A compact vocabulary of paratope-epitope interactions enables predictability of antibody-antigen binding
Rahmad Akbar, Philippe A. Robert, Milena Pavlović, Jeliazko R. Jeliazkov, Igor Snapkov, Andrei Slabodkin, Cédric R. Weber, Lonneke Scheffer, Enkelejda Miho, Ingrid Hobæk Haff, Dag Trygve Tryslew Haug, Fridtjof Lund‐Johansen, Yana Safonova, Geir Kjetil Sandve, Victor Greiff
Abstract
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.