  Computational antibody design seeks to automatically create an antibody that
binds to an antigen. The binding affinity is governed by the 3D binding
interface where antibody residues (paratope) closely interact with antigen
residues (epitope). Thus, predicting 3D paratope-epitope complex (docking) is
the key to finding the best paratope. In this paper, we propose a new model
called Hierarchical Equivariant Refinement Network (HERN) for paratope docking
and design. During docking, HERN employs a hierarchical message passing network
to predict atomic forces and use them to refine a binding complex in an
iterative, equivariant manner. During generation, its autoregressive decoder
progressively docks generated paratopes and builds a geometric representation
of the binding interface to guide the next residue choice. Our results show
that HERN significantly outperforms prior state-of-the-art on paratope docking
and design benchmarks.
