Proteins are macromolecules responsible for essential functions in almost all
living organisms. Designing reasonable proteins with desired functions is
crucial. A protein's sequence and structure are strongly correlated and they
together determine its function. In this paper, we propose NAEPro, a model to
jointly design Protein sequence and structure based on automatically detected
functional sites. NAEPro is powered by an interleaving network of attention and
equivariant layers, which can capture global correlation in a whole sequence
and local influence from nearest amino acids in three dimensional (3D) space.
Such an architecture facilitates effective yet economic message passing at two
levels. We evaluate our model and several strong baselines on two protein
datasets, $\beta$-lactamase and myoglobin. Experimental results show that our
model consistently achieves the highest amino acid recovery rate, TM-score, and
the lowest RMSD among all competitors. These findings prove the capability of
our model to design protein sequences and structures that closely resemble
their natural counterparts. Furthermore, in-depth analysis further confirms our
model's ability to generate highly effective proteins capable of binding to
their target metallocofactors. We provide code, data and models in Github.

设计合理的蛋白质序列和结构是至关重要的，我们提出了一种基于功能位点的 NAEPro 模型，该模型通过网络注意力和等变层来联合设计蛋白质序列和结构，在两个层次上进行有效而经济的信息传递，实验证明该模型能够设计出与自然蛋白质序列和结构非常相似且具有高度效能的蛋白质。