End-to-End Full-Atom Antibody Design
This work addresses antibody design for therapeutics and biology, offering an end-to-end solution that is incremental by building on existing learning-based methods.
The paper tackles the problem of antibody design by addressing the limitations of existing methods that handle only subtasks or omit full-atom details, proposing an end-to-end full-atom model called dyMEAN. The result shows superiority in epitope-binding CDR-H3 design, complex structure prediction, and affinity optimization, though no concrete numbers are provided in the abstract.
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.