Antibodies are crucial proteins produced by the immune system in response to
foreign substances or antigens. The specificity of an antibody is determined by
its complementarity-determining regions (CDRs), which are located in the
variable domains of the antibody chains and form the antigen-binding site.
Previous studies have utilized complex techniques to generate CDRs, but they
suffer from inadequate geometric modeling. Moreover, the common iterative
refinement strategies lead to an inefficient inference. In this paper, we
propose a deep generative model that can co-design 1D sequences and 3D
structures of CDRs in a one-shot manner. To achieve this, we decouple the
antibody CDR design into two stages: (i) geometric modeling of protein
structures and (ii) sequence-structure co-learning. We develop a protein
complex invariant embedding that captures both intra- and inter-component
interactions among the backbone atoms including C$\alpha$, N, C, and O atoms to
achieve comprehensive geometric modeling. Then, we introduce a cross-gate MLP
for sequence-structure co-learning, which allows sequence and structure
representations to implicitly refine each other. This enables our model to
design desired sequences and structures in a one-shot manner. Extensive
experiments are conducted to evaluate our results at both the sequence and
structure level, which demonstrate that our model achieves superior performance
compared to the state-of-the-art antibody CDR design methods.

本文提出了一种深度生成模型，可在一次试验中协同设计抗体 CDR 的一维序列和三维结构，并通过广泛实验证明其性能优于现有方法。