# Combining deep mutational scanning and SPR binning approaches for large-scale epitope identification of anti-ricin antibodies

Ophélie Kot, Lois Lequesne, Hans Werner Mages, Steven Dubois, Paloma Piquet, François Becher, Bernard Maillère, Brigitte G Dorner, Stéphanie Simon, Daniel Stern, et al.

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Ophélie Kot, Lois Lequesne, Hans Werner Mages, Steven Dubois, Paloma Piquet, et al. Combining deep mutational scanning and SPR binning approaches for large-scale epitope identification of anti-ricin antibodies. mAbs, 2025, 17 (1), pp.2544922. 10.1080/19420862.2025.2544922. cea-05314161

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# Combining deep mutational scanning and SPR binning approaches for large-scale epitope identification of anti-ricin antibodies

Ophélie Kot, Lois Lequesne, Hans Werner Mages, Steven Dubois, Paloma Piquet, François Becher, Bernard Maillère, Brigitte G. Dorner, Stéphanie Simon, Daniel Stern & Hervé Nozach

**To cite this article:** Ophélie Kot, Lois Lequesne, Hans Werner Mages, Steven Dubois, Paloma Piquet, François Becher, Bernard Maillère, Brigitte G. Dorner, Stéphanie Simon, Daniel Stern & Hervé Nozach (2025) Combining deep mutational scanning and SPR binning approaches for large-scale epitope identification of anti-ricin antibodies, mAbs, 17:1, 2544922, DOI: 10.1080/19420862.2025.2544922

**To link to this article:** https://doi.org/10.1080/19420862.2025.2544922

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# Combining deep mutational scanning and SPR binning approaches for large-scale epitope identification of anti-ricin antibodies

Ophélie Kot<sup>a*</sup>, Lois Lequesne<sup>Id b*</sup>, Hans Werner Mages<sup>c</sup>, Steven Dubois<sup>a</sup>, Paloma Piquet<sup>b</sup>, François Becher<sup>Id b</sup>, Bernard Maillère<sup>Id a</sup>, Brigitte G. Dorner<sup>Id c</sup>, Stéphanie Simon<sup>Id b</sup>, Daniel Stern<sup>Id c*</sup>, and Hervé Nozach<sup>Id a*</sup>

<sup>a</sup>Département Médicaments et Technologies pour la Santé (DMTS), SIMoS, Université Paris Saclay, CEA, INRAE, Gif-sur-Yvette, France; <sup>b</sup>Département Médicaments et Technologies pour la Santé (DMTS), SPI, Université Paris Saclay, CEA, INRAE, Gif-sur-Yvette, France; <sup>c</sup>Biological Toxins (ZBS3), Centre for Biological Threats and Special Pathogens, Robert Koch Institute, Berlin, Germany

## ABSTRACT

Ricin, a ribosome-inactivating lectin from *Ricinus communis* seeds, has been used as a bioterrorism agent in multiple cases. While passive immunotherapy with anti-ricin antibodies shows promise in preclinical studies, no approved countermeasure exists. Developing effective monoclonal antibodies (mAbs) is challenging, requiring epitope targeting that ensures neutralization of the two most dominant natural ricin isoforms (D and E). Moreover, high-affinity binding does not always correlate with toxin neutralization, highlighting the importance of epitope specificity in driving protection. Here, we characterized a panel of 17 anti-ricin antibodies, including VHH and IgG mAbs, to determine their affinities, selectivity, and epitopes. Using surface plasmon resonance (SPR) and biolayer interferometry (BLI), we evaluated antibody affinities for the two ricin isoforms (D and E), as well as for ricin agglutinin, a related lectin with markedly lower toxicity. Epitope determination was performed using (1) SPR-based epitope binning, enhanced by network analysis for streamlined bin visualization, and (2) deep mutational scanning with yeast surface display to identify key epitope residues. BLI effectively distinguished low- and high-affinity interactions, while SPR provided superior resolution for determining the highest affinities and lowest dissociation rates. Both epitope-mapping strategies yielded highly consistent results, allowing the identification of critical epitopes associated with potent neutralization and cross-reactivity between ricin isoforms. This study advances our understanding of ricin neutralization by this panel of antibodies, providing key insights into their affinity, epitope specificity, and cross-reactivity. These findings contribute to the rational design of antibody-based therapeutics for ricin intoxication.

## ARTICLE HISTORY

Received 14 May 2025
Revised 28 July 2025
Accepted 3 August 2025

## KEYWORDS

Yeast surface display;
epitope mapping; surface
plasmon resonance; biolayer
interferometry; ricin;
monoclonal antibodies; deep
mutational scanning;
epitope binning

## Introduction

Ricin toxin is a type II ribosome-inactivating protein derived from the seeds of the castor plant *Ricinus communis* that is particularly dangerous due to the widespread availability of castor seeds and the relative ease with which crude ricin preparations can be made. It has been used in a number of poisoning incidents,¹ including suicide attempts and small-scale criminal attacks, and incorporated into improvised explosive devices in attempted acts of bioterrorism.²

The toxicity of ricin is mediated by two distinct chains: the A chain (RTA), which exhibits ribosome-inactivating protein (RIP) enzymatic activity, and the B chain (RTB), which functions as a lectin. The two chains are linked by an interchain disulfide bond, forming a toxin with a molecular weight of 64 kDa. Ricin exists in two natural isoforms (D and E) that share 94.3% sequence identity, with identical A chains and minor variations limited to specific regions of the B chain.<sup>3-5</sup> Ricin agglutinin, another related but less toxic lectin derived from *Ricinus communis*,

**CONTACT** Daniel Stern ⬒ SternD@rki.de ⬒ Biological Toxins (ZBS3), Centre for Biological Threats and Special Pathogens, Robert Koch Institute, Berlin 13353, Germany; Hervé Nozach ⬒ herve.nozach@cea.fr ⬒ Département Médicaments et Technologies Pour la Santé (DMTS), SIMoS, Université Paris Saclay, CEA, INRAE, Gif-Sur-Yvette 91191, France

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Supplemental data for this article can be accessed online at https://doi.org/10.1080/19420862.2025.2544922

<footnote>*Contributed equally to this work.</footnote>
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shares approximately 89% sequence identity with ricin isoform D.⁶,⁷ RTB plays a crucial role in facilitating cellular uptake by binding to terminal galactose residues on glycoproteins and glycolipids on the cell surface, thereby enabling the internalization of the toxin. Subsequently, ricin is transported to early endosomes, where the majority of the protein is either recycled back to the cell surface or directed to lysosomes for degradation. Nevertheless, a minor proportion of the protein evades degradation and is conveyed via retrograde transport through the trans-Golgi network (TGN) to the endoplasmic reticulum (ER), where the A and B chains are separated. From the ER, the A chain is translocated to the cytoplasm, where it exerts its catalytic activity⁸,⁹. RTA catalyzes the hydrolysis of an adenine residue in the sarcin-ricin loop of the 28S rRNA, thereby inhibiting protein synthesis and ultimately leading to cell death¹⁰,¹¹.

At present, there are no approved and commercially available therapeutic countermeasures for ricin poisoning. The only recombinant vaccines, RVEcTM and RiVax®, which are based on RTA, are still in clinical development¹² and are not suited for post-exposure treatment due to the time required to generate a protective immune response. Potential post-exposure strategies include the use of small molecule inhibitors and passive immunotherapy with monoclonal or polyclonal antibodies.¹³ Of these, monoclonal antibodies (mAbs) have emerged as a particularly promising approach due to their high specificity and potential for effective neutralization of ricin, both *in vitro* and *in vivo*.³,¹⁴⁻²¹ Nevertheless, although numerous ricin-specific mAbs have been characterized, none have entered clinical trials or been approved for clinical use. The precise mechanisms by which antibodies neutralize ricin remain incompletely understood, but two principal modes of action have been proposed. First, antibodies can prevent the toxin from binding to cell surface receptors, thereby blocking its internalization. Second, they can disrupt ricin's intracellular trafficking, inhibiting its transport to the trans-Golgi network and subsequent downstream processes.²²⁻²⁴

Many RTA-specific antibodies predominantly influence intracellular trafficking. These antibodies (e.g., PB10, SyH7, GD12, IB2), targeting various epitopes, lead to accumulation of ricin in late endosomes and lysosomes, thereby reducing the amount of ricin that reaches the TGN or the ER and ultimately the cytosol.²⁵ Depending on their epitope, RTB-specific antibodies may either prevent cellular entry, as observed with SyH3, JB4, and RB37, or impair intracellular trafficking, as demonstrated by 24B11, which targets a distinct, though uncharacterized, epitope.³,²⁶ Hence, binding to diverse epitopes by different mAbs allows them to intervene at multiple stages of ricin's entry and trafficking, offering potentially complementary neutralization strategies.

In addition to targeting a relevant epitope, two other key parameters are critical for effective ricin-neutralizing antibodies: 1) high affinity for the antigen and 2) cross-reactivity with both natural ricin isoforms, ricin D and E. While some antibodies, such as PB10, can neutralize ricin despite having moderate affinity (40 nM)²⁷ by targeting a critical epitope, others with high affinity fail to neutralize the toxin effectively due to binding outside of neutralizing epitopes, illustrating that each parameter is necessary, but not sufficient on its own. Moreover, some antibodies that neutralize ricin *in vitro* do not provide *in vivo* protection, particularly in cases of severe intoxication when treatment is delayed.⁵,²⁸ Therefore, understanding the precise molecular interactions defined by both the affinities between ricin-specific mAbs and ricin and the exact binding epitopes is essential for the development of effective therapies.

To address these challenges, we used an optimized pipeline combining biophysical methods, namely surface plasmon resonance (SPR) and biolayer interferometry (BLI), to efficiently characterize a panel of 17 mAb candidates against ricin D, ricin E, and agglutinin. This integrated pipeline enabled precise measurements of binding kinetics and affinities, allowing rapid and accurate assessment of antibody interactions. Using SPR-based epitope binning, we grouped the antibodies based on their binding epitopes, while high-resolution epitope maps were generated through yeast surface display coupled with deep mutational scanning (DMS) to identify, at the amino acid level, the ricin residues involved in functional recognition. The SPR analysis ranked the antibodies by binding affinity, with the strongest candidates exhibiting affinities as low as 50 pM. Consistent epitope mapping results across methods revealed six unique, partially overlapping epitope bins. This streamlined approach facilitates efficient characterization and supports the targeted selection and further testing of individual mAbs and oligoclonal cocktails for *in vitro* and *in vivo* applications, advancing the development of potent therapeutic antibodies against ricin toxin.
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# Materials and methods

## Antibodies and toxins

Binding kinetics and epitopes were determined for a panel of 17 mAbs targeting ricin (Supp. Table S1). All antibodies were selected for reactivity against ricin, some have been implemented in assays for ricin detection, while others were shown to be neutralizing in either *in vivo* and/or *in vitro* neutralization assays. Briefly, mAbs R18, R109, and R21 were raised against native ricin coupled to microbeads and implemented in a bead-based multiplex assay for toxin detection<sup>29</sup>, a Sandwich-ELISA<sup>30</sup>, as enrichment antibodies for subsequent MALDI-TOF MS detection<sup>31</sup>, as well as an SPR-based sensor for differentiation between ricin and agglutinin<sup>32</sup>. mAbs RA35, RA36, RB34, and RB37 were generated by immunization with isolated A (for RA35 and RA36) or B (for RB34 and RB37) chains of ricin<sup>21</sup> and also implemented in a Sandwich-ELISA highly specific for ricin<sup>30</sup>. These mAbs generally demonstrated *in vitro* or *in vivo* efficacy against ricin isoform D<sup>3,21</sup>. mAbs RCH1, 1RK1<sup>30,33</sup>, and mAb 43RCA-G1<sup>17,20</sup> all target the A chain of ricin. As controls, we used antibodies with known binding epitopes that were previously published: single domain camelid VHH antibodies E5, V2A11, and V2G10 were generated by immunization of alpacas<sup>14,34</sup> with RiVax*. The VHH antibody V2C11 is not part of the panel of studied antibodies and was solely used as conformational control in the yeast surface display (YSD) experiment to identify mutations destabilizing the ricin B chain. It was obtained by immunization of alpacas with various mixtures of ricin toxoid, RiVax*, and the ricin B chain.<sup>15,34</sup> Binding epitopes of VHH E5 (PDB ID 4LGP)<sup>35</sup>, V2G10 (PDB ID 6OCA), V2A11 (PDB ID 6OBC)<sup>36</sup> and V2C11 (PDB ID 7KD0)<sup>37</sup> have been determined by X-ray crystallography. mAbs RicE4, RicE5, RicE7, and RicE8 were generated in mice after immunization with inactivated ricin E as previously described.<sup>5</sup> For SPR measurements, ricin, ricin D, ricin E, and *Ricinus communis* agglutinin (RCA) were isolated, purified, and characterized from castor beans of the *R. c.* Carmencita cultivar as described previously<sup>38,39</sup>. In the context of affinity measurements, we use the term “ricin” to designate a mixture of the two isolectins from the seeds of the Carmencita cultivar. Ricin D and E refer to purified isolectins D and E. For BLI measurements, ricin D and ricin E were also isolated and purified from the same cultivar, but using the procedure described by Orsini *et al.*<sup>3</sup>

## Surface plasmon resonance measurements

SPR measurements were performed with a Biacore™ T200 SPR system (Cytiva) at an analysis temperature of 25°C using HBS-EP+ buffer (10 mM HEPES, 150 mM NaCl, 3 mM EDTA, 0.05 Tween-20, pH 7.4). For the determination of the binding kinetics, the running buffer was supplemented with 10 mg/mL D-Galactose to suppress unspecific binding.<sup>32</sup> As sensor chips, we used Series S Sensor Chips CM5, which were coupled with polyclonal rabbit anti-mouse or a monoclonal mouse anti-human IgG (Fc) capture antibodies using the Amine Coupling Kit, the Mouse Antibody Capture Kit, and the Human Antibody Capture Kit (all Cytiva) as recommended by the manufacturer. ChromPure polyclonal mouse IgG (015–000–003) and normal human serum (009–000–121) used for blocking were produced by Jackson ImmunoResearch and obtained from Dianova. After covalent immobilization of the anti-mouse antibody, mouse capture sensor chips were conditioned three times by injecting mouse IgG (c = 100 µg/mL) for 120 s at a flow rate of 10 µL/min followed by regeneration with 10 mM Glycine-HCl pH 1.7 for 180 s at a flow rate of 10 µL/min on all flow cells.

Binding kinetics for mAbs carrying a murine Fc were determined by capturing mAbs diluted to a concentration of 2.5 µg/mL in running buffer for 30 s at a flow rate of 10 µL/min to the surface of the mouse capture modified sensor chip on flow cells 2 or 4. Five consecutive 1:3 dilutions of the selected ricin antigen (calculated with an MW of ~60 kDa), or agglutinin (MW ~ 120 kDa) ranging from 3.08 nM to 250 nM (corresponding to 15 µg/mL ricin or 30 µg/mL agglutinin) were injected at flow rates of 30 µL/min for association times of 120 s, followed by 600 s of buffer injection to monitor the dissociation phase in a single-cycle measurement over all flow cells. Regeneration was performed as described above. Sensorgrams were double referenced by 1) subtracting responses on the respective control flow cells 3 and 1 from the responses on the measurement flow cells 4 and 2 (first reference) and 2) by subtracting buffer only injections from measurement cycles with toxin injections (second reference).<sup>40</sup> Binding kinetics for mAbs carrying a human
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Fc (43RCA-G1, E5, V2A11, V2G10) were determined similarly with the following changes: For capturing using an anti-human IgG modified sensor chip CM5, the mAbs were diluted to 0.5 µg/mL and injected for 30 s at a flow rate of 5 µL/min to reduce the surface density, dissociation times were elongated to 1800 s, and regeneration was performed with 3 M MgCl₂ for 30 s at a flow rate of 20 µL/min. For each mAb, binding kinetics were recorded at least twice as technical replicates.

For the epitope binning, all possible combinations of all mAbs used as capture antibody and as enhancement antibody were tested ($n = 17^2$ combinations). To this aim, antibodies were diluted to 10 µg/mL and captured for 120 s at a flow rate of 10 µL/min to flow cells 2, 3, or 4. Subsequently, anti-mouse or anti-human IgG binding sites on capture antibodies were saturated by injection of mouse IgG ($c = 100$ µg/mL) or human serum ($c = 6$ mg/mL) over all flow cells for 300 s at a flow rate of 5 µg/mL before ricin ($c = 20$ µg/mL) was injected for 120 s at a flow rate of 10 µg/mL over all flow cells. Finally, enhancement injection was performed over all flow cells with mAb concentrations of 10 µg/mL at a flow rate of 10 µL/min for 120 s. After each cycle, mouse capture chips were regenerated twice, while human capture chips were regenerated once as described above. Sensorgrams were double referenced as described above before data analysis.

## Data analysis of SPR measurements

Results of the SPR measurements recorded with the Biacore T200 Control Software (Cytiva, Version 3.2.1) were analyzed using the Biacore T200 Evaluation Software (Cytiva, Version 3.2.1). Binding kinetics were determined by fitting 1:1 Langmuir models or heterogeneous ligand models to the double referenced sensorgrams, hereby determining the association rate constants $k_a$ (s⁻¹M⁻¹), the dissociation rate constants $k_d$ (s⁻¹), and the equilibrium dissociation constants $K_D$ (M). Results were then analyzed in R (Version 4.3.0)⁴¹ using R Studio (Version 2024.04.0, Posit Software). For data handling, wrangling and plotting the packages *rio*, *tidyverse*⁴², *reshape*⁴³, *stringi*⁴⁴, *readxl*, *data.table*, *ggthemes*, *ggraph*, *igraph*⁴⁵, *extrafont*, *gplots*, *splitstackshape*, *rlist*, *ggpubr*, *broom*, *scales*, *ggthemes*, and *viridis* were used.

To improve the accuracy of the dissociation rate constants for high affinity mAbs, sensorgrams were normalized to local maximal response unit (RU) values of the dissociation phase (time after first injection > 1000 s) and transformed to the natural logarithm. Linear models were fit to the transformed data with the slopes of the fit representing the novel dissociation rate constants ($k_d fit$).

To analyze the results of the SPR binning, both text files of the sensorgrams and report point tables containing measured responses for each cycle at different report points were imported into R. For plotting, sensorgrams were normalized to baseline binding levels and injection time points. To determine binning clusters, relative responses for capture levels for each mAb, binding levels after injection of ricin, and enhancement levels after injection of the enhancement antibody were imported from the report point tables. The enhancement response was first normalized for each capture mAb by subtracting the enhancement response when the capture mAbs is injected (ΔEnh). This represents a “full-blockage” scenario, as the same antibody was used for capture and enhancement. Second, the subtracted enhancement responses were normalized to the sample level after ricin injection (NormΔEnh) by calculating the ratio between the normalized enhancement injection and the ricin binding level, grouped by each capture antibody. The differences due to varying capture levels and ricin binding capacities are thereby accounted for. To further account for the differences in maximum binding capacity of each mAb during the enhancement injection, the enhancement response for each mAb used as an enhancement antibody was normalized to the maximum signal observed for that specific enhancement mAb (MaxNormΔEnh).

Subsequently, normalized enhancement responses were transformed into a matrix. A correlation matrix using Pearson's r correlation coefficients was calculated and plotted as a heatmap to visualize clusters of mAbs showing a similar pattern regarding their normalized enhancement responses. The normalized enhancement responses were transformed into an adjacency matrix by setting all values above a cutoff of 0.1 to 0 and all values below the cutoff to 1 indicating that those mAbs share a common bin as indicated by mutual blockage of binding. Using the igraph package⁴⁵, we constructed an undirected network graph from the adjacency matrix with mAbs representing nodes and mutual blockage in the binning representing an edge. Before plotting, loops were removed and communities of subgraphs were identified via greedy optimization of modularity. Hereby, bins are defined as groups of tightly connected nodes.
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## BLI measurements

BLI measurements were performed as described in Lequesne et al.⁵ with some minor adaptations and in the absence of galactose, as compared to SPR experiments. VHH-Fc antibodies were immobilized on anti-human (AHC) Fc biosensors from forteBio® (Sartorius) at 2.5 µg/mL, instead of 10 or 5 µg/mL for IgG antibodies, because of their smaller molecular weight. The 1RK1, R18, RCH1, R21, R109 antibodies were immobilized on anti-mouse (AMC) Fc biosensors from forteBio® (Sartorius) at 10 µg/mL. Their affinities were measured as described in Lequesne et al.⁵, except that BLI sensors were saturated with a polyclonal solution of irrelevant mouse antibodies during 250 s after the antibody loading step to reduce any potential nonspecific interactions of ricin with the BLI sensors.

## Epitope mapping experiments

Epitopes were determined by saturation mutagenesis using yeast display libraries of RTA or RTB (isoform D) as described in Pruvost et al. with some adaptations.⁴⁶ Briefly, five RTA and five RTB libraries with single amino acid mutations were constructed using Splicing by Overlap Extension PCR (SOE-PCR) and NNK-containing degenerate primers. NNK primers, encoding all 20 amino acids with 32 codons, were used to introduce single point mutations across the ricin sequence, which was divided into five libraries per ricin chain for Illumina sequencing. For RTA, the five libraries cover the following amino acid ranges: 1–54, 55–107, 108–161, 162–215, and 216–267. For RTB, the five libraries cover the following amino acid ranges: 1–51, 52–104, 105–156, 157–209, and 210–262. The libraries for RTA and RTB span approximately 50 residues in the sequence, which correspond to a theoretical size of approximately 1,600 unique clones (50 residues × 32 codons). Consequently, both the A and B chains include approximately 8,500 unique clones in total.

Following the mutagenesis, genes were assembled and amplified by SOE-PCR. Preparation of competent yeast cells EBY100 (ATCC® MYA-4941) and library transformation were performed according to Benatuil et al.⁴⁷ Libraries were cloned into yeast cells by gap repair cloning by electroporation with 1 µg of digested vector and a molar ratio of 1.5:1 (library genes/digested vector). Transformation efficiency was determined by taking a small aliquot to plate serial dilutions on selective agar plates. Transformed cells were cultured for 2 d in SD-CAA medium (6.7 g/L yeast nitrogen base without casamino acids, 20 g/L glucose, 5 g/L casamino acids, 100 mM sodium phosphate, pH 6.0), at 30°C with shaking. After a passage to an OD₆₀₀ of 0.25, cells were grown at 30°C until OD₆₀₀ 0.5–1.0 and re-suspended in 50 mL of SG-CAA (6.7 g/L yeast nitrogen base without casamino acids, 20 g/L galactose, 5 g/L casamino-acids, 100 mM sodium phosphate, pH 6.0) for induction of the surface expression of the mutated RTA or RTB libraries and incubated for 18 h at 20°C.

Binding of murine IgG antibodies to the surface-expressed RTA or RTB libraries was revealed either using a secondary antibody (goat anti-mouse APC, Invitrogen 31,981, 1:100 dilution), or by using the corresponding biotinylated IgG molecules using streptavidin-PE (Molecular Probes™ S866, 1:100 dilution) as a secondary reporter. 43RCA-G1, being a humanized antibody, was covalently labeled with DyLight 550 fluorescent probe (Thermo Scientific), according to the manufacturer's protocol. VHH antibodies were detected via goat anti-VHH-PE (Ozyme, JIR128-115-230, 1:100 dilution) secondary antibody. Fab antibodies were detected by mouse anti-human kappa light-APC (Invitrogen; MH10515, 1:100 dilution). Alternatively, VHH and Fab constructs were expressed with a V5 or HA tag at the C-terminus of their sequence and revealed via a mouse anti-V5 APC (Invitrogen 451,098, 1:100 dilution) or mouse anti-HA-APC (Invitrogen 26183D650, 1:100 dilution) secondary antibody.

For each antibody tested, a second antibody was used as a conformational control to confirm the surface expression of correctly folded RTA or RTB fragments. Various pairwise combinations of antibodies were analyzed using flow cytometry to identify suitable mAb combinations for epitope mapping, ensuring no cross-reaction between molecules and secondary reagents (Supp. Table S2).

After induction, 10⁶ yeast cells from each RTA or RTB library were incubated with 0.1 or 1 nM of the studied antibody, depending on its $K_D$. Incubations were performed with both the studied antibody and the conformational control antibody simultaneously in either 14 mL (final concentration of 0.1 nM) or 1 mL (final concentration of 1 nM) in PBSF buffer (phosphate-buffered saline (PBS), bovine serum albumin (BSA) 0.1%), respectively. After incubation for 2 h at 20°C, cells were washed with 1 mL ice-
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cold PBSF (4000 g, 3 min, 4°C). Secondary labeling with adapted fluorescent reporters was performed in PBSF for 15 min on ice. Cells were washed with 1 mL of ice-cold PBSF before sorting of cells with a BD FACS Aria™ III cytometer (Becton Dickinson) using BD FACSdiva™ v8 software. Cells exhibiting decreased binding to the antibody of interest, but not to the conformational control antibody, were sorted and cultured at 30°C for 2 d in SD-CAA medium. Plasmids were extracted with Zymoprep Yeast Plasmid Miniprep II kit (Zymo Research, ZD2004). Regions of interest (spanning the RTA or RTB sequences) were amplified in a first PCR step before the addition of Illumina sequencing primers as previously described.⁴⁶ Next-generation sequencing (NGS) was performed with an Illumina 3ISeq (Illumina) device (2 × 150 bp kit 300 cycles) with at least 500,000 reads per population. Sequencing data was analyzed using R (Version 4.3.0) using the RStudio IDE. Briefly, paired reads were joined (Fastq Joiner), trimmed (Fastq Trimmer)⁴⁸ to retain only the region of interest in the correct frame, and filtered (Filter FASTQ) to eliminate reads with a minimum quality score below 30, as previously described.⁴⁶ The enrichment of each mutation (i.e., the ratio of the frequency of the mutation in the sorted population to the frequency in the initial library) was calculated for each amino acid position. The results are presented as heatmaps, illustrating the enrichment levels of each mutation in the sorted library. Ricin amino acids at a specific position were considered part of the epitope if they exhibited at least five distinct highly enriched mutations, each with an enrichment factor exceeding 4.

## Determination of the inhibition of RTA enzymatic activity by an adenine release assay

Ricin spiked at 10 ng/mL was bound to an anti-B-chain antibody (mAb RB34) immobilized on Dynabeads M-280 tosyl-activated magnetic beads (Life Technologies, Oslo, Norway). Bound ricin was incubated for 1 h at 25°C with increasing amount of the anti-A-chain antibodies, at molar ratio to ricin of 0, 1, 10, 100 and 1000-fold. Magnetic beads were resuspended in ammonium acetate 10 mM, pH 4 and incubated with the 14-mer RNA substrate as described in Becher et al.⁴⁹ Released adenine was determined by targeted high-resolution mass spectrometry (MS) on a Dionex Ultimate 3000 system coupled to a Q-Exactive mass spectrometer (Thermo Fisher Scientific, Germany).

## Results

### Characterisation of antibody kinetics and affinity by SPR and BLI

Given the critical role of binding strength between an antibody and its toxin antigen in neutralizing the toxin, we assessed the binding kinetics of mAbs to ricin using both BLI and SPR, two complementary and widely used techniques. Our objective was to assess the reliability of these methods in ranking the mAbs based on their overall binding affinities. For the SPR measurements, we performed single-cycle titration measurements using a Biacore T200 SPR system, targeting ricin isoforms D and E, as well as agglutinin, to evaluate the cross-reactivity of each mAb tested (Figure 1 and Supp. Table S3). For BLI measurements, we used the Octet® RED96e from the ForteBio® system to assess the association and dissociation rate constants of ricin D and E separately with the various antibodies immobilized on BLI sensors (Supp. Table S4).

Using SPR, we observed that for mAbs with slow dissociation rate constants (below ~2 × 10⁻⁵ s⁻¹, Figure 1), the standard evaluation software overestimated binding stability by fitting dissociation rates below 10⁻⁶ s⁻¹, which are beyond the resolution capabilities of SPR. A detailed analysis of the sensorgrams revealed detectable, albeit slow, dissociation.

To enhance the accuracy of dissociation phase modeling, we applied linear regression to the natural logarithm-transformed, normalized dissociation phase data (time after first analyte injection >1000 s). This method enabled more accurate fitting of the dissociation phase and allowed determination of dissociation rate constants independently of association rate constants (Supp. Figure S1).

By employing this approach, we obtained reliable and plausible dissociation values even for highly stable complexes, allowing the precise ranking of ricin-specific mAbs by binding affinity (Figure 1C). Notably, binding affinities between 50 pM and 200 pM were resolved with high confidence for the strongest binders to ricin D. Even the lowest affinity mAb in our panel, R21, exhibited a high-affinity interaction with ricin D, with a dissociation constant of 5 nM, consistent with the preselection of already potent anti-ricin antibodies.
<figure><img src="image_18.png" /></figure>

<figure><img src="image_19.png" /></figure>

**Figure 1.** SPR binding kinetics of a panel of mAbs against ricin D or ricin E. A) and B): sensorgrams with measured (red line) and fitted (black line) resonance units (RU) of single cycle kinetics, plotted over time in seconds for ricin D and E, respectively. Corresponding sensorgrams for agglutinin are shown in Supp. Fig. S2. C) heatmap color-coded by binding affinities $K_D$ of the mAb panel against ricin D, ricin E, and agglutinin. White panels represent no binding or low-affinity cross reactivity that did not follow a 1:1 Langmuir interaction. On the left, Panel A presents SPR sensorgrams depicting the binding kinetics of monoclonal antibodies (mAbs) to ricin D, while Panel B, in the middle, shows sensorgrams for ricin E. The curves differ among antibodies, with some exhibiting lower affinity for ricin D (43RCA-G1, R109, RB37, R21, RicE7) and others for ricin E (RB34, 43RCA-G1, R109, RB37, R21) compared to other mAbs. Certain antibodies preferentially bind one isoform over the other: R109, RB34, and RB37 display stronger affinity for ricin D, whereas RicE7 preferentially binds ricin E. On the right, Panel C presents SPR binding affinities as a heatmap of $log_{10}(K_D)$ values for each mAb against ricin D, ricin E, and agglutinin. High-affinity interactions ($log_{10}(K_D)$ around -11) are shown in dark blue, while low-affinity interactions ($log_{10}(K_D)$ around -8) are represented in yellow, with intermediate values transitioning through green.

When comparing mAb specificity for ricin D, E, or agglutinin, we found that most antibodies bound ricin D and E with similar affinity, exhibiting a clear preference of ricin over agglutinin (Figure 1, Supp. Figure S2). Notable exceptions included mAbs RB34, RB37, and R109, which showed preferential binding to ricin D over E. In contrast, RicE7 demonstrated stronger binding to ricin E and agglutinin than to ricin D.

Furthermore, the affinity of the diverse mAbs for ricin was also evaluated through BLI, another widely used method for studying antibody-antigen binding kinetics. The BLI data indicate that all the antibodies exhibit a high affinity for ricin D, with $K_D$ values in the sub-nanomolar range. In comparison with SPR data (Figure 2), the majority of antibodies showed slower dissociation rates, with $k_d$ values below $10^{-5} s^{-1}$, or in some cases, no measurable dissociation at all (Supp. Figure S3, Supp. Table S4). This discrepancy, along with the lack of detectable dissociation using BLI in those conditions, leads to artificially low $K_D$ values in BLI and minimizes differences for certain antibodies (e.g., RCH1, R18, RicE5) that exhibit significant affinity variations in SPR.

## Identification of binding epitopes

To better understand the mode of action and identify antibodies capable of simultaneous antigen binding, which is critical for *in vitro* diagnostics (e.g., Sandwich-ELISA) and therapeutic applications (e.g., oligoclonal mAb cocktails), we characterized the binding epitopes using two complementary approaches.

First, SPR-based binning was used to classify antibodies into clusters based on their ability to either competitively inhibit or permit simultaneous binding to ricin. Second, to precisely map the epitopes, we combined deep mutational scanning with yeast display to identify key amino acids mediating individual mAb-ricin interactions.
<figure><img src="image_20.png" /></figure>

<figure><img src="image_21.png" /><figcaption>**Figure 2.** Plot of association rate constants ($k_a$) over dissociation rate constants ($k_d$) for binding to ricin D for the panel of antibodies as determined SPR. Isoaffinity lines indicating regions of similar affinity, antibodies colored according to epitope bins (Figure 4), differentiated by different symbols. Scatter plots of dissociation rate constants ($k_d$) vs. association rate constants ($k_a$) for antibody binding to ricin D, measured by SPR. Isoaffinity lines indicating regions of similar affinity, antibodies colored according to epitope bins (Figure 4), differentiated by different symbols.</figcaption></figure>

## Identification of epitope bins using SPR

To identify epitope bins by SPR, we conducted pairwise binding experiments for all possible mAb combinations using a capture-based approach (Figure 3A). Each mAb was non-covalently immobilized on sensor chips via species-specific Fc capture, and the remaining binding sites were blocked prior to ricin injection. Following ricin binding, a second (enhancement) mAb was injected to assess whether simultaneous binding to ricin occurred, indicating recognition of distinct epitopes. This approach enabled the assessment of 289 interactions ($n = 17^2$) using only two sensor chips (mouse and human IgG capture chips), leveraging the different Fc regions of the mAbs without requiring further modifications.

To facilitate interpretation of the data, we developed a straightforward yet effective method to visualize epitope relationships. Enhancement responses were normalized to account for variations in capture levels, ricin binding, and enhancement mAb binding (Figure 3B). These normalized values were transformed into an adjacency matrix, allowing the generation of clustered network plots (Figure 3C).

Sensorgrams showing the sample binding response after ricin injection and the enhancement binding response after the injection of enhancement antibodies for all capture and enhancement mAb combinations are presented in Supp. Figure S4. Clear differences are evident between antibody pairs that can simultaneously bind to ricin targeting different epitopes (enhancement of the binding response) and those that
<figure><img src="image_22.png" /></figure>

<figure><img src="image_23.png" /><figcaption>**Figure 3.** Schematic overview of data collection and analysis of SPR-based epitope binning. A) mAbs are immobilized via mouse or human specific anti-species capture antibodies. Unoccupied binding sites on the capture antibodies are blocked before ricin is injected, for which sample binding levels are recorded. Finally, the injection of enhancement antibodies leads to the enhancement binding levels. For non-overlapping epitopes, additional binding is observed, while for overlapping epitopes, mutual binding blockage prevents additional binding. B) to account for slight differences between the capture, sample, and enhancement levels of different antibodies, binding responses are normalized. For each capture mAb tested, enhancement responses after injection of enhancement mAbs are normalized by subtracting the enhancement response after injection of the capture mAb (leading to ΔEnh). Sample levels are normalized to the capture level of each mAbs (Norm<sub>Sam</sub>). The enhancement response is then normalized by dividing the delta enhancement response by the normalized sample levels (Norm<sub>ΔEnh</sub>). Finally, for each enhancement mAb, the enhancement responses are normalized to the maximum enhancement response (MaxNorm<sub>ΔEnh</sub>). C) normalized enhancement responses are transformed into an adjacency matrix, from which a network graph is constructed and clusters are analyzed using the *igraph* package in *R*. Created in BioRender.
Stern, D. (2025). Panel A illustrates SPR-based epitope binning, showing the functionalized SPR chip and three key steps: (1) antibody capture, (2) ricin capture, and (3) second mAb addition. Binding signal after step 3 indicates positive enhancement (binding signal increases for non-overlapping epitopes) or negative enhancement (binding signal decreases for overlapping epitopes). Panel B details data normalization, calculating the normalized enhancement responses (positive or negative) for each mAb pair tested, represented in a matrix. Panel C transforms the data into an adjacency matrix and visualizes epitope binning as a network graph, clustering antibodies into colored circles, where mAbs in the same circle share overlapping epitopes.</figcaption></figure>

mutually block each other's binding (targeting the same or a close-by epitope; no enhancement of the binding response).

However, clustering mAbs into bins based on this data was challenging, due to the large number of sensorgrams requiring comparison. Therefore, we computed a heatmap of the Pearson correlation coefficients based on the normalized enhancement responses, providing a clearer depiction of antibodies that grouped into similar epitope bins (Figure 4A).

To further resolve spatial relationships between epitopes, we used network graph visualization (Figure 4B). This approach not only delineated mAbs within the same bin but also revealed spatial proximity between distinct bins and identified antibodies that bridged bins, suggesting intermediate or overlapping epitopes.

SPR-based epitope binning clustered the panel of 17 mAbs into six distinct, partly overlapping bins defined by mutual binding interference: three located on the A chain and three on the B chain of ricin.
<figure><img src="image_24.png" /></figure>

<figure><img src="image_25.png" /><figcaption>**Figure 4.** Results of the epitope binning approach by SPR. A) heatmap of Pearson's correlation coefficients calculated between the normalized enhancement responses of the different antibody pairs to visualize clusters of different mAbs. B) network graph with clustered subnetworks representing epitope bins as an alternative visual representation of epitope bins of the tested antibodies on ricin. The heatmap represents antibody pair compatibility, with dark blue indicating antibodies that cannot bind simultaneously to ricin (sharing similar epitopes), transitioning to red for those that bind without interference, and white for partial binding impact. On the right, the data is visualized as a network graph, clustering compatible mAbs into colored bins. RA35, E5, 1RK1, and R21 form a bin on RTA, near the bin including V2G10, V2A11, RA36, and 43RCA-G1. Further on RTA, RCH1, R18, and RicE5 cluster together, with RCH1 linking closely to an RTB bin containing RB37 and RicE7. Two additional RTB bins include RicE8, RB34, and RicE4, while R109 remains separate.</figcaption></figure>

Hereby, mAbs linking different epitope bins indicate close spatial proximity between those bins. Notably, two A-chain bins comprising eight mAbs showed extensive cross-blocking, with mAbs such as RA36 and 43RCA-G1 occupying central positions, hereby interfering with binding in both bins. Conversely, mAbs capable of simultaneous binding with antibodies from other bins likely reside on the periphery of the epitope region.

Importantly, network visualization effectively captured the spatial organization of ricin epitopes. A particularly striking example involved an A-chain epitope recognized by R18, RicE5, and RCH1, which was linked to a B-chain epitope through mutual binding interference between RCH1 and RB37, highlighting a potential interchain epitope interface.

## Identification of mutations affecting mAb binding to ricin chain A or to ricin chain B

The second objective of this study was to identify the amino acids in the toxin's A or B chain that are involved in the recognition of the various mAbs that were selected for analysis. To this end, a deep mutational scanning approach was used separately for each ricin chain using the yeast surface display technique (Figure 5). In this approach, the individual chain A or B was expressed in fusion with the Aga2p protein, which enabled its anchoring in the yeast cell wall. A mutation was engineered into the ricin A chain sequence to reduce its toxicity to cells. Specifically, the E177D mutation was introduced at the active site, as previously reported.⁵⁰ Additionally, the cysteine residues involved in the interchain disulfide bridge were substituted with serine residues.

We first demonstrated that these constructs were effectively expressed and that the selected antibodies were capable of binding to the cognate ricin chain (Figure 5B). In order to distinguish mutations directly involved in antibody recognition (i.e., in the epitope) from those affecting the expression, folding or general conformation of the antigen, a conformational control was included in the labeling process (Figure 5B).
<figure><img src="image_26.png" /></figure>

<figure><img src="image_27.png" /><figcaption>**Figure 5.** General principle of the epitope mapping process combining deep mutational scanning, yeast surface display screening and large-scale sequencing. A) DMS libraries incorporate a single mutation per clone, using a single NNK codon in the gene sequence for each position. The five libraries generated for each chain (A or B) encompass the amino acids 36 to 302 and 315 to 576, respectively, of the ricin protein. Libraries are transformed into *S. cerevisiae* cells using gap repair recombination. B) following the induction phase, each library is incubated with the antibody under study, together with a second antibody (VHH-Fc or conventional) that is used as a conformational control. During FACS analysis, clones that no longer exhibited recognition by the antibody under study (red gates) were sorted C) corresponding DNA is sequenced using Illumina deep sequencing. The DNA sequences corresponding to these cells are obtained by Illumina NGS sequencing, and bioinformatics processing is used to generate NGS-based heat maps that calculate enrichment, i.e., the change in frequency fold between sorted and unsorted populations for a given amino acid substitution. Identified amino acid positions are then represented in color on the ricin crystallographic structure (PDB entry #3RTI) and are defined as the functional epitope. On the left, a schematic illustrates the cloning of DMS library inserts – spanning five regions of RTA or RTB with single amino acid mutations – into a yeast plasmid. To the right, two cases of yeast displaying mutated ricin chains are shown. The binding of fluorescently labeled IgG antibody and VHH-Fc conformational control is shown on a flow cytogram, with IgG fluorescence on the y-axis and VHH-Fc fluorescence on the x-axis. In the first case, where the mutation is outside both antibody epitopes, binding occurs, placing yeast in the upper-right quadrant. In the second case, where the mutation disrupts IgG binding, yeast shifts to the lower-right quadrant. These yeast populations are gated for FACS, followed by bioinformatics analysis. An arrow leads to a representative heatmap highlighting a frequently substituted position in the sorted population, which is mapped onto a 3D ricin structure, pinpointing a residue within the IgG epitope.</figcaption></figure>

This required a second antibody whose binding was compatible with the first antibody under investigation. The most suitable conformational control was then identified for each antibody (Supp. Table S2). Single mutant libraries of ricin chains were generated via SOE-PCR using degenerate primers (Figure 5A). Due to the substantial size of each ricin chain and to facilitate NGS procedures, their sequences were divided into five sub-libraries for each chain. Upon induction, each set of five yeast libraries was simultaneously labeled with the studied antibody and the appropriate conformational control antibody. Subsequently, a fluorescence-activated cell sorting (FACS) procedure was used to sort the mutants of ricin that cause a loss of recognition by the antibody under study (Figure 5B). The respective mutations were identified through NGS (Figure 5C) and corresponding positions defined as part of the functional epitope, as previously described for other antigens.<sup>46,51</sup> Finally, the positions identified in this process can be colored on the crystallographic structure of ricin, providing a spatial representation of the epitope (Figure 5C). As a control, we mapped the epitopes of VHHs V2A11 and V2G10. The results show that the amino acids identified by the DMS technique are in direct contact with the VHHs, specifically interacting with residues of the hypervariable CDR3 loop in both cases (Supp. Figure S5).

The epitopes identified by DMS show a high degree of consistency with the bins of epitopes identified by SPR, confirming that the bins represent antibody groups with mutually exclusive antigen binding due to
<figure><img src="image_28.png" /></figure>

<figure><img src="image_29.png" /><figcaption>**Figure 6.** Graphical view of the crystallographic structure of ricin (PDB ID 3RTI) with the functional epitopes of the different anti-A chain antibodies tested. The functional epitopes are displayed in color according to the different bins that were identified in SPR (left). mAbs targeting epitopes close to the active site are shown in panel A, and antibodies on the side opposite the active site are shown in panel B. Additionally, graphical representations of the crystallographic structures corresponding to VHH E5, V2A11, and V2G10 antibodies are provided for reference. The active site of the A chain of ricin contains a nucleotide analog, a formycin monophosphate molecule (in red sticks). In the upper panel, the epitopes of IgG mAbs from the RA35, R21, E5, and 1RK1 SPR bin, along with those from RA36, 43RCA-G1, V2G10, and V2A11 in the neighboring bin, are highlighted on a surface representation of RTA. The crystallographic complex structures of VHH V2G10, V2A11, and E5 bound to RTA are also shown. All of these mAbs bind around the active site region, with V2A11 and V2G10 specifically targeting the active cleft. In the lower panel, the epitopes of RicE5, R18, and RCH1, which belong to the same bin, are mapped onto RTA, located behind the active site. RicE5 and R18 share a similar epitope, while RCH1 binds near the RTA-RTB interface.</figcaption></figure>

steric hindrance (Figure 6). Antibodies within the same epitope cluster exhibit close (e.g., RA35, R21 or 1RK1, Figure 6A) or even overlapping (e.g., RicE5 and R18, Figure 6B) epitopes. On the B chain, epitope mapping confirms that RB37 targets a distinct, more distal epitope compared to RB34, RicE4, and RicE8, in agreement with the binning data (Figure 7). Furthermore, DMS confirmed the spatial proximity between epitopes recognized by RCH1 (A chain) and RB37 (B chain) (Supp. Figure S6A) consistent with mutual binding interference observed in SPR.

In this set of ricin-specific antibodies, the functional epitopes consist of a variable number of amino acids, typically forming contiguous surfaces that align with interaction with a single paratope. The number of residues per epitopes varied between 2 and 21 amino acids, depending on both the antibody and its format. It can be observed that certain epitopes, including those of 1RK1, RA35 and 43RCA-G1, when used as full
<figure><img src="image_30.png" /></figure>

<figure><img src="image_31.png" /><figcaption>**Figure 7.** Graphical view of the crystallographic structure of ricin (PDB ID 3RTI) with the functional epitopes of the different anti-B chain antibodies tested. The first antibody bin identified by SPR is shown in panel A and the second bin in panel B, together with the high-affinity galactose binding site and the substitutions present between isoforms D and E. The epitopes of antibodies from the three SPR-identified bins on RTB are highlighted in different colors on various angled views of the surface representation of RTB. RicE4 and RicE8 share very similar epitopes, positioned close to – but apart from – the low-affinity galactose binding site. The RB34 epitope is located near theirs, spanning two distinct regions on the protein. The RB37 epitope is situated around the high-affinity galactose binding site of RTB, where substitutions between the ricin D and E isoforms are also highlighted in a separate representation of the RTB structure. The epitope of R109 is located on the opposite side of RTB from the other epitopes (Supp. Fig S6).</figcaption></figure>

IgG molecules, are constituted of a small number of residues (Figure 8). Conversely, Fab-reformatted antibodies facilitate the identification of a greater number of amino acids, likely attributable to the elimination of avidity effects.

To confirm the correlation between epitope mapping data and the functional properties of the antibodies, we systematically evaluated their capacity to neutralize ricin's enzymatic activity using an adenine release assay. The results demonstrate that antibodies targeting the immediate vicinity of the active site (depicted in blue in Figure 6) effectively inhibit ricin's enzymatic activity (Figure 9). This inhibitory effect is particularly pronounced for VHH-Fc V2G10 and V2A11, which nearly abolish enzymatic activity at a 1:1 ricin-to-antibody ratio. In contrast, antibodies interacting only near, but not directly at, the catalytic site (in orange in Figure 6) generally do not inhibit activity, except for R21 and VHH E5 at high antibody-to-ricin ratios. Additionally, antibodies binding opposite the active site (depicted in red in Figure 6) do not appear to reduce enzymatic activity and may even slightly enhance it, as observed with RicE5.

## Discussion

Understanding the molecular interactions between mAbs and their antigens has become a critical element in the discovery of antibody-based therapeutics. In particular, binding epitopes are key determinants of the efficacy of therapeutic mAb candidates used for the post-exposure treatment of ricin poisoning.⁵² Additionally, higher binding affinity is correlated with higher toxin-neutralizing activity, although targeting neutralizing epitopes is crucial.³⁵ Hence, in this work, we determined both binding affinities and epitopes of a panel of 17 pre-selected ricin specific antibodies by two orthogonal methods. Hereby, we wanted to contribute to a deeper understanding of how mAbs offer protection against ricin by determining these key
<figure><img src="image_32.png" /></figure>

<figure><img src="image_33.png" /></figure>

<table><thead><tr><th>specificity</th><th>Name</th><th>Fab</th><th>IgG</th></tr></thead><tbody><tr><td rowspan="8">Chain A</td><td>RicE5</td><td>7</td><td>5</td></tr><tr><td>R18</td><td>10</td><td>5</td></tr><tr><td>R21</td><td>n.d.</td><td>5</td></tr><tr><td>1RK1</td><td>n.d.</td><td>4</td></tr><tr><td>RCH1</td><td>n.d.</td><td>3</td></tr><tr><td>RA35</td><td>n.d.</td><td>2</td></tr><tr><td>RA36</td><td>n.d.</td><td>3</td></tr><tr><td>43RCA-G1</td><td>n.d.</td><td>4</td></tr><tr><td rowspan="5">Chain B</td><td>RicE4</td><td>n.d.</td><td>7</td></tr><tr><td>RicE8</td><td>7</td><td>n.d.</td></tr><tr><td>R109</td><td>15</td><td>n.d.</td></tr><tr><td>RB34</td><td>21</td><td>n.d.</td></tr><tr><td>RB37</td><td>13</td><td>n.d.</td></tr><tr><td colspan="2">Mean ± SD</td><td>12,2 ± 5,4</td><td>4,2 ± 1,5</td></tr></tbody></table>

**Figure 8.** Influence of antibody format on epitope determination. A) comparative analysis of the functional epitopes identified by DMS for RicE5 and R18 antibodies, as IgG or Fab molecules. B) number of amino acids identified for each antibody as part of the functional epitope, either for full IgG or Fab antibodies. These data show the influence of antibody format on epitope determination by DMS. The left panel shows a surface representation of the RTA structure, with the epitopes of RicE5 and R18 (determined as Fab or IgG antibodies) colored. The Fab format allowed for the identification of a larger epitope region. On the right, a table summarizes the number of residues identified as part of the epitope for all the tested antibodies in the panel, when expressed as Fab or IgG antibodies. Fab versions of R21, 1RK1, RCH1, RA35, RA36, 43RCA-G1, and RicE4 were not tested, nor were IgG versions of RicE8, R109, RB34, and RB37 (n.d. specified). The RicE7 epitope was not determined by epitope mapping. Between 2 and up to 21 residues were identified in the epitope, with the RB34 Fab format identifying the maximum number of residues. When expressed as FabS, two additional residues in the RicE5 epitope and five additional residues in the R18 epitope were identified compared to the IgG versions.

parameters, which can then be correlated to *in vitro* and *in vivo* toxin neutralization assays.⁵ To explicitly connect these findings to therapeutic development, the precise identification of binding epitopes and affinities of anti-ricin antibodies is crucial not only to understand their individual neutralization

<figure><img src="image_34.png" /></figure>

**Figure 9.** Enzymatic activity inhibition by anti-A-chain antibodies. The three panels (blue, yellow, and red) correspond to the different antibody bins identified by SPR for anti-A-chain mAbs. Ricin (10 ng) was mixed with equimolar ratios or 10-, 100-, 1000-fold molar excess of each antibody. Adenine released by the A chain enzymatic activity on a 14-mer RNA substrate, in absence or presence of the antibody, was determined by liquid chromatography coupled to tandem mass spectrometry. Each point represents the mean ± SD of two independent repetitions. Three plots display the percentage of released adenine (y-axis) against the ricin-to-antibody molar ratio (x-axis) for each anti-RTA mAb, grouped by epitope cluster. The three panels (blue, yellow, and red) correspond to the different antibody bins identified by SPR for anti-chain A mAbs. The left plot shows that V2A11, V2G10, RA36, and 43RCA-G1 significantly reduced adenine release at molar ratios of 1 for V2G10 and V2A11, and 10 for the others, indicating inhibition of RTA's enzymatic activity. The middle plot shows adenine release curves for E5, R21, RA35, and 1RK1, with E5 inhibiting at ratio 10 and R21 inhibiting only at ratio 1000. The right plot shows that RCH1, RicE5, and R18 antibodies exhibit no inhibition, as no decrease in released adenine was observed at any ricin-to-antibody ratio.
<figure><img src="image_35.png" /></figure>

mechanisms but also to rationally design antibody cocktails. Such cocktails can target multiple, non-overlapping epitopes to maximize synergistic toxin neutralization and minimize escape mutants.

To compare binding affinities, we used both BLI and SPR, using established protocols optimized within our laboratories. This direct comparison allowed us to assess potential method-dependent differences, which may arise from differences in ligand density, injection times, buffer composition, and signal detection sensitivity. Notably, the presence or absence of galactose can influence the binding of certain antibodies targeting the B chain of ricin, as demonstrated by Orsini.³ This may also contribute to discrepancies between SPR and BLI measurements, as SPR includes galactose in the running buffer, whereas BLI does not.

Our results indicated that, under our experimental conditions, SPR yields more accurate dissociation constant measurements than BLI. This is largely attributed to SPR's higher sensitivity and its microfluidic flow system, which facilitates the efficient removal of dissociated analytes and minimizes rebinding artifacts. In contrast, BLI's use of static wells increases the likelihood of rebinding events, particularly when measuring high-affinity interactions. Although previous studies have demonstrated that BLI performance can be improved by lowering surface ligand densities,⁵³ we observed that reduced surface loading led to lower signal-to-noise ratios, which introduced variability and compromised the reliability of high-affinity measurements, despite some improvement in resolution. While BLI remains well suited for high-throughput screening and for distinguishing between low- and high-affinity interactions, its lower sensitivity, rebinding, and evaporation effects limit its utility for accurately resolving the highest-affinity antibodies.⁵⁴ SPR, with lower surface functionalization, minimizes artifacts and provides more precise kinetic measurements, essential for detecting subtle differences.

Besides affinity, the epitope is arguably the key to understanding the diverse functional characteristics of antibodies. As antibodies can interact with their antigen in various ways, each epitope has a defining role in the functional properties of mAbs, such as specificity or efficacy when used as therapeutic. Understanding these interactions is crucial for designing effective mAb-based therapies and diagnostics. Moreover, unlike other properties such as affinity or pharmacokinetics, which can be modified by engineering, the epitope represents an intrinsic property that is inherent to the antibody itself. Probably due to the often-limited throughput of the various techniques used, detailed epitope mapping is rarely performed early in the drug discovery process. For technical and cost reasons, it is often considered more straightforward to screen the diverse functional properties of antibodies individually using high-throughput techniques than to define the epitopes themselves. Various antibody properties, such as antigen affinity, specificity, selectivity, and *in vitro* neutralization ability, can be efficiently screened in high-throughput assays. However, epitope mapping, which aims to determine how antibodies bind to specific antigenic regions and assess molecular complementarities, are typically conducted at lower throughput due to the greater complexity of these methods.

Among the techniques available for determining epitopes, structural methods such as X-ray crystallography and cryo-electron microscopy (cryo-EM) remain the gold standard for providing high-resolution data on epitopes. Additionally, these techniques facilitate the acquisition of valuable information on paratopes, which are of paramount importance for guiding molecular engineering. These techniques, however, are very labor-intensive and low throughput, and the chances of success remain highly uncertain. Over the years, a wide array of other techniques has been used to identify antibody epitopes within antigens.⁵⁵,⁵⁶ In addition to functional binning and mutational approaches, these varied methods include peptide-based techniques that are of limited relevance to the study of conformational epitopes, and MS-based methodologies, including cross-linking MS and hydrogen-deuterium exchange (HDX-MS), as well as *in silico* antibody/antigen docking. The recent advances in artificial intelligence, in particular with tools such as AlphaFold²,⁵⁷ RoseTTAFold⁵⁸ and AlphaFold³,⁵⁹ have considerably enhanced the quality and accuracy of protein/protein interface prediction, particularly in docking models for antigen/antibody complexes. However, accurate modeling of hypervariable loops remains challenging and experimental validation of predicted epitopes is essential to ensure their reliability.⁶⁰

Label-free techniques have emerged as powerful tools for high-throughput epitope binning, utilizing platforms such as BLI or SPR, in addition to multiplex assays.⁶¹⁻⁶³ A key advantage is the ability to assess pairwise binding without antibody labeling, reducing reagent consumption and hands-on time while allowing antibody binding from unpurified supernatants by using capture approaches. Additionally, this method enhances throughput by eliminating the need for sensor regeneration optimization. While epitope
<figure><img src="image_36.png" /></figure>

binning is a high-throughput method for identifying epitope clusters and molecular compatibilities,⁶⁴,⁶⁵ it lacks the resolution needed to precisely define the targeted regions at the amino acids level.

In recent years, DMS has become a powerful tool for characterizing protein-protein interactions and identifying mutations that either enhance or disrupt epitope-paratope binding.⁴⁶,⁵⁶,⁶⁶,⁶⁷ The data presented in this study, together with previous research, demonstrate a strong convergence between epitopes identified by DMS and those determined by X-ray crystallography,⁴⁶,⁶⁸ reinforcing the robustness and accuracy of the DMS approach for epitope mapping. Unlike epitope binning methods, DMS provides a high-resolution approach to mapping both linear and conformational epitopes. By analyzing escape mutations, DMS pinpoints the critical interaction points, i.e., the amino acids whose mutations are prone to disrupt recognition by antibodies.

To ensure both high-throughput analysis and precise epitope resolution in our anti-ricin antibody panel, we integrated SPR-based epitope binning with DMS. This approach leverages SPR to rapidly classify antibodies into epitope bins while utilizing DMS to identify key amino acids involved in antibody-antigen interactions, enabling a more detailed and comprehensive epitope characterization. Our contribution to the existing literature on this topic is two-fold. First, we developed new automated scripts using network analysis tools to streamline data processing, generating clear visual representations that accurately depict epitope groups. Although commercial software solutions are available for visualizing epitope bins using network plots or heatmaps, these tools are typically tied to specific SPR instruments or proprietary analysis workflows.⁶⁹ In contrast, our analysis pipeline is fully independent and relies exclusively on openly available tools. The accompanying R scripts are freely accessible, modifiable, and can be readily integrated into existing protocols or adapted to fit individual experimental workflows. Second, this methodology was applied to an extensive dataset of mAbs, resulting in a comprehensive characterization of the affinity and specificity of ricin-specific antibodies, together with their compatibility and complementarities.

Our results demonstrate strong agreement between epitope bins identified using the SPR binning approach and the exact binding epitopes determined by the more precise DMS method. To our knowledge, this study represents a significant benchmark, as it is the first to comprehensively characterize and directly compare epitope binning and high-resolution epitope mapping across a panel of mAbs using these two orthogonal approaches. This indicates that epitope clusters can be confidently determined at higher throughput using the established SPR approach. Including antibodies with known binding epitopes from the literature can further enhance the significance of SPR binning results. These well-defined antibodies serve as “anchor bins,” allowing bin assignments to be mapped onto the antigen surface and facilitating the spatial contextualization of newly characterized mAbs. In contrast, the combined DMS and YSD mutation screening approach identified the key amino acids involved in antibody recognition for 13 antibodies in the panel.

As this approach allows the identification of amino acids that contribute to the binding affinity between antigen and antibody, we have previously proposed the term ‘functional epitope’ to designate them.⁴⁶ Our epitope mapping approach enables the identification of a wide range of distinct, yet partially overlapping, epitopes, offering comprehensive coverage. This technique enhances our understanding of the epitopes targeted by anti-ricin antibodies, revealing critical insights into their mechanisms of action and specificity. Notably, many of the antibodies tested share epitope similarities with those previously documented, allowing us to hypothesize their mode of action and infer their therapeutic potential. Indeed, previous studies have characterized various anti-ricin VHH and IgG antibodies using X-ray crystallography and HDX-MS, aiming to correlate their epitopes and binding affinities with their neutralizing or protective efficacy.¹⁵,³⁷,⁷⁰,⁷¹ While several studies demonstrate that antibodies targeting specific epitopes can effectively neutralize the toxin, others, despite high affinity, fail to produce similar outcomes.⁵,²⁸,⁵² Additionally, certain neutralizing antibodies show efficacy in vitro, but do not provide protection in vivo, particularly in severe intoxication models where treatment is delayed for several hours post-exposure.⁵

This is particularly true of the RicE8 and RicE4 antibodies, which target the same epitope as SylH3 and JB4, both highly neutralizing antibodies (Supp. Fig. S7).⁷² Notably, SylH3 has demonstrated protective efficacy in prophylactic models.⁷²,⁷³ These antibodies significantly reduce ricin binding to host cells, although they do not directly interact with the carbohydrate binding sites of RTB. Despite its very high
<figure><img src="image_37.png" /></figure>

affinity and *in vitro* neutralizing capacity, RicE8 shows reduced post-exposure protective efficacy.⁵ Given the ability of SylH3 to protect in a prophylactic model, this suggests that the mechanism of action of these antibodies, which blocks ricin's entry into cells, is highly effective before exposure, but shows diminished efficacy after exposure.

It is worth noting that two of the antibodies under investigation, RicE5 and R18, have been observed to target an original epitope that appears to diverge significantly from those previously described in the literature. The newly identified epitope is located on the opposite side of the active site, with helix D and the loop b-c at its center (Supp. Fig. S8, Supp. Fig. S9). This loop has previously been identified as part of the epitope for neutralizing antibodies that offered only weak *in vivo* protection²⁸ and as an immunodominant B-cell epitope in patients treated with RTA immunotoxin for Hodgkin's lymphoma.⁷⁴ While helix D is included in the epitopes of numerous VHH antibodies described in the literature,⁷⁵ it is seldom the central element. Other antibodies that interact primarily with the αD-helix have demonstrated limited or no neutralizing activity, which may be related to their moderate binding affinity for ricin.

In contrast, recent studies have demonstrated that RicE5 is a highly neutralizing and protective antibody when administered post-exposure to ricin, including at late treatment times.⁵ Our data suggest that targeting this new epitope is a promising avenue for the generation of new antibodies with potential therapeutic applications, and that R18 may also possess this type of property, which will be the subject of future research. The precise mechanism of action of the antibodies targeting this particular epitope remains to be determined. One hypothesis is that targeting very distinct secondary structures with high affinity could induce conformational stabilization of the ricin A chain, thereby preventing the unfolding required for translocation into the cytoplasm, as previously proposed.⁷⁶ This hypothesis will have to be verified in the future through mechanistic studies.

The majority of antibodies in this set demonstrate comparable efficacy in detecting both ricin D and E isolectins. However, there are notable exceptions, with some interesting mAbs such as RB34 and RB37, exhibiting a distinct preference for ricin D and RicE7 exhibiting a distinct preference for ricin E. These mAbs may offer a potential avenue for differentiating between the two isoforms, especially for forensics applications. However, to maximize therapeutic efficacy and provide optimal *in vivo* protection, anti-ricin antibody candidates should preferably display cross-reactivity, enabling the efficient neutralization of both isoforms that coexist in the majority of the cultivars of *Ricinus communis*.³⁸ In this regard, epitope mapping offers invaluable insight by accurately identifying the specific substitutions responsible for the observed difference in affinity. This is exemplified by the case of RB34 and RB37, whose epitope partially overlaps the 2α and 2γ subdomains, which contain a significant number of mutations that differentiate the two isoforms (Figure 7 and Supp. Fig. S10). These observations are also fully consistent with the previously observed reduced ability of these antibodies to neutralize ricin in *in vitro* cell-based assays and their limited ability to protect mice in *in vivo* models that include both ricin isoforms.³,⁵,²¹ Other anti-B-chain antibodies, however, show interesting cross-reactivity, in particular RicE4 and RicE8, which appears consistent with the location of their epitope in the 1β domain, at a distance from substitutions between isoforms.

In addition, cross-reactivity to the closely related agglutinin, which is approx. 90% identical to ricin,³⁸ was assessed. It is plausible that agglutinin could act as a 'sink' for anti-ricin antibodies with potential neutralizing capabilities if crude ricin preparations containing both ricin and agglutinin were used in a bioterrorist attack. Assessing binding to ricin agglutinin can also be important for forensic and detection applications, as agglutinin is often co-extracted with ricin from castor beans. Distinguishing between the two can be helpful to enhance the specificity of detection methods and prevents false positives in toxin identification. The high sequence identity between ricin and agglutinin makes their differentiation challenging. mAbs with defined epitopes can selectively target either toxin, a feature particularly useful for forensic applications. For instance, VHH-Fc V2G10 and V2A11, along with RicE7, 43RCA-G1, and to a lesser extent VHH-Fc E5, exhibit the ability to recognize and bind both agglutinin and ricin. This cross-reactivity distinguishes them from other antibodies, which are specific to ricin.

The presented data suggest that avidity effects can play a significant role in a YSD setup with bivalent antibodies, as evidenced by the discrepancy between the number of amino acids identified in the functional epitopes of Fab and IgG molecules. To mitigate avidity effects, we utilized antibodies in a monomeric Fab format whenever feasible. By facilitating the selection of lower-affinity clones during FACS, this strategy helps identify core epitope/paratope residues while also detecting neighboring amino acids that, though
<figure><img src="image_38.png" /></figure>

non-essential, may influence binding. However, reformatting antibodies into Fabs demands significant time and resources for recombinant expression and cloning, which can limit screening throughput. In contrast, IgGs derived from hybridomas can be screened directly without modification, offering higher throughput but potentially identifying fewer epitope residues due to avidity effects.

In conclusion, this work establishes a framework for the selection of anti-ricin antibodies that target specific epitopes, with a particular focus on those that are highly neutralizing and protective, and exhibit ricin D/E cross-reactivity. By conducting competitive selections with well-characterized molecules and performing detailed epitope mapping, it is possible to streamline the identification of promising candidates. Given the low-throughput nature of animal experiments, it is preferable to reserve these for later stages of development. Importantly, our integrated use of DMS and epitope binning now provides a powerful and coherent framework to identify antibodies with distinct, non-overlapping epitopes. This is essential for the rational design of therapeutic cocktails, as combining antibodies that target multiple distinct neutralizing epitopes with high affinity can potentially lead to synergistic neutralization and improved therapeutic efficacy. Such an approach also reduces the risk of escape mutants that could limit treatment efficacy. Future work will focus on evaluating these rationally designed antibody cocktails *in vitro* and *in vivo* to confirm their improved protective potential.

**Disclosure statement**

No potential conflict of interest was reported by the author(s).

**Funding**

This work was supported by grants from the German Federal Ministry of Education and Research [project number 13N15462 to B.G.D.] and the French ANR [project number ANR-20-SEBM-0001] in the bilateral French-German research project "PLANT" and partly by the European Union's Horizon 2020 research and innovation program [EuroBioTox project, Grant Agreement no. 740189, to B.G.D and S.S.]. The authors appreciate the delivery of two monoclonal antibodies for the PLANT project by Dr Matthias Wittwer, Spiez Laboratory, Federal Office for Civil Protection, Spiez, Switzerland.

**ORCID**

Lois Lequesne http://orcid.org/0009-0004-9800-8007

François Becher http://orcid.org/0000-0002-1075-9980

Bernard Maillère http://orcid.org/0000-0001-5580-4194

Brigitte G. Dorner http://orcid.org/0000-0002-5100-2546

Stéphanie Simon http://orcid.org/0000-0002-6071-8255

Daniel Stern http://orcid.org/0000-0001-9057-4283

Hervé Nozach http://orcid.org/0000-0002-4021-5204

**Data availability statement**

Data files and R scripts for the analysis of binding kinetics and epitope binning are publicly available here: https://github.com/RKI-ZBS/SPR_kinetics_binning.

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