Structural insights of Quercetin and its derivatives against multi-drug resistant Proteus mirabilis: In silico approach

Structural insights of Quercetin and its derivatives against multi-drug resistant Proteus mirabilis: In silico approach | 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 Structural insights of Quercetin and its derivatives against multi-drug resistant Proteus mirabilis: In silico approach Charu Jaiswal, Medha Chakraborty, Priti Choudhary, Kiran Lokhande This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-5753353/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Aims This study aims to explore natural compounds as potential inhibitors against multidrug-resistant Proteus mirabilis hemolysin, a key virulence factor contributing to catheter-associated urinary tract infections (CAUTIs). The emergence of multidrug-resistant bacterial strains poses a significant threat to global public health, with Proteus mirabilis being a notable contributor to hospital-acquired infections such as CAUTIs. Hemolysin, a toxin produced by P. mirabilis , plays a crucial role in its pathogenesis, making it an attractive target for antimicrobial therapy. The objective of this study is to investigate the binding affinity and stability of natural compounds, particularly derivatives of quercetin, with P. mirabilis hemolysin through molecular docking and dynamics simulations. Crystal structure retrieval of hemolysin from P. mirabilis was conducted, and the protein was prepared for molecular docking studies. Molecular docking analysis was performed to evaluate the binding affinity of natural compounds with hemolysin. Molecular dynamics simulations were then employed to assess the stability and dynamics of protein-ligand complexes. Molecular docking analysis revealed several lead compounds, including derivatives of quercetin, exhibiting strong binding affinity with P. mirabilis hemolysin. Molecular dynamics simulations demonstrated stable interactions between the lead compounds and hemolysin, with certain compounds showing reduced structural deviations and increased stability within the binding cavity. Natural compounds, particularly derivatives of quercetin, show promising antimicrobial activity against multidrug-resistant Proteus mirabilis hemolysin. These findings highlight the potential of natural compounds as effective inhibitors for combating multidrug-resistant bacterial infections, offering new avenues for therapeutic development in the fight against antibiotic resistance. Further research and clinical trials are warranted to validate the efficacy and safety of these compounds for clinical use. Antimicrobial resistance Proteus mirabili drug discovery molecular docking dynamic simulation h bond network Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Figure 8 Figure 9 1. Introduction One of the most significant threats to global public health revolves around the rapid surge in multi-drug resistance (MDR) among bacterial strains. This phenomenon is primarily fueled by the pervasive issue of antibiotics being excessively employed. These misguided practices have emerged as the primary reason behind the emergence of drug-resistant bacteria. Concurrently, other factors such as like long duration of stay in the hospital, intense surgical procedures, presence of immunosuppressive patients, and the incapability to control the spread of infections from one patient to other have further contributed to this concerning trend ( Boev, 2017; Prestinaci et al. , 2015 ). In a study conducted recently, it was found that there 30% of unnecessary outpatient antibiotics were prescribed along with acute respiratory infections at 50% of unnecessary use of antibiotics (Eggermont et al. , 2018). As per the National Healthcare Safety Network, Enterobacter, Klebsiella pneumoniae , Proteus mirabilis , Enterococcus, E Coli, Pseudomonas aeruginosa, Staphylococcus aureus , coagulase-negative Staphylococcus are the causative organisms for nosocomial infections. The World Health Organization (WHO) has published a list of MDR bacteria that urgently require the development of novel antibiotics. P. mirabilis falls under the urgent category of amoxillin resistant (CR) Enterobacteriaceae ( Girlich et al. , 2020) . The gram-negative bacteria including P. mirabilis use LuxI/LuxR type quorum sensing (QS) systems (Ruby, 1996; Hastings & Greenberg, 1999) . Here the reaction between S-adenosylmethionine (SAM) and acyl carrier protein (ACP) is catalyzed by autoinducer (AI) synthase which is homologous to LuxI. This reaction produces acyl-homoserine lactone (AHL) AI (Engebrecht & Silverman, 1984; More et al ., 1996; Schaefer et al. , 1996; Ng & Bassler, 2009) . High concentrations result in the binding of AHL AIs to cognate cytoplasmic LuxR- like transcription factors (TFs). On the other hand, LuxR-type proteins are degraded if not bound to AI, this prevents QS short-circuiting in bacteria. Often the binding of AI stabilizes the LuxR-type proteins, ultimately resulting in the activation of target genes transcription (Engebrecht et al. , 1983; Engebrecht & Silverman, 1984; Stevens et al. , 1994; Zhu & Winans 1999, 2001). Different bacteria produce AHLs possessing different length side chains and side-chain arrangements (Fuqua et al., 2001; Ng & Bassler., 2009) allowing bacteria to interact with a diverse population (Watson et al., 2002; Gould et al., 2004; Rutherford & Bassler, 2012). The mechanism of QS i.e. , the cell-to-cell communication is regulated by the membrane components and the cytoplasmic factors (Rutherford & Bassler., 2012) . Thus, the expression of the virulence factor responsible for the pathogenesis of P.mirabilis is controlled by QS (Stankowska et al. , 2012). Rauprich et al. , 1996 suggested a tremendously ordered swarm cycle that coordinates the mechanism in a multicellular organism and the fact that P. mirabilis is involved in biofilm formation which shows the mechanism of QS making CAUTI complex to treat. Catheter-associated urinary tract infection (CAUTI) is a disease that is typically caused by the bacterial species of Escherichia coli (E. coli), Candida, Enterococcus, Pseudomonas, Proteus, and Klebsiella (Majumder et al. , 2018). However, the bacteria of Proteus species are one of the major causes of the disease i.e., around 10–44% of cases of CAUTI are caused by P. mirabilis (Jacobsen & Shirtliff., 2011; Schaffer & Pearson., 2015) due to the causes of catheter blockage and development of urinary stone. According to a report published by Centres for Disease Control and Prevention (CDC), CAUTI has been considered the most frequently occurring hospital-acquired infection, which includes around 75% cases from long-term catheterization, and 15–25% during hospital bid. P. mirabilis has the innate ability to develop crystalline biofilms which are formed majorly by virulence factors such as urease enzyme and capsule polysaccharides (CPSs) (Jacobsen & Shirtliff, 2011) , ultimately giving rise to blocked and encrusted catheters causing difficulties in such infections (Jones et al. , 2007) . As a result, they could lead to reflux and retention of urine and, in critical conditions, in addition to trauma, endotoxic shock, and septicemia to the urethra and bladder mucosa as a consequence of removing the catheter (Chen et al. , 2012; Vaidyanathan et al. , 2010; Wasfi et al. , 2020) . As per the 2019 report of the CDC, the Standardized Infection Ratio for CAUTI was 0.74 across general acute care hospitals (Majumder et al. , 2018) . The attachment of fimbriae such as mannose-resistant hemagglutinins on the fluid-derived protein coat of the catheter surface is widely responsible for the initial biofilm formation stages (Jacobsen et al. , 2008; Donlan, 2002) . Mannose-resistant/Proteus-like (MR/P) mutants fimbriae have also been found to play a major role in biofilm formation (Scavone et al. , 2016) to the resistance to antibiotics in P. mirabilis is conferred by the means of genes present in plasmids, transposons, and integrons and further expand by the means of horizontal gene transfer thus resulting in swift spreading and treatment failure (Hall et al. , 2003; Mirzaei et al. , 2021) . The Beta-lactam resistance pattern in P. mirabilis is of grave concern. In a recent study, it has been reported that clinical isolates of P. mirabilis carried (intI1, intI2, intI3, blaTEM, blaCTX-M, blaSHV, mcr1, and mcr2) antibiotic resistance genes among which intI1 and blaTEM genes were found most abundantly in the ESBL and integrase gene family (Mirzaei et al., 2021). The frequent and inappropriate use of antibiotics has also contributed to the development of multidrug resistance (MDR) in P. mirabilis , so the generation of novel therapeutics against MDR bacteria is the need of the hour. Developing novel health interventions to block the action of the gene, protein, or enzyme responsible for the MDR, can aid with the treatment of the diseases. Hemolysin is such a protein that contributes to MDR in bacteria, so blocking their function through the use of inhibitors derived from natural compounds namely, Quercetin can help combat the complications related to MDR. We have used Quercetin in our experiment as it is a naturally occurring compound mainly present in plants and fungi. and hence it can serve as a clinically potent medicine. It has a phenolic hydroxyl group as well as contained a double bond due to which it shows a high antioxidant property. Quercetin has been clinically identified to possess the antibacterial activity and diminish the bacterial biofilm formation by inhibiting the expression of the genes involved in its formation ( Yang et al. , 2020 ). This study is also intended to look for natural alternatives since they have fewer side effects than chemical drugs. Moreover, they form complexes due to the presence of various compounds, such as polysaccharides, mucilage, and tannins that contribute to the modification of the active component’s effect making them more favorable (Kopaei., 2012, 2013) . We aimed to develop a lead compound against multidrug-resistant P. mirabilis and found pertinent alternative compounds to that of small drugs by docking the available drug molecules (acting as inhibitors), against our target proteins. This study also aids to find a potential drug lead for the MDR bacterial disease, by using the binding affinity scores of the ligand to the protein. 2. Material and methods 2.1 Crystal structure retrieval of hemolysin A from P. mirabilis and its preparation: The structural analysis of the hemolysin protein was conducted utilizing the Uniprot database, accessible through the link: https://www.uniprot.org/uniprot/P16466#structure . Within this repository, a total of thirteen distinct structures of the hemolysin protein from P. mirabilis were subjected to thorough examination. These analyses were undertaken employing methodologies such as X-ray crystallography or NMR spectroscopy. The primary objectives of this investigation were to assess the protein's length and resolution quality. From the collection of thirteen examined structures, one particular structure, denoted as 4W8Q, emerged as the preferred choice. This selection was predicated on specific criteria, including its superior resolution quality, characterized by a low-resolution value. In comparison to the remaining structures, 4W8Q exhibited a greater length, rendering it an ideal candidate for further in-depth investigation. This truncated hemolysinA structure from P. mirabilis , bearing the designation 4W8Q, boasted a resolution of 1.43 Å within the positional range of 30–265. The origin of this structure was traced back to the literature, with a corresponding DOI of 10.2210/pdb4w8q/pdb . It is noteworthy that the hemolysin protein exclusively comprises a single chain, designated as ChainA. Subsequently, the isolated hemolysin structure, defined by the single ChainA protein, underwent meticulous preparation using the Schrodinger Protein Preparation Wizard. This software tool occupies a pivotal role in facilitating modeling applications. The rationale underlying protein preparation stems from the inherent complexity of PDB structures, often containing co-crystallized components, heavy atoms, and occasionally multimeric entities. These factors necessitate streamlining for analytical efficacy. The preparatory steps encompass the integration of missing elements, simplification of intricate components, and removal of redundancies. This process compensates for disruptions that may have transpired during X-ray crystallization, such as hydrogen atom crystallization, sulfide bond disruption, and altered amino acid properties. Given that protein preparation serves as a prerequisite for subsequent docking studies, it encompasses tasks such as hydrogen addition, bond order assignment, zero bond order establishment for metals, incorporation of absent loops or chains, formation of disulfide bonds, and selenomethionine-to-methionine conversion. A final phase entails a comprehensive review and adjustment for structural integrity for further use. 2.2 Structure retrieval of the quercetin and its derivatives: The molecular structure of Quercetin, exhibiting a remarkable 94% similarity to an inhibitor of our designated target protein, hemolysin, has been sourced from the PubChem database ( https://pubchem.ncbi.nlm.nih.gov/ ), which provides access to structures presented in the SDF format, characterized by a three-dimensional configuration. The process of retrieval involved identifying molecules that bear resemblance to Quercetin, resulting in the procurement of a total of 605 analogous compounds. This selection was governed by the application of the Lipinski Rule of Five, an established criterion initially formulated by Lipinski in 2004 and subsequently highlighted by Jayaram et al. in 2012 . The application of this rule serves the purpose of differentiating molecules with potential as drug candidates from those that are less amenable to drug development. Furthermore, it contributes to the prognostication of success rates or potential setbacks in the realm of drug discovery. The Quercetin compound, distinguished by the DrugBank accession number DB04216, underwent a process of verification within the DrugBank database. This verification, conducted via the link: https://go.drugbank.com/drugs/DB04216 , enabled the comprehensive acquisition of information pertaining to the Quercetin compound itself and its associated drug target. This step serves to augment our comprehension of the specific attributes and implications linked to the Quercetin compound, particularly concerning its role as a potential inhibitor of the target protein, hemolysin. 2.3 Molecular docking analysis by FlexX: The molecular docking of 605 retrieved inhibitors that serves as our ligand was docked against the target protein hemolysin whose structure was retrieved using Schrodinger's Maestro 12.8 tool. By using FlexX software, the small molecules were docked into the active site of the protein (Rarey et al. , 1996) . The binding cavity of the receptor was defined with the help of the FlexX receptor preparation wizard. The active sites within the hemolysin structure were delineated by the following residues: ASN-30-A, ALA-83-A, GLU-85-A, ALA-86-A, GLY-87-A, GLN-88-A, SER-89-A, GLN-90-A, GLU-126-A, VAL-127-A, PHE-128-A, GLY-129-A, ILE-130-A, ILE-150-A, and ASN-151-A. The ligand-binding was driven by Enthalpy and Entropy (Hybrid Approach) with a maximum allowed overlap volume of 2.9 Å between protein-ligand clashes, and a clash -factor of 0.6 within the ligands. All 606 compounds (1 compound of Quercetin and 605 similar compounds) were docked against the binding site of hemolysin to find the best binding pose. The ligand is docked on the receptor after the ligand’s conformational expansion, resulting in the generation of various conformations (Lokhande et al. , 2020) . The molecular docking of 116 retrieved inhibitors that serves as our ligand was docked against the target protein hemolysin whose structure was retrieved using Schrodinger's Maestro 12.8 tool. By using the FlexX software, the small molecules were docked into the active site of the protein (Rarey et al. , 1996). The binding cavity of the receptor was defined with the help of the FlexX receptor preparation wizard. The ligand-binding was driven by Enthalpy and Entropy (Hybrid Approach) with a maximum allowed overlap volume of 2.9 Å between protein-ligand clashes, and a clash -factor of 0.6 within the ligands. FlexX considers the following factors for determining the best configuration that including -loss of entropy upon ligand binding, hydrogen bond interaction, solvation energy, Van der Waals, and electrostatic interaction (Bursulaya et al. , 2003) . By determining the binding and docking energy, the optimal binding pose is determined (Lokhande et al. , 2020) . 2.4 Molecular dynamic simulation: The Desmond software developed by D. E. Shaw was employed to conduct extensive 100 nanoseconds (ns) all-atom molecular dynamics (MD) simulations, aiming to assess the binding affinity and the interactions between the top five best-docked ligands with the target protein Haemolysin (4W8Q), alongside a standard reference compound. Each complex system was immersed within a solvated cubic box spanning 10 angstroms (Å), incorporating periodic boundary conditions to mimic a realistic environment. Solvation was achieved using the TIP3P water model, ensuring a realistic representation of the solvent environment. To maintain overall system neutrality, counter ions were added to neutralize the total charges present in the system. The number of water molecules added, along with the concentration of counter ions in the complex, are tabulated in Table 1 for reference. During the 100 ns MD simulations, the OPLS 2005 force field was employed, with initial energy minimization performed using the steepest descent approach. The systems underwent linear heating within the NVT ensemble for the first 400 picoseconds (ps), gradually raising the temperature from 0 to 300 Kelvin (K). Subsequently, equilibration of the systems was achieved utilizing an NPT ensemble at a pressure of 1 bar and a temperature of 300 K. Temperature and pressure regulation throughout the simulations was facilitated by the Noose-Hoover chain thermostat method and the Martyna-Tobias-Klein barostat method, respectively, employing an isotropic coupling style. Integration of the equations of motion was executed using the leap-frog approach with a time step of 2 femtoseconds (fs). Additionally, coulomb short-range interactions were maintained within a cut-off radius of 9.0 Å. Following the production run, trajectory analysis was conducted to evaluate structural dynamics throughout the simulation period. Root Mean Square Deviation (RMSD) and Root Mean Square Fluctuation (RMSF) analyses were performed to elucidate any significant conformational changes within the protein-ligand complexes, indicating stability and equilibration of the simulation. RMSD provided insights into the stability of compound structures, while RMSF highlighted fluctuations in specific regions of the complexes, aiding in the characterization of dynamic behavior during the simulation. Table 1 Number of water molecules and ions required for MD simulation study. Complex Number of water molecules added Number of counterions Concentration of counterions 4W8Q_5280343 5213 3 10.463 4W8Q_9814421 5223 3 10.443 4W8Q_9966216 5212 3 10.465 4W8Q_140034695 5213 3 10.463 4W8Q_688798 5212 3 10.465 4W8Q_676310 5212 3 10.465 3. Result and Discussion 3.1 Molecular docking analysis: Binding energies : Based on the comprehensive investigation conducted, the molecules listed in Table 2 have emerged as promising hit compounds following docking studies and molecular dynamics (MD) simulations. The reference compound, along with the top five hits, exhibited robust docking scores (DDRC: -21.898 kcal/mol; DHFC: -21.567 kcal/mol; THDF: -21.536 kcal/mol; DHTC: -21.417 kcal/mol; TTDF: -21.117 kcal/mol). These scores indicate a strong binding affinity between the compounds and the target receptor, suggesting favorable interactions within the binding pocket. The high docking scores imply that the derivatives are well-suited for binding within the active site of the hemolysin protein, a significant toxin responsible for virulence in P. mirabilis. This finding is particularly promising as hemolysin contributes significantly to virulence and poses a challenge in combating multidrug resistance (MDR) in P. mirabilis infections. The strong interaction observed between the hit compounds and the active site residues of the target receptor underscores their potential as effective therapeutic agents against MDR in P. mirabilis . The robust binding affinity displayed by these compounds suggests their ability to disrupt crucial molecular pathways associated with virulence, thus offering a promising avenue for the development of novel antimicrobial agents. Table 2 Docking score and intermolecular interaction analysis of Quercetin and the selected lead compounds with Hemolysin. Compound Name PubChem CID Binding Affinity (Kcal/mol) Interacting Amino Acids Polarity Type of Inter-molecular Bond Bond Distance (Å) Quercetin 5280343 -15.053 GLU 85 Hydrophilic H -Bond 2.05, 2.06 GLN 88 Hydrophilic H -Bond 1.91 GLU 126 Hydrophilic H -Bond 1.77 GLY 129 Hydrophobic H -Bond 2.13 2-(2,4-Dihydroxyphenyl)-7,8-dihydroxychromen-4-one 9814421 -21.898 GLU 85 Hydrophilic H -Bond 2.27 GLU 126 Hydrophilic H -Bond 1.94 GLY 129 Hydrophobic H -Bond 2.06, 2.08, 2.13 PHE 128 Hydrophobic Pi-Pi Stacking 5.47 2-(3,4-Dihydroxyphenyl)-3-fluoro-7,8-dihydroxychromen-4-one 9966216 -21.567 GLU 85 Hydrophilic H -Bond 1.67, 1.67 GLU 126 Hydrophilic H -Bond 1.57, 1.60 GLY 129 Hydrophobic H -Bond 1.89 PHE 128 Hydrophobic Pi-Pi Stacking 4.80 7,8,3'-Trihydroxyflavone 676310 -21.536 GLU 85 Hydrophilic H -Bond 1.67, 1.67 GLU 126 Hydrophilic H -Bond 1.57, 1.60 GLY 129 Hydrophobic H -Bond 1.89 PHE 128 Hydrophobic Pi-Pi Stacking 4.80 2-(2,3-dihydroxyphenyl)-5,7,8-trihydroxychromen-4-one 140034695 -21.417 GLU 85 Hydrophilic H -Bond 1.58, 1.78, 2.39 GLN 88 Hydrophilic H -Bond 2.07 VAL 127 Hydrophobic H -Bond 1.52, 1.92 7,8,3',4'-Tetrahydroxyflavone 688798 -21.117 GLU 85 Hydrophilic H -Bond 1.65, 1.65 GLY 129 Hydrophobic H -Bond 1.88 GLU 126 Hydrophilic H -Bond 1.55, 1.60 PHE 128 Hydrophobic Pi-Pi Stacking 1.60, 4.78 Interaction study : Quercetin : Quercetin ( PubChem CID : 5280343) was a reference ligand that has antimicrobial activity. It inhibits the pathogenesis of P.mirabilis by acting on the haem receptor of the hemolysin protein. However, we worked to check out the efficacy of other derivatives against hemolysin as well, along with the Quercetin. Henceforth, a comparison of Quercetin with other hit compounds was performed along with studying the efficacy of Quercetin. Quercetin binds in the cavity of protein with an affinity of -15.053kcal/mol. In the 2D structure of Quercetin, 4 functional groups are present that facilitate this binding are GLU-85, GLN 88, GLU 126, and GLY 129 are bound to the cavity of the hemolysin via hydrogen bonds. The GLU 85 and GLN 88 forms 2 hydrogen bonds each. GLU 85 forms the hydrogen bonds of bond lengths 2.06 Å and 2.05 Å, and the GLN 88 forms hydrogen bond of lengths 1.89 Å and 1.91 Å, while, GLU 126 and GLY 129 form only 1 hydrogen bond with the target protein of bond length 1.77 Å and 2.13 Å respectively. As the GLN 88 forms 2 hydrogen bond that is more than GLU 126, therefore, the functional group GLN 88 in the ligand majorly contributes to the binding with hemolysin (Fig. 1 ). 2-(2,4-Dihydroxyphenyl)-7,8-dihydroxychromen-4-one (DDRC) : The compound 2-(2,4-Dihydroxyphenyl)-7,8-dihydroxychromen-4-one ( PubChem CID : 9814421) is a derivative of Quercetin was observed to be a potential lead compound to inhibit the activity of P.mirabilis because it shows higher binding affinity than Quercetin and other derivatives. Figure 3 shows the binding pose of 2-(2,4-Dihydroxyphenyl)-7,8-dihydroxychromen-4-one within the cavity of hemolysin. The docking score of 2-(2,4-Dihydroxyphenyl)-7,8-dihydroxychromen-4-one is -21.898 kcal/mol, which suggests strong binding of this compound within the binding cavity of hemolysin in P.mirabilis . It forms 5 hydrogen bonds and a pi-pi stacking with the protein, of which GLU 85 forms a hydrogen bond of a bond length of 2.27 Å, GLU 126 forms a hydrogen bond of 1.94 Å, and the GLY 129 forms 3 hydrogen bonds of bond length 2.13 Å, 2.06 Å, and 2.08 Å. A pi-pi stacking is often observed with PHE 128 having a bond length of 5.47 Å. Therefore, this structure has a high affinity, due to the presence of both strong intermolecular bonds that ensures efficient binding and non-covalent pi-pi stacking which provides stability to this compound. The GLY 129 is a major residue, significantly involved in the bonding of the lead compound to a receptor. Moreover, the comparison of its affinity to that of Quercetin shows it binds more efficiently. 2-(3,4-Dihydroxyphenyl)-3-fluoro-7,8-dihydroxychromen-4-one (DHFC) : The compound 2-(3,4-Dihydroxyphenyl)-3-fluoro-7,8-dihydroxychromen-4-one (PubChem CID: 9966216) a derivative of Quercetin was observed to be a potential lead compound to inhibit the activity of P.mirabilis because it shows high binding affinity with the hemolysin. Figure 3 shows the binding pose of 2-(2,4-Dihydroxyphenyl)-7,8-dihydroxychromen-4-one within the cavity of hemolysin. It forms 6 hydrogen bonds and a pi-pi stacking with the protein, of which the GLU 85 forms two hydrogen bonds of 1.67 Å and 1.67 Å; GLU 126 forms two hydrogen bonds of 1.57 Å and 1.60 Å, while GLY 129 forms one hydrogen bond of 1.89 Å, along with a pi-pi stacking formed of 4.80 Å by PHE 128. 7,8,3'-Trihydroxyflavone (THDF) : The docking score of Trihydroxyflavone ( PubChem CID : 676310) is -21.536 which suggests a strong bonding of this compound from Quercetin but less than 2-(3,4-Dihydroxyphenyl)-3-fluoro-7,8-dihydroxychromen-4-one, within the binding cavity of hemolysin. It has 5 hydrogen bonds of length 1.67 Å, and 1.67 Å, for GLU 85, 1.57 Å, and 1.60 Å, for GLU 126, and 1.89 Å, for GLY 129 as well as consists of 1 pi-pi stacking with PHE 128 with a bond distance of 4.80 Å. GLU is the major contributing residue in this compound (Fig. 4 ). 2-(2,3-dihydroxyphenyl)-5,7,8-trihydroxychromen-4-one (DHTC) : The docking score of 2-(2,3-dihydroxyphenyl)-5,7,8-trihydroxychromen-4-one ( PubChem CID :140034695) is -21.4177. It forms a total of 6 hydrogen bonds of which GLU 85 forms hydrogen bonds of length 2.39 Å, 1.58 Å, and 1.78 Å; GLU 85 forms a hydrogen bond of 2.07 Å in GLN 88, as well as hydrogen bonds of bond length 1.52 Å, and 1.92 Å is formed by VAL 127 (Fig. 5 ). From here we inferred GLU is a major residue, significantly involved in the bonding of the lead compound to a receptor. Moreover, the comparison of its affinity to that of Quercetin shows it binds more efficiently. 7,8,3',4'-Tetrahydroxyflavo (TTDF) : In the 2D workspace 5 hydrogen bonds and 1 pi -pi stacking were observed in 7,8,3',4'-Tetrahydroxyflavone (PubChem CID: 688798) having a binding energy of -21.117Kcal/mol. The GLU 85 forms 2 hydrogen bonds of length 1.65 Å, GLY 129 forms a hydrogen bond of 1.88 Å, and GLU 126 forms a 2hydrogen bond of length 1.55 Å and 1.60 Å. GLU is the major binding group here. A pi-pi stacking of bond length 4.78 was often observed in PHE 128 (Fig. 6 ). 3.2 MD simulation The molecular dynamic simulation was carried out to explore the protein-ligand dynamic conformational perturbation of the best five docked ligands. A run of 100ns simulations was performed using the OPLS-2005 force field for the hemolysin receptor. For the purpose of visualization of the interaction among the selected ligand derivatives and their reference compound, the Desmond MD system was used. The net charge of all the systems of standard reference complex of Hemolysin protein with the reference ligand (4W8Q: Quercitin) and complex systems of the lead compounds (4W8Q: DDRC, 4W8Q: DHFC, 4W8Q: THDF, 4W8Q: DHTC, 4W8Q: TTDF) was equilibrated by the incorporation of sodium ions (Na+) and was solvated by the means of explicit TIP3P water molecules in the filledcubicboxof1Å spacing detailed in Table 3 . Figure 7 . Is a representation of the strength and stability of the ligand with the protein receptor. 50ns was the time taken by DDRC to reach a state of convergent equilibrium as compared to THDF which required only 25ns, followed by TTDF with a time of 10ns to reach equilibrium and showing most stability throughout the process. DHFC displayed stability from 10 to 55ns and was observed to show minor fluctuations from 60-100ns whereas DHTC was found to be stable during the entire process. A decline in the potential energy was observed which indicates the system is stable. RMSD (Root mean square deviation) was calculated once the conformation analysis was done. The shift in selected atoms can be calculated by observing the average change for the particular frame with respect to the reference frame. RMSD Figure 7 illustrates the Root Mean Square Deviation (RMSD) of the protein throughout the simulation period. Observationally, it is evident that the fluctuations experienced by the atoms reached a maximum peak limit of 2.2–2.5 Å across all complex systems, as depicted in the network processing frame. This observation suggests potential helical winding and unwinding of the protein receptor, indicating dynamic structural changes occurring within the protein environment. In general, compounds demonstrating lower RMSD values are considered satisfactory, indicating stable binding interactions with the protein target. Analyzing the RMSD for the complex systems of derivatives of quercetin, it was observed that 4W8Q: TTDF exhibited the least variation in RMSD, followed by 4W8Q: TTDF, 4W8Q: THDF, and 4W8Q: THDF. Conversely, 4W8Q: DHFC displayed minor fluctuations in RMSD. These findings suggest that certain compounds, particularly 4W8Q: TTDF, exhibit more stable interactions within the binding cavity of hemolysin compared to others. The lower RMSD values indicate reduced structural deviation from the initial conformation, indicating a higher degree of stability in the protein-ligand complex. This stability is crucial for the development of effective inhibitors targeting hemolysin, as it ensures sustained binding interactions essential for therapeutic efficacy against multidrug-resistant pathogens like P. mirabilis. RMSF An in-depth analysis was conducted by comparing the Root Mean Square Fluctuation (RMSF) of C-alpha atoms of all residues using trajectories generated during 100 ns of MD simulations. This analysis aimed to elucidate structural alterations in the hemolysin protein upon binding to the anti-bacterial compounds. The RMSF of each complex system provided insights into residue mobility and ligand flexibility within the receptor network. A rigid protein structure suggests minimal fluctuations and increased flexibility of the ligand within the receptor, reflected by lower RMSF values. Conversely, higher RMSF values indicate a loosely bonded structure, suggesting greater flexibility within the complex ( Pathak et al. , 2018 ). The RMSF trajectories of the receptor-ligand complex stability correlate with rigid and stable conformations. The RMSF graph revealed that upon ligand binding, notable changes were observed primarily in the region spanning residues 247 to 254, with a shift ranging from 1 to 3.5 angstroms (Å). Specifically, the complex 4W8Q: DHFC exhibited the highest fluctuations, indicating increased flexibility within this particular complex (Fig. 8 ). Table 3 RMSF values of hemolysin binding pocket residues after binding of lead compounds. Residue 5280343 9814421 9966216 676310 GLU85 0.713 0.689 0.62 0.644 GLU86 0.672 0.693 0.616 0.643 GLN88 0.814 0.708 0.642 0.659 GLU126 0.364 0.388 0.347 0.37 VAL127 0.363 0.405 0.368 0.374 PHE128 0.396 0.415 0.369 0.382 GLY129 0.524 0.493 0.466 0.478 GLU185 0.581 0.659 0.473 0.571 The analysis suggests that the lead compounds form stable complexes with the hemolysin protein and exhibit thermodynamic stability. The observed structural changes, particularly in specific regions of the protein, highlight the dynamic nature of the receptor-ligand interactions. These findings underscore the potential of the lead compounds as effective inhibitors against multidrug-resistant pathogens, such as P. mirabilis , by targeting key proteins involved in virulence pathways. H-Bond monitoring Reports To assess the stability of the binding between the lead compounds and hemolysin, we conducted an analysis of intermolecular hydrogen bond (H-bond) interactions. Utilizing Maestro software, 1000 frames were extracted from the 100 nsMD simulation trajectory data to evaluate the H-bonding patterns. Figure 9 depicts the presence of intermolecular H-bonding between the lead compounds and the hemolysin receptor. Figure 12(a) illustrates that the quercetin-hemolysin complex initially stabilizes with 1 to 3 H-bonds, maintaining stability with a single H-bond until 50,000 ns, followed by bond breakage. In Fig. 9 (b) , the main lead compound, DDRC, forms 3 to 5 H-bonds initially, with some trajectories forming up to 6 H-bonds during the 50 ns MD simulation. However, bond breakage is observed over time. Figure 9 (c) demonstrates that the lead compound DHFC maintains stability by forming 3 to 5 H-bonds initially, followed by 4 to 5 H-bonds in the middle phase, and eventually 2 to 5 H-bonds towards the end of the dynamics, thereby ensuring complex stability. Figure 9 (d) depicts the hydrogen bonds between the ligand THDF and hemolysin. Initially, 1 to 3 H-bonds stabilize the complex, increasing to 2 to 4 H-bonds, maintaining stability throughout. Figure 9 (e) shows the TTDF-hemolysin complex stabilized by 1 to 7 H-bonds initially, maintaining the same number of bonds throughout the 1000 ns simulation. Figure 9 (f) indicates that the TTDF-hemolysin complex forms 3 to 5 H-bonds initially, maintaining stability throughout the simulation. Overall, the results suggest that the lead compounds form stable complexes with hemolysin, maintaining stability over the 100 ns MD simulation period. This stability is crucial for the development of effective inhibitors against multidrug-resistant pathogens like P. mirabilis. 4. Conclusion The escalating threat of multidrug-resistant (MDR) bacterial strains, particularly Proteus mirabilis, poses a significant challenge to global public health. The overuse and misuse of antibiotics, coupled with other factors such as prolonged hospital stays and invasive medical procedures, have exacerbated the proliferation of drug-resistant bacteria. The emergence of MDR strains like P. mirabilis in catheter-associated urinary tract infections (CAUTIs) underscores the urgent need for novel therapeutic approaches. This study investigates the molecular mechanisms underlying P. mirabilis virulence, particularly focusing on the role of hemolysin, a crucial toxin contributing to its pathogenesis. Molecular docking analysis revealed several lead compounds, including derivatives of quercetin, which demonstrated strong binding affinity with the hemolysin protein. These compounds exhibited promising antimicrobial activity against MDR P. mirabilis, highlighting their potential as therapeutic agents. Furthermore, molecular dynamics simulations provided valuable insights into the stability and dynamics of the protein-ligand complexes. The analysis revealed stable interactions between the lead compounds and hemolysin, suggesting their efficacy as inhibitors against MDR pathogens. Notably, certain compounds exhibited reduced structural deviations and increased stability within the binding cavity of hemolysin, indicating their potential for therapeutic development. Overall, this study underscores the importance of exploring natural alternatives and novel therapeutic strategies to combat MDR bacterial infections effectively. The identification of lead compounds with potent antimicrobial activity against P. mirabilis hemolysin represents a significant step towards developing targeted therapies for CAUTIs and other MDR-related infections. Further research and clinical trials are warranted to validate the efficacy and safety of these compounds for clinical use, ultimately addressing the pressing global health challenge posed by antibiotic resistance. Declarations Acknowledgements: The authors are thankful to Dr. D. Y. Patil Biotechnology and Bioinformatics Institute, Dr. D. Y. Patil Vidyapeeth, Pune for the physical infrastructure and Department of Science and Technology Science and Engineering Research Board (DST-SERB), Govt. of India, New Delhi, (File Number: YSS/2015/002035) for utilizing an Optimized Supercomputer for docking and dynamics calculations. Disclosure statement: There is no conflict of interest declared by all authors. 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Comput Biol Chem 76:32–41. https://doi.org/10.1016/j.compbiolchem.2018.05.015 Additional Declarations No competing interests reported. Cite Share Download PDF Status: Posted Version 1 posted 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-5753353","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":397105323,"identity":"2d61a817-e842-4e1a-81d5-354d694d259d","order_by":0,"name":"Charu Jaiswal","email":"","orcid":"","institution":"Dr. D. Y. Patil Vidyapeeth (Deemed to be University)","correspondingAuthor":false,"prefix":"","firstName":"Charu","middleName":"","lastName":"Jaiswal","suffix":""},{"id":397105324,"identity":"e47dd677-8e3e-42e8-8e80-342e4573bd6e","order_by":1,"name":"Medha Chakraborty","email":"","orcid":"","institution":"Dr. D. Y. Patil Vidyapeeth (Deemed to be University)","correspondingAuthor":false,"prefix":"","firstName":"Medha","middleName":"","lastName":"Chakraborty","suffix":""},{"id":397105325,"identity":"0e549620-fe63-4b1b-bfae-802d40200a21","order_by":2,"name":"Priti Choudhary","email":"","orcid":"","institution":"K. R. Mangalam University","correspondingAuthor":false,"prefix":"","firstName":"Priti","middleName":"","lastName":"Choudhary","suffix":""},{"id":397105326,"identity":"4ec91bc8-793d-4356-81de-02bc530a6bd7","order_by":3,"name":"Kiran Lokhande","email":"data:image/png;base64,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","orcid":"","institution":"Dr. D. Y. Patil Vidyapeeth (Deemed to be University)","correspondingAuthor":true,"prefix":"","firstName":"Kiran","middleName":"","lastName":"Lokhande","suffix":""}],"badges":[],"createdAt":"2025-01-02 17:08:25","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-5753353/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-5753353/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":73693631,"identity":"c63e4acd-24e4-4ec2-9d73-d117014d18cd","added_by":"auto","created_at":"2025-01-13 15:56:14","extension":"jpg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":1012475,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003e(a)\u003c/strong\u003e 2-D representation of hemolysin (protein)- Quercetin (ligand) interaction at a width of 1200 Å and height of 1668 Å, where the hydrogen bonding is represented by purple. \u003cstrong\u003e(b) \u003c/strong\u003e3-D representation of hemolysin(protein)- Quercetin (ligand) interaction, where the hydrogen bond is represented in black. The lead compound is represented by the ball and stick model while the protein is represented in ribbon form. Here, the hydrogen bonds are represented in black.\u003c/p\u003e","description":"","filename":"Figure1.jpg","url":"https://assets-eu.researchsquare.com/files/rs-5753353/v1/054180a8395648ba60c3b5c1.jpg"},{"id":73692634,"identity":"70124a0c-8a95-4a8e-aef5-322492c14f6e","added_by":"auto","created_at":"2025-01-13 15:48:15","extension":"jpg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":1045866,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003e(a) \u003c/strong\u003eThe intermolecular interaction between hemolysin(protein) and 2-(3,4-Dihydroxyphenyl)-3-fluoro-7,8-dihydroxychromen-4-one (ligand), where the purple-colored arrow represents the hydrogen bond while green color arrow represents the pi-pi stacking. \u003cstrong\u003e(b) \u003c/strong\u003eThe intermolecular interaction between hemolysin(protein) and 2-(3,4-Dihydroxyphenyl)-3-fluoro-7,8-dihydroxychromen-4-one(ligand) in 3D. The lead compound is represented by the ball and stick model while the protein is represented in ribbon form. Here, the hydrogen bonds and pi-pi stacking are represented by black and blue colors respectively.\u003c/p\u003e","description":"","filename":"Figure2.jpg","url":"https://assets-eu.researchsquare.com/files/rs-5753353/v1/0f85341df08b35a28f22a94b.jpg"},{"id":73692628,"identity":"35ec5fa3-c28b-4911-bc02-033865e14c63","added_by":"auto","created_at":"2025-01-13 15:48:14","extension":"jpg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":892071,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003e(a) \u003c/strong\u003eThe intermolecular interaction between hemolysin (protein) and 2-(3,4-Dihydroxyphenyl)-3-fluoro-7,8-dihydroxychromen-4-one (ligand), where the purple-colored arrow represents the hydrogen bond while green color arrow represents the pi-pi stacking. \u003cstrong\u003e(b)\u003c/strong\u003eThe intermolecular interaction between hemolysin(protein) and 2-(3,4-Dihydroxyphenyl)-3-fluoro-7,8-dihydroxychromen-4-one(ligand) in 3D. The lead compound is represented by the ball and stick model while the protein is represented in ribbon form. Here, the hydrogen bonds and pi-pi stacking are represented by black and blue colors respectively.\u003c/p\u003e","description":"","filename":"Figure3.jpg","url":"https://assets-eu.researchsquare.com/files/rs-5753353/v1/1e0800f8dc803eea5e6ad152.jpg"},{"id":73692229,"identity":"e5710aa5-2bdf-402d-b5d8-64907aa66bbc","added_by":"auto","created_at":"2025-01-13 15:40:14","extension":"jpg","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":892494,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003e(a) \u003c/strong\u003eThe intermolecular interaction between hemolysin(protein) and 7,8,3'-Trihydroxyflavone (ligand) in 2D, where the purple-colored arrow represents the hydrogen bond while the green color arrow represents the pi-pi stacking \u003cstrong\u003e(b)\u003c/strong\u003eThe intermolecular interaction between hemolysin(protein) and 7,8,3'-Trihydroxyflavone(ligand) in 3D structure. 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Here, the hydrogen bonds are represented by black color.\u003c/p\u003e","description":"","filename":"Figure5.jpg","url":"https://assets-eu.researchsquare.com/files/rs-5753353/v1/3ba325035702e19d1536a900.jpg"},{"id":73692251,"identity":"ad74ead3-6b5e-4f99-8c42-85d5778813d5","added_by":"auto","created_at":"2025-01-13 15:40:15","extension":"jpg","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":971548,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003e(a) \u003c/strong\u003eThe intermolecular interaction between hemolysin (protein) and 7,8,3',4'-Tetrahydroxyflavone (ligand) in 2D, where the purple-colored arrow represents the hydrogen bond while the green color arrow represents the pi-pi stacking.\u003cstrong\u003e (b) \u003c/strong\u003eThe intermolecular interaction between hemolysin(protein) and 7,8,3',4'-Tetrahydroxyflavone in 3D. The lead compound is represented by the ball and stick model while the protein is represented in ribbon form. Here, the hydrogen bonds and pi-pi stacking are represented by black and blue colors respectively.\u003c/p\u003e","description":"","filename":"Figure6.jpg","url":"https://assets-eu.researchsquare.com/files/rs-5753353/v1/f8cf586de6da60c6f54e0bff.jpg"},{"id":73692227,"identity":"f45e588e-865f-4850-82b2-447de31f755e","added_by":"auto","created_at":"2025-01-13 15:40:14","extension":"jpg","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":2864129,"visible":true,"origin":"","legend":"\u003cp\u003eProtein-Ligand root mean square deviation (RMSD) graphs for reference compound and its respective docked hits during 100ns molecular dynamic simulation.\u003c/p\u003e","description":"","filename":"Figure7.jpg","url":"https://assets-eu.researchsquare.com/files/rs-5753353/v1/b90ab47ad8cebf5c9926a3c9.jpg"},{"id":73692244,"identity":"7e5d6aff-cff9-4424-bc1f-b55b026ad272","added_by":"auto","created_at":"2025-01-13 15:40:15","extension":"jpg","order_by":8,"title":"Figure 8","display":"","copyAsset":false,"role":"figure","size":1923095,"visible":true,"origin":"","legend":"\u003cp\u003eProtein-Ligand root mean square fluctuation RMSF graphs of reference compound and its respective docked hits during 100ns molecular dynamic simulation. Peaks are indicative of the areas of protein that undergo the most fluctuation during the simulation.\u003c/p\u003e","description":"","filename":"Figure8.jpg","url":"https://assets-eu.researchsquare.com/files/rs-5753353/v1/5fecfa9d25faf6b5ab6d08f5.jpg"},{"id":73692224,"identity":"05f43083-5fac-43fd-9f41-b4e7440e5b4a","added_by":"auto","created_at":"2025-01-13 15:40:14","extension":"jpg","order_by":9,"title":"Figure 9","display":"","copyAsset":false,"role":"figure","size":1290878,"visible":true,"origin":"","legend":"\u003cp\u003eH-bond monitoring report of \u003cstrong\u003e(a)\u003c/strong\u003e Quercetin (5280343), \u003cstrong\u003e(b)\u003c/strong\u003e DDRC (9814421), \u003cstrong\u003e(c)\u003c/strong\u003e DHFC (9966216), \u003cstrong\u003e(d) \u003c/strong\u003eTHDF (676310), \u003cstrong\u003e(e)\u003c/strong\u003e DHTC (140034695), \u003cstrong\u003e(f)\u003c/strong\u003e TTDF (688798) complexes during 100 ns MD simulation.\u003c/p\u003e","description":"","filename":"Figure9.jpg","url":"https://assets-eu.researchsquare.com/files/rs-5753353/v1/39bdb76da4521f8ff0fdb208.jpg"},{"id":76414075,"identity":"364449dc-5b93-49a1-a1f3-49b2af0df9b2","added_by":"auto","created_at":"2025-02-17 02:16:44","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":13158926,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-5753353/v1/cbc7d268-75d5-4fe2-995c-c16c03958823.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Structural insights of Quercetin and its derivatives against multi-drug resistant Proteus mirabilis: In silico approach","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eOne of the most significant threats to global public health revolves around the rapid surge in multi-drug resistance (MDR) among bacterial strains. This phenomenon is primarily fueled by the pervasive issue of antibiotics being excessively employed. These misguided practices have emerged as the primary reason behind the emergence of drug-resistant bacteria. Concurrently, other factors such as like long duration of stay in the hospital, intense surgical procedures, presence of immunosuppressive patients, and the incapability to control the spread of infections from one patient to other have further contributed to this concerning trend (\u003cb\u003eBoev, 2017; Prestinaci\u003c/b\u003e \u003cb\u003eet al.\u003c/b\u003e, \u003cb\u003e2015\u003c/b\u003e). In a study conducted recently, it was found that there 30% of unnecessary outpatient antibiotics were prescribed along with acute respiratory infections at 50% of unnecessary use of antibiotics \u003cb\u003e(Eggermont\u003c/b\u003e \u003cb\u003eet al.\u003c/b\u003e, \u003cb\u003e2018).\u003c/b\u003e As per the National Healthcare Safety Network, Enterobacter, \u003cem\u003eKlebsiella pneumoniae\u003c/em\u003e, \u003cem\u003eProteus mirabilis\u003c/em\u003e, \u003cem\u003eEnterococcus, E Coli, Pseudomonas aeruginosa, Staphylococcus aureus\u003c/em\u003e, coagulase-negative \u003cem\u003eStaphylococcus\u003c/em\u003e are the causative organisms for nosocomial infections. The World Health Organization (WHO) has published a list of MDR bacteria that urgently require the development of novel antibiotics. \u003cem\u003eP. mirabilis\u003c/em\u003e falls under the urgent category of amoxillin resistant (CR) \u003cem\u003eEnterobacteriaceae\u003c/em\u003e (\u003cb\u003eGirlich\u003c/b\u003e \u003cb\u003eet al.\u003c/b\u003e, \u003cb\u003e2020)\u003c/b\u003e. The gram-negative bacteria including \u003cem\u003eP. mirabilis\u003c/em\u003e use LuxI/LuxR type quorum sensing (QS) systems \u003cb\u003e(Ruby, 1996; Hastings \u0026amp; Greenberg, 1999)\u003c/b\u003e. Here the reaction between S-adenosylmethionine (SAM) and acyl carrier protein (ACP) is catalyzed by autoinducer (AI) synthase which is homologous to LuxI. This reaction produces acyl-homoserine lactone (AHL) AI \u003cb\u003e(Engebrecht \u0026amp; Silverman, 1984; More\u003c/b\u003e \u003cb\u003eet al\u003c/b\u003e., \u003cb\u003e1996; Schaefer\u003c/b\u003e \u003cb\u003eet al.\u003c/b\u003e, \u003cb\u003e1996; Ng \u0026amp; Bassler, 2009)\u003c/b\u003e. High concentrations result in the binding of AHL AIs to cognate cytoplasmic LuxR- like transcription factors (TFs). On the other hand, LuxR-type proteins are degraded if not bound to AI, this prevents QS short-circuiting in bacteria. Often the binding of AI stabilizes the LuxR-type proteins, ultimately resulting in the activation of target genes transcription \u003cb\u003e(Engebrecht\u003c/b\u003e \u003cb\u003eet al.\u003c/b\u003e, \u003cb\u003e1983; Engebrecht \u0026amp; Silverman, 1984; Stevens\u003c/b\u003e \u003cb\u003eet al.\u003c/b\u003e, \u003cb\u003e1994; Zhu \u0026amp; Winans 1999, 2001).\u003c/b\u003e Different bacteria produce AHLs possessing different length side chains and side-chain arrangements \u003cb\u003e(Fuqua et al., 2001; Ng \u0026amp; Bassler., 2009)\u003c/b\u003e allowing bacteria to interact with a diverse population \u003cb\u003e(Watson et al., 2002; Gould et al., 2004; Rutherford \u0026amp; Bassler, 2012).\u003c/b\u003e The mechanism of QS \u003cem\u003ei.e.\u003c/em\u003e, the cell-to-cell communication is regulated by the membrane components and the cytoplasmic factors \u003cb\u003e(Rutherford \u0026amp; Bassler., 2012)\u003c/b\u003e. Thus, the expression of the virulence factor responsible for the pathogenesis of \u003cem\u003eP.mirabilis\u003c/em\u003e is controlled by QS \u003cb\u003e(Stankowska\u003c/b\u003e \u003cb\u003eet al.\u003c/b\u003e, \u003cb\u003e2012). Rauprich\u003c/b\u003e \u003cb\u003eet al.\u003c/b\u003e, \u003cb\u003e1996\u003c/b\u003e suggested a tremendously ordered swarm cycle that coordinates the mechanism in a multicellular organism and the fact that \u003cem\u003eP. mirabilis\u003c/em\u003e is involved in biofilm formation which shows the mechanism of QS making CAUTI complex to treat.\u003c/p\u003e \u003cp\u003eCatheter-associated urinary tract infection (CAUTI) is a disease that is typically caused by the bacterial species of \u003cem\u003eEscherichia coli (E. coli), Candida, Enterococcus, Pseudomonas, Proteus, and Klebsiella\u003c/em\u003e \u003cb\u003e(Majumder\u003c/b\u003e \u003cb\u003eet al.\u003c/b\u003e, \u003cb\u003e2018).\u003c/b\u003e However, the bacteria of \u003cem\u003eProteus\u003c/em\u003e species are one of the major causes of the disease i.e., around 10\u0026ndash;44% of cases of CAUTI are caused by \u003cem\u003eP. mirabilis\u003c/em\u003e \u003cb\u003e(Jacobsen \u0026amp; Shirtliff., 2011; Schaffer \u0026amp; Pearson., 2015)\u003c/b\u003e due to the causes of catheter blockage and development of urinary stone. According to a report published by Centres for Disease Control and Prevention (CDC), CAUTI has been considered the most frequently occurring hospital-acquired infection, which includes around 75% cases from long-term catheterization, and 15\u0026ndash;25% during hospital bid. \u003cem\u003eP. mirabilis\u003c/em\u003e has the innate ability to develop crystalline biofilms which are formed majorly by virulence factors such as urease enzyme and capsule polysaccharides (CPSs) \u003cb\u003e(Jacobsen \u0026amp; Shirtliff, 2011)\u003c/b\u003e, ultimately giving rise to blocked and encrusted catheters causing difficulties in such infections \u003cb\u003e(Jones\u003c/b\u003e \u003cb\u003eet al.\u003c/b\u003e, \u003cb\u003e2007)\u003c/b\u003e. As a result, they could lead to reflux and retention of urine and, in critical conditions, in addition to trauma, endotoxic shock, and septicemia to the urethra and bladder mucosa as a consequence of removing the catheter \u003cb\u003e(Chen\u003c/b\u003e \u003cb\u003eet al.\u003c/b\u003e, \u003cb\u003e2012; Vaidyanathan\u003c/b\u003e \u003cb\u003eet al.\u003c/b\u003e, \u003cb\u003e2010; Wasfi\u003c/b\u003e \u003cb\u003eet al.\u003c/b\u003e, \u003cb\u003e2020)\u003c/b\u003e. As per the 2019 report of the CDC, the Standardized Infection Ratio for CAUTI was 0.74 across general acute care hospitals \u003cb\u003e(Majumder\u003c/b\u003e \u003cb\u003eet al.\u003c/b\u003e, \u003cb\u003e2018)\u003c/b\u003e. The attachment of fimbriae such as mannose-resistant hemagglutinins on the fluid-derived protein coat of the catheter surface is widely responsible for the initial biofilm formation stages \u003cb\u003e(Jacobsen\u003c/b\u003e \u003cb\u003eet al.\u003c/b\u003e, \u003cb\u003e2008; Donlan, 2002)\u003c/b\u003e. Mannose-resistant/Proteus-like (MR/P) mutants fimbriae have also been found to play a major role in biofilm formation \u003cb\u003e(Scavone\u003c/b\u003e \u003cb\u003eet al.\u003c/b\u003e, \u003cb\u003e2016)\u003c/b\u003e to the resistance to antibiotics in \u003cem\u003eP. mirabilis\u003c/em\u003e is conferred by the means of genes present in plasmids, transposons, and integrons and further expand by the means of horizontal gene transfer thus resulting in swift spreading and treatment failure \u003cb\u003e(Hall\u003c/b\u003e \u003cb\u003eet al.\u003c/b\u003e, \u003cb\u003e2003; Mirzaei\u003c/b\u003e \u003cb\u003eet al.\u003c/b\u003e, \u003cb\u003e2021)\u003c/b\u003e. The Beta-lactam resistance pattern in \u003cem\u003eP. mirabilis\u003c/em\u003e is of grave concern.\u003c/p\u003e \u003cp\u003eIn a recent study, it has been reported that clinical isolates of \u003cem\u003eP. mirabilis\u003c/em\u003e carried (intI1, intI2, intI3, blaTEM, blaCTX-M, blaSHV, mcr1, and mcr2) antibiotic resistance genes among which intI1 and blaTEM genes were found most abundantly in the ESBL and integrase gene family (Mirzaei et al., 2021). The frequent and inappropriate use of antibiotics has also contributed to the development of multidrug resistance (MDR) in \u003cem\u003eP. mirabilis\u003c/em\u003e, so the generation of novel therapeutics against MDR bacteria is the need of the hour. Developing novel health interventions to block the action of the gene, protein, or enzyme responsible for the MDR, can aid with the treatment of the diseases. Hemolysin is such a protein that contributes to MDR in bacteria, so blocking their function through the use of inhibitors derived from natural compounds namely, Quercetin can help combat the complications related to MDR. We have used Quercetin in our experiment as it is a naturally occurring compound mainly present in plants and fungi. and hence it can serve as a clinically potent medicine. It has a phenolic hydroxyl group as well as contained a double bond due to which it shows a high antioxidant property. Quercetin has been clinically identified to possess the antibacterial activity and diminish the bacterial biofilm formation by inhibiting the expression of the genes involved in its formation (\u003cb\u003eYang\u003c/b\u003e \u003cb\u003eet al.\u003c/b\u003e, \u003cb\u003e2020\u003c/b\u003e).\u003c/p\u003e \u003cp\u003eThis study is also intended to look for natural alternatives since they have fewer side effects than chemical drugs. Moreover, they form complexes due to the presence of various compounds, such as polysaccharides, mucilage, and tannins that contribute to the modification of the active component\u0026rsquo;s effect making them more favorable \u003cb\u003e(Kopaei., 2012, 2013)\u003c/b\u003e. We aimed to develop a lead compound against multidrug-resistant \u003cem\u003eP. mirabilis\u003c/em\u003e and found pertinent alternative compounds to that of small drugs by docking the available drug molecules (acting as inhibitors), against our target proteins. This study also aids to find a potential drug lead for the MDR bacterial disease, by using the binding affinity scores of the ligand to the protein.\u003c/p\u003e"},{"header":"2. Material and methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e2.1 Crystal structure retrieval of hemolysin A from \u003cem\u003eP. mirabilis\u003c/em\u003e and its preparation:\u003c/h2\u003e \u003cp\u003eThe structural analysis of the hemolysin protein was conducted utilizing the Uniprot database, accessible through the link: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.uniprot.org/uniprot/P16466#structure\u003c/span\u003e\u003cspan address=\"https://www.uniprot.org/uniprot/P16466#structure\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e. Within this repository, a total of thirteen distinct structures of the hemolysin protein from \u003cem\u003eP. mirabilis\u003c/em\u003e were subjected to thorough examination. These analyses were undertaken employing methodologies such as X-ray crystallography or NMR spectroscopy. The primary objectives of this investigation were to assess the protein's length and resolution quality. From the collection of thirteen examined structures, one particular structure, denoted as 4W8Q, emerged as the preferred choice. This selection was predicated on specific criteria, including its superior resolution quality, characterized by a low-resolution value. In comparison to the remaining structures, 4W8Q exhibited a greater length, rendering it an ideal candidate for further in-depth investigation. This truncated hemolysinA structure from \u003cem\u003eP. mirabilis\u003c/em\u003e, bearing the designation 4W8Q, boasted a resolution of 1.43 \u0026Aring; within the positional range of 30\u0026ndash;265.\u003c/p\u003e \u003cp\u003eThe origin of this structure was traced back to the literature, with a corresponding DOI of \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.2210/pdb4w8q/pdb\u003c/span\u003e\u003cspan address=\"10.2210/pdb4w8q/pdb\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e. It is noteworthy that the hemolysin protein exclusively comprises a single chain, designated as ChainA. Subsequently, the isolated hemolysin structure, defined by the single ChainA protein, underwent meticulous preparation using the Schrodinger Protein Preparation Wizard. This software tool occupies a pivotal role in facilitating modeling applications. The rationale underlying protein preparation stems from the inherent complexity of PDB structures, often containing co-crystallized components, heavy atoms, and occasionally multimeric entities. These factors necessitate streamlining for analytical efficacy. The preparatory steps encompass the integration of missing elements, simplification of intricate components, and removal of redundancies. This process compensates for disruptions that may have transpired during X-ray crystallization, such as hydrogen atom crystallization, sulfide bond disruption, and altered amino acid properties. Given that protein preparation serves as a prerequisite for subsequent docking studies, it encompasses tasks such as hydrogen addition, bond order assignment, zero bond order establishment for metals, incorporation of absent loops or chains, formation of disulfide bonds, and selenomethionine-to-methionine conversion. A final phase entails a comprehensive review and adjustment for structural integrity for further use.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e2.2 Structure retrieval of the quercetin and its derivatives:\u003c/h2\u003e \u003cp\u003eThe molecular structure of Quercetin, exhibiting a remarkable 94% similarity to an inhibitor of our designated target protein, hemolysin, has been sourced from the PubChem database (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://pubchem.ncbi.nlm.nih.gov/\u003c/span\u003e\u003cspan address=\"https://pubchem.ncbi.nlm.nih.gov/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e), which provides access to structures presented in the SDF format, characterized by a three-dimensional configuration. The process of retrieval involved identifying molecules that bear resemblance to Quercetin, resulting in the procurement of a total of 605 analogous compounds. This selection was governed by the application of the Lipinski Rule of Five, an established criterion initially formulated by Lipinski in 2004 and subsequently highlighted by \u003cb\u003eJayaram\u003c/b\u003e \u003cb\u003eet al.\u003c/b\u003e \u003cb\u003ein 2012\u003c/b\u003e. The application of this rule serves the purpose of differentiating molecules with potential as drug candidates from those that are less amenable to drug development. Furthermore, it contributes to the prognostication of success rates or potential setbacks in the realm of drug discovery. The Quercetin compound, distinguished by the DrugBank accession number DB04216, underwent a process of verification within the DrugBank database. This verification, conducted via the link: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://go.drugbank.com/drugs/DB04216\u003c/span\u003e\u003cspan address=\"https://go.drugbank.com/drugs/DB04216\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e, enabled the comprehensive acquisition of information pertaining to the Quercetin compound itself and its associated drug target. This step serves to augment our comprehension of the specific attributes and implications linked to the Quercetin compound, particularly concerning its role as a potential inhibitor of the target protein, hemolysin.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003e2.3 Molecular docking analysis by FlexX:\u003c/h2\u003e \u003cp\u003eThe molecular docking of 605 retrieved inhibitors that serves as our ligand was docked against the target protein hemolysin whose structure was retrieved using Schrodinger's Maestro 12.8 tool. By using FlexX software, the small molecules were docked into the active site of the protein \u003cb\u003e(Rarey\u003c/b\u003e \u003cb\u003eet al.\u003c/b\u003e, \u003cb\u003e1996)\u003c/b\u003e. The binding cavity of the receptor was defined with the help of the FlexX receptor preparation wizard. The active sites within the hemolysin structure were delineated by the following residues: ASN-30-A, ALA-83-A, GLU-85-A, ALA-86-A, GLY-87-A, GLN-88-A, SER-89-A, GLN-90-A, GLU-126-A, VAL-127-A, PHE-128-A, GLY-129-A, ILE-130-A, ILE-150-A, and ASN-151-A. The ligand-binding was driven by Enthalpy and Entropy (Hybrid Approach) with a maximum allowed overlap volume of 2.9 \u0026Aring; between protein-ligand clashes, and a clash -factor of 0.6 within the ligands. All 606 compounds (1 compound of Quercetin and 605 similar compounds) were docked against the binding site of hemolysin to find the best binding pose. The ligand is docked on the receptor after the ligand\u0026rsquo;s conformational expansion, resulting in the generation of various conformations \u003cb\u003e(Lokhande\u003c/b\u003e \u003cb\u003eet al.\u003c/b\u003e, \u003cb\u003e2020)\u003c/b\u003e.\u003c/p\u003e \u003cp\u003eThe molecular docking of 116 retrieved inhibitors that serves as our ligand was docked against the target protein hemolysin whose structure was retrieved using Schrodinger's Maestro 12.8 tool. By using the FlexX software, the small molecules were docked into the active site of the protein \u003cb\u003e(Rarey\u003c/b\u003e \u003cb\u003eet al.\u003c/b\u003e, \u003cb\u003e1996).\u003c/b\u003e The binding cavity of the receptor was defined with the help of the FlexX receptor preparation wizard. The ligand-binding was driven by Enthalpy and Entropy (Hybrid Approach) with a maximum allowed overlap volume of 2.9 \u0026Aring; between protein-ligand clashes, and a clash -factor of 0.6 within the ligands. FlexX considers the following factors for determining the best configuration that including -loss of entropy upon ligand binding, hydrogen bond interaction, solvation energy, Van der Waals, and electrostatic interaction \u003cb\u003e(Bursulaya\u003c/b\u003e \u003cb\u003eet al.\u003c/b\u003e, \u003cb\u003e2003)\u003c/b\u003e. By determining the binding and docking energy, the optimal binding pose is determined \u003cb\u003e(Lokhande\u003c/b\u003e \u003cb\u003eet al.\u003c/b\u003e, \u003cb\u003e2020)\u003c/b\u003e.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003e2.4 Molecular dynamic simulation:\u003c/h2\u003e \u003cp\u003eThe Desmond software developed by D. E. Shaw was employed to conduct extensive 100 nanoseconds (ns) all-atom molecular dynamics (MD) simulations, aiming to assess the binding affinity and the interactions between the top five best-docked ligands with the target protein Haemolysin (4W8Q), alongside a standard reference compound. Each complex system was immersed within a solvated cubic box spanning 10 angstroms (\u0026Aring;), incorporating periodic boundary conditions to mimic a realistic environment. Solvation was achieved using the TIP3P water model, ensuring a realistic representation of the solvent environment. To maintain overall system neutrality, counter ions were added to neutralize the total charges present in the system. The number of water molecules added, along with the concentration of counter ions in the complex, are tabulated in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e for reference. During the 100 ns MD simulations, the OPLS 2005 force field was employed, with initial energy minimization performed using the steepest descent approach. The systems underwent linear heating within the NVT ensemble for the first 400 picoseconds (ps), gradually raising the temperature from 0 to 300 Kelvin (K). Subsequently, equilibration of the systems was achieved utilizing an NPT ensemble at a pressure of 1 bar and a temperature of 300 K. Temperature and pressure regulation throughout the simulations was facilitated by the Noose-Hoover chain thermostat method and the Martyna-Tobias-Klein barostat method, respectively, employing an isotropic coupling style. Integration of the equations of motion was executed using the leap-frog approach with a time step of 2 femtoseconds (fs). Additionally, coulomb short-range interactions were maintained within a cut-off radius of 9.0 \u0026Aring;.\u003c/p\u003e \u003cp\u003eFollowing the production run, trajectory analysis was conducted to evaluate structural dynamics throughout the simulation period. Root Mean Square Deviation (RMSD) and Root Mean Square Fluctuation (RMSF) analyses were performed to elucidate any significant conformational changes within the protein-ligand complexes, indicating stability and equilibration of the simulation. RMSD provided insights into the stability of compound structures, while RMSF highlighted fluctuations in specific regions of the complexes, aiding in the characterization of dynamic behavior during the simulation.\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\u003eNumber of water molecules and ions required for MD simulation study.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eComplex\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNumber of water molecules added\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNumber of counterions\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eConcentration of counterions\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e4W8Q_5280343\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e5213\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e10.463\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e4W8Q_9814421\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e5223\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e10.443\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e4W8Q_9966216\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e5212\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e10.465\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e4W8Q_140034695\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e5213\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e10.463\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e4W8Q_688798\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e5212\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e10.465\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e4W8Q_676310\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e5212\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e10.465\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"3. Result and Discussion","content":"\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003e3.1 Molecular docking analysis:\u003c/h2\u003e \u003cp\u003e \u003cb\u003eBinding energies\u003c/b\u003e:\u003c/p\u003e \u003cp\u003eBased on the comprehensive investigation conducted, the molecules listed in Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e have emerged as promising hit compounds following docking studies and molecular dynamics (MD) simulations. The reference compound, along with the top five hits, exhibited robust docking scores (DDRC: -21.898 kcal/mol; DHFC: -21.567 kcal/mol; THDF: -21.536 kcal/mol; DHTC: -21.417 kcal/mol; TTDF: -21.117 kcal/mol). These scores indicate a strong binding affinity between the compounds and the target receptor, suggesting favorable interactions within the binding pocket. The high docking scores imply that the derivatives are well-suited for binding within the active site of the hemolysin protein, a significant toxin responsible for virulence in P. mirabilis. This finding is particularly promising as hemolysin contributes significantly to virulence and poses a challenge in combating multidrug resistance (MDR) in \u003cem\u003eP. mirabilis\u003c/em\u003e infections.\u003c/p\u003e \u003cp\u003eThe strong interaction observed between the hit compounds and the active site residues of the target receptor underscores their potential as effective therapeutic agents against MDR in \u003cem\u003eP. mirabilis\u003c/em\u003e. The robust binding affinity displayed by these compounds suggests their ability to disrupt crucial molecular pathways associated with virulence, thus offering a promising avenue for the development of novel antimicrobial agents.\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 and intermolecular interaction analysis of Quercetin and the selected lead compounds with Hemolysin.\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=\"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 \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCompound Name\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePubChem CID\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eBinding Affinity (Kcal/mol)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eInteracting Amino Acids\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003ePolarity\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eType of Inter-molecular Bond\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eBond Distance (\u0026Aring;)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003eQuercetin\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003e5280343\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003e-15.053\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eGLU 85\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eHydrophilic\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eH -Bond\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e2.05, 2.06\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eGLN 88\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eHydrophilic\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eH -Bond\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e1.91\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eGLU 126\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eHydrophilic\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eH -Bond\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e1.77\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eGLY 129\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eHydrophobic\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eH -Bond\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e2.13\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003e2-(2,4-Dihydroxyphenyl)-7,8-dihydroxychromen-4-one\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003e9814421\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003e-21.898\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eGLU 85\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eHydrophilic\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eH -Bond\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e2.27\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eGLU 126\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eHydrophilic\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eH -Bond\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e1.94\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eGLY 129\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eHydrophobic\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eH -Bond\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e2.06,\u003c/p\u003e \u003cp\u003e2.08,\u003c/p\u003e \u003cp\u003e2.13\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003ePHE 128\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eHydrophobic\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003ePi-Pi Stacking\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e5.47\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003e2-(3,4-Dihydroxyphenyl)-3-fluoro-7,8-dihydroxychromen-4-one\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003e9966216\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003e-21.567\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eGLU 85\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eHydrophilic\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eH -Bond\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e1.67,\u003c/p\u003e \u003cp\u003e1.67\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eGLU 126\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eHydrophilic\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eH -Bond\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e1.57,\u003c/p\u003e \u003cp\u003e1.60\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eGLY 129\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eHydrophobic\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eH -Bond\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e1.89\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003ePHE 128\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eHydrophobic\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003ePi-Pi Stacking\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e4.80\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003e7,8,3'-Trihydroxyflavone\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003e676310\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003e-21.536\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eGLU 85\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eHydrophilic\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eH -Bond\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e1.67,\u003c/p\u003e \u003cp\u003e1.67\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eGLU 126\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eHydrophilic\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eH -Bond\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e1.57,\u003c/p\u003e \u003cp\u003e1.60\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eGLY 129\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eHydrophobic\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eH -Bond\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e1.89\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003ePHE 128\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eHydrophobic\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003ePi-Pi Stacking\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e4.80\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e2-(2,3-dihydroxyphenyl)-5,7,8-trihydroxychromen-4-one\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e140034695\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e-21.417\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eGLU 85\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eHydrophilic\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eH -Bond\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e1.58,\u003c/p\u003e \u003cp\u003e1.78,\u003c/p\u003e \u003cp\u003e2.39\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eGLN 88\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eHydrophilic\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eH -Bond\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e2.07\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eVAL 127\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eHydrophobic\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eH -Bond\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e1.52,\u003c/p\u003e \u003cp\u003e1.92\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003e7,8,3',4'-Tetrahydroxyflavone\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003e688798\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003e-21.117\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eGLU 85\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eHydrophilic\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eH -Bond\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e1.65,\u003c/p\u003e \u003cp\u003e1.65\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eGLY 129\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eHydrophobic\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eH -Bond\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e1.88\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eGLU 126\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eHydrophilic\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eH -Bond\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e1.55,\u003c/p\u003e \u003cp\u003e1.60\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003ePHE 128\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eHydrophobic\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003ePi-Pi Stacking\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e1.60,\u003c/p\u003e \u003cp\u003e4.78\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\u003cb\u003eInteraction study\u003c/b\u003e:\u003c/p\u003e \u003cp\u003e \u003cb\u003eQuercetin\u003c/b\u003e: Quercetin (\u003cb\u003ePubChem CID\u003c/b\u003e: 5280343) was a reference ligand that has antimicrobial activity. It inhibits the pathogenesis of \u003cem\u003eP.mirabilis\u003c/em\u003e by acting on the haem receptor of the hemolysin protein. However, we worked to check out the efficacy of other derivatives against hemolysin as well, along with the Quercetin. Henceforth, a comparison of Quercetin with other hit compounds was performed along with studying the efficacy of Quercetin.\u003c/p\u003e \u003cp\u003eQuercetin binds in the cavity of protein with an affinity of -15.053kcal/mol. In the 2D structure of Quercetin, 4 functional groups are present that facilitate this binding are GLU-85, GLN 88, GLU 126, and GLY 129 are bound to the cavity of the hemolysin via hydrogen bonds. The GLU 85 and GLN 88 forms 2 hydrogen bonds each. GLU 85 forms the hydrogen bonds of bond lengths 2.06 \u0026Aring; and 2.05 \u0026Aring;, and the GLN 88 forms hydrogen bond of lengths 1.89 \u0026Aring; and 1.91 \u0026Aring;, while, GLU 126 and GLY 129 form only 1 hydrogen bond with the target protein of bond length 1.77 \u0026Aring; and 2.13 \u0026Aring; respectively. As the GLN 88 forms 2 hydrogen bond that is more than GLU 126, therefore, the functional group GLN 88 in the ligand majorly contributes to the binding with hemolysin (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003cb\u003e2-(2,4-Dihydroxyphenyl)-7,8-dihydroxychromen-4-one (DDRC)\u003c/b\u003e: The compound 2-(2,4-Dihydroxyphenyl)-7,8-dihydroxychromen-4-one (\u003cb\u003ePubChem CID\u003c/b\u003e: 9814421) is a derivative of Quercetin was observed to be a potential lead compound to inhibit the activity of \u003cem\u003eP.mirabilis\u003c/em\u003e because it shows higher binding affinity than Quercetin and other derivatives. Figure\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e shows the binding pose of 2-(2,4-Dihydroxyphenyl)-7,8-dihydroxychromen-4-one within the cavity of hemolysin. The docking score of 2-(2,4-Dihydroxyphenyl)-7,8-dihydroxychromen-4-one is -21.898 kcal/mol, which suggests strong binding of this compound within the binding cavity of hemolysin in \u003cem\u003eP.mirabilis\u003c/em\u003e. It forms 5 hydrogen bonds and a pi-pi stacking with the protein, of which GLU 85 forms a hydrogen bond of a bond length of 2.27 \u0026Aring;, GLU 126 forms a hydrogen bond of 1.94 \u0026Aring;, and the GLY 129 forms 3 hydrogen bonds of bond length 2.13 \u0026Aring;, 2.06 \u0026Aring;, and 2.08 \u0026Aring;. A pi-pi stacking is often observed with PHE 128 having a bond length of 5.47 \u0026Aring;. Therefore, this structure has a high affinity, due to the presence of both strong intermolecular bonds that ensures efficient binding and non-covalent pi-pi stacking which provides stability to this compound. The GLY 129 is a major residue, significantly involved in the bonding of the lead compound to a receptor. Moreover, the comparison of its affinity to that of Quercetin shows it binds more efficiently.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003cb\u003e2-(3,4-Dihydroxyphenyl)-3-fluoro-7,8-dihydroxychromen-4-one (DHFC)\u003c/b\u003e: The compound 2-(3,4-Dihydroxyphenyl)-3-fluoro-7,8-dihydroxychromen-4-one (PubChem CID: 9966216) a derivative of Quercetin was observed to be a potential lead compound to inhibit the activity of \u003cem\u003eP.mirabilis\u003c/em\u003e because it shows high binding affinity with the hemolysin. Figure\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e shows the binding pose of 2-(2,4-Dihydroxyphenyl)-7,8-dihydroxychromen-4-one within the cavity of hemolysin. It forms 6 hydrogen bonds and a pi-pi stacking with the protein, of which the GLU 85 forms two hydrogen bonds of 1.67 \u0026Aring; and 1.67 \u0026Aring;; GLU 126 forms two hydrogen bonds of 1.57 \u0026Aring; and 1.60 \u0026Aring;, while GLY 129 forms one hydrogen bond of 1.89 \u0026Aring;, along with a pi-pi stacking formed of 4.80 \u0026Aring; by PHE 128.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003cb\u003e7,8,3'-Trihydroxyflavone (THDF)\u003c/b\u003e: The docking score of Trihydroxyflavone (\u003cb\u003ePubChem CID\u003c/b\u003e: 676310) is -21.536 which suggests a strong bonding of this compound from Quercetin but less than 2-(3,4-Dihydroxyphenyl)-3-fluoro-7,8-dihydroxychromen-4-one, within the binding cavity of hemolysin. It has 5 hydrogen bonds of length 1.67 \u0026Aring;, and 1.67 \u0026Aring;, for GLU 85, 1.57 \u0026Aring;, and 1.60 \u0026Aring;, for GLU 126, and 1.89 \u0026Aring;, for GLY 129 as well as consists of 1 pi-pi stacking with PHE 128 with a bond distance of 4.80 \u0026Aring;. GLU is the major contributing residue in this compound (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003cb\u003e2-(2,3-dihydroxyphenyl)-5,7,8-trihydroxychromen-4-one (DHTC)\u003c/b\u003e: The docking score of 2-(2,3-dihydroxyphenyl)-5,7,8-trihydroxychromen-4-one (\u003cb\u003ePubChem CID\u003c/b\u003e:140034695) is -21.4177. It forms a total of 6 hydrogen bonds of which GLU 85 forms hydrogen bonds of length 2.39 \u0026Aring;, 1.58 \u0026Aring;, and 1.78 \u0026Aring;; GLU 85 forms a hydrogen bond of 2.07 \u0026Aring; in GLN 88, as well as hydrogen bonds of bond length 1.52 \u0026Aring;, and 1.92 \u0026Aring; is formed by VAL 127 (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e). From here we inferred GLU is a major residue, significantly involved in the bonding of the lead compound to a receptor. Moreover, the comparison of its affinity to that of Quercetin shows it binds more efficiently.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003cb\u003e7,8,3',4'-Tetrahydroxyflavo (TTDF)\u003c/b\u003e: In the 2D workspace 5 hydrogen bonds and 1 pi -pi stacking were observed in 7,8,3',4'-Tetrahydroxyflavone (PubChem CID: 688798) having a binding energy of -21.117Kcal/mol. The GLU 85 forms 2 hydrogen bonds of length 1.65 \u0026Aring;, GLY 129 forms a hydrogen bond of 1.88 \u0026Aring;, and GLU 126 forms a 2hydrogen bond of length 1.55 \u0026Aring; and 1.60 \u0026Aring;. GLU is the major binding group here. A pi-pi stacking of bond length 4.78 was often observed in PHE 128 (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003e3.2 MD simulation\u003c/h2\u003e \u003cp\u003eThe molecular dynamic simulation was carried out to explore the protein-ligand dynamic conformational perturbation of the best five docked ligands. A run of 100ns simulations was performed using the OPLS-2005 force field for the hemolysin receptor. For the purpose of visualization of the interaction among the selected ligand derivatives and their reference compound, the Desmond MD system was used. The net charge of all the systems of standard reference complex of Hemolysin protein with the reference ligand (4W8Q: Quercitin) and complex systems of the lead compounds (4W8Q: DDRC, 4W8Q: DHFC, 4W8Q: THDF, 4W8Q: DHTC, 4W8Q: TTDF) was equilibrated by the incorporation of sodium ions (Na+) and was solvated by the means of explicit TIP3P water molecules in the filledcubicboxof1\u0026Aring; spacing detailed in Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e. Figure\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003e. Is a representation of the strength and stability of the ligand with the protein receptor. 50ns was the time taken by DDRC to reach a state of convergent equilibrium as compared to THDF which required only 25ns, followed by TTDF with a time of 10ns to reach equilibrium and showing most stability throughout the process. DHFC displayed stability from 10 to 55ns and was observed to show minor fluctuations from 60-100ns whereas DHTC was found to be stable during the entire process. A decline in the potential energy was observed which indicates the system is stable. RMSD (Root mean square deviation) was calculated once the conformation analysis was done. The shift in selected atoms can be calculated by observing the average change for the particular frame with respect to the reference frame.\u003c/p\u003e \u003cp\u003e \u003cb\u003eRMSD\u003c/b\u003e \u003c/p\u003e \u003cp\u003eFigure \u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003e illustrates the Root Mean Square Deviation (RMSD) of the protein throughout the simulation period. Observationally, it is evident that the fluctuations experienced by the atoms reached a maximum peak limit of 2.2\u0026ndash;2.5 \u0026Aring; across all complex systems, as depicted in the network processing frame. This observation suggests potential helical winding and unwinding of the protein receptor, indicating dynamic structural changes occurring within the protein environment. In general, compounds demonstrating lower RMSD values are considered satisfactory, indicating stable binding interactions with the protein target. Analyzing the RMSD for the complex systems of derivatives of quercetin, it was observed that 4W8Q: TTDF exhibited the least variation in RMSD, followed by 4W8Q: TTDF, 4W8Q: THDF, and 4W8Q: THDF. Conversely, 4W8Q: DHFC displayed minor fluctuations in RMSD.\u003c/p\u003e \u003cp\u003eThese findings suggest that certain compounds, particularly 4W8Q: TTDF, exhibit more stable interactions within the binding cavity of hemolysin compared to others. The lower RMSD values indicate reduced structural deviation from the initial conformation, indicating a higher degree of stability in the protein-ligand complex. This stability is crucial for the development of effective inhibitors targeting hemolysin, as it ensures sustained binding interactions essential for therapeutic efficacy against multidrug-resistant pathogens like P. mirabilis.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003cb\u003eRMSF\u003c/b\u003e \u003c/p\u003e \u003cp\u003eAn in-depth analysis was conducted by comparing the Root Mean Square Fluctuation (RMSF) of C-alpha atoms of all residues using trajectories generated during 100 ns of MD simulations. This analysis aimed to elucidate structural alterations in the hemolysin protein upon binding to the anti-bacterial compounds. The RMSF of each complex system provided insights into residue mobility and ligand flexibility within the receptor network. A rigid protein structure suggests minimal fluctuations and increased flexibility of the ligand within the receptor, reflected by lower RMSF values. Conversely, higher RMSF values indicate a loosely bonded structure, suggesting greater flexibility within the complex (\u003cb\u003ePathak\u003c/b\u003e \u003cb\u003eet al.\u003c/b\u003e, \u003cb\u003e2018\u003c/b\u003e). The RMSF trajectories of the receptor-ligand complex stability correlate with rigid and stable conformations. The RMSF graph revealed that upon ligand binding, notable changes were observed primarily in the region spanning residues 247 to 254, with a shift ranging from 1 to 3.5 angstroms (\u0026Aring;). Specifically, the complex 4W8Q: DHFC exhibited the highest fluctuations, indicating increased flexibility within this particular complex (Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eRMSF values of hemolysin binding pocket residues after binding of lead compounds.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"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 \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eResidue\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5280343\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e9814421\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e9966216\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e676310\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGLU85\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.713\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.689\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.62\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.644\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGLU86\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.672\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.693\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.616\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.643\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGLN88\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.814\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.708\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.642\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.659\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGLU126\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.364\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.388\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.347\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.37\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVAL127\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.363\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.405\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.368\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.374\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePHE128\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.396\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.415\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.369\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.382\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGLY129\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.524\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.493\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.466\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.478\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGLU185\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.581\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.659\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.473\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.571\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eThe analysis suggests that the lead compounds form stable complexes with the hemolysin protein and exhibit thermodynamic stability. The observed structural changes, particularly in specific regions of the protein, highlight the dynamic nature of the receptor-ligand interactions. These findings underscore the potential of the lead compounds as effective inhibitors against multidrug-resistant pathogens, such as \u003cem\u003eP. mirabilis\u003c/em\u003e, by targeting key proteins involved in virulence pathways.\u003c/p\u003e \u003cp\u003e \u003cb\u003eH-Bond monitoring Reports\u003c/b\u003e \u003c/p\u003e \u003cp\u003eTo assess the stability of the binding between the lead compounds and hemolysin, we conducted an analysis of intermolecular hydrogen bond (H-bond) interactions. Utilizing Maestro software, 1000 frames were extracted from the 100 nsMD simulation trajectory data to evaluate the H-bonding patterns. Figure\u0026nbsp;\u003cspan refid=\"Fig9\" class=\"InternalRef\"\u003e9\u003c/span\u003e depicts the presence of intermolecular H-bonding between the lead compounds and the hemolysin receptor. Figure\u0026nbsp;12(a) illustrates that the quercetin-hemolysin complex initially stabilizes with 1 to 3 H-bonds, maintaining stability with a single H-bond until 50,000 ns, followed by bond breakage.\u003c/p\u003e \u003cp\u003eIn Fig.\u0026nbsp;\u003cspan refid=\"Fig9\" class=\"InternalRef\"\u003e9\u003c/span\u003e\u003cb\u003e(b)\u003c/b\u003e, the main lead compound, DDRC, forms 3 to 5 H-bonds initially, with some trajectories forming up to 6 H-bonds during the 50 ns MD simulation. However, bond breakage is observed over time. Figure\u0026nbsp;\u003cspan refid=\"Fig9\" class=\"InternalRef\"\u003e9\u003c/span\u003e\u003cb\u003e(c)\u003c/b\u003e demonstrates that the lead compound DHFC maintains stability by forming 3 to 5 H-bonds initially, followed by 4 to 5 H-bonds in the middle phase, and eventually 2 to 5 H-bonds towards the end of the dynamics, thereby ensuring complex stability. Figure\u0026nbsp;\u003cspan refid=\"Fig9\" class=\"InternalRef\"\u003e9\u003c/span\u003e\u003cb\u003e(d)\u003c/b\u003e depicts the hydrogen bonds between the ligand THDF and hemolysin. Initially, 1 to 3 H-bonds stabilize the complex, increasing to 2 to 4 H-bonds, maintaining stability throughout. Figure\u0026nbsp;\u003cspan refid=\"Fig9\" class=\"InternalRef\"\u003e9\u003c/span\u003e\u003cb\u003e(e)\u003c/b\u003e shows the TTDF-hemolysin complex stabilized by 1 to 7 H-bonds initially, maintaining the same number of bonds throughout the 1000 ns simulation. Figure\u0026nbsp;\u003cspan refid=\"Fig9\" class=\"InternalRef\"\u003e9\u003c/span\u003e\u003cb\u003e(f)\u003c/b\u003e indicates that the TTDF-hemolysin complex forms 3 to 5 H-bonds initially, maintaining stability throughout the simulation.\u003c/p\u003e \u003cp\u003eOverall, the results suggest that the lead compounds form stable complexes with hemolysin, maintaining stability over the 100 ns MD simulation period. This stability is crucial for the development of effective inhibitors against multidrug-resistant pathogens like P. mirabilis.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"4. Conclusion","content":"\u003cp\u003eThe escalating threat of multidrug-resistant (MDR) bacterial strains, particularly Proteus mirabilis, poses a significant challenge to global public health. The overuse and misuse of antibiotics, coupled with other factors such as prolonged hospital stays and invasive medical procedures, have exacerbated the proliferation of drug-resistant bacteria. The emergence of MDR strains like \u003cem\u003eP. mirabilis\u003c/em\u003e in catheter-associated urinary tract infections (CAUTIs) underscores the urgent need for novel therapeutic approaches. This study investigates the molecular mechanisms underlying \u003cem\u003eP. mirabilis\u003c/em\u003e virulence, particularly focusing on the role of hemolysin, a crucial toxin contributing to its pathogenesis. Molecular docking analysis revealed several lead compounds, including derivatives of quercetin, which demonstrated strong binding affinity with the hemolysin protein. These compounds exhibited promising antimicrobial activity against MDR P. mirabilis, highlighting their potential as therapeutic agents. Furthermore, molecular dynamics simulations provided valuable insights into the stability and dynamics of the protein-ligand complexes. The analysis revealed stable interactions between the lead compounds and hemolysin, suggesting their efficacy as inhibitors against MDR pathogens. Notably, certain compounds exhibited reduced structural deviations and increased stability within the binding cavity of hemolysin, indicating their potential for therapeutic development.\u003c/p\u003e \u003cp\u003eOverall, this study underscores the importance of exploring natural alternatives and novel therapeutic strategies to combat MDR bacterial infections effectively. The identification of lead compounds with potent antimicrobial activity against \u003cem\u003eP. mirabilis\u003c/em\u003e hemolysin represents a significant step towards developing targeted therapies for CAUTIs and other MDR-related infections. Further research and clinical trials are warranted to validate the efficacy and safety of these compounds for clinical use, ultimately addressing the pressing global health challenge posed by antibiotic resistance.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAcknowledgements:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors are thankful to Dr. D. Y. Patil Biotechnology and Bioinformatics Institute, Dr. D. Y. Patil Vidyapeeth, Pune for the physical infrastructure and Department of Science and Technology Science and Engineering Research Board (DST-SERB), Govt. of India, New Delhi, (File Number: YSS/2015/002035) for utilizing an Optimized Supercomputer for docking and dynamics calculations.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eDisclosure statement:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThere is no conflict of interest declared by all authors.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe author(s) reported there is no funding associated with the work featured in this article.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData availability statement:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe docking structures are available upon request from the corresponding author\u003c/p\u003e\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eK.B.L: Conceived and designed the experiments, Analyzed the data, Review and editing of the manuscript; C.J. and M.C.: Performed the experiments, Analyzed the data, Wrote the manuscript; P.C.: Review and editing of the manuscript, Proofread of final version.All authors have approved final version of the manuscript.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eFriis SD, Johansson MJ, Ackermann L (2020) Cobalt-catalysed C-H methylation for late-stage drug diversification. 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Comput Biol Chem 76:32\u0026ndash;41. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.compbiolchem.2018.05.015\u003c/span\u003e\u003cspan address=\"10.1016/j.compbiolchem.2018.05.015\" 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":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Antimicrobial resistance, Proteus mirabili, drug discovery, molecular docking, dynamic simulation, h bond network","lastPublishedDoi":"10.21203/rs.3.rs-5753353/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-5753353/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eAims\u003c/h2\u003e \u003cp\u003eThis study aims to explore natural compounds as potential inhibitors against multidrug-resistant Proteus mirabilis hemolysin, a key virulence factor contributing to catheter-associated urinary tract infections (CAUTIs). The emergence of multidrug-resistant bacterial strains poses a significant threat to global public health, with Proteus mirabilis being a notable contributor to hospital-acquired infections such as CAUTIs. Hemolysin, a toxin produced by \u003cem\u003eP. mirabilis\u003c/em\u003e, plays a crucial role in its pathogenesis, making it an attractive target for antimicrobial therapy. The objective of this study is to investigate the binding affinity and stability of natural compounds, particularly derivatives of quercetin, with \u003cem\u003eP. mirabilis\u003c/em\u003e hemolysin through molecular docking and dynamics simulations. Crystal structure retrieval of hemolysin from P. mirabilis was conducted, and the protein was prepared for molecular docking studies. Molecular docking analysis was performed to evaluate the binding affinity of natural compounds with hemolysin. Molecular dynamics simulations were then employed to assess the stability and dynamics of protein-ligand complexes. Molecular docking analysis revealed several lead compounds, including derivatives of quercetin, exhibiting strong binding affinity with \u003cem\u003eP. mirabilis\u003c/em\u003e hemolysin. Molecular dynamics simulations demonstrated stable interactions between the lead compounds and hemolysin, with certain compounds showing reduced structural deviations and increased stability within the binding cavity. Natural compounds, particularly derivatives of quercetin, show promising antimicrobial activity against multidrug-resistant Proteus mirabilis hemolysin. These findings highlight the potential of natural compounds as effective inhibitors for combating multidrug-resistant bacterial infections, offering new avenues for therapeutic development in the fight against antibiotic resistance. Further research and clinical trials are warranted to validate the efficacy and safety of these compounds for clinical use.\u003c/p\u003e","manuscriptTitle":"Structural insights of Quercetin and its derivatives against multi-drug resistant Proteus mirabilis: In silico approach","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-01-13 15:40:08","doi":"10.21203/rs.3.rs-5753353/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"aff51822-43ff-423c-8fc1-d73fa6418276","owner":[],"postedDate":"January 13th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2025-02-17T02:08:32+00:00","versionOfRecord":[],"versionCreatedAt":"2025-01-13 15:40:08","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-5753353","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-5753353","identity":"rs-5753353","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}