Antibodies are versatile proteins that bind to pathogens like viruses and
stimulate the adaptive immune system. The specificity of antibody binding is
determined by complementarity-determining regions (CDRs) at the tips of these
Y-shaped proteins. In this paper, we propose a generative model to
automatically design the CDRs of antibodies with enhanced binding specificity
or neutralization capabilities. Previous generative approaches formulate
protein design as a structure-conditioned sequence generation task, assuming
the desired 3D structure is given a priori. In contrast, we propose to
co-design the sequence and 3D structure of CDRs as graphs. Our model unravels a
sequence autoregressively while iteratively refining its predicted global
structure. The inferred structure in turn guides subsequent residue choices.
For efficiency, we model the conditional dependence between residues inside and
outside of a CDR in a coarse-grained manner. Our method achieves superior
log-likelihood on the test set and outperforms previous baselines in designing
antibodies capable of neutralizing the SARS-CoV-2 virus.

本文提出了一种基于生成模型的方法来自动设计具有增强的结合特异性或中和能力的抗体的 CDRs。该方法系统地利用了序列和三维结构之间的关系，并在设计中具有更高的效率和灵活性，最终成功设计出能够中和 SARS-CoV-2 病毒的抗体。