We consider the problem of antibody sequence design given 3D structural
information. Building on previous work, we propose a fine-tuned inverse folding
model that is specifically optimised for antibody structures and outperforms
generic protein models on sequence recovery and structure robustness when
applied on antibodies, with notable improvement on the hypervariable CDR-H3
loop. We study the canonical conformations of complementarity-determining
regions and find improved encoding of these loops into known clusters. Finally,
we consider the applications of our model to drug discovery and binder design
and evaluate the quality of proposed sequences using physics-based methods.

给定 3D 结构信息，我们提出了一种经过精调的逆折叠模型，专门针对抗体结构进行优化，在抗体中的序列恢复和结构鲁棒性方面超越通用蛋白质模型，尤其在高变性 CDR-H3 环上改进显著。我们研究了互补决定区的规范构象，发现将这些环编码到已知簇中的方法有所改进。最后，我们考虑了我们模型在药物发现和结合物设计方面的应用，并利用基于物理的方法评估了所提出序列的质量。