Skip to main content
NCBI home page
Food Chemistry: Molecular Sciences logoLink to Food Chemistry: Molecular Sciences
. 2026 Jan 22;12:100359. doi: 10.1016/j.fochms.2026.100359

Performance difference of enrofloxacin monoclonal antibody and nanobody elicited from the same immunogen and the underlying molecular mechanism

Yujie Chen a, Zhaoxiang Wang b, Qingqing He a, Haiyan Xing a, Simin Shen b, Rui Feng a, Yixuan Wu a, Baomin Wang a,⁎, Qing X Li c
PMCID: PMC12874607  PMID: 41660674

Abstract

Enrofloxacin (ENR) is a widely used fluoroquinolone antibiotic in animal husbandry, aquaculture, and humans. Here, a monoclonal antibody (mAb 2D3) and a nanobody (Nb22) against ENR were generated with the same immunogen. Nb22 had the nanobody common property of greater stability in harsh conditions, but its assay sensitivity was approximately 30-fold lower than that of mAb 2D3. Nb22 showed better selectivity, which the cross-reactivity to each of ENR analogs was less than or equal to that of mAb 2D3. The VH and VL gene sequences were amplified from the hybridoma cell line 2D3. Molecular docking revealed that mAb 2D3 had stronger hydrogen bonds and formed a flat and wide binding pocket to accommodate other analogs of ENR. The average recoveries of ENR from milk, milk powder, egg and fish determined by mAb 2D3 and Nb22 based ic-ELISAs ranged from 77.7% to 119% and 88.2% to 116%, respectively. This study confirmed that the direct application of nanobody in immunoassay is no better than the conventional monoclonal antibody. Improving the sensitivity of nanobody is an essential prerequisite for taking advantage of its stability and specificity.

Keywords: Enrofloxacin, Monoclonal antibody, Nanobody, Molecular docking, Immunoassay

Graphical abstract

Unlabelled Image

Open in a new tab

Highlights

  • •

    Enrofloxacin mAb and nanobody were generated with the same immunogen.

  • •

    Performance of mAb and nanobody based ic-ELISA in residue detection was compared.

  • •

    Nb22 gene was found unable to express in the conventional pComb3X vector system.

  • •

    Property difference of enrofloxacin mAb and nanobody was elucidated at molecular level.

1. Introduction

Enrofloxacin (ENR) (1-cyclopropyl-7-(4-ethylpiperazin-1-yl)-6-fluoro-4-oxoquinoline-3-carboxylic acid) is a synthetic antibiotic belonging to the fluoroquinolone group that possesses a broad spectrum of activity against most gram-negative pathogens. ENR is widely used for the prevention and treatment of various diseases in animal husbandry and aquaculture. However, long-term irregular or excessive use of ENR could enrich the residues in foodstuffs of animal origin and bring attention to its safety (Donkor et al., 2011; Masi et al., 2017) The European Union established the maximum residue level (MRL) of ENR in animal tissues at 100 μg/kg (Skrypal', 1998) In China, the MRLs of ENR in milk, milk powder, egg and fish are 100, 100, 10, 100 μg/kg, respectively (GB 31650-2019, GB3160.1-2022). Therefore, it is important to develop a sensitive method for the detection of ENR residues in food products.

Many methods have been developed for ENR detection in food samples. Among these, liquid chromatography-mass spectrometry techniques exhibit high accuracy and reproducibility. However, their application in routine food safety inspection is limited by expensive instrumentation, skilled personnel requirement and tedious sample pretreatment (Cinquina et al., 2003; Panzenhagen et al., 2016; Tan et al., 2020) Owing to their advantages of sensitivity, specificity and high throughput, enzyme-linked immunosorbent assay (ELISA) and lateral flow immunoassay (LFIA) become popular in residue detection of food and environmental safety. Polyclonal antibodies (pAbs) and monoclonal antibodies (mAbs) against ENR have been reported in various immunoassay formats. Although the immunoassays were sensitive to ENR and the IC50 values were between 0.32 ng/mL and 10 ng/mL, the selectivity of these antibodies were not very well, which had a broad cross-reactivity with other ENR structural analogs (Table S1) (Kato et al., 2007; Kim et al., 2015; Lu et al., 2023; Luo et al., 2020; Suryoprabowo et al., 2014). However, the performances of pAbs and mAbs were easily affected by environmental factors such as temperature, pH, organic solvent, and salt ionic strength in actual sample analysis (Singh et al., 2016).

Nanobodies were derived from the variable region of the heavy chain-only antibody (VHH) of camelids and screened from immune or naive libraries (Asaadi et al., 2021). Previous studies showed nanobodies were more stable in various harsh environments. The anti-atrazine nanobody Nb3 could tolerate higher temperatures than mAb 5D9 and the IC50 value barely change in organic solvents.(He et al., 2023) Anti-caffeine nanobody could recover its bioactivity after being exposure to temperatures up to 90 °C and was able to bind the coating antigen at 70 °C (Ladenson et al., 2006). Wang et al. reported an anti-triazophos nanobody that the IC50 value did not obviously shift at pH 5.0–10.0.(Wang et al., 2019) Nanobodies also showed more specific than conventional antibodies (Asaadi et al., 2021; Hassanzadeh-Ghassabeh et al., 2013). Yang et al. immunized mice and an alpaca with a sulfonamide immunogen, and the mAb 1D4 had cross-reactivity with four sulfacetamide analogs (sulfachloropyridazine, sulfameter, sulfaethoxypyridazine and sulfisomidine), but the nanobody H1–17 had no observable cross-reactivity with other structural analogs of sulfadimethoxine and was ultra-specific to sulfadimethoxine.(Yang et al., 2023) Anti-parathion pAb showed cross-reactivity with coumaphos (1520%), triazophos (207%), dichlofenthion (250%), and phoxin (2160%), however, nanobody VHH9-AP developed from the same immunogen had very low cross-reactivity with parathion analogs.(Xu et al., 2009; Zhang et al., 2018) Ochratoxin A (OTA) was conjugated with carrier protein via water-soluble carbodiimide to immunize mice and alpaca. The mAb OTA 2 reacted equally with OTA and OTB (98.8%) but weakly with OTC (42.0%). The nanobody VHH-28 showed excellent specificity for OTA and its analogs, except for OTB, which had 3.5% cross-reactivity (Kawamura et al., 1989; Liu et al., 2014). The anti-2,4-D nanobody NB3-9 had better specificity than the rabbit pAb #1518, with no cross-reactivity with 2,4-D analogs.(Li et al., 2021).

The immunoassay sensitivity is an important parameter for sample detection (Li et al., 2012). Some studies showed that nanobodies could exhibit dissociation constants in the low picomolar range (Ingram et al., 2018; Sockolosky et al., 2016; Zhao et al., 2025). However, the sensitivity of most reported anti-small molecular nanobodies was not as good as that of conventional antibodies, with assay IC50 values usually 10-fold or even 100-fold lower (Chen et al., 2024). He et al. developed anti-atrazine Nb3 and mAb 5D3, and the IC50 of the former (36.7 ng/mL) was 3.5-fold higher than that of the latter (10.2 ng/mL) (He et al., 2023). The assay sensitivities of the nanobody VHH-T1 (IC50 = 9.3 ng/mL) and the anti-triazophos mAb (IC50 = 0.65 ng/mL) were 14-fold different (Gui et al., 2006; Wang et al., 2019). The IC50 value of the anti-dicamba mAb was 12.3 ng/mL, whereas that of the nanobody Nb-242 was 930 ng/mL (Huo et al., 2019; Wang et al., 2024). Yang et al. prepared an ENR nanobody and developed a competitive ELISA using the fusion protein of nanobody-horseradish peroxidase. The IC50 value was 37.41 ng/mL (Yang et al., 2025). Li et al. utilized AlphaFold to guide the modification of the amino acid sequence and prepare two anti-ENR nanobodies. The IC50 values of Nb-C1-M29 and Nb-G20 based immunoassays were 231.9 ng/mL and 71.6 ng/mL, respectively (Li, Liu, Guo, et al., 2025; Li, Liu, Qiu, et al., 2025). Despite nanobodies have several good biochemical properties such as stability and specificity, their low sensitivity remains the primary limiting factor for their application in immunoassays.

In the present study, the same ENR complete antigen was used to immunize mice and alpaca, and ENR monoclonal antibody and nanobody were developed, respectively. The aim was to figure out whether the selectivity of nanobody was superior to that of mAb and whether the assay sensitivity of nanobody could be the same as that of mAb. Moreover, we explored the difference in the molecular interactions between ENR and the two types of antibodies.

2. Materials and methods

2.1. Chemicals and reagents

E. coli BL21(DE3) competent cells and E. coli DH5α competent cells were purchased from TransGen Biotech (Beijing, China). T4 DNA ligase, restriction enzyme, and the pMDTM18-T vector were purchased from Takara (Dalian, China). B-PER complete bacterial protein extraction solution and Ni-NTA resin were purchased from Thermo Fisher Scientific, Inc. (Fremont, CA). Kits for plasmid purification and gel extraction were purchased from TianGen (Beijing, China). Bovine serum albumin (BSA), ovalbumin (OVA), PEG2000, mouse antibody isotyping kit, goat anti-mouse IgG conjugated horseradish peroxidase (IgG-HRP), and goat anti-alpaca IgG (H + L) conjugated horseradish peroxidase were purchased from Sigma-Aldrich (St. Louis, MO). The pComb3X vector, pET30(+) vector, E. coli TOP10F′, E. coli ER2738, E. coli DH5α, M13K07, mouse hybridoma cell line SP2/0 and anti-His mAb conjugated to horseradish peroxidase (His-HRP) were available from our previous research. ENR and its analogs were purchased from Inno-chem (Beijing, China). All other reagents were purchased from Beijing Chemical Reagents Co. (Beijing, China). The real samples of milk, milk powder, egg and fish were purchased from local supermarkets (Beijing, China). The Multiskan MK3 microplate reader (Thermo Labsystems, Pittsburgh, PA) was used to measure the absorbance.

Female Balb/c mice (6-week-old) were obtained from the Beijing HFK Bioscience Co., LTD (Beijing, China). LAT-10-0090 euthanasia Device for Laboratory Animals was from Beijing Lab Anim Tech Develop Co., LTD. Two-year-old male alpaca was provided from Ximagezhuang Farm, Daxing District, Beijing. All animal experiments were conducted at the Laboratory Animal Platform and approved by the Ethical Committee for Animal Experiments of China Agricultural University (permit numbers: CAU20190506-3 and CAU20220904-2). The animal experiment was performed strictly according to the Guide for the Care and Use of Laboratory Animals (National Research Council Commission on Life Sciences, 1996 edition).

2.2. Mice immunization and monoclonal antibody development

ENR was conjugated to the carrier protein via the method according to activated ester method as previously described (Kato et al., 2007). Five six-week-old female BALB/c mice were immunized subcutaneously with 100 μg ENR-BSA with Freund's complete adjuvant at the first immunization, followed by two immunizations with Freund's incomplete adjuvant emulsified ENR-BSA every two weeks. Seven days after the third immunization, the titer and inhibition rate of mice serum were detected via ic-ELISA (supporting information 1.1). The spleen cells of the mouse with the best titer and inhibition rate were collected and applied for fusion with SP 2/0 myeloma cells via PEG 2000. The positive monoclonal hybridoma cell line was selected by limit dilution and used for ascites production. The ENR mAb was purified via ammonium sulfate precipitation and stored at −40 °C. The immunoglobulin isotype was determined with a mouse antibody isotyping kit according to the manufacturer's instruction.

2.3. Alpaca immunization, phage library construction and selection of nanobody

Two-year-old male alpaca was immunized subcutaneously with 200 μg ENR-BSA with Freund's complete adjuvant at the first immunization, followed by five injections with Freund's incomplete adjuvant emulsified ENR-BSA every two weeks. After 15 days of the last booster, 5 mL of blood was collected in blood collection bags, and the titer and inhibition rate of serum were detected via ic-ELISA (supporting information 1.1). Peripheral blood mononuclear cells (PBMCs) were isolated via Ficoll gradient centrifugation. Total RNA was extracted via the TRIzol method. The concentration and integrity of the total RNA was measured with Nanodrop and 1% agarose gels, respectively. The total RNA was reverse transcribed to cDNA according to the manufacturer's instruction. The VHH genes were amplified via nested PCR using pairs of primers (Table S5), cloned, and inserted into the phage display vector pComb3x. The ligated materials were electroporated into the competent cells of E. coli ER 2738, and with the rescue of the helper phage M13K07, an alpaca-derived phage display library was constructed. Twenty-four clones were randomly selected from the titer plates to evaluate the diversity of phage library, and the phage titer was estimated by counting the clones from the highest dilutions.

VHHs specific for ENR were isolated from the constructed library using a serial of decreasing concentration gradient of coating antigen ENR-OVA and standard ENR. Briefly, microtiter plates were coated with ENR-OVA (100 μL/well) in coating buffer (14.2 mM Na2CO3 and 35.8 mM NaHCO3, pH 9.6) and subsequently incubated at 4 °C for 12 h. After washing three times, each well was blocked with 3% skimmed milk in PBS (1.5 mM KH2PO4, 154 mM NaCl, 8.30 mM Na2HPO4·12H2O, 0.1% (v/v), pH 7.4) at room temperature for 1 h. Then the library was added to each well (100 μL/well) and incubated at room temperature for 3 h on a vibrating platform (500 rpm). After washing 15 times, the ENR standard was added to each well and incubated at room temperature for 1 h on a vibrating platform (500 rpm). This step would elute the bound phage to the immobilized coating antigen by competitive ENR standard. Collected the supernatant, 10 μL of the eluted phage was used to test the elute output titer, and the remainder was infected 3 mL of E. coli ER2738 and incubated for 30 min without shaking. After being cultured and rescued with helper phage M13K07, the supernatant containing phage particles were collected and tested by indirect competitive phage ELISA. The optimal positive clones were selected for prokaryotic expression and purification.

2.4. Prokaryotic expression and purification of nanobody

Nanobody Nb22 genes were amplified with specific primers (Table S5), cloned, and inserted into pET-30a (+) for prokaryotic expression vector which fused with a His-tag for purification. The plasmid selected from the positive clones was transformed into competent cells E. coli BL21 (DE3). A single colony was inoculated into LB medium and shaken (220 rpm) at 37 °C when the OD600 nm reached 0.6. Nanobody was induced with 1 mM IPTG (final concentration) and grown on overnight at 18 °C shaken at 150 rpm. (Chen et al., 2024).

The harvested bacteria were extracted with B-PER complete bacterial protein extraction solution and purified at 4 °C with 1 mL of Ni-NTA resin. After washes with two resin bed volumes of washing buffer (50 mM NaH2PO4, 300 mM NaCl and 10 mM imidazole, pH 7.4), the captured nanobody was eluted with three resin bed volumes of elution buffer (50 mM NaH2PO4, 300 mM NaCl, 25 mM, 250 mM and 2 M imidazole, pH 7.4). The eluted nanobody was dialyzed against PBS four times and ddH2O twice. The size of the nanobody was estimated with 12% sodium dodecyl sulfate polyacrylamide gel electrophoresis (SDS-PAGE).

2.5. mAb and nanobody based ic-ELISA

For ic-ELISA, the 96-well microtiter plates were coated with 1 μg/mL ENR-OVA in coating buffer and incubated at 37 °C for 3 h. After three times washes with PBST, 50 μL of 1 μg/mL ENR standard or PBST buffer, and 50 μL of ENR mAb or nanobody-fused a His tag were successively added to each well, followed by incubation at 37 °C for 30 min. After washing with PBST three times, the ENR mAb was detected with goat anti-mouse IgG-HRP, and the nanobody was detected with anti-His-HRP, followed by incubation at 37 °C for 30 min. The peroxidase activity was measured with 3,3′,5,5′-tetramethylbenzidine (TMB) (10 μL of 30% H2O2 added to 10 mL of citrate–phosphate buffer containing 0.35 mg/mL TMB, pH 5.5). A 50 μL aliquot of 1 M HCl was added to stop the reaction. The absorbance was measured at 450 nm with a Multiskan MK3 microplate reader. The data were analyzed with Origin 8.0 software. The standard curve was fitted using the four-parameter logistic model.

2.6. Cross-reactivity of mAb and nanobody

For the cross-reactivity of the ENR mAb and nanobody, the procedure was the same as that of ic-ELISA. ENR analogs include norfloxacin, difloxacin, ciprofloxacin, orbifloxacin, ofloxacin, sarafloxacin, pefloxacin, marbofloxacin, lomefloxacin, gatifloxacin, flumequine, sparfloxacin, fleroxacin, enoxacin and balofloxacin. The cross-reactivity was calculated with the equation: cross-reactivity (%) = [IC50 (ENR)/IC50 (analogs)] × 100%.

2.7. Stability of mAb and nanobody

The optimal dilutions of ENR-OVA and antibodies were used to evaluate the heat, pH, sodium chloride and organic solvents tolerance of the ENR monoclonal antibody and nanobody. ENR mAb and nanobody were heated at 25, 37, 50, 60, 70, 80, and 90 °C for 10 min, meanwhile, incubated at 90 °C for 10, 20, 30, 40, 50, and 60 min. Different pH value (3.0, 5.0, 7.0, 9.0, and 11.0), sodium chloride concentration (0, 0.15, 0.5, 1, 2, and 4 M), and organic solvent (5, 10, 20, and 40%, v/v) were prepared and used to test the tolerance of the mAb and nanobody.

2.8. Recovery analysis of ENR from milk, milk powder, egg and fish samples

A serial of concentrations of ENR standard was added to milk, milk powder, egg and fish for recovery analysis. The egg and fish were minced in the pretreatment procedure. Then, two grams of spiked milk, milk powder, egg or fish samples were extracted with 10 mL of acetonitrile and centrifuged at 4000 g for 10 min at 4 °C. The supernatant was collected, removed acetonitrile by rotary evaporation at 45 °C, and then dissolved in 2 mL of PBST. The recovery was calculated with the equation: Recovery (%) = (measured amount/spiked amount) × 100%.

2.9. Sequencing and analysis of ENR mAb variable regions

The total RNA of hybridoma cell line 2D3 were obtained via the TRIzol method according to the previously described procedures (Chen et al., 2016). The variable regions of the VH and VL chains were amplified with subtype-specific primers (Table S5). The VH and VL chains were subsequently cloned, inserted into the pMD™18-T vector, and transformed into competent cells E. coli DH5α. The positive clones were selected for sequencing.

ENR mAb variable regions were analyzed with the NCBI database and classified with the IMGT of complementarity determining regions (CDRs) and framework regions (FRs).

2.10. Molecular docking analysis of the ENR mAb and nanobody

To explore the recognition characteristics of antibodies against ENR and ENR analogs, an ENR mAb and nanobody 3D model were constructed with SWISS-MODEL. The resulting models were submitted to the Structure Analysis and Verification Server (SAVES) (https://services.mbi.ucla.edu/SAVES/) and evaluated using PROCHECK program (Laskowski et al., 1996). The 3D model of ENR antibodies and ENR or ENR analogs were docked with AutoDock vina software (Eberhardt et al., 2021). The results were analyzed with Pymol program and BIOVIA Discovery Studio (Dassault Systèmes, Paris, France). The method of alanine scanning validation was in the supplementary information 1.2.

3. Results and discussion

3.1. Development of monoclonal antibody

After the third immunization, the mouse serum with the best titer was more than 1:8000, and the maximal inhibition rate was 62.9% with 1 μg/mL ENR standard (Table S3). The best monoclonal clone, defined as mAb 2D3, was obtained via hybridoma antibody technology and applied to produce ascites and purified by the ammonium sulfate technique. Isotype analysis showed mAb 2D3 belonged to IgG2b subclass, and the light chain type was designated κ.

3.2. Library construction, isolation, and identification of nanobody

After the sixth immunization with the same immunogen of the monoclonal antibody, the titer and inhibition rate of alpaca serum against ENR were determined via ic-ELISA. The serum titer was approximately 1:8000, and the maximum inhibition rate was 56.4% with 1 μg/mL ENR standard (Table S4). Total RNA was extracted from PBMCs (Fig. 1C) and reverse transcribed to cDNA for two-step nested PCR to amplify the VHH genes. The capacity of the library was 5 × 108 cfu (Fig. 1D), and more than 90% of the transformants had a VHH insert. The titer of the phage library was 1.5 × 1013 pfu after rescuing with the helper phage M13K07.

Fig. 1.

Fig. 1

Open in a new tab

(A) Agarose gel electrophoresis of total RNA extracted from mAb 2D3 hybridoma cell line. Lane 1, DNA maker; lane 2–3, total RNA. (B) Agarose gel electrophoresis of VH and VL of mAb 2D3. Lane 1, VL fragment; lane 2, VH fragment, lane 3, DNA maker. The size of VL and VH were 450 and 500 bp, respectively. (C) Agarose gel electrophoresis of total RNA extracted from alpaca PBMCs. Lane 1, DNA maker; lane 2–3, total RNA. (D) Titer plates of enrofloxacin nanobody library with gradient dilutions. (E) Identification of randomly selected 24 clones with indirect phage ELISA. (F) SDS–PAGE analysis of Nb22. Lane 1: protein ladder, lane 2: Nb22 eluted with 25 mM imidazole, lane 3: Nb22 eluted with 250 mM imidazole, lane 4: Nb22 eluted with 2 M imidazole. The molecular weight of Nb22 was 18 kDa.

The concentrations of both ENR-OVA and ENR standard were gradually decreased for the purpose to capture high-affinity binders. After the fifth round of panning, the randomly selected 24 clones showed high binding ability to ENR-OVA. The inhibition rate of the best clone, designed as Nb22, was greater than 85% in the presence with 1 μg/mL ENR standard and used for sequencing analysis (Fig. 1E).

The VHH genes of Nb22 were amplified, cloned, and inserted into the pET30a (+) vector, and the resulting plasmid was selected from the positive clone and transformed into BL21(DE3) competent cells. After the addition 1 mM IPTG, Nb22 was expressed and purified with Ni-NTA resin. Fig. 1F showed that Nb22 migrated on SDS–PAGE gel at a molecular weight of 18 kDa, which agreed with the predicted size.

The selected positive plasmid was usually chemically transformed into E. coli strain TOP10F′ for prokaryotic expression.(He et al., 2023; He et al., 2024) However, despite extensive optimization of prokaryotic expression conditions (including temperature, time and IPTG concentration) after the plasmid clone Nb22 was transformed, no active nanobody was successfully expressed using the pComb3x phage display system. Previous studies showed that the activity of recombinant protein had a direct link with the expression vector. The urokinase-type plasminogen activator (uPA) nanobody nAb-C3 and nAb-C8 only expressed minute amounts with pFab74 phagemid vector. After the sequences were cloned into pET22-B (+) and optimized for BL21 (DE3) expression, the yield of nanobody was significantly improved (Kaczmarek & Skottrup, 2015) The deoxynivalenol (DON) scFv gene was cloned and inserted into pET-29b (+), pET-26b (+), and pGEX-5x-3 vector, which expressed merely inactive inclusion body proteins without any binding activity.(Choi et al., 2004) Lu et al. cloned the artemether-scFv gene and inserted it into vectors pET32a (+), pET22b (+), pGEX-2T, and pMAL-p5x to express scFv, but only pMAL-p5x could be induced an active scFv. In this study, Nb22 gene fragment was cloned and inserted into pET30a (+) vector system, transformed into E. coli BL21(DE3) strain cells and successfully expressed an active nanobody.(Lu et al., 2022).

3.3. Characterization of mAb and nanobody

The sensitivity, specificity, and cross-reactivity of mAb 2D3 and Nb22 based ic-ELISA were evaluated separately. The IC50 value of mAb 2D3 based ic-ELISA was 2.76 ng/mL, and the working range (IC20–IC80) was 0.68–17.6 ng/mL (Fig. 2A). The IC50 value of Nb22 based ic-ELISA was 92.6 ng/mL, and the working range (IC20–IC80) was 28.2–345 ng/mL (Fig. 2B). The sensitivity of Nb22 was obviously not as good as that of mAb 2D3. Monoclonal antibody-based commercial ENR ELISA kits typically had IC50 values of 0.1–0.5 ng/mL, and the reported ENR mAbs and nanobodies ranged from 0.32 to 10 ng/mL, 37.41–231.9 ng/mL, respectively (Table S1, S2). Although those antibodies were elicited from different ENR haptens, all the results also confirmed that ENR mAbs were far more sensitive than nanobodies.

Fig. 2.

Fig. 2

Open in a new tab

Standard inhibition curves of mAb 2D3 (A) and Nb22 (B) based ic-ELISAs. The data was average value of three replicates performed on different plates. The IC50 value of mAb 2D3 and Nb22 based ic-ELISAs was 2.76 ng/mL and 92.6 ng/mL, respectively.

The cross-reactivity of mAb 2D3 and Nb22 had significantly difference. Table 1 showed that mAb 2D3 had the maximal cross-reactivity of 77.6% with gatifloxacin, followed by ofloxacin, marbofloxacin, difloxacin, and lomefloxacin, 47.3%, 40.2%, 39.8%, and 33.6%, respectively. Five other compounds had cross-reactivity rates between 10% and 25% (pefloxacin, fleroxacin, ciprofloxacin, enoxacin, and norfloxacin). Three other compounds with less than 10% cross-reactivity included orbifloxacin, flumequine, and sarafloxacin. Sparfloxacin and balofloxacin had no cross-reactivity with mAb 2D3. Nevertheless, there were 5 compounds with cross-reactivity rates between 10% and 30% to Nb22, including norfloxacin (27.7%), difloxacin (26.6%), ciprofloxacin (19.5%), orbifloxacin (14.6%), and ofloxacin (11.1%). The cross-reactivity of the other 10 compounds with Nb22 were less than 10%. Similarly, the reported ENR nanobodies also showed a slightly better specificity than those mAbs (Table S1).

Table 1.

Cross-reactivity of the mAb 2D3 and Nb22 against enrofloxacin analogs.

Compound Structure mAb 2D3
 Nb22
IC50 (ng/mL) CR (%) IC50 (ng/mL) CR (%)
Enrofloxacin Image 1 2.76 100 92.6 100
Norfloxacin Image 2 12.7 21.7 334 27.7
Difloxacin Image 3 6.90 39.8 348 26.6
Ciprofloxacin Image 4 13.9 20.0 475 19.5
Orbifloxacin Image 5 29.3 9.42 635 14.6
Ofloxacin Image 6 5.80 47.3 833 11.1
Sarafloxacin Image 7 65.6 4.21 1830 5.06
Pefloxacin Image 8 12.2 22.7 2220 4.17
Marbofloxacin Image 9 6.90 40.2 2700 3.43
Lomefloxacin Image 10 8.20 33.6 2830 3.27
Gatifloxacin Image 11 3.60 77.6 3590 2.58
Flumequine Image 12 49.6 5.56 3880 2.39
Sparfloxacin Image 13 >500 <1 >10,000 <1
Fleroxacin Image 14 13.0 21.2 >10,000 <1
Enoxacin Image 15 15.3 18.1 >10,000 <1
Balofloxacin Image 16 >500 <1 >10,000 <1
Open in a new tab

3.4. Stability of mAb and nanobody

The antibody ability to bind antigen was affected by temperature, pH, ionic strength and organic solvents. To test the thermal stability, mAb 2D3 and Nb22 were incubated at temperatures ranging from 25 to 90 °C for 10 min. Fig. 3A showed that Nb22 had better thermal tolerance than mAb 2D3. As the temperature increased, the binding activity of both antibodies gradually decreased, while mAb 2D3 was almost completely inactive at 70 °C. Nb22 maintained approximately 70% binding activity at 90 °C for 10 min. In addition, when two types of antibodies were incubated at 90 °C for 10–60 min, Nb22 still maintained approximately 50% binding activity after 30 min treatment (Fig. 3B). There was no significant change in binding activity for Nb22 at pH 3.0–11.0. In contrast, the binding activity of mAb 2D3 changed with pH, and the maximum binding activity was at pH 7 (Fig. 3C). Nb22 binding activity changed slightly along with the NaCl concentration in PB buffer from 0 M to 4 M. Higher concentrations of NaCl greatly reduced the binding activity of mAb 2D3 and it was more sensitive to ionic strength than nanobody Nb22 (Fig. 3D). Methanol, acetonitrile and DMSO in the range of 0–40% (v/v) were used to test the organic solvent tolerance of mAb 2D3 and nanobody Nb22. Fig. 4A–C showed the IC50 values of mAb 2D3 based ic-ELISA varied from 4.64 to 55.2 ng/mL when different volume of organic solvent was added in sample buffer. Nevertheless, the sensitivity of Nb22 changed slightly, with IC50 values ranging from 63.9 to 488 ng/mL. The IC50 values of mAb 2D3 based ic-ELISA increased 20-fold, whereas the IC50 values of Nb22 based ic-ELISA increased only 5-fold. The radar chart clearly demonstrated that Nb22 exhibited superior stability, better specificity and less sensitivity compared to mAb 2D3. (Fig. 4D).

Fig. 3.

Fig. 3

Open in a new tab

Effects of temperature (A), heating time at 90 °C (B), pH (C) and NaCl concentration (D) on the binding activity of mAb 2D3 and Nb22. Each point indicates the mean ± standard deviation of triplicate samples.

Fig. 4.

Fig. 4

Open in a new tab

Solvent tolerance of mAb 2D3 and Nb22 to methanol (A), DMSO (B), and acetonitrile (C). Each point indicates the mean ± standard deviation of triplicate samples. (D) Radar chart of mAb 2D3 and Nb22 in sensitivity, cross-reactivity, and stability.

3.5. Sample recovery of mAb and nanobody based ic-ELISA

To evaluate the reliability of the developed assay in practical applications, milk, milk powder, egg and fish samples were fortified with ENR standard at concentrations ranging from 0 to 20 and 0–500 μg/kg for mAb 2D3 and Nb22, respectively. The average recoveries of ENR detected with mAb 2D3 were 77.7–87.3%, 97.0–107%, 85.0–119% and 93.7–104% for milk, milk powder, egg and fish samples, respectively. The corresponding average recoveries measured with Nb22 were 98.1–106%, 86.4–108%, 88.2–116%, and 97.3–110%, respectively (Table 2). The spike tests showed that the ENR residue in foodstuffs less than 50 μg/kg could not be detected in actual samples with Nb22.

Table 2.

Recovery of enrofloxacin spiked in samples determined by mAb 2D3 and Nb22 based ic-ELISAs.

Sample Spiked (μg/kg) mAb 2D3
 CV Spiked (μg/kg) Nb22
 CV
Detected (μg/kg) Recovery (%) Detected (μg/kg) Recovery (%)
Milk 0 ND ND / 0 ND ND /
1 0.77 ± 0.05 77.7 6.49 50 49.1 ± 1.2 98.1 2.44
4 3.49 ± 0.12 87.3 3.44 150 155 ± 4.3 106 2.77
20 16.9 ± 0.54 84.7 3.20 500 528 ± 14 103 2.65



Milk powder 0 ND ND / 0 ND ND /
1 0.97 ± 0.08 97.0 8.25 50 54.2 ± 2.7 108 4.98
4 4.12 ± 0.24 103 5.83 150 148 ± 5.6 98.7 3.78
20 21.5 ± 2.2 107 10.2 500 432 ± 12 86.4 2.78



Egg 0 ND ND / 0 ND ND /
1 0.85 ± 0.06 85.0 7.06 50 58.2 ± 1.4 116 2.41
4 4.79 ± 0.17 119 3.55 150 164 ± 3.2 109 1.95
20 19.5 ± 1.2 97.7 6.15 500 441 ± 13 88.2 2.72



Fish 0 ND ND / 0 ND ND /
1 0.93 ± 0.03 93.7 3.23 50 55.3 ± 2.1 110 3.80
4 3.85 ± 0.19 96.3 4.94 150 146 ± 2.9 97.3 1.98
20 20.8 ± 1.4 104 6.73 500 514 ± 10 103 1.95
Open in a new tab

3.6. Molecular mechanism of mAb 2D3 and Nb22

Total RNA was extracted from hybridoma cell line mAb 2D3 and reverse transcribed to cDNA for subtype-specific primers to amplify the VH and VL gene (Fig. 1A). The size of VH and VL were 450 and 500 bp, respectively (Fig. 1B). Sequence analysis of the variable regions of mAb 2D3 and Nb22 was showed in Fig. 5A. Molecular docking is an important method for underlying the binding mechanism between antibody and antigen. The 3D models of mAb 2D3 was constructed using the the PDB: A2KBC7, which exhibited a similarity score of 0.51 and a sequence identity of 70.74% (Fig. 5B). The 3D model of Nb22 was constructed using the PDB: 7TGF. The similarity score and sequence identity were 0.54, 77.44%, respectively. (Fig. 5C). Ramachandran plots were applied to verify the predicted torsion angles in 3D models and evaluate the fitness of the protein sequence to ensure rationality, which showed that the constructed models of mAb 2D3 and Nb22 had 85.0% and 89.6% domain amino acid residues located in the allowed region, respectively (Fig. S1, S2). The results indicated that the 3D models constructed were reasonable.

Fig. 5.

Fig. 5

Open in a new tab

(A) Alignment of amino acid sequence of mAb 2D3 VH, VL and Nb22. (B) The 3D model of mAb 2D3 and Nb22. The CDR1 was marked as red, CDR2 was marked as yellow, and CDR3 was marked as green. (For interpretation of the references to colour in this figure legend, the reader is referred to the web version of this article.)

The VH and VL of mAb 2D3 were connected through three modeling connecting peptides (Gly4Ser) and had a typical single-chain antibody structure with anti-parallel β-sheet and loop regions. ENR, gatifloxacin, and ofloxacin were inserted into the binding pocket and fit well with a narrow cavity formed by mAb 2D3 CDR regions. There was one conventional hydrogen bond (Trp 48), two carbon hydrogen bonds (Asp 63, Asp 159), nine van der Waals (Glu 47, Leu 46, Tyr 61, Ser 137, Gly 155, Glu 255, Arg 258, Ser 259, Glu 260) and two hydrophobic bonds (Ala 62, Val 161) formed between ENR and mAb 2D3 (Fig. 6A). Moreover, gatifloxacin and ofloxacin could form one conventional hydrogen bond (Trp 48), one halogen bond (Glu 47), and some van der Waals and hydrophobic bonds (Fig. 6B, C). The 3D model of nanobody Nb22 was docked with ENR in a simulation box of the possible binding sites surrounding three CDR regions. As shown in Fig. 6D, ENR (or ENR analogs) was wrapped deeply in the pocket formed by CDR2 and CDR3. The binding pocket of Nb22 had many interactions with ENR, including three conventional hydrogen bonds (Gly 56, Thr 58, and Ser 104), van der Waals bonds (Ser 52, Ser 54 and Tyr 102) and hydrophobic bonds (Tyr 103). Norfloxacin and difloxacin could also form three conventional hydrogen bonds, van der Waals and hydrophobic bonds, but the binding energies of norfloxacin and difloxacin were slightly lower than ENR (Fig. 6E, F). This docking study illuminated that mAb 2D3 could develop strong interactive forces compared with Nb22, and the binding pattern of antibodies against ENR, which matched the ic-ELISA results in the cross-reactivity study to some extent. Nanobodies have one single CDR, whereas monoclonal antibodies have two variable regions of the VH and VL chains, which could provide more amino acid residues to interactions with the active site of small molecules. The affinity between mAb 2D3 and ENR was −7.6 kcal/mol, which was lower 1.8 kcal/mol than that of Nb22 and ENR (−5.8 kcal/mol). This explained why the mAb 2D3 exhibited better sensitivity than Nb22. To fully identify the critical residues affecting the sensitivity of Nb22, alanine scanning was performed on the key amino acids. Molecular docking analysis showed four amino acids (Gly 56, Thr 58, Tyr 103, and Ser 104) of Nb22 played a key role in ENR recognition. Accordingly, the single-site mutation of alanine was introduced into Nb22. The sensitivity of Nb22-G56A, Nb22-T58A, Nb22-Y103A and Nb22-S104A was significantly decreased compared with that of the wild type Nb22, with corresponding IC50 values of 5.23, 31.9, 10.6 and greater than 50 μg/mL, respectively.

Fig. 6.

Fig. 6

Open in a new tab

Molecular docking between antibodies and enrofloxacin and its analogs. The recognition mode between mAb 2D3 and enrofloxacin (A), gatifloxacin (B), and ofloxacin (C). The recognition mode between Nb22 and enrofloxacin (D), norfloxacin (E), and difloxacin (F).

A sensitive antibody is necessary for sample analysis. In the present study, the IC50 value of mAb 2D3 based ic-ELISA was 2.76 ng/mL, which was approximately 30-fold more sensitivities than Nb22 (IC50 = 92.6 ng/mL). Although nanobody had unique biophysical features, such as thermal stability, organic solvent tolerance and refolding capacity, the sensitivity of small molecule nanobody was far lower than conventional antibody in practical applications. Expression of nanobody-enzyme fusion, constructing bivalent/multivalent nanobodies, fusing stabilizing domains, and optimizing expression hosts could improve the sensitivity of nanobody based immunoassays. One-step immunoassay for 2,4-D based on a nanobody-alkaline phosphatase fusion was developed with IC50 of 1.9 ng/mL, which was more sensitivity than the corresponding nanobody based ic-ELISA (IC50 = 29.2 ng/mL) (Li et al., 2021). Liao et al. (2024) constructed homo- and heterodimers Salmonella nanobodies, the affinity of homo- and heterodimers nanobodies improved by 2 orders of magnitude than that of monovalent nanobody, which the KD values were 0.386 nM and 1.534 nM, respectively. Anbuhl et al. (2024) generated CXCR4 nanobodies with different stabilizing domains. The results showed C-terminal of nanobody fused with human Fc could improve 2-log-fold and 3-log-fold potency. The expression hosts also effected nanobody sensitivity, our laboratory expressed atrazine nanobody in pComb3X and pET30a (+), the IC50 values were 36.7 ng/mL and 84.4 ng/mL, respectively (Chen et al., 2024; He et al., 2023). Molecular evolution in vitro was an effect method to improve the sensitivity of nanobody. Cheng et al. reported that an CD47 mutation improved the sensitivity with 87.4-fold and 7.36 °C greater thermal stability (Cheng et al., 2019). Li et al. used rational site-directed saturation mutation to construct a rapid and effective platform for parathion nanobody evolution, the sensitivity of Nb—D5 and Nb—D12 were 3.5-fold and 3.1-fold greater than that of Nb9, respectively (Li et al., 2023). Fang et al. obtained an affinity-matured mutant coumaphos, Nb 3G, from the secondary library, with a 6.4-fold improvement over the parent Nb A4 (Fang et al., 2024).

Although mAb 2D3 and Nb22 were generated from the same complete hapten, they showed different cross-reactivity with ENR analogs. The mAb 2D3 could react with ENR and 10 ENR analogs, with cross-reactivity rate more than 10%. As shown in Fig. 6A, there was a key hydrogen bond between Trp 48 and the hydrogen on the carbon chain on the benzene ring to ensure the specific of scFv, however, most interaction forces between ENR analogs and scFv could be observed, including the other three hydrogen bonds, halogen bonds, and hydrophobic force. The reason for better specificity of Nb22 may be that it could form only one hydrogen bond with small molecular. Compared with Nb22, mAb 2D3 had more binding sites to form stable interactive forces with ENR and ENR analogs. Moreover, mAb 2D3 formed a flat and wide binding pocket to accommodate other analogs of the antigen easily. Previous studies showed that the CDR1, CDR2 and CDR3 of mAb VH and VL contribute equally to the binding of an antigen, whereas the CDR3 of the nanobody had significantly greater contact proclivity with the antigen and act as the main contributor to binding site affinity (Jin et al., 2023). These results indicated that preparation of broad-spectrum antibody should be performed with monoclonal antibody rather than nanobody.

4. Conclusion

In the present study, monoclonal antibody 2D3 and nanobody Nb22 were developed with the same complete enrofloxacin antigen. Unlike other nanobodies expressed in the pComb3x vector in our lab, the active nanobody of Nb22 only could be expressed in the pET30a (+) vector. The mAb 2D3 had higher sensitivity, significantly broader cross-reactivity with ENR analogs, but poorer stability in harsh environments than that of Nb22. The binding pocket of mAb 2D3 could form more stable interactive force to recognize ENR and ENR analogs, indicated that preparation of broad-spectrum antibody should be first considered with monoclonal antibody rather than nanobody. The relatively low sensitivity of Nb22 limits the further application in residue detection. Molecular evolution in vitro plus one-step ELISA with nanobody-enzyme fusion to improve the sensitivity may be the viable approach for practical use of nanobody based ELISA.

CRediT authorship contribution statement

Yujie Chen: Writing – original draft, Methodology, Investigation, Formal analysis, Data curation, Conceptualization. Zhaoxiang Wang: Methodology, Investigation, Formal analysis. Qingqing He: Methodology, Investigation. Haiyan Xing: Writing – review & editing, Methodology. Simin Shen: Methodology, Investigation. Rui Feng: Methodology. Yixuan Wu: Methodology. Baomin Wang: Writing – review & editing, Funding acquisition, Conceptualization. Qing X. Li: Writing – review & editing, Funding acquisition.

Funding

The project was supported by the National Key Research and Development Program of China (2018YFC1602900) and the USDA (HAW05044R).

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.

Footnotes

Appendix A

Supplementary data to this article can be found online at https://doi.org/10.1016/j.fochms.2026.100359.

Appendix A. Supplementary data

Supplementary material Described method of serum testing of mouse and alpaca and alanine scanning validation. Tables of the reported ENR monoclonal antibody and nanobody, the titer and inhibition rate of mouse and alpaca antiserum after immunizations, and sequence of the primers. Figure of the PROCHECK evaluation result of mAb 2D3 and Nb22 homology models, characterizations of Nb22 alanine scanning mutants.

mmc1.docx (1MB, docx)

Data availability

All the data generated or analyzed during this study are included in this published article.

References

  1. Anbuhl S.M., Dervillez X., Neubacher S., Schriek A.I., Bobkov V., de Taeye S.W.…Heukers R. Multivalent CXCR4-targeting nanobody formats differently affect affinity, receptor clustering, and antagonism. Biochemical Pharmacology. 2024;227:14. doi: 10.1016/j.bcp.2024.116457. [DOI] [PubMed] [Google Scholar]
  2. Asaadi Y., Jouneghani F.F., Janani S., Rahbarizadeh F. A comprehensive comparison between camelid nanobodies and single chain variable fragments. Biomarker Research. 2021;9(1) doi: 10.1186/s40364-021-00332-6. [DOI] [PMC free article] [PubMed] [Google Scholar]
  3. Chen X., Lu Y., Tan G., Cao Z., Liu W., Wang B., Zhang M., Li Z. Functional and binding characterization of a single chain fv antibody to abscisic acid and conjugated abscisic acid. Food and Agricultural Immunology. 2016;27(5):624–642. [Google Scholar]
  4. Chen Y., He Q., Shen S., Wang Z., Xing H., Feng R.…Li Q.X. Nanobody mediated atrazine resistance in plants. Journal of Agricultural and Food Chemistry. 2024;72(29):16368–16377. doi: 10.1021/acs.jafc.4c00717. [DOI] [PubMed] [Google Scholar]
  5. Cheng X., Wang J., Kang G., Hu M., Yuan B., Zhang Y., Huang H. Homology modeling-based in silico affinity maturation improves the affinity of a nanobody. International Journal of Molecular Sciences. 2019;20(17) doi: 10.3390/ijms20174187. [DOI] [PMC free article] [PubMed] [Google Scholar]
  6. Choi G.H., Lee D.H., Min W.K., Cho Y.J., Kweon D.H., Son D.H.…Seo J.H. Cloning, expression, and characterization of single-chain variable fragment antibody against mycotoxin deoxynivalenol in recombinant Escherichia coli. Protein Expression and Purification. 2004;35(1):84–92. doi: 10.1016/j.pep.2003.12.008. [DOI] [PubMed] [Google Scholar]
  7. Cinquina A.L., Roberti P., Giannetti L., Longo F., Draisci R., Fagiolo A., Brizioli N.R. Determination of enrofloxacin and its metabolite ciprofloxacin in goat milk by high-performance liquid chromatography with diode-array detection – Optimization and validation. Journal of Chromatography A. 2003;987(1–2):221–226. doi: 10.1016/s0021-9673(02)01800-9. [DOI] [PubMed] [Google Scholar]
  8. Donkor E.S., Newman M.J., Tay S.C.K., Dayie N.T.K.D., Bannerman E., Olu-Taiwo M. Investigation into the risk of exposure to antibiotic residues contaminating meat and egg in Ghana. Food Control. 2011;22(6):869–873. [Google Scholar]
  9. Eberhardt J., Santos-Martins D., Tillack A.F., Forli S. AutoDock vina 1.2.0: New docking methods, expanded force field, and python bindings. Journal of Chemical Information and Modeling. 2021;61(8):3891–3898. doi: 10.1021/acs.jcim.1c00203. [DOI] [PMC free article] [PubMed] [Google Scholar]
  10. Fang R., Li Y., Liu F., Liang Y., Wang Y., Zhong G.…Wang H. A new strategy to generate nanobodies for the coumaphos based on the synthesized nanobody libraries. Food Chemistry. 2024;455 doi: 10.1016/j.foodchem.2024.139684. [DOI] [PMC free article] [PubMed] [Google Scholar]
  11. Gui W.J., Jin R.Y., Chen Z.L., Cheng J.L., Zhu G.N. Hapten synthesis for enzyme-linked immunoassay of the insecticide triazophos. Analytical Biochemistry. 2006;357(1):9–14. doi: 10.1016/j.ab.2006.07.023. [DOI] [PubMed] [Google Scholar]
  12. Hassanzadeh-Ghassabeh G., Devoogdt N., De Pauw P., Vincke C., Muyldermans S. Nanobodies and their potential applications. Nanomedicine. 2013;8(6):1013–1026. doi: 10.2217/nnm.13.86. [DOI] [PubMed] [Google Scholar]
  13. He Q., Chen Y., Wang Z., Shen S., Zhao Y., Xing H., Zhang J., Wu Y., Zhang X., Wang B. Efficient selection of the 2,4-dichlorophenoxyacetic acid nanobody gene from the phage library constructed with sorted specific cells and expression in plants to confer herbicide resistance. Journal of Agricultural and Food Chemistry. 2024;72(50):27850–27860. doi: 10.1021/acs.jafc.4c11018. [DOI] [PubMed] [Google Scholar]
  14. He, Q., Wang, M., Zhao, Y., Tan, G., Zhang, M., Feng, R., Chen, Y., Wang, B., & Li, Q. X. (2023). Isolation of atrazine nanobodies enhanced by depletion of anti-carrier protein phages and performance comparison between the nanobody and monoclonal antibody derived from the same immunogen. Analytica Chimica Acta, 1244. [DOI] [PubMed]
  15. Huo J., Barnych B., Li Z., Wan D., Li D., Vasylieva N.…Hammock B.D. Hapten synthesis, antibody development, and a highly sensitive indirect competitive chemiluminescent enzyme immunoassay for detection of dicamba. Journal of Agricultural and Food Chemistry. 2019;67(20):5711–5719. doi: 10.1021/acs.jafc.8b07134. [DOI] [PMC free article] [PubMed] [Google Scholar]
  16. Ingram J.R., Schmidt F.I., Ploegh H.L. Exploiting nanobodies’ singular traits. Annual Review of Immunology. 2018;36:695–715. doi: 10.1146/annurev-immunol-042617-053327. [DOI] [PubMed] [Google Scholar]
  17. Jin B., Odongo S., Radwanska M., Magez S. NANOBODIES®: A review of diagnostic and therapeutic applications. International Journal of Molecular Sciences. 2023;24(6):5994. doi: 10.3390/ijms24065994. [DOI] [PMC free article] [PubMed] [Google Scholar]
  18. Kaczmarek J.Z., Skottrup P.D. Selection and characterization of camelid nanobodies towards urokinase-type plasminogen activator. Molecular Immunology. 2015;65(2):384–390. doi: 10.1016/j.molimm.2015.02.011. [DOI] [PubMed] [Google Scholar]
  19. Kato M., Ihara Y., Nakata E., Miyazawa M., Sasaki M., Kodaira T., Nakazawa H. Development of enrofloxacin ELISA using a monoclonal antibody tolerating an organic solvent with broad cross-reactivity to other new quinolones. Food and Agricultural Immunology. 2007;18(3–4):179–187. [Google Scholar]
  20. Kawamura O., Sato S., Kajii H., Nagayama S., Ohtani K., Chiba J., Ueno Y. A sensitive enzyme-linked immunosorbent assay of ochratoxin a based on monoclonal antibodies. Toxicon (Oxford) 1989;27(8):887. doi: 10.1016/0041-0101(89)90100-1. [DOI] [PubMed] [Google Scholar]
  21. Kim N., Kim M., Park Y., Jung T., Son S., So B., Kang H. Magnetic nanoparticle based purification and enzyme-linked immunosorbent assay using monoclonal antibody against enrofloxacin. Journal of Veterinary Science. 2015;16(4):431–437. doi: 10.4142/jvs.2015.16.4.431. [DOI] [PMC free article] [PubMed] [Google Scholar]
  22. Ladenson R.C., Crimmins D.L., Landt Y., Ladenson J.H. Isolation and characterization of a thermally stable recombinant anti-caffeine heavy-chain antibody fragment. Analytical Chemistry. 2006;78(13):4501–4508. doi: 10.1021/ac058044j. [DOI] [PubMed] [Google Scholar]
  23. Laskowski R.A., Rullmannn J.A., MacArthur M.W., Kaptein R., Thornton J.M. AQUA and PROCHECK-NMR: Programs for checking the quality of protein structures solved by NMR. Journal of Biomolecular NMR. 1996;8(4):477–486. doi: 10.1007/BF00228148. [DOI] [PubMed] [Google Scholar]
  24. Li G., Liu C., Guo X., Chen Y., Cao L., Wang K., Lin H., Sui J. Rapid transformation of nanobodies affinity based on AlphaFold2's high-accuracy predictions and interaction analysis for enrofloxacin detection in coastal fish. Biosensors & Bioelectronics. 2025;267:9. doi: 10.1016/j.bios.2024.116785. [DOI] [PubMed] [Google Scholar]
  25. Li G., Liu C., Qiu S., Wei L., Cao L., Wang K.…Sui J. From non-affinity to high-affinity: Rapid preparation of nanobodies utilizing high-precision alphafold and structural-interaction analysis for detection of enrofloxacin in marine fish. Journal of Hazardous Materials. 2025;488 doi: 10.1016/j.jhazmat.2025.137394. [DOI] [PubMed] [Google Scholar]
  26. Li J., Shen X., Xu Z., Liang Y., Shen Y., Yang J., Wang H. Molecular evolution of antiparathion nanobody with enhanced sensitivity and specificity based on structural analysis. Journal of Agricultural and Food Chemistry. 2023;71(40):14758–14768. doi: 10.1021/acs.jafc.3c05176. [DOI] [PubMed] [Google Scholar]
  27. Li X., Li P., Zhang Q., Li Y., Zhang W., Ding X. Molecular characterization of monoclonal antibodies against aflatoxins: A possible explanation for the highest sensitivity. Analytical Chemistry. 2012;84(12):5229–5235. doi: 10.1021/ac202747u. [DOI] [PubMed] [Google Scholar]
  28. Li Z., Dong J., Vasylieva N., Cui Y., Wan D., Hua X.…Hammock B.D. Highly specific nanobody against herbicide 2,4-dichlorophenoxyacetic acid for monitoring of its contamination in environmental water. Science of the Total Environment. 2021;753 doi: 10.1016/j.scitotenv.2020.141950. [DOI] [PMC free article] [PubMed] [Google Scholar]
  29. Liao X., Zhang Y., Liang Y., Zhang L., Wang P., Wei J., Yin X., Wang J., Wang H., Wang Y. Enhanced sandwich immunoassay based on bivalent nanobody as an efficient immobilization approach for foodborne pathogens detection. Analytica Chimica Acta. 2024;1289:8. doi: 10.1016/j.aca.2024.342209. [DOI] [PubMed] [Google Scholar]
  30. Liu X., Xu Y., Xiong Y., Tu Z., Li Y., He Z.…Hammock B.D. VHH phage-based competitive real-time immuno-polymerase chain reaction for ultrasensitive detection of ochratoxin a in cereal. Analytical Chemistry. 2014;86(15):7471–7477. doi: 10.1021/ac501202d. [DOI] [PMC free article] [PubMed] [Google Scholar]
  31. Lu F., Zhang F., Qian J., Huang T., Chen L., Huang Y., Wang B., Cui L., Guo S. Preparation and application of a specific single-chain variable fragment against artemether. Journal of Pharmaceutical and Biomedical Analysis. 2022;220 doi: 10.1016/j.jpba.2022.115020. [DOI] [PMC free article] [PubMed] [Google Scholar]
  32. Lu N., Bu M., Zhang C., Gao Q., Wang X., Zhou X., Ding D., Zhang H. Development of a rapid detection method for enrofloxacin in food. Biotechnology & Genetic Engineering Reviews. 2023:1–19. doi: 10.1080/02648725.2023.2204701. ahead-of-print(ahead-of-print) [DOI] [PubMed] [Google Scholar]
  33. Luo M., Xing K., Guo Z., Guo D., Lai W., Peng J. Sensitive immunoassays based on a monoclonal antibody for detection of marbofloxacin in milk. Journal of Dairy Science. 2020;103(9):7791–7800. doi: 10.3168/jds.2019-18108. [DOI] [PubMed] [Google Scholar]
  34. Masi M., Refregiers M., Pos K.M., Pages J. Mechanisms of envelope permeability and antibiotic influx and efflux in Gram-negative bacteria. Nature Microbiology. 2017;2(3) doi: 10.1038/nmicrobiol.2017.1. [DOI] [PubMed] [Google Scholar]
  35. Panzenhagen P.H., Aguiar W.S., Gouvea R., de Oliveira A.M., Barreto F., Pereira V.L., Aquino M.H. Investigation of enrofloxacin residues in broiler tissues using ELISA and LC-MS/MS. Food Additives and Contaminants Part a-Chemistry Analysis Control Exposure & Risk Assessment. 2016;33(4):639–643. doi: 10.1080/19440049.2016.1143566. [DOI] [PubMed] [Google Scholar]
  36. Singh A., Pasha S.K., Manickam P., Bhansali S. Single-domain antibody based thermally stable electrochemical immunosensor. Biosensors & Bioelectronics. 2016;83:162–168. doi: 10.1016/j.bios.2016.04.054. [DOI] [PubMed] [Google Scholar]
  37. Skrypal’ I.H. Information on the Proposals of the Council of the European Economic Community on the Protection of Workers at risk related to work with dangerous biological objects (on the execution of article 118(a) of the treaty of the European Economic Community (EEC) and the refining of directive 90/679 of the EEC council) Mikrobiolohichnyĭ Zhurnal. 1998;60(3):101–111. [PubMed] [Google Scholar]
  38. Sockolosky J.T., Dougan M., Ingram J.R., Ho C.C.M., Kauke M.J., Almo S.C.…Garcia K.C. Durable antitumor responses to CD47 blockade require adaptive immune stimulation. Proceedings of the National Academy of Sciences of the United States of America. 2016;113(19):E2646–E2654. doi: 10.1073/pnas.1604268113. [DOI] [PMC free article] [PubMed] [Google Scholar]
  39. Suryoprabowo S., Liu L., Peng J., Kuang H., Xu C. Development of a broad specific monoclonal antibody for fluoroquinolone analysis. Food Analytical Methods. 2014;7(10):2163–2168. [Google Scholar]
  40. Tan G., Zhao Y., Wang M., Chen X., Wang B., Li Q.X. Ultrasensitive quantitation of imidacloprid in vegetables by colloidal gold and time-resolved fluorescent nanobead traced lateral flow immunoassays. Food Chemistry. 2020;311 doi: 10.1016/j.foodchem.2019.126055. [DOI] [PubMed] [Google Scholar]
  41. Wang K., Liu Z., Ding G., Li J., Vasylieva N., Li Q.X.…Xu T. Development of a one-step immunoassay for triazophos using camel single-domain antibody-alkaline phosphatase fusion protein. Analytical and Bioanalytical Chemistry. 2019;411(6):1287–1295. doi: 10.1007/s00216-018-01563-7. [DOI] [PMC free article] [PubMed] [Google Scholar]
  42. Wang Y., Zhou H., Fu Y., Wang Z., Gao Q., Yang D.…Huo J. Establishment of an indirect competitive immunoassay for the detection of dicamba based on a highly specific nanobody. Science of the Total Environment. 2024;917 doi: 10.1016/j.scitotenv.2024.170567. [DOI] [PMC free article] [PubMed] [Google Scholar]
  43. Xu Z., Xie G., Li Y., Wang B., Beier R.C., Lei H.…Sun Y. Production and characterization of a broad-specificity polyclonal antibody for o,o-diethyl organophosphorus pesticides and a quantitative structure–activity relationship study of antibody recognition. Analytica Chimica Acta. 2009;647(1):90–96. doi: 10.1016/j.aca.2009.05.025. [DOI] [PubMed] [Google Scholar]
  44. Yang H., Vasylieva N., Wang J., Li Z., Duan W., Chen S.…Wang Z. Precise isolation and structural origin of an ultra-specific nanobody against chemical compound. Journal of Hazardous Materials. 2023;458 doi: 10.1016/j.jhazmat.2023.131958. [DOI] [PMC free article] [PubMed] [Google Scholar]
  45. Yang M., Xu Q., Gu K., Wen R., Zhou C., Zhao Y.…Wang H. Development of a nanobody-horseradish peroxidase fusion-based competitive ELISA to rapidly and sensitively detect enrofloxacin residues in animal-derived foods. Spectrochimica Acta. Part A, Molecular and Biomolecular Spectroscopy. 2025;327 doi: 10.1016/j.saa.2024.125309. [DOI] [PubMed] [Google Scholar]
  46. Zhang Y., Xu Z., Wang F., Cai J., Dong J., Zhang J., Si R., Wang C., Wang Y., Shen Y., Sun Y., Wang H. Isolation of bactrian camel single domain antibody for parathion and development of one-step dc-FEIA method using VHH-alkaline phosphatase fusion protein. Analytical Chemistry. 2018;90(21):12886–12892. doi: 10.1021/acs.analchem.8b03509. [DOI] [PubMed] [Google Scholar]
  47. Zhao Y., Xiao C., Ren L., Ma Y., Wang Y., Zhang H., Luo Y. The dual nondestructive amplification strategy realizes the ultrasensitive detection of soluble TRAIL in human plasma. Analytical Chemistry. 2025;97(45):25295–25303. doi: 10.1021/acs.analchem.5c05121. [DOI] [PubMed] [Google Scholar]

Associated Data

This section collects any data citations, data availability statements, or supplementary materials included in this article.

Supplementary Materials

Supplementary material Described method of serum testing of mouse and alpaca and alanine scanning validation. Tables of the reported ENR monoclonal antibody and nanobody, the titer and inhibition rate of mouse and alpaca antiserum after immunizations, and sequence of the primers. Figure of the PROCHECK evaluation result of mAb 2D3 and Nb22 homology models, characterizations of Nb22 alanine scanning mutants.

mmc1.docx (1MB, docx)

Data Availability Statement

All the data generated or analyzed during this study are included in this published article.

Articles from Food Chemistry: Molecular Sciences are provided here courtesy of Elsevier

RESOURCES