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.

翻译：抗体设计是治疗学与生物学等多个领域中一项重要且具有挑战性的任务。当前基于学习的方法存在两个主要缺陷：1）仅处理整个抗体设计流程中的某个子任务，导致性能次优或资源消耗大；2）忽略框架区或侧链，因此无法捕获全原子几何结构。为解决这些问题，我们提出动态多通道等变图网络（dyMEAN），这是一个针对给定抗原表位与抗体不完整序列的E(3)等变抗体设计的端到端全原子模型。具体而言，我们首先探索结构初始化作为抗体结构的知识性猜测，然后提出阴影互补位来桥接抗原表位-抗体连接。通过自适应多通道等变编码器，可同时更新一维序列与三维结构，该编码器在处理全原子时能处理可变尺寸的蛋白质残基。最后，通过阴影互补位的对齐，将更新后的抗体对接至抗原表位。在表位结合CDR-H3设计、复合物结构预测和亲和力优化实验中的结果表明，我们的端到端框架与全原子建模具有优越性。