EnzyControl: Adding Functional and Substrate-Specific Control for Enzyme
  Backbone Generation
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.
6904224b2c556835fa67f0a5
Chao Song
6904224b2c556835fa67f0a6
Zhiyuan Liu
6904224b2c556835fa67f0a7
Han Huang
6904224b2c556835fa67f0a8
Liang Wang
6904224b2c556835fa67f0a9
Qiong Wang
6904224b2c556835fa67f0aa
Jianyu Shi
6904224b2c556835fa67f0ab
Hui Yu
6904224b2c556835fa67f0ac
Yihang Zhou
6904224b2c556835fa67f0ad
Yang Zhang
EnzyControl: Adding Functional and Substrate-Specific Control for Enzyme
  Backbone Generation
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.
EnzyControl, a method using EnzyAdapter, enhances enzyme backbone generation with substrate-specific functionality and achieves superior performance in designability and catalytic efficiency.
enzyme backbones substrate-specific functionality computational protein engineering generative models EnzyBind EnzyControl MSA-annotated catalytic sites motif-scaffolding model EnzyAdapter two-stage training paradigm EnzyBench benchmarks designability catalytic efficiency