EnzyControl: Adding Functional and Substrate-Specific Control for Enzyme
  Backbone Generation
Authors: name: Chao Song, name: Zhiyuan Liu, name: Han Huang, name: Liang Wang, name: Qiong Wang, name: Jianyu Shi, name: Hui Yu, name: Yihang Zhou, name: Yang Zhang
Organization: National University of Singapore

Designing enzyme backbones with substrate-specific functionality is a
critical challenge in computational protein engineering. Current generative
models excel in protein design but face limitations in binding data,
substrate-specific control, and flexibility for de novo enzyme backbone
generation. To address this, we introduce EnzyBind, a dataset with 11,100
experimentally validated enzyme-substrate pairs specifically curated from
PDBbind. Building on this, we propose EnzyControl, a method that enables
functional and substrate-specific control in enzyme backbone generation. Our
approach generates enzyme backbones conditioned on MSA-annotated catalytic
sites and their corresponding substrates, which are automatically extracted
from curated enzyme-substrate data. At the core of EnzyControl is EnzyAdapter,
a lightweight, modular component integrated into a pretrained motif-scaffolding
model, allowing it to become substrate-aware. A two-stage training paradigm
further refines the model's ability to generate accurate and functional enzyme
structures. Experiments show that our EnzyControl achieves the best performance
across structural and functional metrics on EnzyBind and EnzyBench benchmarks,
with particularly notable improvements of 13\% in designability and 13\% in
catalytic efficiency compared to the baseline models. The code is released at
https://github.com/Vecteur-libre/EnzyControl.