  Enzymes are genetically encoded biocatalysts capable of accelerating chemical
reactions. How can we automatically design functional enzymes? In this paper,
we propose EnzyGen, an approach to learn a unified model to design enzymes
across all functional families. Our key idea is to generate an enzyme's amino
acid sequence and their three-dimensional (3D) coordinates based on
functionally important sites and substrates corresponding to a desired
catalytic function. These sites are automatically mined from enzyme databases.
EnzyGen consists of a novel interleaving network of attention and neighborhood
equivariant layers, which captures both long-range correlation in an entire
protein sequence and local influence from nearest amino acids in 3D space. To
learn the generative model, we devise a joint training objective, including a
sequence generation loss, a position prediction loss and an enzyme-substrate
interaction loss. We further construct EnzyBench, a dataset with 3157 enzyme
families, covering all available enzymes within the protein data bank (PDB).
Experimental results show that our EnzyGen consistently achieves the best
performance across all 323 testing families, surpassing the best baseline by
10.79% in terms of substrate binding affinity. These findings demonstrate
EnzyGen's superior capability in designing well-folded and effective enzymes
binding to specific substrates with high affinities.
