Miho, EnkelejdaAkbar, RahmadPavlovic, MilenaSnapkov, IgorSlabodkin, AndreiScheffer, LonnekeHaff, Ingrid HobaedTryslew Haug, Dag TrygveLund-Johanson, FridtjofSafonova, YanaGreiff, VictorRobert, PhilippeJeliazkov, JeliazkoWeber, CedricSandve, Geir2022-02-182022-02-182021-03-162639-18562211-124710.1016/j.celrep.2021.108856https://irf.fhnw.ch/handle/11654/33315https://doi.org/10.26041/fhnw-4106Antibody-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.enAttribution-NonCommercial 3.0 United Statesantibodyantigenparatopeepitopestructurepredictiondeep learningmachine learning01A - Beitrag in wissenschaftlicher Zeitschrift1-21