article · 14/10/2024
Inference and design of antibody specificity: From experiments to models and back
Résumé
Exquisite binding specificity is essential for many protein functions but is difficult to engineer. Many biotechnological or biomedical applications require the discrimination of very similar ligands, which poses the challenge of designing protein sequences with highly specific binding profiles. Experimental methods for generating specific binders rely on in vitro selection, which is limited in terms of library size and control over specificity profiles. Additional control was recently demonstrated through high-throughput sequencing and downstream computational analysis. Here we follow such an approach to demonstrate the design of specific antibodies beyond those probed experimentally. We do so in a context where very similar epitopes need to be discriminated, and where these epitopes cannot be experimentally dissociated from other epitopes present in the selection. Our approach involves the identification of different binding modes, each associated with a particular ligand against which the antibodies are either selected or not. Using data from phage display experiments, we show that the model successfully disentangles these modes, even when they are associated with chemically very similar ligands. Additionally, we demonstrate and validate experimentally the computational design of antibodies with customized specificity profiles, either with specific high affinity for a particular target ligand, or with cross-specificity for multiple target ligands. Overall, our results showcase the potential of leveraging a biophysical model learned from selections against multiple ligands to design proteins with tailored specificity, with applications to protein engineering extending beyond the design of antibodies.
Citer cet article
Fernandez-de-Cossio-Diaz, J., Uguzzoni, G., Ricard, K., Anselmi, F., Nizak, C., Pagnani, A., & Rivoire, O. (2024). Inference and design of antibody specificity: From experiments to models and back. PLoS Computational Biology. https://doi.org/10.1371/journal.pcbi.1012522@article{FernandezdeCossioDiaz2024_463,
author = {Fernandez-de-Cossio-Diaz, Jorge and Uguzzoni, Guido and Ricard, Kévin and Anselmi, Francesca and Nizak, Clément and Pagnani, Andrea and Rivoire, Olivier},
year = {2024},
month = {10},
title = {Inference and design of antibody specificity: From experiments to models and back},
journal = {PLoS Computational Biology},
abstract = {Exquisite binding specificity is essential for many protein functions but is difficult to engineer. Many biotechnological or biomedical applications require the discrimination of very similar ligands, which poses the challenge of designing protein sequences with highly specific binding profiles. Experimental methods for generating specific binders rely on in vitro selection, which is limited in terms of library size and control over specificity profiles. Additional control was recently demonstrated through high-throughput sequencing and downstream computational analysis. Here we follow such an approach to demonstrate the design of specific antibodies beyond those probed experimentally. We do so in a context where very similar epitopes need to be discriminated, and where these epitopes cannot be experimentally dissociated from other epitopes present in the selection. Our approach involves the identification of different binding modes, each associated with a particular ligand against which the antibodies are either selected or not. Using data from phage display experiments, we show that the model successfully disentangles these modes, even when they are associated with chemically very similar ligands. Additionally, we demonstrate and validate experimentally the computational design of antibodies with customized specificity profiles, either with specific high affinity for a particular target ligand, or with cross-specificity for multiple target ligands. Overall, our results showcase the potential of leveraging a biophysical model learned from selections against multiple ligands to design proteins with tailored specificity, with applications to protein engineering extending beyond the design of antibodies.},
url = {https://journals.plos.org/ploscompbiol/article?id=10.1371/journal.pcbi.1012522},
doi = {10.1371/journal.pcbi.1012522},
}TY - JOUR
AU - Fernandez-de-Cossio-Diaz, Jorge
AU - Uguzzoni, Guido
AU - Ricard, Kévin
AU - Anselmi, Francesca
AU - Nizak, Clément
AU - Pagnani, Andrea
AU - Rivoire, Olivier
PY - 2024
DA - 2024/10/14
TI - Inference and design of antibody specificity: From experiments to models and back
JO - PLoS Computational Biology
AB - Exquisite binding specificity is essential for many protein functions but is difficult to engineer. Many biotechnological or biomedical applications require the discrimination of very similar ligands, which poses the challenge of designing protein sequences with highly specific binding profiles. Experimental methods for generating specific binders rely on in vitro selection, which is limited in terms of library size and control over specificity profiles. Additional control was recently demonstrated through high-throughput sequencing and downstream computational analysis. Here we follow such an approach to demonstrate the design of specific antibodies beyond those probed experimentally. We do so in a context where very similar epitopes need to be discriminated, and where these epitopes cannot be experimentally dissociated from other epitopes present in the selection. Our approach involves the identification of different binding modes, each associated with a particular ligand against which the antibodies are either selected or not. Using data from phage display experiments, we show that the model successfully disentangles these modes, even when they are associated with chemically very similar ligands. Additionally, we demonstrate and validate experimentally the computational design of antibodies with customized specificity profiles, either with specific high affinity for a particular target ligand, or with cross-specificity for multiple target ligands. Overall, our results showcase the potential of leveraging a biophysical model learned from selections against multiple ligands to design proteins with tailored specificity, with applications to protein engineering extending beyond the design of antibodies.
DO - 10.1371/journal.pcbi.1012522
UR - https://journals.plos.org/ploscompbiol/article?id=10.1371/journal.pcbi.1012522
ER -