Bioinformatic Characterization of a Candidate Antimicrobial Peptide 13_4 from Bacillus spizizenii ATCC 6633: A Multifunctional Inhibitor of Essential Metabolic Targets and β-Lactamases

Bioinformatic Characterization of a Candidate Antimicrobial Peptide 13_4 from Bacillus spizizenii ATCC 6633: A Multifunctional Inhibitor of Essential Metabolic Targets and β-Lactamases | 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 Bioinformatic Characterization of a Candidate Antimicrobial Peptide 13_4 from Bacillus spizizenii ATCC 6633: A Multifunctional Inhibitor of Essential Metabolic Targets and β-Lactamases Ana Paula Palacios Rodriguez, Elias Jorge Muniz Seif, Pedro Ismael Silva Junior This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8768089/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract The global rise of multidrug-resistant bacteria underscores the urgent need for alternative antimicrobial strategies targeting non-classical bacterial mechanisms. In this study, we performed an in-silico characterization of peptide 13_4, a short anionic peptide candidate derived from Bacillus spizizenii ATCC 6633, to explore its potential interactions with essential metabolic enzymes and resistance-associated proteins. Physicochemical analysis revealed a highly flexible, negatively charged peptide, compatible with non-membranolytic mechanisms of action. Target prospecting and molecular docking identified high-affinity interactions with key bacterial enzymes, including carbapenemases (OXA-23, OXA 24, OXA-58), Malate Synthase G, Aspartate Aminotransferase, and UDP-N-acetylglucosamine 1-carboxyvinyltransferase. Molecular dynamics simulations demonstrated stable peptide–protein complexes, supported by persistent hydrogen bonding networks and adaptive conformational flexibility, particularly for carbapenemase targets. Network analysis further highlighted the involvement of these targets in essential metabolic and resistance pathways. Collectively, these results suggest that peptide 13_4 may act as a multifunctional bioactive molecule targeting intracellular bacterial processes and resistance mechanisms, supporting its prioritization as a candidate for future experimental validation. antimicrobial peptide Bacillus spizizenii molecular docking molecular dynamics simulations in silico analysis Figures Figure 1 Figure 2 Figure 3 Figure 4 1. Introduction The escalating threat of multidrug-resistant (MDR) bacteria represents one of the most pressing challenges to global public health. Infections caused by these pathogens severely compromise the efficacy of conventional antimicrobial therapies, resulting in prolonged illness, increased mortality, and substantial economic burden[ 1 ]. In 2021, bacterial antimicrobial resistance was associated with an estimated 4.71 million deaths worldwide, including 1.14 million deaths directly attributable to resistant infections, underscoring the urgent need for innovative antimicrobial strategies [ 2 ]. Several of the most problematic MDR pathogens are grouped under the acronym ESKAPEE, which includes Enterococcus faecium, Staphylococcus aureus, Klebsiella pneumoniae, Acinetobacter baumannii, Pseudomonas aeruginosa , Enterobacter species, and Escherichia coli [ 3 ]. Among these, carbapenem-resistant Acinetobacter baumannii (CRAB) is particularly alarming due to its global dissemination, high mortality rates, and the limited efficacy of current therapeutic options. Beyond resistance enzymes, the inhibition of essential metabolic enzymes and other intracellular bacterial targets has emerged as a promising strategy to impair pathogen viability and limit resistance development [ 4 ]. Similarly, Mycobacterium tuberculosis continues to pose a major global health burden, ranking as the second leading cause of death from a single infectious agent in 2022. The increasing incidence of rifampicin-resistant strains further emphasizes the urgent need for alternative antimicrobial interventions that act through mechanisms distinct from conventional antibiotics [ 5 ]. In this context, antimicrobial peptides (AMPs) have emerged as promising therapeutic candidates due to their structural diversity and functional versatility. While many classic AMPs exert their activity through membrane disruption, accumulating evidence indicates that several peptides act through non-membranolytic mechanisms, including interactions with intracellular targets such as metabolic enzymes, ribosomal components, and resistance-associated proteins [ 4 , 6 – 10 ]. Notably, anionic antimicrobial peptides have been increasingly associated with intracellular modes of action rather than direct membrane lysis, which may contribute to reduced cytotoxicity toward mammalian cells and enhanced selectivity for bacterial targets[ 11 ]. These properties make them particularly attractive candidates for targeting essential enzymatic pathways involved in bacterial metabolism and antibiotic resistance. Computational approaches play a central role in the discovery and functional evaluation of peptide-based antimicrobials. Techniques such as molecular docking, in silico protein–peptide interaction analysis, and structure-based modeling enable the prediction of binding affinities, identification of key interaction residues, and exploration of potential multitarget effects across bacterial enzymes. Complementarily, molecular dynamics simulations, machine learning–based classifiers, and curated antimicrobial peptide databases, have become essential tools for identifying novel AMP candidates, characterizing their putative mechanisms of action, and prioritizing sequences with favorable biological profiles [ 12 , 13 ]. Therefore, the aim of this study was to investigate potential bacterial protein targets of the anionic antimicrobial peptide candidate 13_4 from Bacillus spizizenii ATCC 6633 using structure-based molecular docking and in silico binding analyses, to gain mechanistic insights into its predicted mode of action. 2. Materials and Methods 2.1. Peptide characterization and folding In this study, we investigated the antimicrobial peptide candidate 13_4 (“KDSMEEY”), isolated from Bacillus spizizenii ATCC 6633. Mass spectrometry analysis revealed the presence of an oxidized methionine variant (manuscript submitted); however, all computational analyses were performed using the non-oxidized peptide sequence. This approach was adopted to evaluate the intrinsic structural and interaction properties of the native peptide backbone, minimizing potential biases associated with post-translational modifications or oxidation artifacts arising from sample preparation. The physicochemical properties of peptide 13_4 was assessed using computational tools. The theoretical molecular weight, isoelectric point (pI), net charge at pH 7, and extinction coefficient were calculated using the PepCalc online server ( http://www.pep-calc.com ) [ 14 ]. Additionally, the instability index and aliphatic index were estimated using the ProtParam tool ( https://web.expasy.org/protparam/ ) available through the ExPASy platform [ 15 ]. The three-dimensional structure of peptide 13_4 was constructed using Chimera version 1.19[ 16 ]. Hydrogen atoms were added, partial charges were assigned, and screening radii were defined according to the AMBER ff14SB force field. Energy minimization was performed without positional restraints, using 1,000 steps of steepest descent with a step size of 0.02 Å. The resulting minimized structure was exported in mol2 and PDB formats for subsequent in silico analyses [ 17 ]. 2.2. Targets prospection To identify potential molecular targets for Peptide 13_4, a reverse pharmacophore mapping approach was performed using the PharmMapper server ( http://www.lilab-ecust.cn/pharmmapper/ ) [ 18 ] The peptide was submitted with Conformer Generation enabled (“YES”), allowing the generation of up to 300 conformations. Full pharmacophore mapping was carried out against the entire database (v2010, containing 7,302 targets), retaining the top 300 matched targets [ 19 ]. From these, the 50 best-ranked hits based on the Normalized Fit Score were selected and subsequently filtered to include only proteins from pathogenic or opportunistic bacteria, as well as enzymes related to bacterial resistance. Furthermore, because Acinetobacter baumannii is classified as a critical-priority pathogen, eight carbapenemase enzymes belonging to the OXA-type β-lactamase family (class D, oxacillin-hydrolyzing enzymes) from this microorganism were selected for further analysis. 2.3. Molecular docking analysis Molecular docking was performed using ClusPro 2.0 [ 20 ], AutoDock Vina [ 21 ], and HPEPDOCK server [ 22 ]. ClusPro 2.0 was employed as the primary global docking platform, generating thousands of rigid-body conformations that were filtered based on desolvation energy and clustered according to interface RMSD. Resulting complexes were ranked using a weighted scoring function that integrates electrostatic, van der Waals, and desolvation energy terms. The top-scoring complex for each receptor was selected based on geometric stability and favorable stereochemical properties, making it suitable for subsequent molecular dynamics simulations. AutoDock Vina was then used to refine docking within predicted or canonical binding pockets, providing binding affinity estimates and improved pose discrimination, while HPEPDOCK server complemented the analysis by incorporating hierarchical peptide conformational sampling to account for peptide flexibility. 2.4. Interaction analysis and binding site validation Docked complexes were analyzed to characterize non-covalent interactions and to confirm peptide binding within functionally relevant regions of the target proteins. Interaction profiling was performed using the Protein-Ligand Interaction Profiler (PLIP) web server, which identified hydrogen bonds, hydrophobic contacts, and salt bridges for each protein–peptide complex [ 23 ]. In addition, the Computed Atlas of Surface Topography of proteins (CASTp) v3.0 web server was employed to define the geometric boundaries of canonical binding pockets [ 24 ]. The spatial positioning of peptide 13_4 relative to residues identified by PLIP and CASTp was subsequently evaluated to assess its potential to interfere with catalytic activity or substrate recognition. Structural visualization and analysis of the docking complexes were performed using UCSF ChimeraX v1.10 [ 25 ]. Key interactions were further visualized using LigPlot+ v2.2.7, generating two-dimensional diagrams that highlight hydrogen bonding and hydrophobic contacts [ 26 ]. 2.5. Protein-protein interactions To provide functional context for the selected targets, protein–protein interaction networks were explored using STRING, which allowed identification of biologically relevant pathways and associations without implying direct validation of docking predictions [ 27 ] 2.6. Molecular Dynamics simulations Molecular dynamics simulations were conducted at the Bioinformatics Center of the Butantan Institute using GROMACS v.2023.5 ( https://www.gromacs.org )[ 28 ]. Three peptide–protein complexes, corresponding to the top-ranked docking poses of the antimicrobial peptide candidate 13_4 bound to bacterial target proteins retrieved from the Protein Data Bank (PDB codes: 4OH0, 1N8W, and 4K0X), were selected for molecular dynamics simulations. Simulations employed the OPLS-AA/L force field. Each complex was placed in a cubic box with 1 nm padding and solvated with TIP3P water molecules. Cl − or Na + ions were added to neutralize the system. Energy minimization was performed for 1 ns with a maximum force threshold of < 1000 kJ/mol/nm, followed by equilibration at 300K and 1 bar for 0.1 ns. Production dynamics were run for 100 ns [ 19 ]. 2.7. Data analysis Trajectory outputs were analyzed using GROMACS tools: gmx rms (root mean squared deviation), gmx rmsf (root mean squared fluctuation), gmx hbond (hydrogen bond interactions), gmx gyrate (radius of gyration), and gmx sasa (solvent-accessible surface area). An index was created to track the 13_4 peptide trajectory. Graphs and statistical analyses were generated using R v4.4.2 within RStudio with the packages ‘ggplot2’, ‘dplyr’, and ‘grid’. 3. Results 3.1. Peptide structure The physicochemical characterization of peptide 13_4 indicates that it is a short heptapeptide with a net charge of − 2, a molecular weight of 900.96 g/mol, and an isoelectric point of 4.14. The peptide exhibits a high instability index (82.66) and a strongly hydrophilic profile (GRAVY = − 2.086), features commonly associated with flexible, non-membranolytic antimicrobial peptides. Its Boman index (4.15 kcal/mol) suggests a strong propensity for protein binding, consistent with the hypothesis of a mechanism involving interaction with intracellular targets rather than membrane disruption. The initial three-dimensional structure of peptide 13_4 was energy-minimized using UCSF Chimera v1.19 through steepest descent followed by conjugate gradient optimization (Fig. 1 ). This process reduced the potential energy from 953 to 269 kJ/mol, relieving steric clashes and producing a stable conformation suitable for target prospecting, molecular docking, and molecular dynamics simulations. 3.2. Target prospecting by PharmMapper Pharmacophore-based target screening using PharmMapper identified 300 potential protein targets for peptide 13_4. Based on normalized fit scores and biological relevance, the top 50 microbial-related targets were examined in detail, leading to the selection of 15 functionally significant receptors (Table 1 ). Priority was given to enzymes involved in essential metabolic pathways and antimicrobial resistance mechanisms. Among the highest-ranked targets were Aspartate Aminotransferase (AspC, PDB ID: 1AIB), UDP-N-acetylglucosamine 1-carboxyvinyltransferase (MurA, PDB ID: 1UAE), Malate Synthase G (GlcB, PDB ID: 1N8W), and the β-lactamase regulatory protein BlaR1 (PDB ID: 1XA1). In addition, given the clinical relevance of carbapenem resistance, several β-lactamases not highlighted by PharmMapper were deliberately incorporated, including TEM and metallo-β-lactamases as well as OXA-type carbapenemases (OXA-23, OXA-24/40, OXA-58, and OXA-143) from Acinetobacter baumannii . This integrative selection strategy ensured that subsequent docking and molecular dynamics analyses focused on targets with both high pharmacophore compatibility and critical functional roles in bacterial survival and resistance. 3.3. Docking molecular and receptor-peptide interactions A total of 28 bacterial proteins were evaluated by molecular docking with peptide 13_4 using a hierarchical strategy. Initial rigid-body docking was performed using ClusPro 2.0, and the top-ranked ClusPro pose was selected for subsequent analyses. To independently validate the docking results, parallel docking simulations were performed using AutoDock Vina and HPEPDOCK, providing complementary binding scores and pose comparisons (Table 2 ). Protein–peptide interaction patterns, including hydrogen bonds, hydrophobic contacts, and salt bridges, were systematically analyzed using PLIP (Table S1). Across all docking platforms, peptide 13_4 consistently exhibited more favorable predicted binding toward carbapenemase enzymes compared to other screened targets, despite these enzymes not being prioritized by the initial PharmMapper-based screening. OXA-type, including OXA-58 (PDB ID: 4OH0) and OXA-23 (PDB ID: 4K0X), displayed recurrent high-ranking poses and coherent interaction profiles. Table 1 PharmMapper results using peptide 13_4 as the ligand. PM Rank PDB ID Target name Normalized fit score Origin 1 1AIB Aspartate aminotransferase 0.9996 Escherichia coli 4 1QI1 NADP-dependent glyceraldehyde-3-phosphate dehydrogenase 0.9989 Streptococcus mutans 6 1PQP Aspartate-semialdehyde dehydrogenase 0.9982 Haemophilus influenzae RdKW20 7 1KC7 Pyruvate, phosphate dikinase 0.9979 Clostridium symbiosum 11 1Y79 Peptidyl-dipeptidase dcp 0.9968 Escherichia coli 17 1BLH Beta-lactamase 0.9956 Staphylococcus aureus 18 1HOT Glucosamine 6-phosphate deaminase 0.9954 Escherichia coli 22 1GRO Isocitrate dehydrogenase (NADP) 0.9941 Escherichia coli 25 1UAE UDP-N-acetylglucosamine 1-carboxyvinyltransferase 0.9936 Escherichia coli 27 1XFF Glucosamine-fructose-6-phosphate aminotransferase 0.9935 Escherichia coli 29 1KFL Phospho-2-dehydro-3-deoxyheptonate aldolase, Phesensitive 0.9927 Staphylococcus aureus 30 1XA1 Regulatory protein blaR1 0.9919 Staphylococcus aureus 35 1N8W Malate synthase G 0.9919 Mycobacterium tuberculosis 45 1U3L 2-C-methyl-D-erythritol 2,4-cyclodiphosphate synthase 0.9900 Escherichia coli 47 1W55 Bifunctional enzyme ispD/ispF 0.9895 Campylobacter jejuni 84 1MQO Metallo-β-Lactamase II 0.9747 Bacillus cereus 103 1M2X Metallo-β-Lactamase (B − 1 type) 0.9654 Elizabethkingia meningoseptica 144 1HLK Metallo-β-Lactamase II 0.9394 Bacteroides fragilis 236 1AXB β-Lactamase (TEM) 0.8825 Escherichia coli 299 1DD6 Metallo-β-Lactamase (IMP − 1 type) 0.8338 Pseudomonas aeruginosa - 4JF4 Carbapenemase OXA-23 - Acinetobacter baumannii - 4K0X Carbapenemase OXA-23 - Acinetobacter baumannii - 2JC7 Carbapenemase OXA-24 - Acinetobacter baumannii - 3FV7 Carbapenemase OXA-24 - Acinetobacter baumannii - 4WM9 Carbapenemase OXA-24 - Acinetobacter baumannii - 7RP8 Carbapenemase OXA-24/40 - Acinetobacter baumannii - 4OH0 Carbapenemase OXA-58 - Acinetobacter baumannii - 5IY2 Carbapenemase OXA-143 - Acinetobacter baumannii PM Rank corresponds to the general PharmMapper ranking. PDB ID refers to the Protein Data Bank identification code. The normalized fit score was calculated as the ratio between the fit score and the number of pharmacophore features. Origin indicates the bacterial species from which the target protein was isolated. Among these targets, OXA-58 (PDB ID: 4OH0) emerged as the most promising candidate, presenting the most favorable consensus docking scores (ClusPro: −687.5 kcal/mol; Vina: −6.9 kcal/mol; HPEPDOCK: −130.732) and a large, well-defined docking cluster comprising 1000 members. Structural inspection revealed an extensive protein–peptide interface involving 169 non-covalent contacts, with recurrent interactions involving residues previously implicated in substrate recognition and catalytic stabilization, including Ser83, Val132, Tyr135, Trp223, Met225, and Arg263 (Fig. 2 a-b and Table 3 ). Comparable interaction patterns were observed for Malate Synthase G (PDB ID: 1N8W), where peptide 13_4 localized within the acetyl-CoA binding region, interacting with amino acid residues Arg125, Phe126, Asn129, Arg312, and Lys621 (Fig. 2 c-d and Table 3 ), and for OXA-23 carbapenemase (PDB ID: 4K0X), with contacts involving catalytically relevant residues such as Ser79 and Trp219 (Fig. 2 e–f and Table 3 ). Favorable docking scores and coherent binding poses were also obtained for Aspartate Aminotransferase (PDB ID: 1AIB) and UDP-N-acetylglucosamine 1-carboxyvinyltransferase (PDB ID: 1UAE), with peptide binding localized to substrate-associated regions critical for enzymatic function. Table 2 Docking scores of the peptide13_4 against selected bacterial protein targets using ClusPro 2.0, AutoDock Vina, and HPEPDOCK PDB ID ClusPro 2.0 AutoDock Vina binding afinity (kcal/mol) HPEPDOCK score Cluster size Lower energy (kcal/mol) 4OH0 1000 -687.5 -6.9 -130.732 3FV7 533 -631.3 -6.9 -131.862 1N8W 513 -593.2 -7.0 -138.450 5IY2 607 -570.5 -7.3 -131.698 4JF4 365 -566.5 -6.3 -119.731 4K0X 958 -557.8 -6.4 -119.386 1AIB 394 -556.2 -8.0 -132.749* 1UAE 306 -549.4 -8.0 -138.756 2JC7 508 -528.1 -7.2 -128.565 7RP8 657 -527.4 -6.7 -124.137 4WM9 180 -518.0 -7.1 -135.900 1DD6 678 -517.7 -6.7 -136.367* 1XA1 204 -498.0 -6.2 -139.188* 1HLK 310 -484.5 -6.6 -129.993* 1BLH 357 -394.7 -5.4 -127.558 1AXB 113 -389.7 -6.6 -117.280 An asterisk (*) indicates targets for which the lowest-energy pose predicted by HPEPDOCK did not correspond to the same binding site identified by ClusPro and AutoDock Vina. Table 3. Comparison of experimentally reported active-site residues with peptide 13_4 binding residues predicted by ClusPro docking and analyzed using PLIP. Shared residues are highlighted in bold. PDB ID Protein Binding / Active Site Ligand bind site residues (native) Ligand bind site residues (found) 4OH0 Carbapenemase OXA-58 Active site 83, 86, 130, 169, 170, 220, 263 83 , 132, 135, 223, 225, 226, 260 , 263 3FV7 Carbapenemase OXA-24 Active site 81, 111 , 112 , 130 , 168, 221 111 , 112 , 115, 128, 130 , 169, 218, 221 , 258, 261 1N8W Malate synthase G Acetyl-CoA binding site 118, 125 , 126 , 129 , 312 , 621 123, 125 , 126 , 129 , 140 , 312 , 621 5IY2 Carbapenemase OXA-143 Active site 81 , 84, 167, 219, 261 81 , 109, 111, 112, 128, 130, 161, 162, 258, 259, 261 4JF4 Carbapenemase OXA-23 Active site 79, 126 ,166, 217, 219 , 259 79 , 110, 113, 128, 219, 256, 258, 259 4K0X Carbapenemase OXA-23 Active site 79 , 109 , 110, 126, 217, 219, 221 , 259 79 , 109 , 126 , 132, 136, 221 , 259 1AIB Aspartate aminotransferase Active site 108, 109 , 140 , 194 , 255, 257, 258, 266 109 , 140, 194 , 266, 386 1UAE UDP-N-acetylglucosamine 1-carboxyvinyltransferase FFQ / UD1 binding site 22, 23 , 91 , 115 , 120 , 121 , 124, 162, 163 , 164, 305, 371 , 397 23 , 91 , 95, 115 , 120 , 121 , 159, 160 163 , 190, 232, 328, 371 , 397 2JC7 Carbapenemase OXA-24 Active site 81, 84, 111, 112 , 115, 128, 130, 218, 219 , 220, 221 , 223, 261 112 , 219 , 221 , 258, 261 7RP8 Carbapenemase OXA-24/40 Active site 81, 128 , 165, 168, 219, 221, 261 112, 114, 115, 128 , 261, 262 4WM9 Carbapenemase OXA-24 Active site 81, 112, 128 , 218, 219, 221, 223, 261 115, 127, 128 , 206, 218 , 261 , 262 1DD6 Metallo-β-Lactamase (IMP−1 type) Active site 23, 25 , 31 , 33 , 51, 81 , 139, 161 , 166, 167 , 197 23 , 25 , 28, 31, 33 , 51 , 81, 161 , 167 , 197 1XA1 Regulatory protein blaR1 Active site 59 , 62 , 107 , 196, 197, 236 59 , 62 , 91, 93, 107 , 147, 199, 236 1HLK Metallo-β-Lactamase II Active site 49 , 99, 101 , 103 , 162, 184 , 193 36, 45, 46, 47, 49 , 55, 101, 103 , 184, 193 , 195, 196, 223 1BLH β -lactamase Active site 70 , 105, 237 , 239 70 , 73, 167, 170, 237 , 239 , 270, 273 1AXB β-Lactamase (TEM) Active site 70 , 130 , 132 , 166, 170 , 237 , 240 70 , 73, 105, 130 , 132, 167, 168, 170 , 237 , 240 , 244, 272, 273 * Residues shared between native functional sites and peptide 13_4 docking-predicted binding regions indicate potential interference with catalytic or substrate-recognition regions. 3.4. Protein-protein interactions Protein–protein interaction (PPI) network analysis revealed that the high-affinity targets of peptide 13_4 cluster into two major functional modules: central metabolic pathways and antimicrobial resistance regulation. Aspartate aminotransferase (AspC, PDB ID: 1AIB) is involved in oxaloacetate production, the TCA cycle, and amino acid and nucleotide biosynthesis (Fig. 3 a). Interaction of peptide 13_4 with AspC is predicted to perturb multiple metabolic pathways, potentially triggering a cascade of metabolic failures. Similarly, UDP-N-acetylglucosamine 1-carboxyvinyltransferase (MurA, PDB ID: 1UAE) plays a central role in bacterial cell wall biosynthesis in E. coli K12 (Fig. 3 b), and interaction with peptide 13_4 may disrupt peptidoglycan formation, compromising cell wall integrity and bacterial viability. Malate Synthase G (GlcB) was confirmed as a key component of the glyoxylate shunt in M. tuberculosis , coordinated with Isocitrate Lyase (Fig. 3 c), a pathway required for survival under nutrient-limited conditions. Finally, the β-lactamase regulatory protein BlaR1 controls β-lactamase expression and stress response pathways in S. aureus (Fig. 3 d). Collectively, these findings suggest that peptide 13_4 has the potential to simultaneously interfere with essential metabolic and resistance mechanisms. 3.5. Molecular dynamics Molecular dynamics simulations were performed over 100 ns to evaluate the stability and conformational behavior of the peptide–protein complexes. RMSD analysis of the peptide backbone indicated that all systems reached equilibrium after approximately 30 ns and remained stable for the remainder of the simulation (Fig. 4 a). Among the evaluated complexes, the Malate synthase G (PDB ID: 1N8W) complex exhibited the lowest overall deviations, whereas the Carbapenemase OXA-23 (PDB ID: 4K0X) complex displayed slightly higher flexibility. The RMSF analysis revealed moderate fluctuations along the peptide sequence, particularly in central residues, while terminal regions remained comparatively stable across all complexes (Fig. 4 b). Hydrogen bond analysis revealed persistent peptide–protein interactions throughout the simulations. Among the evaluated complexes, carbapenemase OXA-58 (PDB ID: 4OH0) formed the highest number of intermolecular hydrogen bonds, whereas 1N8W and 4K0X formed fewer but more consistent contacts over time (Fig. 4 c). In contrast, the peptide displayed a limited and fluctuating number of intramolecular hydrogen bonds (Fig. 4 d), suggesting that its conformational stability is primarily driven by interactions with the protein surface rather than by internal hydrogen bonding. Analysis of peptide compactness and solvent exposure revealed receptor-dependent conformational differences. Specifically, the peptide adopted a more extended and solvent-exposed conformation in the 4OH0 complex, as reflected by higher radius of gyration and SASA values, while remaining more compact in the 1N8W complex (Fig. 4 e–f). Collectively, these results indicate that peptide 13_4 forms stable yet flexible complexes with diverse bacterial targets, supporting its capacity for structural adaptation to different protein environments. 4. Discussion The present study provides an in silico exploration of peptide 13_4, a short anionic peptide candidate derived from Bacillus spizizenii ATCC 6633 (manuscript submitted), aimed at identifying plausible protein targets associated with bacterial metabolism and antimicrobial resistance. Owing to its anionic and hydrophilic nature [ 11 , 29 ], peptide 13_4 is unlikely to exert antimicrobial activity through classical membranolytic mechanisms typically described for cationic antimicrobial peptides [ 10 , 30 ]. Instead, its physicochemical profile is consistent with a putative intracellular mode of action, which constitutes the central focus of this investigation. Accordingly, this work does not aim to demonstrate definitive antimicrobial inhibition, but rather to prioritize biologically relevant protein targets and propose mechanistic hypotheses to guide future experimental validation. Target bioprospecting combined with molecular docking analyses identified enzymes involved in central metabolism, cell wall biosynthesis, and antimicrobial resistance regulation as prominent interaction partners for peptide 13_4 (Table 1 , Fig. 3 ). Notably, consistent predicted binding was observed for several class D β-lactamases from Acinetobacter baumannii , including OXA-23, OXA-24/40, OXA-58, and OXA-143, despite these enzymes not being ranked among the top pharmacophore matches in the initial PharmMapper screening. This recurrent interaction pattern suggests an intrinsic structural complementarity between peptide 13_4 and conserved features of OXA-type enzymes, rather than an effect driven by target preselection. Given the pivotal role of carbapenemases in multidrug resistance and the increasing failure of last-resort antibiotics such as colistin [ 31 ], the identification of resistance-associated enzymes as potential peptide interaction partners is particularly relevant. Importantly, while many antimicrobial peptides reported against resistant A. baumannii originate from animal sources [ 32 , 33 ], peptide 13_4 derives from a bacterial producer, highlighting B. spizizenii as an underexplored reservoir of non-canonical bioactive peptide scaffolds. OXA-type enzymes share a conserved catalytic architecture characterized by a serine-based active site and a hydrophobic substrate-binding groove that accommodates carbapenem antibiotics. Docking analyses indicated that peptide 13_4 preferentially interacts with residues located within or adjacent to the catalytic cleft of several OXA enzymes (Table 2 ), including OXA-23 [ 34 , 35 ], OXA-24/40 [ 36 ], OXA-58 [ 37 ], and OXA-143[ 38 ]. These residues have been previously implicated in substrate recognition and catalytic stabilization (Table 3 ), suggesting that peptide binding may sterically hinder substrate access or perturb the local active-site environment. Docking-derived complexes were further evaluated by molecular dynamics simulations, which revealed stable peptide–protein associations [ 39 ]. RMSD profiles showed that all systems remained close to their initial conformations after equilibration, indicating stable binding modes rather than transient interactions (Fig. 4 a). Residue-level fluctuations (RMSF) were moderate and mainly localized (Fig. 4 b), consistent with binding-induced flexibility without global destabilization [ 40 ] Intermolecular hydrogen bonds between peptide 13_4 and the protein targets were persistently maintained throughout the simulations, particularly in the OXA-58 (PDB ID: 4OH0) complex (Fig. 4 c). In contrast, intramolecular hydrogen bonds within the peptide were fewer and transient (Fig. 4 d), indicating that peptide stability is primarily supported by interactions with the protein surface rather than a rigid internal fold. This structural adaptability is a feature commonly associated with short antimicrobial and bioactive peptides and may enable simultaneous modulation of multiple cellular pathways. Receptor-dependent differences in radius of gyration and solvent accessibility further suggest that peptide 13_4 adopts distinct yet stable conformations depending on the target enzyme (Fig. 4 e-f). Despite these insights, many limitations must be acknowledged. The present study does not address peptide uptake, intracellular availability, competition with native substrates, or direct enzymatic inhibition, nor does it account for the complexity of the cellular environment. Consequently, the predicted interactions should be interpreted as structural hypotheses rather than evidence of functional inhibition. Experimental validation through biochemical assays and cellular studies will be required to confirm the proposed mechanisms. Overall, this work provides a systematic computational framework for the identification of intracellular targets of non-canonical antimicrobial peptides and highlights peptide 13_4 as a promising scaffold capable of engaging both resistance-associated enzymes and essential metabolic pathways. These findings lay the groundwork for future experimental validation and rational optimization strategies, including peptide truncation, sequence refinement, and structure–activity relationship studies, aimed at developing novel peptide-based modulators of bacterial metabolism and antimicrobial resistance. 5. Conclusions This study presents an integrative in silico characterization of peptide 13_4, a short anionic peptide candidate derived from Bacillus spizizenii ATCC 6633, highlighting its potential to interact with intracellular bacterial targets involved in both essential metabolism and antimicrobial resistance. Rather than acting through classical membrane-disruptive mechanisms, peptide 13_4 is predicted to engage catalytically relevant regions of metabolic enzymes and class D β-lactamases, supporting a multifunctional and non-membranolytic mode of action. The convergence of target prospecting, molecular docking, molecular dynamics simulations, and network analysis underscores the utility of computational strategies for prioritizing bioactive candidates and elucidating plausible mechanisms at early discovery stages. Although the proposed interactions remain predictive and require experimental validation, these findings identify peptide 13_4 as a promising molecular scaffold and position B. spizizenii as an underexplored source of antimicrobial candidates. Collectively, this work provides a rational framework to guide future in vitro and in vivo studies aimed at validating peptide-based strategies to address multidrug-resistant bacterial infection. Declarations Funding: This research was funded by the Research Support Foundation of the State of São Paulo (FAPESP/CeTICS) (Grant No. 2013/07467-1), by the Brazilian National Council for Scientific and Technological Development (CNPq) (Grant No. 472744/2012-7), by the Coordenação de Aperfeiçoamento de Pessoal de Nível Superior - Brasil (CAPES) - Finance Code 001 Author Contribution Conceptualization, A.P.P.R and P.I.S.J.; Data curation, A.P.P.R.; Formal analysis, A.P.P.R.; Funding acquisition, P.I.S.J.; Investigation, A.P.P.R.; Methodology, A.P.P.R, E.J.M.S and P.I.S.J.; Project administration, P.I.S.J.; Resources, E.J.M.S and P.I.S.J.; Supervision, P.I.S.J.; Validation, A.P.P.R., E.J.M.S and P.I.S.J.; Writing-original draft, A.P.P.R; Writing-review and editing, A.P.P.R, E.J.M.S. and P.I.S.J. Acknowledgement We thank all members of the Protein Chemistry Laboratory at the Laboratory for Applied Toxinology (LETA—Butantan Institute, Brazil) for their continuous support and encouragement, as well as to the Bioinformatics Center of the Butantan Institute for their valuable assistance. References Ahmed SK, Hussein S, Qurbani K, Ibrahim RH, Fareeq A, Mahmood KA et al (2024) Antimicrobial resistance: Impacts, challenges, and future prospects. J Med Surg Public Health 2:100081. https://doi.org/10.1016/J.GLMEDI.2024.100081 Naghavi M, Vollset SE, Ikuta KS, Swetschinski LR, Gray AP, Wool EE et al (2024) Global burden of bacterial antimicrobial resistance 1990–2021: a systematic analysis with forecasts to 2050. Lancet 404:1199–1226. https://doi.org/10.1016/S0140-6736(24)01867-1 Gajic I, Tomic N, Lukovic B, Jovicevic M, Kekic D, Petrovic M et al (2025) A Comprehensive Overview of Antibacterial Agents for Combating Multidrug-Resistant Bacteria: The Current Landscape, Development, Future Opportunities, and Challenges. Antibiotics 14:221. https://doi.org/10.3390/ANTIBIOTICS14030221/S1 Le CF, Fang CM, Sekaran SD (2017) Intracellular targeting mechanisms by antimicrobial peptides. Antimicrob Agents Chemother 61:10–1128. https://doi.org/https://doi.org/10.1128/aac.02340-16 Xiong XS, Zhang X, Di, Yan JW, Huang TT, Li ZK, Wang L et al (2024) Identification of Mycobacterium tuberculosis Resistance to Common Antibiotics: An Overview of Current Methods and Techniques. Infect Drug Resist 17:1491–1506. https://doi.org/10.2147/IDR.S457308 Caulier S, Nannan C, Gillis A, Licciardi F, Bragard C, Mahillon J (2019) Overview of the antimicrobial compounds produced by members of the Bacillus subtilis group. 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Nucleic Acids Res 45:W356–W360. https://doi.org/10.1093/NAR/GKX374 Muniz Seif EJ, Icimoto MY, Silva Júnior PI (2024) In silico bioprospecting of receptors associated with the mechanism of action of Rondonin, an antifungal peptide from spider Acanthoscurria rondoniae haemolymph. Silico Pharmacol 12. https://doi.org/10.1007/s40203-024-00224-1 Kozakov D, Hall DR, Xia B, Porter KA, Padhorny D, Yueh C et al (2017) The ClusPro web server for protein-protein docking. Nat Protoc 12:255–278. https://doi.org/10.1038/NPROT.2016.169 Trott O, Olson AJ (2010) AutoDock Vina: Improving the speed and accuracy of docking with a new scoring function, efficient optimization, and multithreading. J Comput Chem 31:455–461. https://doi.org/10.1002/jcc.21334 Zhou P, Jin B, Li H, Huang SY (2018) HPEPDOCK: a web server for blind peptide-protein docking based on a hierarchical algorithm. Nucleic Acids Res 46:W443–W450. https://doi.org/10.1093/NAR/GKY357 Schake P, Bolz SN, Linnemann K, Schroeder MPLIP (2025) : introducing protein-protein interactions to the protein-ligand interaction profiler. Nucleic Acids Res 2025;53:W463–5. https://doi.org/10.1093/NAR/GKAF361 Tian W, Chen C, Lei X, Zhao J, Liang J (2018) CASTp 3.0: Computed atlas of surface topography of proteins. Nucleic Acids Res 46:W363–W367. https://doi.org/10.1093/nar/gky473 Meng EC, Goddard TD, Pettersen EF, Couch GS, Pearson ZJ, Morris JH et al (2023) UCSF ChimeraX: Tools for structure building and analysis. Protein Sci 32. https://doi.org/10.1002/pro.4792 Laskowski RA, Swindells MB, LigPlot+ (2011) Multiple ligand-protein interaction diagrams for drug discovery. J Chem Inf Model 51:2778–2786. https://doi.org/10.1021/ci200227u Szklarczyk D, Kirsch R, Koutrouli M, Nastou K, Mehryary F, Hachilif R et al (2023) The STRING database in 2023: protein-protein association networks and functional enrichment analyses for any sequenced genome of interest. Nucleic Acids Res 51:D638–D646. https://doi.org/10.1093/nar/gkac1000 Berendsen HJC, Van Der Spoel D, Van Drunen R (1995) GROMACS: A message-passing parallel molecular dynamics implementation. Comput Phys Commun. ;91:43–56 Lai R, Liu H, Hui Lee W, Zhang Y (2002) An anionic antimicrobial peptide from toad Bombina maxima. Biochem Biophys Res Commun 295:796–799. https://doi.org/10.1016/S0006-291X(02)00762-3 Li X, Zuo S, Wang B, Zhang K, Wang Y (2022) Antimicrobial Mechanisms and Clinical Application Prospects of Antimicrobial Peptides. Molecules 27:2675. https://doi.org/https://doi.org/10.3390/molecules27092675 Novović K, Jovčić B (2023) Colistin Resistance in Acinetobacter baumannii: Molecular Mechanisms and Epidemiology. Antibiotics 2023, Vol 12, Page 516. ;12:516. https://doi.org/10.3390/ANTIBIOTICS12030516 Neshani A, Sedighian H, Mirhosseini SA, Ghazvini K, Zare H, Jahangiri A (2020) Antimicrobial peptides as a promising treatment option against Acinetobacter baumannii infections. Microb Pathog 146:104238. https://doi.org/10.1016/J.MICPATH.2020.104238 Rangel K, Lechuga GC, Provance DW, Morel CM, De Simone SG (2023) An Update on the Therapeutic Potential of Antimicrobial Peptides against Acinetobacter baumannii Infections. Pharmaceuticals 2023, Vol 16, Page 1281. ;16:1281. https://doi.org/10.3390/PH16091281 Smith CA, Antunes NT, Stewart NK, Toth M, Kumarasiri M, Chang M et al (2013) Structural basis for carbapenemase activity of the OXA-23 β-Lactamase from Acinetobacter baumannii. Chem Biol 20:1107–1115. https://doi.org/10.1016/j.chembiol.2013.07.015 Kaitany KCJ, Klinger NV, June CM, Ramey ME, Bonomo RA, Powers RA et al (2013) Structures of the class D carbapenemases OXA-23 and OXA-146: Mechanistic basis of activity against carbapenems, extended-spectrum cephalosporins, and aztreonam. Antimicrob Agents Chemother 57:4848–4855. https://doi.org/10.1128/AAC.00762-13 Santillana E, Beceiro A, Bou G, Romero A (2007) Crystal structure of the carbapenemase OXA-24 reveals insights into the mechanism of carbapenem hydrolysis. Proceedings of the National Academy of Sciences. ;104:5354–9. https://doi.org/doi/10.1073/pnas.0607557104 Smith CA, Antunes NT, Toth M, Vakulenko SB (2014) Crystal structure of carbapenemase OXA-58 from Acinetobacter baumannii. Antimicrob Agents Chemother 58:2135–2143. https://doi.org/https://doi.org/10.1128/aac.01983-1 Higgins PG, Poirel L, Lehmann M, Nordmann P, Seifert H (2009) OXA-143, a novel carbapenem-hydrolyzing class D β-lactamase in Acinetobacter baumannii. Antimicrob Agents Chemother 53:5035–5038. https://doi.org/https://doi.org/10.1128/aac.00856-09 Palmer N, Maasch JRMA, Torres MDT, De La Fuente-Nunez C (2021) Molecular dynamics for antimicrobial peptide discovery. Infect Immun 89. https://doi.org/10.1128/iai.00703-20 . https://doi.org/ Awdhesh Kumar Mishra R, Kodiveri Muthukaliannan G In-silico and in-vitro study of novel antimicrobial peptide AM1 from Aegle marmelos against drug-resistant Staphylococcus aureus. Sci Rep 2024 14:1 2024;14:25822-. https://doi.org/10.1038/s41598-024-76553-0 Additional Declarations No competing interests reported. Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-8768089","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":588874685,"identity":"3187c249-1516-454f-b120-5af8f96a4da9","order_by":0,"name":"Ana Paula Palacios Rodriguez","email":"","orcid":"","institution":"Butantan Institute","correspondingAuthor":false,"prefix":"","firstName":"Ana","middleName":"Paula Palacios","lastName":"Rodriguez","suffix":""},{"id":588874686,"identity":"d54f0583-d952-4b5f-a5af-8a51b5bacbcc","order_by":1,"name":"Elias Jorge Muniz Seif","email":"","orcid":"","institution":"Federal University of São Paulo (UNIFESP)","correspondingAuthor":false,"prefix":"","firstName":"Elias","middleName":"Jorge Muniz","lastName":"Seif","suffix":""},{"id":588874687,"identity":"bdc1178d-24b9-4c5d-8d75-1bdf038f33da","order_by":2,"name":"Pedro Ismael Silva Junior","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA70lEQVRIiWNgGAWjYDACZgYGA4aKAyhibERoOXOAgYeBgbGBOC0gwNhGihZzdvYHxZXz7iTuZz97/MHPHQx2/RIJbI8r8GixbOYxMDy77VliD09eYmPvGYbkmTMS2A3P4NFicJiHwbBx2+HEHoYcwwbeNoZkgzMH2CQb8Gphf2DYOAeohf+NYeNf4rQwGBg2NgC1SOQYNgNtsTM43oBfC9gvDceeGffceGM4W7ZNIkGyvbHdEJ8Wc/7jzwwbau7ItvfnGHx822Zjz8/MfOwhXocBY8EAiS+R2ICIH5xamB8gC9jjVT4KRsEoGAUjEgAAe5tO5jdIsiAAAAAASUVORK5CYII=","orcid":"","institution":"Butantan Institute","correspondingAuthor":true,"prefix":"","firstName":"Pedro","middleName":"Ismael Silva","lastName":"Junior","suffix":""}],"badges":[],"createdAt":"2026-02-02 18:23:16","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-8768089/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-8768089/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":102613875,"identity":"70d52507-9b41-45cf-9c00-3af4a5ff71de","added_by":"auto","created_at":"2026-02-13 15:12:40","extension":"jpeg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":20032,"visible":true,"origin":"","legend":"\u003cp\u003eThree-dimensional structure of 13_4 peptide\u003c/p\u003e","description":"","filename":"groupimage1.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-8768089/v1/b1bdf6460bef899821fc595c.jpeg"},{"id":102613878,"identity":"f6b7de3f-b9b6-4023-bb05-390f52955892","added_by":"auto","created_at":"2026-02-13 15:12:40","extension":"jpeg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":3750797,"visible":true,"origin":"","legend":"\u003cp\u003eMolecular docking of peptide 13_4 with bacterial target proteins. Three-dimensional views (a, c, e) show peptide 13_4 (ligand, purple spheres) bound to the protein receptors (blue) corresponding to PDB IDs 4OH0, 1N8W, and 4K0X, respectively. The corresponding two-dimensional LigPlot+ diagrams (b, d, f) display ligand residues in blue and receptor residues in green, with hydrogen bonds shown as green dashed lines and hydrophobic interactions as red arcs.\u003c/p\u003e","description":"","filename":"floatimage2.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-8768089/v1/e4733ab0de166a635cf05445.jpeg"},{"id":102613876,"identity":"a9b571b3-a83f-47aa-a32e-6804e916b1be","added_by":"auto","created_at":"2026-02-13 15:12:40","extension":"jpeg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":651727,"visible":true,"origin":"","legend":"\u003cp\u003eProtein–protein interaction (PPI) network analysis of the selected molecular targets using the STRING server. The network shows high-confidence functional associations between the target proteins (red) and other enzymes involved in essential metabolic pathways and resistance mechanisms. Central nodes include: (a) Aspartate aminotransferase (AspC, PDB ID: 1AIB); (b) UDP-N-acetylglucosamine 1-carboxyvinyltransferase (MurA, PDB ID: 1UAE); (c) Malate synthase G (GlcB, PDB ID: 1N8W); and (d) β-lactamase regulatory protein (BlaR1, KDP11884.1, 1XA1 homolog).\u003c/p\u003e","description":"","filename":"floatimage3.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-8768089/v1/f6a43afda85260fa75580e57.jpeg"},{"id":102613877,"identity":"58209c64-1809-495e-b332-2b20135006c7","added_by":"auto","created_at":"2026-02-13 15:12:40","extension":"jpeg","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":1896729,"visible":true,"origin":"","legend":"\u003cp\u003eMolecular dynamics analysis of peptide 13_4–protein complexes. (a) Backbone RMSD of peptide 13_4 over 100 ns simulations, (b) RMSF per peptide residue, (c) Number of intermolecular hydrogen bonds between peptide 13_4 and the protein targets over time, (d) Intramolecular hydrogen bonds within peptide 13_4 during the simulations, (e) Radius of gyration (Rg) of peptide 13_4 and (f) Solvent-accessible surface area (SASA) of peptide 13_4. Simulations were performed for complexes with Malate Synthase G (PBD ID: 1N8W), Carbapenemase OXA-23 (PBD ID: 4K0X), and Carbapenemase OXA-58 (PBD ID: 4OH0).\u003c/p\u003e","description":"","filename":"floatimage4.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-8768089/v1/011a94ea3790871fb17c3054.jpeg"},{"id":104573100,"identity":"a3903e04-3ed2-44c7-805c-6e4c0d23cb6c","added_by":"auto","created_at":"2026-03-13 13:11:24","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":7557614,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8768089/v1/f37bb10e-1d55-4ffb-98ba-c9f9ce847b02.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Bioinformatic Characterization of a Candidate Antimicrobial Peptide 13_4 from Bacillus spizizenii ATCC 6633: A Multifunctional Inhibitor of Essential Metabolic Targets and β-Lactamases","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eThe escalating threat of multidrug-resistant (MDR) bacteria represents one of the most pressing challenges to global public health. Infections caused by these pathogens severely compromise the efficacy of conventional antimicrobial therapies, resulting in prolonged illness, increased mortality, and substantial economic burden[\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. In 2021, bacterial antimicrobial resistance was associated with an estimated 4.71\u0026nbsp;million deaths worldwide, including 1.14\u0026nbsp;million deaths directly attributable to resistant infections, underscoring the urgent need for innovative antimicrobial strategies [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eSeveral of the most problematic MDR pathogens are grouped under the acronym ESKAPEE, which includes \u003cem\u003eEnterococcus faecium, Staphylococcus aureus, Klebsiella pneumoniae, Acinetobacter baumannii, Pseudomonas aeruginosa\u003c/em\u003e, \u003cem\u003eEnterobacter\u003c/em\u003e species, and \u003cem\u003eEscherichia coli\u003c/em\u003e [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. Among these, carbapenem-resistant \u003cem\u003eAcinetobacter baumannii\u003c/em\u003e (CRAB) is particularly alarming due to its global dissemination, high mortality rates, and the limited efficacy of current therapeutic options. Beyond resistance enzymes, the inhibition of essential metabolic enzymes and other intracellular bacterial targets has emerged as a promising strategy to impair pathogen viability and limit resistance development [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. Similarly, \u003cem\u003eMycobacterium tuberculosis\u003c/em\u003e continues to pose a major global health burden, ranking as the second leading cause of death from a single infectious agent in 2022. The increasing incidence of rifampicin-resistant strains further emphasizes the urgent need for alternative antimicrobial interventions that act through mechanisms distinct from conventional antibiotics [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eIn this context, antimicrobial peptides (AMPs) have emerged as promising therapeutic candidates due to their structural diversity and functional versatility. While many classic AMPs exert their activity through membrane disruption, accumulating evidence indicates that several peptides act through non-membranolytic mechanisms, including interactions with intracellular targets such as metabolic enzymes, ribosomal components, and resistance-associated proteins [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e, \u003cspan additionalcitationids=\"CR7 CR8 CR9\" citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. Notably, anionic antimicrobial peptides have been increasingly associated with intracellular modes of action rather than direct membrane lysis, which may contribute to reduced cytotoxicity toward mammalian cells and enhanced selectivity for bacterial targets[\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. These properties make them particularly attractive candidates for targeting essential enzymatic pathways involved in bacterial metabolism and antibiotic resistance.\u003c/p\u003e \u003cp\u003eComputational approaches play a central role in the discovery and functional evaluation of peptide-based antimicrobials. Techniques such as molecular docking, \u003cem\u003ein silico\u003c/em\u003e protein\u0026ndash;peptide interaction analysis, and structure-based modeling enable the prediction of binding affinities, identification of key interaction residues, and exploration of potential multitarget effects across bacterial enzymes. Complementarily, molecular dynamics simulations, machine learning\u0026ndash;based classifiers, and curated antimicrobial peptide databases, have become essential tools for identifying novel AMP candidates, characterizing their putative mechanisms of action, and prioritizing sequences with favorable biological profiles [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eTherefore, the aim of this study was to investigate potential bacterial protein targets of the anionic antimicrobial peptide candidate 13_4 from \u003cem\u003eBacillus spizizenii\u003c/em\u003e ATCC 6633 using structure-based molecular docking and \u003cem\u003ein silico\u003c/em\u003e binding analyses, to gain mechanistic insights into its predicted mode of action.\u003c/p\u003e"},{"header":"2. Materials and Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e2.1. Peptide characterization and folding\u003c/h2\u003e \u003cp\u003e \u003cdiv class=\"BlockQuote\"\u003e \u003cp\u003eIn this study, we investigated the antimicrobial peptide candidate 13_4 (\u0026ldquo;KDSMEEY\u0026rdquo;), isolated from \u003cem\u003eBacillus spizizenii\u003c/em\u003e ATCC 6633. Mass spectrometry analysis revealed the presence of an oxidized methionine variant (manuscript submitted); however, all computational analyses were performed using the non-oxidized peptide sequence. This approach was adopted to evaluate the intrinsic structural and interaction properties of the native peptide backbone, minimizing potential biases associated with post-translational modifications or oxidation artifacts arising from sample preparation.\u003c/p\u003e \u003cp\u003eThe physicochemical properties of peptide 13_4 was assessed using computational tools. The theoretical molecular weight, isoelectric point (pI), net charge at pH 7, and extinction coefficient were calculated using the PepCalc online server (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://www.pep-calc.com\u003c/span\u003e\u003cspan address=\"http://www.pep-calc.com\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. Additionally, the instability index and aliphatic index were estimated using the ProtParam tool (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://web.expasy.org/protparam/\u003c/span\u003e\u003cspan address=\"https://web.expasy.org/protparam/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) available through the ExPASy platform [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. The three-dimensional structure of peptide 13_4 was constructed using Chimera version 1.19[\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]. Hydrogen atoms were added, partial charges were assigned, and screening radii were defined according to the AMBER ff14SB force field. Energy minimization was performed without positional restraints, using 1,000 steps of steepest descent with a step size of 0.02 \u0026Aring;. The resulting minimized structure was exported in mol2 and PDB formats for subsequent \u003cem\u003ein silico\u003c/em\u003e analyses [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e].\u003c/p\u003e \u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e2.2. Targets prospection\u003c/h2\u003e \u003cp\u003e \u003cdiv class=\"BlockQuote\"\u003e \u003cp\u003eTo identify potential molecular targets for Peptide 13_4, a reverse pharmacophore mapping approach was performed using the PharmMapper server (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://www.lilab-ecust.cn/pharmmapper/\u003c/span\u003e\u003cspan address=\"http://www.lilab-ecust.cn/pharmmapper/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e] The peptide was submitted with Conformer Generation enabled (\u0026ldquo;YES\u0026rdquo;), allowing the generation of up to 300 conformations. Full pharmacophore mapping was carried out against the entire database (v2010, containing 7,302 targets), retaining the top 300 matched targets [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]. From these, the 50 best-ranked hits based on the Normalized Fit Score were selected and subsequently filtered to include only proteins from pathogenic or opportunistic bacteria, as well as enzymes related to bacterial resistance. Furthermore, because \u003cem\u003eAcinetobacter baumannii\u003c/em\u003e is classified as a critical-priority pathogen, eight carbapenemase enzymes belonging to the OXA-type β-lactamase family (class D, oxacillin-hydrolyzing enzymes) from this microorganism were selected for further analysis.\u003c/p\u003e \u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003e2.3. Molecular docking analysis\u003c/h2\u003e \u003cp\u003e \u003cdiv class=\"BlockQuote\"\u003e \u003cp\u003eMolecular docking was performed using ClusPro 2.0 [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e], AutoDock Vina [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e], and HPEPDOCK server [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]. ClusPro 2.0 was employed as the primary global docking platform, generating thousands of rigid-body conformations that were filtered based on desolvation energy and clustered according to interface RMSD. Resulting complexes were ranked using a weighted scoring function that integrates electrostatic, van der Waals, and desolvation energy terms. The top-scoring complex for each receptor was selected based on geometric stability and favorable stereochemical properties, making it suitable for subsequent molecular dynamics simulations. AutoDock Vina was then used to refine docking within predicted or canonical binding pockets, providing binding affinity estimates and improved pose discrimination, while HPEPDOCK server complemented the analysis by incorporating hierarchical peptide conformational sampling to account for peptide flexibility.\u003c/p\u003e \u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003e2.4. Interaction analysis and binding site validation\u003c/h2\u003e \u003cp\u003e \u003cdiv class=\"BlockQuote\"\u003e \u003cp\u003eDocked complexes were analyzed to characterize non-covalent interactions and to confirm peptide binding within functionally relevant regions of the target proteins. Interaction profiling was performed using the Protein-Ligand Interaction Profiler (PLIP) web server, which identified hydrogen bonds, hydrophobic contacts, and salt bridges for each protein\u0026ndash;peptide complex [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]. In addition, the Computed Atlas of Surface Topography of proteins (CASTp) v3.0 web server was employed to define the geometric boundaries of canonical binding pockets [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]. The spatial positioning of peptide 13_4 relative to residues identified by PLIP and CASTp was subsequently evaluated to assess its potential to interfere with catalytic activity or substrate recognition.\u003c/p\u003e \u003cp\u003eStructural visualization and analysis of the docking complexes were performed using UCSF ChimeraX v1.10 [\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]. Key interactions were further visualized using LigPlot+ v2.2.7, generating two-dimensional diagrams that highlight hydrogen bonding and hydrophobic contacts [\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e].\u003c/p\u003e \u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003e2.5. Protein-protein interactions\u003c/h2\u003e \u003cp\u003e \u003cdiv class=\"BlockQuote\"\u003e \u003cp\u003eTo provide functional context for the selected targets, protein\u0026ndash;protein interaction networks were explored using STRING, which allowed identification of biologically relevant pathways and associations without implying direct validation of docking predictions [\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e]\u003c/p\u003e \u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003e2.6. Molecular Dynamics simulations\u003c/h2\u003e \u003cp\u003e \u003cdiv class=\"BlockQuote\"\u003e \u003cp\u003eMolecular dynamics simulations were conducted at the Bioinformatics Center of the Butantan Institute using GROMACS v.2023.5 (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.gromacs.org\u003c/span\u003e\u003cspan address=\"https://www.gromacs.org\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e)[\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e]. Three peptide\u0026ndash;protein complexes, corresponding to the top-ranked docking poses of the antimicrobial peptide candidate 13_4 bound to bacterial target proteins retrieved from the Protein Data Bank (PDB codes: 4OH0, 1N8W, and 4K0X), were selected for molecular dynamics simulations. Simulations employed the OPLS-AA/L force field. Each complex was placed in a cubic box with 1 nm padding and solvated with TIP3P water molecules. Cl\u003csup\u003e\u0026minus;\u003c/sup\u003e or Na\u003csup\u003e+\u003c/sup\u003e ions were added to neutralize the system. Energy minimization was performed for 1 ns with a maximum force threshold of \u0026lt;\u0026thinsp;1000 kJ/mol/nm, followed by equilibration at 300K and 1 bar for 0.1 ns. Production dynamics were run for 100 ns [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e].\u003c/p\u003e \u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003e2.7. \u003cem\u003eData analysis\u003c/em\u003e\u003c/h2\u003e \u003cp\u003e \u003cdiv class=\"BlockQuote\"\u003e \u003cp\u003eTrajectory outputs were analyzed using GROMACS tools: gmx rms (root mean squared deviation), gmx rmsf (root mean squared fluctuation), gmx hbond (hydrogen bond interactions), gmx gyrate (radius of gyration), and gmx sasa (solvent-accessible surface area). An index was created to track the 13_4 peptide trajectory. Graphs and statistical analyses were generated using R v4.4.2 within RStudio with the packages \u0026lsquo;ggplot2\u0026rsquo;, \u0026lsquo;dplyr\u0026rsquo;, and \u0026lsquo;grid\u0026rsquo;.\u003c/p\u003e \u003c/div\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"3. Results","content":"\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003e3.1. Peptide structure\u003c/h2\u003e \u003cp\u003e \u003cdiv class=\"BlockQuote\"\u003e \u003cp\u003eThe physicochemical characterization of peptide 13_4 indicates that it is a short heptapeptide with a net charge of \u0026minus;\u0026thinsp;2, a molecular weight of 900.96 g/mol, and an isoelectric point of 4.14. The peptide exhibits a high instability index (82.66) and a strongly hydrophilic profile (GRAVY\u0026thinsp;=\u0026thinsp;\u0026minus;\u0026thinsp;2.086), features commonly associated with flexible, non-membranolytic antimicrobial peptides. Its Boman index (4.15 kcal/mol) suggests a strong propensity for protein binding, consistent with the hypothesis of a mechanism involving interaction with intracellular targets rather than membrane disruption.\u003c/p\u003e \u003cp\u003eThe initial three-dimensional structure of peptide 13_4 was energy-minimized using UCSF Chimera v1.19 through steepest descent followed by conjugate gradient optimization (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). This process reduced the potential energy from 953 to 269 kJ/mol, relieving steric clashes and producing a stable conformation suitable for target prospecting, molecular docking, and molecular dynamics simulations.\u003c/p\u003e \u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003e3.2. Target prospecting by PharmMapper\u003c/h2\u003e \u003cp\u003e \u003cdiv class=\"BlockQuote\"\u003e \u003cp\u003ePharmacophore-based target screening using PharmMapper identified 300 potential protein targets for peptide 13_4. Based on normalized fit scores and biological relevance, the top 50 microbial-related targets were examined in detail, leading to the selection of 15 functionally significant receptors (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). Priority was given to enzymes involved in essential metabolic pathways and antimicrobial resistance mechanisms.\u003c/p\u003e \u003cp\u003eAmong the highest-ranked targets were Aspartate Aminotransferase (AspC, PDB ID: 1AIB), UDP-N-acetylglucosamine 1-carboxyvinyltransferase (MurA, PDB ID: 1UAE), Malate Synthase G (GlcB, PDB ID: 1N8W), and the β-lactamase regulatory protein BlaR1 (PDB ID: 1XA1). In addition, given the clinical relevance of carbapenem resistance, several β-lactamases not highlighted by PharmMapper were deliberately incorporated, including TEM and metallo-β-lactamases as well as OXA-type carbapenemases (OXA-23, OXA-24/40, OXA-58, and OXA-143) from \u003cem\u003eAcinetobacter baumannii\u003c/em\u003e.\u003c/p\u003e \u003cp\u003eThis integrative selection strategy ensured that subsequent docking and molecular dynamics analyses focused on targets with both high pharmacophore compatibility and critical functional roles in bacterial survival and resistance.\u003c/p\u003e \u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003e3.3. Docking molecular and receptor-peptide interactions\u003c/h2\u003e \u003cp\u003e \u003cdiv class=\"BlockQuote\"\u003e \u003cp\u003eA total of 28 bacterial proteins were evaluated by molecular docking with peptide 13_4 using a hierarchical strategy. Initial rigid-body docking was performed using ClusPro 2.0, and the top-ranked ClusPro pose was selected for subsequent analyses. To independently validate the docking results, parallel docking simulations were performed using AutoDock Vina and HPEPDOCK, providing complementary binding scores and pose comparisons (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). Protein\u0026ndash;peptide interaction patterns, including hydrogen bonds, hydrophobic contacts, and salt bridges, were systematically analyzed using PLIP (Table S1).\u003c/p\u003e \u003cp\u003eAcross all docking platforms, peptide 13_4 consistently exhibited more favorable predicted binding toward carbapenemase enzymes compared to other screened targets, despite these enzymes not being prioritized by the initial PharmMapper-based screening. OXA-type, including OXA-58 (PDB ID: 4OH0) and OXA-23 (PDB ID: 4K0X), displayed recurrent high-ranking poses and coherent interaction profiles.\u003c/p\u003e \u003c/div\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\u003ePharmMapper results using peptide 13_4 as the ligand.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePM Rank\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePDB ID\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eTarget name\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eNormalized fit score\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eOrigin\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1AIB\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eAspartate aminotransferase\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.9996\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cem\u003eEscherichia coli\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1QI1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNADP-dependent glyceraldehyde-3-phosphate dehydrogenase\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.9989\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cem\u003eStreptococcus mutans\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1PQP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eAspartate-semialdehyde dehydrogenase\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.9982\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cem\u003eHaemophilus influenzae\u003c/em\u003e RdKW20\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1KC7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePyruvate, phosphate dikinase\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.9979\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cem\u003eClostridium symbiosum\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1Y79\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePeptidyl-dipeptidase dcp\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.9968\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cem\u003eEscherichia coli\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1BLH\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eBeta-lactamase\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.9956\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cem\u003eStaphylococcus aureus\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1HOT\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eGlucosamine 6-phosphate deaminase\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.9954\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cem\u003eEscherichia coli\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e22\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1GRO\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eIsocitrate dehydrogenase (NADP)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.9941\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cem\u003eEscherichia coli\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1UAE\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eUDP-N-acetylglucosamine 1-carboxyvinyltransferase\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.9936\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cem\u003eEscherichia coli\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e27\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1XFF\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eGlucosamine-fructose-6-phosphate aminotransferase\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.9935\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cem\u003eEscherichia coli\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e29\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1KFL\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePhospho-2-dehydro-3-deoxyheptonate aldolase, Phesensitive\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.9927\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cem\u003eStaphylococcus aureus\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1XA1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eRegulatory protein blaR1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.9919\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cem\u003eStaphylococcus aureus\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e35\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1N8W\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMalate synthase G\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.9919\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cem\u003eMycobacterium tuberculosis\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e45\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1U3L\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2-C-methyl-D-erythritol 2,4-cyclodiphosphate synthase\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.9900\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cem\u003eEscherichia coli\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e47\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1W55\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eBifunctional enzyme ispD/ispF\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.9895\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cem\u003eCampylobacter jejuni\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e84\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1MQO\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMetallo-β-Lactamase II\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.9747\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cem\u003eBacillus cereus\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e103\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1M2X\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMetallo-β-Lactamase (B\u0026thinsp;\u0026minus;\u0026thinsp;1 type)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.9654\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cem\u003eElizabethkingia meningoseptica\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e144\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1HLK\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMetallo-β-Lactamase II\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.9394\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cem\u003eBacteroides fragilis\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e236\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1AXB\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eβ-Lactamase (TEM)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.8825\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cem\u003eEscherichia coli\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e299\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1DD6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMetallo-β-Lactamase (IMP\u0026thinsp;\u0026minus;\u0026thinsp;1 type)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.8338\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cem\u003ePseudomonas aeruginosa\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4JF4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCarbapenemase OXA-23\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\u003cem\u003eAcinetobacter baumannii\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4K0X\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCarbapenemase OXA-23\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\u003cem\u003eAcinetobacter baumannii\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2JC7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCarbapenemase OXA-24\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\u003cem\u003eAcinetobacter baumannii\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3FV7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCarbapenemase OXA-24\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\u003cem\u003eAcinetobacter baumannii\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4WM9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCarbapenemase OXA-24\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\u003cem\u003eAcinetobacter baumannii\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e7RP8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCarbapenemase OXA-24/40\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\u003cem\u003eAcinetobacter baumannii\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4OH0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCarbapenemase OXA-58\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\u003cem\u003eAcinetobacter baumannii\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5IY2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCarbapenemase OXA-143\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\u003cem\u003eAcinetobacter baumannii\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"BlockQuote\"\u003e \u003cp\u003ePM Rank corresponds to the general PharmMapper ranking. PDB ID refers to the Protein Data Bank identification code. The normalized fit score was calculated as the ratio between the fit score and the number of pharmacophore features. Origin indicates the bacterial species from which the target protein was isolated.\u003c/p\u003e \u003c/div\u003e \u003c/p\u003e \u003cp\u003eAmong these targets, OXA-58 (PDB ID: 4OH0) emerged as the most promising candidate, presenting the most favorable consensus docking scores (ClusPro: \u0026minus;687.5 kcal/mol; Vina: \u0026minus;6.9 kcal/mol; HPEPDOCK: \u0026minus;130.732) and a large, well-defined docking cluster comprising 1000 members. Structural inspection revealed an extensive protein\u0026ndash;peptide interface involving 169 non-covalent contacts, with recurrent interactions involving residues previously implicated in substrate recognition and catalytic stabilization, including Ser83, Val132, Tyr135, Trp223, Met225, and Arg263 (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003ea-b and Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). Comparable interaction patterns were observed for Malate Synthase G (PDB ID: 1N8W), where peptide 13_4 localized within the acetyl-CoA binding region, interacting with amino acid residues Arg125, Phe126, Asn129, Arg312, and Lys621 (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003ec-d and Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e), and for OXA-23 carbapenemase (PDB ID: 4K0X), with contacts involving catalytically relevant residues such as Ser79 and Trp219 (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003ee\u0026ndash;f and Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). Favorable docking scores and coherent binding poses were also obtained for Aspartate Aminotransferase (PDB ID: 1AIB) and UDP-N-acetylglucosamine 1-carboxyvinyltransferase (PDB ID: 1UAE), with peptide binding localized to substrate-associated regions critical for enzymatic function.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eDocking scores of the peptide13_4 against selected bacterial protein targets using ClusPro 2.0, AutoDock Vina, and HPEPDOCK\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003ePDB ID\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003eClusPro 2.0\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eAutoDock Vina binding afinity\u003c/p\u003e \u003cp\u003e(kcal/mol)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eHPEPDOCK score\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCluster size\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eLower energy\u003c/p\u003e \u003cp\u003e(kcal/mol)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e4OH0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-687.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-6.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-130.732\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e3FV7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e533\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-631.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-6.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-131.862\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1N8W\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e513\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-593.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-7.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-138.450\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e5IY2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e607\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-570.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-7.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-131.698\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e4JF4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e365\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-566.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-6.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-119.731\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e4K0X\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e958\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-557.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-6.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-119.386\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1AIB\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e394\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-556.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-8.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-132.749*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1UAE\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e306\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-549.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-8.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-138.756\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2JC7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e508\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-528.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-7.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-128.565\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e7RP8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e657\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-527.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-6.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-124.137\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e4WM9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e180\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-518.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-7.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-135.900\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1DD6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e678\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-517.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-6.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-136.367*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1XA1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e204\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-498.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-6.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-139.188*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1HLK\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e310\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-484.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-6.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-129.993*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1BLH\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e357\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-394.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-5.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-127.558\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1AXB\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e113\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-389.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-6.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-117.280\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"BlockQuote\"\u003e \u003cp\u003eAn asterisk (*) indicates targets for which the lowest-energy pose predicted by HPEPDOCK did not correspond to the same binding site identified by ClusPro and AutoDock Vina.\u003c/p\u003e \u003c/div\u003e \u003c/p\u003e \u003cp\u003e\u003cstrong\u003eTable 3.\u003c/strong\u003e\u0026nbsp; Comparison of experimentally reported active-site residues with peptide 13_4 binding residues predicted by ClusPro docking and analyzed using PLIP. Shared residues are highlighted in bold.\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"629\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e\u003cstrong\u003ePDB ID\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 184px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eProtein\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 113px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eBinding / Active\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eSite\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 121px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eLigand bind site residues\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003e(native)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 143px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eLigand bind site residues (found)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e4OH0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 184px;\"\u003e\n \u003cp\u003eCarbapenemase OXA-58\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 113px;\"\u003e\n \u003cp\u003eActive site\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 121px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cu\u003e83,\u003c/u\u003e\u003c/strong\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e86, 130, 169, 170, 220,\u003cstrong\u003e\u0026nbsp;\u003cu\u003e263\u003c/u\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 143px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cu\u003e83\u003c/u\u003e\u003c/strong\u003e\u003cstrong\u003e,\u0026nbsp;\u003c/strong\u003e132, 135, 223, 225, 226, 260\u003cstrong\u003e, \u003cu\u003e263\u003c/u\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e3FV7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 184px;\"\u003e\n \u003cp\u003eCarbapenemase OXA-24\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 113px;\"\u003e\n \u003cp\u003eActive site\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 121px;\"\u003e\n \u003cp\u003e81, \u003cstrong\u003e\u003cu\u003e111\u003c/u\u003e\u003c/strong\u003e, \u003cstrong\u003e\u003cu\u003e112\u003c/u\u003e\u003c/strong\u003e, \u003cstrong\u003e\u003cu\u003e130\u003c/u\u003e\u003c/strong\u003e, 168, \u003cstrong\u003e\u003cu\u003e221\u003c/u\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 143px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cu\u003e111\u003c/u\u003e\u003c/strong\u003e\u003cstrong\u003e, \u003cu\u003e112\u003c/u\u003e\u003c/strong\u003e, 115, 128, \u003cstrong\u003e\u003cu\u003e130\u003c/u\u003e,\u003c/strong\u003e 169, 218,\u003cstrong\u003e\u003cu\u003e\u0026nbsp;221\u003c/u\u003e\u003c/strong\u003e, 258, 261\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e1N8W\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 184px;\"\u003e\n \u003cp\u003eMalate synthase G\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 113px;\"\u003e\n \u003cp\u003eAcetyl-CoA binding site\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 121px;\"\u003e\n \u003cp\u003e118, \u003cstrong\u003e\u003cu\u003e125\u003c/u\u003e, \u003cu\u003e126\u003c/u\u003e, \u003cu\u003e129\u003c/u\u003e, \u003cu\u003e312\u003c/u\u003e, \u003cu\u003e621\u003c/u\u003e\u003c/strong\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 143px;\"\u003e\n \u003cp\u003e123, \u003cstrong\u003e\u003cu\u003e125\u003c/u\u003e, \u003cu\u003e126\u003c/u\u003e, \u003cu\u003e129\u003c/u\u003e,\u0026nbsp;\u003c/strong\u003e140\u003cstrong\u003e, \u003cu\u003e312\u003c/u\u003e, \u003cu\u003e621\u003c/u\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e5IY2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 184px;\"\u003e\n \u003cp\u003eCarbapenemase OXA-143\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 113px;\"\u003e\n \u003cp\u003eActive site\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 121px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cu\u003e81\u003c/u\u003e\u003c/strong\u003e\u003cstrong\u003e,\u003c/strong\u003e 84, 167, 219, \u003cstrong\u003e\u003cu\u003e261\u003c/u\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 143px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cu\u003e81\u003c/u\u003e\u003c/strong\u003e, 109, 111, 112, \u0026nbsp;128, 130, 161, 162, 258, 259, \u003cstrong\u003e\u003cu\u003e261\u0026nbsp;\u003c/u\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e4JF4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 184px;\"\u003e\n \u003cp\u003eCarbapenemase OXA-23\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 113px;\"\u003e\n \u003cp\u003eActive site\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 121px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cu\u003e79,\u003c/u\u003e\u003c/strong\u003e 126 ,166, 217, \u0026nbsp;\u003cstrong\u003e\u003cu\u003e219\u003c/u\u003e\u003c/strong\u003e, \u003cstrong\u003e\u003cu\u003e259\u003c/u\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 143px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cu\u003e79\u003c/u\u003e\u003c/strong\u003e, 110, 113, 128, \u003cstrong\u003e\u003cu\u003e219,\u003c/u\u003e\u003c/strong\u003e 256, 258, \u003cstrong\u003e\u003cu\u003e259\u003c/u\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e4K0X\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 184px;\"\u003e\n \u003cp\u003eCarbapenemase OXA-23\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 113px;\"\u003e\n \u003cp\u003eActive site\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 121px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cu\u003e79\u003c/u\u003e\u003c/strong\u003e\u003cstrong\u003e, \u003cu\u003e109\u003c/u\u003e\u003c/strong\u003e, 110, \u003cstrong\u003e\u003cu\u003e126,\u003c/u\u003e\u003c/strong\u003e 217, 219,\u003cu\u003e\u0026nbsp;\u003cstrong\u003e221\u003c/strong\u003e\u003c/u\u003e\u003cstrong\u003e, \u003cu\u003e259\u003c/u\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 143px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cu\u003e79\u003c/u\u003e\u003c/strong\u003e\u003cstrong\u003e, \u003cu\u003e109\u003c/u\u003e, \u003cu\u003e126\u003c/u\u003e\u003c/strong\u003e, 132, 136, \u003cstrong\u003e\u003cu\u003e221\u003c/u\u003e, \u003cu\u003e259\u003c/u\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e1AIB\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 184px;\"\u003e\n \u003cp\u003eAspartate aminotransferase\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 113px;\"\u003e\n \u003cp\u003eActive site\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 121px;\"\u003e\n \u003cp\u003e108, \u003cstrong\u003e\u003cu\u003e109\u003c/u\u003e\u003c/strong\u003e, \u003cstrong\u003e\u003cu\u003e140\u003c/u\u003e\u003c/strong\u003e, \u003cstrong\u003e\u003cu\u003e194\u003c/u\u003e\u003c/strong\u003e, 255, 257, 258, \u003cstrong\u003e\u003cu\u003e266\u003c/u\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 143px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cu\u003e109\u003c/u\u003e\u003c/strong\u003e\u003cstrong\u003e, \u003cu\u003e140,\u003c/u\u003e \u003cu\u003e194\u003c/u\u003e\u003c/strong\u003e, \u003cstrong\u003e\u003cu\u003e266,\u0026nbsp;\u003c/u\u003e\u003c/strong\u003e386\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e1UAE\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 184px;\"\u003e\n \u003cp\u003eUDP-N-acetylglucosamine 1-carboxyvinyltransferase\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 113px;\"\u003e\n \u003cp\u003eFFQ / UD1 binding site\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 121px;\"\u003e\n \u003cp\u003e22, \u003cstrong\u003e\u003cu\u003e23\u003c/u\u003e\u003c/strong\u003e, \u003cstrong\u003e\u003cu\u003e91\u003c/u\u003e\u003c/strong\u003e, \u003cstrong\u003e\u003cu\u003e115\u003c/u\u003e\u003c/strong\u003e, \u003cstrong\u003e\u003cu\u003e120\u003c/u\u003e\u003c/strong\u003e, \u003cstrong\u003e\u003cu\u003e121\u003c/u\u003e\u003c/strong\u003e, 124, 162, \u003cstrong\u003e\u003cu\u003e163\u003c/u\u003e\u003c/strong\u003e, 164, 305, \u003cstrong\u003e\u003cu\u003e371\u003c/u\u003e\u003c/strong\u003e, \u003cstrong\u003e\u003cu\u003e397\u003c/u\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 143px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cu\u003e23\u003c/u\u003e\u003c/strong\u003e, \u003cstrong\u003e\u003cu\u003e91\u003c/u\u003e\u003c/strong\u003e, 95, \u003cstrong\u003e\u003cu\u003e115\u003c/u\u003e\u003c/strong\u003e, \u003cstrong\u003e\u003cu\u003e120\u003c/u\u003e\u003c/strong\u003e, \u003cstrong\u003e\u003cu\u003e121\u003c/u\u003e\u003c/strong\u003e, 159, 160 \u003cstrong\u003e\u003cu\u003e163\u003c/u\u003e\u003c/strong\u003e, 190, 232, 328, \u003cstrong\u003e\u003cu\u003e371\u003c/u\u003e\u003c/strong\u003e, \u003cstrong\u003e\u003cu\u003e397\u003c/u\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e2JC7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 184px;\"\u003e\n \u003cp\u003eCarbapenemase OXA-24\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 113px;\"\u003e\n \u003cp\u003eActive site\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 121px;\"\u003e\n \u003cp\u003e81, 84, 111, \u003cstrong\u003e\u003cu\u003e112\u003c/u\u003e\u003c/strong\u003e, 115, 128, 130, 218, \u003cstrong\u003e\u003cu\u003e219\u003c/u\u003e\u003c/strong\u003e, 220, \u003cstrong\u003e\u003cu\u003e221\u003c/u\u003e\u003c/strong\u003e, 223, \u003cstrong\u003e\u003cu\u003e261\u003c/u\u003e\u003c/strong\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 143px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cu\u003e112\u003c/u\u003e\u003c/strong\u003e, \u003cstrong\u003e\u003cu\u003e219\u003c/u\u003e\u003c/strong\u003e, \u003cstrong\u003e\u003cu\u003e221\u003c/u\u003e\u003c/strong\u003e, 258, \u003cstrong\u003e\u003cu\u003e261\u003c/u\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e7RP8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 184px;\"\u003e\n \u003cp\u003eCarbapenemase OXA-24/40\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 113px;\"\u003e\n \u003cp\u003eActive site\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 121px;\"\u003e\n \u003cp\u003e81, \u003cstrong\u003e\u003cu\u003e128\u003c/u\u003e\u003c/strong\u003e, 165, 168, 219, 221, \u003cstrong\u003e\u003cu\u003e261\u003c/u\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 143px;\"\u003e\n \u003cp\u003e112, 114, 115, \u003cstrong\u003e\u003cu\u003e128\u003c/u\u003e\u003c/strong\u003e, \u003cstrong\u003e\u003cu\u003e261,\u003c/u\u003e\u003c/strong\u003e 262\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e4WM9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 184px;\"\u003e\n \u003cp\u003eCarbapenemase OXA-24\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 113px;\"\u003e\n \u003cp\u003eActive site\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 121px;\"\u003e\n \u003cp\u003e81, 112, \u003cstrong\u003e\u003cu\u003e128\u003c/u\u003e\u003c/strong\u003e, \u003cstrong\u003e\u003cu\u003e218,\u003c/u\u003e\u003c/strong\u003e 219, 221, 223, \u003cstrong\u003e\u003cu\u003e261\u003c/u\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 143px;\"\u003e\n \u003cp\u003e115, 127, \u003cstrong\u003e\u003cu\u003e128\u003c/u\u003e\u003c/strong\u003e, 206, \u003cstrong\u003e\u003cu\u003e218\u003c/u\u003e\u003c/strong\u003e, \u003cstrong\u003e\u003cu\u003e261\u003c/u\u003e\u003c/strong\u003e, 262\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e1DD6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 184px;\"\u003e\n \u003cp\u003eMetallo-\u0026beta;-Lactamase (IMP\u0026minus;1 type)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 113px;\"\u003e\n \u003cp\u003eActive site\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 121px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cu\u003e23,\u003c/u\u003e\u003c/strong\u003e \u003cstrong\u003e\u003cu\u003e25\u003c/u\u003e\u003c/strong\u003e, \u003cstrong\u003e\u003cu\u003e31\u003c/u\u003e\u003c/strong\u003e, \u003cstrong\u003e\u003cu\u003e33\u003c/u\u003e\u003c/strong\u003e, \u003cstrong\u003e\u003cu\u003e51,\u003c/u\u003e\u003c/strong\u003e \u003cstrong\u003e\u003cu\u003e81\u003c/u\u003e\u003c/strong\u003e, 139, \u003cstrong\u003e\u003cu\u003e161\u003c/u\u003e\u003c/strong\u003e, 166, \u003cstrong\u003e\u003cu\u003e167\u003c/u\u003e\u003c/strong\u003e, \u003cstrong\u003e\u003cu\u003e197\u003c/u\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 143px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cu\u003e23\u003c/u\u003e\u003c/strong\u003e, \u003cstrong\u003e\u003cu\u003e25\u003c/u\u003e\u003c/strong\u003e, \u0026nbsp;28, \u003cstrong\u003e\u003cu\u003e31,\u003c/u\u003e\u003c/strong\u003e \u003cstrong\u003e\u003cu\u003e33\u003c/u\u003e\u003c/strong\u003e, \u0026nbsp;\u003cstrong\u003e\u003cu\u003e51\u003c/u\u003e\u003c/strong\u003e, \u003cstrong\u003e\u003cu\u003e81,\u003c/u\u003e\u003c/strong\u003e\u0026nbsp; \u003cstrong\u003e\u003cu\u003e161\u003c/u\u003e\u003c/strong\u003e, \u003cstrong\u003e\u003cu\u003e167\u003c/u\u003e\u003c/strong\u003e, \u003cstrong\u003e\u003cu\u003e197\u003c/u\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e1XA1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 184px;\"\u003e\n \u003cp\u003eRegulatory protein blaR1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 113px;\"\u003e\n \u003cp\u003eActive site\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 121px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cu\u003e59\u003c/u\u003e\u003c/strong\u003e, \u003cstrong\u003e\u003cu\u003e62\u003c/u\u003e\u003c/strong\u003e, \u003cstrong\u003e\u003cu\u003e107\u003c/u\u003e\u003c/strong\u003e, 196, 197, \u003cstrong\u003e\u003cu\u003e236\u003c/u\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 143px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cu\u003e59\u003c/u\u003e\u003c/strong\u003e, \u003cstrong\u003e\u003cu\u003e62\u003c/u\u003e\u003c/strong\u003e, 91, \u0026nbsp;93, \u003cstrong\u003e\u003cu\u003e107\u003c/u\u003e\u003c/strong\u003e, 147, 199, \u0026nbsp;\u003cstrong\u003e\u003cu\u003e236\u003c/u\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e1HLK\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 184px;\"\u003e\n \u003cp\u003eMetallo-\u0026beta;-Lactamase II\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 113px;\"\u003e\n \u003cp\u003eActive site\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 121px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cu\u003e49\u003c/u\u003e\u003c/strong\u003e, 99, \u003cstrong\u003e\u003cu\u003e101\u003c/u\u003e\u003c/strong\u003e, \u003cstrong\u003e\u003cu\u003e103\u003c/u\u003e\u003c/strong\u003e, 162, \u003cstrong\u003e\u003cu\u003e184\u003c/u\u003e\u003c/strong\u003e, \u003cstrong\u003e\u003cu\u003e193\u003c/u\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 143px;\"\u003e\n \u003cp\u003e36, 45, 46, 47, \u003cstrong\u003e\u003cu\u003e49\u003c/u\u003e\u003c/strong\u003e, 55, \u003cstrong\u003e\u003cu\u003e101,\u003c/u\u003e\u003c/strong\u003e \u003cstrong\u003e\u003cu\u003e103\u003c/u\u003e\u003c/strong\u003e, \u003cstrong\u003e\u003cu\u003e184,\u003c/u\u003e\u003c/strong\u003e \u003cstrong\u003e\u003cu\u003e193\u003c/u\u003e\u003c/strong\u003e, 195, 196, 223\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e1BLH\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 184px;\"\u003e\n \u003cp\u003e\u0026beta; -lactamase\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 113px;\"\u003e\n \u003cp\u003eActive site\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 121px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cu\u003e70\u003c/u\u003e\u003c/strong\u003e, 105, \u003cstrong\u003e\u003cu\u003e237\u003c/u\u003e\u003c/strong\u003e, \u003cstrong\u003e\u003cu\u003e239\u003c/u\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 143px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cu\u003e70\u003c/u\u003e\u003c/strong\u003e, 73, 167, 170, \u003cstrong\u003e\u003cu\u003e237\u003c/u\u003e\u003c/strong\u003e, \u003cstrong\u003e\u003cu\u003e239\u003c/u\u003e\u003c/strong\u003e, 270, 273\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e1AXB\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 184px;\"\u003e\n \u003cp\u003e\u0026beta;-Lactamase (TEM)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 113px;\"\u003e\n \u003cp\u003eActive site\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 121px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cu\u003e70\u003c/u\u003e\u003c/strong\u003e, \u003cstrong\u003e\u003cu\u003e130\u003c/u\u003e\u003c/strong\u003e, \u003cstrong\u003e\u003cu\u003e132\u003c/u\u003e\u003c/strong\u003e, 166, \u003cstrong\u003e\u003cu\u003e170\u003c/u\u003e\u003c/strong\u003e, \u003cstrong\u003e\u003cu\u003e237\u003c/u\u003e\u003c/strong\u003e,\u003cu\u003e\u0026nbsp;\u003cstrong\u003e240\u003c/strong\u003e\u003c/u\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 143px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cu\u003e70\u003c/u\u003e\u003c/strong\u003e, 73, 105, \u003cstrong\u003e\u003cu\u003e130\u003c/u\u003e\u003c/strong\u003e, \u003cstrong\u003e\u003cu\u003e132,\u003c/u\u003e\u003c/strong\u003e 167, 168, \u003cstrong\u003e\u003cu\u003e170\u003c/u\u003e\u003c/strong\u003e, \u003cstrong\u003e\u003cu\u003e237\u003c/u\u003e\u003c/strong\u003e, \u003cstrong\u003e\u003cu\u003e240\u003c/u\u003e\u003c/strong\u003e, 244, 272, 273\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003csup\u003e*\u003c/sup\u003eResidues shared between native functional sites and peptide 13_4 docking-predicted binding regions indicate potential interference with catalytic or substrate-recognition regions.\u003c/p\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003e3.4. Protein-protein interactions\u003c/h2\u003e \u003cp\u003e \u003cdiv class=\"BlockQuote\"\u003e \u003cp\u003eProtein\u0026ndash;protein interaction (PPI) network analysis revealed that the high-affinity targets of peptide 13_4 cluster into two major functional modules: central metabolic pathways and antimicrobial resistance regulation.\u003c/p\u003e \u003cp\u003eAspartate aminotransferase (AspC, PDB ID: 1AIB) is involved in oxaloacetate production, the TCA cycle, and amino acid and nucleotide biosynthesis (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003ea). Interaction of peptide 13_4 with AspC is predicted to perturb multiple metabolic pathways, potentially triggering a cascade of metabolic failures.\u003c/p\u003e \u003cp\u003eSimilarly, UDP-N-acetylglucosamine 1-carboxyvinyltransferase (MurA, PDB ID: 1UAE) plays a central role in bacterial cell wall biosynthesis in \u003cem\u003eE. coli\u003c/em\u003e K12 (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eb), and interaction with peptide 13_4 may disrupt peptidoglycan formation, compromising cell wall integrity and bacterial viability.\u003c/p\u003e \u003cp\u003eMalate Synthase G (GlcB) was confirmed as a key component of the glyoxylate shunt in \u003cem\u003eM. tuberculosis\u003c/em\u003e, coordinated with Isocitrate Lyase (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003ec), a pathway required for survival under nutrient-limited conditions. Finally, the β-lactamase regulatory protein BlaR1 controls β-lactamase expression and stress response pathways in \u003cem\u003eS. aureus\u003c/em\u003e (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003ed). Collectively, these findings suggest that peptide 13_4 has the potential to simultaneously interfere with essential metabolic and resistance mechanisms.\u003c/p\u003e \u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003e3.5. Molecular dynamics\u003c/h2\u003e \u003cp\u003e \u003cdiv class=\"BlockQuote\"\u003e \u003cp\u003eMolecular dynamics simulations were performed over 100 ns to evaluate the stability and conformational behavior of the peptide\u0026ndash;protein complexes. RMSD analysis of the peptide backbone indicated that all systems reached equilibrium after approximately 30 ns and remained stable for the remainder of the simulation (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003ea). Among the evaluated complexes, the Malate synthase G (PDB ID: 1N8W) complex exhibited the lowest overall deviations, whereas the Carbapenemase OXA-23 (PDB ID: 4K0X) complex displayed slightly higher flexibility. The RMSF analysis revealed moderate fluctuations along the peptide sequence, particularly in central residues, while terminal regions remained comparatively stable across all complexes (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eb).\u003c/p\u003e \u003cp\u003eHydrogen bond analysis revealed persistent peptide\u0026ndash;protein interactions throughout the simulations. Among the evaluated complexes, carbapenemase OXA-58 (PDB ID: 4OH0) formed the highest number of intermolecular hydrogen bonds, whereas 1N8W and 4K0X formed fewer but more consistent contacts over time (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003ec). In contrast, the peptide displayed a limited and fluctuating number of intramolecular hydrogen bonds (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003ed), suggesting that its conformational stability is primarily driven by interactions with the protein surface rather than by internal hydrogen bonding.\u003c/p\u003e \u003cp\u003eAnalysis of peptide compactness and solvent exposure revealed receptor-dependent conformational differences. Specifically, the peptide adopted a more extended and solvent-exposed conformation in the 4OH0 complex, as reflected by higher radius of gyration and SASA values, while remaining more compact in the 1N8W complex (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003ee\u0026ndash;f). Collectively, these results indicate that peptide 13_4 forms stable yet flexible complexes with diverse bacterial targets, supporting its capacity for structural adaptation to different protein environments.\u003c/p\u003e \u003c/div\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"4. Discussion","content":"\u003cp\u003eThe present study provides an \u003cem\u003ein silico\u003c/em\u003e exploration of peptide 13_4, a short anionic peptide candidate derived from \u003cem\u003eBacillus spizizenii\u003c/em\u003e ATCC 6633 (manuscript submitted), aimed at identifying plausible protein targets associated with bacterial metabolism and antimicrobial resistance. Owing to its anionic and hydrophilic nature [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e, \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e], peptide 13_4 is unlikely to exert antimicrobial activity through classical membranolytic mechanisms typically described for cationic antimicrobial peptides [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e, \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e]. Instead, its physicochemical profile is consistent with a putative intracellular mode of action, which constitutes the central focus of this investigation. Accordingly, this work does not aim to demonstrate definitive antimicrobial inhibition, but rather to prioritize biologically relevant protein targets and propose mechanistic hypotheses to guide future experimental validation.\u003c/p\u003e \u003cp\u003eTarget bioprospecting combined with molecular docking analyses identified enzymes involved in central metabolism, cell wall biosynthesis, and antimicrobial resistance regulation as prominent interaction partners for peptide 13_4 (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e, Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). Notably, consistent predicted binding was observed for several class D β-lactamases from \u003cem\u003eAcinetobacter baumannii\u003c/em\u003e, including OXA-23, OXA-24/40, OXA-58, and OXA-143, despite these enzymes not being ranked among the top pharmacophore matches in the initial PharmMapper screening. This recurrent interaction pattern suggests an intrinsic structural complementarity between peptide 13_4 and conserved features of OXA-type enzymes, rather than an effect driven by target preselection. Given the pivotal role of carbapenemases in multidrug resistance and the increasing failure of last-resort antibiotics such as colistin [\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e], the identification of resistance-associated enzymes as potential peptide interaction partners is particularly relevant. Importantly, while many antimicrobial peptides reported against resistant \u003cem\u003eA. baumannii\u003c/em\u003e originate from animal sources [\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e, \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e], peptide 13_4 derives from a bacterial producer, highlighting \u003cem\u003eB. spizizenii\u003c/em\u003e as an underexplored reservoir of non-canonical bioactive peptide scaffolds.\u003c/p\u003e\u003cp\u003eOXA-type enzymes share a conserved catalytic architecture characterized by a serine-based active site and a hydrophobic substrate-binding groove that accommodates carbapenem antibiotics. Docking analyses indicated that peptide 13_4 preferentially interacts with residues located within or adjacent to the catalytic cleft of several OXA enzymes (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e), including OXA-23 [\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e, \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e], OXA-24/40 [\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e], OXA-58 [\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e], and OXA-143[\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e]. These residues have been previously implicated in substrate recognition and catalytic stabilization (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e), suggesting that peptide binding may sterically hinder substrate access or perturb the local active-site environment.\u003c/p\u003e \u003cp\u003eDocking-derived complexes were further evaluated by molecular dynamics simulations, which revealed stable peptide\u0026ndash;protein associations [\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e]. RMSD profiles showed that all systems remained close to their initial conformations after equilibration, indicating stable binding modes rather than transient interactions (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003ea). Residue-level fluctuations (RMSF) were moderate and mainly localized (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eb), consistent with binding-induced flexibility without global destabilization [\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e]\u003c/p\u003e \u003cp\u003eIntermolecular hydrogen bonds between peptide 13_4 and the protein targets were persistently maintained throughout the simulations, particularly in the OXA-58 (PDB ID: 4OH0) complex (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003ec). In contrast, intramolecular hydrogen bonds within the peptide were fewer and transient (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003ed), indicating that peptide stability is primarily supported by interactions with the protein surface rather than a rigid internal fold. This structural adaptability is a feature commonly associated with short antimicrobial and bioactive peptides and may enable simultaneous modulation of multiple cellular pathways. Receptor-dependent differences in radius of gyration and solvent accessibility further suggest that peptide 13_4 adopts distinct yet stable conformations depending on the target enzyme (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003ee-f).\u003c/p\u003e \u003cp\u003eDespite these insights, many limitations must be acknowledged. The present study does not address peptide uptake, intracellular availability, competition with native substrates, or direct enzymatic inhibition, nor does it account for the complexity of the cellular environment. Consequently, the predicted interactions should be interpreted as structural hypotheses rather than evidence of functional inhibition. Experimental validation through biochemical assays and cellular studies will be required to confirm the proposed mechanisms.\u003c/p\u003e \u003cp\u003eOverall, this work provides a systematic computational framework for the identification of intracellular targets of non-canonical antimicrobial peptides and highlights peptide 13_4 as a promising scaffold capable of engaging both resistance-associated enzymes and essential metabolic pathways. These findings lay the groundwork for future experimental validation and rational optimization strategies, including peptide truncation, sequence refinement, and structure\u0026ndash;activity relationship studies, aimed at developing novel peptide-based modulators of bacterial metabolism and antimicrobial resistance.\u003c/p\u003e"},{"header":"5. Conclusions","content":"\u003cp\u003eThis study presents an integrative \u003cem\u003ein silico\u003c/em\u003e characterization of peptide 13_4, a short anionic peptide candidate derived from \u003cem\u003eBacillus spizizenii\u003c/em\u003e ATCC 6633, highlighting its potential to interact with intracellular bacterial targets involved in both essential metabolism and antimicrobial resistance. Rather than acting through classical membrane-disruptive mechanisms, peptide 13_4 is predicted to engage catalytically relevant regions of metabolic enzymes and class D β-lactamases, supporting a multifunctional and non-membranolytic mode of action. The convergence of target prospecting, molecular docking, molecular dynamics simulations, and network analysis underscores the utility of computational strategies for prioritizing bioactive candidates and elucidating plausible mechanisms at early discovery stages. Although the proposed interactions remain predictive and require experimental validation, these findings identify peptide 13_4 as a promising molecular scaffold and position \u003cem\u003eB. spizizenii\u003c/em\u003e as an underexplored source of antimicrobial candidates. Collectively, this work provides a rational framework to guide future \u003cem\u003ein vitro\u003c/em\u003e and \u003cem\u003ein vivo\u003c/em\u003e studies aimed at validating peptide-based strategies to address multidrug-resistant bacterial infection.\u003c/p\u003e"},{"header":"Declarations","content":"\u003ch2\u003eFunding:\u003c/h2\u003e \u003cp\u003eThis research was funded by the Research Support Foundation of the State of S\u0026atilde;o Paulo (FAPESP/CeTICS) (Grant No. 2013/07467-1), by the Brazilian National Council for Scientific and Technological Development (CNPq) (Grant No. 472744/2012-7), by the Coordena\u0026ccedil;\u0026atilde;o de Aperfei\u0026ccedil;oamento de Pessoal de N\u0026iacute;vel Superior - Brasil (CAPES) - Finance Code 001\u003c/p\u003e\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eConceptualization, A.P.P.R and P.I.S.J.; Data curation, A.P.P.R.; Formal analysis, A.P.P.R.; Funding acquisition, P.I.S.J.; Investigation, A.P.P.R.; Methodology, A.P.P.R, E.J.M.S and P.I.S.J.; Project administration, P.I.S.J.; Resources, E.J.M.S and P.I.S.J.; Supervision, P.I.S.J.; Validation, A.P.P.R., E.J.M.S and P.I.S.J.; Writing-original draft, A.P.P.R; Writing-review and editing, A.P.P.R, E.J.M.S. and P.I.S.J.\u003c/p\u003e\u003ch2\u003eAcknowledgement\u003c/h2\u003e\u003cp\u003eWe thank all members of the Protein Chemistry Laboratory at the Laboratory for Applied Toxinology (LETA\u0026mdash;Butantan Institute, Brazil) for their continuous support and encouragement, as well as to the Bioinformatics Center of the Butantan Institute for their valuable assistance.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eAhmed SK, Hussein S, Qurbani K, Ibrahim RH, Fareeq A, Mahmood KA et al (2024) Antimicrobial resistance: Impacts, challenges, and future prospects. 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Infect Immun 89. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1128/iai.00703-20\u003c/span\u003e\u003cspan address=\"10.1128/iai.00703-20\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e. https://doi.org/\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAwdhesh Kumar Mishra R, Kodiveri Muthukaliannan G In-silico and in-vitro study of novel antimicrobial peptide AM1 from Aegle marmelos against drug-resistant Staphylococcus aureus. Sci Rep 2024 14:1 2024;14:25822-. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1038/s41598-024-76553-0\u003c/span\u003e\u003cspan address=\"10.1038/s41598-024-76553-0\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"antimicrobial peptide, Bacillus spizizenii, molecular docking, molecular dynamics simulations, in silico analysis","lastPublishedDoi":"10.21203/rs.3.rs-8768089/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8768089/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThe global rise of multidrug-resistant bacteria underscores the urgent need for alternative antimicrobial strategies targeting non-classical bacterial mechanisms. In this study, we performed an \u003cem\u003ein-silico\u003c/em\u003e characterization of peptide 13_4, a short anionic peptide candidate derived from \u003cem\u003eBacillus spizizenii\u003c/em\u003e ATCC 6633, to explore its potential interactions with essential metabolic enzymes and resistance-associated proteins. Physicochemical analysis revealed a highly flexible, negatively charged peptide, compatible with non-membranolytic mechanisms of action. Target prospecting and molecular docking identified high-affinity interactions with key bacterial enzymes, including carbapenemases (OXA-23, OXA 24, OXA-58), Malate Synthase G, Aspartate Aminotransferase, and UDP-N-acetylglucosamine 1-carboxyvinyltransferase. Molecular dynamics simulations demonstrated stable peptide\u0026ndash;protein complexes, supported by persistent hydrogen bonding networks and adaptive conformational flexibility, particularly for carbapenemase targets. Network analysis further highlighted the involvement of these targets in essential metabolic and resistance pathways. Collectively, these results suggest that peptide 13_4 may act as a multifunctional bioactive molecule targeting intracellular bacterial processes and resistance mechanisms, supporting its prioritization as a candidate for future experimental validation.\u003c/p\u003e","manuscriptTitle":"Bioinformatic Characterization of a Candidate Antimicrobial Peptide 13_4 from Bacillus spizizenii ATCC 6633: A Multifunctional Inhibitor of Essential Metabolic Targets and β-Lactamases","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-02-13 15:12:35","doi":"10.21203/rs.3.rs-8768089/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":"d3c6b3ef-c569-4096-9fef-cb53be4bba2b","owner":[],"postedDate":"February 13th, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2026-03-13T13:10:22+00:00","versionOfRecord":[],"versionCreatedAt":"2026-02-13 15:12:35","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8768089","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8768089","identity":"rs-8768089","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}