Antibodies are produced by the immune system in response to infection or vaccina-tion. While sequencing of the individual antibody repertoire is becoming routine,identifying the antigens they recognize requires costly low-throughput experiments.Even when the antigen is known, epitope mapping is still challenging: experimentalapproaches are low-throughput and computational ones are not sufficiently accurate.Recently, AlphaFold2 has revolutionized structural biology by predicting highlyaccurate protein structures and complexes. However, it relies on an evolutionaryinformation that is not available for antibody-antigen interactions. Traditionalcomputational epitope mapping is based on structure modeling (folding) of theantibodies followed by docking the predicted structure to the corresponding antigen.The problem with this sequential approach is that the folding step does not considerthe structural changes of the antibody upon antigen binding and the docking stepis inaccurate because the antibody is considered rigid. Here, we develop a deeplearning end-to-end model, that given an antibody sequence and its correspondingantigen structure can simultaneously perform folding and docking tasks. Themodel produces the 3D coordinates of the entire antibody-antigen (Ab-Ag) ornanobody-antigen (Nb-Ag) complex, including the side chains. An accurate modelis detected among the Top-5 predictions for 75% of the test set. In addition tomining antibody repertoires, such a method can have the potential to be used inantibody-based drug design, as well as in the vaccine design.