Advances in computational structure-based antibody design

Antibodies are currently the most important class of biotherapeutics and are used to treat numerous diseases. Recent advances in computational methods are ushering in a new era of antibody design, driven in part by accurate structure prediction. Previously, structure-based antibody design has been limited to a relatively small number of cases where accurate structures or models of both the target antigen and antibody were available. As we move towards a time where it is possible to accurately model most antibodies and antigens, and to reliably predict their binding site, there is vast potential for true computational antibody design. In this review, we describe the latest methods that promise to launch a paradigm shift towards entirely in silico structure-based antibody design. [Display omitted] •Deep learning is rapidly improving protein structure prediction, paratope-epitope prediction and antibody-antigen docking.•Structure-based design is accelerating due to the increased availability of accurate antibody and antigen structure models.•Full in silico antibody design is not yet viable but the foundations have been laid.