  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 \textit{simple yet effective} 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 problem into two stages: (i) geometric modeling of
protein complex structures and (ii) sequence-structure co-learning. We develop
a novel macromolecular structure invariant embedding, typically for protein
complexes, 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 simple cross-gate MLP
for sequence-structure co-learning, allowing 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 levels, which demonstrate that our model achieves superior
performance compared to the state-of-the-art antibody CDR design methods.
