimicrobial resistance (AMR), which complicates the management of LF-related bacterial infections. The emergence of drug-resistant pathogens has made treating these secondary infections more challenging. Patients suffering from co-infections particularly those with both bacterial infections and filarial disease are at a significantly higher risk of poor clinical outcomes. Studies have shown that individuals with polymicrobial infections are four times more likely to experience fatal complications compared to those with isolated bacterial infections [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. Recently our lab has shown that among the bacteria isolated from the wound of an infected LF patients, \u003cem\u003ePseudomonas aeruginosa\u003c/em\u003e remains the most significant multidrug resistant gram-negative pathogen [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. This pathogen is a problematic nosocomial pathogen that seriously threatens critically ill and immunocompromised patients. The resistance of \u003cem\u003eP. aeruginosa\u003c/em\u003e and other microbes is significantly heightened due to their biofilm-forming ability. There is therefore a strong demand for effective treatment strategies to eradicate biofilms associated with \u003cem\u003eP. aeruginosa\u003c/em\u003e. The production of biofilms and the control of virulence factors in \u003cem\u003eP. aeruginosa\u003c/em\u003e result from cell-to-cell communication among bacteria, a process known as quorum sensing (QS). This bacterial communication is facilitated by diffusible chemical signaling molecules called autoinducers (AIs) [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eBacterial population growth causes signaling molecules to build up in the environment, amplifying a feedback mechanism that promotes gene expression for biofilm formation and the regulation of other population-dependent behaviors. Two interconnected QS regulatory systems are crucial for the development of biofilms, pathogenicity, and a host of other features like antibiotic, detergent, and biocide tolerance. Signaling molecules build up in the environment around bacteria as their population rises, amplifying a feedback mechanism that encourages the expression of genes that lead to biofilm development and control. The acylhomoserine lactone synthase (lasI) and the transcriptional activator (lasR) form the first system (Las) [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. A transcriptional activator (rhlR) and an acyl homoserine lactone synthase (rhlI) make up the second system (Rhl). Pseudomonas Quinolone Signal (PQS), which employs quinolone as the signaling chemical, and Integrated Quorum System (IQS) are two other QS systems that have just recently been identified. LasR and PqsR systems produce 100 folds of virulence factors [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e], [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e], [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e], [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. This emphasizes LasR's and PqsR\u0026rsquo;s significance as a potential target for suppressing quorum sensing and thereby regulating biofilm development.\u003c/p\u003e \u003cp\u003eThe current treatments for lymphatic filariasis, which include diethylcarbamazine (DEC), ivermectin, and albendazole, have made significant strides in reducing the disease's burden. However, these treatments have notable limitations. One major drawback is their predominant action against microfilariae, the immature larvae, while leaving adult worms largely unaffected [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. As a result, adult worms can continue producing microfilariae for years, perpetuating the infection. Additionally, concerns about drug resistance have emerged due to prolonged mass drug administration (MDA) programs, particularly in regions with overlapping parasitic infections. For instance, resistance to ivermectin in \u003cem\u003eOnchocerca volvulus\u003c/em\u003e infections raises alarms about the potential for similar challenges in lymphatic filariasis [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. Another limitation involves the adverse side effects associated with these medications. DEC can cause severe allergic reactions, especially in individuals with high microfilarial loads, while ivermectin is known to trigger Mazzotti-like reactions in those co-infected with \u003cem\u003eOnchocerca\u003c/em\u003e [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e], [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e], [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. Geographical coverage presents yet another hurdle; many endemic regions face logistical difficulties due to socio-economic challenges, weak healthcare infrastructure, and community non-compliance with MDA programs. Moreover, the current drugs exhibit limited efficacy against Wolbachia, a bacterial endosymbiont essential for the parasite's survival and reproduction. Although doxycycline has shown promise in targeting Wolbachia, its use is restricted in children and pregnant women, limiting its broader application [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e], [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e], [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e], [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. Again, the increasing resistance to routinely used antiparasitic and antimicrobial agents is particularly concerning in Sub-Saharan Africa, where high levels of resistance have been observed [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. For instance, resistance to ivermectin, one of the key drugs used in LF treatment, has already been documented in Ghana. Additionally, widespread resistance to benzimidazole compounds, including albendazole, has been linked to mutations in the β-tubulin gene, which are responsible for conferring resistance to these drugs [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]. This resistance compromises the efficacy of treatment regimens and poses a significant barrier to achieving the long-term eradication of LF [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]. Further complicating the situation is the suboptimal efficacy of diethylcarbamazine (DEC), another cornerstone of LF treatment [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e], [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]. Research has demonstrated that DEC does not provide complete protection or cure in all cases, further highlighting the need for new treatment approaches and a deeper understanding of the disease's progression [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. Moreover, our field observations have indicated that patients with filarial lymphedema are more susceptible to secondary bacterial infections, likely due to their compromised lymphatic systems [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]. These secondary infections can be recurrent and often exacerbate the severity of the lymphedema, leading to further complications and increased morbidity [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eGiven these limitations, natural product-derived therapies offer a promising alternative. Natural compounds provide an extensive repertoire of bioactive molecules with structural diversity that can target multiple stages of the parasite's lifecycle. Plant-derived alkaloids, flavonoids, terpenoids, and polyphenols have demonstrated significant antifilarial activity in various studies. In addition to their antiparasitic properties, many natural products have antibacterial effects that could disrupt Wolbachia symbiosis, thereby weakening the parasite. Examples such as, extracts from \u003cem\u003eAzadirachta indica\u003c/em\u003e (neem) and \u003cem\u003eClerodendrum\u003c/em\u003e species have shown encouraging antifilarial potential with fewer side effects than synthetic drugs [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]. The chance to identify novel medications from natural products is always present, and this is also true with fungi-derived compounds. According to reports, marine fungi generate polyketides as one of their main secondary metabolites as a form of protection. Additionally, various experimental findings have been published that discuss several polyketides' antibiofilm and antimicrobial properties [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e], [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e], [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e], [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e], [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eIn this study, we present an \u003cem\u003ein-silico\u003c/em\u003e investigation of compounds derived from marine sources, targeting the biofilm and quorum sensing proteins LasR and PqsR through molecular docking and molecular dynamics simulations. Virtual screening of over 100 compounds identified two promising candidates Ketidocillinone B and Ketidocillinone C from the polyketide category as exhibiting higher binding affinities against LasR and PqsR compared to their natural autoinducers and other compounds from different categories which some of the showed no affinity for the active sites of these proteins or reported binding affinities less than that of the autoinducers for both proteins. While computational method such as virtual screening and molecular docking can rapidly screen large datasets, they often lack the ability to fully capture the complexity of biological systems, such as protein dynamics. As a result of this failure, molecular dynamics simulation was employed to further examine protein dynamics to some extent and also assess the stability of these compounds within the ligand-binding domains of LasR and PqsR, demonstrating interactions that could potentially block the autoinducer binding and disrupt signal transduction. As a result, the production of virulence factors would be halted. Based on these findings, we propose that Ketidocillinone B and C hold potential as alternative leads for treating secondary infections caused by resistant pathogenic strains in filarial lymphedema patients, particularly in LF-hyperendemic regions.\u003c/p\u003e"},{"header":"Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eProtein selection and preparation\u003c/h2\u003e \u003cp\u003e \u003cem\u003ePseudomonas aeruginosa\u003c/em\u003e utilizes the LasR and PqsR receptors as key regulators for biofilm formation. Given their critical roles, this study focuses on these two receptors. Inhibiting LasR and PqsR leads to the disruption of biofilm formation, a process known as biofilm quenching. For this study, the 3D structures of LasR and PqsR proteins from \u003cem\u003eP. aeruginosa\u003c/em\u003e complexed with their respective autoinducers (OHN for LasR and NNQ for PqsR) were retrieved from the Protein Data Bank (PDB IDs: 6V7X and 4JVD). Chain B of LasR was selected and prepared by removing water molecules, heteroatoms, the anti-activator (Aqs1), and the autoinducer (OHN). Similarly, Chain A of PqsR was chosen, with water molecules, heteroatoms, and the autoinducer (NNQ) removed [\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]. Major groups were removed because, these groups such as water molecules are not need for the biological activities of both proteins. Also, autoinducers and Aqs1 were moved before the apo state of the protein in currently not available at the PDB database. The backbone structures of the proteins were then energy minimized using the AMBERff4SB force field, with Gasteiger charges computed via Antechamber in Chimera to further refine the models for subsequent molecular dynamics simulations.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eLigand preparation\u003c/h3\u003e\n\u003cp\u003eLigand preparation in this study involved using Spartan \u0026rsquo;14 software to model and optimize selected compounds. Structural optimizations and energy minimizations were performed with density functional theory (DFT) using the B3LYP/6-31G* basis set to ensure accurate molecular geometries and electronic properties [\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]. DFT is a powerful tool for ligand energy minimization but has limitations. It struggles with accurately capturing dispersion forces, solvent effects, and self-interaction errors, which can influence binding affinity predictions hence the docking was replicated. The compounds were saved in .pdb and .sdf formats for further computational analysis. These optimized ligands, identified as potential inhibitors of quorum sensing and biofilm formation in \u003cem\u003ePseudomonas aeruginosa\u003c/em\u003e, were evaluated for their interactions with the LasR and PqsR receptors. Figure\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e highlights the hit compounds selected for their promising binding properties and stability, forming the basis for further therapeutic exploration.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e\n\u003ch3\u003eMolecular docking\u003c/h3\u003e\n\u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003eVirtual Screening and Precision Docking\u003c/h2\u003e \u003cp\u003eThe docking process involved the use of both global and precision docking approaches to evaluate the interaction between modeled ligands and prepared proteins. Initially, a global search of over 100 marine-derived compounds, known for their antibiofilm activity in \u003cem\u003ePseudomonas aeruginosa\u003c/em\u003e, was conducted using AutoDock Vina embedded in PyRx [\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e]. The search grid was set to cover the entire protein and allow the compounds to search for their preferred binding sites. Virtual screening was done for more than 100 compounds from marine source with antibiofilm activity in \u003cem\u003ePseudomonas aeruginosa\u003c/em\u003e with reported IC\u003csub\u003e50\u003c/sub\u003e values from experimental researches. Compounds with reported IC\u003csub\u003e50\u003c/sub\u003e values\u0026thinsp;\u0026le;\u0026thinsp;10 \u0026micro;g/mL and have shown antibiofilm activity were selected. Selected compounds were grouped into alkaloids, steroids, flavones, macrolids, and polyketides. Since the molecular docking approach considered proteins as rigid and does not account for solvent effect, the docking was done in replicates (3times) and the average binding scores were calculated. The x, y and z dimensions of the box were approximately 50. 9317, 44. 2871 and 57. 7354 \u0026Aring;, respectively, while the x, y and centers were; 22. 7209, 4. 0930 and \u0026minus;\u0026thinsp;13. 2669 respectively for LasR. For the PqsR, the x, y and z dimensions of the box were approximately 48. 2587, 44. 8693 and 48. 6144 \u0026Aring;, respectively, while the x, y and centers were; -27. 1696, 64. 3398 and 9. 8905. Precision docking was then performed using AutoDock Vina in UCSF Chimera, focusing on compounds that preferentially bound to the ligand-binding domain of the LasR and PqsR proteins. The OHN, and NNQ were redocked in the ligand binding domain of LasR and PqsR to validate the docking protocol in precision docking using the x, y and z dimensions of the box that fit the active site. For the LasR, the x, y and z dimension used in the study was; 22. 9078, -3. 37362 and \u0026minus;\u0026thinsp;4. 80686 \u0026Aring; and radius of 19. 2 \u0026Aring;. For PqsR, the x, y and z dimension used in the study was; -33. 3486, 57. 8889 and 9. 39797 \u0026Aring; and radius of 15. 8 \u0026Aring;. The active site residues were obtained from the PDBSUM entry for 6V7X and 4JVD with binding site residues. For 6V7X, the identified active site residues are; Trp60, Tyr93, Ser129, Asp73, Ile52, Ala50, Ala127, Leu125, Leu40, Val76, and Cys79. For 4JVD, the identified active site residues were; Ala 168, Ile149, Leu207, Ile263, Leu189, Ile186, Ile236, Ala102, Val170, and Tyr258. The accuracy of the docking protocol was validated by ensuring a low root-mean-square deviation (RMSD) of less than 2 \u0026Aring; between the redocked ligands and their original co-crystallized orientations. Additionally, the interactions observed in the PDB structures were successfully replicated, further affirming the reliability of the docking procedure. This comprehensive docking strategy provided insights into the binding affinities and interactions of the selected compounds with the LasR and PqsR proteins, highlighting their potential as biofilm inhibitors.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eMolecular dynamics simulation study\u003c/h3\u003e\n\u003cp\u003eAll-atom molecular dynamics simulations were conducted for hit molecules, NNQ, and OHN for 200 ns using GROMACS V.2018.6 [\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e] on the Lengau cluster (Centre for High-Performance Computing, Cape Town). While 200 ns is sufficient for many systems, some protein-ligand interactions may require longer simulations to fully capture conformational changes. Ligand poses with the lowest energy conformations from docking was saved in the mol2 format and were prepared with hydrogens added and corresponding charges using CHARMM force fields. Ligand topologies were generated using the CGenFF force field server [\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e], while protein topologies were generated with the pdb2gmx tool with the CHARMM36 force field. CHARMM force field was used in this study since this forcefield accurately give proper accounts for atoms in small molecules and macromolecules. TIP3P water model was employed in the solvation process in a dodecahedron box maintaining a margin of 1.0 nm. Ionization was achieved by adding Na\u003csup\u003e+\u003c/sup\u003e and Cl\u003csup\u003e\u0026minus;\u003c/sup\u003e ions where necessary, replacing water molecules until neutrality was achieved. In a dodecahedron box, the system was solvated with the TIP3P water model. Energy minimization was performed for 50,000 steps, followed by NVT and NPT equilibration at 300 K and 1 bar for 100 ps. Compounds were then subjected to MD simulation utilizing PME for long-range electrostatics with cut-offs of 1.2 nm for both Coulomb and Van der Waals interactions, and a time step of 2 fs.During the production run, coordinate trajectories were recorded every 10 ps, applying three-dimensional periodic boundary conditions (PBC) throughout the 200 ns production run [\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e].The Molecular Mechanics Poisson-Boltzmann Surface Area (MM-PBSA) method implemented in GROMACS was employed to calculate binding energies.\u003c/p\u003e \u003cp\u003eThe Molecular Mechanics Poisson-Boltzmann Surface Area (MM-PBSA) method implemented in GROMACS was used to calculate binding energies [\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e].\u003c/p\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003ePost-MD analysis\u003c/h2\u003e \u003cp\u003eThe stabilities of all complexes were investigated using simulation output files obtained after MD productions. Parameters such as the Root mean square deviation (RMSD), root mean square fluctuation (RMSF), radius of gyration (RoG), and the principal component analysis (PCA) values were generated. Output files were graphed using XMGRACE. By extracting frames at 200 ns, trajectory analysis of the complexes was undertaken. All hydrogen bonds were estimated using a bond angle and bond length of 180\u0026deg; and 3 \u0026Aring;, respectively as previously described [\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e], [\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e]. Further analysis helped retrieve the number of hydrogen bonding interactions and their occupancies during the 200 ns MD simulation.\u003c/p\u003e \u003cp\u003eFor stable complexes, energy calculations were computed using the last 50 ns of the simulation period. G_mmpbsa was used to calculate the binding energies of the complexes [\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e]. The MM-PBSA method estimated the binding energy based on the energetic terms that correspond to the change in the potential energy in vacuum. These include bond angle, torsional energies, van der Waals and electrostatic interactions, desolvation of different species, which includes the polar and non-polar solvation energy using the implicit solvation model, and the configurational entropy associated with the complex formation. Molecular mechanical potential energy (EMM), polar (Gpol), and apolar (Gapol) solvation parameters were included in the estimated binding energies.\u003c/p\u003e \u003cp\u003eThe MM-PBSA method calculated the binding energy by examining energetic terms related to changes in potential energy in a vacuum. These terms include bond angles, torsional energies, van der Waals interactions, electrostatic interactions, and the desolvation of various species, which comprises both polar and non-polar solvation energy using a deducible solvation model. The configurational entropy associated with complex formation was also considered. The estimated binding energies also included molecular mechanical potential energy (EMM), as well as polar (Gpol) and apolar (Gapol) solvation parameters.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eADME-Tox evaluation\u003c/h3\u003e\n\u003cp\u003eThe drug-likeness and pharmacokinetic properties of the selected compounds were evaluated using ADMETLab2.0 (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://admetmesh.scbdd.com/\u003c/span\u003e\u003cspan address=\"https://admetmesh.scbdd.com/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) and SwissADME (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ewww.swissadme.ch\u003c/span\u003e\u003cspan address=\"http://www.swissadme.ch\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e)\u003c/span\u003e web servers [\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e], [\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e], [\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e], [\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e], [\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e]. The 3D structures of the ligands were downloaded from PubChem and converted to SMILES strings for analysis. ADMETLab2.0 assessed drug-likeness and various ADME (Absorption, Distribution, Metabolism, Excretion) parameters, including potential bioavailability, tissue distribution, metabolic pathways, excretion routes, and toxicity risks. SwissADME further analyzed lipophilicity, polarity, drug-likeness, and pharmacokinetics to determine the compounds' suitability as therapeutic agents. Even though theses webservers are in silico predictions, it provides initial evaluation and screening parameter for compounds used in this study. Some associated limitations of in silico ADME-T predictions are that, they are susceptible to experimental evaluation and validation. This comprehensive evaluation provided insights into the compounds' potential efficacy and safety.\u003c/p\u003e"},{"header":"Results and Discussion","content":"\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eMolecular docking\u003c/h2\u003e \u003cdiv id=\"Sec12\" class=\"Section3\"\u003e \u003ch2\u003eVirtual screening\u003c/h2\u003e \u003cp\u003eResults from the virtual screening, suggests that two polyketide compounds ketidocillinone B and C, (Ket B and Ket C) had the most binding poses with high affinity for the active site of both proteins under study (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). Ketidocillinone B (Ket B) and Ketidocillinone C (Ket C) emerged as promising candidates based on stringent selection criteria during virtual screening, including high binding affinity, stability within the binding pocket. Comparing the binding affinities recorded by other compounds from various categories, Ket B and Ket C had better binding affinity when compared among compounds of the same category and other categories as both compounds exhibit better binding affinity when compound to co-crystalized ligands (OHN and NNQ). These compounds were then subjected to precision docking. Binding affinity does not account for pharmacokinetic properties such as absorption, distribution, metabolism, and excretion (ADME), which are critical for drug development, hence further ADME-T profiling shows that both compounds are excellent candidates.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003eValidation of the molecular docking protocol used in this study\u003c/h2\u003e \u003cp\u003eIn the quest to validate the molecular docking protocol employed in this study, we performed a re-docking experiment using co-crystalized ligands, to the receptors. By comparing the re-docked conformation of co-crystallized ligands with its experimental alignment, the validation of the docking was evaluated. Using the LasR receptor, N-3-oxo-dodecanoyl-L-homoserine lactone (OHN), and that for the PqsR receptor, 2-nonylquinolin-4(1H)-one (NNQ) was used. OHN was bound at the active site of the LasR receptor with a binding energy of -8.3 kcal/mol and the RMSD (Root Mean Square Deviation) of 1.199 \u0026Aring; (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003ea). For the PqsR receptor, NNQ had a binding energy of -6.9 kcal/mol and a RMSD value of 0.919 \u0026Aring; (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eb). An RMSD value\u0026thinsp;\u0026lt;\u0026thinsp;2 \u0026Aring; clearly indicates that the docking protocol employed is validated [\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e].\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003ePrecision docking\u003c/h2\u003e \u003cdiv id=\"Sec15\" class=\"Section3\"\u003e \u003ch2\u003eLasR-ligand complexes\u003c/h2\u003e \u003cp\u003eFrom the analysis, good docking scores (kcal/mol) were recorded for both compounds (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). Analysis from the LasR-ligand complexes showed a binding affinity of -9.3 kcal/mol and \u0026minus;\u0026thinsp;9.5 kcal/mol, for Ket B and Ket C, respectively. The interactions observed between LasR and Ket B were mostly hydrogen bond and hydrophobic interactions. Amino acid residues; Tyr 43, Ala 66, and Thr 111 formed hydrogen bond interactions. Hydrophobic interactions with Asp 69, Trp 84, Ala 101, and Phe 97. Hydrophobic interactions were predominant in the LasR-Ket B complex (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e (a) left). For the LasR-Ket C complex, hydrophobic interaction was predominant. The hydrophobic interactions established were two amino acid residues: Trp 84 and Asp 69. The Pi-Pi T-shaped interaction was established between the benzyl moiety, Trp 84, and Pi-anion interaction with Asp 69 (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e (a) right). From a study conducted, the native ligand acyl homoserine lactone (AHL) binds to the LasR active site with a definite pose [\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e], [\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e], [\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e]. In this pose, AHL establishes both hydrophobic and hydrogen bond interactions are observed. The LasR-AHL complex's hydrophobic interactions sequester additional non-polar residues into a beneficial hydrophobic environment while protecting the ligand binding pocket from bulk solvent[\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]. These interactions must be precise and vital for LasR to be activated. After activation, LasR dimers and binds to the promoter gene to initiate transcription of the proteins required for the synthesis of virulence factors. In the absence of AHL, the loss of these helpful interactions causes loop flexibility, this then exposes the buried hydrophobic residues in the pocket to bulk solvent and causes protein aggregation [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. On this account, an antagonist is therefore recommended not to mimic the aforementioned interactions. From the visual interaction analysis, both Ket B and Ket C did not mimic the interactions and can be proposed as potential inhibitors of the LasR receptor. In-depth was drawn from the molecular dynamics simulations as both ligands were bound at the active site throughout the simulation period.\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\u003eDocking score with established hydrogen bond and hydrophobic bond interactions by the polyketides against the LasR protein evaluated from molecular docking with specific amino acid residues\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"7\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eCompounds\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"4\" nameend=\"c5\" namest=\"c2\"\u003e \u003cp\u003eDocking score (kcal/mol)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003eMost established interactions\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1st docking\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2nd docking\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3rd docking\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eAverage\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eHydrogen bond interactions\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eHydrophobic interactions\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eKet B\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-9.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-9.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-9.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-9.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eAla66, Tyr43, Thr111\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eAsp69, Trp84, Phe97, Ala101\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eKet C\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-9.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-9.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-9.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-9.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eTrp84, Asp69, Tyr60, Ala66, Phe97, Ala101\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOHN\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-8.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-8.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-8.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-8.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eTrp60, Ser129, Asp73\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eAla127, Ala50, Ile52, Leu40, Val76, Leu125, Cys79\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \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\u003eDocking score with established hydrogen bond and hydrophobic bond interactions by the polyketides against the PqsR protein evaluated from molecular docking with specific amino acid residues\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"7\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eCompounds\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"4\" nameend=\"c5\" namest=\"c2\"\u003e \u003cp\u003eDocking score (kcal/mol)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003eMost established interactions\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1st docking\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2nd docking\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3rd docking\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eAverage\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eHydrogen bond interactions\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eHydrophobic interactions\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eKet B\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-7.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-7.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-7.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-7.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eGln101, Ile143, Ser103, Leu 115\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eAla75, Ile56\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eKet C\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-8.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-8.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-8.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-8.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eIle143, Leu104, Ser103, Gln101, Ile102\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eAla9, Ala75, Ile56\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNNQ\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-6.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-6.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-6.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-6.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eAla168, Val170, Ala102, Ile149, Ile263, Ile236, Leu207\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 \u003c/div\u003e \u003cdiv id=\"Sec16\" class=\"Section2\"\u003e \u003ch2\u003ePqsR-ligand complexes\u003c/h2\u003e \u003cp\u003eThe docking score for both Ket B and Ket C was \u0026minus;\u0026thinsp;7.9 kcal/mol and \u0026minus;\u0026thinsp;8.8 kcal/mol, respectively (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). Hydrophobic and hydrogen bond interactions were predominant in both complexes. For the PqsR-Ket B system, the hydrogen bond interactions were between amino acids such as; Ile 143, Gln 101, Ser 103, and Leu 115. Hydrogen bonds were seen between Ala 75 and Ile 56 amino acid residues (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e (b) left). Similar observations were made for the PqsR-Ket C system. Both hydrogen and hydrophobic bond interactions were predominant. Amino acids such as; Ile 143, Leu 104, Ile 102, Ser 103, and Gln 101 were involved in hydrogen bonding while Ala 9, Ala 75, and Ile 56 were involved in hydrophobic bond interaction (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e (b) right).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eThe ligand binding site in PqsR is a hydrophobic cavity, as demonstrated by crystal soaking tests conducted with NNQ.\u003c/p\u003e \u003cp\u003eThere are no electrostatic interactions involved; hydrophobic interactions are what stabilize the PqsR-NNQ complex. Nevertheless, an obvious switch involving two isosteres switches a quinazolinone (QZN) agonist into an antagonist, which also affects the activation of the synthesis of bacterial virulence factors[\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e]. Hence the inactivation of PqsR requires a strong interaction between the proposed antagonist and the receptor since the earlier research clearly shows the involvement of the exchanged isosteres. Therefore, with the different structural properties of Ket B and Ket C as compared to the agonist (NNQ), the ligands under study can be potential inhibitors of the receptor.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec17\" class=\"Section2\"\u003e \u003ch2\u003eMolecular Dynamics Simulations\u003c/h2\u003e \u003cp\u003eAnalyzing internal motions through MD simulations opens up avenues to explore diverse biological functions, including the synthesis of new molecules with specific conformations and structural characteristics. MD simulations in an explicit solvent were conducted to explore the potential inhibition of LasR and PqsR protein targets by Ket B and Ket C. The stability of ligand interactions with the selected protein targets was evaluated using molecular dynamics simulations, taking into accounts aspects such as protein structure, solvation, ionic effects, and entropic contributions. In assessing stability, various factors were considered, such as the RMSD of the protein backbone and ligands, RMSF of protein side chains, Rg (Radius of Gyration), and Principal Component analysis of the protein. While RMSD is a useful measure of protein stability, it does not provide information about local conformational changes that could affect ligand binding. Similarly, RMSF and Rg provide insights into protein flexibility but do not capture specific interactions between the ligand and protein residues. Molecular dynamics simulations offer a comprehensive understanding of ligand-protein interaction stability by considering characteristics beyond those addressed by molecular docking alone. These simulations account for dynamic aspects, providing a deeper insight into the stability of such interactions.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec18\" class=\"Section2\"\u003e \u003ch2\u003eLasR-Ket B and Ket C complexes\u003c/h2\u003e \u003cp\u003eBoth Ket B and Ket C exhibited stability and effectively bound to the active site of the LasR protein. Examination of the LasR-Ket B complex indicated stable binding of Ket B, Ket C, and the native ligand (OHN) within the active site, as illustrated in Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e (a). The RMSD value of Ket B was 0.6 nm, which is less than 2 nm hence shows that the compound is stable [\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e], [\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e], [\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e]. Also, the RMSD value of Ket C was 0.3 nm. Comparing the stability of the compounds clearly shows that Ket C had the least stability, and this can be explained by the rigidity of the compound. Both hydrogen and hydrophobic interactions were observed in both ligand complexes. Hydrophobic interactions were predominant as also seen from the docking. This is because of the hydrophobic nature of the active site. Even though hydrophobic interactions play a critical role in the mechanism of action of drug-like molecules against protein targets, hydrogen bonds are also needed in enzymatic activity inhibition. With this, hydrogen bond occupancy for the ligands (Ket C and Ket C), was evaluated. Ket C had the most hydrogen bond occupancy with a hydrogen bond count of 9 while Ket C had the least hydrogen bond occupancy with a count of 3 (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e (b)). This also explains the extent of stability of both ligands. The stability of the ligands was analyzed based on their RMSD.\u003c/p\u003e \u003cp\u003eThe analysis also included assessing the RMSD of the protein backbone. The Apo protein exhibited notable deviation around ~\u0026thinsp;195\u0026ndash;200 ns. In contrast, the LasR-AQS1 bound protein backbone showed no significant deviation. A similar trend was observed for the LasR-Ket B bound protein, although a slight deviation occurred around ~\u0026thinsp;15 ns. However, considerable protein backbone deviation persisted throughout the simulation period for the LasR-Ket C bound protein. Figure\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e (c) illustrates the trajectory of the RMSD plot for both bound and unbound protein systems. Similar observation of the LasR protein was made elsewhere, where LasR exhibited a low RMSD value of \u0026sim;0.3 \u0026Aring; upon favorable binding with hydrophorones [\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]. The Rg analysis revealed that the flexibility of the unbound protein (Apo protein) and the LasR-Ahl systems was comparable, as depicted in Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e (d). This suggests that the native ligand had minimal influence on the protein's structural features. Consequently, the protein maintained similar structural integrity whether in the unbound state or when bound to the native ligand. This observation is logical considering that the native ligand serves as a natural substrate for the protein, necessitating consistent structural integrity for effective signal transduction upon binding. In contrast, both Ket B and Ket C bound systems exhibited reduced protein flexibility, indicating a noticeable impact on the protein structure by these ligands. This is evidenced by the increased compactness of the protein in these bound states. Figure\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e (d) illustrates the Rg plot for both bound and unbound systems.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eFurthermore, analysis of RMS fluctuation of amino acid residues showed no significant deviations between the bound and unbound systems, indicating stability in the protein's amino acid residues regardless of the ligand binding state. Figure\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003e illustrates the protein's structural stability while Ket B and Ket C remain bound to the active site throughout the simulation duration. Nonetheless, notable discrepancies among all systems primarily concern amino acid residues situated in the loop regions of the protein, as depicted in Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e (e). [\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e] discovered a similar pattern where similar fluctuations were observed when hydrophorones were bonded to LasR. The PCA analysis of the Cα-atoms was conducted to examine the slow and functional motions exhibited by these atoms across the amino acid residues. This assessment involved scrutinizing the vigorous motion of the Cα-atom through eigenvectors, which represent the overall direction of atom motion, and eigenvalues, indicating the atomic contribution to motion. Upon reviewing the PCA plots of both bound and unbound systems, it became evident that the bound protein systems (LasR-Ket B and LasR-Ket C) displayed reduced collective motion of the Cα-atoms compared to the unbound system, as depicted in Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e (f). These findings correspond to the results obtained from the Rg analysis, which indicated higher flexibility in the unbound protein compared to the bound protein. Consequently, this analysis suggests that the motion of amino acid residues was constrained when the ligands were bound to the protein.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec19\" class=\"Section2\"\u003e \u003ch2\u003ePqsR-Ket B and Ket C complexes\u003c/h2\u003e \u003cp\u003eBoth Ket B and Ket C demonstrated stability as they securely bound to the active site of the PqsR protein. Upon analyzing the PqsR-Ket B complex, a slight deviation was observed at approximately 49 ns, followed by another deviation around 70 ns. Despite these deviations, the RMSD of Ket B remained below 2 nm, ranging from approximately 0.45 to 1.5 nm, as illustrated in Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003e (a). This deviation primarily stemmed from the flipping motion of the rotatable bonds, which persisted throughout the simulation. Notably, both hydrogen and hydrophobic interactions played significant roles in maintaining Ket B within the active site, with observable bond distances.\u003c/p\u003e \u003cp\u003eConversely, the binding poses of Ket C remained stable throughout the simulation period, with no deviations observed, as depicted in Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003e (a). Hydrophobic interactions were crucial in anchoring Ket C within the active site pocket, alongside established hydrogen bond interactions. The RMSD of Ket C was approximately 0.25 nm. Moreover, both compounds exhibited a higher frequency of hydrogen bond interactions, with Ket B and Ket C recording 9 and 7 hydrogen bond counts, respectively, as shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003e (b).\u003c/p\u003e \u003cp\u003eThe analysis of RMSD was conducted for both bound and unbound proteins. The RMSD for the unbound protein exhibited a consistent range between 0.3 and 0.4 nm. When Ket B was bound to the protein, a slight increase in RMSD occurred around 50 ns, reaching approximately 0.35 nm for the remainder of the simulation period, as shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003e (c). Similarly, Ket C bound protein displayed slight deviations throughout the simulation duration, as depicted in Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003e (c). These findings indicate the overall stability of the protein over the simulation period.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eAssessment of the RMSF of amino acid residues revealed significant fluctuations between bound and unbound proteins, particularly in the loop regions of the protein, as illustrated in Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003e (e) and Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003e. Additionally, the PCA analysis indicated more collective motions of the Cα-atoms in the presence of Ket B compared to the unbound protein, while for PqsR-Ket C, collective motions of the Cα-atoms were less pronounced, as depicted in Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003e (f).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eIn both Ket B and Ket C bound systems, the protein's compactness was evident as protein flexibility decreased. This observation aligns closely with the results obtained from the Rg analysis, as illustrated in Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003e (d). Furthermore, Fig.\u0026nbsp;\u003cspan refid=\"Fig9\" class=\"InternalRef\"\u003e9\u003c/span\u003e illustrates the protein's structural stability when Ket B and Ket C were bound to the active region during the simulation.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec20\" class=\"Section2\"\u003e \u003ch2\u003eBinding Energy Calculations\u003c/h2\u003e \u003cdiv id=\"Sec21\" class=\"Section3\"\u003e \u003ch2\u003eMMPBSA\u003c/h2\u003e \u003cp\u003eAlthough molecular docking offers the lowest energy and the ligand-bound protein's binding conformation, it does not take into account the protein's natural conformational changes, solvation and ionic effects, or the entropic contributions to the total binding free energies [\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e], [\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e]. The stability and corresponding energy of ligands while bound to their targets are estimated via molecular dynamics, which also takes into consideration these docking restrictions. Whiles MMPBSA calculation is important, its reliance on a single trajectory and its inability to fully capture entropic contributions to binding. Calculated binding free energies were used to assess the compounds' affinities for the pocket residues of LasR and PqsR. Van der Waals, electrostatic, polar, and non-polar solvation energies are only a few of the energy components that contribute to the overall binding energy. Van der Waals, electrostatic, and non-polar energies all significantly contribute to the binding of the complexes, according to analyses of the complexes. However, the polar solvation energy had a negative impact on how well all the complexes bound.\u003c/p\u003e \u003cp\u003eFrom the binding energy calculations, Ket B bound to the LasR protein had a total binding energy of -82.559 kJ/mol with Van der Waals and electrostatic contributions of -166.041 kJ/mol and \u0026minus;\u0026thinsp;18.632 kJ/mol respectively. Non-polar solvation energy of -18.212 kJ/mol and polar solvation energy of 120.355 kJ/mol. For Ket C bound to LasR, a total binding energy of -68.680 kJ/mol, Van der Waals contribution was \u0026minus;\u0026thinsp;131.480 kJ/mol, electrostatic contribution was \u0026minus;\u0026thinsp;42.134 kJ/mol, and polar solvation of 119.901 kJ/mol with a non-polar solvation energy of -14.967 kJ/mol. The results clearly indicate that, the recorded binding free energies of Ket B and Ket C was favorable and this was due to the positive effect establish by Van der Waals and electrostatic contributions which is accompanied by the non-polar solvation energy. In a study by [\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e], the AHL is unable to interact favorably with the L3 loop to create a favorable hydrophobic environment. This mechanism highlights the major fluctuations seen in this loop region. In our study, the positive impact by Van der Waals and electrostatic contributions indicates that the compounds were not able to interact with the amino acid at the loop region during the simulation. This may lead to changes in the L3 loop, exposing the pocket to the surrounding solvent. Disruption of interactions between the compounds and the pocket residues results from the availability of solvent molecules in the binding pocket. As a result, this could contribute to the observed decline in Van der Waals energy, nonpolar energy, and electrostatic energy between the compounds and the pocket residues, similar to findings reported in other studies [\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e]. With this scenario [\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e], suggested that exposing buried hydrophobic residues to the surrounding solvent leads to protein assemblage. Calculated binding energies revealed that even in the presence of solvent molecules, all compounds spontaneously bound to the protein during the simulation indicating the compounds\u0026rsquo; stability within the binding domain. Therefore, we propose that the compounds bind to LasR for a sufficiently long duration which causes the bulk solvent to accumulate in the ligand binding domain leading to protein aggregation. The energy calculations of Ket B and Ket C against LasR protein are summarized in Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e3\u003c/span\u003ea.\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\u003e\u003cb\u003ea\u003c/b\u003e. MM-PBSA calculations of binding energy for Ket B and Ket C against the LasR receptor.\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\"\u003e \u003cp\u003eComplex\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eΔE\u003csub\u003evdW\u003c/sub\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eΔE\u003csub\u003eelect\u003c/sub\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eΔG\u003csub\u003ePB\u003c/sub\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eΔG\u003csub\u003eSASA\u003c/sub\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eΔG\u003csub\u003ebind\u003c/sub\u003e\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLasR-Ket B\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-166.041\u0026thinsp;\u0026plusmn;\u0026thinsp;9.590\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-18.632\u0026thinsp;\u0026plusmn;\u0026thinsp;9.972\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e120.355\u0026thinsp;\u0026plusmn;\u0026thinsp;17.953\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-18.212\u0026thinsp;\u0026plusmn;\u0026thinsp;0.777\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-82.529\u0026thinsp;\u0026plusmn;\u0026thinsp;15.153\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLasR-Ket C\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-131.480\u0026thinsp;\u0026plusmn;\u0026thinsp;10.425\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-42.134\u0026thinsp;\u0026plusmn;\u0026thinsp;12.105\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e119.901\u0026thinsp;\u0026plusmn;\u0026thinsp;10.459\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-14.967\u0026thinsp;\u0026plusmn;\u0026thinsp;0.726\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-68.680\u0026thinsp;\u0026plusmn;\u0026thinsp;11.185\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLasR-OHN\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-182.9\u0026thinsp;\u0026plusmn;\u0026thinsp;12.10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-131.8\u0026thinsp;\u0026plusmn;\u0026thinsp;13.50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-258.7\u0026thinsp;\u0026plusmn;\u0026thinsp;16.60\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-20.3\u0026thinsp;\u0026plusmn;\u0026thinsp;0.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-76.3\u0026thinsp;\u0026plusmn;\u0026thinsp;14.6\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\u003eConsidering the compounds against the PqsR, the total binding energies recorded are 86.855 kJ/mol and \u0026minus;\u0026thinsp;90.342 kJ/mol for Ket B and Ket C respectively. For Ket B, Van der Waals contribution was \u0026minus;\u0026thinsp;129.798 kJ/mol, electrostatic contribution was \u0026minus;\u0026thinsp;22.608 kJ/mol, and polar solvation of 82.155 kJ/mol with a non-polar solvation energy of -16.605 kJ/mol. Also, in the case of Ket C, -150.552 kJ/mol was contributed by van der Waals, -35.803 kJ/mol by electrostatics, and 112.041 kJ/mol was contributed by polar solvation with a non-polar solvation energy of -16.028 kJ/mol. All of the compounds spontaneously bound to the protein throughout the simulation in the presence of solvent molecules, demonstrating the compounds' stability in the pocket, according to the predicted binding energies. As a result, we propose that the compounds bind to PqsR for a long enough time to result in the bulk solvent gathering in the ligand binding domain, which subsequently results in protein clumping. Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e3\u003c/span\u003eb provides a summary of the energy estimations for Ket B and Ket C against the PqsR protein.\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 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003e\u003cb\u003eb\u003c/b\u003e. MM-PBSA calculations of binding energy for Ket B and Ket C against the PqsR receptor.\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\"\u003e \u003cp\u003eComplex\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eΔE\u003csub\u003evdW\u003c/sub\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eΔE\u003csub\u003eelect\u003c/sub\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eΔG\u003csub\u003ePB\u003c/sub\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eΔG\u003csub\u003eSASA\u003c/sub\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eΔG\u003csub\u003ebind\u003c/sub\u003e\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePqsR-Ket B\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-129.798\u0026thinsp;\u0026plusmn;\u0026thinsp;12.994\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-22.608\u0026thinsp;\u0026plusmn;\u0026thinsp;9.138\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e82.155\u0026thinsp;\u0026plusmn;\u0026thinsp;13.422\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-16.605\u0026thinsp;\u0026plusmn;\u0026thinsp;1.126\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-86.855\u0026thinsp;\u0026plusmn;\u0026thinsp;12.012\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePqsR-Ket C\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-150.552\u0026thinsp;\u0026plusmn;\u0026thinsp;8.983\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-35.803\u0026thinsp;\u0026plusmn;\u0026thinsp;4.634\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e112.041\u0026thinsp;\u0026plusmn;\u0026thinsp;5.446\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-16.028\u0026thinsp;\u0026plusmn;\u0026thinsp;0.624\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-90.342\u0026thinsp;\u0026plusmn;\u0026thinsp;8.418\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePqsR-NNQ\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-147.5\u0026thinsp;\u0026plusmn;\u0026thinsp;11.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-12.7\u0026thinsp;\u0026plusmn;\u0026thinsp;6.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e76.7\u0026thinsp;\u0026plusmn;\u0026thinsp;9.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-18.5.3\u0026thinsp;\u0026plusmn;\u0026thinsp;1.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-102.0\u0026thinsp;\u0026plusmn;\u0026thinsp;11.5\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\u003ePolyketides' antibacterial activity has also been emphasized in earlier in vitro research. According to [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e] Ket B and Ket C have been tested for their ability to inhibit the growth of \u003cem\u003ePseudomonas aeruginosa\u003c/em\u003e. According to the study, the compounds are highly effective at inhibiting the growth of biofilms and \u003cem\u003eP. aeruginosa\u003c/em\u003e strains. Therefore, this work offers a mechanistic explanation for the pharmacological actions observed in \u003cem\u003ein vitro\u003c/em\u003e experiments.\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec22\" class=\"Section2\"\u003e \u003ch2\u003eLigand Orientations, Interactions and Atom Fluctuations\u003c/h2\u003e \u003cdiv id=\"Sec23\" class=\"Section3\"\u003e \u003ch2\u003eLasR-ligand systems\u003c/h2\u003e \u003cp\u003eThroughout the MD simulations, AHL, Ket B and Ket C established various orientations to enhance stability at the active site. For AHL, hydrophobic interactions were established with amino acid residues; Val76, Leu36, Ile52, Ala127, Ala50, Leu40, and Tyr47. Hydrogen bond interactions were established with amino acids such as; Trp60, Arg61 and Ser129. Ket B established only hydrophobic interactions with amino acids such as; Trp60, Tyr64, Arg61, Leu36, Ile52, Ala50, Leu125, and Cys79. Hydrophobic interactions observed between Ket C and LasR were with amino acids such as; Phe101, Trp60, Ala105, Trp88, Tyr64, Leu36, Ile52 and Arg61. Ket C establish some hydrogen bonding with Asp73, Tyr56 and Leu110. In general, all compounds (AHL, Ket B and Ket C), establish common interactions with residues such as; Trp60, Arg61, Leu36, Ile52 and Ala50. From the study by [\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e] highlighted that, residues play crucial role in maintaining autoinducer at the active site. Some of the roles played by these residues include; (i) Tyr56-OH with the amide 1-oxo (2.65 \u0026Aring;), (ii) likewise Ser129-OG with the 1-oxo (2.70 \u0026Aring;), (iii) Trp60-NE with the lactone carbonyl (3.01 \u0026Aring;), (iv) Asp73-OD2 with the amide NH (2.76 \u0026Aring;), (v) Thr75-OG1 likewise (3.39 \u0026Aring;), and (vi) an indirect H-bond between the 3-oxo group and a water molecule (2.85 \u0026Aring;) and then Arg61-NE1 (2.90 \u0026Aring;) and NH1 (2.84 \u0026Aring;). Also, 12-carbon AHL is housed in a large hydrophobic pocket formed by residues; Leu36, Gly38, Leu39, Leu40, Tyr47, Glu48, Ala50, Ile52, Tyr56, Trp60, Arg61, Tyr64, Asp65, Gly68, Tyr69, Ala70, Asp73, Pro74, Thr75, Val76, Cys79, Thr80, Trp88, Tyr93, Phe101, Phe102, Ala105, Leu110, Thr115, Leu125, Gly126, Ala127, and Ser129. These interactions were similarly observed in this study. From this, Ket B and Ket C interacting with similar residues help in maintaining these compounds at the active throughout the simulations as shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig10\" class=\"InternalRef\"\u003e10\u003c/span\u003e. Other researchers demonstrated that potential inhibitors such as furanones[\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e] interacts with these residues to cause inhibition and this is same in the case of other marine metabolites that have shown inhibitory activity against the LasR protein through a computational study[\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e]. [\u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e]also, highlighted that allosteric binding at the interface between the ligand-binding domain and DNA-binding domain, caused displacement of most hydrophobic interactions between AHL and active site residues. Also, fungi secondary metabolites have demonstrated that biofilm inhibition activity by establishing strong interactions at the active site of LasR[\u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e], causing significant structural changes which is also observed in this study.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eUnderstanding the fluctuations of moieties in Ket B and Ket C will helped to explain how these compounds are interacting with active site residues during the MD run. Hence atomic-wise fluctuation analysis was done for Ket B and Ket C respectively. From this it was clearly observed that, methyl side-groups of both Ket B and Ket C played an important role in establishing hydrophobic interactions with active site residues during the simulations. [\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e] research showed that, the long 12C chain of AHL is stabilized by interacting amino acids at the hydrophobic pocket. Similarly, the methyl group of both Ket B (C\u003csub\u003e12\u003c/sub\u003e) and Ket C (C\u003csub\u003e11\u003c/sub\u003e) interacted with residues such as Ala50, Ile52, Tyr56, Trp60, Arg61, Val76, Cys79, Thr80, Trp88, Ala105, Leu110, Ala127, and Ser129 as shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig11\" class=\"InternalRef\"\u003e11\u003c/span\u003e. Also, major fluctuation was observed for in C\u003csub\u003e15\u003c/sub\u003e Ket C, this is accompanied by opposite fluctuation of C\u003csub\u003e12\u0026thinsp;\u0026minus;\u0026thinsp;15\u003c/sub\u003e. This was different in Ket B because of the alkene functionality between C\u003csub\u003e10\u0026thinsp;\u0026minus;\u0026thinsp;11\u003c/sub\u003e as seen Fig.\u0026nbsp;\u003cspan refid=\"Fig11\" class=\"InternalRef\"\u003e11\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec24\" class=\"Section2\"\u003e \u003ch2\u003ePqsR-ligand systems\u003c/h2\u003e \u003cp\u003eFor PqsR-ligand systems, notable interactions observed between NNQ and the receptor were only hydrophobic interactions. Hydrophobic (Pi-sigma, Pi-alkyl, and alkyl) interactions between NHQ with Ala168, Ile149, Leu207, Ile263, Ile236, Ala102, Leu189, Val170, Ile186, and Tyr258 were observed. Ket B established hydrogen bond interaction with Thr265 and hydrophobic interactions with residues such as; Gln194, Leu208, Ile149, Phe221, Ala109, Pro126, Pro238, Ala168, Tyr258, and Leu207. For Ket C, hydrogen bond interactions were established with Ser196, Leu197, Tyr258, and Thr265. Hydrophobic interactions were established with Leu207, Leu208, Ile236, Gln194, and Ala168 (Fig.\u0026nbsp;\u003cspan refid=\"Fig12\" class=\"InternalRef\"\u003e12\u003c/span\u003e). Over the years, a lot of studies has demonstrated the importance of inhibiting the PqsR receptor to curb biofilm formation. Furanones from marine sources have been demonstrated as potential inhibitors of PqsR[\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e], and similarly, other marine secondary metabolites have also been reported to inhibit \u003cem\u003eP. aeruginosa\u003c/em\u003e PqsR receptor[\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e]. [\u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e]reported a competitive PqsR inhibitor (M64), which established interactions with Tyr258 and Gln194. These interactions are crucial for inhibition as reported by [\u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e]. Another study by [\u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e51\u003c/span\u003e] demonstrated the importance of interactions with some residues such as; Gln194, Ile236 Leu208, and Tyr258. From these findings, it is evident that an inhibitor should establish strong interactions with these residues evaluated by other studies and from this study.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eHaving established the fact that some interactions are important to cause inhibition, understanding the fluctuations of moieties used for these interactions are of great importance. Hence this study considered the fluctuations observed in various moieties of Ket B and Ket C. As observed in LasR, the methyl group (C\u003csub\u003e12\u003c/sub\u003e) on Ket B had major fluctuations during the MD run. Also, it was observed that, the methyl group (C\u003csub\u003e17\u003c/sub\u003e) from the ester had major fluctuations in Ket B when bound at the active site of the PqsR protein. Slight fluctuations were observed at CH\u003csub\u003e2\u003c/sub\u003e (C\u003csub\u003e13\u003c/sub\u003e) (Fig.\u0026nbsp;\u003cspan refid=\"Fig13\" class=\"InternalRef\"\u003e13\u003c/span\u003ea). For Ket C, fluctuations were observed for the methyl group (C\u003csub\u003e11\u003c/sub\u003e), oxygen at the carbonyl functionality and methyl group (C\u003csub\u003e15\u003c/sub\u003e) (Fig.\u0026nbsp;\u003cspan refid=\"Fig13\" class=\"InternalRef\"\u003e13\u003c/span\u003eb). These fluctuations helped to established strong hydrophobic and hydrogen bond interactions at the active site throughout the simulations.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cdiv id=\"Sec25\" class=\"Section3\"\u003e \u003ch2\u003eADME-T Prediction\u003c/h2\u003e \u003cp\u003eThe ADME-T characteristics of the two primary compounds identified through molecular docking and molecular dynamics were investigated using the ADMETlab 2.0 and SwissADME web servers. Various criteria such as Lipinski's rule of five, oral bioavailability, solubility class, gastrointestinal absorption, P-glycoprotein substrate status, blood-brain barrier (BBB) penetration, metabolism analysis, excretion, and toxicity predictions were assessed (Table\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e4\u003c/span\u003e). These attributes play a pivotal role in identifying potential drug candidates for disease treatment. Ket B, in this research, demonstrated compliance with Lipinski's rules and displayed high water solubility. Moreover, it exhibited high gastrointestinal absorption and BBB permeability, while also lacking P-gp substrate status. Ket C also showed excellent ADME-T profiling such as, high gastrointestinal absorption and BBB permeability, while also lacking P-gp substrate status. Since drugs with high molecular weight and logP values may have poor solubility or membrane permeability, which could limit their effectiveness in vivo. The molecular weight of both Ket B and Ket C have lower molecular weight and hence better clog P (lipophilicity) values, and this support their T\u003csub\u003e1/2\u003c/sub\u003e; Half-life respectively (Table\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e4\u003c/span\u003e) .\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab5\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eADME-T properties of hit compounds from the study. These properties highlight the importance of ADME-T predictions for future perspective of these compounds for novel therapeutics against \u003cem\u003eP. aeruginosa\u003c/em\u003e receptors.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"15\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c11\" colnum=\"11\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c12\" colnum=\"12\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c13\" colnum=\"13\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c14\" colnum=\"14\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c15\" colnum=\"15\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eCompound\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"6\" nameend=\"c7\" namest=\"c2\"\u003e \u003cp\u003ePhysicochemical\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e \u003cp\u003eMedicinal\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c11\" namest=\"c10\"\u003e \u003cp\u003eAbsorption\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c13\" namest=\"c12\"\u003e \u003cp\u003eExcretion\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c15\" namest=\"c14\"\u003e \u003cp\u003eSolubility\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMW\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003en HA\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003en HD\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003en Rot\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eTPSA\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eclog P\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003eLipinski\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c9\"\u003e \u003cp\u003ePfizer\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c10\"\u003e \u003cp\u003eGI\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c11\"\u003e \u003cp\u003eP-gp sub\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c12\"\u003e \u003cp\u003eCl\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c13\"\u003e \u003cp\u003eT \u003csub\u003e1/2\u003c/sub\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c14\"\u003e \u003cp\u003eESOL log\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c15\"\u003e \u003cp\u003eClass\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eKet B\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e261.14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e66.76\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e3.271\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eAccepted\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eAccepted\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003eHigh\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c12\"\u003e \u003cp\u003e16.306\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c13\"\u003e \u003cp\u003e0.918\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c14\"\u003e \u003cp\u003e-3.35\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c15\"\u003e \u003cp\u003eSoluble\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eKet C\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e238.46\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e66.76\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e2.251\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eAccepted\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eAccepted\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003eHigh\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c12\"\u003e \u003cp\u003e14.881\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c13\"\u003e \u003cp\u003e0.888\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c14\"\u003e \u003cp\u003e-3.35\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c15\"\u003e \u003cp\u003eSoluble\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\u003eMW; Molecular weight, nHA; number of hydrogen bond acceptors, nHD; number of hydrogen bond donor, nRot; number of rotatable bonds, cLog P; lipophilicity, TPSA; Topological Polar Surface Area, GI; gastrointestinal absorption, Cl; clearance, T\u003csub\u003e1/2\u003c/sub\u003e; Half-life, H. soluble; highly soluble\u003c/p\u003e \u003cp\u003eMetabolism analysis revealed that Ket B did not inhibit the majority of cytochrome P450 enzymes. Its predicted clearance rate was notably high at 16.306 mL/min/kg, yielding a bioavailability score of 0.55, indicating favorable drug-like characteristics. Similarly, Ket C adhered to Lipinski's rules, displayed high water solubility, and exhibited robust gastrointestinal absorption while not being a P-gp substrate. Metabolism analysis indicated no inhibition of cytochrome P450 enzymes by Ket C. Its clearance rate was moderate at 14.881 mL/min/kg, resulting in a bioavailability score of 0.55. In summary, both polyketides investigated in this study demonstrated outstanding ADME-T properties and minimal toxicity. Consequently, these compounds offer promise as an alternative for addressing antimicrobial resistance in patients with filarial lymphedema. While the findings regarding the ADME-T properties of Ket B and Ket C are promising for their potential as drug candidates for filarial lymphedema, it is important to acknowledge some limitations of this study: the study primarily relied on computational tools and \u003cem\u003ein silico\u003c/em\u003e predictions to assess ADME-T characteristics. Experimental validation through \u003cem\u003ein vitro\u003c/em\u003e and \u003cem\u003ein vivo\u003c/em\u003e studies is necessary to confirm the predicted properties and safety of these compounds. Correlation between in silico and experimental studies highlights that, Ket B and Ket B antibiofilm activity is evident from the experimental research done by [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]. However, future assays like enzymatic assay can be explored to further probe into the inhibition mechanism observed through the \u003cem\u003ein silico\u003c/em\u003e study conducted.\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e"},{"header":"Conclusion","content":"\u003cp\u003eTo elucidate the molecular basis of the anti-biofilm and anti-quorum sensing activities of fungal polyketides, this study integrates molecular docking and molecular dynamics simulations. Virtual screening of 100 compounds identified two with substantial affinity for LasR and PqsR, marking them as promising candidates for further computational and experimental investigations. Molecular dynamics simulations confirmed the stability of these compounds within the binding pocket, even in the presence of solvent molecules. Notably, their interactions with LasR induced the formation of bulk solvents in the binding pocket, a phenomenon that may contribute to protein aggregation, similar to what was observed with PqsR. This study provides computational evidence supporting the antibiofilm potential of these polyketides by analyzing their stability and interactions. The absence of autoinducer-like activity further reinforces their antibiofilm effects, aligning with experimental findings. Additionally, these compounds exhibit favorable drug-like properties and ADMET profiles, highlighting their therapeutic potential. Ketidocillinones B and C emerge as promising anti-biofilm agents that could aid in combating antibiotic resistance, particularly in patients with filarial lymphedema in Ghana and the world at large. Their broader applicability in addressing antibiotic resistance in neglected tropical diseases underscores their significance as potential therapeutic agents.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAcknowledgments\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe acknowledge the Borquaye Research Group (www.borquayelab.com) for their support for this work.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003ePM, PBN, and AK conceived the study. All experiments were designed by \u0026nbsp;PM, PBN, POP, and AK. Computations were done by PM, and PBN. Data analysis was done by PM, PBN and POP. The initial manuscript draft was prepared by PM, PBN, POP, and AK. All authors read and approved of the final manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNo funding is available for this study.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData Availability\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll data generated or analyzed during this study are included in this published article.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics approval\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent to Publish\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot Applicable\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eClinical trial number\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConflict of interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll the authors report no conflicts of interest.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare no competing interests.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eC. Abbafati \u003cem\u003eet al.\u003c/em\u003e, \u0026ldquo;Global burden of 369 diseases and injuries in 204 countries and territories, 1990\u0026ndash;2019: a systematic analysis for the Global Burden of Disease Study 2019,\u0026rdquo; Lancet, vol. 396, no. 10258, pp. 1204\u0026ndash;1222, 2020, doi: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1016/S0140-6736(20)30925-9\u003c/span\u003e\u003cspan address=\"10.1016/S0140-6736(20)30925-9\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eK. M. Pfarr, A. Y. Debrah, S. Specht, and A. Hoerauf, \u0026ldquo;Filariasis and lymphoedema,\u0026rdquo; Parasite Immunol., vol. 31, no. 11, pp. 664\u0026ndash;672, 2009, doi: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1111/j.1365-3024.2009.01133.x\u003c/span\u003e\u003cspan address=\"10.1111/j.1365-3024.2009.01133.x\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eP. E. Simonsen, E. M. Pedersen, R. T. Rwegoshora, M. N. Malecela, Y. A. Derua, and S. M. Magesa, \u0026ldquo;Lymphatic filariasis control in Tanzania: Effect of repeated mass drug administration with ivermectin and albendazole on infection and transmission,\u0026rdquo; PLoS Negl. Trop. Dis., vol. 4, no. 6, 2010, doi: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1371/journal.pntd.0000696\u003c/span\u003e\u003cspan address=\"10.1371/journal.pntd.0000696\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eH. F. McGarry, G. L. Egerton, and M. J. Taylor, \u0026ldquo;Population dynamics of Wolbachia bacterial endosymbionts in Brugia malayi,\u0026rdquo; Mol. Biochem. Parasitol., vol. 135, no. 1, pp. 57\u0026ndash;67, 2004, doi: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1016/j.molbiopara.2004.01.006\u003c/span\u003e\u003cspan address=\"10.1016/j.molbiopara.2004.01.006\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eF. Corona and J. L. Martinez, \u0026ldquo;Phenotypic resistance to antibiotics,\u0026rdquo; Antibiotics, vol. 2, no. 2, pp. 237\u0026ndash;255, 2013, doi: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.3390/antibiotics2020237\u003c/span\u003e\u003cspan address=\"10.3390/antibiotics2020237\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eS. Wireko \u003cem\u003eet al.\u003c/em\u003e, \u0026ldquo;Prevalence of Methicillin-Resistant Staphylococcus Species Among Filarial Lymphedema Patients in Ahanta West District of Ghana,\u0026rdquo; \u003cem\u003eFront. Trop. Dis.\u003c/em\u003e, vol. 2, no. November, pp. 1\u0026ndash;8, 2021, doi: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.3389/fitd.2021.786378\u003c/span\u003e\u003cspan address=\"10.3389/fitd.2021.786378\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eL. Zhou, Y. Zhang, Y. Ge, X. Zhu, and J. Pan, \u0026ldquo;Regulatory Mechanisms and Promising Applications of Quorum Sensing-Inhibiting Agents in Control of Bacterial Biofilm Formation,\u0026rdquo; \u003cem\u003eFront. Microbiol.\u003c/em\u003e, vol. 11, no. October, pp. 1\u0026ndash;11, 2020, doi: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.3389/fmicb.2020.589640\u003c/span\u003e\u003cspan address=\"10.3389/fmicb.2020.589640\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eJ. Lee and L. Zhang, \u0026ldquo;The hierarchy quorum sensing network in Pseudomonas aeruginosa,\u0026rdquo; Protein Cell, vol. 6, no. 1, pp. 26\u0026ndash;41, 2015, doi: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1007/s13238-014-0100-x\u003c/span\u003e\u003cspan address=\"10.1007/s13238-014-0100-x\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eS. Wang, G. F. Payne, and W. E. Bentley, \u0026ldquo;Quorum Sensing Communication: Molecularly Connecting Cells, Their Neighbors, and even Devices,\u0026rdquo; Annu. Rev. Chem. Biomol. Eng., vol. 11, pp. 447\u0026ndash;468, 2020, doi: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1146/annurev-chembioeng-101519-124728\u003c/span\u003e\u003cspan address=\"10.1146/annurev-chembioeng-101519-124728\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eP. K. Taylor, A. T. Y. Yeung, and R. E. W. Hancock, \u0026ldquo;Antibiotic resistance in Pseudomonas aeruginosa biofilms: Towards the development of novel anti-biofilm therapies,\u0026rdquo; J. Biotechnol., vol. 191, pp. 121\u0026ndash;130, 2014, doi: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1016/j.jbiotec.2014.09.003\u003c/span\u003e\u003cspan address=\"10.1016/j.jbiotec.2014.09.003\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eM. Y. Osei-Atweneboana, K. Awadzi, S. K. Attah, D. A. Boakye, J. O. Gyapong, and R. K. Prichard, \u0026ldquo;Phenotypic evidence of emerging ivermectin resistance in Onchocerca volvulus,\u0026rdquo; PLoS Negl. Trop. Dis., vol. 5, no. 3, pp. 1\u0026ndash;11, 2011, doi: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1371/journal.pntd.0000998\u003c/span\u003e\u003cspan address=\"10.1371/journal.pntd.0000998\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eS. Mand \u003cem\u003eet al.\u003c/em\u003e, \u0026ldquo;Doxycycline improves filarial lymphedema independent of active filarial infection: A randomized controlled trial,\u0026rdquo; Clin. Infect. Dis., vol. 55, no. 5, pp. 621\u0026ndash;630, 2012, doi: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1093/cid/cis486\u003c/span\u003e\u003cspan address=\"10.1093/cid/cis486\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eM. J. Taylor, W. H. Makunde, H. F. McGarry, J. D. Turner, S. Mand, and A. Hoerauf, \u0026ldquo;Macrofilaricidal activity after doxycycline treatment of Wuchereria bancrofti: A double-blind, randomised placebo-controlled trial,\u0026rdquo; Lancet, vol. 365, no. 9477, pp. 2116\u0026ndash;2121, 2005, doi: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1016/S0140-6736(05)66591-9\u003c/span\u003e\u003cspan address=\"10.1016/S0140-6736(05)66591-9\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eM. J. Taylor, A. Hoerauf, and M. Bockarie, \u0026ldquo;Lymphatic filariasis and onchocerciasis,\u0026rdquo; Lancet, vol. 376, no. 9747, pp. 1175\u0026ndash;1185, 2010, doi: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1016/S0140-6736(10)60586-7\u003c/span\u003e\u003cspan address=\"10.1016/S0140-6736(10)60586-7\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eS. J. Leopold, F. van Leth, H. Tarekegn, and C. Schultsz, \u0026ldquo;Antimicrobial drug resistance among clinically relevant bacterial isolates in sub-Saharan Africa: A systematic review,\u0026rdquo; J. Antimicrob. Chemother., vol. 69, no. 9, pp. 2337\u0026ndash;2353, 2014, doi: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1093/jac/dku176\u003c/span\u003e\u003cspan address=\"10.1093/jac/dku176\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eD. W. Bailey, D. C. Anderson, and D. E. Doornbos, \u0026ldquo;Comparison of Ivermectin and Conventional External Parasite Control in Young Beef Cows,\u0026rdquo; Prof. Anim. Sci., vol. 13, no. 3, pp. 129\u0026ndash;132, 1997, doi: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.15232/S1080-7446(15)31865-9\u003c/span\u003e\u003cspan address=\"10.15232/S1080-7446(15)31865-9\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eF. Cobo, \u0026ldquo;Determinants of parasite drug resistance in human lymphatic filariasis,\u0026rdquo; Rev. Esp. Quimioter., vol. 29, no. 6, pp. 288\u0026ndash;295, 2016.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eA. Kwarteng \u003cem\u003eet al.\u003c/em\u003e, \u0026ldquo;Shift in the skin microbiome among individuals presenting with filarial lymphedema compared to non-filarial healthy individuals in Ghana,\u0026rdquo; Sci. African, vol. 16, p. e01237, 2022, doi: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1016/j.sciaf.2022.e01237\u003c/span\u003e\u003cspan address=\"10.1016/j.sciaf.2022.e01237\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eF. A. Fordjour \u003cem\u003eet al.\u003c/em\u003e, \u0026ldquo;Use of medicinal plants as a remedy against lymphatic filariasis: Current status and future prospect,\u0026rdquo; Heal. Sci. Reports, vol. 6, no. 5, pp. 1\u0026ndash;8, 2023, doi: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1002/hsr2.1295\u003c/span\u003e\u003cspan address=\"10.1002/hsr2.1295\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eT. Oja \u003cem\u003eet al.\u003c/em\u003e, \u0026ldquo;Effective antibiofilm polyketides against Staphylococcus aureus from the pyranonaphthoquinone biosynthetic pathways of Streptomyces species,\u0026rdquo; Antimicrob. Agents Chemother., vol. 59, no. 10, pp. 6046\u0026ndash;6052, 2015, doi: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1128/AAC.00991-15\u003c/span\u003e\u003cspan address=\"10.1128/AAC.00991-15\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eM. V. Arasu, V. Duraipandiyan, and S. Ignacimuthu, \u0026ldquo;Antibacterial and antifungal activities of polyketide metabolite from marine Streptomyces sp. AP-123 and its cytotoxic effect,\u0026rdquo; Chemosphere, vol. 90, no. 2, pp. 479\u0026ndash;487, 2013, doi: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1016/j.chemosphere.2012.08.006\u003c/span\u003e\u003cspan address=\"10.1016/j.chemosphere.2012.08.006\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eM. Shah \u003cem\u003eet al.\u003c/em\u003e, \u0026ldquo;Antibacterial polyketides from Antarctica sponge-derived fungus Penicillium sp. HDN151272,\u0026rdquo; Mar. Drugs, vol. 18, no. 2, pp. 1\u0026ndash;9, 2020, doi: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.3390/md18020071\u003c/span\u003e\u003cspan address=\"10.3390/md18020071\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eM. A. Almalki, \u0026ldquo;Isolation and characterization of polyketide drug molecule from Streptomyces species with antimicrobial activity against clinical pathogens,\u0026rdquo; J. Infect. Public Health, vol. 13, no. 1, pp. 125\u0026ndash;130, 2020, doi: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1016/j.jiph.2019.07.002\u003c/span\u003e\u003cspan address=\"10.1016/j.jiph.2019.07.002\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eW. Mozirandi, D. Tagwireyi, and S. Mukanganyama, \u0026ldquo;Evaluation of antimicrobial activity of chondrillasterol isolated from Vernonia adoensis (Asteraceae),\u0026rdquo; BMC Complement. Altern. Med., vol. 19, no. 1, pp. 1\u0026ndash;11, 2019, doi: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1186/s12906-019-2657-7\u003c/span\u003e\u003cspan address=\"10.1186/s12906-019-2657-7\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eJ. O. Mensah \u003cem\u003eet al.\u003c/em\u003e, \u0026ldquo;Computational Studies Provide a Molecular Basis for the Quorum Sensing Inhibitory Action of Compounds from Dioon spinulosum Dyer Ex Eichler,\u0026rdquo; ChemistrySelect, vol. 8, no. 1, 2023, doi: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1002/slct.202203773\u003c/span\u003e\u003cspan address=\"10.1002/slct.202203773\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eJ. M. Martell and H. Hengtai, \u0026ldquo;Molecular decompositions of acetaldehyde and formamide: Theoretical studies using Hartree-Fock, Moller-Plesset and density functional theories,\u0026rdquo; Mol. Phys., vol. 92, no. 3, pp. 497\u0026ndash;502, 1997, doi: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1080/002689797170248\u003c/span\u003e\u003cspan address=\"10.1080/002689797170248\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eA. J. O. O. Trott, \u0026ldquo;Software News and Updates Gabedit \u0026mdash; A Graphical User Interface for Computational Chemistry Softwares,\u0026rdquo; J. Comput. Chem., vol. 32, pp. 174\u0026ndash;182, 2010, doi: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1002/jcc\u003c/span\u003e\u003cspan address=\"10.1002/jcc\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eC. Kutzner, S. P\u0026aacute;ll, M. Fechner, A. Esztermann, B. L. de Groot, and H. Grubm\u0026uuml;ller, \u0026ldquo;More bang for your buck: Improved use of GPU nodes for GROMACS 2018,\u0026rdquo; J. Comput. Chem., vol. 40, no. 27, pp. 2418\u0026ndash;2431, 2019, doi: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1002/jcc.26011\u003c/span\u003e\u003cspan address=\"10.1002/jcc.26011\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eK. Vanommeslaeghe \u003cem\u003eet al.\u003c/em\u003e, \u0026ldquo;CHARMM general force field: A force field for drug-like molecules compatible with the CHARMM all‐atom additive biological force fields,\u0026rdquo; J. Comput. Chem., vol. 31, no. 4, pp. 671\u0026ndash;690, 2010.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eM. T. Che Omar, \u0026ldquo;Data analysis of molecular dynamics simulation trajectories of β-sitosterol, sonidegib and cholesterol in smoothened protein with the CHARMM36 force field,\u0026rdquo; \u003cem\u003eData Br.\u003c/em\u003e, vol. 33, no. 2020, p. 106350, 2020, doi: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1016/j.dib.2020.106350\u003c/span\u003e\u003cspan address=\"10.1016/j.dib.2020.106350\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eL. K. Kyei, E. N. Gasu, G. B. Ampomah, J. O. Mensah, and L. S. Borquaye, \u0026ldquo;An In Silico Study of the Interactions of Alkaloids from Cryptolepis sanguinolenta with Plasmodium falciparum Dihydrofolate Reductase and Dihydroorotate Dehydrogenase,\u0026rdquo; \u003cem\u003eJ. Chem.\u003c/em\u003e, vol. 2022, pp. 1\u0026ndash;26, 2022, doi: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1155/2022/5314179\u003c/span\u003e\u003cspan address=\"10.1155/2022/5314179\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eL. S. Borquaye \u003cem\u003eet al.\u003c/em\u003e, \u0026ldquo;Alkaloids from Cryptolepis sanguinolenta as Potential Inhibitors of SARS-CoV-2 Viral Proteins: An in Silico Study,\u0026rdquo; \u003cem\u003eBiomed Res. Int.\u003c/em\u003e, vol. 2020, 2020, doi: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1155/2020/5324560\u003c/span\u003e\u003cspan address=\"10.1155/2020/5324560\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eR. Kumari, R. Kumar, and A. Lynn, \u0026ldquo;g-mmpbsa -A GROMACS tool for high-throughput MM-PBSA calculations,\u0026rdquo; J. Chem. Inf. Model., vol. 54, no. 7, pp. 1951\u0026ndash;1962, 2014, doi: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1021/ci500020m\u003c/span\u003e\u003cspan address=\"10.1021/ci500020m\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eP. Manu, J. O. Mensah, E. N. Gasu, and L. S. Borquaye, \u0026ldquo;The Amaryllidaceae alkaloid, montanine, is a potential inhibitor of the Trypanosoma cruzi trans-sialidase enzyme,\u0026rdquo; J. Biomol. Struct. Dyn., 2023, doi: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1080/07391102.2023.2272750\u003c/span\u003e\u003cspan address=\"10.1080/07391102.2023.2272750\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eD. Amon \u003cem\u003eet al.\u003c/em\u003e, \u0026ldquo;Antimicrobial resistance modifying effects and molecular docking studies of Affinine, derived from Tabernaemontana crassa,\u0026rdquo; \u003cem\u003eSci. African\u003c/em\u003e, vol. 26, no. July, p. e02382, 2024, doi: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1016/j.sciaf.2024.e02382\u003c/span\u003e\u003cspan address=\"10.1016/j.sciaf.2024.e02382\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eP. Manu, P. B. Nketia, P. Osei-poku, and A. Kwarteng, \u0026ldquo;Computational Mutagenesis and Inhibition of Staphylococcus aureus AgrA LytTR Domain Using Phenazine Scaffolds: Insight From a Biophysical Study,\u0026rdquo; vol. 2024, 2024, doi: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1155/2024/8843954\u003c/span\u003e\u003cspan address=\"10.1155/2024/8843954\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eP. B. Nketia, E. N. Gasu, J. O. Mensah, and L. S. Borquaye, \u0026ldquo;In silico identification of α-bisabolol and letestuianin C as potential inhibitors of Trypanosoma brucei trypanothione reductase,\u0026rdquo; J. Biomol. Struct. Dyn., vol. 0, no. 0, pp. 1\u0026ndash;13, 2023, doi: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1080/07391102.2023.2247084\u003c/span\u003e\u003cspan address=\"10.1080/07391102.2023.2247084\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eA. R. McCready, J. E. Paczkowski, B. R. Henke, and B. L. Bassler, \u0026ldquo;Structural determinants driving homoserine lactone ligand selection in the Pseudomonas aeruginosa LasR quorum-sensing receptor,\u0026rdquo; \u003cem\u003eProc. Natl. Acad. Sci. U. S. A.\u003c/em\u003e, vol. 116, no. 1, pp. 245\u0026ndash;254, 2019, doi: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1073/pnas.1817239116\u003c/span\u003e\u003cspan address=\"10.1073/pnas.1817239116\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eS. Fernandes, A. Borges, I. B. Gomes, S. F. Sousa, and M. Sim\u0026otilde;es, \u0026ldquo;Curcumin and 10-undecenoic acid as natural quorum sensing inhibitors of LuxS/AI-2 of Bacillus subtilis and LasI/LasR of Pseudomonas aeruginosa,\u0026rdquo; \u003cem\u003eFood Res. Int.\u003c/em\u003e, vol. 165, no. October 2022, 2023, doi: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1016/j.foodres.2023.112519\u003c/span\u003e\u003cspan address=\"10.1016/j.foodres.2023.112519\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eG. D. Geske, J. C. O. Neill, and H. E. Blackwell, \u0026ldquo;Antagonize or Superagonize Quorum Sensing in Vibrio fischeri,\u0026rdquo; vol. 2, no. 5, pp. 315\u0026ndash;319, 2009, doi: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1021/cb700036x.N\u003c/span\u003e\u003cspan address=\"10.1021/cb700036x.N\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eA. Ilangovan \u003cem\u003eet al.\u003c/em\u003e, \u0026ldquo;Structural Basis for Native Agonist and Synthetic Inhibitor Recognition by the Pseudomonas aeruginosa Quorum Sensing Regulator PqsR (MvfR),\u0026rdquo; PLoS Pathog., vol. 9, no. 7, 2013, doi: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1371/journal.ppat.1003508\u003c/span\u003e\u003cspan address=\"10.1371/journal.ppat.1003508\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eA. Dalal \u003cem\u003eet al.\u003c/em\u003e, \u0026ldquo;Computational investigations on the potential role of hygrophorones as quorum sensing inhibitors against LasR protein of Pseudomonas aeruginosa,\u0026rdquo; J. Biomol. Struct. Dyn., vol. 41, no. 6, pp. 2249\u0026ndash;2259, 2023, doi: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1080/07391102.2022.2029570\u003c/span\u003e\u003cspan address=\"10.1080/07391102.2022.2029570\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eM. G\u0026ouml;tte, \u0026ldquo;Free energy calculations of protein-ligand complexes with computational molecular dynamics,\u0026rdquo; p. 116, 2008, [Online]. Available: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://webdoc.sub.gwdg.de/diss/2008/goette/\u003c/span\u003e\u003cspan address=\"http://webdoc.sub.gwdg.de/diss/2008/goette/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eY. Zou and S. K. Nair, \u0026ldquo;Molecular Basis for the Recognition of Structurally Distinct Autoinducer Mimics by the Pseudomonas aeruginosa LasR Quorum-Sensing Signaling Receptor,\u0026rdquo; Chem. Biol., vol. 16, no. 9, pp. 961\u0026ndash;970, 2009, doi: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1016/j.chembiol.2009.09.001\u003c/span\u003e\u003cspan address=\"10.1016/j.chembiol.2009.09.001\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eM. J. Bottomley, E. Muraglia, R. Bazzo, and A. Carf\u0026igrave;, \u0026ldquo;Molecular insights into quorum sensing in the human pathogen Pseudomonas aeruginosa from the structure of the virulence regulator LasR bound to its autoinducer,\u0026rdquo; J. Biol. Chem., vol. 282, no. 18, pp. 13592\u0026ndash;13600, 2007, doi: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1074/jbc.M700556200\u003c/span\u003e\u003cspan address=\"10.1074/jbc.M700556200\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eA. Boakye, M. P. Seidu, A. Adomako, M. K. Laryea, and L. S. Borquaye, \u0026ldquo;Marine-Derived Furanones Targeting Quorum-Sensing Receptors in Pseudomonas aeruginosa: Molecular Insights and Potential Mechanisms of Inhibition,\u0026rdquo; Bioinform. Biol. Insights, vol. 18, 2024, doi: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1177/11779322241275843\u003c/span\u003e\u003cspan address=\"10.1177/11779322241275843\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eM. P. Seidu, A. Adomako, A. Boakye, M. K. Laryea, and L. S. Borquaye, \u0026ldquo;Targeting Quorum Sensing in Pseudomonas aeruginosa Using Marine-Derived Metabolites \u0026mdash; An In Silico Approach,\u0026rdquo; vol. 2024, 2024, doi: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1155/joch/2172452\u003c/span\u003e\u003cspan address=\"10.1155/joch/2172452\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eP. B. Nketia, P. Manu, P. Osei-poku, and A. Kwarteng, \u0026ldquo;Phenazine Scaffolds as a Potential Allosteric Inhibitor of LasR Protein in Pseudomonas aeruginosa,\u0026rdquo; 2025, doi: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1177/11779322251319594\u003c/span\u003e\u003cspan address=\"10.1177/11779322251319594\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eP. Manu, A. Abakah, P. Kwesi, A. Priscilla, O. Poku, and A. Kwarteng, \u0026ldquo;Disrupting quorum sensing by exploring conformational dynamics and active site flexibility of LasR protein in Pseudomonas aeruginosa,\u0026rdquo; Discov. Chem., 2025, doi: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1007/s44371-025-00121-2\u003c/span\u003e\u003cspan address=\"10.1007/s44371-025-00121-2\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eT. Kitao \u003cem\u003eet al.\u003c/em\u003e, \u0026ldquo;Molecular insights into function and competitive inhibition of pseudomonas aeruginosa multiple virulence factor regulator,\u0026rdquo; MBio, vol. 9, no. 1, 2018, doi: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1128/mBio.02158-17\u003c/span\u003e\u003cspan address=\"10.1128/mBio.02158-17\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eH. Xiao \u003cem\u003eet al.\u003c/em\u003e, \u0026ldquo;Multidimensional Criteria for Virtual Screening of PqsR Inhibitors Based on Pharmacophore, Docking, and Molecular Dynamics,\u0026rdquo; Int. J. Mol. Sci., vol. 25, no. 3, 2024, doi: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.3390/ijms25031869\u003c/span\u003e\u003cspan address=\"10.3390/ijms25031869\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"discover-chemistry","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"","sideBox":"Learn more about [Discover Chemistry](https://link.springer.com/journal/44371)","snPcode":"44371","submissionUrl":"https://submission.nature.com/new-submission/44371/3","title":"Discover Chemistry","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Discover Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Lymphatic Filariasis (LF), Molecular Docking, Molecular Dynamics Simulations, Biofilm Formation, Polyketides","lastPublishedDoi":"10.21203/rs.3.rs-5690135/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-5690135/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eLymphatic filariasis (LF) remains a significant public health challenge, particularly in endemic regions where secondary bacterial infections exacerbate the morbidity associated with chronic lymphedema. Among these infections, \u003cem\u003ePseudomonas aeruginosa\u003c/em\u003e stands out due to its biofilm-forming ability and resistance to conventional antibiotics. This study underscores the importance of targeting \u003cem\u003eP. aeruginosa\u003c/em\u003e in LF patients, as biofilm-associated infections are prevalent in chronic wounds, complicating treatment and increasing healthcare burdens. Leveraging molecular docking and molecular dynamics simulations, we screened 100 fungal polyketides against LasR and PqsR, quorum-sensing proteins critical to \u003cem\u003eP. aeruginosa\u003c/em\u003e biofilm formation. Ketidocillinone B (Ket B) and Ketidocillinone C (Ket C) emerged as promising candidates with notable binding affinities of -9.3 kcal/mol and \u0026minus;\u0026thinsp;9.5 kcal/mol to LasR, and \u0026minus;\u0026thinsp;7.9 kcal/mol and \u0026minus;\u0026thinsp;8.8 kcal/mol to PqsR, respectively. Molecular dynamics simulations revealed sustained stability of both compounds within the active sites, with binding energies of -82.559 kJ/mol (Ket B) and \u0026minus;\u0026thinsp;68.680 kJ/mol (Ket C) for LasR, and \u0026minus;\u0026thinsp;86.855 kJ/mol (Ket B) and \u0026minus;\u0026thinsp;90.342 kJ/mol (Ket C) for PqsR. Pharmacokinetic evaluations indicated high gastrointestinal absorption, solubility, and favorable metabolic profiles, with Ket B exhibiting a clearance rate of 16.306 mL/min/kg and Ket C 14.881 mL/min/kg. These findings highlight the potential of Ket B and Ket C as therapeutic agents against \u003cem\u003eP. aeruginosa\u003c/em\u003e infections in LF patients, through computational investigation. Future experimental validation could help by offering a novel approach to mitigate complications associated with this neglected tropical disease using KetB and Ket C as starting scaffold.\u003c/p\u003e","manuscriptTitle":"Computational Insight into Biofilm Inhibitory Activity of Ketidocillinone B and C against Pseudomonas aeruginosa: A Computational Study","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-04-04 11:10:39","doi":"10.21203/rs.3.rs-5690135/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"editorAssigned","content":"","date":"2025-04-09T09:54:05+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-04-03T13:59:15+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"198064412541546211457593027347907862242","date":"2025-04-03T13:53:27+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-04-03T09:11:24+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-04-02T07:15:08+00:00","index":"","fulltext":""},{"type":"submitted","content":"Discover Chemistry","date":"2025-03-30T11:20:09+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"discover-chemistry","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"","sideBox":"Learn more about [Discover Chemistry](https://link.springer.com/journal/44371)","snPcode":"44371","submissionUrl":"https://submission.nature.com/new-submission/44371/3","title":"Discover Chemistry","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Discover Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"f124050e-0425-4b95-8c41-95052b3e41aa","owner":[],"postedDate":"April 4th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2025-04-10T13:08:30+00:00","versionOfRecord":[],"versionCreatedAt":"2025-04-04 11:10:39","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-5690135","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-5690135","identity":"rs-5690135","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}