Combining deep mutational scanning and SPR binning approaches for large-scale epitope identification of anti-ricin antibodies
Year: 2025
Type: article
Abstract: 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.
Source: mAbs
Language: English
Subfield: Immunology
Field: Immunology and Microbiology
Domain: Life Sciences
Open Access status: gold
Estimated APC paid: $3,575
Funder: Agence Nationale de la Recherche
OpenAlex ID: W4413806792
