EnzyControl: Adding Functional and Substrate-Specific Control for Enzyme Backbone Generation
Chao Song, Zhiyuan Liu, Han Huang, Liang Wang, Qiong Wang, Jianyu Shi, Hui Yu, Yihang Zhou, Yang Zhang
2025-10-31
Summary
This research focuses on designing the core structure, or 'backbone,' of enzymes to work with specific molecules, called substrates. It introduces a new method called EnzyControl to help build these enzyme backbones more effectively.
What's the problem?
Currently, designing enzymes with specific functions is difficult. Existing computer programs can create protein structures, but they struggle to accurately predict how well an enzyme will bind to its target molecule, control which molecule it works on, and create entirely new enzyme structures from scratch. There was a lack of good data specifically linking enzymes to the molecules they interact with.
What's the solution?
The researchers created a dataset called EnzyBind containing information on over 11,000 enzyme-substrate pairings. Then, they developed EnzyControl, a system that builds enzyme backbones based on the characteristics of the catalytic site – the part of the enzyme that interacts with the substrate – and the substrate itself. They added a component called EnzyAdapter to an existing protein design model, making it 'aware' of the substrate. The model was trained in two steps to improve its accuracy in creating functional enzyme structures.
Why it matters?
This work is important because it significantly improves the ability to design enzymes that can perform specific tasks. EnzyControl outperforms previous methods, showing a 13% improvement in both how well the designed enzymes are structurally sound and how efficiently they catalyze reactions. This could lead to the creation of new enzymes for various applications, like industrial processes or medicine.
Abstract
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.