Integrative Genomic and in silico Structural Analysis of Carbapenemase in Pseudomonas for Environmental Surveillance

Integrative Genomic and in silico Structural Analysis of Carbapenemase in Pseudomonas for Environmental Surveillance | 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 Integrative Genomic and in silico Structural Analysis of Carbapenemase in Pseudomonas for Environmental Surveillance Shing Wei Siew, Nazmi Harith-Fadzilah, Miah Roney, Mohd Fadhlizil Fasihi Mohd Aluwi, and 1 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8745308/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 emergence of carbapenem-resistant bacteria in environmental reservoirs represents a growing public health concern, particularly in settings associated with healthcare waste. In this study, we reported the genomic and structural characterisation of a carbapenem-resistant Pseudomonas isolate (CW003PS) recovered from microwave-treated healthcare waste. Whole-genome sequencing and comparative genomic analyses revealed that CW003PS is closely related to Pseudomonas wenzhouensis but harbors a distinct antimicrobial resistance gene repertoire, including class D β-lactamase OXA-10 and metallo-β-lactamases VIM-2 and VIM-6, alongside multiple efflux systems and porin-associated alterations. VIM and OXA enzymes displayed significant binding affinity to ertapenems, an interaction not previously characterized in this species. To explore structural features associated with carbapenem resistance, protein structure modeling, molecular docking, and molecular dynamics simulations were applied to key β-lactamases identified in the genome. These analyses revealed differential structural conformations and binding behaviors with carbapenem antibiotics, revealing sequence-dependent structural and dynamic variability in enzyme–ligand interactions, providing testable hypotheses for future functional validation. While these computational analyses do not establish enzymatic activity, they provide structural hypotheses that complement genomic predictions and highlight features that may contribute to resistance phenotypes. Overall, this study integrates environmental genomics with in silico structural analysis to provide insights into the antimicrobial resistance architecture of a healthcare waste-associated Pseudomonas strain. The findings underscore the role of environmental reservoirs in disseminating carbapenemase-encoding bacteria and establish a framework for future experimental validation of resistance mechanisms. whole genome sequencing antimicrobial resistance carbapenem molecular docking molecular dynamics simulation beta-lactams inhibitor Figures Figure 1 Figure 2 Figure 3 Figure 4 1. Introduction Antimicrobial resistance (AMR) is a silent global threat, spreading resistance genes in people and the environment, making infections harder to treat and risking more deaths by 2050 (Ahmed et al., 2024 ; Akram et al., 2023 ). Healthcare waste can spread virulence factors and antimicrobial resistance genes from infected patients, increasing risks to both people and the environment (Ajekiigbe et al., 2025 ; Gashaw et al., 2024 ). Carbapenem-resistant Pseudomonas aeruginosa (CRPA) is a major concern because it carries genes for β-lactamases and carbapenemases, making it resistant to many antibiotics (Bunduki et al., 2025 ; Reyes et al., 2023 ). Past studies reported increasing cases of carbapenem-resistant bacteria in the environment (Lamba et al., 2017 ; Oliveira et al., 2021 ; Sahoo et al., 2025 ). Because carbapenems are last-resort antibiotics, their resistance limits the remaining treatment options (Sheu et al., 2019 ). Pseudomonas aeruginosa causes 7–7.3% of hospital infections with up to 61% mortality (Jibril et al., 2025 ; Scheffler et al., 2022 ). Carbapenem resistance in Gram-negative bacteria is rising fast, causing 1.03 million infections and 216,000 deaths in 2021 (Naghavi et al., 2024). The antibiotic resistance assessment of bacteria relies on the culture-based antibiotic susceptibility testing (AST) to determine the minimum inhibitory concentration (MIC) of an antimicrobial agent (Kadeřábková et al., 2024 ), but lacking details in explaining mutation in regulatory regions of bacteria in response to emergence of new AMR (Hassall et al., 2024 ). Current AST methods are limited, highlighting the need for faster, more accurate tools to detect antimicrobial resistance and guide public health action. Recent AMR studies use whole genome sequencing (WGS) to detect resistance genes and mobile elements. Combined with computational tools and AI, WGS can predict protein structures and enhance in silico analysis (Chen et al., 2024 ). Molecular docking and dynamics (MD) simulations deepen understanding of how AMR-related proteins interact with antibiotics and help identify new inhibitor compounds for therapy (Chio et al., 2023 ; García Hernández et al., 2025 ). This study is intentionally framed as a computational and genome-resolved analysis. The structural modelling, molecular docking, and molecular dynamics simulations presented here are not intended to demonstrate enzymatic activity or confirm resistance phenotypes. Rather, they aim to generate structure-informed hypotheses by integrating whole-genome context with predicted protein–ligand interactions. Experimental validation, while essential for mechanistic confirmation, is beyond the scope of the present work. 2. Materials and methods 2.1 Isolation, sequencing, and identification of antibiotic-resistant Pseudomonas species The presumed colony of antibiotic-resistant Pseudomonas was isolated from healthcare waste on LB media containing 100 mg/mL of ampicillin (AMP). The genetic materials of the selected bacteria was extracted and subjected to Sanger sequencing as described in our previous study (Siew et al., 2025 ). The species was identified on the basis of 16S rRNA gene sequence’s similarity obtained using National Center for Biotechnology Information (NCBI) database and BLAST algorithm. 2.2 Library preparation for long-read sequencing and raw data analysis of a novel Pseudomonas species For WGS, 400 ng of DNA was used as input for library preparation with a ligation sequencing kit (SQK-LSK110; Oxford Nanopore Technologies, Oxford, UK) with slight modifications (Zainulabid et al., 2023 ). The prepared library was purified using 1.0X AMPure XP magnetic beads and washed with 1:1 ratio of Short Fragment Buffer and Long Fragment Buffer during final washing step to avoid excessive size selection as previously described (Soffian et al., 2023 ; Zainulabid et al., 2022 ). The prepared library was sequenced on a Flongle flow cell (R9.4.1; Oxford Nanopore Technologies) for 24 hours and basecalling was done using Guppy v6.3.9 (R9.4.1 super accurate model) with modifications (Musa et al., 2023 ; Tay et al., 2023 ). The raw data analysis was performed with default parameters unless. Briefly, the raw fastq reads were filtered to retain quality reads longer than 2,000 bp, followed by de novo assembly using flye v2.9.2 based on estimated genome size for Pseudomonas species (Kolmogorov et al., 2019 ). Subsequently, the assembled reads were polished with Medaka v.1.9.1 (Vaser et al., 2017 ) and the completeness of assembled genome was assessed qualitatively using BUSCO v5.4.7 based on Pseudomonadales odb10 lineage database (Seppey et al., 2019 ). 2.3 Deep whole-genome and functional analyses The sequence was uploaded to web-based type strain genome server (TYGS) to identify for the most similar genomes in the TYGS database using Genome BLAST Distance Phylogeny (GBDP) approach (Meier-Kolthoff and Göker, 2019 ). FastANI v1.34 was used directly on Proksee server to determine the whole-genome Average Nucleotide Identity (ANI) between CW003PS and reference genome (Jain et al., 2018 ). The genome was annotated via NCBI Prokaryotic Genome Annotation Pipeline (PGAP) to predict protein coding genes (Haft et al., 2024 ; Tatusova et al., 2016 ), followed by a comparison of Pseudomonas sp. CW003PS and the identified reference genome, and visualization of their annotated sequences using proksee (Grant et al., 2023 ). The presence of acquired ARGs was predicted using the program ResFinder 4.1 ( https://cge.cbs.dtu.dk/services/ResFinder/ ), and ABRicate ( https://github.com/tseemann/abricate ). A comprehensive genome analysis was performed for using BV-BRC web server v3.49.1 to identify the AMR determinants and mechanisms detected the bacterial isolate (Olson et al., 2023 ). The genes of interest related to antibiotic resistance genes was annotated in using BlastKOALA in Kyoto Encyclopedia of Genes and Genomes (KEGG) web server for characterization of genes and pathway mapping (Kanehisa et al., 2016 ). 2.4 Pangenome analysis with carbapenem resistant Pseudomonas aeruginosa Pseudomonas CW003PS was compared to the reference genome and four other Pseudomonas aeruginosa genome assemblies featuring carbapenem-resistant isolates from humans and hospitals (1 strain in Malaysia, 1 strain in the Philippines, and 2 strains in China). The sequences were obtained from the GenBank database and evaluated using the PanExplorer web server using the Roary approach and a 95 percent identity setting (Dereeper et al., 2022 ; Page et al., 2015 ). 2.5 Protein structure modelling and quality evaluation The CW003PS Bla OXA10 , Bla VIM2 and Bla VIM6 sequences were searched against Pseudomonas genera (taxonomy id: 286) against the SwissProt database via protein-protein BLASTp (Altschul et al., 1990 ). The top-aligned blast hit was used to infer whether the sequences are monomeric, dimeric or more (Rodriguez et al., 2019 ). The beta-lactamases’ sequences of interests were modelled using three different modelling software: AlphaFold3, Swiss Model and TrRosetta. Each model is evaluated based on the MolProbity score (Williams et al., 2018 ), QMean (Benkert et al., 2011 ), ERRAT score (Colovos and Yeates, 1993 ), Verify3D (Lüthy et al., 1992 ) scores and Procheck assessment (Laskowski et al., 1993 ). The information on the selected model for each CW003PS beta-lactamases and its quality assessment scores were summarised in ( Table S7 ). The chimera energy minimisation tool was used to perform 5000 steepest gradiant and 1000 conjugate steps energy minimisation on the selected model. Then, the models were submitted to the DALI webserver ( http://ekhidna.biocenter.helsinki.fi/dali_server/ ) to search for the most similar structures of proteins available in the Protein Data Bank (PDB) database (Holm, 2022 ). The matched proteins with the closest structural similarity were selected based on z-scores as references for inferring ligand-binding sites for the CW003PS β-lactamases. 2.6 Molecular docking of beta-lactamase with antibiotics and inhibitors Molecular docking was performed to evaluate the binding affinity of Bla OXA10 , Bla VIM2 and Bla VIM6 with assayed antibiotics. The simplified molecular input line entry system (SMILES) strings for each assayed antibiotic were acquired from ChEMBL online database ( https://www.ebi.ac.uk/chembl/ ). The beta-lactamase inhibitors SMILEs were acquired from the ChEMBL online database. Only inhibitors demonstrated via beta-lactamase inhibition via binding assays were selected. A customised RDKit python script was used to build the 3D model of each antibiotic and inhibitor from their corresponding SMILEs ( https://www.rdkit.org ). Their structures were energy-minimised for 500 steps utilising OpenBabel (version 2.4.1) (O’Boyle et al., 2011 ). The CB-DOCK2 ( https://cadd.labshare.cn/cb-dock2/index.phpmolecular ) docking webserver was used to infer putative ligand binding cavities of Bla OXA10 , Bla VIM2 and Bla VIM6 by performing docking with ertapenem. Bound ligand poses of the CW003PS beta-lactamases were compared with that of the reference protein model acquired from DALI searches prior via Chimera Matchmaker tool (Pettersen et al., 2004 ). The CW003PS beta-lactamases' ligand binding cavities with overlapping region with their respective reference proteins were declared as the putative ligand binding site (Harith-Fadzilah and Alias, 2024 ). The MzDOCK molecular docking tool was used to perform high throughput docking of antibiotics and inhibitors against CW003PS beta-lactamases. The docking region was restricted to only the putative ligand binding region of each beta-lactamase inferred from CB-DOCK2. The intermolecular interaction between CW003PS beta-lactamases and the antibiotics and inhibitors were visualised using BIOVIA Discovery Studio (version 21.1.0.20298). 2.7 Molecular dynamics (MD) simulation and post hoc analysis of beta-lactamases The molecular dynamics (MD) simulation was performed for the Bla OXA10 , Bla VIM2 and Bla VIM6 with and antibiotics ertapenem to obtain further insights into the interaction behaviour under a polar solvation system. The CHARMMGUI webserver ( https:charmm-gui.org ) was used to prepare the Bla VIM6 -ertapenem complex for MD simulation using CHARMMGUI solution builder tool (Jo et al., 2008 ). The system parameters were as follows: octahedral system, with 0.15 M sodium chloride solvation, Charmm36m forcefield, with NPT Ensemble dynamics input generation with the temperature set at 310 K, the WFY (tryptophan, tyrosine and phenylalanine) cation parameter and mass hydrogen repartitioning parameters were enabled. Other parameters were kept as default. The MD simulation was carried out using NAMD3 (Phillips et al., 2020 ). The energy minimisation of 100, 000 steps was performed on each complex. The simulation was performed for 50 ns with 0.1 ns trajectory intervals recorded, generating 500 simulation frames. From the MD simulation trajectories recorded, the root mean square deviation (RMSD), root mean square fluctuation (RMSF), number of intermolecular hydrogen bonds formed (Inter-H), difference in solvent-accessible surface area (ΔSASA) trajectories, and the molecular mechanics / generalised Born surface area (mmGBSA) ligand free binding energy were calculated in visual molecular dynamics (VMD) (version 1.9.3) utilising our in-house script (Humphrey et al., 1996 ). The ΔSASA is calculated as follows: ΔSASA = SASA complex – SASA receptor - SASA ligand In addition, the contact frequencies of the interacting beta-lactamases’ residues with ertapenem were also calculated from the simulation trajectory. The contact threshold is defined as the amino acid on the receptor molecule being under 4 Å distance from the ligand (Humphrey et al., 2016 ). 3. Result 3.1 Phylogenetic tree and comprehensive genome analysis Phylogenetic reconstruction placed the isolate in close proximity to Pseudomonas wenzhouensis . Supporting this placement, FastANI analysis revealed a 94.23% average nucleotide identity between Pseudomonas CW003PS and P. wenzhouensis A20, indicating that CW003PS likely represents a novel strain of P. wenzhouensis (Fig. 1 A). The generated genome formed a complete circular genome with 15,383 reads length and a N50 length of 10,906 bp. The assembly using Flye generated the final consensus assembly that consisted of 4,523,787 bp, 62.3% G + C content, and 0.8x genome coverage ( Figure S1 ). The genome comparison between CW003PS and P. wenzhouensis A20 revealed distinct gene sets despite phylogenetically similar. The zoomed-in genome map focused on the sequences 1475 to 1480 kb pairs to identify the genes that are specific to CW003PS and related to resistance. Several ARGs, including aadA1 , Class D beta-lactamase (QDX81_07315), subclass B1 beta-lactamase (QDX81_07320) and intl1 genes were found in CW003PS strain, while absent in A20 strain (Fig. 1 B). The result identified a total of 2,395 genes shared by both strains, representing their conserved genetic backbone, while 1,460 and 1,423 genes were unique to A20 and CW003PS, respectively ( Table S1 to S3 ). Notably, within the shared gene set, multiple multidrug efflux transporters, including the MdtD multidrug transporter subunit, and NorM family multidrug efflux MATE transporter were identified in both A20 and CW003PS (Fig. 2 A ) . In contrast, strain-specific genes in CW003PS including oxacillin-hydrolyzing class D beta-lactamase OXA-10, metallo-beta-lactamase VIM-2, and VIM-6, which are well recognized for conferring resistance to beta-lactams and carbapenems (Fig. 2 B). Moreover, a comparative genome analysis was performed to identify the core and strain-specific gene that distinguish between P. wenzhouensis A20 and Pseudomonas sp. CW003PS ( Figure S2) . To investigate the genetic basis of this resistance, the screening of ARGs was completed using ABRicate and Resfinder, showcased the presence of carbapenem resistance genes OXA-10, VIM-6 , and VIM-2 ( Table S4 and S5 ). 3.2 Prediction of antibiotic resistance determinants and mechanisms Carbapenem resistance in strain CW003PS was identified by AST ( Table S6 ), and WGS was subsequently performed to characterise the underlying AMR genes. The comprehensive genome analysis was then performed via BV-BRC platform uncovered the ARGs and AMR mechanisms present in this CW003PS strain, as summarized in Table 1 . These findings revealed the presence of AMR genes, and mechanisms, including antibiotic activation and inactivation enzymes, altered antibiotic targets, diverse efflux pump systems, cell wall and permeability modifications, and regulatory mechanisms, which contribute to the ability of the strain to evade the actions of multiple antibiotics. KEGG database revealed the loss of multiple porin-coding genes, including OprD, OmpF, OmpC ,and OmpU which known to play a role in the porin-mediated resistance of Class D and Class B beta-lactamases ( Figure S3 ). Table 1 Antimicrobial resistance genes and AMR mechanisms identified in CW003PS strain AMR Mechanism Genes Antibiotic activation enzyme KatG Antibiotic inactivation enzyme AAC(6')-Ib/AAC(6')-II, Mph(F) family, OXA-10 family, PDC family, VIM f amily Antibiotic target in susceptible species Alr, Ddl, dxr, EF-G, EF-Tu, folA, Dfr, folP, gyrA, gyrB, inhA, fabI, Iso-tRNA, kasA, MurA, rho, rpoB, rpoC, S10p, S12p Efflux pump conferring antibiotic resistance MdtABC-OMF, MdtABC-TolC, MexAB-OprM, MexEF-OprN, MexEF-OprN system, MexJK-OprM/OpmH, MexPQ-OpmE, OprM/OprM family, TolC/OpmH Gene conferring resistance via absence gidB Protein altering cell wall charge conferring antibiotic resistance GdpD, PgsA Protein modulating permeability to antibiotic OccD4/OpdT, OccK6/OpdQ, OccK8/OprE, OprD family, OprF Regulator modulating expression of antibiotic resistance genes OxyR 3.3 Beta-lactamase molecular docking with antibiotics We performed molecular docking of Bla OXA10 , Bla VIM2 and Bla VIM6 on with antibiotics reported in the databases. From DALI analysis, the all three beta-lactamases showed closest similarity to dimerised beta-lactamases. Furthermore, sequence analysis showed a very high sequence similarity between Bla VIM2 and Bla VIM6 , of which they had only two amino acid difference on 60th and 148th amino acid, where Bla VIM2 had Glu60 and Asn147, whereas Bla VIM6 had Arg60 and Ser147. Thus, Bla VIM2 and Bla VIM6 shared the same reference structure, (PDB ID: 7AFX). However, these two amino acid differences led to a significantly different structure with RMSD of 1.140 Å. The notable difference was the first 50 amino acids of each chain, whereby in the Bla VIM2 dimer was dangled out of the core complex, whereas in Bla VIM6 , these regions was looped closely to the other monomer structure (Fig. 3 ). The lividomycin displayed the strongest binding affinity across three beta-lactamases whereas, fosfomycin displayed the weakest binding affinity despite our strain was not resistant to both lividomycin and fosfomycin. The binding affinity of beta-lactamases with carbepenems were weaker in comparison to binding affinity to lividomycin. Among the three carbepenems tested, ertapenem displayed the strongest ligand binding affinity to all three beta-lactamases ( Table S8 ). Bla VIM2 and Bla VIM6 displayed quite a different ligand binding affinity against meropenem, despite both these beta-lactamases shared very high sequence similarity. 3.4 Molecular dynamics simulation Within the 50 ns simulation there were significant differences among the three beta-lactamase-ertapenem complexes. The mmGBSA free binding energy of Bla OXA10 was the largest, with − 30.73 kcal/mol, followed by Bla VIM6 and Bla VIM2 , with − 22.32 kcal/mol and − 11.49 kcal/mol respectively (Table 2 ). We analysed the RMSD trajectory to evaluate the stability of the protein-complex within the water simulation system. The Bla VIM6 had the smallest RMSD, followed by Bla VIM2 and Bla OXA10 with an average RMSD of 6.62 Å, 9.58 Å and 16.65 Å respectively. Although Bla VIM6 had the lowest overall RMSD, suggesting highest stability, there were notable fluctuations within the first 10 ns simulation, and with some more smaller fluctuations between 25–30 ns and 40–45 ns (Fig. 4 A). Bla VIM2 had marginally larger RMSD than Bla OXA10 however, it had a much smaller fluctuations of its trajectory. In contrast, the Bla VIM6 had a more consistent but larger fluctuations than Bla OXA10 and Bla VIM2 . Table 2 Molecular dynamics (MD) simulation post hoc analysis Bla VIM6 Bla VIM2 Bla OXA10 mmGBSA (kcal/mol) –22.32 –11.59 –30.73 Average RMSD (Å) 6.62 9.58 16.65 Average RMSF (Å) 3.37 2.78 4.41 Average Intermolecular hydrogen bonds 1.62 1.042 1.93 ΔSASA (Å 2 ) –904.48 –574.69 –820.21 The RMSF analysis was performed to determine the regions of the protein that are flexible and rigid. Among the three complexes, the Bla VIM2 RMSF exhibited the least fluctuations, with and 2.78 Å average (Fig. 4 B). A notable fluctuation in Bla VIM2 was observed between 250–300th amino acid residue positions. This region consisting of the terminal position of Bla VIM2 monomers which are exposed to the solvent (Fig. 3 B). Several amino acid residues within this fluctuating region interacted with ertapenem ligand during the MD simulation, specifically within the PROA chain ( Table S9 ). No PROB amino acid residues from the high fluctuation region had interaction with ertapenem. Notable residues among them were His259 (13.74% frequency), Arg262 (16.61% frequency), Ser263 (15.02% frequency), and Val264 (10.86% frequency) for having interacted with ertapenem with notably higher frequency compared to the rest of the amino acid within the 250–300th residue region. Bla VIM6 complex had a notable fluctuation within approximately 400–430th amino acid region which belonged exclusively on the PROB chain from Tyr134 to Ala164 (Fig. 4 B; Fig. 3 C ). This high RMSF region is located on the external region of the complex, having more interaction with the system solution, rather than within the interior dimer cavity. Expectedly contact frequency analysis revealed none of these residues had any interaction with ertapenem ligand ( Table S9 ). Bla OXA10 showed a significant fluctuation on the first 10 and between 267–281st amino acid, which consisted of terminal regions of the dimer which are exposed to the solvent (Fig. 4 B; Fig. 3 D). None of these high RMSF residues had any interaction with ertapenem ligand as well ( Table S9 ). Bla VIM2 complex formed the least number of intermolecular hydrogen bonds, with average 1.04 bonds throughout the simulation period (Table 2 ; Fig. 4 C). These hydrogen bonds were formed on Arg60, Tyr67, Asp118, His240, Tyr201, Arg205, Ser207 and Asn210 ( Table S10 ). Bla VIM6 has a marginally higher number of hydrogen bonds, with an average of 1.62 bonds, mediated by Asp63, tyr67, trp87, his116, asp117, gly209, asn210, his240, asp213, glu146, his179, arg205, arg262. Bla OXA10 had the highest average of 1.934 bonds, but were mediated by the least number of amino acids among the three beta-lactamases: Ser67, arg104, gln101, ser115, gln113, lys205, thr206, phe208, ser209, glu199, arg250. The ΔSASA is the change in the SASA when the protein binds to a ligand or another protein to form a complex. A bigger difference indicates the ligand is buried deep into the protein, indicative of a strong binding Bla VIM2 had a much smaller ΔSASA, with average of − 574.69 Å 2 (Table 2 ). Bla VIM6 had the largest of all three beta-lactamases, with average ΔSASA, − 904.48 Å 2 , whereas Bla OXA10 had an average of − 820.21 Å 2 indicating that this complex has the most deeply buried ligand. In addition, Bla VIM6 had the most stable ΔSASA trajectory with the least fluctuations among the three beta-lactamases. 3.5 Beta-lactamase molecular docking with putative inhibitors 1356 beta-lactamase inhibitor entries were mined from CHEMBL database with binding assay data. After removing duplicates, we have narrowed down the number of inhibitors to 818. 52 of the compounds fail to produce 3D structures suitable for molecular docking, leaving 767 the analysis. Beta-lactamase inhibitor for this novel Bla OXA10 , Bla VIM2 and Bla VIM6 was screened utilising molecular docking tools. Some differences of beta-lactamase inhibitor efficacy were observed. The highest inhibitor binding affinity for Bla OXA10 was CHEMBL1554784 (6-(4-benzylpiperidin-1-yl)-9,10,10-trioxo-N-[[3-(trifluoromethyl)phenyl]methyl]thioxanthene-1-carboxamide) with − 10.4 kcal/mol ( Table S11 ). The other carboxamides sharing the same 4-benzylpiperidin-1-yl piperidine core (CHEMBL1436864, CHEMBL1726287, CHEMBL1434843) shared the second strongest binding affinity to Bla OXA10 , with − 10 kcal/mol ( Table S12 ). Bla VIM2 also had a 4-benzylpiperidin-1-yl piperidine core carboxamide, CHEMBL1436864 (6-(4-benzylpiperidin-1-yl)-N-[(3-fluorophenyl)methyl]-9,10,10-trioxothioxanthene-1-carboxamide) as the strongest binding beta-lactamase inhibitor, with − 14.2 kcal/mol. CHEMBL1554784 which had the strongest binding affinity to Bla OXA10 , was the second strongest ligand bound to Bla VIM2 , with − 13.1 kcal/mol. However, following these two carboxamides were vastly different compounds with different pharmacophores. CHEMBL1293246 has amines and sulfone, CHEMBL1293246 possesses coumarin lactone and sulfonamide, and CHEMBL1293244 possesses tetrazole and biphenyl structures. Bla VIM6 showed highest affinity with CHEMBL1293246 (9,10,10-trioxo-6-(4-piperidin-1-ylpiperidin-1-yl)-N-[[2-(trifluoromethyl)phenyl]methyl]thioxanthene-1-carboxamide). This inhibitor is also a with 4-benzylpiperidin-1-yl piperidine core. 4. Discussion Carbapenem-resistant gram-negative bacteria have emerged as a critical global health threat since the late 1990s (De Pascale et al., 2025 ; European Centre for Disease Prevention and Control, 2025 ; Tesalona et al., 2025 ). The rapid dissemination of carbapenems across region and species has severely compromised the efficacy of existing antibiotics, leaving clinicians with severely limited therapeutic options. Hospitals are hotspots for antibiotic-resistant bacteria (ARB) because of heavy antibiotic use. When leftover drugs and ARBs enter the waste stream, they help these bacteria survive and share resistance genes with others in the waste and environment. In this study, Pseudomonas sp. CW003PS was isolated from healthcare waste. Our analysis revealed that CW003PS is closely related to the newly identified Pseudomonas wenzhouensis A20 strain, sharing 94.23% of its genetic makeup with 0.8x genome coverage. P. wenzhouensis is a newly classified Pseudomonas species isolated from sewage discharged from animal farm in China. This strain is identified with novel AmpC beta-lactamase encoding gene, Bla PRC–1 , which is associated with to many beta-lactam antibiotics (Zhang et al., 2021 ). However, unlike the P. wenzhouensis strain, CW003PS has acquired a distinct set of genes. These include the oxacillin-hydrolyzing class D beta-lactamase OXA-10 , the subclass B1 metallo-beta-lactamase VIM-2 , and VIM-6 that confer resistance to aminoglycoside, beta-lactam and carbapenem antibiotics, respectively (Alonso-García et al., 2023 ; Mei et al., 2023 ; Oelschlaeger et al., 2023 ). Given the scarce study reported on P. wenzhouensis and the variation of this species remain unexplored, we sequenced the CW003PS genome, screened for antimicrobial determinants and performed molecular simulations to understand the molecular interaction between antibiotics and enzymes responsible for its resistance properties. The CW003PS strain exhibited resistance primarily in beta-lactam antibiotics, hence we postulated the beta-lactamase could be the primary arsenal for the strain’s antimicrobial resistance. Bla OXA10 is a member of class D beta-lactamases, which considered as narrow spectrum enzyme that hydrolyzes penicillin and some older generation of cephalosporins, with weak carbapenem-hydrolyzing activity (Gill et al., 2021 ). The contribution of Bla OXA10 in carbapenem resistance is significant when combined with porin deficiency (Alonso-García et al., 2023 ). Bla VIM are more potent Class B metallo-beta-lactamase, which incorporates zinc ions to effectively hydrolyze and inactivate the beta-lactam ring, conferring resistance to penicillins, cephalosporins, and carbapenems (AlBahrani et al., 2024 ; Boyd et al., 2020 ). The genes for VIM enzymes are often located on mobile genetic elements, enable them to spread rapidly between different species of bacteria (Caliskan-Aydogan and Alocilja, 2023 ). While OXA-10 resistance represents a significant clinical challenge, VIM enzymes, including VIM-2 and VIM-6, directly target and inactivate carbapenems, representing a more advanced and life-threatening form of resistance. In addition to beta-lactamases, our analysis identified a loss or severe reduction of several porins in the metabolic pathway, including OprD, OmpF, OmpC, OmpU . Porins serves as channels in the outer membrane for diffusion of nutrients and antimicrobial drugs into the cell, and their loss can greatly influence the sensitivity of microorganisms to the antibiotics (Zhou et al., 2023 ). The outer membrane proteins (OMP) OmpF , and OmpC facilitates the entry and resistance of beta-lactam antibiotics (Kim et al., 2020 ; Zhou et al., 2023 ), while the loss of OprD can reduce the uptake of carbapenem (Wang et al., 2025 ). Furthermore, CW003PS strain possess multiple efflux pump that actively remove toxic compounds, including antibiotics from the cell, contribute to a broader range of multidrug resistance phenotype. It is highly probable that this CW003PS isolate has the remarkable ability to evolve and adapt, as a result of thriving in the selective pressures of the healthcare environment. The acquisition of these resistance determinants likely occurred within the healthcare setting where the bacteria were exposed to various antibiotics, leading to gene mutations and the opportunity to exchange genetic material with other resistant microorganisms. Based on our sequence analysis, the beta-lactamases, Bla OXA10 , Bla VIM2 and Bla VIM6 were postulated as key resistance-associated determinants within the genomic context of this isolate. Bla VIM2 was first isolated from France (Poirel et al., 2000 ), a year after the first identification of VIM gene from P. aeruginosa in Italy (Lauretti et al., 1999 ). Bla VIM6 was first identified in Singapore with reported difference from Bla VIM2 at amino acids 59 (Gln to Arg) and 165 (Asn to Ser) (Walsh et al., 2005 ). The MD simulation data showed high mutational sensitivity of the beta-lactamases. In our study, Bla VIM2 and Bla VIM6 , which only had two amino acid differences, displayed a notable difference conformation of the first 40 amino acids and to ertapenem binding characteristics. Bla VIM2 demonstrated a more stable complexes with ertapenem, as indicated by minimal RMSD and RMSF fluctuations and a larger mmGBSA estimations than Bla VIM6 counterpart. On the other hand, Bla OXA10 exhibited high RMSD and RMSF fluctuations, attributed primarily to the dangling terminal region of the dimer. The exposed terminal region may confer reduced stability to the dimer structure and thus, a less stable interaction with ertapenem. However, Bla OXA10 also has a high mutational sensitivity as a previous research reported that only two amino acids changes to Bla OXA10 could enhance resistance against cephalosporins, of which Bla OXA10 was originally had weak activity on (Evans and Amyes, 2014 ). Across all three enzymes, the formation of intermolecular hydrogen bonds appeared to be a less significant contributor to ligand binding, with non-polar interactions within the large binding cavities likely function as the primary driver. This observation may inform future structure-guided studies aimed at understanding β-lactamase–ligand interactions. From docking analysis, carboxamide-containing compounds appear to have generally high binding affinity to all three beta-lactamases. Several examples of these compounds such as relebactam and avibactam have been used as antibiotic adjuvants (Heo, 2021 ; Matesanz and Mensa, 2021). Relebactam and avibactam may be examined in subsequent studies incorporating biochemical or microbiological validation. Furthermore, novel CHEMBL1554784, CHEMBL1436864 and CHEMBL1293246 compounds may be of our interest to explore as beta-lactamase inhibitors against P. aeruginosa and other Gram-negative bacteria. While our current analysis provide a genotypic resistance profile of Pseudomonas CW003PS, the AST result represent the phenotypic resistance from our previous study, was partially consistent with these findings (Siew et al., 2025 ). The AST results showed that the CW003PS susceptible to cefepime but resistance to ceftazidime/avibactam. In contrast, ResFinder analysis indicated resistance to cefepime but susceptible to ceftazidime/avibactam. The molecular docking analysis supported resistance to ceftazidime and cefepime due to the presence of Bla OXA10 . Despite both ResFinder and molecular docking demonstrating resistance to ertapenem, the AST did not test for this antibiotic, hence the phenotypic result remains unknown. The discrepancies between genotypic and phenotypic resistance may be influenced by mutations in regulatory mechanisms that do not always exhibit phenotypic resistance, or the silent mutations in resistance genes that alter the resistance mechanisms but does not captured by the genotypic analysis (Mou et al., 2024 ; Silva and Khare, 2024 ). The differences observed underscore the importance of multifaceted approach that combines both genotypic and phenotypic data for comprehensive assessment of antimicrobial resistance. While genotypic information reveals the structural features for resistance and inhibition activities, phenotypic testing demonstrates the actual effectiveness of an antimicrobial agent against a specific microorganism to ensure the right antimicrobial agents is prescribed at the correct dose for the right microorganisms, therefore ensuring effective therapeutic outcome. While whole-genome sequencing provides a robust framework for predicting antimicrobial resistance potential, genotypic predictions do not always translate directly into phenotypic resistance. Similarly, molecular docking and dynamics simulations offer structural insights but cannot substitute for biochemical or microbiological validation of enzyme activity or inhibition. The observed discrepancies between genotypic predictions and available phenotypic data highlight the limitations of resistance inference based solely on gene presence. These inconsistencies underscore the value of structure-informed computational analyses in exploring potential resistance architectures that are not directly captured by standard susceptibility testing. 5. Conclusion Our data suggest environmental isolates may act as reservoirs of carbapenemases and offer structural reference models for hypothesis-driven studies of enzyme–ligand interactions. This study provides a comprehensive insight into the carbapenem resistance properties of Pseudomonas CW003PS, an isolate from the microwave treated healthcare waste, by combining the whole-genome sequence study with molecular docking analysis. Above findings suggest that there is no fundamental limitation in exploring the antimicrobial resistance genes in bacterial isolate and understanding the interaction between drugs and targeted enzyme, as well as identify structural features associated with predicted β-lactamase–ligand interactions. However, discrepancies observed between genotypic and phenotypic resistance profiles of CW003PS highlighted the complexity of antimicrobial resistance. This underscores the importance of combined strategy for monitoring of resistance evolution in microorganisms particularly in high-risk environments like healthcare settings and inform future studies aimed at understanding antimicrobial resistance mechanisms. Hence, we recommend further laboratory and field study to incorporate this dual strategy for monitoring the resistance evolution and understand the dissemination patterns of antimicrobial resistance in a broader perspective. Additionally, future experiments can focus on experimental validation of identified inhibitors to bridge the gap from simulation to a tangible therapeutic solution. Declarations Data Availability The whole genome sequencing data of Pseudomonas CW003PS was accessible in the NCBI database under the BioProject accession number PRJNA955726, BioSample accession number SAMN34187649 with Sequence Read Archive (SRA) accessions SRR24173875. The assembled genome annotated with NCBI PGAP was deposited under GenBank accession number CP123623 and assembly accession number GCA_029873275.1. All additional input files are provided in supplementary file. Funding This work was supported by Universiti Malaysia Pahang Al-Sultan Abdullah, Malaysia for the Made-in-UMP Grant (PDU223001-1) and the Postgraduate Research Grant Scheme (PGRS); PGRS220382. Author information Authors and Affiliations B-Crobes Laboratory Sdn. Bhd, 18 & 20, Lintasan Perajurit 17`G, Taman Teknologi Industri & Perusahaan Ipoh, 31400 Ipoh, Perak, Malaysia Shing Wei Siew Faculty of Industrial Sciences and Technology, Universiti Malaysia Pahang Al-Sultan Abdullah, Lebuh Persiaran Tun Khalil Yaakob, 26300, Gambang, Pahang, Malaysia Nazmi Harith-Fadzilah, Miah Roney, Mohd Fadhlizil Fasihi Mohd Aluwi, Hajar Fauzan Ahmad The Microbiome Lab (TML), Universiti Malaysia Pahang Al-Sultan Abdullah, Lebuh Persiaran Tun Khalil Yaakob, 26300, Gambang, Pahang, Malaysia Shing Wei Siew, Hajar Fauzan Ahmad Centre for Artificial Intelligence and Data Science (CAIDaS), Universiti Malaysia Pahang Al-Sultan Abdullah, Lebuh Persiaran Tun Khalil Yaakob, 26300, Gambang, Pahang, Malaysia Nazmi Harith-Fadzilah, Hajar Fauzan Ahmad Contributions Shing Wei Siew: writing – original draft, writing – review & editing, investigation, methodology, formal analysis, visualization, data curation. Nazmi Harith-Fadzilah : conceptualization, writing – original draft, writing – review & editing, software, visualization, validation, data curation. Miah Roney: conceptualization, writing – review & editing. Mohd Fadhlizil Fasihi Mohd Aluwi: conceptualization, writing – review & editing. Hajar Fauzan Ahmad: conceptualization, supervision, writing – original draft, writing – review & editing, resources, project administration, methodology, validation, funding acquisition All authors have given approval to the final version of the manuscript. Corresponding author Correspondence to Hajar Fauzan Ahmad. Ethics declarations Ethics approval and consent to participate Not applicable. Patient consent for publication Not applicable. Clinical trial number Not applicable. Competing interests The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-8745308","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":597808346,"identity":"2ad469b3-aec2-4d2d-83ab-3796590f3af9","order_by":0,"name":"Shing Wei Siew","email":"","orcid":"","institution":"Universiti Malaysia Pahang Al-Sultan Abdullah, Lebuh Persiaran Tun Khalil Yaakob","correspondingAuthor":false,"prefix":"","firstName":"Shing","middleName":"Wei","lastName":"Siew","suffix":""},{"id":597808350,"identity":"03bf9c8c-8c06-4632-9783-e6fbf321c29c","order_by":1,"name":"Nazmi Harith-Fadzilah","email":"","orcid":"","institution":"Universiti Malaysia Pahang Al-Sultan Abdullah, Lebuh Persiaran Tun Khalil Yaakob","correspondingAuthor":false,"prefix":"","firstName":"Nazmi","middleName":"","lastName":"Harith-Fadzilah","suffix":""},{"id":597808352,"identity":"9bef46cd-1974-48f7-9e83-90fdb1be8bdb","order_by":2,"name":"Miah Roney","email":"","orcid":"","institution":"Universiti Malaysia Pahang Al-Sultan Abdullah, Lebuh Persiaran Tun Khalil Yaakob","correspondingAuthor":false,"prefix":"","firstName":"Miah","middleName":"","lastName":"Roney","suffix":""},{"id":597808353,"identity":"f6187091-3911-4c1b-9a7d-d86c3119e0c7","order_by":3,"name":"Mohd Fadhlizil Fasihi Mohd Aluwi","email":"","orcid":"","institution":"Universiti Malaysia Pahang Al-Sultan Abdullah, Lebuh Persiaran Tun Khalil Yaakob","correspondingAuthor":false,"prefix":"","firstName":"Mohd","middleName":"Fadhlizil Fasihi Mohd","lastName":"Aluwi","suffix":""},{"id":597808355,"identity":"da7987a2-abb0-46f6-bafb-c7ccd4487612","order_by":4,"name":"Hajar Fauzan Ahmad","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA/klEQVRIiWNgGAWjYBAC9gYInQDEhg9gohL4tPAcQGgxNiBZixlcJX4t7GeMP/zcYZen2354WzVvzh17/gbmg7d5GLYlNuDSwpNjYNh7JrnY7Exa2W3ebc8SZxxgS7bmYbiNU4s9Q45BAm8bc+K2AzlmQC2HExgO8JhJ49PCw//G4ODftvrEbeffmBUDtdjLH+D/hl+LRI5hM2/b4cRtN3LMmIFaGDcc4GEjoOVZMbNs23GglmfFknOBftl4mM3Yco7BbWPcDkve/PFtWzXQYckbP7zddsde7njzwxtvKm7L4tKCDg4wMDCDaAMGRxK0QIE9kTpGwSgYBaNg+AMAWNde6T2pNU8AAAAASUVORK5CYII=","orcid":"","institution":"Universiti Malaysia Pahang Al-Sultan Abdullah, Lebuh Persiaran Tun Khalil Yaakob","correspondingAuthor":true,"prefix":"","firstName":"Hajar","middleName":"Fauzan","lastName":"Ahmad","suffix":""}],"badges":[],"createdAt":"2026-01-30 23:23:33","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-8745308/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-8745308/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":103628652,"identity":"5c964bdf-3a65-48f1-a66b-5f66bd12cdd7","added_by":"auto","created_at":"2026-02-27 21:43:58","extension":"jpg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":45965,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eA)\u003c/strong\u003e Average nucleotide identity (ANI)–based phylogenomic tree showing the relationship of \u003cem\u003ePseudomonas\u003c/em\u003e CW003PS with closely related Pseudomonas species. The tree was constructed using pairwise ANI distances derived from whole-genome comparisons. Bootstrap support values (%) are indicated at branch nodes. The isolate \u003cem\u003ePseudomonas\u003c/em\u003eCW003PS is highlighted in red, and \u003cstrong\u003eB) \u003c/strong\u003eOxacillin-hydrolyzing class D beta-lactamase OXA-10 (QDX81_07315) and subclass B1 metallo-beta-lactamase VIM-6 encoded gene (QDX81_07320) were identified in the \u003cem\u003ePseudomonas\u003c/em\u003e sp. CW003PS while absent in the reference genome \u003cem\u003eP. wenzhouensis\u003c/em\u003e A20. Notes: Lane 1: A20 genes (+), Lane 2: A20 genes (-), Lane 3: Backbone (Contigs), Lane 4: CW003PS genes (+), Lane 5: CW003PS genes (-), Lane 6: GC Skew, and Lane 7: GC Content\u003c/p\u003e","description":"","filename":"Picture1.jpg","url":"https://assets-eu.researchsquare.com/files/rs-8745308/v1/f07df28d1fc96ef7dbc3f17e.jpg"},{"id":104399510,"identity":"49ddd836-98e3-4430-bcad-952f0c787b38","added_by":"auto","created_at":"2026-03-11 12:06:26","extension":"jpg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":94256,"visible":true,"origin":"","legend":"\u003cp\u003eClinker comparison illustrating\u003cstrong\u003e (A) \u003c/strong\u003ethe conserved genomic regions between \u003cem\u003eP. wenzhouensis \u003c/em\u003eA20 and \u003cem\u003ePseudomonas\u003c/em\u003e sp. CW003PS, including genes encoding MdtAB multidrug efflux transporters, and \u003cstrong\u003e(B) \u003c/strong\u003estrain-specific genomic regions in CW003PS carrying antimicrobial resistance determinants such as class D β-lactamase OXA-10 and metallo-β-lactamases VIM-2 and VIM-6.\u003c/p\u003e","description":"","filename":"Picture2.jpg","url":"https://assets-eu.researchsquare.com/files/rs-8745308/v1/9ddba4e9ed34c7f86f7628b7.jpg"},{"id":103628653,"identity":"54424a4a-3eb9-45e5-8819-fc7b0902ddaf","added_by":"auto","created_at":"2026-02-27 21:43:58","extension":"jpg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":61639,"visible":true,"origin":"","legend":"\u003cp\u003eBeta-lactamase molecular docking with antibiotics. (\u003cstrong\u003eA) \u003c/strong\u003eSuperimposed \u003cem\u003eBla\u003c/em\u003e\u003csub\u003eVIM2 \u003c/sub\u003e(white) and \u003cem\u003eBla\u003c/em\u003e\u003csub\u003eVIM6\u003c/sub\u003e (red). The first 40 amino acids of \u003cem\u003eBla\u003c/em\u003e\u003csub\u003eVIM2\u003c/sub\u003e is coloured yellow, and coloured blue for \u003cem\u003eBla\u003c/em\u003e\u003csub\u003eVIM6\u003c/sub\u003e. (\u003cstrong\u003eB) \u003c/strong\u003e\u003cem\u003eBla\u003c/em\u003e\u003csub\u003eVIM2\u003c/sub\u003e-dimer-ertapenem complex. First chain monomer (PROA) is coloured green. Second chain (PROB) coloured blue. The 250-300th amino acid residue position is coloured yellow. (\u003cstrong\u003eC) \u003c/strong\u003e\u003cem\u003eBla\u003c/em\u003e\u003csub\u003eVIM6\u003c/sub\u003e-dimer-ertapenem complex. First chain monomer (PROA) is coloured green. Second chain (PROB) coloured blue. The ertapenem is coloured in red. The 400–430th amino acid region is coloured yellow. (\u003cstrong\u003eD) \u003c/strong\u003e\u003cem\u003eBla\u003c/em\u003e\u003csub\u003eOXA10-\u003c/sub\u003edimer-ertapenem complex. First chain monomer (PROA) is coloured green. Second chain (PROB) coloured blue. The ertapenem is coloured in red. The first 10 and between 267–281st amino acid is coloured yellow.\u003c/p\u003e","description":"","filename":"Picture3.jpg","url":"https://assets-eu.researchsquare.com/files/rs-8745308/v1/57c5c5a8b59f656e48d86dbf.jpg"},{"id":103628656,"identity":"00d5ddc0-666c-4003-b7a0-4a29314385bd","added_by":"auto","created_at":"2026-02-27 21:43:58","extension":"jpg","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":57879,"visible":true,"origin":"","legend":"\u003cp\u003eMolecular dynamics simulation analysis of carbapenemase–ligand complexes, showing \u003cstrong\u003eA)\u003c/strong\u003e root mean square deviation (RMSD), \u003cstrong\u003eB)\u003c/strong\u003e root mean square fluctuation (RMSF), \u003cstrong\u003eC)\u003c/strong\u003e radius of gyration (Rg), and \u003cstrong\u003eD)\u003c/strong\u003e solvent-accessible surface area (SASA) over a 50-ns simulation period.\u003c/p\u003e","description":"","filename":"Picture4.jpg","url":"https://assets-eu.researchsquare.com/files/rs-8745308/v1/bf8a1709e720754d27bed5eb.jpg"},{"id":104407643,"identity":"01e232b4-5ca6-4944-9c7a-86c96b5e8ecf","added_by":"auto","created_at":"2026-03-11 12:39:20","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1511481,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8745308/v1/9fb0d4b5-c6f8-4ee9-9dcc-12042fd2637e.pdf"},{"id":103628655,"identity":"6a0c850b-4585-4fbe-a30c-7a99d28594e0","added_by":"auto","created_at":"2026-02-27 21:43:58","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":6362585,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryFigures.docx","url":"https://assets-eu.researchsquare.com/files/rs-8745308/v1/03978680800727249170588e.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Integrative Genomic and in silico Structural Analysis of Carbapenemase in Pseudomonas for Environmental Surveillance","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eAntimicrobial resistance (AMR) is a silent global threat, spreading resistance genes in people and the environment, making infections harder to treat and risking more deaths by 2050 (Ahmed et al., \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Akram et al., \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Healthcare waste can spread virulence factors and antimicrobial resistance genes from infected patients, increasing risks to both people and the environment (Ajekiigbe et al., \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2025\u003c/span\u003e; Gashaw et al., \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Carbapenem-resistant Pseudomonas aeruginosa (CRPA) is a major concern because it carries genes for β-lactamases and carbapenemases, making it resistant to many antibiotics (Bunduki et al., \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2025\u003c/span\u003e; Reyes et al., \u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Past studies reported increasing cases of carbapenem-resistant bacteria in the environment (Lamba et al., \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Oliveira et al., \u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Sahoo et al., \u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). Because carbapenems are last-resort antibiotics, their resistance limits the remaining treatment options (Sheu et al., \u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). \u003cem\u003ePseudomonas aeruginosa\u003c/em\u003e causes 7\u0026ndash;7.3% of hospital infections with up to 61% mortality (Jibril et al., \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2025\u003c/span\u003e; Scheffler et al., \u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Carbapenem resistance in Gram-negative bacteria is rising fast, causing 1.03\u0026nbsp;million infections and 216,000 deaths in 2021 (Naghavi et al., 2024).\u003c/p\u003e \u003cp\u003eThe antibiotic resistance assessment of bacteria relies on the culture-based antibiotic susceptibility testing (AST) to determine the minimum inhibitory concentration (MIC) of an antimicrobial agent (Kadeř\u0026aacute;bkov\u0026aacute; et al., \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2024\u003c/span\u003e), but lacking details in explaining mutation in regulatory regions of bacteria in response to emergence of new AMR (Hassall et al., \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Current AST methods are limited, highlighting the need for faster, more accurate tools to detect antimicrobial resistance and guide public health action. Recent AMR studies use whole genome sequencing (WGS) to detect resistance genes and mobile elements. Combined with computational tools and AI, WGS can predict protein structures and enhance in silico analysis (Chen et al., \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Molecular docking and dynamics (MD) simulations deepen understanding of how AMR-related proteins interact with antibiotics and help identify new inhibitor compounds for therapy (Chio et al., \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Garc\u0026iacute;a Hern\u0026aacute;ndez et al., \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2025\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThis study is intentionally framed as a computational and genome-resolved analysis. The structural modelling, molecular docking, and molecular dynamics simulations presented here are not intended to demonstrate enzymatic activity or confirm resistance phenotypes. Rather, they aim to generate structure-informed hypotheses by integrating whole-genome context with predicted protein\u0026ndash;ligand interactions. Experimental validation, while essential for mechanistic confirmation, is beyond the scope of the present work.\u003c/p\u003e"},{"header":"2. Materials and methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e2.1 Isolation, sequencing, and identification of antibiotic-resistant \u003cem\u003ePseudomonas\u003c/em\u003e species\u003c/h2\u003e \u003cp\u003eThe presumed colony of antibiotic-resistant \u003cem\u003ePseudomonas\u003c/em\u003e was isolated from healthcare waste on LB media containing 100 mg/mL of ampicillin (AMP). The genetic materials of the selected bacteria was extracted and subjected to Sanger sequencing as described in our previous study (Siew et al., \u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). The species was identified on the basis of 16S rRNA gene sequence\u0026rsquo;s similarity obtained using National Center for Biotechnology Information (NCBI) database and BLAST algorithm.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e2.2 Library preparation for long-read sequencing and raw data analysis of a novel \u003cem\u003ePseudomonas\u003c/em\u003e species\u003c/h2\u003e \u003cp\u003eFor WGS, 400 ng of DNA was used as input for library preparation with a ligation sequencing kit (SQK-LSK110; Oxford Nanopore Technologies, Oxford, UK) with slight modifications (Zainulabid et al., \u003cspan citationid=\"CR72\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). The prepared library was purified using 1.0X AMPure XP magnetic beads and washed with 1:1 ratio of Short Fragment Buffer and Long Fragment Buffer during final washing step to avoid excessive size selection as previously described (Soffian et al., \u003cspan citationid=\"CR62\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Zainulabid et al., \u003cspan citationid=\"CR71\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). The prepared library was sequenced on a Flongle flow cell (R9.4.1; Oxford Nanopore Technologies) for 24 hours and basecalling was done using Guppy v6.3.9 (R9.4.1 super accurate model) with modifications (Musa et al., \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Tay et al., \u003cspan citationid=\"CR64\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). The raw data analysis was performed with default parameters unless. Briefly, the raw fastq reads were filtered to retain quality reads longer than 2,000 bp, followed by \u003cem\u003ede novo\u003c/em\u003e assembly using flye v2.9.2 based on estimated genome size for \u003cem\u003ePseudomonas\u003c/em\u003e species (Kolmogorov et al., \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). Subsequently, the assembled reads were polished with Medaka v.1.9.1 (Vaser et al., \u003cspan citationid=\"CR66\" class=\"CitationRef\"\u003e2017\u003c/span\u003e) and the completeness of assembled genome was assessed qualitatively using BUSCO v5.4.7 based on Pseudomonadales odb10 lineage database (Seppey et al., \u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e2019\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003e2.3 Deep whole-genome and functional analyses\u003c/h2\u003e \u003cp\u003eThe sequence was uploaded to web-based type strain genome server (TYGS) to identify for the most similar genomes in the TYGS database using Genome BLAST Distance Phylogeny (GBDP) approach (Meier-Kolthoff and G\u0026ouml;ker, \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). FastANI v1.34 was used directly on Proksee server to determine the whole-genome Average Nucleotide Identity (ANI) between CW003PS and reference genome (Jain et al., \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). The genome was annotated \u003cem\u003evia\u003c/em\u003e NCBI Prokaryotic Genome Annotation Pipeline (PGAP) to predict protein coding genes (Haft et al., \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Tatusova et al., \u003cspan citationid=\"CR63\" class=\"CitationRef\"\u003e2016\u003c/span\u003e), followed by a comparison of \u003cem\u003ePseudomonas\u003c/em\u003e sp. CW003PS and the identified reference genome, and visualization of their annotated sequences using proksee (Grant et al., \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). The presence of acquired ARGs was predicted using the program ResFinder 4.1 (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://cge.cbs.dtu.dk/services/ResFinder/\u003c/span\u003e\u003cspan address=\"https://cge.cbs.dtu.dk/services/ResFinder/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e), and ABRicate (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://github.com/tseemann/abricate\u003c/span\u003e\u003cspan address=\"https://github.com/tseemann/abricate\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e). A comprehensive genome analysis was performed for using BV-BRC web server v3.49.1 to identify the AMR determinants and mechanisms detected the bacterial isolate (Olson et al., \u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). The genes of interest related to antibiotic resistance genes was annotated in using BlastKOALA in Kyoto Encyclopedia of Genes and Genomes (KEGG) web server for characterization of genes and pathway mapping (Kanehisa et al., \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2016\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003e2.4 Pangenome analysis with carbapenem resistant \u003cem\u003ePseudomonas aeruginosa\u003c/em\u003e\u003c/h2\u003e \u003cp\u003e \u003cem\u003ePseudomonas\u003c/em\u003e CW003PS was compared to the reference genome and four other \u003cem\u003ePseudomonas aeruginosa\u003c/em\u003e genome assemblies featuring carbapenem-resistant isolates from humans and hospitals (1 strain in Malaysia, 1 strain in the Philippines, and 2 strains in China). The sequences were obtained from the GenBank database and evaluated using the PanExplorer web server using the Roary approach and a 95 percent identity setting (Dereeper et al., \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Page et al., \u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e2015\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003e2.5 Protein structure modelling and quality evaluation\u003c/h2\u003e \u003cp\u003eThe CW003PS \u003cem\u003eBla\u003c/em\u003e\u003csub\u003eOXA10\u003c/sub\u003e, \u003cem\u003eBla\u003c/em\u003e\u003csub\u003eVIM2\u003c/sub\u003e and \u003cem\u003eBla\u003c/em\u003e\u003csub\u003eVIM6\u003c/sub\u003e sequences were searched against \u003cem\u003ePseudomonas\u003c/em\u003e genera (taxonomy id: 286) against the SwissProt database \u003cem\u003evia\u003c/em\u003e protein-protein BLASTp (Altschul et al., \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e1990\u003c/span\u003e). The top-aligned blast hit was used to infer whether the sequences are monomeric, dimeric or more (Rodriguez et al., \u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). The beta-lactamases\u0026rsquo; sequences of interests were modelled using three different modelling software: AlphaFold3, Swiss Model and TrRosetta. Each model is evaluated based on the MolProbity score (Williams et al., \u003cspan citationid=\"CR69\" class=\"CitationRef\"\u003e2018\u003c/span\u003e), QMean (Benkert et al., \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2011\u003c/span\u003e), ERRAT score (Colovos and Yeates, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e1993\u003c/span\u003e), Verify3D (L\u0026uuml;thy et al., \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e1992\u003c/span\u003e) scores and Procheck assessment (Laskowski et al., \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e1993\u003c/span\u003e). The information on the selected model for each CW003PS beta-lactamases and its quality assessment scores were summarised in (\u003cb\u003eTable S7\u003c/b\u003e). The chimera energy minimisation tool was used to perform 5000 steepest gradiant and 1000 conjugate steps energy minimisation on the selected model. Then, the models were submitted to the DALI webserver (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://ekhidna.biocenter.helsinki.fi/dali_server/\u003c/span\u003e\u003cspan address=\"http://ekhidna.biocenter.helsinki.fi/dali_server/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) to search for the most similar structures of proteins available in the Protein Data Bank (PDB) database (Holm, \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). The matched proteins with the closest structural similarity were selected based on z-scores as references for inferring ligand-binding sites for the CW003PS β-lactamases.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003e2.6 Molecular docking of beta-lactamase with antibiotics and inhibitors\u003c/h2\u003e \u003cp\u003eMolecular docking was performed to evaluate the binding affinity of \u003cem\u003eBla\u003c/em\u003e\u003csub\u003eOXA10\u003c/sub\u003e, \u003cem\u003eBla\u003c/em\u003e\u003csub\u003eVIM2\u003c/sub\u003e and \u003cem\u003eBla\u003c/em\u003e\u003csub\u003eVIM6\u003c/sub\u003e with assayed antibiotics. The simplified molecular input line entry system (SMILES) strings for each assayed antibiotic were acquired from ChEMBL online database (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.ebi.ac.uk/chembl/\u003c/span\u003e\u003cspan address=\"https://www.ebi.ac.uk/chembl/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e). The beta-lactamase inhibitors SMILEs were acquired from the ChEMBL online database. Only inhibitors demonstrated \u003cem\u003evia\u003c/em\u003e beta-lactamase inhibition \u003cem\u003evia\u003c/em\u003e binding assays were selected. A customised RDKit python script was used to build the 3D model of each antibiotic and inhibitor from their corresponding SMILEs (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.rdkit.org\u003c/span\u003e\u003cspan address=\"https://www.rdkit.org\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e). Their structures were energy-minimised for 500 steps utilising OpenBabel (version 2.4.1) (O\u0026rsquo;Boyle et al., \u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e2011\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe CB-DOCK2 (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://cadd.labshare.cn/cb-dock2/index.phpmolecular\u003c/span\u003e\u003cspan address=\"https://cadd.labshare.cn/cb-dock2/index.phpmolecular\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) docking webserver was used to infer putative ligand binding cavities of \u003cem\u003eBla\u003c/em\u003e\u003csub\u003eOXA10\u003c/sub\u003e, \u003cem\u003eBla\u003c/em\u003e\u003csub\u003eVIM2\u003c/sub\u003e and \u003cem\u003eBla\u003c/em\u003e\u003csub\u003eVIM6\u003c/sub\u003e by performing docking with ertapenem. Bound ligand poses of the CW003PS beta-lactamases were compared with that of the reference protein model acquired from DALI searches prior \u003cem\u003evia\u003c/em\u003e Chimera Matchmaker tool (Pettersen et al., \u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e2004\u003c/span\u003e). The CW003PS beta-lactamases' ligand binding cavities with overlapping region with their respective reference proteins were declared as the putative ligand binding site (Harith-Fadzilah and Alias, \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2024\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe MzDOCK molecular docking tool was used to perform high throughput docking of antibiotics and inhibitors against CW003PS beta-lactamases. The docking region was restricted to only the putative ligand binding region of each beta-lactamase inferred from CB-DOCK2. The intermolecular interaction between CW003PS beta-lactamases and the antibiotics and inhibitors were visualised using BIOVIA Discovery Studio (version 21.1.0.20298).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003e2.7 Molecular dynamics (MD) simulation and post hoc analysis of beta-lactamases\u003c/h2\u003e \u003cp\u003eThe molecular dynamics (MD) simulation was performed for the \u003cem\u003eBla\u003c/em\u003e\u003csub\u003eOXA10\u003c/sub\u003e, \u003cem\u003eBla\u003c/em\u003e\u003csub\u003eVIM2\u003c/sub\u003e and \u003cem\u003eBla\u003c/em\u003e\u003csub\u003eVIM6\u003c/sub\u003e with and antibiotics ertapenem to obtain further insights into the interaction behaviour under a polar solvation system. The CHARMMGUI webserver (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps:charmm-gui.org\u003c/span\u003e\u003cspan address=\"https:charmm-gui.org\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) was used to prepare the \u003cem\u003eBla\u003c/em\u003e\u003csub\u003eVIM6\u003c/sub\u003e-ertapenem complex for MD simulation using CHARMMGUI solution builder tool (Jo et al., \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2008\u003c/span\u003e). The system parameters were as follows: octahedral system, with 0.15 M sodium chloride solvation, Charmm36m forcefield, with NPT Ensemble dynamics input generation with the temperature set at 310 K, the WFY (tryptophan, tyrosine and phenylalanine) cation parameter and mass hydrogen repartitioning parameters were enabled. Other parameters were kept as default. The MD simulation was carried out using NAMD3 (Phillips et al., \u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). The energy minimisation of 100, 000 steps was performed on each complex. The simulation was performed for 50 ns with 0.1 ns trajectory intervals recorded, generating 500 simulation frames.\u003c/p\u003e \u003cp\u003eFrom the MD simulation trajectories recorded, the root mean square deviation (RMSD), root mean square fluctuation (RMSF), number of intermolecular hydrogen bonds formed (Inter-H), difference in solvent-accessible surface area (ΔSASA) trajectories, and the molecular mechanics / generalised Born surface area (mmGBSA) ligand free binding energy were calculated in visual molecular dynamics (VMD) (version 1.9.3) utilising our in-house script (Humphrey et al., \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e1996\u003c/span\u003e). The ΔSASA is calculated as follows:\u003c/p\u003e \u003cp\u003eΔSASA\u0026thinsp;=\u0026thinsp;SASA\u003csub\u003ecomplex\u003c/sub\u003e \u0026ndash; SASA\u003csub\u003ereceptor\u003c/sub\u003e - SASA\u003csub\u003eligand\u003c/sub\u003e\u003c/p\u003e \u003cp\u003eIn addition, the contact frequencies of the interacting beta-lactamases\u0026rsquo; residues with ertapenem were also calculated from the simulation trajectory. The contact threshold is defined as the amino acid on the receptor molecule being under 4 \u0026Aring; distance from the ligand (Humphrey et al., \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2016\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e"},{"header":"3. Result","content":"\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003e3.1 Phylogenetic tree and comprehensive genome analysis\u003c/h2\u003e \u003cp\u003ePhylogenetic reconstruction placed the isolate in close proximity to \u003cem\u003ePseudomonas wenzhouensis\u003c/em\u003e. Supporting this placement, FastANI analysis revealed a 94.23% average nucleotide identity between \u003cem\u003ePseudomonas\u003c/em\u003e CW003PS and \u003cem\u003eP. wenzhouensis\u003c/em\u003e A20, indicating that CW003PS likely represents a novel strain of \u003cem\u003eP. wenzhouensis\u003c/em\u003e (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e1\u003c/span\u003eA). The generated genome formed a complete circular genome with 15,383 reads length and a N50 length of 10,906 bp. The assembly using Flye generated the final consensus assembly that consisted of 4,523,787 bp, 62.3% G\u0026thinsp;+\u0026thinsp;C content, and 0.8x genome coverage (\u003cb\u003eFigure S1\u003c/b\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eThe genome comparison between CW003PS and \u003cem\u003eP. wenzhouensis\u003c/em\u003e A20 revealed distinct gene sets despite phylogenetically similar. The zoomed-in genome map focused on the sequences 1475 to 1480 kb pairs to identify the genes that are specific to CW003PS and related to resistance. Several ARGs, including \u003cem\u003eaadA1\u003c/em\u003e, Class D beta-lactamase (QDX81_07315), subclass B1 beta-lactamase (QDX81_07320) and \u003cem\u003eintl1\u003c/em\u003e genes were found in CW003PS strain, while absent in A20 strain (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e1\u003c/span\u003eB).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eThe result identified a total of 2,395 genes shared by both strains, representing their conserved genetic backbone, while 1,460 and 1,423 genes were unique to A20 and CW003PS, respectively (\u003cb\u003eTable S1 to S3\u003c/b\u003e). Notably, within the shared gene set, multiple multidrug efflux transporters, including the MdtD multidrug transporter subunit, and NorM family multidrug efflux MATE transporter were identified in both A20 and CW003PS (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e2\u003c/span\u003eA\u003cb\u003e)\u003c/b\u003e. In contrast, strain-specific genes in CW003PS including oxacillin-hydrolyzing class D beta-lactamase OXA-10, metallo-beta-lactamase VIM-2, and VIM-6, which are well recognized for conferring resistance to beta-lactams and carbapenems (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e2\u003c/span\u003eB). Moreover, a comparative genome analysis was performed to identify the core and strain-specific gene that distinguish between \u003cem\u003eP. wenzhouensis A20\u003c/em\u003e and \u003cem\u003ePseudomonas\u003c/em\u003e sp. CW003PS (\u003cb\u003eFigure S2)\u003c/b\u003e. To investigate the genetic basis of this resistance, the screening of ARGs was completed using ABRicate and Resfinder, showcased the presence of carbapenem resistance genes \u003cem\u003eOXA-10, VIM-6\u003c/em\u003e, and \u003cem\u003eVIM-2\u003c/em\u003e (\u003cb\u003eTable S4 and S5\u003c/b\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003e3.2 Prediction of antibiotic resistance determinants and mechanisms\u003c/h2\u003e \u003cp\u003eCarbapenem resistance in strain CW003PS was identified by AST (\u003cb\u003eTable S6\u003c/b\u003e), and WGS was subsequently performed to characterise the underlying AMR genes. The comprehensive genome analysis was then performed \u003cem\u003evia\u003c/em\u003e BV-BRC platform uncovered the ARGs and AMR mechanisms present in this CW003PS strain, as summarized in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. These findings revealed the presence of AMR genes, and mechanisms, including antibiotic activation and inactivation enzymes, altered antibiotic targets, diverse efflux pump systems, cell wall and permeability modifications, and regulatory mechanisms, which contribute to the ability of the strain to evade the actions of multiple antibiotics. KEGG database revealed the loss of multiple porin-coding genes, including \u003cem\u003eOprD, OmpF, OmpC\u003c/em\u003e,and \u003cem\u003eOmpU\u003c/em\u003e which known to play a role in the porin-mediated resistance of Class D and Class B beta-lactamases (\u003cb\u003eFigure S3\u003c/b\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\u003eAntimicrobial resistance genes and AMR mechanisms identified in CW003PS strain\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"2\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAMR Mechanism\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGenes\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAntibiotic activation enzyme\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eKatG\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAntibiotic inactivation enzyme\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eAAC(6')-Ib/AAC(6')-II, Mph(F)\u003c/em\u003e family, \u003cem\u003eOXA-10\u003c/em\u003e family, \u003cem\u003ePDC family, VIM f\u003c/em\u003eamily\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAntibiotic target in susceptible species\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eAlr, Ddl, dxr, EF-G, EF-Tu, folA, Dfr, folP, gyrA, gyrB, inhA, fabI, Iso-tRNA, kasA, MurA, rho, rpoB, rpoC, S10p, S12p\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEfflux pump conferring antibiotic resistance\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eMdtABC-OMF, MdtABC-TolC, MexAB-OprM, MexEF-OprN, MexEF-OprN\u003c/em\u003e system, \u003cem\u003eMexJK-OprM/OpmH, MexPQ-OpmE, OprM/OprM\u003c/em\u003e family, \u003cem\u003eTolC/OpmH\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGene conferring resistance via absence\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003egidB\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eProtein altering cell wall charge conferring antibiotic resistance\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eGdpD, PgsA\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eProtein modulating permeability to antibiotic\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eOccD4/OpdT, OccK6/OpdQ, OccK8/OprE, OprD\u003c/em\u003e family, \u003cem\u003eOprF\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRegulator modulating expression of antibiotic resistance genes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eOxyR\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 \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003e3.3 Beta-lactamase molecular docking with antibiotics\u003c/h2\u003e \u003cp\u003eWe performed molecular docking of \u003cem\u003eBla\u003c/em\u003e\u003csub\u003eOXA10\u003c/sub\u003e, \u003cem\u003eBla\u003c/em\u003e\u003csub\u003eVIM2\u003c/sub\u003e and \u003cem\u003eBla\u003c/em\u003e\u003csub\u003eVIM6\u003c/sub\u003e on with antibiotics reported in the databases. From DALI analysis, the all three beta-lactamases showed closest similarity to dimerised beta-lactamases. Furthermore, sequence analysis showed a very high sequence similarity between \u003cem\u003eBla\u003c/em\u003e\u003csub\u003eVIM2\u003c/sub\u003e and \u003cem\u003eBla\u003c/em\u003e\u003csub\u003eVIM6\u003c/sub\u003e, of which they had only two amino acid difference on 60th and 148th amino acid, where \u003cem\u003eBla\u003c/em\u003e\u003csub\u003eVIM2\u003c/sub\u003e had Glu60 and Asn147, whereas \u003cem\u003eBla\u003c/em\u003e\u003csub\u003eVIM6\u003c/sub\u003e had Arg60 and Ser147. Thus, \u003cem\u003eBla\u003c/em\u003e\u003csub\u003eVIM2\u003c/sub\u003e and \u003cem\u003eBla\u003c/em\u003e\u003csub\u003eVIM6\u003c/sub\u003e shared the same reference structure, (PDB ID: 7AFX). However, these two amino acid differences led to a significantly different structure with RMSD of 1.140 \u0026Aring;. The notable difference was the first 50 amino acids of each chain, whereby in the \u003cem\u003eBla\u003c/em\u003e\u003csub\u003eVIM2\u003c/sub\u003e dimer was dangled out of the core complex, whereas in \u003cem\u003eBla\u003c/em\u003e\u003csub\u003eVIM6\u003c/sub\u003e, these regions was looped closely to the other monomer structure (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e3\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe lividomycin displayed the strongest binding affinity across three beta-lactamases whereas, fosfomycin displayed the weakest binding affinity despite our strain was not resistant to both lividomycin and fosfomycin. The binding affinity of beta-lactamases with carbepenems were weaker in comparison to binding affinity to lividomycin. Among the three carbepenems tested, ertapenem displayed the strongest ligand binding affinity to all three beta-lactamases (\u003cb\u003eTable S8\u003c/b\u003e). \u003cem\u003eBla\u003c/em\u003e\u003csub\u003eVIM2\u003c/sub\u003e and \u003cem\u003eBla\u003c/em\u003e\u003csub\u003eVIM6\u003c/sub\u003e displayed quite a different ligand binding affinity against meropenem, despite both these beta-lactamases shared very high sequence similarity.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003e3.4 Molecular dynamics simulation\u003c/h2\u003e \u003cp\u003eWithin the 50 ns simulation there were significant differences among the three beta-lactamase-ertapenem complexes. The mmGBSA free binding energy of \u003cem\u003eBla\u003c/em\u003e\u003csub\u003eOXA10\u003c/sub\u003e was the largest, with \u0026minus;\u0026thinsp;30.73 kcal/mol, followed by \u003cem\u003eBla\u003c/em\u003e\u003csub\u003eVIM6\u003c/sub\u003e and \u003cem\u003eBla\u003c/em\u003e\u003csub\u003eVIM2\u003c/sub\u003e, with \u0026minus;\u0026thinsp;22.32 kcal/mol and \u0026minus;\u0026thinsp;11.49 kcal/mol respectively (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). We analysed the RMSD trajectory to evaluate the stability of the protein-complex within the water simulation system. The \u003cem\u003eBla\u003c/em\u003e\u003csub\u003eVIM6\u003c/sub\u003e had the smallest RMSD, followed by \u003cem\u003eBla\u003c/em\u003e\u003csub\u003eVIM2\u003c/sub\u003e and \u003cem\u003eBla\u003c/em\u003e\u003csub\u003eOXA10\u003c/sub\u003e with an average RMSD of 6.62 \u0026Aring;, 9.58 \u0026Aring; and 16.65 \u0026Aring; respectively. Although \u003cem\u003eBla\u003c/em\u003e\u003csub\u003eVIM6\u003c/sub\u003e had the lowest overall RMSD, suggesting highest stability, there were notable fluctuations within the first 10 ns simulation, and with some more smaller fluctuations between 25\u0026ndash;30 ns and 40\u0026ndash;45 ns (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e4\u003c/span\u003eA). \u003cem\u003eBla\u003c/em\u003e\u003csub\u003eVIM2\u003c/sub\u003e had marginally larger RMSD than \u003cem\u003eBla\u003c/em\u003e\u003csub\u003eOXA10\u003c/sub\u003e however, it had a much smaller fluctuations of its trajectory. In contrast, the \u003cem\u003eBla\u003c/em\u003e\u003csub\u003eVIM6\u003c/sub\u003e had a more consistent but larger fluctuations than Bla\u003csub\u003eOXA10\u003c/sub\u003e and \u003cem\u003eBla\u003c/em\u003e\u003csub\u003eVIM2\u003c/sub\u003e.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eMolecular dynamics (MD) simulation post hoc analysis\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eBla\u003c/em\u003e\u003csub\u003eVIM6\u003c/sub\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003eBla\u003c/em\u003e\u003csub\u003eVIM2\u003c/sub\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003eBla\u003c/em\u003e\u003csub\u003eOXA10\u003c/sub\u003e\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003emmGBSA (kcal/mol)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e\u0026ndash;22.32\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u0026ndash;11.59\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026ndash;30.73\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAverage RMSD (\u0026Aring;)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e6.62\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e9.58\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e16.65\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAverage RMSF (\u0026Aring;)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e3.37\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2.78\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e4.41\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAverage Intermolecular hydrogen bonds\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.62\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.042\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.93\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eΔSASA (\u0026Aring;\u003csup\u003e2\u003c/sup\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e\u0026ndash;904.48\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u0026ndash;574.69\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026ndash;820.21\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eThe RMSF analysis was performed to determine the regions of the protein that are flexible and rigid. Among the three complexes, the \u003cem\u003eBla\u003c/em\u003e\u003csub\u003eVIM2\u003c/sub\u003e RMSF exhibited the least fluctuations, with and 2.78 \u0026Aring; average (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e4\u003c/span\u003eB). A notable fluctuation in \u003cem\u003eBla\u003c/em\u003e\u003csub\u003eVIM2\u003c/sub\u003e was observed between 250\u0026ndash;300th amino acid residue positions. This region consisting of the terminal position of \u003cem\u003eBla\u003c/em\u003e\u003csub\u003eVIM2\u003c/sub\u003e monomers which are exposed to the solvent (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e3\u003c/span\u003eB). Several amino acid residues within this fluctuating region interacted with ertapenem ligand during the MD simulation, specifically within the PROA chain (\u003cb\u003eTable S9\u003c/b\u003e). No PROB amino acid residues from the high fluctuation region had interaction with ertapenem. Notable residues among them were His259 (13.74% frequency), Arg262 (16.61% frequency), Ser263 (15.02% frequency), and Val264 (10.86% frequency) for having interacted with ertapenem with notably higher frequency compared to the rest of the amino acid within the 250\u0026ndash;300th residue region.\u003c/p\u003e \u003cp\u003e \u003cem\u003eBla\u003c/em\u003e \u003csub\u003eVIM6\u003c/sub\u003e complex had a notable fluctuation within approximately 400\u0026ndash;430th amino acid region which belonged exclusively on the PROB chain from Tyr134 to Ala164 (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e4\u003c/span\u003eB; Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e3\u003c/span\u003eC\u003cb\u003e).\u003c/b\u003e This high RMSF region is located on the external region of the complex, having more interaction with the system solution, rather than within the interior dimer cavity. Expectedly contact frequency analysis revealed none of these residues had any interaction with ertapenem ligand (\u003cb\u003eTable S9\u003c/b\u003e).\u003c/p\u003e \u003cp\u003e \u003cem\u003eBla\u003c/em\u003e \u003csub\u003eOXA10\u003c/sub\u003e showed a significant fluctuation on the first 10 and between 267\u0026ndash;281st amino acid, which consisted of terminal regions of the dimer which are exposed to the solvent (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e4\u003c/span\u003eB; Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e3\u003c/span\u003eD). None of these high RMSF residues had any interaction with ertapenem ligand as well (\u003cb\u003eTable S9\u003c/b\u003e).\u003c/p\u003e \u003cp\u003e \u003cem\u003eBla\u003c/em\u003e \u003csub\u003eVIM2\u003c/sub\u003e complex formed the least number of intermolecular hydrogen bonds, with average 1.04 bonds throughout the simulation period (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e; Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e4\u003c/span\u003eC). These hydrogen bonds were formed on Arg60, Tyr67, Asp118, His240, Tyr201, Arg205, Ser207 and Asn210 (\u003cb\u003eTable S10\u003c/b\u003e). \u003cem\u003eBla\u003c/em\u003e\u003csub\u003eVIM6\u003c/sub\u003e has a marginally higher number of hydrogen bonds, with an average of 1.62 bonds, mediated by Asp63, tyr67, trp87, his116, asp117, gly209, asn210, his240, asp213, glu146, his179, arg205, arg262. \u003cem\u003eBla\u003c/em\u003e\u003csub\u003eOXA10\u003c/sub\u003e had the highest average of 1.934 bonds, but were mediated by the least number of amino acids among the three beta-lactamases: Ser67, arg104, gln101, ser115, gln113, lys205, thr206, phe208, ser209, glu199, arg250.\u003c/p\u003e \u003cp\u003eThe ΔSASA is the change in the SASA when the protein binds to a ligand or another protein to form a complex. A bigger difference indicates the ligand is buried deep into the protein, indicative of a strong binding \u003cem\u003eBla\u003c/em\u003e\u003csub\u003eVIM2\u003c/sub\u003e had a much smaller ΔSASA, with average of \u0026minus;\u0026thinsp;574.69 \u0026Aring;\u003csup\u003e2\u003c/sup\u003e (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). \u003cem\u003eBla\u003c/em\u003e\u003csub\u003eVIM6\u003c/sub\u003e had the largest of all three beta-lactamases, with average ΔSASA, \u0026minus;\u0026thinsp;904.48 \u0026Aring;\u003csup\u003e2\u003c/sup\u003e, whereas \u003cem\u003eBla\u003c/em\u003e\u003csub\u003eOXA10\u003c/sub\u003e had an average of \u0026minus;\u0026thinsp;820.21 \u0026Aring;\u003csup\u003e2\u003c/sup\u003e indicating that this complex has the most deeply buried ligand. In addition, \u003cem\u003eBla\u003c/em\u003e\u003csub\u003eVIM6\u003c/sub\u003e had the most stable ΔSASA trajectory with the least fluctuations among the three beta-lactamases.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003e3.5 Beta-lactamase molecular docking with putative inhibitors\u003c/h2\u003e \u003cp\u003e1356 beta-lactamase inhibitor entries were mined from CHEMBL database with binding assay data. After removing duplicates, we have narrowed down the number of inhibitors to 818. 52 of the compounds fail to produce 3D structures suitable for molecular docking, leaving 767 the analysis. Beta-lactamase inhibitor for this novel \u003cem\u003eBla\u003c/em\u003e\u003csub\u003eOXA10\u003c/sub\u003e, \u003cem\u003eBla\u003c/em\u003e\u003csub\u003eVIM2\u003c/sub\u003e and \u003cem\u003eBla\u003c/em\u003e\u003csub\u003eVIM6\u003c/sub\u003e was screened utilising molecular docking tools. Some differences of beta-lactamase inhibitor efficacy were observed.\u003c/p\u003e \u003cp\u003eThe highest inhibitor binding affinity for \u003cem\u003eBla\u003c/em\u003e\u003csub\u003eOXA10\u003c/sub\u003e was CHEMBL1554784 (6-(4-benzylpiperidin-1-yl)-9,10,10-trioxo-N-[[3-(trifluoromethyl)phenyl]methyl]thioxanthene-1-carboxamide) with \u0026minus;\u0026thinsp;10.4 kcal/mol (\u003cb\u003eTable S11\u003c/b\u003e). The other carboxamides sharing the same 4-benzylpiperidin-1-yl piperidine core (CHEMBL1436864, CHEMBL1726287, CHEMBL1434843) shared the second strongest binding affinity to \u003cem\u003eBla\u003c/em\u003e\u003csub\u003eOXA10\u003c/sub\u003e, with \u0026minus;\u0026thinsp;10 kcal/mol (\u003cb\u003eTable S12\u003c/b\u003e). \u003cem\u003eBla\u003c/em\u003e\u003csub\u003eVIM2\u003c/sub\u003e also had a 4-benzylpiperidin-1-yl piperidine core carboxamide, CHEMBL1436864 (6-(4-benzylpiperidin-1-yl)-N-[(3-fluorophenyl)methyl]-9,10,10-trioxothioxanthene-1-carboxamide) as the strongest binding beta-lactamase inhibitor, with \u0026minus;\u0026thinsp;14.2 kcal/mol. CHEMBL1554784 which had the strongest binding affinity to \u003cem\u003eBla\u003c/em\u003e\u003csub\u003eOXA10\u003c/sub\u003e, was the second strongest ligand bound to \u003cem\u003eBla\u003c/em\u003e\u003csub\u003eVIM2\u003c/sub\u003e, with \u0026minus;\u0026thinsp;13.1 kcal/mol. However, following these two carboxamides were vastly different compounds with different pharmacophores. CHEMBL1293246 has amines and sulfone, CHEMBL1293246 possesses coumarin lactone and sulfonamide, and CHEMBL1293244 possesses tetrazole and biphenyl structures. \u003cem\u003eBla\u003c/em\u003e\u003csub\u003eVIM6\u003c/sub\u003e showed highest affinity with CHEMBL1293246 (9,10,10-trioxo-6-(4-piperidin-1-ylpiperidin-1-yl)-N-[[2-(trifluoromethyl)phenyl]methyl]thioxanthene-1-carboxamide). This inhibitor is also a with 4-benzylpiperidin-1-yl piperidine core.\u003c/p\u003e \u003c/div\u003e"},{"header":"4. Discussion","content":"\u003cp\u003eCarbapenem-resistant gram-negative bacteria have emerged as a critical global health threat since the late 1990s (De Pascale et al., \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2025\u003c/span\u003e; European Centre for Disease Prevention and Control, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2025\u003c/span\u003e; Tesalona et al., \u003cspan citationid=\"CR65\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). The rapid dissemination of carbapenems across region and species has severely compromised the efficacy of existing antibiotics, leaving clinicians with severely limited therapeutic options. Hospitals are hotspots for antibiotic-resistant bacteria (ARB) because of heavy antibiotic use. When leftover drugs and ARBs enter the waste stream, they help these bacteria survive and share resistance genes with others in the waste and environment.\u003c/p\u003e \u003cp\u003eIn this study, \u003cem\u003ePseudomonas\u003c/em\u003e sp. CW003PS was isolated from healthcare waste. Our analysis revealed that CW003PS is closely related to the newly identified \u003cem\u003ePseudomonas wenzhouensis\u003c/em\u003e A20 strain, sharing 94.23% of its genetic makeup with 0.8x genome coverage. \u003cem\u003eP. wenzhouensis\u003c/em\u003e is a newly classified \u003cem\u003ePseudomonas\u003c/em\u003e species isolated from sewage discharged from animal farm in China. This strain is identified with novel AmpC beta-lactamase encoding gene, \u003cem\u003eBla\u003c/em\u003e\u003csub\u003ePRC\u0026ndash;1\u003c/sub\u003e, which is associated with to many beta-lactam antibiotics (Zhang et al., \u003cspan citationid=\"CR73\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). However, unlike the \u003cem\u003eP. wenzhouensis\u003c/em\u003e strain, CW003PS has acquired a distinct set of genes. These include the oxacillin-hydrolyzing class D beta-lactamase \u003cem\u003eOXA-10\u003c/em\u003e, the subclass B1 metallo-beta-lactamase \u003cem\u003eVIM-2\u003c/em\u003e, and \u003cem\u003eVIM-6\u003c/em\u003e that confer resistance to aminoglycoside, beta-lactam and carbapenem antibiotics, respectively (Alonso-Garc\u0026iacute;a et al., \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Mei et al., \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Oelschlaeger et al., \u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Given the scarce study reported on \u003cem\u003eP. wenzhouensis\u003c/em\u003e and the variation of this species remain unexplored, we sequenced the CW003PS genome, screened for antimicrobial determinants and performed molecular simulations to understand the molecular interaction between antibiotics and enzymes responsible for its resistance properties.\u003c/p\u003e \u003cp\u003eThe CW003PS strain exhibited resistance primarily in beta-lactam antibiotics, hence we postulated the beta-lactamase could be the primary arsenal for the strain\u0026rsquo;s antimicrobial resistance. \u003cem\u003eBla\u003c/em\u003e\u003csub\u003eOXA10\u003c/sub\u003e is a member of class D beta-lactamases, which considered as narrow spectrum enzyme that hydrolyzes penicillin and some older generation of cephalosporins, with weak carbapenem-hydrolyzing activity (Gill et al., \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). The contribution of \u003cem\u003eBla\u003c/em\u003e\u003csub\u003eOXA10\u003c/sub\u003e in carbapenem resistance is significant when combined with porin deficiency (Alonso-Garc\u0026iacute;a et al., \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). \u003cem\u003eBla\u003c/em\u003e\u003csub\u003eVIM\u003c/sub\u003e are more potent Class B metallo-beta-lactamase, which incorporates zinc ions to effectively hydrolyze and inactivate the beta-lactam ring, conferring resistance to penicillins, cephalosporins, and carbapenems (AlBahrani et al., \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Boyd et al., \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). The genes for VIM enzymes are often located on mobile genetic elements, enable them to spread rapidly between different species of bacteria (Caliskan-Aydogan and Alocilja, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). While OXA-10 resistance represents a significant clinical challenge, VIM enzymes, including VIM-2 and VIM-6, directly target and inactivate carbapenems, representing a more advanced and life-threatening form of resistance.\u003c/p\u003e \u003cp\u003eIn addition to beta-lactamases, our analysis identified a loss or severe reduction of several porins in the metabolic pathway, including \u003cem\u003eOprD, OmpF, OmpC, OmpU\u003c/em\u003e. Porins serves as channels in the outer membrane for diffusion of nutrients and antimicrobial drugs into the cell, and their loss can greatly influence the sensitivity of microorganisms to the antibiotics (Zhou et al., \u003cspan citationid=\"CR74\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). The outer membrane proteins (OMP) \u003cem\u003eOmpF\u003c/em\u003e, and \u003cem\u003eOmpC\u003c/em\u003e facilitates the entry and resistance of beta-lactam antibiotics (Kim et al., \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Zhou et al., \u003cspan citationid=\"CR74\" class=\"CitationRef\"\u003e2023\u003c/span\u003e), while the loss of \u003cem\u003eOprD\u003c/em\u003e can reduce the uptake of carbapenem (Wang et al., \u003cspan citationid=\"CR68\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). Furthermore, CW003PS strain possess multiple efflux pump that actively remove toxic compounds, including antibiotics from the cell, contribute to a broader range of multidrug resistance phenotype. It is highly probable that this CW003PS isolate has the remarkable ability to evolve and adapt, as a result of thriving in the selective pressures of the healthcare environment. The acquisition of these resistance determinants likely occurred within the healthcare setting where the bacteria were exposed to various antibiotics, leading to gene mutations and the opportunity to exchange genetic material with other resistant microorganisms.\u003c/p\u003e \u003cp\u003eBased on our sequence analysis, the beta-lactamases, \u003cem\u003eBla\u003c/em\u003e\u003csub\u003eOXA10\u003c/sub\u003e, \u003cem\u003eBla\u003c/em\u003e\u003csub\u003eVIM2\u003c/sub\u003e and \u003cem\u003eBla\u003c/em\u003e\u003csub\u003eVIM6\u003c/sub\u003e were postulated as key resistance-associated determinants within the genomic context of this isolate. \u003cem\u003eBla\u003c/em\u003e\u003csub\u003eVIM2\u003c/sub\u003e was first isolated from France (Poirel et al., \u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e2000\u003c/span\u003e), a year after the first identification of VIM gene from \u003cem\u003eP. aeruginosa\u003c/em\u003e in Italy (Lauretti et al., \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e1999\u003c/span\u003e). \u003cem\u003eBla\u003c/em\u003e\u003csub\u003eVIM6\u003c/sub\u003e was first identified in Singapore with reported difference from \u003cem\u003eBla\u003c/em\u003e\u003csub\u003eVIM2\u003c/sub\u003e at amino acids 59 (Gln to Arg) and 165 (Asn to Ser) (Walsh et al., \u003cspan citationid=\"CR67\" class=\"CitationRef\"\u003e2005\u003c/span\u003e). The MD simulation data showed high mutational sensitivity of the beta-lactamases. In our study, \u003cem\u003eBla\u003c/em\u003e\u003csub\u003eVIM2\u003c/sub\u003e and \u003cem\u003eBla\u003c/em\u003e\u003csub\u003eVIM6\u003c/sub\u003e, which only had two amino acid differences, displayed a notable difference conformation of the first 40 amino acids and to ertapenem binding characteristics. \u003cem\u003eBla\u003c/em\u003e\u003csub\u003eVIM2\u003c/sub\u003e demonstrated a more stable complexes with ertapenem, as indicated by minimal RMSD and RMSF fluctuations and a larger mmGBSA estimations than \u003cem\u003eBla\u003c/em\u003e\u003csub\u003eVIM6\u003c/sub\u003e counterpart. On the other hand, \u003cem\u003eBla\u003c/em\u003e\u003csub\u003eOXA10\u003c/sub\u003e exhibited high RMSD and RMSF fluctuations, attributed primarily to the dangling terminal region of the dimer. The exposed terminal region may confer reduced stability to the dimer structure and thus, a less stable interaction with ertapenem. However, \u003cem\u003eBla\u003c/em\u003e\u003csub\u003eOXA10\u003c/sub\u003e also has a high mutational sensitivity as a previous research reported that only two amino acids changes to \u003cem\u003eBla\u003c/em\u003e\u003csub\u003eOXA10\u003c/sub\u003e could enhance resistance against cephalosporins, of which \u003cem\u003eBla\u003c/em\u003e\u003csub\u003eOXA10\u003c/sub\u003e was originally had weak activity on (Evans and Amyes, \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2014\u003c/span\u003e). Across all three enzymes, the formation of intermolecular hydrogen bonds appeared to be a less significant contributor to ligand binding, with non-polar interactions within the large binding cavities likely function as the primary driver. This observation may inform future structure-guided studies aimed at understanding β-lactamase\u0026ndash;ligand interactions.\u003c/p\u003e \u003cp\u003eFrom docking analysis, carboxamide-containing compounds appear to have generally high binding affinity to all three beta-lactamases. Several examples of these compounds such as relebactam and avibactam have been used as antibiotic adjuvants (Heo, \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Matesanz and Mensa, 2021). Relebactam and avibactam may be examined in subsequent studies incorporating biochemical or microbiological validation. Furthermore, novel CHEMBL1554784, CHEMBL1436864 and CHEMBL1293246 compounds may be of our interest to explore as beta-lactamase inhibitors against \u003cem\u003eP. aeruginosa\u003c/em\u003e and other Gram-negative bacteria.\u003c/p\u003e \u003cp\u003eWhile our current analysis provide a genotypic resistance profile of \u003cem\u003ePseudomonas\u003c/em\u003e CW003PS, the AST result represent the phenotypic resistance from our previous study, was partially consistent with these findings (Siew et al., \u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). The AST results showed that the CW003PS susceptible to cefepime but resistance to ceftazidime/avibactam. In contrast, ResFinder analysis indicated resistance to cefepime but susceptible to ceftazidime/avibactam. The molecular docking analysis supported resistance to ceftazidime and cefepime due to the presence of \u003cem\u003eBla\u003c/em\u003e\u003csub\u003eOXA10\u003c/sub\u003e. Despite both ResFinder and molecular docking demonstrating resistance to ertapenem, the AST did not test for this antibiotic, hence the phenotypic result remains unknown.\u003c/p\u003e \u003cp\u003eThe discrepancies between genotypic and phenotypic resistance may be influenced by mutations in regulatory mechanisms that do not always exhibit phenotypic resistance, or the silent mutations in resistance genes that alter the resistance mechanisms but does not captured by the genotypic analysis (Mou et al., \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Silva and Khare, \u003cspan citationid=\"CR61\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). The differences observed underscore the importance of multifaceted approach that combines both genotypic and phenotypic data for comprehensive assessment of antimicrobial resistance. While genotypic information reveals the structural features for resistance and inhibition activities, phenotypic testing demonstrates the actual effectiveness of an antimicrobial agent against a specific microorganism to ensure the right antimicrobial agents is prescribed at the correct dose for the right microorganisms, therefore ensuring effective therapeutic outcome.\u003c/p\u003e \u003cp\u003eWhile whole-genome sequencing provides a robust framework for predicting antimicrobial resistance potential, genotypic predictions do not always translate directly into phenotypic resistance. Similarly, molecular docking and dynamics simulations offer structural insights but cannot substitute for biochemical or microbiological validation of enzyme activity or inhibition. The observed discrepancies between genotypic predictions and available phenotypic data highlight the limitations of resistance inference based solely on gene presence. These inconsistencies underscore the value of structure-informed computational analyses in exploring potential resistance architectures that are not directly captured by standard susceptibility testing.\u003c/p\u003e"},{"header":"5. Conclusion","content":"\u003cp\u003eOur data suggest environmental isolates may act as reservoirs of carbapenemases and offer structural reference models for hypothesis-driven studies of enzyme\u0026ndash;ligand interactions. This study provides a comprehensive insight into the carbapenem resistance properties of \u003cem\u003ePseudomonas\u003c/em\u003e CW003PS, an isolate from the microwave treated healthcare waste, by combining the whole-genome sequence study with molecular docking analysis. Above findings suggest that there is no fundamental limitation in exploring the antimicrobial resistance genes in bacterial isolate and understanding the interaction between drugs and targeted enzyme, as well as identify structural features associated with predicted β-lactamase\u0026ndash;ligand interactions. However, discrepancies observed between genotypic and phenotypic resistance profiles of CW003PS highlighted the complexity of antimicrobial resistance. This underscores the importance of combined strategy for monitoring of resistance evolution in microorganisms particularly in high-risk environments like healthcare settings and inform future studies aimed at understanding antimicrobial resistance mechanisms. Hence, we recommend further laboratory and field study to incorporate this dual strategy for monitoring the resistance evolution and understand the dissemination patterns of antimicrobial resistance in a broader perspective. Additionally, future experiments can focus on experimental validation of identified inhibitors to bridge the gap from simulation to a tangible therapeutic solution.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eData Availability\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe whole genome sequencing data of \u003cem\u003ePseudomonas\u003c/em\u003e CW003PS was accessible in the NCBI database under the BioProject accession number PRJNA955726, BioSample accession number SAMN34187649 with Sequence Read Archive (SRA) accessions SRR24173875. The assembled genome annotated with NCBI PGAP was deposited under GenBank accession number CP123623 and assembly accession number GCA_029873275.1. All additional input files are provided in supplementary file.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis work was supported by Universiti Malaysia Pahang Al-Sultan Abdullah, Malaysia for the Made-in-UMP Grant (PDU223001-1) and the Postgraduate Research Grant Scheme (PGRS); PGRS220382.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor information\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors and Affiliations\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eB-Crobes Laboratory Sdn. Bhd, 18 \u0026amp; 20, Lintasan Perajurit 17`G, Taman Teknologi Industri \u0026amp; Perusahaan Ipoh, 31400 Ipoh, Perak, Malaysia\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eShing Wei Siew\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eFaculty of Industrial Sciences and Technology, Universiti Malaysia Pahang Al-Sultan Abdullah, Lebuh Persiaran Tun Khalil Yaakob, 26300, Gambang, Pahang, Malaysia\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eNazmi Harith-Fadzilah, Miah Roney, Mohd Fadhlizil Fasihi Mohd Aluwi, Hajar Fauzan Ahmad\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eThe Microbiome Lab (TML), Universiti Malaysia Pahang Al-Sultan Abdullah, Lebuh Persiaran Tun Khalil Yaakob, 26300, Gambang, Pahang, Malaysia\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eShing Wei Siew, Hajar Fauzan Ahmad\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eCentre for Artificial Intelligence and Data Science (CAIDaS), Universiti Malaysia Pahang Al-Sultan Abdullah, Lebuh Persiaran Tun Khalil Yaakob, 26300, Gambang, Pahang, Malaysia\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eNazmi Harith-Fadzilah, Hajar Fauzan Ahmad\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eContributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eShing Wei Siew:\u003c/strong\u003e writing – original draft, writing – review \u0026amp; editing, investigation, methodology, formal analysis, visualization, data curation. \u003cstrong\u003eNazmi Harith-Fadzilah\u003c/strong\u003e: conceptualization, writing – original draft, writing – review \u0026amp; editing, software, visualization, validation, data curation. \u003cstrong\u003eMiah Roney:\u0026nbsp;\u003c/strong\u003econceptualization, writing – review \u0026amp; editing. \u003cstrong\u003eMohd Fadhlizil Fasihi Mohd Aluwi:\u0026nbsp;\u003c/strong\u003econceptualization, writing – review \u0026amp; editing. \u003cstrong\u003eHajar Fauzan Ahmad:\u003c/strong\u003e conceptualization, supervision, writing – original draft, writing – review \u0026amp; editing, resources, project administration, methodology, validation, funding acquisition\u003c/p\u003e\n\u003cp\u003eAll authors have given approval to the final version of the manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCorresponding author\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eCorrespondence to Hajar Fauzan Ahmad.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics declarations\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ePatient consent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eClinical trial number\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eAhmed SK, Hussein S, Qurbani K, Ibrahim RH, Fareeq A, Mahmood KA, Mohamed MG. 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Microorganisms. 2023;11:1690. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.3390/microorganisms1107169\u003c/span\u003e\u003cspan address=\"10.3390/microorganisms1107169\" 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":"whole genome sequencing, antimicrobial resistance, carbapenem, molecular docking, molecular dynamics simulation, beta-lactams, inhibitor","lastPublishedDoi":"10.21203/rs.3.rs-8745308/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8745308/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThe emergence of carbapenem-resistant bacteria in environmental reservoirs represents a growing public health concern, particularly in settings associated with healthcare waste. In this study, we reported the genomic and structural characterisation of a carbapenem-resistant \u003cem\u003ePseudomonas\u003c/em\u003e isolate (CW003PS) recovered from microwave-treated healthcare waste. Whole-genome sequencing and comparative genomic analyses revealed that CW003PS is closely related to \u003cem\u003ePseudomonas wenzhouensis\u003c/em\u003e but harbors a distinct antimicrobial resistance gene repertoire, including class D β-lactamase OXA-10 and metallo-β-lactamases VIM-2 and VIM-6, alongside multiple efflux systems and porin-associated alterations. VIM and OXA enzymes displayed significant binding affinity to ertapenems, an interaction not previously characterized in this species. To explore structural features associated with carbapenem resistance, protein structure modeling, molecular docking, and molecular dynamics simulations were applied to key β-lactamases identified in the genome. These analyses revealed differential structural conformations and binding behaviors with carbapenem antibiotics, revealing sequence-dependent structural and dynamic variability in enzyme\u0026ndash;ligand interactions, providing testable hypotheses for future functional validation. While these computational analyses do not establish enzymatic activity, they provide structural hypotheses that complement genomic predictions and highlight features that may contribute to resistance phenotypes. Overall, this study integrates environmental genomics with \u003cem\u003ein silico\u003c/em\u003e structural analysis to provide insights into the antimicrobial resistance architecture of a healthcare waste-associated \u003cem\u003ePseudomonas\u003c/em\u003e strain. The findings underscore the role of environmental reservoirs in disseminating carbapenemase-encoding bacteria and establish a framework for future experimental validation of resistance mechanisms.\u003c/p\u003e","manuscriptTitle":"Integrative Genomic and in silico Structural Analysis of Carbapenemase in Pseudomonas for Environmental Surveillance","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-02-27 21:43:53","doi":"10.21203/rs.3.rs-8745308/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":"08c8f63b-e2f5-4313-aa70-9ecc87b3a539","owner":[],"postedDate":"February 27th, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2026-04-21T12:28:07+00:00","versionOfRecord":[],"versionCreatedAt":"2026-02-27 21:43:53","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8745308","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8745308","identity":"rs-8745308","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}