Decoding the Benzaldehyde Pharmacophore: Structural Determinants for Enhancing Antibacterial Efficacy and Food Safety

Abstract

Phytocompounds undoubtedly are structurally diverse and play a crucial role in the development of novel therapeutic agents. 2-Hydroxy-4-methoxybenzaldehyde (HMB), from Hemidesmus indicus, is a potent antibacterial agent. Yet its pharmacophore has not been mechanistically defined. Here, we deconstructed HMB through a panel of structural derivatives to delineate the core structural determinants driving activity against foodborne pathogens. Structure–activity analysis revealed that the core benzaldehyde structure, rather than HMB itself, is the minimal active pharmacophore, with specific functional substitutions modulating antibacterial activity and membrane affinity. Integrating an experimental membrane assay with molecular dynamics simulations provided the first atomistic insight into how these derivatives interact with bacterial membrane lipids, demonstrating that substituent-driven modulation of hydrogen bonding dictates antibacterial potency. Specifically, electron-withdrawing groups enhanced membrane penetration and depolarization, particularly in Gram-positive pathogens. Time–kill kinetics and functional assays confirmed bactericidal action via membrane disruption rather than DNA interaction. Crucially, the active derivatives exhibited negligible cytotoxicity toward mammalian Vero cells, confirming their potential as selective and safe natural preservatives. This work provides a mechanistic blueprint for designing benzaldehyde-based antibacterials to combat antimicrobial resistance.

1. Introduction

Antimicrobial resistance (AMR) has emerged as one of the most formidable challenges to global health, disrupting clinical, agricultural and environmental systems [1]. The escalating prevalence of drug-resistant pathogens has severely limited the efficacy of conventional antibiotics, resulting in prolonged infections, increased mortality and substantial healthcare burden [2,3]. In the food sector, antimicrobial resistance poses an equally alarming threat, as resistant bacteria from livestock, food processing, and contaminated produce can readily disseminate through the food chain, jeopardizing food safety and public health. Environmental reservoirs, including wastewater, soil, and aquatic ecosystems, further amplify the spread of resistance genes, creating a continuous cycle of transmission between humans, animals, and the environment [3].

Amidst this urgent scenario, small-molecule antimicrobial agents have gained renewed interest as adaptable, cost-effective and diverse mechanisms of action. Their low molecular weight, ease of chemical modification, and ability to penetrate bacterial membranes allow them to target essential cellular functions, disrupt quorum sensing, inhibit biofilm formation and interfere with virulence pathways. Natural product-derived compounds, in particular, offer a valuable foundation for designing next-generation anti-infectives capable of circumventing established resistance mechanisms.

Driven by these needs, the search for novel antimicrobial scaffolds has intensified, with small-molecule natural derivatives emerging as highly promising leads [4]. Among these, benzaldehyde-based pharmacophores are noteworthy due to their structural versatility, tunable electronic properties, and inherent antimicrobial potential. Rational modification of the benzaldehyde core, by altering the substituent position, polarity, and electron-donating or electron-withdrawing characteristics, offers a strategic route to overcoming existing resistance mechanisms [5,6]. Such structure–activity-guided drug discovery not only enables the development of compounds capable of disrupting biofilms and virulence pathways but also provides a sustainable avenue to counteract AMR across clinical, food, and environmental interfaces.

Phenolic compounds, including both flavonoids and non-flavonoids, are widely distributed secondary metabolites in the plant kingdom and play a crucial role in plant defense [7]. Synthesized predominantly through the shikimate and phenylpropanoid pathways, these molecules are well-known for their antioxidant properties and their ability to protect biological macromolecules from oxidative damage [7]. Beyond these functions, many plant phenolics possess notable antibacterial properties and have been investigated extensively for their application in food preservation and safety [8]. Compared to flavonoids, non-flavonoids are simple in structure, typically comprising benzene rings with hydroxy and methoxy groups, features that align closely with benzaldehyde pharmacophore architecture.

Traditional medicinal plants remain a valuable reservoir of bioactive compounds with therapeutic potential [9,10]. Hemidesmus indicus is one such plant whose roots are rich in 2-hydroxy-4-methoxybenzaldehyde (HMB) as the major phenolic aldehyde and a compound categorized as Generally Recognized as Safe. The peculiar aroma of these plants’ roots is attributed to HMB. Previous studies have identified HMB as having antibacterial and antivirulence activities against Staphylococcus aureus, Streptococcus pyogenes, and Staphylococcus epidermidis [11,12,13]. Similarly, derivatives of HMB are also reported to have potent fungicidal activity and others [14,15,16]. However, the base structure that confers growth inhibitory activity to HMB remains unexplored. Moreover, previous studies have not quantitatively correlated substituent electronic parameters with antibacterial potency nor mechanistically validated substituent-driven membrane interactions through atomistic molecular dynamics simulations.

In this view, a total of ten different structurally similar compounds, along with HMB (Figure S1), were assessed for their antibacterial efficacy and their mechanism of action against foodborne pathogens, such as S. aureus, Listeria monocytogenes, and Escherichia coli.

The current study aims to decipher the pharmacophore underlying the antibacterial activity of HMB. A comprehensive structure–activity relationship (SAR) investigation was performed using HMB and its structural analogs to identify the substituent-specific determinants governing antibacterial potency. Hammett sigma correlation and molecular dynamics simulations were utilized to interpret the role of the electronic substituent effect and membrane interaction energetics. Alongside these computational insights, experimental assays evaluating the membrane permeability, integrity, and potential of the benzaldehyde pharmacophore were conducted to delineate the mechanistic basis of the benzaldehyde scaffold. Collectively, these integrated approaches decode the benzaldehyde pharmacophore and reveal how specific structural attributes modulate membrane-targeted antibacterial activity, providing a rational framework for developing innovative therapeutics to accelerate future drug discovery efforts and enhance food safety by combating foodborne pathogens.

2. Materials and Methods

2.1. Chemicals and Reagents

The compounds, such as HMB, methoxybenzene, benzaldehyde, carbolic acid, 4-methoxy phenol, salicylaldehyde (SAL), 4-methoxybenzaldehyde, 2-hydroxy-4-methoxybenzophenone, 4-nitrobenzaldehyde (NIT), and 4-chlorobenzaldehyde, were commercially procured from Sigma-Aldrich, St. Louis, MO, USA. Carbolic acid was purchased from the Sisco Research Laboratories, Mumbai, India. The procured compounds were dissolved in methanol at 50 mg/mL as a stock solution, from which the working concentrations were prepared. In all the experiments, methanol was used as a negative control.

2.2. Structure–Activity Relationship (SAR) Design Strategy

This study followed classical structure–activity relationship (SAR) principles. Ten commercially available benzaldehyde analogs were selected and purchased from Sigma Aldrich based on the structural similarities with the parent structural scaffold, HMB. The selected derivatives were intentionally limited to mono-substituted and structurally minimal analogs in order to isolate the independent electronic contribution of single substituents without steric complexity introduced by di- or meta-substituted derivatives (Figure S1). Para-substituted compounds were prioritized because Hammett sigma (σ) constants are most reliably defined for para positions, enabling quantitative correlation analysis. The panel, therefore, represents a controlled electronic gradient rather than an exhaustive chemical library. The electron-donating and electron-withdrawing properties of the substituents showing antibacterial potential were evaluated using Hammett σ constants, which provide a quantitative measure of their electronic effects.

2.3. Bacterial Strains and Culture Conditions

This study used reference bacterial strains, such as methicillin-resistant S. aureus (MRSA) ATCC33591, E. coli ATCC10536, and L. monocytogenes ATCC19111. The MRSA and L. monocytogenes were maintained at 4 °C in Trypticase soya agar (TSA). The E. coli was maintained at 4 °C in Luria Bertani agar (LBA). A 3 h culture of the bacterial strains raised from the overnight cultures was adjusted to 1 × 10⁶ CFU/mL in the respective growth medium to obtain a standard cell suspension for experimental purposes.

2.4. Determination of Minimum Inhibitory Concentration (MIC) and Minimum Bactericidal Concentration (MBC)

The Clinical and Laboratory Standards Institute (CLSI) manual described a standard broth double dilution method to assess the MIC for the test compounds [17]. In short, one percent of the standard cell suspension was added to 1 mL of Muller-Hinton Broth (MHB) containing serially diluted test compounds (1024 to 16 µg/mL) in the wells of a microtiter plate (MTP) and incubated at 37 °C for 24 h. The MIC was determined as the lowest concentration of the test compound that visibly inhibited bacterial growth as a medium control. Subsequently, ten microliters from each concentration was placed on the TSA/LBA after incubation to determine the MBC. The concentration showing no bacterial count was considered the MBC.

2.5. Assessing Metabolic Viability in Bacterial Cells

The influence of the test compounds on the metabolic activity of bacterial cells was assessed using an Alamar blue assay as described elsewhere [18]. The cells cultured in the presence and absence of the test compounds were obtained through centrifugation (10,000× g for 10 min), and the pellet was washed thrice with phosphate-buffered saline (PBS, pH 7.0) and resuspended in the same. Alamar blue at 100 µg/mL (w/v) was added to the cell suspension and incubated in the dark for 4 h. Following incubation, the fluorescent intensity of the reaction setup was read at excitation (560 nm) and emission (590 nm) wavelengths using a multilabel reader (Spectra Max 3, Molecular Devices, San Jose, CA, USA).

2.6. Time–Kill Kinetics Assay

Time–kill kinetics were performed by following the protocol in the Clinical Microbiology Procedure Handbook, 4th Edition [19]. The standard cell suspension was targeted as the starting inoculum for the test compounds at the MIC and 2 × MIC of the test compounds at 0 h. The experiment lasted 9 h. Every 1 h during the experiment, 10 µL from the sample was drawn and plated directly on the respective growth agar plates. Simultaneously, 100 µL of the sample was serially diluted and spread-plated in TSA plates. The plates were then incubated at 37 °C for 24 h. Following incubation, the plates were then enumerated and photographed.

2.7. Determination of Membrane Integrity

2.7.1. Determination of Intracellular Content Leakage Assay

The integrity of the bacterial cell membrane was evaluated by quantifying the discharge of cellular components, such as proteins and nucleic acids, into the supernatant of cell suspensions using a previously published method [20]. Cells were separated by centrifugation at 8000× g for 15 min, washed three times, and resuspended in 2 mL of phosphate-buffered saline (PBS, pH 7.4). The test compounds were introduced to the bacterial cell suspensions at the MIC and 2 × MIC levels, and they were then incubated at 37 °C for 6 h. A UV–visible spectrophotometer was used to measure the nucleic acid release in the supernatant at 260 nm. Bradford’s protein estimation assay was done to quantify the proteins released in the supernatant.

2.7.2. Quantitative Assessment of Cell Membrane Damage

The experiment was performed based on the product information available from Molecular Probes (LIVE/DEAD BacLight^TM Bacterial Viability Kit, Catalogue no. L7012, Thermo Fisher Scientific, Waltham, MA, USA). In short, the untreated and compound-treated bacterial cells were prepared, as mentioned in Section 2.6. Following incubation, the cells were harvested by centrifugation at 10,000× g for 10 min and washed twice with PBS. The harvested cells were then resuspended in the same. For staining, 1 mL of cell suspension was mixed with 3 µL of dye mixture (containing equal volumes of SYTO^® 9 and propidium iodide in 1 mL of filter-sterilized water) and left undisturbed for 15 min in the dark. Following incubation, the cells were again harvested by centrifugation and placed in the glass slides to observe the changes under CLSM (Leica TCS SP8 STED, Leica Microsystems, Nanterre, France). The excitation and emission of SYTO^® 9 and propidium iodide were 485 and 498 nm, and 535 and 617 nm, respectively [21].

2.7.3. Observation of Cellular Morphology by FE-SEM

Further, structural changes in the bacterial pathogens upon compound manifestation were observed using FE-SEM. The untreated and compound-treated bacterial cells were prepared, as mentioned in Section 2.6. Following incubation, the samples were centrifuged at 10,000 rpm for 10 min, and the spent medium was discarded. The resultant pellet was washed twice with sterile PBS. Finally, the resulting cells were dispersed in 50 µL of PBS. The dispersed cells were then placed on the glass slide (1 × 1 cm) and allowed to dry at room temperature for 1 h. Then, the slides were immersed in the glutaraldehyde fixation solution for 3 h in the dark. After incubation, the slides were washed with sterile PBS twice. Then, the slides were dehydrated with increasing ethanol concentrations (10, 20, 40, 60, 80, 90 and 100%) for 10 min each. The dehydrated slides were then sputter-coated and visualized under super-resolution FE-SEM (JSM-7800F, JEOL, Peabody, MA, USA) and photographed [22].

2.8. Determination of Membrane Permeability Assay

2.8.1. Determination of Potassium Ion Efflux

Following the protocol of Zhang et al. [23], with a slight modification, the release of free potassium ions from the test pathogens was investigated to determine the bacterial membrane permeability. Briefly, untreated and compound-treated bacterial cells were prepared, as mentioned in Section 2.6. The cells were separated, and the supernatant was quantified spectrometrically at 405 nm using the predefined protocol described in the potassium estimation kit (C001-3, Nanjing Jiancheng Bioengineering Institute, Nanjing, China) with little modifications for estimating the potassium release.

2.8.2. Determination of Inner Membrane Permeability

The inner membrane permeability of the test pathogens was measured by following the protocol of Gupta et al. [24], with little modifications. In brief, a 3 h culture was prepared from the bacterial cultures grown in LB medium containing 2% lactose at 37 °C. The cells were harvested from the 3 h culture and resuspended in PBS (OD_{600 nm} = 0.4). Further, the cells were subjected to compound treatment, as mentioned in Section 2.6. Following incubation, the cells were harvested by centrifugation at 10,000× g for 10 min and washed twice with PBS. To the cell suspension, o-nitrophenyl-beta-D-galactopyranoside (ONPG, final concentration of 2.5 mM) buffer was added and incubated at 37 °C for a further 15 min. Following incubation, the reaction mixture was read at 420 nm using a spectrophotometer.

2.9. Determination of Membrane Potential Assay Using DiSC3(5)

The membrane potential of the bacterial pathogens was assessed by 3,3′-Dipropylthiadicarbocyanine iodide [DiSC3(5)] following the protocol of earlier studies with a slight modification [25]. In the respective growth medium, the test pathogens were cultured in the presence and absence of test compounds at 37 °C for 6 h. Following incubation, the bacterial suspension was added with 0.8 μM DiSC3(5). An aliquot of 100 μL of the suspension was placed in a 96-well plate and then changes in the fluorescence intensity were recorded on a microplate reader (excitation λ = 622 nm, emission λ = 673 nm). Simultaneously, 10 µL of bacterial sample with the dye was placed in the glass slides and observed for the changes in the membrane potential using CLSM (Leica TCS SP8 STED, Leica Biosystems, Shanghai, China).

2.10. DNA Cleavage Assay

The ability of the compounds to target DNA was assessed by DNA cleavage assay by following the protocol of Gupta et al. [24]. Briefly, the PUC19 plasmid (500 ng in 5 µL) was added to the tube containing the test compounds at the required concentrations (2× concentration in 5 µL) and the reaction mixture (10 µL) was incubated for 6 h at 37 °C in the dark. Following incubation, the complete reaction mixture was mixed with 10× loading dye (1 µL) and was run on 0.8% agarose gel electrophoresis. The gel was stained with nucleic acid dye to visualize the level of damage in the plasmid rendered by the test compounds and then imaged. Methanol used to dissolve compounds was used as a negative control. EcoR1 was used as a positive control.

2.11. Molecular Dynamics Simulation Studies

The molecular dynamics (MD) simulations were performed by the GROMACS 2020.6 [26] package with an integration time step of 2 fs. The study utilized the CHARMM36 force field and the TIP3P model to describe the lipids and the water molecules, respectively [27,28]. The force field of the ligand was generated using the CHARMM-GUI Ligand Reader and Modeler online tool [29]. The periodic boundary conditions in all three dimensions and an NPT (310 K, 1 bar) ensemble were employed in our simulations. The v-rescale thermostat with a coupling constant of 1 ps for temperature coupling was used [30]. The semi-isotropic coupling method and the Parrinello–Rahman barostat with a coupling constant of 5 ps and a compressibility of 4.5 × 10⁻⁵ was used to restrain the pressure of the system [31]. The force-switching method was employed to turn off the van der Waals interactions from 1.0 nm to 1.2 nm [32]. The Particle mesh Ewald (PME) method was used for long-range electrostatic interactions with a cutoff value of 1.2 nm. The LINCS algorithm was used to constrain the bonds involving hydrogen atoms in the simulations [33,34].

Equilibrium simulations were performed to investigate the interactions between the three ligands (HMB, NIT, and SAL) and a POPE: POPG mixture with a mole ratio of 3:1, respectively. The bilayer was constructed by CHARMM-GUI, and multiple ligands were positioned randomly above the membranes during the initial conformation [35]. Each simulation system contained 256 lipids (192 POPE and 64 POPG), ~12,400 water molecules, 20 ligands (with an effective concentration of 0.089 mol/L), and 64 sodium as contour ions to neutralize the simulation system. A relatively high concentration of the ligand was used for obtaining sufficient statistically meaningful ligand–lipid interaction events within microsecond-scale simulations. No persistent ligand aggregation was observed during the trajectory analysis, as confirmed by stable radial distribution functions and the absence of clustered density peaks. The simulations of each of the three systems were performed for 1 μs. Analyses of the hydrogen bonds between the ligands and the lipids were facilitated by the MD Analysis module [36]. A hydrogen bond was formed if the distance between the heavy atoms of the donor and acceptor is smaller than 0.3 nm and the donor hydrogen acceptor is larger than 150°. VMD was used to visualize the trajectories [37].

The free energy profiles for the ligands entering the center of the bilayer were calculated using the umbrella sampling method [38]. The distance between the center of mass of the ligand and the bilayer center was employed as the reaction coordinate. The initial conformation for the simulation of each window was obtained by steered molecular dynamics simulations. Specifically, a ligand was pulled from the bulk water into the bilayer center using a force constant of 2400 kJ mol⁻¹ nm⁻² with a pulling rate of 1 × 10⁻⁵ nm/ps in 0.3 μs simulations. Then, the snapshots were obtained with the desired distances between the ligand and the bilayer center as the initial conformations for the simulations of each window. Sixty-five windows were employed in total, with a window width of 0.05 nm. For each window, 0.02 μs simulations were performed with the distance between the ligand and the bilayer center restrained at the desired value with a force constant of 5000 kJ·mol⁻¹·nm⁻². The other simulation parameters were the same as described above. The last 0.01 μs trajectories of each window were employed to construct the potential of mean force (PMF) profiles using the weighted histogram analysis method (WHAM) with 200 bins (i.e., the bin width of 0.016 nm) [39]. The errors of the energy profiles were estimated using the Bayesian bootstrap analysis [40]. A smaller bilayer was used with more bulk waters in our system for the free energy calculations, with each simulation system containing 128 lipids (POPE: POPG = 3:1), 1 ligand, 12,800 water molecules, and 32 sodium as counter ions.

2.12. Cell Line

The typical cell line (Vero) was obtained from the National Centre for Cell Science (NCCS), Pune, and grown in Eagle’s Minimum Essential Medium containing 10% fetal bovine serum (FBS). The cells were maintained at 37 °C, 5% CO₂, 95% air, and 100% relative humidity. Maintenance cultures were passaged weekly, and the culture medium was changed twice weekly.

2.12.1. Cell Treatment Procedure

The monolayer cells were detached with trypsin–ethylene diamine tetra acetic acid (EDTA) to make single-cell suspensions, and viable cells were counted using a hemocytometer and diluted with medium containing 5% FBS to give a final density of 1 × 10⁵ cells/mL. One hundred microliters per well of cell suspension was seeded into 96-well plates at a plating density of 10,000 cells/well and incubated to allow for cell attachment at 37 °C, 5% CO₂, 95% air, and 100% relative humidity. After 24 h, the cells were treated with serial concentrations of the test samples. They were initially dissolved in neat dimethylsulfoxide (DMSO), and an aliquot of the sample solution was diluted to twice the desired final maximum test concentration with a serum-free medium. An additional four serial dilutions were made to provide a total of five sample concentrations. Aliquots of 100 µL of these different sample dilutions were added to the appropriate wells already containing 100 µL of the medium, resulting in the required final sample concentrations. Following sample addition, the plates were incubated for an additional 48 h at 37 °C, 5% CO₂, 95% air, and 100% relative humidity. The medium without samples served as a control [13].

2.12.2. MTT Assay

3-[4,5-dimethylthiazol-2-yl] 2,5-diphenyltetrazolium bromide (MTT) is a yellow water-soluble tetrazolium salt. A mitochondrial enzyme in living cells, succinate dehydrogenase, cleaves the tetrazolium ring, converting the MTT to an insoluble purple formazan. Therefore, the amount of formazan produced is directly proportional to the number of viable cells.

After 48 h of incubation, 15 µL of MTT (5 mg/mL) in PBS was added to each well and incubated at 37 °C for 4 h. The medium with MTT was then flicked off, and the formed formazan crystals were solubilized in 100 µL of DMSO, and then the absorbance was measured at 570 nm using a microplate reader [13].

2.13. Statistics

All experiments were performed in experimental triplicate at least three times. All data were expressed as arithmetic mean ± standard deviation, and statistical analysis was performed using a statistical software package (SPSS v20.0; SPSS, Armonk, NY, USA). One-way ANOVA and Student t-test were used to compare the control and treated samples, with a p-value less than 0.05 being significant.

3. Results

3.1. Structure–Activity Relationship (SAR) Study Identifies Benzaldehyde Core as the Minimal Antibacterial Pharmacophore

All the nine compounds were assessed for their antibacterial activity against S. aureus, L. monocytogenes and E. coli. Among the compounds tested, HMB, NIT, and SAL showed the lowest MIC value, indicating superior antibacterial potency (

Table 1. MIC values (µg/mL) of HMB and its substituted benzaldehyde derivates determined against the selected foodborne pathogens using the broth microdilution method.

S. No	Compound Name	S. aureus	L. monocytogenes	E. coli
1	HMB	512	512	512
2	SAL	256	256	512
3	NIT	128	256	512
4	Chlorobenzaldehyde	1024	1024	1024

, Figure S2). Chlorobenzaldehyde showed moderate activity. The same was validated by measuring the metabolic activity of the bacterial cells treated with and without benzaldehyde derivatives (Figure S3). Antibacterial profiling revealed that compounds containing the aldehyde group are active, while those lacking it are inactive. It is also important to note that the presence of aldehyde alone in the benzene ring was not sufficient for the antibacterial effectiveness. However, derivatives containing additional polar or electron-withdrawing substituents with benzaldehyde pharmacophore exhibited stronger antibacterial potential. Salicylaldehyde (2–OH), nitrobenzaldehyde (4–NO₂), chlorobenzaldehyde (4–Cl) and HMB (2–OH and 4–OCH₃) displayed growth inhibitory activity, indicating that substituent polarity and orientation play critical roles. These findings establish the minimal benzaldehyde nucleus as the minimal antibacterial pharmacophore.

3.2. Correlation Between Hammett σ Electronic Parameters and Antibacterial Potency

The comparative Hammett σ analysis clearly demonstrated that substituent electronic properties directly influenced the antibacterial potency of the benzaldehyde derivatives (

Table 2. Major substituent groups, corresponding Hammett σ values, and their net electronic effects for the benzaldehyde derivatives evaluated in this study.

Compound	Major Substituent(s)	Approx. Hammett σ Value (Reference Para)	Net Electronic Effect
SAL	–OH (ortho)	−0.37	Strong electron-donating
HMB	–OH (ortho) + –OCH3 (para)	−0.37 + (−0.27)	Strong electron-donating
NIT	–NO2 (para)	+0.78	Strong electron-withdrawing
Chlorobenzaldehyde	–Cl (para)	+0.23	Moderately electron-withdrawing

). The combined regression plot showed a strong negative correlation between the σ values and MIC for Gram-positive bacteria, particularly S. aureus, followed by L. monocytogenes, indicating that electron-withdrawing substituents (high positive σ) enhanced the antibacterial activity. In contrast, E. coli displayed an almost non-responsive flat regression pattern, implying that the Gram-negative outer membrane architecture limited penetration irrespective of σ modulation (Figure 1). Furthermore, compounds bearing strong electron-withdrawing groups (e.g., nitro moiety) exhibited the most pronounced antibacterial effects, whereas electron-donating groups (negative σ) showed weaker activity. Linear regression analysis demonstrated strong inverse correlations between the σ values and antibacterial activity against S. aureus (R² = 0.89, p < 0.01) and L. monocytogenes (R² + 0.81, p < 0.05), whereas E. coli showed a weak correlation (R² = 0.18). Thus, the relationship between the Hammett σ values and bioactivity clearly supports that substituent-driven polarity modulation plays a critical role in driving the antibacterial ability of these benzaldehyde derivatives.

Since SAR and MIC profiling indicated that antibacterial potency was strongly influenced by the nature and polarity of substituents on the benzaldehyde core, we next sought to understand how these chemical modifications translate into differences at the membrane interface. Therefore, molecular dynamics simulations were performed to mechanistically validate whether substituent-dependent polarity affects ligand–lipid interactions, membrane insertion depth and permeability. This integrative approach allowed us to connect the observed antibacterial activity trends with the underlying atomic-level membrane interaction behavior.

3.3. Substituent Polarity Governs Ligand–Lipid Hydrogen Bonding and Membrane Insertion

Molecular dynamics simulation was performed to investigate the interactions between the ligands (HMB, NIT, and SAL) and POPE: POPG (mole ratio 3:1) bilayers. Both the equilibrium simulations and free energy calculations indicated distributions of the ligands at the interface between the bilayers and the bulk water (Figure 2) around the carboxyl groups of the lipids. The trajectories of the ligands in Figure 2A suggested that all three ligands entered the membranes quickly in the first 0.1 μs simulations. Their distributions in the bilayers after equilibrium (Figure 2C) showed peaks that overlap with the distributions of the lipid carboxyl groups, consistent with the positions of the energy minima (Figure 2E) in the free energy profiles.

However, it seemed that NIT entered the bilayer faster than the other two ligands (Figure 2A). The distributions in Figure 2C also showed a slightly higher density for NIT at the hydrophobic center of the bilayer than for HMB and SAL. These results, together with the lower free energy of NIT at the bilayer center in Figure 2E, indicated the higher permeability of NIT into the bilayer center compared to the other two ligands. To illustrate this difference, the hydrogen bond interactions between the ligands and the lipids were analyzed and found that HMB and SAL formed many more hydrogen bonds with the lipids than NIT (Figure 2D). Detailed analyses indicated that HMB and SAL primarily formed hydrogen bonds with the carboxyl groups of the ligands, utilizing their hydroxyl groups as hydrogen bond donors (Figure 2B and Figure S4). In contrast, similar hydrogen bonding between NIT and the carboxyl groups of the lipids was impossible due to the lack of hydroxyl groups in NIT (or other hydrogen bond donors). The existing hydrogen bonds between them are mainly found between the lipid headgroups (specifically, the ammonium of POPE and the hydroxyl group of POPG (served as donors) and the carboxyl group of NIT (served as acceptors)) (Figure 2B and Figure S4). This confirms that increasing the electron-withdrawing character of NIT increases the overall polarity of the aromatic scaffold, strengthens the interaction at the bacterial interface and enhances the membrane damage-based mechanism.

In brief, molecular dynamics simulation demonstrated that substituent polarity dictates the nature of ligand–lipid interactions. Hydroxyl-bearing compounds (HMB and SAL) form stable hydrogen bonds with lipid headgroups, anchoring them at the membrane interface, while NIT penetrated deeper due to reduced hydrogen bonding and increased hydrophobicity. The free energy profiles revealed a lower energy barrier for NIT insertion compared to HMB and SAL, correlating with their antibacterial activity. These findings confirm that substituent polarity acts as a molecular switch controlling hydrogen bonding strength and membrane insertion depth.

3.4. Time–Kill Kinetics Validates Fast Membrane-Targeted Antibacterial Activity

Based on the MIC values obtained, the MIC and 2× MIC of the benzaldehyde derivatives were selected for the time–kill assay. The growth curve profiles (Figure 3) along with the spot assay results (Figure S5) collectively illustrate the bactericidal progression over time. In MRSA, HMB at its MBC markedly reduces the viable cell counts within 3 h of exposure (Figure 3A). In contrast, SAL and NIT showed comparable killing trends at both MIC and 2× MIC levels with no significant difference observed between the two exposure concentrations. For L. monocytogenes, HMB exhibited comparatively weaker activity, as the organism tolerated the MIC and survived up to 6 h at 2× MIC. Conversely, NIT, which performed as the most potent compound against L. monocytogenes, suppressed bacterial growth within 2 h at the MIC and within 1 h at 2× MIC (Figure 3B). Similar to the killing pattern of HMB against MRSA, SAL completely inhibited the growth of L. monocytogenes within 3 h. Among the tested organisms, E. coli was the most susceptible (Figure 3C). HMB achieved complete killing of E. coli within 1 h at 2× MIC, and both SAL and NIT significantly reduced E. coli viability within 2 h at 2× MIC.

To further validate these findings, a spot assay was performed (Figure S5), which supported and confirmed the bacterial count reduction observed in the time–kill growth kinetics assay. Overall, these results clearly indicate that the test compounds exhibit stronger and more rapid bactericidal activity against the Gram-negative pathogen than against Gram-positive strains. The combined MIC and time–kill kinetic analyses therefore confirm that at the MIC and 2× MIC levels, the compounds efficiently interfere with the growth and viability of the evaluated foodborne pathogens.

Based on these time–kill kinetic outcomes, the exposure time points were standardized as follows: for MRSA and L. monocytogenes, 6 h was considered the maximum exposure duration, while for E. coli, the maximum exposure duration was set at 3 h, which will be used for further assays.

Although the MIC values suggested limited σ-dependent modulation in E. coli, once the inhibitory concentration threshold was reached, rapid bactericidal kinetics were observed. This indicates that the Gram-negative outer membrane primarily influences the susceptibility threshold rather than the killing rate, thereby reconciling the apparent discrepancy between the MIC plateau behavior and time–kill efficacy.

3.5. Quantitative and Qualitative Functional Validation of Membrane Targeting

The quantitative assessment of the membrane targeting potential was assessed with an intracellular content release assay. Exposure of the test pathogens to the growth inhibitory concentration of HMB derivatives resulted in a dose-dependent release of intracellular proteins and nucleic acid into the extracellular supernatant (Figure 4A,B). The higher release observed at 2× MIC confirms the enhanced bactericidal action and more severe membrane destabilization. These findings indicate that the test compounds compromise membrane integrity and promote the leakage of intracellular components, ultimately leading to bacterial death.

The qualitative assessment of membrane integrity was assessed with microscopy analyses. Visualization of bacterial cells stained with live/dead stain revealed a progressive increase in PI-associated red fluorescence in the samples treated with benzaldehyde derivatives, implying a significant loss of membrane integrity, while untreated cells predominantly retained SYTO^®9-associated green signals (Figure 4C). SEM analysis provided complementary ultrastructural evidence, wherein untreated cells exhibited smooth and intact morphology, while treated bacterial cells displayed extensive membrane rupture, surface pitting, and cellular collapse (Figure 4D). Together, both quantitative and quantitative analyses conclusively demonstrate that HMB and its derivatives exert antibacterial activity via membrane disruption-driven killing.

3.6. Substituent Polarity Dictates Membrane Permeability and Membrane Depolarization in Gram-Negative and Gram-Positive Pathogens

The membrane permeability assay demonstrated that the benzaldehyde derivatives induced a clear dose-dependent enhancement in intracellular leakage, as reflected by elevated potassium efflux relative to the untreated control cells. Among the derivatives, SAL exhibited the highest leakage-associated membrane disruption, followed by HMB, whereas NIT produced comparatively lower leakage levels despite its strong electron-withdrawing profile (Figure 5A). Meanwhile, the inner membrane permeability of the pathogens was evaluated by quantifying the β-galactosidase activity using ONPG as the chromogenic substrate. Following incubation, the release of o-nitrophenol was measured at 420 nm. A marked increase in absorbance was recorded in the compound-treated samples compared with the untreated control (Figure 5B), demonstrating that exposure of the test compounds facilitated ONPG entry into the cytoplasm through a compromised membrane. These findings also confirm that the benzaldehyde derivatives induce concentration- and time-dependent increases in inner membrane permeability, supporting their membrane-disruptive mode of action.

In parallel, membrane potential studies using DiSC₃(5) dye showed a concentration-dependent fluorescence increase upon compound treatment, confirming the rapid depolarization of the cytoplasmic membrane (Figure 5C and Figure S6). The depolarization trend correlated with the Hammett σ values, where NIT induced the strongest membrane potential collapse, indicating that although NIT does not cause major membrane rupture (as seen with lower K⁺ leakage), it effectively dissipates the membrane potential and likely penetrates the bilayer more efficiently to reach intracellular targets. This dual observation supports a mechanistic distinction: SAL and HMB predominantly act via membrane destabilization and leakage, whereas NIT functions mainly through efficient permeation and membrane depolarization-mediated bactericidal action. Collectively, these results confirm that substituent electronic effects directly control the membrane permeability behavior, extent of depolarization, and intracellular access in both Gram-positive and Gram-negative pathogens, thereby influencing the overall antibacterial mechanism of action.

3.7. Compound–DNA Interactions Are Not a Contributing Antibacterial Mechanism

The analysis is based on the principle that different forms of DNA (supercoiled, nicked/open circular, or linear) migrate at different rates through the agarose gel due to their size and conformation. None of the compounds were found to alter the plasmid structure, confirming that the antibacterial effect arises from membrane disruption rather than DNA interaction (Figure S7). The mention of EcoR1 serves as a valid positive control. Since EcoR1 is known to linearize the plasmid, it confirms the benzaldehyde derivatives do not interact with DNA and confirms that the mechanism of action is likely non-DNA-related, such as membrane disruption.

3.8. Cytotoxic Profiling of Benzaldehyde Derivatives Confirms Selective Targeting of Bacterial Membranes

No appreciable reduction in Vero cell viability was observed following exposure to SAL and NIT across the tested concentration range (18.75–300 µg/mL). The in vitro cytotoxicity assay (Figure 6) therefore confirmed that both compounds were non-toxic toward mammalian cells under the evaluated conditions. This observation is consistent with our previous report [8], in which HMB also exhibited no cytotoxic effects toward the Vero cell line and PBMC up to 300 µg/mL. Taken together, these findings confirm that the active benzaldehyde derivatives retain potent antibacterial efficacy at concentrations that are non-toxic to mammalian cells, thereby supporting their selective membrane-targeted mode of action rather than indiscriminate cytotoxicity.

The selectivity index (SI) was calculated for each tested pathogen using the highest non-cytotoxic concentration (300 µg/mL). For SAL, the SI values were ≥1.17 against S. aureus and L. monocytogenes, and ≥0.58 against E. coli. For NIT, the SI values were ≥2.34 against S. aureus and L. monocytogenes, and ≥1.17 against E. coli. These findings indicate stronger therapeutic selectivity toward the Gram-positive bacterial compared to Gram-negative organism.

4. Discussion

The present work systematically delineates how benzaldehyde scaffolds and their structural derivatives mediate antibacterial activity primarily through membrane-targeted mechanisms. SAR analysis clearly supported the benzaldehyde core as the minimal pharmacophore required for activity and further established that substituent-based electronic perturbation modulates the strength of ligand–lipid interactions. Electron-withdrawing substituents, including −NO₂ and −Cl, enhanced the antibacterial potential relative to the parent benzaldehyde and electron-donating groups, indicating a polarity-dependent mechanism of membrane engagement. This observed polarity–activity relationship aligns with the established principles of small-molecule–membrane interaction, where an increased molecular dipolar character promotes membrane affinity and lipid partitioning. Our findings are strongly supported by molecular dynamics studies on polyphenols, which demonstrate that a molecule’s ability to penetrate and disrupt the lipid bilayer is governed by its capacity to form multiple, simultaneous hydrogen bonds with lipid headgroups and its overall electronic profile. For instance, in proanthocyanidins, the presence of gallate moieties, which introduce significant electron-withdrawing character and polarity, was shown to dramatically increase the binding affinity, drive deeper penetration into the bilayer, and enhance membrane-perturbing potency compared to their nongallated analogs [41]. Similarly, the enhanced activity conferred by electron-withdrawing groups on the benzaldehyde core can be rationalized by an analogous mechanism: these substituents increase the molecule’s dipolar moment, thereby strengthening electrostatic interaction and hydrogen bonding with the lipid bilayer, much like the galloyl groups in polyphenols.

Furthermore, the spatial configuration and rigidity of the ligand, which dictate the orientation and presentation of its polar groups to the membrane, are critical. Studies have shown that more extended and rigid molecular architectures (e.g., A-type proanthocyanidin dimers) facilitate deeper penetration and more efficient contact with lipid acyl chains compared to folded, flexible counterparts [42]. This principle provides a plausible explanation for the differential activities observed within the selected benzaldehyde derivative; specific substitution patterns likely impose distinct conformational constraints that optimize membrane insertion and disruption.

Hammett σ constants correlated strongly with antibacterial potency, supporting a direct link between the substituent electronics and antibacterial outcome. NIT and SAL, which possess higher σ values compared to methoxy-substituted analogs, showed enhanced membrane disruption and faster time–kill kinetics. Such σ-dependent functional behavior has also been observed in the derivatives of chalcone and benzimidole [43,44]. It should also be noted that σ constants are most accurately defined for para substituents; therefore, σ values applied to ortho-substituted SAL (-OH) and HMB (-OCH₃) represent approximations and are interpreted cautiously due to possible intramolecular hydrogen bonding and a steric effect not captured by classical Hammett parameters. These observations further reinforce that hydrophobicity alone is insufficient; rather, the balance between polarity, electron density withdrawal, and anisotropic charge distribution influences membrane binding.

In molecular simulation studies, the ligand concentrations exceed the experimental MIC levels. The objective was a comparative mechanistic evaluation rather than an absolute quantitative replication. The relative free energy trends correlate with the experimental membrane depolarization profiles, supporting translational relevance. This study substantiated these experimental readouts by revealing lower free energy barriers for the insertion of nitro-substituted derivatives into the bilayer, signifying more favorable lipid partitioning. Comparable MD studies on benzaldehyde and aldehyde-functionalized antimicrobials have similarly demonstrated that ligands with stronger dipole alignments traverse the headgroup region with less energetic penalty, thereby accelerating membrane perturbation [45]. The present results support a comparable trajectory, where the polar analogs actively orient into the headgroup region, interact via hydrogen bonding, and subsequently destabilize bilayer packaging. Unlike earlier reports that describe benzaldehyde derivatives as general antimicrobial phytochemicals, the present study mechanistically defines the aldehyde-bearing benzene ring as the minimal antibacterial pharmacophore and quantitatively links substituent electronics to membrane insertion energetics. This combined σ-correlation and atomistic simulation framework represents a mechanistic advancement over prior structure–activity observations.

Functional validation assays confirmed that membrane disruption, not DNA cleavage, is the primary antibacterial mode of action. Potassium leakage, increased DiSC3(5) fluorescence, intracellular biomolecule release, and live/dead CLSM all converged on membrane collapse, the primary phenotypic endpoint. SEM further demonstrated severe membrane deformation, consistent with the increased permeability observed. These multi-layered signatures agree with the membrane-active quinoline derivatives reported for MRSA and Pseudomonas systems [46]. Collectively, this reinforces that the membrane is the principal target, rather than interacting with nucleic acid machinery.

Importantly, the membrane selectivity observed here is notable: none of the active derivatives exhibited cytotoxicity in Vero cells up to 300 ug/mL. This is in line with the earlier findings reported for HMB and other plant-derived aldehydes, which demonstrated preferential action on a bacterial envelope lipid while sparing mammalian cell membranes [13,47]. Selective targeting is likely supported by differences in lipid composition. Bacterial membranes are enriched in cardiolipin and phosphatidylglycerol, whereas mammalian membranes contain cholesterol that confers higher rigidity and reduced susceptibility to electrophilic perturbation.

Though the observed MIC values are higher than those of traditional antibiotics, they align with the functional ranges of other natural food preservatives. Benzaldehyde derivatives are currently GRAS-listed flavoring agents; however, their transition to food preservation requires addressing the volatility, organoleptic changes, and matrix binding effects. To mitigate these challenges, benzaldehyde derivatives are under investigation for their potential synergistic effect with existing preservatives and with different encapsulation strategies to enhance stability and reduce sensory impact. Future commercial applications will necessitate adherence to regulatory thresholds and food-specific intake limits.

On the whole, this study expands the mechanistic framework of benzaldehyde-derived antibacterials by (1) defining the core aldehyde pharmacophore, (2) mapping the electronic substituent effects to membrane binding, (3) correlating Hammett σ descriptors with functional outcomes, and (4) validating membrane collapse as the primary antibacterial mechanism.

The alignment between computational energetics, biophysical readouts and phenotypic outcomes strongly suggests these scaffolds can be rationally tuned via σ-guided design to access next-generation membrane-active antibacterial leads. This strategy aligns with the current emerging direction in non-classical antibiotic development where membrane targeting is increasingly pursued to circumvent resistance evolution and bypass intracellular target mutations [48,49].

5. Conclusions

In summary, this study establishes the benzaldehyde nucleus as the minimal antibacterial pharmacophore and demonstrates that substituent-driven polarity fundamentally dictates bacterial membrane targeting efficiency. By integrating SAR, Hammett σ correlations, MD simulations, and functional membrane assays, we show that electron-withdrawing substituents enhance ligand–lipid interactions and increase membrane penetration, resulting in superior antibacterial kinetics, particularly against key foodborne Gram-positive pathogens. Importantly, the compounds exhibited negligible cytotoxicity toward mammalian cells, confirming their selective membrane disruption without off-target toxicity. Collectively, these findings not only decode the molecular basis underlying benzaldehyde-mediated membrane perturbation but also present a rational design framework for developing membrane-active antibacterial agents that can bypass classical resistance pathways and enhance food safety.