A compact vocabulary of paratope-epitope interactions enables predictability of antibody-antigen binding.

Antibody-antigen binding relies on the specific interaction of amino acids at the paratope-epitope interface. The predictability of antibody-antigen binding is a prerequisite for de novo antibody and (neo-)epitope design. A fundamental premise for the predictability of antibody-antigen binding is the existence of paratope-epitope interaction motifs that are universally shared among antibody-antigen structures. In a dataset of non-redundant antibody-antigen structures, we identify structural interaction motifs, which together compose a commonly shared structure-based vocabulary of paratope-epitope interactions. We show that this vocabulary enables the machine learnability of antibody-antigen binding on the paratope-epitope level using generative machine learning. The vocabulary (1) is compact, less than 10⁴ motifs; (2) distinct from non-immune protein-protein interactions; and (3) mediates specific oligo- and polyreactive interactions between paratope-epitope pairs. Our work leverages combined structure- and sequence-based learning to demonstrate that machine-learning-driven predictive paratope and epitope engineering is feasible. [Display omitted] • Prediction of antibody-antigen binding is a central question in immunology • A motif vocabulary of paratope-epitope interactions governs antibody specificity • Proof of principle that antibody-antigen binding is predictable • Implications for de novo antibody and (neo-)epitope design Prediction of antibody-antigen binding is a central question in immunology and of high relevance for predictive antibody and vaccine design. Akbar et al. prove the predictability of antibody-antigen binding by discovering a universal, compact, and immunity-specific motif vocabulary of paratope-epitope interactions. [ABSTRACT FROM AUTHOR]