Uncovering Novel VIM-2 Inhibitors from Fungal Sources Using Structure-Based Screening and Molecular Dynamics Approaches

Uncovering Novel VIM-2 Inhibitors from Fungal Sources Using Structure-Based Screening and Molecular Dynamics Approaches | 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 Uncovering Novel VIM-2 Inhibitors from Fungal Sources Using Structure-Based Screening and Molecular Dynamics Approaches Sandip Dolui, Amartya Mitra, Kaushik Biswas, Kamalika Mazumder This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6979596/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 Antibiotic resistance is a critical global health concern, with metallo-β-lactamases like Verona integron-encoded metallo-β-lactamase-2 (VIM-2) contributing to the breakdown of β-lactam antibiotics, including carbapenems. The increasing prevalence of VIM-2-mediated resistance highlights the urgent need for novel inhibitors. In this study, a structure-based virtual screening approach was employed to identify potential VIM-2 inhibitors from the Medicinal Fungal Secondary Metabolites and Therapeutics (MeFSAT) library. Molecular docking of 1,830 fungal-derived compounds, followed by molecular dynamics (MD) simulations, led to the identification of three promising candidates: MSID001033, MSID001081, and MSID001168. These compounds showed high docking scores and favorable interactions with the VIM-2 active site, forming stable complexes through π-π stacking, hydrogen bonding, and zinc coordination. MD simulations over 250 ns confirmed the structural stability of the complexes, supported by consistent RMSD, RMSF, and hydrogen bonding profiles. Further validation using MM-GBSA binding energy calculations, radial distribution function (RDF), salt bridge analysis, and anisotropic network model (ANM) cross-correlation reinforced their strong binding affinity. Overall, this study highlights fungal secondary metabolites as promising scaffolds for VIM-2 inhibition and demonstrates the effectiveness of integrated computational methods in accelerating early-stage antibiotic drug discovery. β-lactamase MeFSAT AutoDock Vina Binding Energy RMSD Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Figure 8 Figure 9 Figure 10 Figure 11 Introduction In recent years, the global health community has grown increasingly concerned about the escalating threat of infectious diseases, particularly those caused by bacteria that have developed resistance to multiple classes of antibiotics.[ 1 – 5 ] A key driver of this resistance is the ability of certain bacteria to produce β-lactamase enzymes, which hydrolyze the β-lactam ring present in many antibiotics, rendering them ineffective. These enzymes are broadly classified into four Ambler classes A, C, and D, which are serine β-lactamases (SBLs), and class B, known as metallo-β-lactamases (MBLs), which depend on zinc ions for activity.[ 6 – 9 ] Among the numerous pathogens contributing to the antimicrobial resistance crisis, Pseudomonas aeruginosa has emerged as a particularly dangerous opportunistic organism.[ 10 – 12 ] It is frequently implicated in severe infections such as cystic fibrosis, urinary tract infections, and burn wound complications.[ 13 , 14 ] As a prominent cause of healthcare-associated infections, P. aeruginosa is linked to higher mortality rates, especially in bloodstream infections, than many other bacterial pathogens.[ 15 – 17 ] Recent clinical observations have also highlighted co-infections involving P. aeruginosa in patients with COVID-19.[ 18 , 19 ] Rhoades et al. reported a notable increase in P. aeruginosa colonization in the nasal passages of patients following SARS-CoV-2 infection.[ 20 ] The emergence of extensively drug-resistant (XDR) and multidrug-resistant (MDR) P. aeruginosa strains has created a formidable public health challenge.[ 12 , 21 – 23 ] These resistant strains are associated with significantly higher mortality, with global reports indicating that the death rate from carbapenem-resistant P. aeruginosa infections ranges between 33% and 71%. Resistance to carbapenems, often considered the last line of defence against MDR Gram-negative infections, is driven by a variety of mechanisms.[ 24 , 25 ] Among these, the production of β-lactamases, particularly metallo-β-lactamases, plays a pivotal role. Metallo-β-lactamases (MBLs) are highly efficient enzymes responsible for the inactivation of a wide range of β-lactam antibiotics that lead to antimicrobial resistance, which is a matter of concern for antimicrobial therapy.[ 9 , 26 , 27 ] The unavailability of clinically useful inhibitors against these MBLs is limiting the therapeutic options. These enzymes differ structurally and mechanistically from their serine counterparts. MBLs utilize one or two zinc ions in the active site, along with a hydroxide ion, to catalyze the breakdown of the β-lactam ring. Based on sequence and structural characteristics, MBLs are subdivided into three subclasses B1, B2, and B3 with the B1 subclass being of greatest clinical relevance.[ 28 – 30 ] Notably, subclass B1 enzymes possess a unique di-zinc active site and a water-bridge hydrolysis mechanism, enabling them to inactivate a broad range of β-lactam antibiotics.[ 31 , 32 ] Prominent members of subclass B1 MBLs include Verona integron-encoded metallo-β-lactamase (VIM), New Delhi metallo-β-lactamase (NDM), and imipenemase (IMP).[ 33 , 34 ] Among these, VIM, particularly the VIM-2 variant has become the most widespread MBL in P. aeruginosa .[ 35 ] VIM-2 is now considered the primary driver of carbapenem resistance in P. aeruginosa on a global scale.[ 36 ] The World Health Organization (WHO) has placed a high priority on discovering novel antibiotics and effective MBL inhibitors to combat the growing prevalence of VIM-producing strains.[ 37 ] In many parts of Asia, VIM accounts for over 99% of MBL-positive, multidrug-resistant P. aeruginosa cases. Structurally, VIM-2 is a 266 amino acid long protein with a 32.4% similarity with NDM-1.[ 38 ] VIM-2 differs from VIM-1 by seventeen amino acid residues. Consistent with other subclass B1 metallo-β-lactamases (MBLs), VIM-2 contains two zinc ions situated within the conserved αβ/βα motif characteristic of metal hydrolases, which are crucial for catalysis.[ 39 ] The catalytic site of VIM-2 is composed of two zinc ions, which are essential for its enzymatic activity. Zn1 is coordinated by three conserved histidine residues (His116, His118, His196), while Zn2 is bound by Asp120, Cys221, and His263. This binuclear zinc configuration is critical for substrate binding and catalytic efficiency. The unique coordination geometry also contributes to the enzyme’s resilience against many β-lactamase inhibitors, underscoring the challenge of neutralizing VIM-2 in clinical settings.[ 40 ] In response to the urgent need for effective MBL inhibitors, research efforts have increasingly focused on identifying non-β-lactam compounds that can bypass traditional resistance pathways while offering improved safety and pharmacokinetic profiles. Among natural sources of bioactive molecules, fungal metabolites have attracted considerable interest due to their wide-ranging pharmacological activities, including antibacterial, antiviral, cytotoxic, and anticancer properties. Although no fungal metabolites have yet been approved as antiviral agents, growing evidence supports their potential, with several candidates currently under investigation for clinical development. Fungal secondary metabolites encompass diverse chemical classes such as polyketides, non-ribosomal peptides, terpenoids, alkaloids, and phenylpropanoids. These compounds are known to exert therapeutic effects by modulating cellular processes or inhibiting specific enzymes.[ 41 , 42 ] For example, polyketides like griseofulvin and fusidic acid interfere with microtubule assembly and bacterial protein synthesis, while non-ribosomal peptides target bacterial cell wall biosynthesis. Alkaloids, terpenoids, and other classes contribute further to antimicrobial and enzymatic inhibitory activities. Lovastatin, a well-known fungal metabolite, inhibits HMG-CoA reductase, showcasing the potential of fungal compounds as enzyme-targeted therapies.[ 43 ] Despite their promising properties, the interaction mechanisms between fungal metabolites and metallo-β-lactamases such as VIM-2 remain largely unexplored. In this context, the present study aimed to identify potent inhibitors of VIM-2 by screening a library of 1,830 fungal secondary metabolites using in silico techniques. Through molecular docking, molecular dynamics simulations, and post-simulation analyses, three compounds MSID001033 (Hypholomine B), MSI 001081 (Inoscavin C), and MSID001033 (Melledonol) emerged as promising candidates with strong binding affinity and favorable interaction profiles against the VIM-2 enzyme. These findings not only offer new insights into natural product-based drug discovery but also underscore the power of integrating computational approaches with natural compound libraries to accelerate the development of next-generation therapeutics for combating antimicrobial resistance. Material And Methods Small-molecule database preparation . The secondary metabolites derived from medicinal fungi were obtained from the curated MeFSAT (Medicinal Fungi Secondary Metabolites and Therapeutics) database for this study.[ 42 ] The two-dimensional (2D) chemical structures were converted into their corresponding three-dimensional (3D) conformations using Open Babel, a widely recognized cheminformatics tool. To ensure structural integrity and minimize potential steric clashes, energy minimization and geometry optimization were performed during the conversion process.[ 44 ] A custom Bash script was developed to automate this workflow, utilizing the MMFF94 force field for molecular optimization.[ 45 ] The steepest descent algorithm was applied for 10,000 steps to achieve energetically favorable conformations. The optimized structures were subsequently exported in PDBQT format, rendering them compatible for downstream molecular docking studies. Selection of target protein and preparation The 3-dimensional (3D) coordinates of metallo-β-lactamase, specifically the crystal structure with Protein Data Bank (PDB) identification number 6SP7 was procured from the RCSB-Protein Data Bank.[ 46 ] This Metallo beta-lactamase crystal structure exhibits a resolution of 1.8 Å and an R-value of 0.192. It encompasses 266 amino acid residues that have not undergone mutational modification. To create the desired protein configuration for subsequent analysis, AutoDockTools (ADT) were employed.[ 47 ] ADT facilitated the addition of hydrogen atoms and the removal of all heteroatoms, including water molecules. Additionally, Kollman charges were incorporated into the protein to adjust the partial charges appropriately according to the selected protein crystal structure. Finally, the prepared protein configuration was saved in the preferred file format, such as 'pdbqt,’ to facilitate future analysis. Validation of Docking Protocol The reliability of molecular docking predictions is often evaluated by comparing the predicted ligand-binding pose with that of its experimentally resolved counterpart. To validate our docking protocol, we performed a re-docking experiment using AutoDock Vina, targeting the active site of VIM-2 (PDB ID: 6SP7)[ 46 ], after removing the co-crystallized ligand Taniborbactam (TAN). The re-docked conformation of Taniborbactam showed a close alignment with the original crystal structure, yielding a root-mean-square deviation (RMSD) of 2.0 Å, which is well within the generally accepted threshold of 3 Å for docking accuracy. This strong agreement between predicted and experimental poses confirms the reliability of the chosen docking parameters and supports the suitability of the AutoDock Vina protocol for subsequent structure-based virtual screening efforts.[ 48 ] Molecular Docking Protocol Molecular docking studies were performed using AutoDock Vina on a Linux-based platform to investigate the interactions between selected fungal secondary metabolites and the metallo-β-lactamase enzyme (PDB ID: 6SP7).[ 46 ] A cubic grid box with dimensions of 70 × 70 × 70 Å and a spacing of 0.375 Å was defined to cover the enzyme active site. The grid center and other docking parameters were specified in the AutoDock Vina configuration file, while all remaining settings were left at their default values to maintain consistency with standard protocols. A total of 1,830 fungal-derived secondary metabolite compounds were retrieved from the PubChem database. Before docking, all compounds underwent energy minimization to ensure stable conformations. These ligands were then screened against the VIM-2 enzyme using a conventional docking workflow. To streamline the selection process and identify the most promising candidates with a high likelihood of binding affinity toward metallo-β-lactamases, the binding energies of all docked compounds were systematically analyzed. A cutoff threshold was established based on the lowest binding energy observed among the screened molecules. This benchmark served as a filter, allowing the prioritization of compounds that demonstrated the strongest interactions with the target protein, thereby narrowing the chemical space for subsequent in-depth analyses. Molecular dynamic simulation and post MM-GBSA analysis Molecular dynamics (MD) simulations were carried out using Desmond module within the Maestro interface (Schrodinger Release version 2020-4: Desmond Molecular Dynamics Syatem, D.E. Shaw Research, New York, NY, 2020), applying the OPLS-2005 force field throughout[ 49 ] and dynamic behaviour of the target protein both in its unbound form and when complexed with selected ligands over a 250 nanosecond trajectory. Each simulation system was solvated with a simple point charge (SPC) water model placed within a cubic box maintaining a minimum 10 Å buffer between the solute and box boundaries. To mimic physiological conditions, counter ions were added to neutralize the system, and a 0.15 M salt concentration was applied. Before initiating the production phase, energy minimization was performed using the steepest descent method for up to 2,000 steps, or until the energy gradient dropped below 25 kcal/mol. This was followed by further refinement using the limited-memory Broyden–Fletcher (Goldfarb) Shanno algorithm until convergence was achieved at 1 kcal/mol. The system was then subjected to a five-step equilibration protocol. Initially, Brownian dynamics simulations were performed in the NVT ensemble at 10 K for 100 ps with heavy-atom restraints. This step was followed by 12 ps of restrained NVT dynamics and another 12 ps of restrained NVT dynamics in the NPT ensemble using the Berendsen thermostat. The system was gradually heated to 300 K over a 12 ps period and equilibrated for an additional 24 ps without restraints. Production MD simulations were run for 250 ns under NPT conditions at 300 K. Following the simulations, binding free energy estimations were performed using the thermal_mmgbsa.py script from the Desmond suite on the Schrödinger platform. MM-GBSA analysis evaluated the binding free energy (ΔG bind) based on molecular mechanics and an implicit solvent energy model.[ 50 ] All trajectory analyses and visualizations were performed using the Schrödinger Maestro interface and VMD. Additionally, normal mode analysis was conducted using VMD Timetools, including the Normal Mode Wizard and the ProDy interface. Result and Discussion Molecular docking plays a vital role in drug discovery by elucidating how ligands interact with target proteins, thereby providing valuable structural and energetic insights.[ 51 ] To validate the docking protocol, the co-crystallized ligand, Taniborbactam (TAN), was re-docked into the active site of VIM-2 (PDB ID:6SP7) using AutoDock 4.2. The root-mean-square deviation (RMSD) of 2.0 Å between the experimental and predicted poses confirmed the robustness and reliability of the docking strategy (Fig. 1 ). A benchmark set of 16 β-lactam antibiotics, including penicillins, cephalosporins, carbapenems, and β-lactamase inhibitors, were subsequently docked against VIM-2 to establish a comparative framework for binding affinity. Among these, meropenem demonstrated the most favorable binding score − 8.5 kcal/mol (Table 1 ). Interaction analysis revealed that meropenem formed key hydrogen bonds with Glu149 and Asp236, coordinated with catalytic zinc ions, and engaged in hydrophobic and salt-bridge interactions with residues such as Phe61, Tyr67, Val73, and Phe117 (Fig. 2 ). Following validation, structure-based virtual screening of 1,830 fungal secondary metabolites from the MeFSAT database was performed using AutoDock Vina. The screening identified ten compounds with strong predicted binding affinities, from which the top three candidates MSID001033, MSID001081, and MSID001168 were selected for further analysis based on their docking scores of -9.68, -9.4, and − 9.2 kcal/mol, respectively, all of which surpassed that of meropenem (Table 2 ). In-depth interaction analyses of the top hits revealed favorable binding profiles within the enzyme active site. MSID001033 exhibited multiple stabilizing interactions, including two π-π stacking contacts with Phe 61 and Trp 87, four hydrogen bonds with Trp 87, Lys 90, Gly150, and Asn 165, and coordination with both Zn1 and Zn2 ions (Fig. 3 A,B). These interactions involve residues critical for β-lactam substrate binding, reinforcing the inhibitory potential of the compound. MSID001081 also fit well within the active site, forming three hydrogen bonds with Trp 87, Asp 120, and Asn 165, two π-π stacking interactions with Phe61 and Trp87, and dual zinc coordination bonds (Fig. 3 C,D). MSID001168 showed a relatively simpler binding profile, but still established key interactions, including a hydrogen bond with Asp119 and coordination with both zinc ions (Fig. 3 E,F). All three ligands engaged with conserved catalytic residues and showed consistent interaction patterns involving hydrogen bonding, aromatic stacking, and metal coordination, features essential for effective enzyme inhibition (Table 3 ). Table 1 As part of the docking study, we evaluated the binding affinities of several antibiotics against the VIM-2 enzyme. Molecules Binding Energy (ΔG kcal/mol) Imipenem -8.555 Meropenem -7.611 Ertapenem -6.698 Feropenam -6.629 Cefuroxime -6.497 Piperacillin -6.295 Captropil -6.129 Ampicillin -6.138 PenicillinG -5.842 Avibactam -5.707 Clavulanate -5.561 Cefotaxime -5.404 Tazobactam -5.032 Sulbactam -4.493 Cefepime -3.427 Ceftazimide -3.183 Table 3 The docking analysis of VIM-2 in complex with various ligands revealed that the binding interactions were stabilized through a combination of key forces, including hydrogen bonds, coordination with catalytic zinc ions, π–π stacking, and hydrophobic contacts involving critical residues within the enzyme’s active site. Complex Hydrogen bonds Residue Zinc Interactions π-π stacking π-cation Interaction Hydrophobic Interaction Amino acids Distance (Å) Meropenem Glu149 Asp236 ZN 301, ZN 302 - - Phe61, Tyr67, Trp 87, Phe117 MSID001033 Trp 87, Lys 90, Gly 150, Asn 165 ZN 301, ZN 302 Phe 61, Trp 87 - Phe 61, Trp 87 MSID001081 Trp 87, Asp 120, Asn 165 ZN 301, ZN 302 His122, His 122 - Phe 61, Trp 87,Cys 221 MSID001168 Asp 119 ZN 301, ZN 302 - - Phe 61, Tyr 67, Trp 87, Cys 221, Tyr 224, Ala 235 To further evaluate the binding stability of these ligands, molecular dynamics (MD) simulations[ 52 ] were carried out for the apo form of VIM-2 and lead complexes MSID001033, MSID001081, and MSID001168 over a 250 ns timescale. Several key parameters, RMSD, RMSF, radius of gyration (R g ), hydrogen bonding, MM-GBSA, RDF, and non-covalent interactions were analyzed to gain insight into the structural stability of the systems. RMSD was analyzed to monitor the equilibration and overall stability of the protein backbones throughout the simulation period.[ 53 ] RMSD plots for the apo form and three ligand-bound complexes (MSID001033, MSID001081, and MSID001168) are presented in Fig. 4 A. All four systems showed RMSD fluctuations within the 1–3 Å range across the simulation, indicating the overall structural stability. Notably, slight conformational deviations were observed in the MSID001168-bound system, suggesting potential localized flexibility, while the remaining complexes maintained consistent backbone stability, supporting the formation of stable ligand-protein complexes. The stability of the protein-ligand complexes during simulation was evaluated using radius of gyration (R g ) analysis. An increase in R g indicates structural expansion, whereas a decrease suggests enhanced compactness and rigidity. The results indicated that all the complexes-maintained stability and compactness during the simulation, demonstrating structural stabilization and compaction over time. Figure 4 B illustrates the R g trajectories for the apo- and ligand-bound systems, showing values predominantly within the 16.2–17 Å range. While all systems demonstrated relatively stable folding patterns, the MSID001168-bound complex exhibited slightly elevated R g values, suggesting a subtle conformational disruption during the simulation. Root-mean-square fluctuation (RMSF) analysis of backbone atoms was performed to assess the local conformational flexibility of the VIM-2 enzyme in its apo form and in complexes with the three selected molecules. As illustrated in Fig. 4 C, the overall RMSF profiles exhibited similar trends across all systems, with notable flexibility in the L3 and L10 loop regions. In the L3 loop, RMSF values were recorded as 0.93 Å for the apo form, and slightly reduced to 0.85 Å, 0.92 Å with MSID1033, MSID1081 and but slightly increased 1.45 Å for complex MSID1168, respectively. A more pronounced effect was observed in the L10 loop, where the apo form showed higher fluctuation (3.36 Å) compared to reduced values in the presence of MSID1033 (1.49 Å), MSID1081 (2.57 Å), and MSID1168 (2.35 Å). These reductions suggest that the ligands, particularly MSID1033, contribute to stabilization of the L10 loop conformational dynamics. Additionally, in other structurally relevant regions, namely, the loop encompassing residues 145–150 and the α4 helix spanning residues 218–232 the presence of the three ligands led to a slight decrease in RMSF compared to the apo structure. These findings indicate that ligand binding not only stabilizes critical loop regions, but may also promote a more rigid and stable conformation of VIM-2, potentially could contributing to the inhibition of its enzymatic activity. The stability and structural integrity of protein-ligand complexes are significantly affected by hydrogen bonding.[ 54 , 55 ] In this study, the H-bonds for MSID001033 were found to be 4, and MSID001081 showed the most stable hydrogen-bonding profile, which was found to be 3. For MSID001168, the number of hydrogen bonds was 3. Figure 4 D illustrates that the analysis of hydrogen bonds indicates that all molecules displayed the most stable and extensive hydrogen-bonding network, as evidenced by its highest maximum values. We also identified two additional types of non-covalent π-π and π-cation interactions that play crucial roles in enhancing ligand binding.[ 56 – 58 ] An increase in these interactions generally correlates with greater stability and binding strength of the protein-ligand complex. In our docking studies, we observed two π–π interactions between the ligand and the protein. During the 250 ns molecular dynamics (MD) simulation, this number increased, with three π-π and one π-cation interactions forming between the ligand and metallo-β-lactamase (Fig. 5 A and 5 B). This suggests dynamic adjustment of the ligand within the binding pocket, contributing to a more thermodynamically stable complex. The emergence of these interactions over time reflects a favorable structural reorganization that strengthens binding. π-π stacking interactions, especially those involving aromatic residues, contribute to maintaining the spatial integrity of the complex, whereas π-cation interactions offer additional stabilization through electrostatic forces with positively charged residues. Together, these interactions help preserve the secondary and tertiary structures of the protein and confer flexibility, allowing the complex to adapt to conformational shifts throughout the simulation. Salt bridges, formed through electrostatic attraction between oppositely charged amino acid residues, are key noncovalent interactions that significantly contribute to protein folding, structural integrity, and molecular recognition.[ 59 , 60 ] In this study, notable salt bridge interactions were consistently observed at the binding sites of the highest-ranking VIM-2 complexes MSID001033, MSID001088, and MSID001168. These interactions appear to play a pivotal role in enhancing the structural stability and binding affinity of the enzyme ligand complexes during molecular dynamics simulations. In particular, charged residues such as glutamate (Glu), arginine (Arg), and lysine (Lys) were found to form stable salt bridges with oppositely charged regions on the ligands. These interactions likely reinforce the binding conformation and contribute to the overall thermodynamic stability of the complexes. Representative salt bridge interactions are illustrated in Fig. 6 . To gain deeper insight into the binding affinity and thermodynamic stability of the selected ligands (MSID001033, MSID001081, and MSID001168) with β-lactamase, MM-GBSA binding free energy calculations were performed. The resulting free energy values were found to be -42.89 kcal/mol for MSID001033, -39.62 kcal/mol for MSID001081, and − 30.42 kcal/mol for MSID001168. Notably, MSID001033 demonstrated the most favorable binding, as reflected by its lowest ΔG value (-42.89 kcal/mol), indicating stronger and more stable interactions with the target enzyme. The consistently negative binding free energy values across all three compounds suggested that their interactions with β-lactamase are both spontaneous and thermodynamically favorable. These findings were in good agreement with the molecular docking and molecular dynamics simulation results, reinforcing the potential of these molecules as promising β-lactamase inhibitors. The binding modes of compounds MSID001033, MSID001081, and MSID001168 were further validated through molecular dynamics (MD) simulations, which provided deeper insights into their interactions with the VIM-2 enzyme. Interestingly, the interactions observed at the catalytic site during MD simulations were notably different from those predicted by molecular docking alone. For MSID001033, significant hydrophobic interactions were observed with VIM-2 residues, particularly Phe61 and Trp87 (Fig. 7 A). The ligand also established hydrogen bonds with Trp87 and displayed π-π stacking interactions with this residue. Furthermore, the compound formed two Zn²⁺ coordination bonds: one with catalytic residues His116, His118, His196, and Asp236, and the other with Asp120, Cys221, and His 263. These metal-coordination interactions are crucial because they involve active-site zinc ions essential for VIM-2 enzymatic function. The protein-ligand contact distribution is shown in the histogram (Fig. 7 B). In contrast, molecular docking predicted hydrogen bonds with Trp87, Lys90, Gly150, and Asn165, π-π stacking between Phe61 and Trp87, and hydrophobic contacts involving Phe61, Trp87, and Cys221. MD simulations corroborated many of these interactions, particularly the π-π stacking involving Trp87 and the hydrophobic contributions from Phe61, Trp87, and Cys221. Some residues not highlighted in Fig. 7 A appeared intermittently throughout the MD trajectory, suggesting their transient, but important, roles in ligand binding. The interaction diagram (Fig. 7 C) offers a frame-by-frame view of these dynamic interactions, with residues involved in multiple contacts highlighted in deeper shades of orange. Similarly, MSID001081 demonstrated strong interactions with Phe61 and Trp87 (Fig. 8 A). Hydrogen bonding was observed with Asp120, while π-π stacking interactions occurred with both Phe61 and Trp87. Like MSID001033, this compound formed two Zn²⁺ coordination bonds with His116, His 118, His196, and Asp236, and another with Asp120, Cys221, and His263, underscoring its engagement with the active site. Figure 8 B shows the contact distribution. Docking studies also predicted hydrogen bonds with Trp87, Asp120, and Asn165, as well as π-π stacking with Phe61 and Trp87, alongside hydrophobic contacts involving Phe61, Trp87, and Cys221. These findings were largely validated by MD simulations, which confirmed persistent π-π stacking and hydrophobic interactions involving these residues. Notably, some residues that were absent in the interaction snapshot (Fig. 8 A) were observed transiently during the simulation, highlighting their dynamic involvement. The interaction map in Fig. 8 C illustrates these temporal patterns, with frequently interacting residues marked in darker orange. For the MSID001168-VIM-2 complex, 250 ns MD simulations revealed key interactions, such as hydrogen bonding with Arg228, π-π stacking with His263, and hydrophobic contacts involving Cys221 and Tyr224 (Fig. 9 A). The compound also formed Zn²⁺ coordination bonds with His116, His118, His196, and Asp236, and another with Asp119, Asp120, Cys221, and Tyr224 indicating strong engagement with the catalytic site. The protein-ligand contact distribution is shown in Fig. 9 B. The docking results predicted hydrogen bonding with Asp119 and hydrophobic interactions with Phe61, Tyr67, Cys221, Tyr224, and Ala235. While some of these interactions were confirmed during MD simulations, others appeared transiently, reflecting the dynamic nature of ligand binding. Figure 9 C shows the full spectrum of interactions across the trajectory, emphasizing the residues that frequently engage with the ligand. Anisotropic Network Model (ANM) cross-correlation analysis,[ 61 ] based on molecular dynamics (MD) simulation trajectories, provided detailed insights into the collective motions of ligand-binding residues within the VIM-2 enzyme following interaction with selected fungal secondary metabolites. In the MSID001033-VIM-2 complex, residues such as Phe61, Trp87, Lys90, Gly150, and Asn165, which are crucial for π-π stacking, hydrogen bonding, and zinc coordination, exhibited moderate-to-strong positive cross-correlations (Fig. 10 A). These dynamic couplings suggest concerted motion among the key residues that stabilize the ligand within the active site. Notably, coordination of the ligand with both zinc ions contributed to a more rigid and less flexible catalytic pocket, as indicated by the elevated local correlation values. A similar trend was observed for the MSID001081-VIM-2 complex, where residues Trp87, Asp120, Asn165, and Phe61 displayed synchronized movements, reinforcing stable hydrogen bonding and π-π interactions (Fig. 10 B). The involvement of zinc-coordinating residues indicates a structurally rigid environment. In contrast, the MSID001168-VIM-2 complex, although featuring fewer interactions, showed significant local dynamic correlation around Asp119 and the zinc-binding sites, suggesting a stable yet restricted binding conformation (Fig. 10 C). Altogether, these results indicate that all three ligands induced a constrained and harmonized conformational state in VIM-2, disrupting the enzyme’s catalytic flexibility. Such dynamic stabilization highlights the potential of these compounds as effective inhibitors and emphasizes the value of incorporating flexibility-focused analyses into the rational design of metallo-β-lactamase inhibitors. Radial distribution functions (RDFs) were calculated to understand the spatial distribution of atoms around a reference atom in each of the three ligand-protein complexes.[ 62 ] Fig. 11 shows the RDF plots for complex-MSID001033 (pink), Complex-MSID001081 (blue), and Complex-MSID001168 (deep yellow), providing insights into the structural organization and interaction patterns. In all three complexes, the RDF profiles showed an initial sharp rise beginning around 2.5 Å, indicating the formation of a well-defined first solvation shell. This peak was most pronounced in complex-MSID001033, which reached a maximum distance (Å) value of approximately 3.1 around 4.5 Å. The RDF of Complex-MSID001168 (deep yellow) also demonstrates a prominent peak (~ 3.2) at approximately 4.8 Å, indicating strong local structuring and possibly stronger or more stable interactions between the ligand and surrounding residues or solvent molecules. In contrast, Complex-MSID001081 showed a relatively lower peak (~ 2.7) and broader distribution, suggesting a less ordered interaction environment or a more dynamic solvation shell. This could imply weaker or more transient interactions in this complex compared to those in complexes MSID001033 and MSID001168. Following the primary peak, all complexes show a gradual decay of distance (Å), leveling off beyond 9–10 Å, which is consistent with the bulk-like behavior of the system. complex-MSID001033 and Complex-MSID001168 retain slightly higher distance (Å) values in this region, possibly reflecting persistent long-range structural organization or interactions. Overall, the RDF analysis revealed that complexes MSID001033 and MSID001168 exhibited stronger and more defined interaction patterns compared to complex MSID001081. This suggests that complexes MSID001031 and MSID001168 form more stable or structured interactions with the protein environment, which may correlate with the higher binding affinity or enhanced stability during molecular dynamics simulations. Conclusion Computer-aided drug design (CADD) has become an essential component of modern drug discovery, offering a cost-effective and time-saving alternative to traditional experimental methods. By leveraging in silico techniques, researchers can estimate key molecular descriptors and predict pharmacologically relevant properties using data-driven models and structural analyses. In the context of combating antimicrobial resistance, particularly from enzymes such as Verona integrin-encoded Metallo-β-lactamase (VIM-2), computational strategies have shown significant promise in identifying novel inhibitors and optimizing lead compounds. In this study, 1830 fungal secondary metabolites were virtually screened to identify potential inhibitors targeting VIM-2, a clinically relevant metallo-β-lactamase associated with multidrug-resistant bacterial infections. Using a comprehensive approach that combines molecular docking, molecular dynamics (MD) simulations, post-simulation analyses, salt bridge analysis, anisotropic network model (ANM) cross-correlation and radial distribution function (RDF), we identified three promising candidates: MSID001033, MSID1081, and MSID1168. These metabolites exhibited strong binding affinities and maintained stable interactions within the active site of the enzyme throughout the MD simulations, indicating their potential as effective lead molecules. The focus of this research is the investigation of fungal-derived natural products as sources of VIM-2 inhibitors, an area that remains relatively underexplored. These findings not only contribute to the growing field of antibiotic resistance research but also highlight the value of integrating natural compound libraries with computational screening techniques to accelerate the discovery of next-generation antimicrobial agents. Declarations Author Contributions S.D. envisaged the idea, designed the experiments, analyzed the experimental data and wrote the manuscript; A.M. performed the experiments; K.B. and K.M. for helpful discussion and reviewing the manuscript. Consent to Publish declaration Consent to Publish declaration: not applicable. Consent to Participate declaration Consent to Participate declaration: not applicable. Ethics declaration Ethics declaration: not applicable. Data Availability Statement The datasets generated during and/or analysed during the current study are available from the corresponding author on reasonable request. ACKNOWLEDGEMENTS We acknowledge the Department of Pharmaceutical Chemistry, Eminent College of Pharmaceutical Technology, Barasat, for access to computational infrastructure that facilitated this work. AUTHOR INFORMATION Corresponding Author *Sandip Dolui. Eminent College of Pharmaceutical Technology, Moshpukr, Barbaria, Paschim Khilkapur, Barasat, West Bengal, India. E-mail : [email protected] Phone: 9609534743 References Ahmed SK, Hussein S, Qurbani K, et al. Antimicrobial resistance: Impacts, challenges, and future prospects. J Med Surg Public Health. 2024;2:100081. Salam MA, Al-Amin MY, Salam MT, et al. Antimicrobial Resistance: A Growing Serious Threat for Global Public Health. Healthc (Basel). 2023;11(13):1946. Akram F, Imtiaz M, Haq Iul. Emergent crisis of antibiotic resistance: A silent pandemic threat to 21st century. Microb Pathog. 2023;174:105923. Huemer M, Mairpady Shambat S, Brugger SD, Zinkernagel AS. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-6979596","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":490224892,"identity":"fd8edbe3-6da4-4f83-b336-c0e3388001ef","order_by":0,"name":"Sandip Dolui","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA/UlEQVRIiWNgGAWjYJACiQQGZgY+BsYGIPuAHEjkwANitLBBtRiDtSQQ0sIA1gIGBxJBGhnwaZFvP3vwxsMd1nJsEskNzDwMd9Lnhx1+CLTFTk63AbsWgzN5yRaJZ9KN2SQSQVqe5W68nWYA1JJsbHYAhxaGHDOJxLbDiW0gLTkMh3M3zk4AaTmQuA2HFvn+N6ha0g1np3/Aq4XhBpotCfLSOfhtMbjxxhjiF56HDYf/GDwz3CCdU3AgwQC3X+T7cwxv/gSGGD97+sOHMyruyMvPTt/84UOFnRwuLWAAjkQgOAAMDQaDA5BgwQ9gWiD2NuBQNQpGwSgYBSMWAAAqZGJdqGN0dwAAAABJRU5ErkJggg==","orcid":"","institution":"Eminent College of Pharmaceutical Technology","correspondingAuthor":true,"prefix":"","firstName":"Sandip","middleName":"","lastName":"Dolui","suffix":""},{"id":490224894,"identity":"85aa1b3d-4f93-46f0-99cb-800b82508127","order_by":1,"name":"Amartya Mitra","email":"","orcid":"","institution":"BCDA College of Pharmacy \u0026Technology","correspondingAuthor":false,"prefix":"","firstName":"Amartya","middleName":"","lastName":"Mitra","suffix":""},{"id":490224895,"identity":"bf817629-e841-42e3-9cef-cf2a7daf8635","order_by":2,"name":"Kaushik Biswas","email":"","orcid":"","institution":"Eminent College of Pharmaceutical Technology","correspondingAuthor":false,"prefix":"","firstName":"Kaushik","middleName":"","lastName":"Biswas","suffix":""},{"id":490224897,"identity":"9e84d5a6-5bf9-4aeb-a1a3-eda4231ab3bf","order_by":3,"name":"Kamalika Mazumder","email":"","orcid":"","institution":"BCDA College of Pharmacy \u0026Technology","correspondingAuthor":false,"prefix":"","firstName":"Kamalika","middleName":"","lastName":"Mazumder","suffix":""}],"badges":[],"createdAt":"2025-06-26 05:08:23","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6979596/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6979596/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":87597630,"identity":"db67f4a5-0831-4b27-a012-2904a3f61c75","added_by":"auto","created_at":"2025-07-25 16:09:48","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":208538,"visible":true,"origin":"","legend":"\u003cp\u003eThe figure illustrates the results of the docking protocol validation conducted using AutoDock 4.2. In the image, the native crystallographic conformation of the ligand TAN is shown in deep green, while the predicted binding pose generated through docking is displayed in light green. The close spatial alignment between these two conformations confirms the accuracy and reliability of the docking methodology, demonstrating its ability to reproduce experimentally determined binding orientations effectively.\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-6979596/v1/ddc27b63c0f3d591a4c4383d.png"},{"id":87597624,"identity":"13f7487b-7f9b-4408-a093-06c4a8efa4df","added_by":"auto","created_at":"2025-07-25 16:09:48","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":221446,"visible":true,"origin":"","legend":"\u003cp\u003eThe molecular docking of Meropenem with VIM-2, (A) The binding of meropenem at the active site pocket of VIM-2, (B) Schematic 2D interaction diagram depicting the most frequent interactions of meropenem with key residues.\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-6979596/v1/f90fd70b67f846ce49952ff9.png"},{"id":87597627,"identity":"56467c91-ed11-444f-8306-9fef40451634","added_by":"auto","created_at":"2025-07-25 16:09:48","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":208383,"visible":true,"origin":"","legend":"\u003cp\u003eMolecular docking studies were performed on the crystal structure of VIM-2 focusing on the protein-ligand interactions of the top three molecules: MSID001033, MSID001081, and MSID001168. (\u003cstrong\u003eA\u003c/strong\u003e) Binding of MSID001033 at the active site of VIM-2, (B) A schematic 2D interaction diagram shows the most frequent interactions of MSID001033 with key residues,(C) The binding of MSID001081 at the active site pocket of VIM-2, (D) Schematic 2D interaction diagram depicting the most frequent interactions of MSID001081 with key residues. (E) Binding of MSID001168 at the active site of VIM-2, (F) Schematic interaction diagram showing the most frequent interactions of MSID001168 with key residues.\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-6979596/v1/f9926784a2f063e002ea7d03.png"},{"id":87598042,"identity":"27cfb090-2b91-41ef-b3b9-baf7cf4f6906","added_by":"auto","created_at":"2025-07-25 16:17:48","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":50212,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003e(A)\u003c/strong\u003e Time evolution of the backbone RMSD of VIM-2 at 300 K, comparing the apo form (unbound, red) with the enzyme bound to each of the three selected ligands: MSID001033 (pink), MSID001081 (blue), and MSID001168 (deep yellow). \u003cstrong\u003e(B)\u003c/strong\u003e Changes in the radius of gyration (R\u003csub\u003eg\u003c/sub\u003e) of VIM-2 over the simulation period, indicating variations in structural compactness with (MSID001033, pink; MSID001081, blue; MSID001168, deep yellow) and without (apo, red) ligand binding.\u0026nbsp; \u003cstrong\u003e(C) \u003c/strong\u003eResidue-wise backbone flexibility of VIM-2 analyzed through RMSF, revealing dynamic regions influenced by without (red) and with ligand interactions: MSID001033 (pink), MSID001081 (blue), and MSID001168 (deep yellow).\u0026nbsp; \u003cstrong\u003e(D)\u003c/strong\u003e Time-dependent profile of the total number of hydrogen bonds formed between VIM-2 and each ligand throughout the simulation. Colour coding: apo (red), MSID001033 (pink), MSID001081 (blue), and MSID001168 (deep yellow).\u003c/p\u003e","description":"","filename":"4.png","url":"https://assets-eu.researchsquare.com/files/rs-6979596/v1/5ec90c0234836a87c3f340da.png"},{"id":87598896,"identity":"6c0289c6-7781-4b30-a53f-a691584010d0","added_by":"auto","created_at":"2025-07-25 16:25:48","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":25528,"visible":true,"origin":"","legend":"\u003cp\u003eTotal number of π–π and π–cation interactions between VIM-2 and the three ligand molecules MSID001033 (pink), MSID001081 (blue), and MSID001168 (deep yellow) over time at 300 K.\u003c/p\u003e","description":"","filename":"5.png","url":"https://assets-eu.researchsquare.com/files/rs-6979596/v1/9285f3081394dc6687ff10e8.png"},{"id":87598050,"identity":"b0a044db-b357-4429-a779-6f35de8eb268","added_by":"auto","created_at":"2025-07-25 16:17:49","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":237860,"visible":true,"origin":"","legend":"\u003cp\u003eSalt bridge analysis for the VIM-2 complexes with MSID001033 (A), MSID001081 (B), and MSID001168 (C).\u003c/p\u003e","description":"","filename":"6.png","url":"https://assets-eu.researchsquare.com/files/rs-6979596/v1/063b78e7b2ba2b91b26ad3df.png"},{"id":87598048,"identity":"9c8a8606-8176-4de0-97fb-753f41dab250","added_by":"auto","created_at":"2025-07-25 16:17:49","extension":"png","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":135931,"visible":true,"origin":"","legend":"\u003cp\u003eInteraction analysis of the MSID001033 molecule with the VIM-2 protein during MD simulations: (A) Schematic representation of the ligand-protein interactions observed throughout the simulation. Only interactions persisting for more than 30% of the simulation time are shown. (B) Contact frequency between MSID001033 and individual VIM-2 residues, with interaction fractions representing the percentage of simulation time each contact was maintained. (C) Interaction map from the final frame of the 250 ns MD simulation, showing residues in contact with the ligand across trajectory frames. Residues forming multiple contacts are highlighted in a darker orange shade.\u003c/p\u003e","description":"","filename":"7.png","url":"https://assets-eu.researchsquare.com/files/rs-6979596/v1/3238e78361a123e2629fcd76.png"},{"id":87598045,"identity":"fd20a0b8-bf49-4e93-a313-9cd45167c24c","added_by":"auto","created_at":"2025-07-25 16:17:48","extension":"png","order_by":8,"title":"Figure 8","display":"","copyAsset":false,"role":"figure","size":126018,"visible":true,"origin":"","legend":"\u003cp\u003eInteraction analysis of the MSID001081 molecule with the VIM-2 protein during MD simulations: (A) Schematic representation of the ligand-protein interactions observed throughout the simulation. Only interactions persisting for more than 30% of the simulation time are shown. (B) Contact frequency between MSID001081 and individual VIM-2 residues, with interaction fractions representing the percentage of simulation time each contact was maintained. (C) Interaction map from the final frame of the 250 ns MD simulation, showing residues in contact with the ligand across trajectory frames. Residues forming multiple contacts are highlighted in a darker orange shade.\u003c/p\u003e","description":"","filename":"8.png","url":"https://assets-eu.researchsquare.com/files/rs-6979596/v1/996eb95659bb82b6c736a2f1.png"},{"id":87597641,"identity":"fe4d7a63-1eb3-4593-bfdf-874ca68dd14e","added_by":"auto","created_at":"2025-07-25 16:09:49","extension":"png","order_by":9,"title":"Figure 9","display":"","copyAsset":false,"role":"figure","size":145181,"visible":true,"origin":"","legend":"\u003cp\u003eInteraction analysis of the MSID001168 molecule with the VIM-2 protein during MD simulations: (A) Schematic representation of the ligand-protein interactions observed throughout the simulation. Only interactions persisting for more than 30% of the simulation time are shown. (B) Contact frequency between MSID001168 and individual VIM-2 residues, with interaction fractions representing the percentage of simulation time each contact was maintained. (C) Interaction map from the final frame of the 250 ns MD simulation, showing residues in contact with the ligand across trajectory frames. Residues forming multiple contacts are highlighted in a darker orange shade.\u003c/p\u003e","description":"","filename":"9.png","url":"https://assets-eu.researchsquare.com/files/rs-6979596/v1/8da892202f68778725e975d2.png"},{"id":87597636,"identity":"8829bdad-180d-473b-824f-5807bd0e83fc","added_by":"auto","created_at":"2025-07-25 16:09:49","extension":"png","order_by":10,"title":"Figure 10","display":"","copyAsset":false,"role":"figure","size":435623,"visible":true,"origin":"","legend":"\u003cp\u003eAnisotropic Network Model (ANM) cross-correlation analysis of the four systems. Cross-correlation matrices are shown for: (A) apo VIM-2, (B) VIM-2-MSID001033 complex, (C) VIM-2-MSID001081 complex, and (D) VIM-2-MSID001168 complex.\u003c/p\u003e","description":"","filename":"10.png","url":"https://assets-eu.researchsquare.com/files/rs-6979596/v1/3cb9ebafb0a14fcb5f9e82d7.png"},{"id":87597640,"identity":"c30bc503-cb8e-4610-ba03-266bca874c86","added_by":"auto","created_at":"2025-07-25 16:09:49","extension":"png","order_by":11,"title":"Figure 11","display":"","copyAsset":false,"role":"figure","size":29333,"visible":true,"origin":"","legend":"\u003cp\u003eRadial distribution function (RDF) of the protein around the three different ligands: MSID001033 (pink), MSID001081 (blue), and MSID001168 (deep yellow).\u003c/p\u003e","description":"","filename":"11.png","url":"https://assets-eu.researchsquare.com/files/rs-6979596/v1/2488e7d09a4b2001a348e89b.png"},{"id":103507296,"identity":"99f55230-4bfc-479c-97a0-dd07432667e0","added_by":"auto","created_at":"2026-02-26 13:40:55","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2573104,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6979596/v1/de7e07e2-47ff-4516-97b2-5f231016ab72.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Uncovering Novel VIM-2 Inhibitors from Fungal Sources Using Structure-Based Screening and Molecular Dynamics Approaches","fulltext":[{"header":"Introduction","content":"\u003cp\u003eIn recent years, the global health community has grown increasingly concerned about the escalating threat of infectious diseases, particularly those caused by bacteria that have developed resistance to multiple classes of antibiotics.[\u003cspan additionalcitationids=\"CR2 CR3 CR4\" citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e] A key driver of this resistance is the ability of certain bacteria to produce β-lactamase enzymes, which hydrolyze the β-lactam ring present in many antibiotics, rendering them ineffective. These enzymes are broadly classified into four Ambler classes A, C, and D, which are serine β-lactamases (SBLs), and class B, known as metallo-β-lactamases (MBLs), which depend on zinc ions for activity.[\u003cspan additionalcitationids=\"CR7 CR8\" citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e] Among the numerous pathogens contributing to the antimicrobial resistance crisis, \u003cem\u003ePseudomonas aeruginosa\u003c/em\u003e has emerged as a particularly dangerous opportunistic organism.[\u003cspan additionalcitationids=\"CR11\" citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e] It is frequently implicated in severe infections such as cystic fibrosis, urinary tract infections, and burn wound complications.[\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e] As a prominent cause of healthcare-associated infections, \u003cem\u003eP. aeruginosa\u003c/em\u003e is linked to higher mortality rates, especially in bloodstream infections, than many other bacterial pathogens.[\u003cspan additionalcitationids=\"CR16\" citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e] Recent clinical observations have also highlighted co-infections involving \u003cem\u003eP. aeruginosa\u003c/em\u003e in patients with COVID-19.[\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e, \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e] Rhoades et al. reported a notable increase in \u003cem\u003eP. aeruginosa\u003c/em\u003e colonization in the nasal passages of patients following SARS-CoV-2 infection.[\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e] The emergence of extensively drug-resistant (XDR) and multidrug-resistant (MDR) \u003cem\u003eP. aeruginosa\u003c/em\u003e strains has created a formidable public health challenge.[\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e, \u003cspan additionalcitationids=\"CR22\" citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e] These resistant strains are associated with significantly higher mortality, with global reports indicating that the death rate from carbapenem-resistant \u003cem\u003eP. aeruginosa\u003c/em\u003e infections ranges between 33% and 71%. Resistance to carbapenems, often considered the last line of defence against MDR Gram-negative infections, is driven by a variety of mechanisms.[\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e, \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e] Among these, the production of β-lactamases, particularly metallo-β-lactamases, plays a pivotal role.\u003c/p\u003e\u003cp\u003eMetallo-β-lactamases (MBLs) are highly efficient enzymes responsible for the inactivation of a wide range of β-lactam antibiotics that lead to antimicrobial resistance, which is a matter of concern for antimicrobial therapy.[\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e, \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e, \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e] The unavailability of clinically useful inhibitors against these MBLs is limiting the therapeutic options. These enzymes differ structurally and mechanistically from their serine counterparts. MBLs utilize one or two zinc ions in the active site, along with a hydroxide ion, to catalyze the breakdown of the β-lactam ring. Based on sequence and structural characteristics, MBLs are subdivided into three subclasses B1, B2, and B3 with the B1 subclass being of greatest clinical relevance.[\u003cspan additionalcitationids=\"CR29\" citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e] Notably, subclass B1 enzymes possess a unique di-zinc active site and a water-bridge hydrolysis mechanism, enabling them to inactivate a broad range of β-lactam antibiotics.[\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e, \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e] Prominent members of subclass B1 MBLs include Verona integron-encoded metallo-β-lactamase (VIM), New Delhi metallo-β-lactamase (NDM), and imipenemase (IMP).[\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e, \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e] Among these, VIM, particularly the VIM-2 variant has become the most widespread MBL in \u003cem\u003eP. aeruginosa\u003c/em\u003e.[\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e] VIM-2 is now considered the primary driver of carbapenem resistance in \u003cem\u003eP. aeruginosa\u003c/em\u003e on a global scale.[\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e] The World Health Organization (WHO) has placed a high priority on discovering novel antibiotics and effective MBL inhibitors to combat the growing prevalence of VIM-producing strains.[\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e] In many parts of Asia, VIM accounts for over 99% of MBL-positive, multidrug-resistant \u003cem\u003eP. aeruginosa\u003c/em\u003e cases. Structurally, VIM-2 is a 266 amino acid long protein with a 32.4% similarity with NDM-1.[\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e] VIM-2 differs from VIM-1 by seventeen amino acid residues. Consistent with other subclass B1 metallo-β-lactamases (MBLs), VIM-2 contains two zinc ions situated within the conserved αβ/βα motif characteristic of metal hydrolases, which are crucial for catalysis.[\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e] The catalytic site of VIM-2 is composed of two zinc ions, which are essential for its enzymatic activity. Zn1 is coordinated by three conserved histidine residues (His116, His118, His196), while Zn2 is bound by Asp120, Cys221, and His263. This binuclear zinc configuration is critical for substrate binding and catalytic efficiency. The unique coordination geometry also contributes to the enzyme\u0026rsquo;s resilience against many β-lactamase inhibitors, underscoring the challenge of neutralizing VIM-2 in clinical settings.[\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e] In response to the urgent need for effective MBL inhibitors, research efforts have increasingly focused on identifying non-β-lactam compounds that can bypass traditional resistance pathways while offering improved safety and pharmacokinetic profiles. Among natural sources of bioactive molecules, fungal metabolites have attracted considerable interest due to their wide-ranging pharmacological activities, including antibacterial, antiviral, cytotoxic, and anticancer properties. Although no fungal metabolites have yet been approved as antiviral agents, growing evidence supports their potential, with several candidates currently under investigation for clinical development.\u003c/p\u003e\u003cp\u003eFungal secondary metabolites encompass diverse chemical classes such as polyketides, non-ribosomal peptides, terpenoids, alkaloids, and phenylpropanoids. These compounds are known to exert therapeutic effects by modulating cellular processes or inhibiting specific enzymes.[\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e, \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e] For example, polyketides like griseofulvin and fusidic acid interfere with microtubule assembly and bacterial protein synthesis, while non-ribosomal peptides target bacterial cell wall biosynthesis. Alkaloids, terpenoids, and other classes contribute further to antimicrobial and enzymatic inhibitory activities. Lovastatin, a well-known fungal metabolite, inhibits HMG-CoA reductase, showcasing the potential of fungal compounds as enzyme-targeted therapies.[\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e] Despite their promising properties, the interaction mechanisms between fungal metabolites and metallo-β-lactamases such as VIM-2 remain largely unexplored. In this context, the present study aimed to identify potent inhibitors of VIM-2 by screening a library of 1,830 fungal secondary metabolites using in silico techniques. Through molecular docking, molecular dynamics simulations, and post-simulation analyses, three compounds MSID001033 (Hypholomine B), MSI 001081 (Inoscavin C), and MSID001033 (Melledonol) emerged as promising candidates with strong binding affinity and favorable interaction profiles against the VIM-2 enzyme. These findings not only offer new insights into natural product-based drug discovery but also underscore the power of integrating computational approaches with natural compound libraries to accelerate the development of next-generation therapeutics for combating antimicrobial resistance.\u003c/p\u003e"},{"header":"Material And Methods","content":"\u003cp\u003e\u003cb\u003eSmall-molecule database preparation\u003c/b\u003e.\u003c/p\u003e\u003cp\u003eThe secondary metabolites derived from medicinal fungi were obtained from the curated MeFSAT (Medicinal Fungi Secondary Metabolites and Therapeutics) database for this study.[\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e] The two-dimensional (2D) chemical structures were converted into their corresponding three-dimensional (3D) conformations using Open Babel, a widely recognized cheminformatics tool. To ensure structural integrity and minimize potential steric clashes, energy minimization and geometry optimization were performed during the conversion process.[\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e] A custom Bash script was developed to automate this workflow, utilizing the MMFF94 force field for molecular optimization.[\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e] The steepest descent algorithm was applied for 10,000 steps to achieve energetically favorable conformations. The optimized structures were subsequently exported in PDBQT format, rendering them compatible for downstream molecular docking studies.\u003c/p\u003e\u003cp\u003e\u003cb\u003eSelection of target protein and preparation\u003c/b\u003e\u003c/p\u003e\u003cp\u003eThe 3-dimensional (3D) coordinates of metallo-β-lactamase, specifically the crystal structure with Protein Data Bank (PDB) identification number 6SP7 was procured from the RCSB-Protein Data Bank.[\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e] This Metallo beta-lactamase crystal structure exhibits a resolution of 1.8 \u0026Aring; and an R-value of 0.192. It encompasses 266 amino acid residues that have not undergone mutational modification. To create the desired protein configuration for subsequent analysis, AutoDockTools (ADT) were employed.[\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e] ADT facilitated the addition of hydrogen atoms and the removal of all heteroatoms, including water molecules. Additionally, Kollman charges were incorporated into the protein to adjust the partial charges appropriately according to the selected protein crystal structure. Finally, the prepared protein configuration was saved in the preferred file format, such as 'pdbqt,\u0026rsquo; to facilitate future analysis.\u003c/p\u003e\u003cp\u003e\u003cb\u003eValidation of Docking Protocol\u003c/b\u003e\u003c/p\u003e\u003cp\u003eThe reliability of molecular docking predictions is often evaluated by comparing the predicted ligand-binding pose with that of its experimentally resolved counterpart. To validate our docking protocol, we performed a re-docking experiment using AutoDock Vina, targeting the active site of VIM-2 (PDB ID: 6SP7)[\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e], after removing the co-crystallized ligand Taniborbactam (TAN). The re-docked conformation of Taniborbactam showed a close alignment with the original crystal structure, yielding a root-mean-square deviation (RMSD) of 2.0 \u0026Aring;, which is well within the generally accepted threshold of 3 \u0026Aring; for docking accuracy. This strong agreement between predicted and experimental poses confirms the reliability of the chosen docking parameters and supports the suitability of the AutoDock Vina protocol for subsequent structure-based virtual screening efforts.[\u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e]\u003c/p\u003e\u003cp\u003e\u003cb\u003eMolecular Docking Protocol\u003c/b\u003e\u003c/p\u003e\u003cp\u003eMolecular docking studies were performed using AutoDock Vina on a Linux-based platform to investigate the interactions between selected fungal secondary metabolites and the metallo-β-lactamase enzyme (PDB ID: 6SP7).[\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e] A cubic grid box with dimensions of 70 \u0026times; 70 \u0026times; 70 \u0026Aring; and a spacing of 0.375 \u0026Aring; was defined to cover the enzyme active site. The grid center and other docking parameters were specified in the AutoDock Vina configuration file, while all remaining settings were left at their default values to maintain consistency with standard protocols. A total of 1,830 fungal-derived secondary metabolite compounds were retrieved from the PubChem database. Before docking, all compounds underwent energy minimization to ensure stable conformations. These ligands were then screened against the VIM-2 enzyme using a conventional docking workflow. To streamline the selection process and identify the most promising candidates with a high likelihood of binding affinity toward metallo-β-lactamases, the binding energies of all docked compounds were systematically analyzed. A cutoff threshold was established based on the lowest binding energy observed among the screened molecules. This benchmark served as a filter, allowing the prioritization of compounds that demonstrated the strongest interactions with the target protein, thereby narrowing the chemical space for subsequent in-depth analyses.\u003c/p\u003e\u003cp\u003e\u003cb\u003eMolecular dynamic simulation and post MM-GBSA analysis\u003c/b\u003e\u003c/p\u003e\u003cp\u003eMolecular dynamics (MD) simulations were carried out using Desmond module within the Maestro interface (Schrodinger Release version 2020-4: Desmond Molecular Dynamics Syatem, D.E. Shaw Research, New York, NY, 2020), applying the OPLS-2005 force field throughout[\u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e] and dynamic behaviour of the target protein both in its unbound form and when complexed with selected ligands over a 250 nanosecond trajectory. Each simulation system was solvated with a simple point charge (SPC) water model placed within a cubic box maintaining a minimum 10 \u0026Aring; buffer between the solute and box boundaries. To mimic physiological conditions, counter ions were added to neutralize the system, and a 0.15 M salt concentration was applied. Before initiating the production phase, energy minimization was performed using the steepest descent method for up to 2,000 steps, or until the energy gradient dropped below 25 kcal/mol. This was followed by further refinement using the limited-memory Broyden\u0026ndash;Fletcher (Goldfarb) Shanno algorithm until convergence was achieved at 1 kcal/mol. The system was then subjected to a five-step equilibration protocol. Initially, Brownian dynamics simulations were performed in the NVT ensemble at 10 K for 100 ps with heavy-atom restraints. This step was followed by 12 ps of restrained NVT dynamics and another 12 ps of restrained NVT dynamics in the NPT ensemble using the Berendsen thermostat. The system was gradually heated to 300 K over a 12 ps period and equilibrated for an additional 24 ps without restraints. Production MD simulations were run for 250 ns under NPT conditions at 300 K. Following the simulations, binding free energy estimations were performed using the thermal_mmgbsa.py script from the Desmond suite on the Schr\u0026ouml;dinger platform. MM-GBSA analysis evaluated the binding free energy (ΔG bind) based on molecular mechanics and an implicit solvent energy model.[\u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e] All trajectory analyses and visualizations were performed using the Schr\u0026ouml;dinger Maestro interface and VMD. Additionally, normal mode analysis was conducted using VMD Timetools, including the Normal Mode Wizard and the ProDy interface.\u003c/p\u003e"},{"header":"Result and Discussion","content":"\u003cp\u003eMolecular docking plays a vital role in drug discovery by elucidating how ligands interact with target proteins, thereby providing valuable structural and energetic insights.[\u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e51\u003c/span\u003e] To validate the docking protocol, the co-crystallized ligand, Taniborbactam (TAN), was re-docked into the active site of VIM-2 (PDB ID:6SP7) using AutoDock 4.2. The root-mean-square deviation (RMSD) of 2.0 \u0026Aring; between the experimental and predicted poses confirmed the robustness and reliability of the docking strategy (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). A benchmark set of 16 β-lactam antibiotics, including penicillins, cephalosporins, carbapenems, and β-lactamase inhibitors, were subsequently docked against VIM-2 to establish a comparative framework for binding affinity. Among these, meropenem demonstrated the most favorable binding score \u0026minus;\u0026thinsp;8.5 kcal/mol (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). Interaction analysis revealed that meropenem formed key hydrogen bonds with Glu149 and Asp236, coordinated with catalytic zinc ions, and engaged in hydrophobic and salt-bridge interactions with residues such as Phe61, Tyr67, Val73, and Phe117 (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). Following validation, structure-based virtual screening of 1,830 fungal secondary metabolites from the MeFSAT database was performed using AutoDock Vina. The screening identified ten compounds with strong predicted binding affinities, from which the top three candidates MSID001033, MSID001081, and MSID001168 were selected for further analysis based on their docking scores of -9.68, -9.4, and \u0026minus;\u0026thinsp;9.2 kcal/mol, respectively, all of which surpassed that of meropenem (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). In-depth interaction analyses of the top hits revealed favorable binding profiles within the enzyme active site. MSID001033 exhibited multiple stabilizing interactions, including two π-π stacking contacts with Phe 61 and Trp 87, four hydrogen bonds with Trp 87, Lys 90, Gly150, and Asn 165, and coordination with both Zn1 and Zn2 ions (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eA,B). These interactions involve residues critical for β-lactam substrate binding, reinforcing the inhibitory potential of the compound. MSID001081 also fit well within the active site, forming three hydrogen bonds with Trp 87, Asp 120, and Asn 165, two π-π stacking interactions with Phe61 and Trp87, and dual zinc coordination bonds (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eC,D). MSID001168 showed a relatively simpler binding profile, but still established key interactions, including a hydrogen bond with Asp119 and coordination with both zinc ions (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eE,F). All three ligands engaged with conserved catalytic residues and showed consistent interaction patterns involving hydrogen bonding, aromatic stacking, and metal coordination, features essential for effective enzyme inhibition (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eAs part of the docking study, we evaluated the binding affinities of several antibiotics against the VIM-2 enzyme.\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"2\"\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\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMolecules\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eBinding Energy (ΔG kcal/mol)\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eImipenem\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-8.555\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMeropenem\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-7.611\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eErtapenem\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e\u003cb\u003e-6.698\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eFeropenam\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e\u003cb\u003e-6.629\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eCefuroxime\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e\u003cb\u003e-6.497\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003ePiperacillin\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e\u003cb\u003e-6.295\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eCaptropil\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e\u003cb\u003e-6.129\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eAmpicillin\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e\u003cb\u003e-6.138\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003ePenicillinG\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e\u003cb\u003e-5.842\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eAvibactam\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" 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4x03btxXzoX+8CU1EagcWc8AAADIRYoiAAAAchEoAgAAIBeBIgAAAHIRKAIAACAXgSIAAAByESgCAAAgF4EiAAAAcnVKoKhGkdv64nundZV7QbleS6dGVuMXaXcV2mftu44BAACgo3V4oKjA7v7770+H2qaSdenVXWpTPPv+4kanIFFvSQEAAOgsHR4o6rVqY8eOTYfapp7rag8K9mpNyVRgO3HixHQIAACg41FGsR2RIggAALqyDgsUVY5Q5e1KlU306aXm0TifriznPC+//HLLPF5u0cfpMx5WuT9fZ3Z799xzT8t64q5UWUGfrnVr37xcpChYVL/G69O35eUP1cXrjbe9ZMmSdOze58ePPz5eAACAeuuQQFFBjpcVnDJlylfKFWr6lVdeGaarkzh4U/+tt94apk2fPj0s74Ff7IEHHrBx48aF+SZPnhzmueyyy9KpxcDKh4cOHRr2ZeHChaHzLGJ93nnnnWGc1uNZ2+rXMWQpaNM+afrdd98dxvmxirKP1a9scu2bU9aylospSBw/fnyYX536nY5Hy2u/tS659NJLbfTo0S3bAgAAqKd2DxQVnD377LMtwY2CqLhcoU+/44470jHWEsDFZfx8uoIjBUb6jCnYHDVq1F7r0TxxMBYPa/3al2zwp1Q8BWM+fsyYMeGzXM3jW265JXwqmPPjrIUC1AkTJqRD1hJ4Oh1bHNRqnxRgAwAAtId2DxSXLVuW9uXLm+5BmoK2OPu1FAWa6uIgq1Y33HBDCMY8xXLGjBl7BY5Zt99+e5jfs4B9uWqVC0RjSlW86667Qv+kSZPC/gIAALSHDiuj2FoglBdgDR48OHQKxMrx7FcFi8q+bQsFhArGlEWtwE+pfErhLEXze1axaLlKg748rQXWcariokWLSgawAAAAbdXugaK3XxhXQFEZQwU7Knuo6Uqxi8sSKgjSOE3z5b1yiigQywsIla2swM6zZmuhgFUBmAd/6soFYzouD3I9WMzS/njwGAe9nrWsYxUFvHFNaU8h1fQ4+FQgq/mU1Q4AANBukuCm3SXBkSKolm7s2LGFJPhJpxZp2Kdnp0m8vE9PAqu9xiWB4l7zPfTQQ3sN/+3f/u1ewxMnTtxrOAnAvjLOO20rj5aJ90PLOx+v43U+nzotq0+dHxefBy2Xne6y58i3BQAAUC899F8SYCDlqYPZyjJKFRw5cmRDZPUqdVHlE+OKOwAAAPXWYWUUu4o4CzymbOBGKQ947733UokFAAC0OwLFDJVz9IoscaembzqTUjR9X1Q2kUosAACgvZH1DAAAgFykKAIAACAXgSIAAAByESgCAAAgF4EiAAAAchEoAgAAIBeBIgAAAHIRKAIAACAXgSIAAAByESgCAAAgF4EiAAAAchEoAgAAIBeBIgAAAHIRKAIAACAXgSIAAAByESgCAAAgF4EiAAAAchEoAgAAIBeBIgAAAHIRKAIAACAXgSIAAAByESgCAAAgF4EiAAAAchEoAgAAIBeBIgAAAHIRKAIAACAXgSIAAAByESgCAAAgF4EiAAAAchEoAgAAIBeBIgAAAHIRKAIAACAXgSIAAAByESgCAAAgF4EiAAAAchEoAgAAIBeBIgAAAHIRKAIAACAXgSIAAAByESgCAAAgF4EiAAAAchEoAgAAIBeBIgAAAHIRKAIAACAXgSIAAAByESgCAAAgF4EiAAAAchEoAgAAIBeBIgAAAHIRKAIAACAXgSIAAAByESgCAAAgF4EiAAAAchEoAgAAIBeBIgAAAHIRKAIAACBXpwaK1157rfXo0aOl+/jjj9MpZieeeGLL+Ntuuy0dWz/xtuPtdmUvv/xyyzF5Jw8++GCY1szi71PHi/YT35s613nX1z333JP2ta+O2k6ltD9+btR1d3q2cj46hn4n43Od7eLfOZ+3lt+F+Br332b/7WmP3+pGo+PUM7A76dRAcfLkyTZx4sR0yGzo0KFpn9lHH31k48aNs+nTp9t9992Xjq0fbbuZ6Aa97LLLwjksFAotnS7qm2++OZ2reen7HD16dDqE9qKAXA9Jv75eeumlr1xfHfVj0Wg/SvqxvPPOO8N1qGeXNMsfobU64YQTbOHChekQ2pN+J/V76fwe9edi/PvaFnfccUfL9Y3uoSGynuOLTj9EbvDgwTZw4MB0CKXoL7z7778/9CvAjulBwU2Nenn22WfDj7/Tj1P8x55SF/1abE8dtZ1qzJgxI+0r/pjq3ovPFdAZ7rrrrrSv+MeM6L7V9XnppZeG4bbSerS+9kjUaTQ6zuzvbNNLDrpTJT8yheSvoEISzBS0O+rGjh3bMi35azR0Pi356yhM8+Hkr6QwrGV8nJbzfq1X64+HXTwub7po/T5N+yHxtjTO5/HpHc33JbvveeJzE+9z9pz5MWXPtw/H50zjstNdfK78u5LsuPg8S7nv02WvC98PzY/24ec7/i5d9tpSJ96v78fvxfiaKXUNSPwdq9P3n7ed+HrU9LxrxtevT1+vbzue358/ebL7o+Uku0/Z+8DFzyJ1vrzE5yRePh4fb0f92WHxYR1bfE617ZiPV+fnqJLz6MNadzy/P0skHh/3o33F15fz68evqex3nB32/ux9EF9LvozmyT6Hs8PxszwWX9fe+f2Ylbdf2WvT98+P08Xb8XsgPmaN83nUn73HvYu3oc6VOmfxccfzd0Wdvvc6yf7lxSdW49X5w8e/DL8IfDi+sHxZjSt38Tof9i/XLxatOx4Wv0icL6txvi++rx0pfjD4fpfi8+pYJD4+8WF1OpbsTeHD/n35/H7+/Obz9fuwz69+LSO+L35O4+Nwvj19lvv+JF6+tfOA2vm1HndO35Hfw34NiC+j71Hfv+bRZyXXQDzdr4dS2/Hr0b9/H/Z54u2p03h9+nifL15Hlqb5Naz5NazlxY/Tp2f5NezT1e/H5sejeXw+v6982JeNz5PmiY9L4ulaVnzY+TkQ3+/seWvtPPo4Pw++v74+H/ZjU4f2FX83ceffpdN3H4/371ideL9fPz5d6xef7t+xXwOaT/waUKd+vwb8GoqH/fr26zHLj8mX9XVKfG1pPX5cvp8+XdN8n5zPq0+fFp8/LePH5duOp8fD2X3z8b4fpY6tq2ioWs9Ktk5OaOhXuSeVf6rFhAkTvpI9Vs7tt98ePm+55ZbwqeWVRK9stuTCD+OUDZ5cOF8p/Kt5b7rpJl01DZ/NdPfdd4dPHYv4/mYrBCQXfZjmBXaTCz98ljJq1KjwefHFF4fP8ePHh0+V1xIvPqDvVuc0ew5bo3Oc5RVW/PtR1of3o/34te73qagcrL6PSq5/lSXV/VhJGWH/jq+55prwqeyeetxnuo+1LmUP69PvC79+dWx515zfJ759v498+dZMmjQpfF555ZXhU8ei7asco2eja92+fo3LlnHUuYuL45R7tuk4fF1+b+icqtM58Pt70KBB4TPOoqyE1q9zmOXnzp8L/nxFx9L1pU7fvX5PdZ+2VmY271mvZfTcFs+q1m9EJbRtPTOyfD90D/k1qmsyb/8quT/9Nyumdeke8vvAr3N/rjit359r8b2lZfwenzZtWvjMKrVvvh7VG9B+dPWs6oZrHic+oe1VBinvYixFN0hcIWTZsmXh03V2Gcp4+7UG1osWLUr7KpM9B9Vq6/LVfH+oL69AovvUf4QkL7DKqram4JIlS9K++tLDPI8e6rrXPZCsVKXzVnufyfLly9O+1mm/26Kty3NfNqb4j7J777037atMNddfJfwe8KBO93h83WSDvVg192f8G6P5tZyWl+xzxQNIibev/fJ5y+2XZPdN8/v9pOdNV68N3nCBorT1gdVW8UWhL9n/MlOX99dRZ9K+jh07NvRPmTIlfGZl/4LKGjJkSNrXMeIbE11L9o83/QjpHqkmsKpWqeu63pSa4vd5NcdTbQBcKnUiT0f+IVoqgAbqTSnh+t1SrpNfd7rvyqn1/hRfTl1eKnhM84j2S/un/WwtVzJv3/TbrGEtr+dma7/DjawhA0Wd4GwSeDa4qCQFo1L+V5OvU1klSmLXhaKg1b9gfWa/7Hr/xVULXcS+r3GtcVF2mac0etaS/5Xkf8ndcMMN4bNWvn6v9XnrrbeGT8+e8HOk/dN+6txmfwAfeOCBtK8yHrArxdePw7NH0L70l7PTudf36t95LPtXdPbh3to14NdlfA9qnXEKhPh24j/wNE+p60Hri3mRkzjrNXsfif/A+Pb9PvLlWzNmzJjwqf3y4hfaTvzHntbt61dqbXxM1dJx+rr8XOi+Uaf70L8PT33x77DS81hKXIRHZs6cGT7ROeL70K/Bauh6iIsuSDV/7OTx69yDK3WlVHp/ZsX77cVGdN9li1plc7g0PQ78ygWJpfZN2/F9bC3I7BKSE9Fpki8xFPhUlzwo07F7qIBo8rBLh746v/drvng4eQjuNa+G1fmwponGaf0+Xl28vew038fsthpFcnHvtb/qdG5i2Xn8eLPj42NUF59PdeLj4ml+bl25cxVPy/aX275vIy5YnJ0n/h5RP/peJD7v8TUW3zO6pqTUvFLuGpDsd+zrzNtOPE7XWvZ6iJ8Bvn4Xb9vnzxNvQ51vO3v/ZO8Dl53PC7tL3jUu2fHxccTT1Ok4/JxpvnjeeFvi49WV+g7zzqP3q8tu389HfD6z+4/2kb2Gs53Tdx2Pzw7H35e+f1dqnp/97Gd7TcsOZ68RXYel9jV7jbq8+zPvnouHXTzOr7/sMcfi8d75eYjvJ+2T5O1b9hh9u11VD/2XHAhQNf3FpNSG5IZtuCx5oLtSaobKTCU/alVn0QEdQamJuj6zGuGaVYqiV8SMJcFlq9nWzaohs54BAEBzUna4Ai+lU3mnBIdqy/vWmwJYBYkLFy7ca99Gjx7dUgO6OyJFETXJ/tXVnf/aAhpJXIZUeMSj0aisY/bVnwrGKmk2q715Tlmsu+eaESgCAAAgF1nPAAAAyEWgCAAAgFwEigAAAMhFoAgAAFqo9m+2UpSotnL2pROq2Kh51WWntYWaefL1xo2Gi7aTHddW2eNQDWxvGL+7I1AEAACBgiS1Zzh9+vR0zB56FV32rWhq7UI1lutNb/Aam76xKEv7UMtbZsrRccRtO6o9R7VHqgCyuyNQBAAAISVRzdaoORgFajEPmNR0jObrTNqH7P61BwXLagauu6csEigiyCa7e3+cvB+PVxe/b1PJ9D4+nk83WLzu+IaLsxbidQEAOt69994bPvPaDFy0aFFLitukSZPCZx7PtvbO6bekkvGtpeDp98VTGn0Z/f7EvzPah3i9cWCb/R0rF/QqGNUx33333emY7olAEUGcfaC/KL15TWU1+I3kDaRqmubVX3W66STOjnjppZfCX6SipHvxYb/htE5N0w2v9SmZv7UHBACg/eh5n5eNrD/qldV76623huHx48eHzzwnnHBCeAGDP9tFz3atW78D/lugcVqvb9PnVQpeueAtznb2delNKqLtioK7UaNGtQx7AKzt6XfM3wqj7cbZzXmuueaahkhF7UwEivgKv/mydGOp019q2ZbrY/fdd58NGjQo9OsmzHtji9+4uplFfxGWe/gAANpPnNuT9cADD4TUtRtuuCEMKzArNb/nQul3wPmzfeTIkSG1Ur8j+l3QOtWvN7JU8vo+BWtKVMhmO/vvjL9mTwFgXqqoJ1T4fApqpZJEiuXLl6d93Q+BIlrlN4huUiXVK7jzvzqXLFkSPiuVfeG7/rrTOhV4+l+FAIDGoOf+kCFDQr8CK3/2K3jM0vNcKYTTpk1LxxSVe7Z7VvCUKVPSMaWDspkzZ4YUvmq1lhqobPXWLFu2LO3rfggUUTFPoq/nOy89C8A7AEDHGzhwYNq3N+X+xLlCd911V/iMiyU55UYpy1l/+Oel0inQi3lWsIJPT90rpz1qO4sHwshHoIiKZB8I2ZTBavnNrvIojgotANA5KgnUxCt4SF6lFmU5K/DTs92DRa984kGmfk+URR2n0rWW6qfpednO1fDte06Yb9Oz1MtRtnm3VQAS48aNU3JeS5fc6C39yUMhzKNPH5fc+C39CxcuLDlNXbwuddqWZLc5ffr0MB4A0PH82e2yz+i8LjuPfg/iYa1T8n5T4nk1zudRv34PfJo6rcd/O1x2ejyc/d2ZOHFiWEaf8Xjtg2SPw/l+dWc99F9yUgAAQDemFLYkMApZyPUsYtRVKWtcrXOoPcW2pGR2dWQ9AwCAkP2sIFHlBlvLCm52On4FiePGjevWQaKQoggAAFp4ymJ3Dg/UXI8qz3T3IFEIFAEAAJCLQBEA2tMrr+h1RWY9eyqZwuz66/XusXQiADQ2yigCQHvSW4y+/32zH/7QbOVKs3feSScAQOMjRREA2tPjj5tt3Wq2e7fZvvuaffObZn37phMBoLERKAJAe9q2zWzx4mKgeOSRZv362bx582zYsGG2zz5k6gBobASKANBB1qxZYxs3bgzvst13333tnHPOIVgE0NB4QgFABznssMNs8+bNdsABB9iOHTts9uzZtnPnznQqADQeUhQBoIMtXLjQtmzZYps2bbJevXrZ6aefHj6zlPr47rvv2qBBg2zFihXWu3dvO+OMM9KpAND+CBQBoBN8+umntm7dupZgUQHghx9+aIsXL7YBAwbYkiVL7Nxzz7W1a9faEUccEbKt9bjetWuXjRgxIl0LALQvAkUA6CTLli2zt99+21auXBmyo6+77rqQNb3//vuHFEdlVXtKox7Vjz76qJ122mlhmgJLyjcCaG8EigDQiV566SUbPHiwHXfccdajlYa4V69ebS+++GJ4vZiCxFNOOSVUiqnYunVmu3Yp6gy1rwGgNfw5CgCdRNnJSjHslwRtrQWJcvjhh9sFF1xgS5cuDVnQKutYsSVLzKZP14t8zR591GzRIiVpFtt4BIASCBQBoJMo4OvZs6cddNBB6ZjWDRw4MFR+0bLKEFJlFwWNpWiez9atsxUKDrdvNzvvPLOjjzabNi35BUh+AshUAlAGWc8A0AkU3M2dO9cOOeQQO/bYY9OxlVMt6FdeeSWUVdywYUP4VNCpZneUPa1yjsqeVnb1qKuusp7z59uBr71mduCBZps3m91wA9nPAFpFoAgAneDzzz8P7SiOHj06HVM9ZV2/+eabIZVx/fr1oSa1akyfp1TDlAJSVZQJlM2sdhuVkujjAKAMAkUA6ARfrFwZUhQvHzUqHVM7rUeP8lNPPTUdAwD1QRlFAOhI69ebvfCCHTF/fl2CRLXD+OWXX9qReo80ANQZgSIAdKQnnggVSPZRLefnn09H1k5ZziqXSKAIoD0QKAJAR1I5weHDzVSBRU3WtJEa3z5atZgBoB00baD48ssvh3bJvGs28bHddttt6dg9NM6nq3Fe+fjjj1vGXXvttWH4nnvuCdN8vHfx8tnOl4nF82u9MW1f430/JN6XvGXKrU/7rvHx+iReZ94+Ag1h4ECzL74we+sts2oay86hIFE1nymbCKDdqDJLMxs6dKgq66RDzWX06NHh2PKOz8ePHTs2HVM8F+PGjQv906dPD9N9WHx9EydOTMfsGbdw4cIwrPVpWOOd1qF1Z/tF8/k+xP0uu34ptz7fvnfx/sfj1cXrBBrKnDmFwmefFbZu21ZYvnx5YVvyWQst++KLL6ZDAFB/ZD13YVdeeWXaZ/bggw+mfcX+JKBKh/aI3+Jw6aWXWhIspkOVu++++ywJ3OzZZ59t2eb48eNbUvf0KjJtRym6SuHTfEOGDAnTTjjhBLv//vtDfznl1ifJdWtJMBv6nVIQdTyalgSf6VigHezebTZ7ttnDD5s99pjZtm3phCooBbB//1BOMQn2Qq3l3VpvlXRPjBw5Mh0CgPojUOziPCCcMGFC+JQlS5a0BGcxBVB33nlnyJpV4KVg8Y477kinVu6aa64Jn3pHrcQBqJsxY0b4AcyjbZdTan0KNBWoio5RblCjwQkdh45HPvroo3Csmh+ou88/N3v9dbPLLzfbbz+zp59OJ1RPr+9TQ9lbt24Nb1iJKXBUG4j640fU7uJDDz0U2l7UvadKLLoPtA4AaC8Eil3cmDFjwqdS7pS6oE6pcHkmT54cUgPlsssuCwFjLTwI9RS+jqYyigp4JQ5GtT86JgWaChaBdqHX5SkVsW9fM9U03rQpnVAbBXpqIFvB4jvvvNMSGL7xxhtJDPp0+Pzxj39s27dvT2LTy61///7hrSxqaPv666+3fdtYzjGXUjfXrTPbuLHYQLc67ZfGqx9At0Gg2MUpFc2zWidNmhS6m266KQznUQClHyIPGBV0VWvRokXhs7NS7BTwetbzLbfcEj5F++NZzwoWqdCCdjFggNnJJ5v98pdmixeb3XhjOqF2CvYULKqZm7feeitkRet6ViWVo446yoYPHx4CxZkzZybx27pkkzeGoid9+vRJ11BnyT0WUk2V+v/++5ZsuFhbW5VwZs1KZwLQHRAoNgEPllS2r5y4drQCRmVb15LyNmXKlPA5Km0s2IPO2MUXXxxeK5bHs4hLKbW+mILhcePG5WZTe8qpB7RAXSVB3S79cfYf/oO9kwRySfhUF3pP8+mnn25z5syx9957L6QwKsX8sMMOs29961vhmr7uuuuSGDUJUtubAkVlrasJH+UcPPNMsZs2rZj1DqDbIFBsAp6CqKApG1DFVJEkTmVTVq2XN6yUltd2lGrn27311ltD1reozJR+0BQMKkVE803Tj0tC28urZJNVan3atrKWPctbgaCvT5Vf4tRR7aNnywP1tmv79pB6/dZrr9mWzZvTsW2nMonKWr7ooouSGO3Y8MfYIYcckk7tQPojUJV1dO8eeqjZEUcUU1L1mWaNA+gmkoddU1ITLzq8uFOTMM0iPi6Jm5FJgrO9pvv4JKgK82XHSzy/Os2bHedd3CSNi+dPgrR0bJG2o/Hx9rx5nriLm+XJW192mXg/svub3QegnrZv3x4+f/3rXxfWrFkT+ttqx44dhZkzZxbee++90N+Ztm/eXCisXFkorFpVKGzdWihs2VIo7N6tnSwUNA1At9FD/yU/rACACqksoSqh/L//9//sD//wD+1Qpbq10bZt2+y5556z4cNPseOP77wa+yofuX7DBjv/vPNsn33IdAK6O54CAFAl/X1d77+xN2/ebGvXrrV+/Y5Ix3Q8bf+LL76w4aecQpAIIOBJAAANQLWaR4wYYQcffHA6puMtXrw4VELrzH0A0FgIFAGgBqp4UsvbVEpZvGiRnXbaaelQx1u2bFlITRw2bFg6BgAIFAGgKlu2bLGlS5eGV0aqXKGasWkrpSbut//+7dN4dgV0HAsWLGi16SoA3Q+VWQCgAps2bQqpbnorygEHHGDnnntuyKpdvXp1aBT7oIMOqqlSiyrGvP7663b++efb/kmwWC8qb6igVpVu/DGffdxrWE1Obdy4MRybt40KAI4UxQakNgH/x//4H+EBHncPPvjgV8a15e0jaoBb69D2qlHrcq3x42vtXdCV0v75eYo732+1x1jvY0Dz2blzZ6gJPGvWrBAU6t3MCupU2eP444+3s846K1REUSqj3pxSSQqjsqy1Pr2eT+94Vjb2p59+mk6tD7UzqvZEtT8KGNXF/d5p37ds3RL2AQCyCBQbjAIXNTj93//7f9/rrSP6y18NXOtzaPrmEmV93XHHHaG/Fvfdd1/aV51al+to/vYZ0bnSudOwzqsa51aD4HfffXcIHoEWei2f3kLywgu2Y8MGe+HFF0OApdfoXXjhheFNKTFlF+ttKXoH+oABA5LFXrAXk2WydP0p5fD3v/99mH7EEUeElEi9m13rVaBZr7cJffLJJy2BoFI6tQ3vjjvuuJZO+6xuxWcr7Oijjw7vmgaAGIFiA1FKnQLFtgR/XZkHwu1ZTsrf1uKvLtQ29faY+PWG6MaWLTN77DGzb3xD7dVYzzfeCEGcUhEV2JX7o0IBmd6mctlll4X7+De/+Y19+OGH9vbbb4fg8JFHHgmBmt6epBRJD9qOPPLIkOV89tln28qVK9tc5lEphJ999lm4l4455piKajArYP3yyy9tzZo1oUILADgCxQahbFC9Ys/f21wpLRdnq2pYqWXqf/jhh1vG64fL+/Oydj07OX4NnvgypZZzvrw6D7o0v4/z1++p8yxfHy/xcSgLOs5mj5eN+XGq0/KVUAqiKNXW6Zzr3Fe6DjQxZb8qUNq40ezAA63H559b3+SzmkomBybzK0D77ne/G1Kylc389a9/3b797W+HFEdN79u3bzr3HgrolKr3/PPPh8ottfIgUcGnXgeYvW/yKEVUWekKdNetW0c2NIAWBIoNQmWbZNCgQeEzywMidXGWtLJPfVhZ0hpW1vDEiRPtxhtvtHHjxoVpCoz046F5ssGollchdmXL6h3LHjApENP8Wk7TlFKSR0GdAi39KKpTv8YpZdCzfvW+Z00TrXPKlCnhc/z48WGc9tv3VTylT5Qdp+MRrVcUPGpftW9arrUUQe27zp2W0briVFs/5/4doBvTtfCd75h9+KFqr5hdd106oTYKDJXSqJS6SgI2pTIqK/j999+vqekdpUiqPGWfPn3C9g4//PB0Snn77bdfuB9VTvLT5Z/aok/qkwUOoOsjUGwQS5YsSfvyKSDyTg/0mIIsL3unVLxJkyaFQCumHyC55pprwnxx6pnWl51f0xVUKeVP9OMlHqjFJkyYED7VUK868XHurrvuSvss7Kv2WeKgt5Tbb7897dvDA0z9GN55551hX8vxMora3s0339xyXOL73Np3gG5AKYdnnWXv9uxpy888M6QqtoXfs2p+plIqC6nrWsFitd588007/fTTQ43nc845Jx1bGaVmKrXzqCOOsgUfLWhTqiaA5kGg2CQ8mPKs1bZYvnx52td4ssGc/xCrq4QCVAXG2WAZiC359FNbkwRbnUUNb69fvz4EfKKsYNW+1qd3GlYwpyBUqY9KSVQWsj5VrrJaCk5VTnLN2jXhPnn11VfTKWV8+aXZj39s9o//aDZ5stmCBcWKQLNmhcpAyY2ZzgigqyJQbBCe4lcrT1VUylpeZRgPsDzL11P0StF0Zf16pQ+vjZlNeRTPylaA6UFmtWUtq6WUUfEUzmzZylI0v6di+jnwfW7rd4Dm0XPffTv1XcfKCj7vvPPs8ccfD8NvvfVWKFKiPwT/7u/+zn70ox/ZX//1X9tVV11lhxxySChDqcozKueo61rtPNZCx6zKNaq4I2oOqKwtW8z69zf7wQ/M5s9XAUlLdlo3mtnSpYo+0xkBdFkFNIQkeNGf3oWJEyeGYX1qOO7yxo0bNy7ML9OnTy8kwWI6VKTpmi8JDluW0XyieX1cvG7N63ycurzlfPvxON8Hze/jtM4k8Nxr2Ps1vx+/d/H+aBvx/JpX8o4pFk/PdjHflq8XeOaZZwoffPBBOlS7J598sjBv3rzCihUr0jGVS/44K/z+979PhwphPU899VTht7/9begmTZpUmDBhQuG//tf/Wkj+gCv89Kc/Laxfvz6du2127twZzsGOHTvSMSV88UWh8ItfFArz5xcK/+t/FQozZhQKDzxQKLzySqHwyCOFwq5d6YwAuirezNJAVCFD2aGTlYVTA6+VHDcvo0ofKsOXBEO5qYEonnfpKu1Dov3pHlTTNSov2BZJYGcnnXRSSPVTGcBKqYkaNauj2tKVpmyqtrO6M888s82vAlR7j9rnU045JR1Tws6dZh98YLZmjSUnTG0Ema1aVfxUaqNS7UlVBLo0sp4biAIVZfV6kzGVUrargp0ZM2bwrtYqKStatbQJEtEoVHNZFVlUGaWa7G/VsFYbiiqj2BZq+FvlHocNG5aOKaNnT7MzzjC7/HLVeDPr109t7aiGmGrJESQCTYBAscEoUFRTMuXaLMxSmSQFO9kydkqdVGqiqKZvNevsDnR+VBubRHVkKeBSZREFTHq7Sa20vCqcZN/mUooqpajMrGrlq6xgtUaOHBneyqL3R9dKzyClJLY1VRJAcyDrGQBSCuyWLVtmU6dODdnOChLVZIze6aw2CdU+YSUUZKoiyJNPPhmallLqnCqoqJJJuTelqKLVqlWr7IILLkjHVE+1pVX55Wtf+1o6pnJaTvtXUWoigG6BQBEAEgsWLAhvJVGq3qmnnhqyYBW09e7dOzRTo1Q+NUujFPxyWcJqIUCpiEqVVICp5V977TXr169feD2fUheVvXzuuedar169tIDym62QbHf7yJG2fxuza7X/ajxeQa3KK1ZK+zR79uxQfKXSgBhA8yNQBNCtrVixIgR3SjFU503DiAJDTfNPVXBRMKmKHmqzMKaKJCrOoLKCekWf3p+cpfcpb9q0KaT67bv//nZgssxgVQjRG2FUtlBvUinxBqRqbNiwwT744IOQhay3rcybNy+0k6jOqV+PfxVZUeqpUhMVIFeaTQ6geyBQBNDtKPBTqp9S+vQKRwV3yhYulVKolEBlJ6uiiKcKKitZNZo3btxo056fZscOOjakOmo9lZTv25LsQ4/p06138mlXXWVJRKeGTs3+4i/SOdpm/vz59s4774SUTQWnHih6sOifSj3Ue6EVMNbSUDdQC/1RNTR9y5ja7G2ttQ+VsffXyKqIhlL2VZHT38qldXm7v6gvAkUA3YoCu1mzZoWgUIGRPiutXaxsXWVDK/VNqYtKrdN7nK+77rqq1tMi2Rf7zW+UBGjWu3cxSGzjawOdHu0KiOPgMI/m03H17Nmz7HxAPela03v6b7jhhhDkjR07tmTrEx4Qxs28edNv05M/tlRcwteX98IJtA21ngF0C/438TPPPBMqqOjHRcFRNcGd5lX2tNo3VICorN1vf/vbVa+nRd++Zt//vt7BafbDH9YtSBT9cGq/lLrpQWxep+kqK0mQiI7ib9QSpQwqRVEtd+RR028KEpWKGLcFrKIgospn4qmTqD8CRQDdgrKaVWFD2cb1qKyhmsnKsgZQH8qOjmlYAaSCQHX6Y8abeRszZkz4VNNvKvIhpCa2DwJFAN2CslfV/I1SFilxA3QetfcpajM4ptTFmGrvi97t7/fsLbfcEj6VI6BsZ1FqI9oPgSKAbkGpEWSvAp1PAaHKEypLWfekPpX9XMqoUaPCp+ZRUKiURqUsKmhUAOnj/XWsqC8CRQAA0G78jzR1Hswpm1hBniqoiN6SVcqSJUvSvj3uvvvukNIoXgkmm3WN+iBQBNBtkKIIdDwv7qEurtmsoFFlDJW6qKxkH6f7VCmGXnnFs6jV/I1SDz2Leoqak0rotZdy5ZVXhk/UF4EiAADoMGraRsGgAj0Fj+UqoShL2bOoxdtb1KemabzaV1TzOlRmaR+0owigW1BD2Xr/8pw5c2zgwIF22mmnpVMAAKWQogigS1Cgt2PHjnSoemor8Oijjw7rUA1oAEDrCBQBNDS9Ok9ZTCqfpPcX653KtVDmiRrnPeigg/Z6nzMAoDSyngE0LL2vWK+h27BhQ3i3sgK8pUuX2patW+zMM860fv36pXOWp+xmvfP40EMPDetQo9sAgNYRKAJoOCtWrAiph3rjwocffmhHHnmkDR8+3Hr37h0azVbKoILIgw8+2M4///wQROZZvHixvfHGG3b22WeH4FCv3wMAVI5AEUBDUIqfgsAZM2aEoE6phQoYFQiqfKG6mFIaP/nkE5s3b1545/IxxxwTAkmtZ9OmTTZ16lQbNGiQXXTRRS3vNQYAVIdAEUCn+/zzz+29996zL774ws4777wwfPzxx4dArxLvvvtuKMt44IEHhjKMCha/8Y1v0G4iALQRgSKATqc20ZS9rKxkBXlquqZnz57p1Mps377dfvGLX4QUSAWbAIC2Iy8GQKcbMGBAyG5WKuKZZ55ZdZCoJm+UbT1s2LCQ/QwAqA8CRQCdTu0aqqs2QIwp61kZJGSSAED9ECgC6HT1CO6orAIA9ceTFUCno9IJADQmAkUATYFgEwDqj0ARQEMg0AOAxkOgCAAAgFwEigA6nVdm+fLLL0NXLdWWVhuMaiZHb2wBANQHDW4D6DRqJPudd96x9evXh/c19+/fv6WZG72VRcFfJbZu3Rre6azl1Fi3Xv8HAGg7AkUAneLll1+2devW2amnnmoHHHBAGKdX+CnoU8C4aNEi69u3b2iAu1zTN9OnT7ctW7aExrr1dpdDDz00nQIAaCsCRQD1s3u3aqUU+/WpbODNm8369FH+cMgaXrJkib366qt2ySWXhMBOwaDT21U2btxob7/9tg0cODBkIy9cuNBOP/10O+6441oa5Faq49KlS8P7oc8555zwVhe95xkAUF8EigDqIwkCbfZsSyI8s08/NRs61JJozpKoz+zkk233eefZM1On2jHJ9LPOOitdqLS5c+faJ598YieeeKJ9+OGHdtJJJ9mQIUNCKuT8+fPt6KOPDuMAAO2HQBFAfWzbZjZxopmyfj/6yOyqq8xeecXshz80Gz/e7BvfMDv22HTmyr300kuh/KI6ZUH36tUrlEMEALQ/aj0DqC+VJ8yWKVRqo7KlazBq1KhQTvH9998nSASADkagCKA+FByecYbZeeeZXX652ZAhZpdeava//7fZiBFmxxyTzli9Pn36hMoqNMoNAB2LrGcA9afHioI6pSIqS3q//cz23TedWBvVblazN6olDQDoGKQoAqg/T/lTKqNqPLcxSHSkKAJAxyJQBNAlECQCQMcjUAQAAEAuAkUAXcJ+vXtbT5V1BAB0GCqzAGg869YVyzVu31781BteVq0y239/s0GD6lbmEQBQHoEigMbz1FNmX35ptnKl2bBhxU811j1njtm/+3dm/fqlMwIA2hNZzwAaT69exdrS6rZsMfviC7PRo82OOqqYuggA6BAEigAaj7KY1WC33hut1MPjjy823K13RyuIBAB0CLKeAQAAkIsURQAAAOQiUAQAAEAuAkUAAADkIlAEAABALgJFAAAA5CJQBAAAQC4CRQAAAOQiUAQAAEAuAkUAAADkIlAEAABALgJFAAAA5CJQBAAAQC4CRQAAAOQiUAQAAEAuAkUAAADkIlAEAABALgJFAAAA5CJQBAAAQC4CRQAAAOQiUAQAAEAuAkUAAADkIlAEAABALgJFAAAA5CJQBAAAQC4CRQAAAOQiUAQAAEAuAkUAAADkIlAEAABALgJFAAAA5OpRSKT9AIA8X35p9qtfmW3bZnbqqWZ//MfpBABobqQoAkBrxo83u/12s//0n8x27jR76610AgA0NwJFAGjNmWeavfyy2cyZZj16mPXvn04AgOZG1jMAlLBz507TA7KXBp57Tv+bHX+82UknFfsBoMkRKAJACW+++aYNGzbMVqxYYSeffHI6FgC6D7KeASDHSy+9ZPvvv79NnjzZDjvssHRsDdasMfs//8fsH/7B7N1305EA0DWQogigW9i1a1fISt5nn32sV6+QmVzSa6+9FuZfu3atnXbaaTZkyJB0SpW2bjV74AGziy8uZlc/+qjZiBFmpE4C6CJIUQTQ8TZtKtYelg0bzLZsMfv882L/7t3F8XU2b948e/zxx+25556z9evXhyBQ3Zo1a5JN7tnmwoULbfv27Xb44YfbOeecU3uQKPvua3booWZffGG2dk3yp3kyrpUgFQAaCSmKADreI4+YnX++2aBBxWzZY44pBoi7dhVT3449Np2x/pRSOHPmTNu8eXMSx+1rO3bssJNOOimM79mzpy1YsMBOOOGEECyqfKJSINskCUbt4YeTP8uT9Vx+uSUrTScAQOMjUATQ8R580Gz//c2OOMLsd78zO+QQs7/5G7MnnzQbMMDsvPPSGTvGe++9Z59//nmyO0fYtGnT7IorrghB4kEHHZTOUbtNW7bY8i++sEP69rVdSTA84Mgj0ykA0PjIegbQ8XbsMPO/UZU926+fJRGa2ezZZtu3F8d3oNNPP92uvvrq8HnJJZeEsoz1CBJl7erV9uqLL9onH31kC+bOTccCQNdAiiKAjvfZZ8Wye336mC1ebHbggcXX5CmAVHa0pnWS5cuX26effmrnK2u8DrQ+1aA+8cQTQzb3RRddlE4BgMZHiiKAjqfsZQWJctxxxSzok0+27aeeau8uW2YbVKmlk6hs4iZVtgEANHegeNttt1mPHj1C96DKRDURPy51Os6s+NiVkiEff/xxy7hrr702DN9zzz1hmo/3Ll4+2/kysXh+rTem7Wu874fE+5K3TKn1vfzyy3st599ra+tD17DfPvvYrh07OjVQUy1ntZuocov1pOsSALqapg0UFczcf//96ZDZzTff3FTBg0oMjB49OvTHx+l83NixY+2jjz4K/ddcc42NGzcuLHvXXXfZ0KFDw3iJ1zdx4kS777779hqnJkM0rPXdeeedIdB0OtdTpkwJ07V+bcdpPg1rmgJFD2pVqzRef6zc+i677LKwjE/T9yrl1oeu5eyzzw7tGG7cuDEd0/EOPPDA0Ng2AHR3TRsoquaiAgd1zerKK69M+1SJdE+KqfoV0GUp2HOXXnqpTZ8+PR2qnAJIBZjPPvtsyzbHjx/fklo4ePDgsB2l/Ckw13zeDp2CubygNqvc+sQDX01Dc9L3/5nKMXYSNYmzbt26UKawnkhVBNDVNG2gqNduOQVNCm4UqDQbDwgnTJgQPmXJkiW5jQQrtU2pgfqxUuClYPGOO+5Ip1bOU/hUQF/iANTNmDEjFOLPo22XU2p9+v50DJquY1CqqFI/0XxGjBhhc+fODU3WdAZda/vtt599oYay20hBp+6FDz74ILTbCABdSbeozKKUqDiQaiZjxowJn0q503GqK5XSpuDZs5uVhVtr6oYHoZ7C15F0DJ69nBdQonlccMEFoWHszrJy5co2Z3/rjS9qwPvoo4+2Qw89NPQrpRIAuoqmDxSVenXLLbeE1LNmpOPywGnSpEmhu+mmm8JwHmXbKjveA8a4rGGlFi1aFD47I4VW+6vv07PNm63sKfY4Mm2YWkUR/u///b9lu5///Of24YcfhvnrRc3jrF69uuZgUSmiTz31VGi4+/rrr7evf/3robjI4sWL7c033wxvggGARtf0geLdd9/dEjg1W81np8BJ9INajlckEQWMcUWXaqiiiYwaNSp8xpVi3MUXX2wDBw5Mh/bWWtBean0K+pVyOnLkyLAOz3YvlcWNrk2NXivb9k/+5E9a7f7sz/7M5s+fX9esar0DWlnFut5/97vfhXdFK4VQAV6pss8+XteqrkvdI/379w+VY3r37h36TznllPAe6X/5l38J7TW2rEuBo7//GgAaRFMHiioQr8BCWazNXIjcA2FlxSqgKkUVSeKmbZQSF9coroSW13aUiunbvfXWW8N5FpWPVKCnQM7LFKpikWh7eZVsskqtz3l2pKckthZ4omt67LHH7KyzzrLnn38+BFblOv3Bo9fv6VPtILaVsp1VmUbrUkrgN77xjVBm8Ze//GUIAnVdbtmyJXSiYE/zKhXxySeftOHDh9tVV10VspuztB7dG9/97nfDvfT2u+8WGxv/1381+8UvzFasSOcEgAaQPOCakg4t202fPj2d2vXFxyXjxo0rJAFV6E+Cs72m+/gkSAvzZcdLPL86zZsd553WkRXPn/z4pWOLtB2Nj7en78Ln927ixInp1NLri/c/ntba+tC1rFq1qvDWW28VpkyZUli+fHk6trRFixaFa2HWrFmFJMBLx9Zmx44dhdmzZxfefPPNQvJHSWHDhg3plD10vU2dOjXMl/zBErrnnnuuMG/evHSOKv3bvxUKzzyjAy8U/uEfCoUtW9IJANC5eIUfgIayefPmkAqtLFql2On9y5VQyuNxxx1nr7zyin3ve9+rKRdBj0PVTla5RGUzDxo0yI499th06ldpviRgDPupLHClFtbk8cfNDjjA7OqrzX76UzVnYNa7dzoRADpPt6j1DKDrUNGCk046yd566y077bTT0rGtO+ecc2z27NmhaR2vcFUtvRFGTeKoHKJqKpcLEkVBorKYvTmdmun91spyfuwxM70Lui3rAoA6IlAE0DBUuUNlDpVSd+GFF4bKLJU65JBD7Nxzzw0VUFR+df369emUyqiM4QsvvGDHDDrGtmzdUlGD7qqwUq5yS8WS/V4+dKhtUGA6cmTyZObRDKAx8DQCUB/btqlNmGK/UvRWrTJ7802zf/ons9mzi+NbsWzZMtuwYUMIFJWiVy1lPX/yySd28sknh6CvGkrJVCWUDes32IUXXGg9e/ZMp5SmeepVWW5WEth+psovBIkAGghPJAD1sXmz2oUp9isw1FtzlKr37W+bvfOOqrDb7mSSyv5lO1ENYNUSVrM0atRdzcnUQrWUtS5lX6tJG6X26VV8pTqlCL799tvhc8WKFda3b9/QnE016hEobtu0yXYl+wMAjYTKLADqQ28c+clPzAYMKKYsqk3Av/5rs+OPN3v9ddu+YIGtVOPw27eH4E0Boj9+lF387rvv2gEHHBDeGqTxlaTolaLl1bzNO0mAethhh7VkD/s2vfMgVc3hqKkofSo1slJq7PvGG28MbTiqge62eOSRR0KKpjoAaBSkKHZhKoellAy1bejZX955G4ONSm1c1vJWGKfGw7PHq/OgT6270Y+/aQ0bZvb975t985tK2tNFajZrll7Wbfv96Z/aMUceacccc0yoTawygMoqVqdh1W5Wap4CuLYEiaKa0++9914os6iyjpdccklob1MNYF9++eV2xRVX2Ne+9rVQEUXdddddFwLVoTmNvQNof3pu+/O8EvoNiF8igfZDoNhFKRDSj5peZXfHHXe0vNJO4/RD2xmv1+soeqCo8fCJEye2pAwpNejOO+8M0/UmDZ0HNYyMDqTmXZIALDj7bLMrr7Qk+lOND7M//3OzXr2K00pQDWPVHFa2cVvoelBq4oABA+yoo45Kx5anlEy9SWhGEtBWw3/UKv1xA/BVSjTQc133rp7d6i9Hz3b9BqBjECh2UQqMxo0b12XfSqI3aEyePDkdqpz+glQgoWOP32ntryQUBcma7q82RAdRIHjMMcV+vaf5oIMsidTMvvUts379iuNbofci67tUFnCtVO5QFVPOOOOMdExl9OOkFE2Va6zUjp3FMoXV1M4GsIcSPfxNXKI3c+kZX+4PfZ7tHYunWxekd1brRrrhhhvSMV+lm8yT8eOs6ThLNh4fZwPH2bqlxsdJ/j4uHq/lfJy2o3326b4e/6sxns/7S72X2/+KzHtV4X333deSkqpzo3PUrO/3blZ9+vQJZQSXLl0a3vVcC7W/6O8hr4aCPd92pe8Pv/6Prg+VbnTN1kqpnwqOVU4TQJFaQMij34m77747HUJHIFDsgl566aXwWS57WSmNnsKmt1x41vSkSZPCpwJJZdUq+1YBlf6i0w0oCsY0v8Y7BVw+Xuv1gM0DQ82r9zr7eKUWalgUtCn1T9tSMKcupvGi/VHWg0yYMCF8xuIgV9mE5fi58XOFrkN/QKxataqmdzZ7TWaVg6yFajurke91rbTB6NepgjzdS6rMsmDBgjCuUgqEtawaB1eWu7K/1a/ylUB3oWe1spvjVEVRueUsT2XMm4b2Q6DYBVVbUeOuu+5K+/Z44IEHwqduOA+qxo8fHz4VCKrmqYJKzx72wE0BmgI9/6H0fgWMfqP7/nn2gNajG3ykGhIuQ9nF5cSBcSUpPnr4VHuu0Biuvvpqe/rpp1tqJVfq0UcftbPPPrtNKXz9+/e3k086yWzLlmLzPps2hWtcAZyC0Dlz5oQ/vh5++OFw/6hijF7fp3kee+yx0MROOQoQFVT+9re/DRV5tNybb75pF1xwgX3jG98IryLUfbd169ZQWxtodv77ovvWy5rnFavS75bK5KNjESh2M5W82kzBn1IIdcO2VjNZAaBubgWjnoLovAyhflSVjVAuBTRLWXF5fBt5lQ78r010fcoGPv7440ObigqkPKVQnVIavdOwZ1HPmjUrlHGstg3ELF3P+yjb69e/1i+T2f/8n7Zz3Tqb8vvfh1f2abuqKa1mcVRZRlnGqqWtdhv146ba1goEVc5S++70ekCN+/DDD23x4sWh9rVSIo888kj79re/bQcffHCY7w/+4A/syiuvDAGj2nfUckCz8KJH3onuG90rnouVl2ig57tyrLSMEjJEwxQvan8Eil1QNQFXKWPGjAmfCuA81U2FiEU3orahbGZPJdQPl9x7773hU9nUWs7LiuhGz2YdiFIn88bXyrOtFcTGgaEeFp5K6vTQqce5Quc466yzwnes1GP94aBO36nevKI/eLxbsnRJePWfUt+UbV2XiiVJAGfHHWf2gx+YHX209Uruiev/6I9Cip9SLEs54ogjQoPfCmDfeOONsK9ffvllmLZ69erwthjtp4JDTVPAqeA2S1nRWo+ORcGijpssaTQDz4XyzukZrlwgJQZ4qqEnRCi49GBSnRel0u9LXKkR7YNAsQvygvoe4Clo87+w9IOiG0vZYl5eUAGUB3Qap5tPN53+arv55pu/cnPq5vO/2vwvO03zsomaJgrCPBDTOE0XL7covq/xzRyXa9S+ax9EWd9eTtKnZWl7elBof7V/2q46lUX0IFL83NRSqQGNQSl1CsqUotarV6+WTql3noqnLrkCbN26dSFF7yDVtK6H5J4wlZF8/vlibe79908nVObUU0+1b37zmyG4U0CoWtjqV1a13kGtfVXbjuXo2M4555zQvqSO7/333w/XdfzjCnR1Hgzqd0DP/Vpaw0D74s0sXZRSTpQCSHmNfPrrVNnhpbKwgVapmIbKwqrilFIX0z+QqqV3VytYVO1oBX9qL1IBb7WWLFkSsrSVZa31AEBHIFDsopSyoJRAJcErdRB7cG7QrF577bXwthmVy1RTQgDQ3sh67qI8C9aznLGHGiNXFgZBIpqR7nuVgQSAjkCg2MWRIPxVym6mEgsAAG1HoAgAXYRXJAOAjkKgCABdBIEigI5GoAgAXURd2ogEgCrw1AGALsTbDgWAjkCgCABdBAEigI5GoAgAXYiCxf2rfFNM1u7du2kxAUBFCBQBoIs48MADbeXKlbZ169Z0THXUUPfatWtDE1Lz58+nPUYAreLNLADQhei90Xr/9cknn2wHH3yw9e3bN51S3urVq+3LL78MAWL//v3DcL9+/ezoo4+2gQMHkq0NIBeBIgB0MZs3b7b3338/1II+5JBD7Pjjjy/5/mdlM8+ePTsEl4cffriddtpptm7dupAyuWzZshBoKitb6xk8eHC6FAAUESgCQBf1+eef29y5c0PAOHLkSOvdu3c6peiTTz4JqYi7du0KKZCHHnpoOsVCkKiAcc2aNbZx48bwfvT169eHtxoddthh6VwAujsCRQDowpS6qGBPgZ/6v/a1r4Vs5RdffNEGDRoUUhuVkliqDcbFixfb0qVLrU+fPiFAfO+99+ykk06y4cOHp3MA6M4IFAGgCShVUIHi66+/HrKRTz/99BD8ZVMZ82zZsiWkLk6fPt2OPPJIO+KII0KZxVNOOcX23XffdC4A3RGBIgA0EWU1L1y40M4888yqmtHRT4HKMy5ZssQOOOCAkNI4YsSI0A+g+6J5HABoIkpF/Oyzz0Jll2ooBVGph8qqVk1oBY0AQKAIAE3EM4na2twNzeUAEAJFAGgiCvAULLa1VBGBIgAhUASAJuJBIoEigHogUAQAfAWBIgAhUAQAAEAuAkUAaDKqsdzWrGcAEAJFAGgyamS71vYP1Wj3jh07Sr7JBUD3wpMAAJrI1q1b7aCDDrLTTjstHVOZ7du327x588L7oZcvX26bNm2yVatWpVMBdFe8mQUAmohexzd37tyQMnjCCSfYwIED0ymlffzxx+EVgAoy1Vi3ltH7oRUw9urVyy688MJ0TgDdDYEiADQZBYnLli2ztWvXhretnHHGGSHgy1JZxueff9769u0b3vGsIFHzDRs2zA477LDw/meNW7NmTUil1Cv9AHQvBIoA0KSUfawg75VXXrGRI0eG1/O5t956K7wX+tRTT7WXX37ZTjzxxBAg6hWA++23XzpXsV3G9evXh8BT2dIXXXSR9evXL50KoNkRKAJAk1PA+Pbbb4eg79JLL7VPP/00pCauXLkydJdddllISYwDxKxdu3aFcoyvvvqqnTZihB392GNmBx5opkozJ51kSdRpNmqU2ZQpZhdfbNa3b7okgK6MQBEAuonVq1fb1KlT7bjjjgvBolIYzzzzzKpqOOsHo8eLL5pt22b29a+bzZlj9rOfmV1wgdlNN5n96ldm3/ue2WGHFRcA0KVR6xkAuglVULnxxhtD2cP+/fvb2WefXXUzOOF9LUpf2LrVkmjTTDWjlar4+utmTzxhtnJl8svCTwvQLLibAaCbUXDYpnYSr7jCbMcOs/nzi8HibbeZff/7ZmPGmI0ebZZTcQZA10TWMwB0M8p+Vk1nmr0B0BpSFAEAAJCLQBEAAAC5CBQBAACQi0ARALoZtYmoDgBaQ6AIAN3MgGOPtSMHDEiHAKA0aj0DQHexe7fZO++YLVuWPP17mF10UbF5m2HDzBYsMDvjjHRGACgiRREAugs1hq2GsY86qhgwqrHsf/5ns4ceMvv1r9OZAGAPAkUA6C42bzbr2bP4ur2TTzbbd1+zgQPNRoww698/nQkA9iBQBIDuQuUSDznE7O/+zuyDD8z69Cm+k/m448wOPjidCQD2oIwiAAAAcpGiCAAAgFwEigAAAMhFoAgAAIBcBIoAAADIRaAIAACAXASKAAAAyEWgCAAAgFwEigAAAMhFoAgAAIBcBIoAAADIRaAIAACAXASKAAAAyEWgCAAAgFwEigAAAMhFoAgAAIBcBIoAAADIRaAIAACAXASKAAAAyEWgCAAAgFwEigAAAMhFoAgAAIBcBIoAAADIRaAIAACAXASKAAAAyEWgCAAAgFwEigAAAMhFoAgAAIBcBIoAAADIRaAIAACAXASKAAAAyEWgCAAAgFw9Com0HwDQ1b3/vtmcOWZ/+qfF/k2bzBYuNHvvPbP99jP7y780O+64dGYAKI8URQBoJj16mH3wgdnUqWZvv2322mtmS5ea/f3fm/3gB2Y//Wk6IwC0jkARAJrJrl1mRx9tNnKk2fHHF1MU9903nZiI+wGgFQSKANBMevcuZi0ffLDZkUeafetbZoceajZlitkTT5jdems6IwC0jjKKAJrOvHnzknipdxIvdcOyeNu3F7u+fYufesTvs4/tfu89W5Ock01J0Dh44MB0ZgAojxRFAE1n9erVtn79+nSom1GFFQWJ3r///ma9etnPZ860I844w5558sniNACoAIEigKayfft227lzp/Xv3z8dAxk2ZIgd2Lt3ODe1IgMK6H4IFAE0lbVr19qWLVvsSJXPq4d168xWrDBbsyYd0TVdccUVdsYZZ9jbqgldg82bN9sbb7zRpkATQNdDoAigqfTo0cN69eqVDrXRkiVmP/uZ2XPPmd13X7Fdwi5KZTbPOuusUH6zFgow+/btaz179kzHAOgOCBQBNJXZs2fb2WefnQ610cqVZiefbPbd75pdconZZ5+lE7qmMWPG2P4qs1iD/fbbLwSKALoXAkUATWXVqlV2qJqDqYfdu4vBoT4/XW62c1c6oXPMmjXLfvSjH9myZcvSMdVRAH3sscemQ5X7LDkHX375pQ0ePDgdA6C7IFAE0DTWrFlj/fr1S4fqYPhwS6IjswkTzPZNHpdnn5VO6BwzZ860//bf/pv967/+azqmOn369LFrr73W5s+fn46pjCqxHHjggekQgO6EQBFA03jzzTft9NNPT4fabs2uXbbsoots+zXXmN1wg1kn16SePn16yP697LLL0jHV2WeffUKq4ieffJKOaZ2CRFViOe+889IxALoTAkUATWPHjh22b51eUbd7926bO2eO7di2zaa8/77tToKszrZy5Uo788wz7RKVl6yRsuY3bNgQgr+legd0KxQo6lwoNRJA90OgCKApeNnEepVPVHuMX3zxha3+8ks7eejQkBrXmT7++ONQNlFZx7WaMWNGSJG84YYbwueSJUvs0UcfDYFjKcrurmcqLYCuhUARQJelYG758uUhoFMQpE/Vzq0HtReoGsJqk7G9UtM2btwY2ieshMpfKnXvz//8z9Mx1XnxxRdDs0FqS1GGDx9u559/fsjGnjp1apiuQDTbTqIC8MMOOywdAtDdECgC6FI2bdpkr776auiUfbpw4UKbO3duCIAOPvhge/7559M520blHU9W0ziJQw45JHzWk4LcZ555Jmxn0aJF6djSdIxDhw61o48+Oh1TuTlz5oQA8Nxzz03HFCmoPuKII+yP//iPw7HOm/dhOK/vvfdeCEpXrFgRKge1x/ED6BoIFAE0LJWNU4qbmmZ5+umnQ6fASilcqoU7cODAkEq2bdu28HnllVfarl276hIsKiBVpyzngw46KB1bP8rSVXCmbF1lKz/77LPhOEtR+cSrr7666nYQFYQqNVIph+Wyz/XKw6uuutpOOeWUcD4nT57cch4VNOq8Auh+eiQPAF7eCaAhqZbv6tWrQ2B44oknhqxlVVZZv3596PT4Ui1epYopCNI0jZs2bVr4vOqqq8KbWqqldb/zzjt2+OGH27p16+ziiy9Op9SHArdXXnkllDf0N50o9e61116zQYMG2XHHHbfXKwi3bt1qP/nJT+zrX/96VbWPldqqQHHUqFFVv61GKZCPP/54CFC1TwokFTAreFeQrvYYazm3ALoWAkUADckDlT/5kz8JqW9K5TrggANCoKIsWAVxpeixptQwreMaNW1TJW1PwdDnn38eyvHVO0VRqXVKTTz++OPTMXsoQFUgqW2ec845IRhTsPbYY4/Zn/3Zn4VX8VVCqaFPPfVUCC7LnatSdO4WLFhgxxxzTMjSVw1plQf1YFzv1Fa/Unwvuuii+rZfCaBhECgCaEgKdJQy+Id/+IchVVFZrtU2+vzcc8+FJnNGjx5dceqXUhCVsqcgSBVZtP16Wrx4cWjwWgFcKdquUgJVVlDlChWovfvuuyGF1Om4VM5Rj3AFz14xxlP8FHBecMEFNQdwOg9K0f3Wt76VjtlD2/DKNer/6KOPQkUYHRMVX4DmQqAIoCH9/ve/D0FSLalhTo83pSyqfF25wEyVPRRoiT4VJCqbW8HYd/We5zpRUPXkk0+GrOBKAioFjHptn4LGAQMGhBRVZYurwWyvpaxj1H4qm1k1lEWB8UMPPdSmSigKBFUb+jvf+U46BkB3RGWWLuq2224LKSTeZb388st7TdewqEyUj1MBeq1HsutTlzdOncqKZcXbu+eee9KxRRr2ab4fEu9LvIzW7+MffPDBdOzefJ3xvvgy6vy40HV5wNYWuhZUwUXl87zcosr7KVtVqYZqQ/CJJ54IAZwCN5V1vOKKK0J34403hutLgVq9KEtbZfsqTXVTszxqXFuB7mmnnRayo5VCqHcuqy3E//Jf/ovdfvvt9sMf/tD+6q/+KnTf+973QoCt1Ma20Llvj0o8ALoYpSiia5o4caJSg0On/tjYsWNbprlx48YVhg4dmg4VQn887OsbPXp0OmbPOC0r06dPb1nvwoULwzh9aljT4n7x5bP9Lrt+p2GN1/QsjVfn2xfts6/b99H3AV3P5s2bC5MnTw6f9TJlypTCr371q8Ljjz9eSALEwuLFiwtJ0FjYtGlTIQnE0rm+6tVXXy28+eab6VDtNmzYUHj66afD9qqhZT799NN0qLwk4A2fSZBdeOihhwo7duwIw7XQ/iZ/2KVDALorUhS7OGUxyYQJE8JnOdm22lSuKC91sJxLL73UkiAu9Huq3aRJk8Knmio54YQTQv8DDzwQPrVfqnggqjkppVIJK6H91fqSa7dlW6JjEaWSaj/QtakShWr+1quha5XXO+qoo0Kq3HXXXWfXX399SJVTuUdl55ZrNubCCy8MKZBtTVnU8qq8ou1VShVIVHml0rYTlYIqKqM4bNiwcNy1Uiqmsp8BdG8Eil3cLbfcEj7VBpuCJFEgNmbMmNAfU7kolWOKs2ZV+7Ja3lSItinZAFSmTJkSPj2Ai7300ktpX3V0XNp/8Sxmd+utt4ZPBZGq5Tp27NgQ1KJrUg3ntmadOr2mTmX4pNIaw1mqFKLA6a233krHVE5/1Ki8o8oP6m0olVJw+uGHH4aAr5Ys+JNOOikEmn7s1VJ2vMpIAujeCBS7uJEjR7ak2HnKnn4Y81LVbrrpppbUwPvvvz8EWrWk7sXr9uC0I3iAeffdd4famOIB7x133NFybB5MomtScKLau0rxqwdV/FDKpNopbMs7i9VMjnillzwq/6gAV83q6N7QO5TVrxRLBX66NhU4VkI1o/XmFJVprIVSFRWYqlxkLbSf1dYyB9B8CBSbgAInGT9+fPhxKvcDq4BKPwAeVN18881VB3tqS83F2b8dRcGxpxZ6yqUqtyhl03+EFQi3JYsbnUfN4igYO/TQQ9MxbaMgUSlySqVsK1US6atUSf1R9o//aPZP/6SXIduWJDhUcYv3338/VJLRHzJKyfMKIaoko1rX+iPud7/7XQgey1Fqntal9gnbQkU11N5hLVnISgE966yz0iEA3RWBYhNQSqEotUIpbD6cFdcEVsDoqXJx4FeJGTNmhE8vHzlkyJDwGfNGjvPKQCoLvC3y9vfOO+9sWe/EiRPDp36U0fUoi7fa19SVomtAAaICL095b7P5883mzTP7/vfNhg0zmzbNdiajlWLYt2/fEBSqHKSyflUe0VPlVNNZtamVja13VCsbu1TAqNYB1Ni2UhTbQoGqUhVryTJX+cZa3isNoLkQKDYJlcmT1lL44sBNbbBJNWX5lPqooEzuu+++8KlmOkRZXJ466WUkVYZSAazG+/ZKBbKt8XUqUPXteNlE8axpDxDr/do1tC+lYKkJG32PCur0NhK/Zmql4E1FLFTWT4Fb3SQBYbJiM71mr2dP65MEdH/wrW+F9ySfeuqpIcCLy9A6jVMgqUasVV5SDWoraIzLYyp4VPZ73h9gtdAzQcH3Z599lo4pTe+aViPler2gUiLzjgFAN1NAlxQ3f6MmZJJgrKVJGG9yJu40Xsv4fPH47Pq8yxunLvkRD8vEtB6frqZtYhr2ab490Xp8vDpvlicep0774eJji8fH21eX16wOOsCsWYXCqlXFfjVftHVroaCmZX71q0JhyZIwWk24qOmVdevWFZJAsPDwww8XJk2aFL5DNQPzySefFJJAJXyqaZpFixaF5aql7fjyjz32WJuaitnL6tWFwo9/XChM+JdC4R/+oVCYNi2dUD01z5MEseH4586dW0gCxsLUqVMrbg6nUknwWXj22WdDfxI8J4ewOpz/JBgPzQbpO3jkkUdCczhqNmjp0qWFL774IswPoHvjzSwA6ufv/97sm980O/NMs0cfLY5T5SK9FeXZZ23bNdfY/P33t08XLw7lSVV+T83PKOVKnTdTo9fHqYyeKnKoqIGyQPPei1yOUumefvrpUGZXr8BT7eG62brV7O23Ve7CbMCAdGTttK/K6lU5W2W7K+u63pRKqFch6t3NSnVX2U2db2WF63tQqqOyqklFBBAjUARQPz/+sQrGFcvuKUDUK+Wuv97svPPMnn/ets6bZ58kgeSJSXClt6WUowoYChbVnqKCRb3CTv2VUg1kvb5P5QS1XL2yctuLssc/+OCDkCX9TQXbdaZKNqoBrnKSOpcAUAnKKAKoH6W0nXuuajoVy++dcopepKwCsWpw03qfc44NHzy41SBRVPlDzdksXrw4pIIpWFSgUyk1TaNaw9pWW9553BF++9vf2m9+85vwusH+/fvb7Nmz0yn1obKeeke0WjlQWdC4TCQAlEOgCKB+Lr/c7OSTzVRbNwkK7eqrzfS+YNW6TQIgGzkynbEyaiJnxIgRIUD0LNNKg0XVQFbWsyqHVPpu5Y6mgO0nP/mJ/ehHP7If/OAH4XiVRa6KPMp+r5d33303NO0jSl1VBTOCRQCVIOsZQMNTNrRS2RTkzJs3LzQdEzfdoiZw1G6hajmraRllOys4VLlG1ZBX8FVNtnVHUFuRf/WXf2nPTZtm//Zv/5bE2EmQnVIWtFIAVX6zrVT2UbWofV1qzHzu3LkhpVVBeLnXFwIAgSKALkFNt6icnVLGlD2rrGVlS6vyhSppKAVOwY+ym1UuUeNVOUOPODX5omZiOqOB+DwKEr/zne+E12D+9Kc/tf/4H/9jOmWPp556KjQur3dU10oB4osvvhiailKFHqfgWQG3KgupSZ+ePXumUwBgb039p6RSFrw2ZbO/pUPH+Dd/8zctx6vO2xp0Go6nlzsnam9R86iR7ni5Ss+j5qt2mWrF29A+ap+zx4zm0a9fv5CS+MILL4Q38qgRaaUcKoXsjDPOCG0TqhF4BUQKfBQkiq6Pr33tayHLupoyjnWnv8l37w69HtD++3//73ODRFGQqGdYW+g1gMrOjoNEUcCscQokSStAI/DfHHXl+DytzYc6UopiM8q2F9jEhxqOzdsNrLQ9Q52fcnw93lahL1tN+4S1LFMttb3ox+PfedxWI5rP2rVrQ/t/mzZtSsdURu0HPvfcc4WPPvooHdOO1A7iv/xLsf+FFwqFZcsKhQceKLa7qPtyzpzC7mTSxo0bi/OU8M4774SuFmqrUm0n7tq1Kx2zN7XhuG3btuKAzuX69cX+3cmeqfN+oJ3pOe7t6Kp93by2eqVU27toX02bonjvvfeGAtvJMdbv1V0N6Nprrw1vZYnfdqJUFdG7n7sTpZIkwWl4Gwyal2ow6+0myl6uhlIYVatY2dULFixIx7YTpRy++67ZM8/olUHF1/69/77Zf/7PZn/8x2F8j507W17vV4pSWVasWFFTxZbXX389LF+qDKLaawyvCJw50+yBB8wmTCi+mnDlyuL+7tihNnVaUkGB9qBcIBXBcHrbln67s6npGtY0/aZPTF/Tio7RtIGiXi/n5ZF00SmYaja6cXSD+avtnAdK8c2mz1KvtLvnnntakvIrzSaOs6MVrLo4O1iNB2fF2Qu+rXiZeF9i2kY8Tyl6naCOu9LjQPejV1aq8ovKOFoSrCU9Xw2GlFDf1ixZBYEDByrPvLj+NWuK4xWcKcitoNaxgmG1eag2Fr1dSDWlo8ovjzzySPiMu0mTJoVAWMGlKve02nbkxo2hfcvQzuUf/ZHZP/9zaMYoucHV6rlq1RAoolNkX9+p+/aOO+4I/Z4woj/80P66RRlFacaya3rnsQzUj1Fk0KBBLamKDyilIKF5897prHOkdzdPnz49dGpnrRLXXHNNCL71191HH30UAjOdYy3v41WOLKbyjp7Kq5Q/zatldNP7/iq49L8WPdhTYKiAWMtqmvbXA+As/+PA3/sMZCkV7dQRI6xvEizaL39pyUVV7JSK9sYbxZmefLKY0lYrpeLpWtQbak49tdgA+SWXhLfT2LRpaqOmGCxWQG1JKvBTKqhSU1WTWzW4vdOwOgWFuv4VBM+aNSuU22y1RrMaRFfbl9qfwYOLqYnr1+uBodo0evF08ViAdqJrVrl+caqi6HesFP1uaBkPHNG+mvoJoMDIs551ETZbKlNeip276667wuf9998fPkvxQPKyyy4LnZQKwpymK2jTuhWIq1+B2cz0h9VTMZRNENP8XgxAr1UTpYDEbr/99rRvD89C1wPFHx6+33m0jWb8wwD1owdfD2ULX3SRLlS1r2P2u9+Z/f73lvyFY/bww2bLlxdnrsURRxRfZShqT1J/zH3nO8VGyM8/f8+0ChWSf2vXrg2VTxQIqlMKo/fHw2pKaPjw4ZW9fUX3qgJavW7xoYfMrrii+EpCtX/5p39qduyxpCii3U1QsYeEfk+UECB5CRtORcuUQIGO0S3+VPQLSo31dhe6yTwoU7atsmTLUWqigmp15W7QmKccqlNWf711p+8LnWDzZjNvekYBnLKG1Si4ArtjjjHbtas4rRbKXtY6RTWO1fyMN0I+YkRxfIVUy/uE408If8hdcsklX+lUe1qd9+vdzUph9NyUVn3728oiMFPRlBtvNDvppGIw27t3MTuapnNQR8pZ0rXpnXiijhIdRDlOpSjBZ9SoUaFfiRYkCrS/pg0UdQHqghS/kFoLlrqa1sofxSl6niWb5Tecp9Apm7e1FEUPJD21UudX51rNecg0Za0lsimeCiz9QeBBYCXfiR+HtuPlVrLlMmPaRqnjBVro2leK9tSp+muymMqnFDSlBg4frtfCpDN2HlVi0RtU2vV6VllKpdTr2JUdrtcr6lM/4plmdYC2UqKCJzCocwoAlbihYkiepazfovi3XMMqsqRO4/XHE8/6DpB8UU1J1eZ1eN41I2/CxpuD8eZo1Klf1K/maXzeuPNma5IArmWcL5fcsC3jHnrooZZ+dRKvT/M6rTMe7/0uHufbj5fR9uN5kqAvzBN/n76P8XKaLppfw75uoKxPPikUXn21UFi1qlDYvl3t1RTHr11bbDKmk7344ouFd999Nx0Cmk/8W+LPe+fT9BsVP++9i3970H54M0sXp2xl/UXVHlm/XZH+KlX5TMqvoKvTm2heffXV0JA4AHQWAsUmoCT45K+tvdpS7I6UNZ38hRnKW1ZazhJoVE888UR4P3NbXuEHAG1FoNgk1D6hmqPpzuU1OAdoFmreZufOnS3lfgGgsxAoAkAD0burVWj/3HPPDe+3BoDOREuqANBA9BaWQw89lCARQEMgUASABqHs5vnz55PlDKBhECgCQINQSSAFiwDQKAgUAaBB9OrVy84555yW12ECQGcjUASABqLX761fvz60owgAnY1AEQAaSJ8+fax///42d+7cdAwAdB4CRQBoMCNGjAipiqtWrUrHAEDnIFAEgAY0atQomzp1ajoEAJ2DQBEAGlDfvn3DKynnzJmTjgGAjsebWQCgQe1OHs+FXbts3wULzPbf32zIEL26xaxnz+Lwhg1mBx+czg0A9UegCACN7MEHzQ45xEzlFfW2FrWzOGCA2RlnmD3xhNl3vpPOCAD1R9YzADQqpSSqmZwrrjC7+WazTz81++ADsxdeMJsyxYya0QDaGYEiADSqXr2K2cy7dxe7HTvMevQopipu357OBADth6xnAGhkv/ud2bRpxaDxm980W7rUbPBgs/POM5s40ewv/iKdEQDqj0ARAAAAuch6BgAAQC4CRQAAAOQiUAQAAEAuAkUAAADkIlAEAABALgJFAAAA5CJQBAAAQC4CRQAAAOQiUAQAAEAuAkUAAADkIlAEAABALgJFAAAA5CJQBAAAQC4CRQAAAOQiUAQAAEAuAkUAAADkIlAEAABALgJFAAAA5CJQBAAAQA6z/w+6N2c01uIhwgAAAABJRU5ErkJggg==\" width=\"584\" height=\"458\"\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\u003eThe docking analysis of VIM-2 in complex with various ligands revealed that the binding interactions were stabilized through a combination of key forces, including hydrogen bonds, coordination with catalytic zinc ions, π\u0026ndash;π stacking, and hydrophobic contacts involving critical residues within the enzyme\u0026rsquo;s active site.\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"6\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003eComplex\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eHydrogen bonds Residue\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003eZinc Interactions\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003eπ-π stacking\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003eπ-cation\u003c/p\u003e\u003cp\u003eInteraction\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003eHydrophobic Interaction Amino acids\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cb\u003eDistance\u003c/b\u003e\u003c/p\u003e\u003cp\u003e\u003cb\u003e(\u0026Aring;)\u003c/b\u003e\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eMeropenem\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eGlu149\u003c/p\u003e\u003cp\u003eAsp236\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eZN 301,\u003c/p\u003e\u003cp\u003eZN 302\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003ePhe61, Tyr67, Trp 87, Phe117\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eMSID001033\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eTrp 87, Lys 90, Gly 150, Asn 165\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eZN 301,\u003c/p\u003e\u003cp\u003eZN 302\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003ePhe 61, Trp 87\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003ePhe 61, Trp 87\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eMSID001081\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eTrp 87, Asp 120, Asn 165\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eZN 301,\u003c/p\u003e\u003cp\u003eZN 302\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eHis122, His 122\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003ePhe 61, Trp 87,Cys 221\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eMSID001168\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eAsp 119\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eZN 301,\u003c/p\u003e\u003cp\u003eZN 302\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003ePhe 61, Tyr 67, Trp 87, Cys 221, Tyr 224, Ala 235\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\u003eTo further evaluate the binding stability of these ligands, molecular dynamics (MD) simulations[\u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e52\u003c/span\u003e] were carried out for the apo form of VIM-2 and lead complexes MSID001033, MSID001081, and MSID001168 over a 250 ns timescale. Several key parameters, RMSD, RMSF, radius of gyration (R\u003csub\u003eg\u003c/sub\u003e), hydrogen bonding, MM-GBSA, RDF, and non-covalent interactions were analyzed to gain insight into the structural stability of the systems. RMSD was analyzed to monitor the equilibration and overall stability of the protein backbones throughout the simulation period.[\u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e53\u003c/span\u003e] RMSD plots for the apo form and three ligand-bound complexes (MSID001033, MSID001081, and MSID001168) are presented in Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eA. All four systems showed RMSD fluctuations within the 1\u0026ndash;3 \u0026Aring; range across the simulation, indicating the overall structural stability. Notably, slight conformational deviations were observed in the MSID001168-bound system, suggesting potential localized flexibility, while the remaining complexes maintained consistent backbone stability, supporting the formation of stable ligand-protein complexes. The stability of the protein-ligand complexes during simulation was evaluated using radius of gyration (R\u003csub\u003eg\u003c/sub\u003e) analysis. An increase in R\u003csub\u003eg\u003c/sub\u003e indicates structural expansion, whereas a decrease suggests enhanced compactness and rigidity. The results indicated that all the complexes-maintained stability and compactness during the simulation, demonstrating structural stabilization and compaction over time. Figure\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eB illustrates the R\u003csub\u003eg\u003c/sub\u003e trajectories for the apo- and ligand-bound systems, showing values predominantly within the 16.2\u0026ndash;17 \u0026Aring; range. While all systems demonstrated relatively stable folding patterns, the MSID001168-bound complex exhibited slightly elevated R\u003csub\u003eg\u003c/sub\u003e values, suggesting a subtle conformational disruption during the simulation.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eRoot-mean-square fluctuation (RMSF) analysis of backbone atoms was performed to assess the local conformational flexibility of the VIM-2 enzyme in its apo form and in complexes with the three selected molecules. As illustrated in Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eC, the overall RMSF profiles exhibited similar trends across all systems, with notable flexibility in the L3 and L10 loop regions. In the L3 loop, RMSF values were recorded as 0.93 \u0026Aring; for the apo form, and slightly reduced to 0.85 \u0026Aring;, 0.92 \u0026Aring; with MSID1033, MSID1081 and but slightly increased 1.45 \u0026Aring; for complex MSID1168, respectively. A more pronounced effect was observed in the L10 loop, where the apo form showed higher fluctuation (3.36 \u0026Aring;) compared to reduced values in the presence of MSID1033 (1.49 \u0026Aring;), MSID1081 (2.57 \u0026Aring;), and MSID1168 (2.35 \u0026Aring;). These reductions suggest that the ligands, particularly MSID1033, contribute to stabilization of the L10 loop conformational dynamics. Additionally, in other structurally relevant regions, namely, the loop encompassing residues 145\u0026ndash;150 and the α4 helix spanning residues 218\u0026ndash;232 the presence of the three ligands led to a slight decrease in RMSF compared to the apo structure. These findings indicate that ligand binding not only stabilizes critical loop regions, but may also promote a more rigid and stable conformation of VIM-2, potentially could contributing to the inhibition of its enzymatic activity.\u003c/p\u003e\u003cp\u003eThe stability and structural integrity of protein-ligand complexes are significantly affected by hydrogen bonding.[\u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e54\u003c/span\u003e, \u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e55\u003c/span\u003e] In this study, the H-bonds for MSID001033 were found to be 4, and MSID001081 showed the most stable hydrogen-bonding profile, which was found to be 3. For MSID001168, the number of hydrogen bonds was 3. Figure\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eD illustrates that the analysis of hydrogen bonds indicates that all molecules displayed the most stable and extensive hydrogen-bonding network, as evidenced by its highest maximum values. We also identified two additional types of non-covalent π-π and π-cation interactions that play crucial roles in enhancing ligand binding.[\u003cspan additionalcitationids=\"CR57\" citationid=\"CR56\" class=\"CitationRef\"\u003e56\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e58\u003c/span\u003e] An increase in these interactions generally correlates with greater stability and binding strength of the protein-ligand complex. In our docking studies, we observed two π\u0026ndash;π interactions between the ligand and the protein. During the 250 ns molecular dynamics (MD) simulation, this number increased, with three π-π and one π-cation interactions forming between the ligand and metallo-β-lactamase (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eA and \u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eB). This suggests dynamic adjustment of the ligand within the binding pocket, contributing to a more thermodynamically stable complex. The emergence of these interactions over time reflects a favorable structural reorganization that strengthens binding. π-π stacking interactions, especially those involving aromatic residues, contribute to maintaining the spatial integrity of the complex, whereas π-cation interactions offer additional stabilization through electrostatic forces with positively charged residues. Together, these interactions help preserve the secondary and tertiary structures of the protein and confer flexibility, allowing the complex to adapt to conformational shifts throughout the simulation.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eSalt bridges, formed through electrostatic attraction between oppositely charged amino acid residues, are key noncovalent interactions that significantly contribute to protein folding, structural integrity, and molecular recognition.[\u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e59\u003c/span\u003e, \u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e60\u003c/span\u003e] In this study, notable salt bridge interactions were consistently observed at the binding sites of the highest-ranking VIM-2 complexes MSID001033, MSID001088, and MSID001168. These interactions appear to play a pivotal role in enhancing the structural stability and binding affinity of the enzyme ligand complexes during molecular dynamics simulations. In particular, charged residues such as glutamate (Glu), arginine (Arg), and lysine (Lys) were found to form stable salt bridges with oppositely charged regions on the ligands. These interactions likely reinforce the binding conformation and contribute to the overall thermodynamic stability of the complexes. Representative salt bridge interactions are illustrated in Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003e.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eTo gain deeper insight into the binding affinity and thermodynamic stability of the selected ligands (MSID001033, MSID001081, and MSID001168) with β-lactamase, MM-GBSA binding free energy calculations were performed. The resulting free energy values were found to be -42.89 kcal/mol for MSID001033, -39.62 kcal/mol for MSID001081, and \u0026minus;\u0026thinsp;30.42 kcal/mol for MSID001168. Notably, MSID001033 demonstrated the most favorable binding, as reflected by its lowest ΔG value (-42.89 kcal/mol), indicating stronger and more stable interactions with the target enzyme. The consistently negative binding free energy values across all three compounds suggested that their interactions with β-lactamase are both spontaneous and thermodynamically favorable. These findings were in good agreement with the molecular docking and molecular dynamics simulation results, reinforcing the potential of these molecules as promising β-lactamase inhibitors.\u003c/p\u003e\u003cp\u003eThe binding modes of compounds MSID001033, MSID001081, and MSID001168 were further validated through molecular dynamics (MD) simulations, which provided deeper insights into their interactions with the VIM-2 enzyme. Interestingly, the interactions observed at the catalytic site during MD simulations were notably different from those predicted by molecular docking alone. For MSID001033, significant hydrophobic interactions were observed with VIM-2 residues, particularly Phe61 and Trp87 (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003eA). The ligand also established hydrogen bonds with Trp87 and displayed π-π stacking interactions with this residue. Furthermore, the compound formed two Zn\u0026sup2;⁺ coordination bonds: one with catalytic residues His116, His118, His196, and Asp236, and the other with Asp120, Cys221, and His 263. These metal-coordination interactions are crucial because they involve active-site zinc ions essential for VIM-2 enzymatic function. The protein-ligand contact distribution is shown in the histogram (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003eB). In contrast, molecular docking predicted hydrogen bonds with Trp87, Lys90, Gly150, and Asn165, π-π stacking between Phe61 and Trp87, and hydrophobic contacts involving Phe61, Trp87, and Cys221. MD simulations corroborated many of these interactions, particularly the π-π stacking involving Trp87 and the hydrophobic contributions from Phe61, Trp87, and Cys221. Some residues not highlighted in Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003eA appeared intermittently throughout the MD trajectory, suggesting their transient, but important, roles in ligand binding. The interaction diagram (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003eC) offers a frame-by-frame view of these dynamic interactions, with residues involved in multiple contacts highlighted in deeper shades of orange. Similarly, MSID001081 demonstrated strong interactions with Phe61 and Trp87 (Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003eA). Hydrogen bonding was observed with Asp120, while π-π stacking interactions occurred with both Phe61 and Trp87. Like MSID001033, this compound formed two Zn\u0026sup2;⁺ coordination bonds with His116, His 118, His196, and Asp236, and another with Asp120, Cys221, and His263, underscoring its engagement with the active site. Figure\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003eB shows the contact distribution. Docking studies also predicted hydrogen bonds with Trp87, Asp120, and Asn165, as well as π-π stacking with Phe61 and Trp87, alongside hydrophobic contacts involving Phe61, Trp87, and Cys221. These findings were largely validated by MD simulations, which confirmed persistent π-π stacking and hydrophobic interactions involving these residues. Notably, some residues that were absent in the interaction snapshot (Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003eA) were observed transiently during the simulation, highlighting their dynamic involvement. The interaction map in Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003eC illustrates these temporal patterns, with frequently interacting residues marked in darker orange. For the MSID001168-VIM-2 complex, 250 ns MD simulations revealed key interactions, such as hydrogen bonding with Arg228, π-π stacking with His263, and hydrophobic contacts involving Cys221 and Tyr224 (Fig.\u0026nbsp;\u003cspan refid=\"Fig9\" class=\"InternalRef\"\u003e9\u003c/span\u003eA). The compound also formed Zn\u0026sup2;⁺ coordination bonds with His116, His118, His196, and Asp236, and another with Asp119, Asp120, Cys221, and Tyr224 indicating strong engagement with the catalytic site. The protein-ligand contact distribution is shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig9\" class=\"InternalRef\"\u003e9\u003c/span\u003eB. The docking results predicted hydrogen bonding with Asp119 and hydrophobic interactions with Phe61, Tyr67, Cys221, Tyr224, and Ala235. While some of these interactions were confirmed during MD simulations, others appeared transiently, reflecting the dynamic nature of ligand binding. Figure\u0026nbsp;\u003cspan refid=\"Fig9\" class=\"InternalRef\"\u003e9\u003c/span\u003eC shows the full spectrum of interactions across the trajectory, emphasizing the residues that frequently engage with the ligand.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eAnisotropic Network Model (ANM) cross-correlation analysis,[\u003cspan citationid=\"CR61\" class=\"CitationRef\"\u003e61\u003c/span\u003e] based on molecular dynamics (MD) simulation trajectories, provided detailed insights into the collective motions of ligand-binding residues within the VIM-2 enzyme following interaction with selected fungal secondary metabolites. In the MSID001033-VIM-2 complex, residues such as Phe61, Trp87, Lys90, Gly150, and Asn165, which are crucial for π-π stacking, hydrogen bonding, and zinc coordination, exhibited moderate-to-strong positive cross-correlations (Fig.\u0026nbsp;\u003cspan refid=\"Fig10\" class=\"InternalRef\"\u003e10\u003c/span\u003eA). These dynamic couplings suggest concerted motion among the key residues that stabilize the ligand within the active site. Notably, coordination of the ligand with both zinc ions contributed to a more rigid and less flexible catalytic pocket, as indicated by the elevated local correlation values. A similar trend was observed for the MSID001081-VIM-2 complex, where residues Trp87, Asp120, Asn165, and Phe61 displayed synchronized movements, reinforcing stable hydrogen bonding and π-π interactions (Fig.\u0026nbsp;\u003cspan refid=\"Fig10\" class=\"InternalRef\"\u003e10\u003c/span\u003eB). The involvement of zinc-coordinating residues indicates a structurally rigid environment. In contrast, the MSID001168-VIM-2 complex, although featuring fewer interactions, showed significant local dynamic correlation around Asp119 and the zinc-binding sites, suggesting a stable yet restricted binding conformation (Fig.\u0026nbsp;\u003cspan refid=\"Fig10\" class=\"InternalRef\"\u003e10\u003c/span\u003eC). Altogether, these results indicate that all three ligands induced a constrained and harmonized conformational state in VIM-2, disrupting the enzyme\u0026rsquo;s catalytic flexibility. Such dynamic stabilization highlights the potential of these compounds as effective inhibitors and emphasizes the value of incorporating flexibility-focused analyses into the rational design of metallo-β-lactamase inhibitors.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eRadial distribution functions (RDFs) were calculated to understand the spatial distribution of atoms around a reference atom in each of the three ligand-protein complexes.[\u003cspan citationid=\"CR62\" class=\"CitationRef\"\u003e62\u003c/span\u003e] Fig.\u0026nbsp;\u003cspan refid=\"Fig11\" class=\"InternalRef\"\u003e11\u003c/span\u003e shows the RDF plots for complex-MSID001033 (pink), Complex-MSID001081 (blue), and Complex-MSID001168 (deep yellow), providing insights into the structural organization and interaction patterns. In all three complexes, the RDF profiles showed an initial sharp rise beginning around 2.5 \u0026Aring;, indicating the formation of a well-defined first solvation shell. This peak was most pronounced in complex-MSID001033, which reached a maximum distance (\u0026Aring;) value of approximately 3.1 around 4.5 \u0026Aring;. The RDF of Complex-MSID001168 (deep yellow) also demonstrates a prominent peak (~\u0026thinsp;3.2) at approximately 4.8 \u0026Aring;, indicating strong local structuring and possibly stronger or more stable interactions between the ligand and surrounding residues or solvent molecules. In contrast, Complex-MSID001081 showed a relatively lower peak (~\u0026thinsp;2.7) and broader distribution, suggesting a less ordered interaction environment or a more dynamic solvation shell. This could imply weaker or more transient interactions in this complex compared to those in complexes MSID001033 and MSID001168. Following the primary peak, all complexes show a gradual decay of distance (\u0026Aring;), leveling off beyond 9\u0026ndash;10 \u0026Aring;, which is consistent with the bulk-like behavior of the system. complex-MSID001033 and Complex-MSID001168 retain slightly higher distance (\u0026Aring;) values in this region, possibly reflecting persistent long-range structural organization or interactions. Overall, the RDF analysis revealed that complexes MSID001033 and MSID001168 exhibited stronger and more defined interaction patterns compared to complex MSID001081. This suggests that complexes MSID001031 and MSID001168 form more stable or structured interactions with the protein environment, which may correlate with the higher binding affinity or enhanced stability during molecular dynamics simulations.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eComputer-aided drug design (CADD) has become an essential component of modern drug discovery, offering a cost-effective and time-saving alternative to traditional experimental methods. By leveraging in silico techniques, researchers can estimate key molecular descriptors and predict pharmacologically relevant properties using data-driven models and structural analyses. In the context of combating antimicrobial resistance, particularly from enzymes such as Verona integrin-encoded Metallo-β-lactamase (VIM-2), computational strategies have shown significant promise in identifying novel inhibitors and optimizing lead compounds. In this study, 1830 fungal secondary metabolites were virtually screened to identify potential inhibitors targeting VIM-2, a clinically relevant metallo-β-lactamase associated with multidrug-resistant bacterial infections. Using a comprehensive approach that combines molecular docking, molecular dynamics (MD) simulations, post-simulation analyses, salt bridge analysis, anisotropic network model (ANM) cross-correlation and radial distribution function (RDF), we identified three promising candidates: MSID001033, MSID1081, and MSID1168. These metabolites exhibited strong binding affinities and maintained stable interactions within the active site of the enzyme throughout the MD simulations, indicating their potential as effective lead molecules. The focus of this research is the investigation of fungal-derived natural products as sources of VIM-2 inhibitors, an area that remains relatively underexplored. These findings not only contribute to the growing field of antibiotic resistance research but also highlight the value of integrating natural compound libraries with computational screening techniques to accelerate the discovery of next-generation antimicrobial agents.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAuthor Contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eS.D. envisaged the idea, designed the experiments, analyzed the experimental data and wrote the manuscript; A.M. performed the experiments; K.B. and K.M. for helpful discussion and reviewing the manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent to Publish declaration\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eConsent to Publish declaration: not applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent to Participate declaration\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eConsent to Participate declaration: not applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics declaration\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eEthics declaration: not applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData Availability Statement\u003c/strong\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe datasets generated during and/or analysed during the current study are available from the corresponding author on reasonable request.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eACKNOWLEDGEMENTS\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe acknowledge the Department of Pharmaceutical Chemistry, Eminent College of Pharmaceutical Technology, Barasat, for access to computational infrastructure that facilitated this work.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAUTHOR INFORMATION\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCorresponding Author\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e*Sandip Dolui.\u003csup\u003e\u0026nbsp;\u003c/sup\u003eEminent College of Pharmaceutical Technology, Moshpukr, Barbaria, Paschim Khilkapur, Barasat, West Bengal, India.\u003c/p\u003e\n\u003cp\u003eE-mail\u003cem\u003e:\u003c/em\[email protected]\u003c/p\u003e\n\u003cp\u003ePhone: 9609534743\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eAhmed SK, Hussein S, Qurbani K, et al. 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Future Med Chem. 2020;12(4):299\u0026ndash;309.\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":"β-lactamase, MeFSAT, AutoDock Vina, Binding Energy, RMSD","lastPublishedDoi":"10.21203/rs.3.rs-6979596/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6979596/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eAntibiotic resistance is a critical global health concern, with metallo-β-lactamases like Verona integron-encoded metallo-β-lactamase-2 (VIM-2) contributing to the breakdown of β-lactam antibiotics, including carbapenems. The increasing prevalence of VIM-2-mediated resistance highlights the urgent need for novel inhibitors. In this study, a structure-based virtual screening approach was employed to identify potential VIM-2 inhibitors from the Medicinal Fungal Secondary Metabolites and Therapeutics (MeFSAT) library. Molecular docking of 1,830 fungal-derived compounds, followed by molecular dynamics (MD) simulations, led to the identification of three promising candidates: MSID001033, MSID001081, and MSID001168. These compounds showed high docking scores and favorable interactions with the VIM-2 active site, forming stable complexes through π-π stacking, hydrogen bonding, and zinc coordination. MD simulations over 250 ns confirmed the structural stability of the complexes, supported by consistent RMSD, RMSF, and hydrogen bonding profiles. Further validation using MM-GBSA binding energy calculations, radial distribution function (RDF), salt bridge analysis, and anisotropic network model (ANM) cross-correlation reinforced their strong binding affinity. Overall, this study highlights fungal secondary metabolites as promising scaffolds for VIM-2 inhibition and demonstrates the effectiveness of integrated computational methods in accelerating early-stage antibiotic drug discovery.\u003c/p\u003e","manuscriptTitle":"Uncovering Novel VIM-2 Inhibitors from Fungal Sources Using Structure-Based Screening and Molecular Dynamics Approaches","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-07-25 16:09:44","doi":"10.21203/rs.3.rs-6979596/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":"758babee-de8e-4650-af2e-87f8fb840b45","owner":[],"postedDate":"July 25th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2026-02-25T04:54:47+00:00","versionOfRecord":[],"versionCreatedAt":"2025-07-25 16:09:44","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-6979596","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6979596","identity":"rs-6979596","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}