  Antibody design is an essential yet challenging task in various domains like
therapeutics and biology. There are two major defects in current learning-based
methods: 1) tackling only a certain subtask of the whole antibody design
pipeline, making them suboptimal or resource-intensive. 2) omitting either the
framework regions or side chains, thus incapable of capturing the full-atom
geometry. To address these pitfalls, we propose dynamic Multi-channel
Equivariant grAph Network (dyMEAN), an end-to-end full-atom model for
E(3)-equivariant antibody design given the epitope and the incomplete sequence
of the antibody. Specifically, we first explore structural initialization as a
knowledgeable guess of the antibody structure and then propose shadow paratope
to bridge the epitope-antibody connections. Both 1D sequences and 3D structures
are updated via an adaptive multi-channel equivariant encoder that is able to
process protein residues of variable sizes when considering full atoms.
Finally, the updated antibody is docked to the epitope via the alignment of the
shadow paratope. Experiments on epitope-binding CDR-H3 design, complex
structure prediction, and affinity optimization demonstrate the superiority of
our end-to-end framework and full-atom modeling.
