Therapeutic antibodies are an essential and rapidly expanding drug modality.
The binding specificity between antibodies and antigens is decided by
complementarity-determining regions (CDRs) at the tips of these Y-shaped
proteins. In this paper, we propose a hierarchical training paradigm (HTP) for
the antibody sequence-structure co-design. HTP consists of four levels of
training stages, each corresponding to a specific protein modality within a
particular protein domain. Through carefully crafted tasks in different stages,
HTP seamlessly and effectively integrates geometric graph neural networks
(GNNs) with large-scale protein language models to excavate evolutionary
information from not only geometric structures but also vast antibody and
non-antibody sequence databases, which determines ligand binding pose and
strength. Empirical experiments show that HTP sets the new state-of-the-art
performance in the co-design problem as well as the fix-backbone design. Our
research offers a hopeful path to unleash the potential of deep generative
architectures and seeks to illuminate the way forward for the antibody sequence
and structure co-design challenge.

我们提出了一种层次训练范式 (HTP) 用于抗体序列结构的联合设计，通过结合几何图神经网络和大规模蛋白质语言模型，从几何结构和抗体序列数据库中挖掘进化信息，以确定配体结合位姿和强度，并取得了目前的最优性能。