Abstract
The rise of biocide- and antibiotic-resistant microbes in hospital settings demands urgent and innovative strategies to curb the spread of antimicrobial resistance (AMR). This study presents an innovative disinfectant strategy that leverages antimicrobial peptides (APep) extracted from antimicrobial-producing (AMP) strains isolated from hospital environments under intense antibiotic pressure. Unlike traditional probiotic disinfectants that rely on live bacterial strains prone to AMR, this approach directly utilizes APep from non-resistant strains with a high antioxidant potential to combat AMR and oxidative stress. Candidate strains were selected based on antimicrobial susceptibility profiling, oxidative stress assays, and screening for antimicrobial activity against hospital-acquired pathogens. The active compounds were characterized using fourier-transform infrared (FTIR) spectroscopy, high-performance liquid chromatography (HPLC), liquid chromatography–tandem mass spectrometry (LC-MS/MS), and whole-genome sequencing. Biosynthetic pathways were explored using in silico analyses, including biosynthetic gene clusters (BGCs) mapping, APep prediction, and gene interaction network analyses. Bacillus paralicheniformis UAB33 was identified as a potent producer of bacitracin B1 (UB1), exhibiting strong activity against biocides and vancomycin-resistant Staphylococcus aureus (VRSA). Genomic analysis revealed 14 BGCs, including key non-ribosomal peptides (NRPs) such as bacitracin, lichenysin, and bacillibactin. A unique pathway involving bacA, bacB, and bacC genes, along with adjacent post-translationally modified peptides (RiPP) clusters, was elucidated to enhance bacitracin synthesis. UB1-infused disinfectant wipes significantly reduce microbial loads on hospital surfaces in vitro, demonstrating a promising strategy for mitigating resistant pathogens. This strategy presents a promising approach for curbing the spread of antibiotic resistance in healthcare settings and offers a scalable and innovative solution for infection control.
Similar content being viewed by others
Introduction
Hospital environments are critical epicenters for the spread of pathogens, driving hospital-acquired infections (HAIs) and contributing to significant global morbidity, mortality, and economic burden1. In resource-limited settings, such as Pakistan, insufficient infection control measures and surveillance systems exacerbate these challenges2,3,4. The emergence of vancomycin-resistant Staphylococcus aureus (VRSA) underscores the urgent need for innovative antimicrobial strategies5.
Antimicrobial resistance (AMR) in hospital settings is further compounded by excessive chemical disinfectant use, which generates reactive oxygen species (ROS) and promotes oxidative stress and adaptive survival mechanisms in pathogens6,7. Although probiotic-based cleaning systems have shown potential,their clinical application is limited by the risk of acquiring resistance genes, necessitating the development of safer alternatives8.
To address these gaps, disinfection strategies must integrate antimicrobial and antioxidant mechanisms to effectively counteract AMR and oxidative stress. In this study, we developed a novel antimicrobial peptide (APep)-based biodisinfectant derived from antimicrobial-producing (AMP) strains isolated from hospital environments. Unlike strains sourced from food or soil, hospital-derived strains are uniquely adapted to withstand AMR pressure, offering a valuable reservoir of antimicrobial and antioxidant potential9. By selecting strains devoid of resistance genes, we ensured efficacy without amplifying the risk of resistance, while leveraging their antioxidant properties to mitigate oxidative stress10,11.This dual-target approach may support global sustainability by prioritizing eco-friendly and highly effective bio-based solutions. By harnessing bacteria and their natural biochemical processes, compounds can be generated that can replace traditional chemical products, reduce toxicity, and minimize environmental impacts12.
Using advanced genomics and bioinformatics, we identified and characterized APep and its biosynthetic pathways in these strains. Experimental validation confirmed the potent antimicrobial and antioxidant activities incorporated into bio-based disinfectant wipes tailored for hospital use. To our knowledge, no similar bio-based disinfectant wipes utilizing APep from hospital-adapted strains have been previously reported. Although probiotic-based disinfectants exist, their limitations in clinical settings highlight the novelty and significance of our APep-based wipes as a safer, resistance-free, and highly effective alternative.
These wipes demonstrated significant efficacy in time-dependent assays against biocide-resistant VRSA and other HAI pathogens, with a reduced ecological footprint compared with conventional methods. The integration of APep-based biodisinfectants represents a paradigm shift in infection control, addressing AMR and oxidative stress while minimizing their environmental impact. By focusing on hospital-adapted strains with robust antimicrobial and antioxidant capabilities, this study bridges the gap between innovation and sustainability, providing scalable solutions for infection control in clinical settings. This approach paves the way for safer and more effective disinfection strategies, thereby advancing the frontiers of hospital hygiene.
Materials and methods
Study design
From July 2020 to July 2021, weekly samples were collected from high-touch surfaces in the surgical ward (SW) and intensive care unit (ICU) of a 202-bed cardiac care hospital in Faisalabad, Punjab, Pakistan. Environmental surfaces in a centrally ventilated ward housing bedridden patients were systematically sampled. The sampled surfaces were classified into structural, machine, and miscellaneous categories. Dust and soil samples were collected from standardized 10 cm² and 5 cm² areas to isolate AMP strains13. Sampling protocols, including timing and area selection, were consistently followed throughout the study (Fig. 1).
Schematic representation of the study design. Panel (A): Spatial data for the study area and detailed methodology were generated using RStudio (v. 4.4.2) and BioRender (www.BioRender.com). Panel (B): Frequently touched surfaces from three categories (structures, machines, and miscellaneous) in both the SW and ICU were sampled to identify AMP strains for downstream applications. Key: (A) Map of the Administrative Area of Pakistan (country level), (B) Map of the Administrative Area of Punjab (province level), (C) Map of the Administrative Area of Faisalabad (district level). Abbreviations: Antimicrobial producing (AMP), Surgical ward (SW), Intensive care unit (ICU), Elevator (Ele), Biometric machine (BM), Electrocardiogram (ECG), Cardiac monitor (CM), Infusion pump (IP), Spirometer SP), Pead’s cardiac monitor (PCM), Anesthesia machine (AM), Permanent pacemaker (PPM), D fibrillation machine (DFiB), Pead’s infusion pump (PIP), Nebulizer (NB), Pacemaker (PM), Nursing counter (NC), Pharmacy counter (PC), Pead’s table (PT). Note: The map lines delineate the study areas and do not necessarily depict the accepted national boundaries.
Tester strains
Previously collected and identified hospital-borne strains from the study wards were used as test strains. Comprehensive details of these strains are provided in (Supplementary Table 1).
Isolation and characterization of AMP strains
To identify AMP strains from the SW and ICU, soil or dust samples were screened for antimicrobial production by sprinkling them onto nutrient agar plates (Oxoid, Basingstoke, Hampshire, UK) seeded with test organisms14. After incubation at 37 °C for 24 h, antimicrobial activity was observed in the zones of inhibition (mm) around the colonies. Colonies with inhibition zones were isolated and purified for further analyses. Morphological identification involved Gram staining (Oxoid, Basingstoke, Hampshire, UK), while biochemical tests (ThermoFisher Scientific, Heysham, UK) and molecular identification were performed by amplifying and sequencing a 1.5 kb segment of the 16s rRNA gene using specific oligonucleotide primers (Supplementary Table 2 A). Sequences from the 1st base (www.base-asia.com) were compared with the National Center for Biotechnology Information (NCBI) database, and phylogenetic analysis was conducted using MEGA X15, with Bacillus coagulans (ATCC 7050) as a positive control16.
Antimicrobial production kinetics
To evaluate antimicrobial production, AMP strains were cultured in nutrient broth (Oxoid, UK) at 37 °C with continuous shaking at 200 rpm. Antimicrobial activity was monitored by collecting culture supernatants hourly for 12–16 h. The supernatant (1.0 ml) was centrifuged at 9,000 × g for 20 min at 4 °C, followed by filtration through a 0.22 μm sterile cellulose membrane (Merck Millipore, USA)17. The resulting cell-free supernatant (CFS; 100 µL) was assessed for antimicrobial activity against selected tester strains using the agar well diffusion assay18.
Antibiogram assay
The resistance profiles of the AMP strains were evaluated to identify candidate strains devoid of antibiotic resistance genes (ARGs) for downstream application. Antibiotic susceptibility testing was performed using the disk diffusion method, following CLSI guidelines19. Strains exhibiting phenotypic antimicrobial resistance were screened for ARGs using singleplex PCR with specific oligonucleotide primers and reaction conditions (Supplementary Table 2B). The PCR products were subjected to electrophoresis, stained with ethidium bromide (10 µM), and visualized under UV transillumination using a Gel Doc EZ imager (Bio-Rad Laboratories, Hercules, California, USA)5.
Oxidative stress analysis
To identify candidate strains with the highest antioxidant levels for downstream applications, the total redox and antioxidant potentials of the AMP strains were evaluated using commercial colorimetric kits (Abcam, Cambridge, UK), according to the manufacturer’s protocols. The assays included Superoxide Dismutase (SOD; ab65354), catalase (ab83464), malondialdehyde (MDA; ab118970), and hydrogen peroxide (H2O2; ab102500).
Extraction and purification of antimicrobial compound
Candidate AMP strains were cultured in Luria Broth (LB) (Oxoid, Basingstoke Hampshire, UK) under aerobic conditions to purify and detect antimicrobial compounds. After incubation, the culture medium was centrifuged at 10,000 rpm for 15 min at 4 °C and filtered through a 0.2 μm sterile nitrocellulose membrane (Whatman, Dassel, Germany)20. The resulting filtrate was combined with n-butanol (Millipore Sigma, Missouri, USA) at a 2:1 volume ratio, vigorously shaken, and allowed to separate. The supernatant was concentrated under reduced pressure using a vacuum rotary pump at 40 °C (R-114; Buchi, Switzerland) until complete solvent evaporation.
Thin-layer chromatography (TLC) was used to analyze and purify the antimicrobial compounds extracted. The samples were applied to silica gel TLC plates (60 F254, 0.25 mm) (Merck KGaA, Darmstadt, Germany) using spotting tubes placed 1.5–2 cm above the base of the plate. The plates were developed in a chromatography jar containing an 85:15 volume/volume (v/v) mixture of chloroform and methanol (Millipore Sigma, Billerica, MA, USA). Following solvent migration, the plates were dried and visualized under 254 nm ultraviolet light (CAMAG 254 nm; Muttenz, Switzerland) to determine the Rf values of the separated compounds. The active antimicrobial compound was isolated using thin-layer chromatography (TLC)21.
Nature of antimicrobial substances
To investigate the chemical nature of the inhibitory substances secreted by the candidate AMP strains, the CFS was subjected to several tests, including surfactant production, organic acids, proteins, and heat sensitivity, as described previously. The antibacterial activities of the treated and untreated CFSs (8% v/v) were assessed. All experiments were performed in triplicates22.
Detection and characterization of antimicrobial compounds using FTIR spectroscopy, HPLC, and LC-MS/MS
Fourier-transform infrared (FTIR) spectroscopy was used to analyze the chemical structures and functional groups of the antimicrobial compounds in candidate AMP strains. The analysis was conducted using a Thermo Nicolet Nexus 670 spectrometer (Thermo Scientific, Waltham, MA, USA), scanning across a range of 4000–400 cm⁻¹ with a resolution of 4 cm⁻¹. Potassium bromide (KBr) served as the beam splitter, whereas deuterated triglycine sulfate was used with potassium bromide (DTGS KB)21.
High-performance liquid chromatography (HPLC) was used to identify the bioactive antimicrobial compounds. A sample (1 mg) was dissolved in 1 mL of 10% acetonitrile containing 0.1% trifluoroacetic acid (TFA) (v/v) (Millipore Sigma, Missouri, USA) and filtered through a 0.2 μm cellulose acetate filter. The filtrate was injected into a Poroshell 120 EC-C18 HPLC column (4 μm, 46 × 150 mm) connected to an Agilent 1260 Infinity II LC system (Agilent Technologies, Santa Clara, CA, USA). The compounds were eluted using a gradient of 25–60% acetonitrile containing 0.1% TFA (v/v) for 25 min, and detected at 230 and 254 nm23.
To further investigate the molecular weight and confirm the structural composition, liquid chromatography–tandem mass spectrometry (LC-MS/MS) was performed using a Waters ACQUITY UPLC® H-Class Plus system coupled with a Xevo TQ-S triple quadrupole mass spectrometer (Waters, MA, USA). Samples were resuspended in 1.0 mL of 20% methanol, filtered through a 0.22 μm nylon membrane, and 10 µL was injected. Chromatographic separation was achieved using a Waters Peptide BEH C18 column (100 × 2.1 mm, 1.7 μm) at 35 °C. The mobile phase consisted of 0.1% formic acid in water (A) and acetonitrile (B), delivered at 0.30 mL/min under the following gradient: 20% B (0–0.2 min), ramped to 28% B (0.2–6.0 min), 80% B (6.0–6.1 min), held until 7.0 min, and re-equilibrated at 20% B until 9.0 min. Mass spectrometry was performed in electrospray ionization (ESI) positive mode under multiple reaction monitoring (MRM), and the data were analyzed using the MassLynx software (v 4.2)24.
Time kill kinetics and antibiofilm activity
The antibacterial activity of a bioactive antimicrobial compound, a commercially synthesized analog compound (Pepmic™), and mupirocin was evaluated against VRSA over defined time intervals.
Exponential-phase cultures in Mueller–Hinton broth (Oxoid, Basingstoke Hampshire, UK) (1 × 10⁶−⁸ colony-forming units per milliliter (CFU/mL)) were treated with each compound at three times their minimum inhibitory concentrations (MICs), determined by microbroth dilution, following the EUCAST guidelines25,26. Cultures were incubated at 37 °C with agitation (110 rpm), and aliquots were collected at specified time points, serially diluted in phosphate-buffered saline (PBS) (Oxoid, Basingstoke Hampshire, UK), and plated on Mueller–Hinton agar (Oxoid, Basingstoke Hampshire, UK) following overnight incubation at 37 °C. Bacterial viability was determined by CFU/mL. All experiments were performed in biological triplicates27.
Biofilm activity was measured in a 96-well plate (Thermo Fisher Scientific, Waltham, MA, USA). Test cultures in brain heart infusion (BHI) broth (Oxoid, Basingstoke Hampshire, UK) were adjusted to OD 600 nm = 0.02 and incubated at 37 °C for 24 h. Planktonic cells were washed three times with PBS, and fresh BHI broth containing antimicrobial compounds at different MIC concentrations was added and incubated for another 24 h. Planktonic cells were removed, and the biofilms were fixed with methanol (Millipore Sigma, St. Louis, MO, USA), stained with crystal violet (Oxoid, Basingstoke Hampshire, UK), and re-solubilized with acetic acid (Millipore, Sigma, Missouri, USA). The optical density at 570 nm was measured using a microplate spectrophotometer (A51119600C, Thermo Scientific, Waltham, Massachusetts, USA) to assess biofilm formation25.
Comprehensive genome analysis: assembly, gene prediction, and functional annotation
Whole-genome sequencing was performed to investigate in silico expression, interaction networks of biosynthetic gene clusters (BGCs), and prediction, structural, and physicochemical modeling of the identified APep. Libraries were prepared using the NextEra XT kit (Illumina) and sequenced on an Illumina MiSeq platform, generating 2 × 250 bp paired-end reads27.
Data were quality-filtered and assembled using SPAdes (https://github.com/ablab/spades)28, and genome annotation and circular mapping were conducted using Prokka (https://github.com/tseemann/prokka)29, Prodigal (https://github.com/hyattpd/Prodigal)30, and BRIG (https://sourceforge.net/projects/brig/)31.
The BGCs and secondary metabolite profiles were predicted using antiSMASH (v 8.0) (https://antismash.secondarymetabolites.org/#!/start)32. Network analysis of antimicrobial biosynthetic genes was performed using STRING (https://string-db.org/ ) and visualized using Cytoscape (https://cytoscape.org/)33.
APep was identified and analyzed using a combination of AMPA (https://tcoffee.crg.eu/apps/ampa/do), CAMPR4 (https://camp.bicnirrh.res.in/campHelp.php), and APD3 (https://aps.unmc.edu/AP/) or activity prediction and similarity assessment with known antimicrobial compounds34. Subsequent structural analyses were performed using PEP-FOLD4(https://bioserv.rpbs.univ-paris-diderot.fr/services/PEP-FOLD4/) to generate three-dimensional peptide models. Notably, these peptide prediction tools primarily focus on linear peptides and may not fully capture the structural intricacies of cyclic or post-translationally modified peptides35. The 3D models generated by PEP-FOLD4 were further refined and visualized using PyMOL (v 4.6) for detailed structural analysis36.
Downstream applications of bioactive antimicrobial compounds and efficacy testing
For downstream applications of the candidate AMP strains, bio-based disinfectant nonwoven wipes (BDWs) were formulated using APep via a double-padding process in a finishing bath containing bioactive antimicrobials at a 1:10 liquid ratio. The wipes were passed through mangles at a rate of 0.5 m per 8 s, achieving an 80% liquid pickup, and then air-dried at room temperature for 2 h. The treated wipes were subjected to controlled in vitro trials, following previously established protocols36,37.
A one-week randomized controlled trial was conducted to assess the antimicrobial efficacy of BDWs against biocide-resistant VRSA on three clinically relevant surfaces: wash basins, floors, and nursing counters. Each surface (10 cm² area) was inoculated in triplicate with a hospital-derived VRSA suspension at a concentration of 1.5 × 10⁵ CFU m⁻²38.
The experimental design included three independent biological replicates per surface type per time point, with each replicate representing a separately prepared and inoculated surface. After a contact time of 5 h with BDWs, microbial survival was assessed through CFU enumeration, performed twice daily (10:00 AM and 4:00 PM) for 7 consecutive days (Supplementary Fig. 1). Moreover, randomization was applied to the order of surface contamination, BDW application, and sampling time to minimize systematic bias. Negative control surfaces (no BDW treatment) were included for each replicate and surface type. Statistical analysis was performed using linear regression and mixed-effects models to evaluate the CFU reduction trends over time and across different surfaces. Model assumptions (normality and homoscedasticity) were verified prior to the analysis. Statistical significance was set at p < 0.05. All statistical analyses were conducted using GraphPad Prism (v. 10.4.1) and R software (v. 4.4.2).
Results
Isolation and molecular identification for AMP strains
A total of 92 soil samples were screened for AMP strains, resulting in the identification of seven isolates (7.6%) with potential antimicrobial activity. These isolates produced medium-sized (5–7 mm) whitish colonies on nutrient agar and were confirmed to be Gram-positive rods by Gram staining. The strains demonstrated tolerance to various stress conditions, including phenol (0.3%), pH (5.0–9.0), bile (0.3%), and NaCl (3%) concentrations. Biochemical profiling revealed positive results for catalase, coagulase, nitrate reductase, and Voges-Proskauer tests, while urease activity varied, and H2S production and indole reactions were consistently negative (Supplementary Table 3).
Molecular identification using 16S rRNA gene amplification confirmed the presence of Bacillus subtilis, Bacillus paralicheniformis, Bacillus megaterium, and Bacillus coagulans. There was a strong agreement between the phenotypic and molecular identification methods, with a high kappa agreement coefficient (1, 95% CI 0.97–1, p < 0.01).
Phylogenetic analysis revealed distinct clusters corresponding to B. subtilis (UAB21, UAB34, and UAB11), B. paralicheniformis (UAB14, and UAB33), B. megaterium (UAB15), and B. coagulans (UAB26), highlighting their genetic divergence (Supplementary Fig. 2). Moreover, B. subtilis and B. paralicheniformis were the most prevalent, with a significantly higher occurrence in the ICU (71.4%, 5/7) than in the SW (28.5%, 2/7) (p < 0.05).
Evaluating the antimicrobial efficacy of AMP strains against HAI pathogens
The antimicrobial activity of various AMP strains was assessed using an agar well diffusion assay combined with k-means clustering analysis, which revealed three distinct clusters accounting for 93.93% of point variability (Fig. 2A). Cluster 1, comprising UAB34, UAB33, UAB26, and UAB21, exhibited the highest average zone of inhibition (ZOI) of 10.29 mm against the test isolates. Within this cluster, UAB33 had the largest ZOI, particularly against biocide-resistant VRSA isolates (13 mm), and the smallest ZOI against Escherichia coli (7.8 mm) (p < 0.05). In contrast, Cluster 3, which included UAB14 and UAB15, showed the lowest average ZOI of 6.6 mm, with UAB15 displaying the smallest ZOI against E. coli (4.9 mm) and the largest ZOI against Klebsiella pneumoniae ATCC BAA-1705 (8.4 mm). Cluster 2, represented solely by UAB11, emerged as an outlier distinct from the other clusters. Moreover, the control strain had an average ZOI of 9.08 mm, which was closely aligned with Cluster 1 but classified within Cluster 2 (Fig. 2B).
Cluster Analysis of AMP Strains: (A) Principal Coordinate Analysis (PCoA) was employed to compare the ZOI measurements (mm) with previously isolated hospital-borne strains. The variability of the cluster points was assessed using the ‘clusplot’ function in RStudio (v. 4.4.2). The analysis identified three distinct clusters, which explained 93.93% of the total data variability. (B) Antimicrobial activity of various AMP strains was evaluated using an agar well diffusion assay. A heatmap was generated using the k-means clustering algorithm to visualize the activity patterns among the test isolates. The color gradient corresponds to the diameter (mm) of the ZOI, ranging from 5 mm (light green) to 13 mm (dark-pink). Green shades indicate lower antimicrobial activity, whereas pink shades indicate higher activity. AMP strains and test isolates were clustered based on the similarities in their antimicrobial profiles. The heatmap was generated using the ‘ggplot2’, ‘reshape2’, and ‘pheatmap’ packages in RStudio (v. 4.4.2). Abbreviations: AMP, antimicrobial-producing (AMP), zone of inhibition (ZOI).
These results highlight the differential antimicrobial effectiveness of the AMP strains, with UAB33 exhibiting the most potent activity against biocide-resistant VRSA isolates.
Selection of candidate AMP strains for downstream applications
Candidate AMP strains for downstream applications were selected based on a thorough evaluation of their antibiotic susceptibility and oxidative stress. Antibiotic susceptibility testing revealed that all AMP strains were completely susceptible (100%) to clindamycin, chloramphenicol, and vancomycin but resistant to ampicillin, gentamicin, kanamycin, streptomycin, tetracycline, and erythromycin. Notably, strain UAB33 exhibited complete susceptibility (100%) to all tested antibiotics, whereas strain UAB15 showed the highest resistance, being resistant to 66% (6/9) of the antibiotics tested (Fig. 3A). The predominant ARGs identified among the AMP strains were blaCTX−M and aadA2, respectively. The phenotypic antibiotic susceptibility data were consistent with the molecular identification of ARGs (kappa agreement coefficient = 1, 95% CI: 0.97–1, p < 0.01) (Fig. 3B).
Antibiotic susceptibility profiles of AMP strains. (A) Proportionalbreakdown of the AMR. This bar graph visualizes the % of antimicrobial resistance among AMP strains against a panel of antibiotics, including Ampicillin (AMP, 10 µg), Gentamycin (CN, 10 µg), Kanamycin (K, 30 µg), Streptomycin (S, 10 µg), Tetracycline (TE, 30 µg), Erythromycin (E, 15 µg), Clindamycin (DA, 2 µg), Chloramphenicol (C, 30 µg), and Vancomycin (VA, 30 µg). The AMR percentage was calculated as the proportion of resistant isolates to the total number of isolates evaluated. (B) Molecular detection of ARGs. The presence of ARGs in AMP strains is depicted in a heatmap, where green indicates the presence and pink indicates the absence of specific resistance genes, as determined by singleplex PCR using the specific primers. (C) Hierarchical clustering analysis of antibiotic resistance profiles. A heatmap was generated using XLSTAT (v. 2025.1) using binary susceptibility data (1 = resistant, 0 = susceptible) was subjected to row-wise Z-score normalization and single-linkage hierarchical clustering. This revealed similarities in resistance patterns among AMP strains and identified distinct clusters based on their antibiotic resistance profiles. Green indicates higher relative resistance and magenta indicates higher susceptibility, based on Z-scores. Key: 1 (ampicillin), 2 (gentamycin), 3 (kanamycin), 4 (streptomycin), 5 (tetracycline), 6 (erythromycin), 7 (clindamycin), 8 (chloramphenicol), 9 (vancomycin). Abbreviations: AMP, antimicrobial-producing (AMP), antimicrobial resistance genes (ARGs), antimicrobial resistance (AMR).
A hierarchical clustering dendrogram based on antibiotic response patterns revealed three primary clusters (A, B, and C) of AMP strains. Cluster A included UAB33 and UAB26, with UAB33 exhibiting the highest similarity in terms of antibiotic susceptibility. Cluster B is comprised of UAB14 and UAB15, with UAB15 showing the least similarity. Cluster C contained UAB34, UAB11, and UAB21, with UAB34 and UAB11 being more similar to each other than to UAB21 (Fig. 3C).
Oxidative stress analysis indicated that UAB15 had the highest oxidative stress levels (MDA 9 nmol, H2O2 7 nmol) and the lowest antioxidant levels (SOD 15%, catalase 2 nmol) (p < 0.05). In contrast, UAB33 displayed the lowest oxidative stress (MDA 1 nmol, H2O2 1 nmol) and the highest antioxidant levels (SOD 95%, catalase 18 nmol) (p < 0.05) (Supplementary Fig. 3). Pair plot analysis revealed a strong positive correlation (r = 0.880) between MDA and H2O2 levels, indicating that strains with higher oxidative stress exhibited lower levels of antioxidants. Additionally, the positive correlation (r = 0.932) between catalase and SOD activity suggests that higher antioxidant levels are associated with reduced oxidative stress (Fig. 4).
Comprehensive Oxidative Stress Analysis. Pair plot analysis was conducted using the’ ggpairs’ function in RStudio (v. 4.4.2) program to visualize the relationships between AMP strains and markers of oxidative stress (MDA and H2O2) and antioxidant activity (Catalase and SOD). This analysis revealed several key patterns and correlations. The diagonal elements display density plots showing the distribution of each variable, whereas the off-diagonal elements present bar plots depicting pairwise relationships and correlation coefficients, with significance levels indicated by asterisks (*p < 0.05, **p < 0.01). Abbreviations: AMP, antimicrobial-producing (AMP), malondialdehyde (MDA), hydrogen peroxide (H2O2), superoxide dismutase (SOD).
These results indicate that AMP strains with robust antioxidant defenses, particularly UAB33, which demonstrates broad antibiotic sensitivity, superior antioxidant capacity, and minimal oxidative stress, are ideal candidates for downstream applications.
Extraction, purification, and identification of bioactive antimicrobial compounds from candidate AMP strain UAB33
In this study, n-butanol was used to extract bioactive metabolites from UAB33, which were then purified using thin-layer chromatography (TLC). The active compound, which appeared as a yellowish solid with an Rf value of 0.79, was collected from the TLC plate and further characterized using FTIR and HPLC. Preliminary tests indicated that the antibacterial compound remained active despite acid neutralization but was significantly inhibited by proteinase K and trypsin, suggesting that it is proteinaceous and a peptide.
The FTIR spectrum of the purified antimicrobial compound (Supplementary Fig. 4A) revealed characteristic bands at 1400, 1450, 2850, and 2960 cm⁻¹, which were consistent with the CH stretching vibrations in the aliphatic chains of hydrophobic amino acids. A band at 1650 cm⁻¹ corresponds to CO-N bond stretching (amide I band), while bands at 1560 and 3420 cm⁻¹ indicate N-H bond deformation combined with C-N stretching (amide II band). These findings confirmed the presence of peptide components. Comparative analysis of the FTIR peaks showed 99.6% similarity to those of WS-5023, which was identified as bacitracin.
HPLC analysis further validated this, showing two peaks at retention times of 14.2 and 18.1 min, corresponding to bacitracin B1 and B2, respectively, as seen in the bacitracin standard (Supplementary Fig. 4B).
Moreover, LC–MS/MS analysis of the isolated compound identified a precursor ion at m/z 1408.74, which was consistent with the molecular weight of bacitracin B1. At a collision energy of 10 eV, the precursor ion exhibited 100% relative abundance, indicating minimal fragmentation. These findings confirmed bacitracin B1 (UB1) as the predominant component, establishing it as the principal antimicrobial metabolite produced by UAB33 and underscoring its potential for downstream therapeutic applications (Supplementary Fig. 5).
Time-kill kinetics and antibiofilm potential of a purified bioactive compound against biocide-resistant VRSA isolates
Time-kill assays were conducted to evaluate the bactericidal kinetics of the natural antimicrobial compound UB1, derived from the candidate strain B. paralicheniformis UAB33, its synthetic analog UB2, and the reference antibiotic mupirocin against biocide-resistant VRSA. UB1 exhibited rapid and potent bactericidal activity, reducing viable counts from 6.0 to 2.9 log₁₀ CFU/mL at 300 min and achieving complete eradication by 360 min. UB2 showed a slower killing trajectory, with counts decreasing from 6.0 to 4.0 log₁₀ CFU/mL at 300 min and reaching no detectable CFU/mL at the 6-hour endpoint. Mupirocin resulted in a modest reduction from 7.0 to 4.8 log₁₀ CFU/mL over the same period but did not achieve the ≥ 3 log₁₀ reduction typically associated with bactericidal activity. No significant changes in bacterial burden were observed in the untreated control group (p < 0.05). These findings highlight the superior bactericidal efficacy and rapid onset of action of UB1 against VRSA (Fig. 5A).
Antimicrobial and Antibiofilm Activity. Time-dependent killing of VRSA cells by UB1 and UB2 and comparator antibiotics at 3x MIC concentration. (B) Antibiofilm activity of UB1 against VRSA biofilms. (C) Antibiofilm activity of UB2 against VRSA biofilms. Three biological replicates were analyzed for each peptide treatment concentration. An antibiofilm activity plot (% biofilm reduction) was generated using the ‘ggplot2’ package in RStudio (v. 4.4.2), with log-linear regression performed using the lm () function. The purple and green lines represent the log-linear regressions. The R2 values of the regression were 0.87 for UB1 and 0.81 for UB2. Key: UB1 refers to a natural antimicrobial compound (bacitracin B1) derived from the B.paralicheniformis UAB33. UB2 is a synthetic peptide analog of UB1 that is commercially synthesized based on the predicted sequence. Note: For time–kill assays, the initial inoculum was standardized to 10⁶ CFU/mL for UB1 and UB2 to reflect contamination-relevant exposure levels. Mupirocin was evaluated at 10⁷ CFU/mL in accordance with established antibiotic testing protocols. Abbreviations: Antimicrobial producing (AMP), minimum inhibitory concentration (MIC), vancomycin resistant S. aureus (VRSA).
We also evaluated the antibiofilm activities of UB1 and UB2 using a 96-well plate biofilm model. At sub-MIC levels (0.25 × and 0.5 × MIC), both peptides showed limited antibiofilm activity, reducing biofilm formation by less than 50%. However, at supra-MIC concentrations (2x and 4x MIC), both peptides significantly reduced biofilm mass by ≥ 50%. UB1 was particularly effective, achieving 85% and 100% reduction at 2x and 4x MIC, respectively (Fig. 5B), outperforming UB2, which achieved 70% and 93% reductions at the same concentrations (Fig. 5C).
These results indicate that UB1 has superior antimicrobial kinetics and antibiofilm activity against biocide-resistant VRSA, suggesting its potential as a therapeutic agent.
Computational discovery and assessment of antibacterial bioactive compound targeting biocide-resistant VRSA isolate
The 4.2 Mb genome of UAB33 was visualized using BRIG version 0.95 (Fig. 6A). Regions with no homology to Bacillus reference strains appeared as gaps in the circular chromosome, indicating areas of high nucleotide divergence that may harbor novel genes.
In silico and computational analyses were performed. The whole genome of UAB33 was sequenced to enable a comprehensive computational analysis. (A) Circular Genome Representation. The 4.2 Mb genome is depicted in a circular map, with nucleotide identity levels compared to those of the reference Bacillus strains. Identity was color-coded as 100% (purple), 70% (pink), and 50% (grey). These gaps indicate regions of high nucleotide divergence or potential novel gene products in the genome. The inner rings show GC content (black) and GC skew (green for positive and purple for negative), providing insights into the genome structure and replication. Key BGCs, including those for bacitracin production (bacA, bacB, bacR, and bcrA/B/C), are indicated in red. (B) Secondary metabolite BGCs identification and comparison. AntiSMASH analysis identified three NRPS clusters with 100% sequence similarity to known clusters: (1) bacitracin, (2) lichenysin, and (3) bacillibactin clusters. The organization of the modules within each cluster was detailed, showing a conserved domain architecture and predicted monomer specificity. The genomic regions were aligned with reference sequences from the MIBiG database, indicating structural and functional similarities. Abbreviations: Non-ribosomal peptide synthetase (NRPs), biosynthetic gene clusters (BGCs), minimum information about a biosynthetic gene cluster database (MIBiG). Key: Domains within modules are depicted as circles, with darker shades representing complete modules and lighter shades indicating domains within incomplete or external ones. The arrows highlight the N-terminal- and C-terminal docking domains, which are crucial for module interactions. Crossed-out symbols denote the inactive domains. The genes or CDS features containing these domains are indicated by black arrows. The module boundaries were labeled with module numbers (e.g., M1 and M2), and the predicted monomer for each module was identified. The iterative modules are indicated by enclosed loop symbols. 6 (c). In silico Characterization and Interaction Network of Bacitracin BGCs. The Bacitracin BGC (bacA, bacB, bacC) is flanked by unclassified gene clusters showing strong similarity to proteins involved in circular bacteriocin synthesis and other secondary metabolites. These proteins, identified through BLASTp analysis, shared over 90% identity and coverage with previously known biosynthetic proteins. Interaction analysis using STRING and Cytoscape demonstrated strong connectivity between these proteins, suggesting their coordinated involvement in bacitracin and other secondary metabolite synthesis.
To explore whether these regions contain secondary metabolite BGCs, such as non-ribosomal peptide synthetases (NRPs), polyketide synthases (PKSs), ribosomally synthesized and post-translationally modified peptides (RiPPs), and other antimicrobial synthases, antiSMASH analysis was performed using strict criteria, including sequence motifs, gene organization, and biosynthetic features. Fourteen putative BGCs were identified in UAB33. Of these, 50% (7/14) were NRPs, with Bacitracin, lichenysin, and bacillibactin exhibiting 100% sequence similarity to known NRP gene clusters. Additionally, 21% (3/14) were RiPPs, including a sactipeptide with 85% similarity, and 29% (4/14) represented other types, such as pulcherriminic acid, with 66% similarity. BGCs with 100% similarity to known NRPS/PKS clusters are shown in Fig. 6B, and those with ≥ 60% similarity are shown in Supplementary Fig. 5A. Notably, the Bacitracin BGC region (bacA, bacB, and bacC) was flanked by unclassified RiPP, T3PKS, and terpene gene clusters (Supplementary Fig. 5B).
To elucidate their potential functions, BLASTp analysis was conducted on the translated sequences of these unclassified gene clusters. This analysis revealed strong similarities to the proteins involved in the synthesis of uberolysin/Carno cyclin (circular bacteriocin) (WP_020452200.1): gasA and cirA; 3-oxoacyl-[acyl-carrier-protein] synthase III C-terminal domain-containing protein (WP_152847193.1): bcsA; and prenyltransferase/squalene oxidase repeat-containing protein (WP_152847267.1): SqhC. The BLASTp results, with e-values < 1e-20, percentage identities above 90%, and coverage exceeding 90%, indicated a strong similarity to known biosynthetic proteins.
Further analysis using STRING and Cytoscape revealed significant interactions between secondary metabolite-related proteins, including gasA, cirA, bacA, bacB, bcsA, and SqhC, all of which displayed high connectivity (confidence scores > 0.9). These findings suggest that multiple BGCs and their associated proteins work in concert, resulting in the efficient synthesis of bacitracin and other secondary metabolites (Fig. 6C). A detailed overview of the complete BGCs study is shown in Supplementary Fig. 6.
Furthermore, the BGCs sequences predicted by AntiSMASH were analyzed for APep structures using the AMPA and CAMPR4 servers. Seven peptide stretches with a propensity of ≤ 50% were identified and compared with the APD3 database, revealing that 70% of the peptides showed the highest sequence similarity to bacitracin (Supplementary Table 4). These results suggest that the antimicrobial activity of UAB33 is due to bacitracin.
Furthermore, structural modeling of these predicted APep with the highest similarity to bacitracin (100% identity), performed using PepFold4 and visualized with PyMOL, revealed a 12-amino-acid structure stabilized by hydrogen bonding, electrostatic interactions, and hydrophobic interactions. These interactions are likely to be critical for ligand binding and antimicrobial activity. The detailed structural and physicochemical properties of the samples are presented in Table 1.
Evaluating the efficacy of bdw’s as a downstream application
The efficacy of the BDW formulation containing 80% UB1 against biocide-resistant VRSA was evaluated using survival assays, quantifying CFU reductions across three independent biological replicates per surface and time point. Linear regression analysis demonstrated a significant and consistent decline in CFU m⁻² over time, with an average daily reduction of 15,143 CFU m⁻². The most substantial decreases occurred on days 4 and 6, with reductions of 11,000 and 9,000 CFU m⁻², respectively (p < 0.05). Despite slightly greater reductions in morning samples, the overall rate of decline remained uniform throughout the 7-day period, as indicated by the consistent regression slope (R² = 0.99) (Fig. 7A).
Time-dependent reduction in VRSA CFU m⁻² following BDW treatment over a 7-day period on selected surfaces: wash basin, floor, and nursing counter. (A) Reduction in VRSA CFU m⁻² after BDW treatment at two daily time points: 10 AM (green line) and 4 PM (purple line). Solid lines represent the observed values, and dotted lines represent the polynomial trend lines. The % decrease in CFU (dark green points) indicates complete eradication by day 7 post-treatment. The blue line shows the daily difference in CFU counts, highlighting a consistent reduction trend (R² = 0.993). (B) Overall time-dependent reduction in CFU m⁻² (%) for the treatment group across all surfaces. (C) CFU m⁻² (%) trends in the control group, where no BDW treatment was applied. These findings demonstrate the efficacy of BDW in significantly reducing microbial load on hospital-relevant surfaces over the trial period. Abbreviations: Biobased disinfectant wipes (BDW), vancomycin-resistant Staphylococcus aureus (VRSA), colony-forming units (CFU).
A mixed-effects model accounting for surface type, time, and replicate variability confirmed a significantly greater reduction in CFU m⁻² in the BDW-treated group (98%) than in the control group (53%) (p < 0.05). Complete eradication was observed on day 4 in the treatment group. Among the surfaces, the nursing counter exhibited the highest reduction, while the wash basin showed the lowest (Supplementary Fig. 7A-C). In contrast, the control group demonstrated significantly lower reductions, particularly on floor surfaces, with reductions of 63% and 50% (p < 0.05) (Fig. 7C).
Overall, these results demonstrate that BDW treatment is highly effective in reducing biocide-resistant VRSA, with efficacy varying by surface type, and confirmed through rigorous replication and statistical modeling.
Discussion
This study successfully isolated an APep-producing B. paralicheniformis strain (UAB33) from high-risk hospital environments, addressing a critically underexplored area of clinical infection control. While hospitals are typically considered reservoirs for ARGs due to intense antimicrobial and biocidal selection pressures5, a notable 7.6% of the screened isolates exhibited potent activity against HAI pathogens, including biocide-resistant VRSA. These findings validate the robustness of our selective screening approach and demonstrate the feasibility of identifying functionally safe ARG-free strains in clinically challenging settings.
Unlike previous strategies, such as the study by Jalalifar et al., which combined melittin peptide with oxacillin for VRSA treatment39, our approach avoids reliance on antibiotics or live probiotics, both of which carry the inherent risks of horizontal gene transfer (HGT) and resistance selection40,41. Although conventional chemical disinfectants are effective, they often contribute to AMR by generating ROS and promoting stress-adaptive survival mechanisms in pathogens42. Although ecologically attractive, probiotic-based cleaning systems have limited clinical applicability because of their potential to acquire ARGs8,41.
Given the typically high prevalence of ARGs and mobile genetic elements in hospital microbiomes43, identifying safe, ARG-free antimicrobial strains for clinical use is critical. In contrast to conventional approaches, our bio-based strategy employs a carefully selected, ARG-free, hospitaladapted AMP-producing strain, offering a safer, more sustainable, and effective alternative. We prioritized strains devoid of detectable ARGs, particularly UAB33, which demonstrated broad-spectrum antimicrobial and antioxidant activities based on whole-genome sequencing and phenotypic assays. This strain did not harbor known ARGs, underscoring their suitability for clinical applications where safety is paramount. Although hospital isolates are generally assumed to accumulate ARGs under selective pressure, UAB33’s ARG-free profile suggests intrinsic genetic or regulatory mechanisms that restrict horizontal gene transfer (HGT), such as active restriction-modification systems, CRISPR-Cas elements, and membrane exclusion properties44,45,46. Future comparative genomics and transcriptomic profiling of related Bacillus strains under stress conditions or in co-culture with VRSA will be essential to elucidate these resistance-avoidance mechanisms in Bacillus.
The bioactive compound produced by UAB33, designated UB1, was conclusively identified as bacitracin B1 via LC-MS/MS analysis, with an exact match to the molecular mass and fragmentation pattern of the certified standard47. Complementary genome mining confirmed the presence of a complete BGC, including precursor peptide, RiPPs, and dedicated transporter genes. This integrated workflow, which combines advanced mass spectrometry with in silico genome mining, enables the high-confidence identification of ribosomally synthesized and RiPPs expressed under selective environmental conditions. Such analytical stringency is essential, given the presence of multiple bacitracin isoforms, each exhibiting distinct molecular weights and bioactivities48,49,50,51. The strong concordance between the predicted gene cluster and experimental LC-MS/MS data provided definitive confirmation of UB1’s identity.
In addition to the characterization of UB1, genome mining has revealed multiple BGCs encoding RiPPs, NRPS, and terpenes. The proximity and adjacency of these clusters suggest potential co-regulation or synergistic expression, particularly under environmental stress or pathogen challenges. Previous studies on Bacillus species have demonstrated that clustered antimicrobial BGCs can be coordinately regulated, enhancing ecological competitiveness and adaptive antimicrobial responses52. Further transcriptomic and metabolomic analyses are necessary to confirm these interactions and determine their roles in niche adaptation.
Recognizing theneed to assess the real-world efficacy of UB1, we developed a BDW and validated its antimicrobial activity on multiple hospital surfaces. The highest activity was observed on smooth, non-porous materials such as nursing counters, with reduced efficacy on porous or moisture-retaining surfaces like wash basins, likely due to shorter contact times and biofilm interference. These results align with the established formulation-surface interaction dynamics53, underscoring the importance of material-specific optimization of disinfectant products.
Following the demonstration of antimicrobial efficacy on hospital surfaces, we assessed the preliminary stability of the UB1-containing formulation under ambient conditions. No visible degradation of UB1 was observed over 7-day period. In comparison, conventional chemical-based disinfectant wipes typically maintain efficacy for up to one month under similar conditions54. To our knowledge, no published studies have evaluated the stability of bacitracin-based or probiotic-derived antimicrobial wipe formulations. Nevertheless, comprehensive long-term stability assessments, including accelerated aging, enzymatic activity retention, and packaging integrity evaluations, are essential to support commercial translation. Optimizing formulation stability and defining product shelf life are pivotal steps toward clinical implementation.
Beyond formulation stability, the safety profiles of antimicrobial agents remain critical determinants of their clinical adoption in hospital environments. Although in vitro cytotoxicity assays were beyond the scope of this study, bacitracin is an FDA-approved, over-the-counter antimicrobial with well-documented safety for topical applications55. Previous studies have demonstrated the low cytotoxicity of bacitracin at therapeutic concentrations in human keratinocytes and epithelial cell lines17. Nonetheless, future work will include cytotoxicity assessments (e.g., MTT and LDH release assays) on human-derived cell lines, such as HaCaT and HEK293, to validate biocompatibility for hospital settings and ensure regulatory compliance.
Finally, these findings are particularly timely, given the increasing global incidence of VRSA and mounting concerns over disinfectant-induced AMR. Recent literature emphasizes the urgent need for sustainable, resistance-free antimicrobial interventions in healthcare settings5,53. By leveraging the antimicrobial and antioxidant properties of hospital-adapted, ARG-free strains, our APep-based BDW formulation represents a paradigm shift in hospital disinfection strategies, addressing AMR, ecological resilience, and environmental safety within a scalable and clinically relevant framework. Despite these encouraging findings, several limitations of this study should be acknowledged to contextualize the results and guide future research.
Study limitations
This study had several limitations. The absence of nuclear magnetic resonance (NMR) analysis precludes the precise determination of bacitracin’s structure and molecular mass.
The lack of functional validation, such as gene knockouts of the identified BGCs, limits insights into their role in bacitracin synthesis. Additionally, the absence of cloning and large-scale production hinders the potential development of peptide-based disinfectants. Although the in vitro antimicrobial results are promising, their efficacy in complex clinical settings remains uncertain. Key factors, including activity against diverse hospital pathogens, potential resistance to peptide biocides, and long-term stability, require further investigation. Moreover, the single-center study design may limit the generalizability of our findings to other populations.
Conclusion and future prospects
This study introduces a novel bio-disinfectant strategy that utilizes APep derived from B. paralicheniformis UAB33, which specifically targets biocide-resistant VRSA. Unlike conventional disinfectants, which may contribute to AMR or rely on live probiotics with the risk of horizontal resistance gene transfer, our approach harnesses an antibiotic ARG-free, hospital-adapted bacterial strain for targeted APep production.
Integrative genomic and biochemical analyses identified bacitracin B1 as the principal antimicrobial component of APep, demonstrating potent anti-VRSA activity, oxidative stress tolerance, and antibiofilm efficacy in vitro. Formulating APep into bioactive disinfectant wipes provides a safe, non-cytotoxic, and highly effective alternative for hospital surface decontamination, addressing the pressing need for sustainable infection control interventions in healthcare environments challenged by multidrug-resistant pathogens.
Future research should prioritize scaling up APep production via bioprocess optimization, along with comprehensive toxicological and in vivo evaluations to ensure clinical safety and efficacy. The application of synthetic biology and genetic engineering could further enhance the peptide yield, stability, and activity spectrum. Additionally, detailed cost analysis and feasibility assessment are essential to inform production scalability and guide clinical translation. Investigating the synergistic potential of APep with existing disinfectants or last-resort antibiotics may reveal combinatorial strategies with superior efficacy against resistant nosocomial pathogens. Collectively, these advances position APep-based formulations as next-generation, eco-friendly disinfectants, offering both environmental safety and clinical robustness in the global effort against AMR.
Data availability
The partial 16S rRNA gene sequences of all studied isolates were submitted to the National Center for Biotechnology Information (NCBI) GenBank, where they were assigned the accession numbers OR717549 to OR717555.
Refrences
Chng, K. R. et al. Cartography of opportunistic pathogens and antibiotic resistance genes in a tertiary hospital environment. Nat. Med. 26 (6), 941–951 (2020).
Älgå, A. et al. Perceptions of healthcare-associated infection and antibiotic resistance among physicians treating Syrian patients with war-related injuries. Int. J. Environ. Res. Public Health. 15 (12), 2709 (2018).
Christenson, E. C. et al. Evidence map and systematic review of disinfection efficacy on environmental surfaces in healthcare facilities. Int. J. Environ. Res. Public Health. 18 (21), 11100 (2021).
Saleem, Z. et al. A multicenter point prevalence survey of healthcare–associated infections in pakistan: findings and implications. Am. J. Infect. Control. 47 (4), 421–424 (2019).
Asghar, M. U. et al. Molecular distribution of biocide resistance genes and susceptibility to biocides among Vancomycin resistant Staphylococcus aureus (VRSA) isolates from intensive care unit (ICU) of cardiac hospital-A first report from Pakistan. Heliyon 9(12), (2023).
Mei, D. et al. PEGylated graphene oxide carried OH-CATH30 to accelerate the healing of infected skin wounds. Int. J. Nanomed. 4769–4780 (2021).
Asghar, M. U. et al. Investigating oxidative stress levels in pregnant patients infected with hepatitis C virus and bacterial vaginosis for better treatment option. Oman Med. J. 38 (5), e549 (2023).
Hu, J. et al. Clinically relevant pathogens on surfaces display differences in survival and transcriptomic response in relation to probiotic and traditional cleaning strategies. Npj Biofilms Microbiomes. 8 (1), 72 (2022).
Bhowmick, S. Exploiting Traditional Chinese Medicine for Potential Anti-microbial Drug Leads (Aberystwyth University, 2021).
Das, D. J. et al. Critical insights into antibiotic resistance transferability in probiotic Lactobacillus. Nutrition 69, 110567 (2020).
Kim, J. et al. Genomic insights and functional evaluation of Lacticaseibacillus paracasei EG005: a promising probiotic with enhanced antioxidant activity. Front. Microbiol. 15, 1477152 (2024).
Qumsani, A. T. The Contribution of Microorganisms To Sustainable Development: Towards a Green Future Through Synthetic Biology and Systems Biologyp. 1–17 (Journal of Umm Al-Qura University for Applied Sciences, 2024).
Griffith, C. Surface sampling and the detection of contamination, in Handbook of hygiene control in the food industry Elsevier. pp. 673–696. (2016).
Jamil, B. et al. Isolation of Bacillus subtilis MH-4 from soil and its potential of polypeptidic antibiotic production. Pak J. Pharm. Sci. 20 (1), 26–31 (2007).
Kumar, S. et al. MEGA X: molecular evolutionary genetics analysis across computing platforms. Mol. Biol. Evol. 35 (6), 1547–1549 (2018).
Riazi, S. et al. Characterization of lactosporin, a novel antimicrobial protein produced by Bacillus coagulans ATCC 7050. J. Appl. Microbiol. 106 (4), 1370–1377 (2009).
Choyam, S., Jain, P. M. & Kammara, R. Characterization of a potent new-generation antimicrobial peptide of Bacillus. Front. Microbiol. 12, 710741 (2021).
Choyam, S. et al. Assessing the antimicrobial activities of ocins. Front. Microbiol. 6, 1034 (2015).
Institute, C. L. S. Performance Standards for Antimicrobial Susceptibility Testing, CLSI supplement M100 (2022).
Azevedo, E. et al. Bacitracin production by a new strain of Bacillus subtilis: extraction, purification, and characterization. Appl. Biochem. Biotechnol. 42, 1–7 (1993).
Al-Thubiani, A. S. et al. Identification and characterization of a novel antimicrobial peptide compound produced by Bacillus megaterium strain isolated from oral microflora. Saudi Pharm. J. 26 (8), 1089–1097 (2018).
Hussain, N. et al. Evaluation of the probiotic and postbiotic potential of lactic acid bacteria from artisanal dairy products against pathogens. J. Infect. Developing Ctries. 15 (01), 102–112 (2021).
Ahire, J. et al. Identification and characterization of antimicrobial peptide produced by indigenously isolated Bacillus paralicheniformis UBBLi30 strain. 3 Biotech, 10(3): p. 112. (2020).
Xu, F., Yu, J. & Wu, Y. Optimal conditions for determination of bacitracin, bacitracin zinc and bacitracin methylene disalicylate in animal feed by ultra-performance liquid tandem mass spectrometry. J. Chromatogr. B. 1243, 124234 (2024).
Oyama, L. B. et al. In Silico identification of two peptides with antibacterial activity against multidrug-resistant Staphylococcus aureus. Npj Biofilms Microbiomes. 8 (1), 58 (2022).
Vading, M. et al. Comparison of disk diffusion, etest and VITEK2 for detection of carbapenemase-producing Klebsiella pneumoniae with the EUCAST and CLSI breakpoint systems. Clin. Microbiol. Infect. 17 (5), 668–674 (2011).
Sheldon, E. J. Exploring the Molecular Mechanisms of Antimicrobial Resistance in Brachyspira hyodysenteriae Using Whole Genome Sequencing (University of Warwick, 2018).
Bankevich, A. et al. SPAdes: a new genome assembly algorithm and its applications to single-cell sequencing. J. Comput. Biol. 19 (5), 455–477 (2012).
Seemann, T. Prokka: rapid prokaryotic genome annotation. Bioinformatics 30 (14), 2068–2069 (2014).
Hyatt, D. et al. Prodigal: prokaryotic gene recognition and translation initiation site identification. BMC Bioinform. 11, 1–11 (2010).
Alikhan, N. F. et al. BLAST ring image generator (BRIG): simple prokaryote genome comparisons. BMC Genom. 12, 1–10 (2011).
Blin, K. et al. Recent development of antismash and other computational approaches to mine secondary metabolite biosynthetic gene clusters. Brief. Bioinform. 20 (4), 1103–1113 (2019).
Anusha, M. et al. Gene network interaction analysis to elucidate the antimicrobial resistance mechanisms in the Clostridium difficile. Microb. Pathog. 178, 106083 (2023).
Hafeez, A. B. et al. Whole-genome sequencing and antimicrobial potential of bacteria isolated from Polish honey. Appl. Microbiol. Biotechnol. 107 (20), 6389–6406 (2023).
Lamiable, A. et al. PEP-FOLD3: faster de Novo structure prediction for linear peptides in solution and in complex. Nucleic Acids Res. 44 (W1), W449–W454 (2016).
Yuan, S., Chan, H. S. & Hu, Z. Using PyMOL as a platform for computational drug design. Wiley Interdisciplinary Reviews: Computational Molecular Science, 7(2): p. e1298. (2017).
Dural-Erem, A. et al. Biocontrol of solid surfaces in hospitals using microbial-based wipes. Text. Res. J. 89 (2), 216–222 (2019).
Fauci, V. et al. An innovative approach to hospital sanitization using probiotics: in vitro and field trials. (2015).
Jalalifar, S. et al. A hope for ineffective antibiotics to return to treatment: investigating the anti-biofilm potential of Melittin alone and in combination with penicillin and Oxacillin against multidrug resistant-MRSA and-VRSA. Microbiol 14, 1269392 (2024). Front.
Khan, R. T. et al. Prevention and potential remedies for antibiotic resistance: current research and future prospects. Front. Microbiol. 15, 1455759 (2024).
Michaelis, C. & Grohmann, E. Horizontal gene transfer of antibiotic resistance genes in biofilms. Antibiotics 12 (2), 328 (2023).
Huang, L. et al. The effects of natural products and environmental conditions on antimicrobial resistance. Molecules 26 (14), 4277 (2021).
Ahmad, I., Malak, H. A. & Abulreesh, H. H. Environmental antimicrobial resistance and its drivers: a potential threat to public health. J. Glob Antimicrob. Resist. 27, 101–111 (2021).
Ishikawa, M. & Hori, K. The elimination of two restriction enzyme genes allows for electroporation-based transformation and CRISPR-Cas9-based base editing in the non-competent Gram-negative bacterium Acinetobacter sp. Tol 5. Appl. Environ. Microbiol. 90 (6), e00400–e00424 (2024).
Xu, C. et al. The DNA phosphorothioation restriction-modification system influences the antimicrobial resistance of pathogenic bacteria. Microbiol. Spectr. 11 (1), e03509–e03522 (2023).
Zohra, T. et al. Cracking the challenge of antimicrobial drug resistance with CRISPR/Cas9, nanotechnology and other strategies in ESKAPE pathogens. Microorganisms 9 (5), 954 (2021).
Suleiman, S. A. et al. Analysis of bacitracin and its related substances by liquid chromatography tandem mass spectrometry. J. Pharm. Anal. 7 (1), 48–55 (2017).
Liu, F., van Heel, A. J. & Kuipers, O. P. Leader-and terminal residue requirements for circularin A biosynthesis probed by systematic mutational analyses. ACS Synth. Biol. 12 (3), 852–862 (2023).
Panel, E. C. Safety evaluation of the food enzyme subtilisin from the non-genetically modified Bacillus paralicheniformis strain LMG S-30155. EFSA J. 21 (6), 7910 (2023).
Wu, Z. et al. Transcriptome analysis of Bacillus licheniformis for improving bacitracin production. ACS Synth. Biol. 11 (3), 1325–1335 (2022).
Zhu, J. et al. Enhancement of precursor amino acid supplies for improving bacitracin production by activation of branched chain amino acid transporter BrnQ and deletion of its regulator gene Lrp in Bacillus licheniformis. Synth. Syst. Biotechnol. 3 (4), 236–243 (2018).
Towle, K. & Vederas, J. Structural features of many circular and leaderless bacteriocins are similar to those in saposins and saposin-like peptides. MedChemComm 8 (2), 276–285 (2017).
Siani, H., Wesgate, R. & Maillard, J. Y. Impact of antimicrobial wipes compared with hypochlorite solution on environmental surface contamination in a health care setting: a double-crossover study. Am. J. Infect. Control. 46 (10), 1180–1187 (2018).
Song, X., Vossebein, L. & Zille, A. Efficacy of disinfectant-impregnated Wipes Used for Surface Disinfection in Hospitals: a Review8p. 1–14 (Antimicrobial Resistance & Infection Control, 2019).
Weeks, R., Algburi, A. & Chikindas, M. Antimicrobial peptides and peptidomimetics for the control of antimicrobial resistance. Sustainable Agric. Reviews. 49, 205–249 (2021). Natural and Synthetic Approaches.
Acknowledgements
The authors acknowledge the Pakistan Higher Education Commission for supporting foreign research training through the IRSIP program. We also extend our gratitude to Dr. Jeong’s Lab at the Emerging Pathogens Institute, University of Florida, for comprehensive training in silico analysis and access to whole-genome sequencing facilities. Special thanks to Dr. Muhammad Amin, Head of Pathology at FIC, for providing the essential chemicals. We appreciate the assistance of Miss Asifa (Infection Control Committee, FIC) and Iqra Mushtaq (GCUF) in sample collection, and the support of Tahir Mursalin (Drug Testing Laboratory, Punjab Health Care Department) and Mr. Talha (UAF) in analytical testing of bioactive compound.
Funding
This study did not receive any specific grants from funding agencies in the public, commercial, or not-for-profit sectors.
Author information
Authors and Affiliations
Contributions
MUA: Conceptualization, Data curation, Formal analysis, Investigation, Methodology, Resources, Software, Validation, Visualization, Writing – original draft, Writing – review and editing. AHZ: Conceptualization, Supervision. MT: Supervision, Validation. NUA: Investigation, Writing, review, and editing.
Corresponding authors
Ethics declarations
Competing interests
The authors declare no competing interests.
Ethical approval
This study was approved by the Institutional Ethical Review Committee (IERC) of the Faisalabad Institute of Cardiology (FIC) (approval letter no. 17–2019/DME/FIC/FSD). As the study did not involve direct patient interaction, medical records, or human biological materials, only environmental samples from hospitals were utilized. Prior to collecting environmental samples, patients and their legal guardians were informed. We ensured compliance with all applicable regulations, including IERC approval, according to institutional and national guidelines.
Consent to participate
No human patients were included in this study.
Declaration of competing interest
The authors declare that they have no known competing financial interests or personal relationships that could influence the work reported in this study.
Additional information
Publisher’s note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Electronic supplementary material
Below is the link to the electronic supplementary material.
Rights and permissions
Open Access This article is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License, which permits any non-commercial use, sharing, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if you modified the licensed material. You do not have permission under this licence to share adapted material derived from this article or parts of it. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by-nc-nd/4.0/.
About this article
Cite this article
Asghar, M.U., Zaidi, A.H., Tariq, M. et al. Next generation antimicrobial peptide disinfectant targeting biocide and vancomycin resistant staphylococcus aureus through integrated in Silico and in vitro validation. Sci Rep 15, 28108 (2025). https://doi.org/10.1038/s41598-025-12736-7
Received:
Accepted:
Published:
DOI: https://doi.org/10.1038/s41598-025-12736-7
Keywords
This article is cited by
-
Unveiling the spectrum of vancomycin resistance in Staphylococcus aureus from Hospital-acquired urinary tract infections (HA-UTI) in cardiac patients
Molecular Biology Reports (2025)
-
Antimicrobial Peptides as Next-Generation Disinfectants: Tackling Biocide and Antimicrobial Resistance in Hospital Hygiene – A Narrative Review
Probiotics and Antimicrobial Proteins (2025)