Protein Complex Invariant Embedding with Cross-Gate MLP is A One-Shot Antibody Designer

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.