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Screening of a broad-spectrum aptamer and its application in the fabrication of an aptasensor for the detection of sulfonamides in animal-derived foods
*Corresponding author: E-mail address: letao@cqnu.edu.cn (T. Le)
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Received: ,
Accepted: ,
Abstract
Sulfonamides (SAs) are commonly used in livestock. However, excessive residues of these substances in animal-derived foods pose health risks to consumers. Therefore, there is an urgent need for simple, high-throughput techniques that can simultaneously detect multiple SAs or groups of SAs, enabling rapid assessment of total SA residues in animal-derived foods. In this study, a sulfadiazine (SDZ)-specific aptamer was successfully selected using a graphene oxide-based-Systematic evolution of ligands by exponential enrichment (SELEX). The aptamer was subsequently optimized and truncated to a 16-nucleotide sequence (SAs16-1: 5′-AGGGCTTCAACGGCAC-3′), which retained comparable binding affinities for SDZ, sulfadimethoxine (SDM), and sulfamethoxypyrimidine (SMP), with dissociation constants ranging from 74.12 to 116.16 nM. A novel Fe₃O₄/gold (Au)/graphitic carbon nitride (g-C₃N₄)-based fluorescent aptasensor, incorporating SAs16-1 for simultaneous detection of SDZ, SDM, and SMP, was further developed. Under optimized conditions, the linear detection ranges of SDZ, SDM, and SMP were 5-120, 3.25-150, and 10-110 ng/mL, respectively, with detection limits of 2.31, 1.94, and 5.86 ng/mL. Spiked recovery experiments in real food samples showed recovery rates of 85.16-111.52%, with a coefficient of variation below 15%. Furthermore, the aptasensor’s results were positively correlated with those obtained using high-performance liquid chromatography (HPLC). In conclusion, this novel aptasensor offers a rapid, cost-effective, and sensitive method for screening total residues of SDZ, SDM, and SMP in animal-derived foods, and holds great potential for widespread application in food safety testing.
Keywords
Aptasensor
Broad-spectrum aptamer
GO-SELEX
Sulfonamides
Molecular dynamics
1. Introduction
Sulfadiazine (SDZ), sulfadimethoxine (SDM), and sulfamethoxypyrimidine (SMP) are sulfonamides (SAs) frequently administered in livestock farming to prevent and control infections caused by bacterial and protozoal pathogens [1]. However, the improper use of these antimicrobials has led to residual contamination in animal-derived food, resulting in the rejection of food imports between countries [2,3]. In response to these concerns, food safety authorities in regions such as the European Union, the United States, and China have established a maximum residue limit of 100 μg/kg for SAs in animal-derived food items [4]. To detect and quantify these residues, a variety of analytical technologies have been developed over the years, including high-performance liquid chromatography (HPLC)-ultraviolet [5], gas chromatography-mass spectrometry [6], liquid chromatography-tandem mass spectrometry [7], liquid chromatography [8], capillary electrophoresis-tandem mass spectrometry [9], and immunological assays [10,11]. Despite their sensitivity and reliability, these conventional methods are often associated with significant drawbacks, such as lengthy sample preparation, high operational costs, and the need for specialized personnel [12,13].
Aptamers are short, single-stranded oligonucleotides composed of either DNA or RNA, which can fold into complex 3D structures, enabling them to bind specifically to target molecules with high affinity [4,14]. They offer several advantages over antibodies, including lower cost, higher specificity, and greater stability [15-17]. In recent years, a wide array of aptamer-based analytical platforms has been developed for the detection of biomolecules [18-20]. Numerous aptasensors have also been designed and reported specifically for identifying SAs residues [21,22]. In our previous study, aptamers that specifically recognize individual SA drugs were screened using graphene oxide-based SELEX [4,14,22-24]. Since there are over 40 types of SAs used in human and veterinary medicine, detecting a single drug is impractical. In farming, multiple SAs are often used in combination to achieve therapeutic effects, leading to residues of several SAs in animal tissues. Single-drug detection methods in multi-drug residue detection systems are not only time-consuming but also increase detection costs. To overcome this limitation, Xu et al. used a mixture of sulfamethoxydiazine (SMD), sulfaquinoxaline (SQX), and sulfamethoxazole (SMZ) for aptamer capture-SELEX and identified aptamers that bind to this mixture [1]. In a similar approach, Li et al. employed a combination of three SA compounds during the selection process to isolate aptamers exhibiting varying binding specificities [21]. However, the traditional screening process for aptamers is similar to “finding a needle in a haystack”, making it difficult to quickly, efficiently, and accurately identify broad-spectrum aptamers. Therefore, it is very important to improve the performance of pre-screened SA aptamers to broaden their recognition spectrum and enable simultaneous detection of multiple SAs.
Recently, Chen et al. used a non-experimental manual editing technique to rapidly generate broad-spectrum aptamers for classes of molecules. These aptamers demonstrated comparable binding affinities to those obtained through experimental screening, but were much easier to obtain [25]. This manual editing technique involves predicting aptamer structures by directly modifying bases to analyze binding properties. Aptamers generated using this method can recognize a broad range of molecules, enabling them to target entire classes of molecules rather than just a single type. Integrating these broad-spectrum aptamers with sensors enables highly selective, broad-spectrum, and sensitive detection of SAs.
In this work, an SA-targeting aptamer was systematically optimized and structurally modified to obtain a broad-spectrum binding capability. Based on this aptamer, a fluorescence-based aptasensor (Figure 1) was constructed using a Fe₃O₄/Au/g-C₃N₄ nanocomposite for the simultaneous detection of SDZ, SDM, and SMP in real sample matrices. To the best of our knowledge, this study represents the first successful application of molecular simulation-guided manual sequence editing to derive a broad-spectrum aptamer, which was subsequently employed in the development of aptasensors for the detection of multiple SA residues. The aptasensor demonstrated excellent sensitivity over a wide detection range: 5-120 ng/mL for SDZ, 3.25-150 ng/mL for SDM, and 10-110 ng/mL for SMP.
- Schematic illustration of the Fe₃O₄/Au/g-C₃N₄-based fluorescent aptasensor for the detection of SDZ, SDM, and SMP.
The molecular recognition mechanisms and structure–activity relationships of the aptamer with SDZ, SDM, and SMP were systematically investigated through molecular dynamics (MD) simulations, complemented by molecular mechanics/generalized born surface area (MM/GBSA) calculations to evaluate the binding free energies. Furthermore, the performance of the developed aptasensor was validated by analyzing SDZ, SDM, and SMP in real samples such as milk and honey, showing strong agreement with results obtained from conventional HPLC. These findings demonstrate that the proposed fluorescent aptasensor serves as a reliable and efficient alternative to traditional analytical techniques for monitoring SA residues in animal-derived foods.
2. Materials and Methods
2.1. Reagents
The truncated aptamer SAs16-1 (5′-AGGGCTTCAACGGCAC-3′), previously identified and validated in our laboratory [24], was utilized in this study. This aptamer is derived from the original SAs16 through truncation, and the specific truncation process has been shown in Figure 2. Analytical standards of SAs, including SDZ, SDM, and SMP, as well as polyethylene glycol (PEG) 20000, iron chloride hexahydrate (FeCl₃·6H₂O), iron sulfate heptahydrate (FeSO₄·7H₂O), urea, chloroauric acid (HAuCl₄), and N,N-dimethylformamide (DMF), were obtained from Sigma-Aldrich (St. Louis, MO, USA). All reagents were of analytical grade. Ultrapure water was produced using a Milli-Q purification system (Millipore, Bedford, MA, USA) and used throughout the experiments.
- (a) The tertiary structure of the truncated aptamer SAs16-1; (b) The specificity of SAs16-1 in recognizing SDZ, SDM, and SMP. (c) Truncation process of the aptamer (from SAs16 to SAs16-1): a large stem-loop structure was retained during truncation, and a terminal base pair was preserved to maintain flexibility at the 3′ end of the aptamer. (d) Binding affinity of the original (untruncated) aptamer SAs16 with SDZ. (e-g) The binding affinity of SAs16-1 with (e) SDZ, (f) SDM, and (g) SMP, respectively. (h-j) The geometric structures and surface electrostatic potential maps (in electron volts, eV) of (h) SDZ, (i) SDM, and (j) SMP.
2.2. Synthesis of Fe3O4/Au/g-C3N4
The Fe₃O₄/Au/g-C₃N₄ nanocomposite was synthesized with minor modifications based on previously reported procedures [22,26]. Briefly, 15 g of urea was subjected to thermal treatment by heating to 550°C at a rate of 2°C/min and maintained at this temperature for 4 h, resulting in the formation of yellow-colored g-C₃N₄. Subsequently, 1 g of the obtained g-C₃N₄ was dispersed in 50 mL of 1.5 mM trisodium citrate solution and sonicated for 20 min. Afterward, 25 mL of a 1 mM HAuCl₄ solution was introduced into the mixture, followed by continuous stirring at 60°C for 2 h. The resulting solid was collected by filtration, thoroughly washed with deionized water and ethanol (four times each), and then freeze-dried to yield Au/g-C₃N₄.
For the preparation of the final composite, 0.15 g of Au/g-C₃N₄, 0.5 mmol of FeCl₃·6H₂O, and 0.73 mmol of FeSO₄·7H₂O were dispersed in 50 mL of deionized water and sonicated for 1 h. The mixture was transferred into a Teflon-lined stainless steel autoclave, followed by the addition of 0.003 mol NaOH. The sealed reactor was then heated to 120°C and held at this temperature for 24 h. Upon cooling, the product was washed repeatedly with deionized water and ethanol, and the magnetic nanocomposite was isolated using a magnet before being freeze-dried to obtain the final Fe₃O₄/Au/g-C₃N₄ material.
2.3. Structural modeling and molecular docking
The aptamer SAs16-1, which exhibits simultaneous binding affinity toward SDZ, SDM, and SMP, was analyzed for its secondary structure. The aptamer SAs16-1, capable of recognizing SDZ, SDM, and SMP simultaneously, was analyzed for its secondary structure using the Mfold web server (https://www.unafold.org/mfold/applications/dna-folding-form.php). The predicted secondary structure is then converted into dot-bracket notation for input into the 3dRNA/DNA program (http://biophy.hust.edu.cn/new/3dRNA) to predict its tertiary structure [27]. The chemical structures of SDZ, SMZ, and SMP were retrieved from the PubChem database (https://pubchem.ncbi.nlm.nih.gov/) in SDF format. Docking was performed using Xdock [28], a tool designed for nucleic acid-small molecule interactions. During docking, SAs16-1 was treated as a rigid structure, while the target molecules remained flexible. A semi-flexible docking approach was applied to generate diverse conformations. The highest-scoring binding conformation, based on Xdock’s scoring criteria, was selected for further analysis. The single-point energy was determined through density functional theory (DFT) computations, utilizing the B3LYP exchange-correlation functional, in conjunction with the def2-TZVP basis set and the GD3(BJ) dispersion model to account for van der Waals interactions. The restrained electrostatic potential (RESP) charges of the small molecules were fitted using Multiwfn (Lu & Chen, 2012). Topology files were generated using the sobtop program.
2.4. MD and binding free energy
MD simulations were performed for the aptamer SAs16-1 in complex with SDZ, SDM, and SMP using the GROMACS (2025.0) software package for a duration of up to 100 ns. Parameterization of the aptamer was carried out using the AMBER ff14SB force field in combination with the parmbsc1 correction. The system was then solvated in a cubic simulation box, maintaining at least 1.0 nm of separation between the solute and the box edges. The aptamer was parameterized using the AMBER ff14SB + parmbsc1 force field and placed in a cubic simulation box with a minimum solute-box edge distance of 1.0 nm. Solvation of the system was performed with the TIP3P explicit water model and neutralized with Na⁺ and Cl⁻ counterions, maintaining a salt concentration of 0.15 M. Prior to initiating production MD, the system underwent energy minimization (EM) and equilibration to alleviate steric clashes. The equilibration process included EM, canonical (NVT) ensemble, and isothermal-isobaric (NPT) ensemble steps. Initially, harmonic restraints (100 kJ·mol⁻1·nm⁻1) were applied, followed by 10,000 steps of EM using the steepest descent method. The system was then heated from 0 K to 300 K over 1.0 ps under NVT conditions. Equilibration was performed in the NPT ensemble at 300 K and 1 atm for 2 ns with periodic boundary conditions in all directions. Finally, a 100 ns production MD simulation was carried out under NPT conditions. Temperature and pressure were controlled using the V-rescale thermostat and C-rescale barostat, respectively. Long-range electrostatics were treated with the particle mesh Ewald (PME) method, applying a 1.0 nm cutoff. Constraints on bonds involving hydrogen atoms were applied using the LINCS algorithm. An integration time step of 2.0 fs was used throughout the simulation, and trajectory frames were recorded every 5,000 steps, resulting in a total of 10,000 frames per system. This extensive sampling enabled a thorough characterization of the aptamer-drug interactions.
Binding free energy calculations were performed using the gmx_MMPBSA program on MD-generated trajectories [29]. The MM/GBSA method was used, incorporating the “GB-Neck2” model to enhance accuracy. The ion concentration was maintained at 0.15 M, and all simulation parameters remained consistent with those used in the MD simulations. Furthermore, binding free energy was analyzed on a per-nucleotide basis to evaluate the contribution of each residue to the overall affinity.
2.5. Preparation of the fluorescent aptasensor
Fe₃O₄/Au/g-C₃N₄ (1 mg) was ultrasonically treated in 1 mL of PBS for 1 h to obtain a uniform dispersion. The resulting suspension was then washed three times with PBS, after which 1 mL of PBS was added to the precipitate to obtain a Fe₃O₄/Au/g-C₃N₄ suspension at a concentration of 1 mg/mL. Subsequently, 200 μL of fluorescein amidite (FAM)-labeled aptamers at a concentration of 100 nM were incubated with various target molecule concentrations in the dark at 25°C for 1 h. After incubation, 30 μL of a 1 mg/mL Fe₃O₄/Au/g-C₃N₄ suspension was added to the mixture, and the total volume was adjusted to 250 μL with PBS if needed. The supernatant was separated magnetically, and its fluorescence intensity was recorded using a Varioskan LUX microplate reader.
2.6. Optimization of detection conditions for aptasensor
Optimization of the mass ratio between SAs16-1 and Fe₃O₄/Au/g-C₃N₄. To identify the optimal mass ratio between SAs16-1 and Fe₃O₄/Au/g-C₃N₄, six sets of 200 μL FAM-labeled aptamer solutions (100 nM) were incubated with Fe₃O₄/Au/g-C₃N₄ at varying mass ratios (ssDNA/Fe₃O₄/Au/g-C₃N₄: 1/0, 1/50, 1/100, 1/150, 1/200, and 1/250). The Fe₃O₄/Au/g-C₃N₄ suspension (1 mg/mL) was incubated in the dark at 25°C for 30 min. Afterwards, the supernatant was separated using a magnetic rack, and its fluorescence intensity was recorded. Each experiment was performed in triplicate to ensure reproducibility.
Optimization of buffer pH. PBS buffer solutions were adjusted to various pH values (5.5, 6.0, 6.5, 7.0, 7.5, 8.0) using diluted hydrochloric acid. A 10 μM FAM-labeled aptamer solution was prepared in each pH-adjusted buffer, then diluted to 100 nM for further use. Six groups of 200 μL aptamer solutions (100 nM) were incubated with a 1 μM target solution at 25°C for 1 h in the dark. Afterward, 25 μL of Fe₃O₄/Au/g-C₃N₄ solution was added, mixed thoroughly, and incubated for 30 min under gentle shaking. The supernatant was collected using a magnetic rack, and fluorescence intensity was measured. The experiment was repeated three times.
Optimization of sealing agent. Polyethylene glycol (PEG) with molecular weights of 20,000 (PEG20000), 4,000 (PEG4000), and 6,000 (PEG6000) was evaluated as a sealing agent for Fe₃O₄/Au/g-C₃N₄. To prepare the sealed nanomaterial, 1 mg of Fe₃O₄/Au/g-C₃N₄ was dispersed in 1 mL of PEG solution (1 mg/mL in PBS, pH 7.5) and ultrasonicated for 1 h. The dispersion was then incubated under gentle shaking for 24 h, followed by magnetic separation to remove the supernatant. The sealed Fe₃O₄/Au/g-C₃N₄ was washed three times with PBS and resuspended for storage. To assess the sealing effect, four groups of 200 μL aptamer solutions (100 nM) were incubated with a 1 μM target solution at 25°C for 1 h in the dark. Three experimental groups were treated with 25 μL of sealed Fe₃O₄/Au/g-C₃N₄, while the control group received unsealed Fe₃O₄/Au/g-C₃N₄. All samples were further incubated under shaking for 30 min. The supernatant was collected using a magnetic rack, and fluorescence intensity was measured. The experiment was repeated in triplicate.
Optimization of aptamer-target incubation time. To determine the optimal incubation time for aptamer-target binding, 200 μL of aptamer solution (100 nM) was incubated with a 1 μM target solution at 25°C for different time intervals (10, 20, 30, 40, 50, and 60 min) in the dark. Following incubation, 25 μL of Fe₃O₄/Au/g-C₃N₄ solution was added, thoroughly mixed, and further incubated under gentle shaking for 30 min. The supernatant was then collected using a magnetic rack, and fluorescence intensity was measured. The experiment was repeated in triplicate.
2.7. Extraction of samples
Control milk and honey samples, which had not been exposed to Sas, were obtained from a local supermarket in Chongqing, China. For preparing honey samples, 2 g of honey was dissolved in 10 mL of distilled water, followed by the addition of 1 mL of acetonitrile as a dispersing agent. Then, 40 μL of chloroform was introduced, and the mixture was vortexed vigorously for 1 minute to obtain a uniform emulsion. The sample was centrifuged at 4000 rpm for 2 min, after which the supernatant was collected. This supernatant was subsequently filtered through a 0.22 μm membrane and stored for later analysis. For milk sample preparation, 10 mL of milk was combined with 20 mL of acetonitrile and centrifuged at 8000 rpm for 5 min. The supernatant was collected into a 100 mL conical flask. The precipitate was subjected to a second extraction by adding another 20 mL of acetonitrile. The two extracts were then pooled, and the combined solution was evaporated at 45°C with continuous rotation until dryness. The residue was subsequently washed sequentially with 0.1 mol/L hydrochloric acid and n-hexane. The resulting washing solution was transferred to a centrifuge tube, vortexed for 30 seconds, and centrifuged again at 8000 rpm for 5 min. The upper n-hexane layer was discarded, retaining the lower aqueous phase. The final extract was filtered through a 0.22 μm membrane and stored for further analysis. SDZ, SDM, and SMP were spiked into the prepared honey and milk samples at concentrations of 50, 100, and 150 μg/kg, respectively. The recoveries and coefficients of variation (CVs) were assessed using the aptasensor assay. The recovery rate was calculated using the formula: Recovery (%) = (measured SDZ concentration in the sample/spiked SDZ, SDM, and SMP concentration) × 100%. Each spiked concentration was analyzed in five replicates to ensure accuracy and reproducibility.
2.8. Validation of aptasensor
The applicability and accuracy of the dual-modal aptasensor were assessed by testing milk and honey samples sourced from a local market. Sample preparation was carried out according to a previously established protocol [24]. After processing, all sample solutions were analyzed by HPLC to confirm the absence of SDZ, SDM, and SMP. The processed samples were spiked with SDZ, SDM, and SMP at final concentrations of 50, 100, and 150 ng/mL, respectively. Each sample was split into three aliquots for analysis by both the aptasensor and HPLC. Five replicates per concentration were performed, and the average recovery and CV were calculated. To assess the aptasensor’s reliability, the correlation between its detection results and those from HPLC was evaluated.
3. Results and Discussion
3.1. Aptamer characterization and recognition principle analysis
The SAs16-1 aptamer, previously screened in our laboratory, was successfully truncated and optimized in this study (Figure 2a). The specificity of SAs16-1 was evaluated, and it exhibited strong binding affinity toward SDZ, SDM, and SMP (Figures 2e-g). Affinity analysis revealed dissociation constants (Kd) of 83.66 nM, 74.12 nM, and 116.16 nM for SDZ, SDM, and SMP, respectively. Furthermore, specificity tests revealed that SAs16-1 showed negligible recognition of structurally similar SAs such as SMZ, SMR, and SQX, as well as unrelated antibiotics including oxytetracycline (OTC), kanamycin (KAN), and doxycycline (DOX), indicating its excellent selectivity (Figure 2b). These results indicate that SAs16-1 exhibits high recognition capability for SDZ, SDM, and SMP. Moreover, compared with the original SAs16, it not only has a broader spectrum, but also the affinity for SDZ has been increased from the original 204.19 nM to 83.66 nM.
Aptamers recognize biopharmaceuticals with high affinity, primarily through hydrogen bonding, a type of weak interaction. These interactions occur between a hydrogen atom (H) attached to an electron-rich donor atom and another electron-rich acceptor atom. As SAs16-1 is an ssDNA with strong anionic properties due to its phosphate groups, the oxygen, nitrogen, and phosphorus atoms within its phosphate backbone and nitrogenous bases can act as effective hydrogen bond donors and acceptors. Consequently, SAs16-1 establishes weak interactions with electron-rich regions of target molecules. As shown in Figures 2(h-j), SDZ, SDM, and SMP contain oxygen (O) and sulfur (S) atoms, which exhibit electron-rich regions, with stronger electron density represented in dark blue. These atoms can serve as hydrogen bond donors and acceptors, thereby facilitating strong hydrogen bond interactions with SAs16-1.
3.2. Molecular simulation analysis
Molecular docking was performed using the Xdock program, where SAs16-1 served as the receptor molecule and SDZ, SDM, and SMP were used as ligand molecules. From the docking results, the complex conformation with the highest docking score was selected for 100 ns MD simulations. To assess the stability of the aptamer-target complex, the root mean square deviation (RMSD) was monitored throughout the simulation. As shown in Figure 3, the RMSD values showed significant fluctuations during the initial 20 ns but gradually stabilized at approximately 0.6 nm, indicating that the aptamer formed a stable complex with the target molecules. Furthermore, the number of hydrogen bonds formed between the aptamer and its targets provides an indirect measure of interaction strength, further supporting the stability of the complex. The MD results showed that the average number of hydrogen bonds between SDZ and SAs16-1 over the 100 ns trajectory was approximately 2 (Figure 3d). In contrast, both SDM and SMP formed an average of 2–3 hydrogen bonds with SAs16-1 (Figures 3 (e and f), confirming strong interactions. Further analysis revealed that the dominant hydrogen bond distance between SDZ and SAs16-1 was 0.28 nm (Figure 3b), with bond angles predominantly ranging from 8° to 15° (Figure 3c). Similarly, for SDM and SMP, the dominant hydrogen bond distance was 0.30 nm, with bond angles ranging from 8° to 25° (Figures 3b and c). However, hydrogen bond analysis provides only a qualitative assessment of aptamer-target interactions. Therefore, to obtain a quantitative evaluation, binding free energy calculations were performed. As shown in Figure 4, subparts Figure 4(a-c), the binding free energies of SAs16-1 with SDZ, SDM, and SMP were −23.73 kcal/mol, −36.67 kcal/mol, and −26.49 kcal/mol, respectively. These values were consistent with the affinity experiments in Figure 1, further confirming the strong recognition ability of SAs16-1 for these three targets. Additionally, decomposition analysis revealed that van der Waals interactions predominantly stabilize the complexes, while electrostatic interactions contribute negligibly to the overall binding energy.
- (a) RMSD fluctuation curves over time for the complexes formed by SAs16-1 with SDZ, SDM, and SMP. (b) Frequency distribution of hydrogen bond distances between the aptamer and target molecules in each system. (c) Frequency distribution of hydrogen bond angles between the aptamer and target molecules in each system. (d–f) Time-dependent fluctuations in the number of hydrogen bonds formed between (d) SAs16-1 and SDZ, (e) SDM, and (f) SMP, respectively.
- (a-c) Binding free energy and decomposition bar charts illustrating the contribution of various energy components to the binding free energy between SAs16-1 and SDZ, SDM, and SMP, respectively. (d-f) Binding free energy decomposition analysis, showing the contributions of different nucleotide bases and drug molecules in the SAs16-1–SDZ, SAs16-1–SDM, and SAs16-1-SMP complexes. Bases not mentioned in the charts indicate a negligible contribution to target binding.
3.3. Binding site analysis
To investigate the conformational relationship and interaction mode of SAs16-1 with SDZ, SDM, and SMP during the binding process, the stable conformations of the aptamer-target complexes were analyzed using free energy landscape (FEL) mapping. Additionally, residue-based energy decomposition and 2D interaction mapping were used to elucidate the binding mechanism. Figures 5(a-c) present the FEL maps of the SAs16-1-SDZ, SAs16-1-SDM, and SAs16-1-SMP complexes, respectively. The FEL analysis revealed that SAs16-1 adopted 2, 1, and 2 free energy wells in its interactions with SDZ, SDM, and SMP, respectively, suggesting that the aptamer adopts distinct stable conformations with each target while maintaining a relatively low free energy state, indicative of stable binding. Figures 4(d-f) show the decomposition of the binding free energy contributions from each base of SAs16-1 and the target molecules. In the SAs16-1-SDZ complex (Figure 4d), bases G3, G4, G5, T6, A15, and C16 significantly contribute to the binding interaction. For the SAs16-1-SDM complex (Figure 4e), the primary contributing bases include G5, T6, T7, C8, A9, G12, G13, and C14. In the case of the SAs16-1-SMP complex (Figure 4f), the bases G4, G5, T6, T7, A9, and C14 play major roles. A comparative analysis indicated that G5 might be a common contributor in all three complexes, while G5, T6, and T7 were shared contributors in the SAs16-1-SDM and SAs16-1-SMP interactions. These findings suggest that SAs16-1 possesses a broad-spectrum binding capability. To further visualize the energy distribution across the aptamer structure, the base-specific energy decomposition was mapped onto a 3D model (Figures 5(d-f). In this model, regions of stronger energy contributions are represented in blue, while white regions signify negligible contributions. Corresponding 2D interaction diagrams (Figures 5g-i) provide a more intuitive representation of the intermolecular interactions. As shown in Figure 4(g), O3’ of base A15 forms a hydrogen bond with O1 of SDZ (4.11 Å), and O3’ of G4 establishes a hydrogen bond with N1 of SDZ (3.34 Å). Additionally, the bases G3, G5, T6, and C16 contribute to van der Waals interactions with SDZ. Similarly, Figures 5 (h and i) demonstrate that SAs16-1 forms four hydrogen bonds with SDM and three hydrogen bonds with SMP. These hydrogen bonding and van der Waals interactions are critical for stabilizing the SAs16-1-target complex.
- (a-c) FEL of the dynamic process of SAs16-1 forming complexes with SDZ, SDM, and SMP, where bluer regions indicate a single, stable conformation with lower free energy. (d-f) Spatial distribution of base-specific binding free energy contributions in the 3D structures of the SAs16-1-SDZ, SAs16-1-SDM, and SAs16-1-SMP complexes, where bluer regions indicate higher energy contributions, and white denotes bases with negligible contributions. (g-i) Two-dimensional interaction diagrams illustrating the binding interactions between SAs16-1 and SDZ, SDM, and SMP, respectively; (Green dashed lines: hydrogen-bonding interactions; eyelashes: van der Waals interactions).
3.4. Optimization of detection conditions
The mass ratio of SAs16-1 to Fe₃O₄/Au/g-C₃N₄ was optimized to maximize fluorescence quenching efficiency, as shown in Figure 6. The quenching efficiency varied with different aptamer batches. For SDZ (Figure 6a), fluorescence was nearly fully quenched at a mass ratio of 1:150, with minimal changes at lower aptamer concentrations. For SDM (Figure 6e) and SMP (Figure 6i), optimal quenching occurred at a mass ratio of 1:200. Thus, the optimal mass ratios for fluorescence sensor detection of SDZ, SDM, and SMP were 1:150, 1:200, and 1:200, respectively.
- Optimization of parameters for the aptasensor to detect SDZ, SDM, and SMP. Optimization of the mass ratio of SAs16-1 to Fe₃O₄/Au/g-C₃N₄ in the systems of (a) SDZ, (e) SDM, and (i) SMP. Optimization of buffer pH in different systems: (b) SDZ, (f) SDM, and (j) SMP. Optimization of the blocker: (c) SDZ, (g) SDM, and (k) SMP. Optimization of the incubation time for the aptamer/target: (d) SDZ, (h) SDM, and (l) SMP.
The binding efficiency between the aptamer and its target is influenced by pH. To assess this effect, fluorescence recovery was examined under varying pH conditions. As shown in Figures 6 (b, f, and j), fluorescence intensity increased with rising pH, peaking at pH 7.5. Beyond pH 7.5, fluorescence intensity declined. Thus, pH 7.5 was selected as the optimal condition for fluorescence sensor detection of SDZ, SDM, and SMP.
Incubation time also plays a crucial role in fluorescence recovery, as it affects the aptamer’s affinity for its target. As shown in Figures 6(c, g and k), the recovery time varied for different targets. Optimal incubation times of 30 min, 60 min, and 40 min were selected for detecting SDZ, SDM, and SMP, respectively.
Surface sealers are often used to reduce nanomaterial activity, improving sensor efficiency and enhancing the signal-to-noise ratio. Fe₃O₄/Au/g-C₃N₄, as a graphene-like material, exhibits strong adsorption properties for free ssDNA. To mitigate this effect, a sealing agent was introduced to partially block the material’s porous structure. As shown in Figures 6 (d, h, and l), fluorescence intensity recovery was highest when polyethylene glycol (PEG20000) was used as the blocking agent, while the control group (without a blocking agent) exhibited the lowest recovery. These results confirm that the sealing agent effectively reduces non-specific adsorption by partially sealing the porous structure of Fe₃O₄/Au/g-C₃N₄.
3.5. Sensitivity and specificity of the aptasensor
Under optimized conditions, Figure 7(a) illustrates a broad linear relationship between fluorescence intensity and SDZ concentration, ranging from 2.5 to 140 ng/mL (y = 0.01567x + 0.0484, R2 = 0.9674), with a limit of detection (LOD) of 2.31 ng/mL. For SDM (Figure 7b), the linear regression was described by y = 0.0137x + 0.028 (R2 = 0.9921), with a LOD of 1.94 ng/mL. For SMP (Figure 7c), the equation was y = 0.01796x − 0.008 (R2 = 0.9891), and the LOD was 5.86 ng/mL. These findings indicate that the truncated aptamer exhibits good accuracy for detecting all three targets within the sensor system.
- (a-c) Standard curve of the aptamer sensor detecting SDZ, SDM, and SMP. (d) Verification of the specificity of the aptasensor.
The specificity of the aptasensor was evaluated by testing SDZ, SDM, SMP, and other antibiotics (chloramphenicol, KAN, OTC, ampicillin, chlorotetracycline, tetracycline, and DOX) at a concentration of 100 ng/mL. As shown in Figure 7(d), the relative fluorescence intensity of SDZ was less than 20%, indicating no cross-reaction between them. These results confirm that the aptasensor can reliably detect SDZ, SDM, and SMP.
3.6. Validation of the aptasensor
To validate the detection performance of the aptasensor, SAs-free food matrices (milk and honey, determined to be free of SAs by HPLC) were spiked with known concentrations of SDZ, SDM, and SMP (50, 100, and 150 μg/kg), and the spiked samples were analyzed using the aptasensor. The detection results have been shown in Table 1. The mean recovery for SDZ ranged from 89.78% to 111.52%, for SDM from 93.53% to 110.41%, and for SMP from 85.16% to 105.92%. The CVs were 3.91-10.68% for SDZ, 3.32-10.12% for SDM, and 6.33-12.09% for SMP. A side-by-side comparison of the aptasensor and HPLC showed excellent correlation between the results (R2 > 0.996), further supporting the potential application of this aptasensor for detecting SDZ, SDM, and SMP in milk and honey.
| Sample | Antibiotics | Spiked (μg/kg) | Aptasensor | HPLC | ||
|---|---|---|---|---|---|---|
| Measured (μg/kg) | Recovery±SD (%) | CV (%) | Measured (μg/kg) | |||
| Milk | SDZ | 50 | 53.28 | 106.56±4.98 | 4.67 | 51.98 |
| 100 | 89.78 | 89.78±8.62 | 9.60 | 99.76 | ||
| 150 | 135.57 | 90.38±9.65 | 10.68 | 148.45 | ||
| SDM | 50 | 47.88 | 95.76±3.18 | 3.32 | 50.51 | |
| 100 | 93.53 | 93.53±10.86 | 11.61 | 99.28 | ||
| 150 | 142.14 | 94.76±7.96 | 8.40 | 152.64 | ||
| SMP | 50 | 43.89 | 87.78±5.82 | 6.63 | 50.8 | |
| 100 | 105.92 | 105.92±12.81 | 12.09 | 101.18 | ||
| 150 | 134.23 | 89.49±8.64 | 9.65 | 153.62 | ||
| Honey | SDZ | 50 | 55.76 | 111.52±6.46 | 5.79 | 48.25 |
| 100 | 106.03 | 106.03±8.27 | 7.80 | 99.63 | ||
| 150 | 138.82 | 92.54±3.62 | 3.91 | 147.65 | ||
| SDM | 50 | 47.32 | 94.64±7.53 | 7.96 | 49.86 | |
| 100 | 109.05 | 109.05±10.58 | 9.70 | 98.58 | ||
| 150 | 165.62 | 110.41±11.24 | 10.12 | 144.12 | ||
| SMP | 50 | 42.58 | 85.16±9.38 | 11.01 | 52.09 | |
| 100 | 87.91 | 87.91±6.96 | 7.92 | 103.65 | ||
| 150 | 133.68 | 89.12±5.65 | 6.33 | 155.87 | ||
4. Conclusions
In this study, a novel fluorescent aptasensor was established based on Fe3O4/Au/g-C3N4 and the truncated broad-spectrum aptamer (SAs16-1) for the simultaneous detection of SDZ, SDM, and SMP in milk and honey. MD simulations and binding free energy analyses revealed that the binding free energies of SAs16-1 with SDZ, SDM, and SMP were -23.73 kcal/mol, -36.67 kcal/mol, and -26.49 kcal/mol, respectively. Hydrogen bonding and van der Waals interactions were crucial in stabilizing the aptamer-target binding. Compared to other methods, the proposed approach exhibited enhanced sensitivity with an LOD of 5.86 ng/mL, a linear range spanning 3.25 to 150.00 ng/mL, and high recovery rates between 85.16% and 111.52% for SDZ, SDM, and SMP in spiked milk and honey samples. In addition, the CVs were below 13%. When applied to spiked sample detection, the aptasensor results showed excellent correlation with those obtained from HPLC, further validating its accuracy. With high sensitivity, broad-spectrum selectivity, and reliable performance, this aptasensor offers a promising tool for rapid detection of SDZ, SDM, and SMP in animal-derived food products.
Acknowledgment
This work was supported by the Science and Technology Research Program of Chongqing Municipal Education Commission (Grant No.KJZD-M202400501).
CRediT authorship contribution statement
Shuang Jiang: Data curation, Formal analysis, Investigation, Software, Visualization, Writing – original draft. Jiaming Tang: Methodology. Ying Yang: Data curation. Tao Le: Funding acquisition, Project administration, Supervision, Writing – review & editing.
Declaration of competing interest
The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.
Declaration of Generative AI and AI-assisted technologies in the writing process
The authors confirm that there was no use of artificial intelligence (AI)-assisted technology for assisting in the writing or editing of the manuscript and no images were manipulated using AI.
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