EnzyControl：为酶骨架生成添加功能与底物特异性调控

EnzyControl: Adding Functional and Substrate-Specific Control for Enzyme Backbone Generation

October 29, 2025

作者: Chao Song, Zhiyuan Liu, Han Huang, Liang Wang, Qiong Wang, Jianyu Shi, Hui Yu, Yihang Zhou, Yang Zhang

cs.AI

摘要

设计具有底物特异性功能的酶骨架是计算蛋白质工程中的关键挑战。当前生成模型在蛋白质设计方面表现出色，但在结合数据、底物特异性控制以及从头生成酶骨架的灵活性方面存在局限。为此，我们推出了包含11,100个实验验证酶-底物对的EnzyBind数据集，该数据集专门从PDBbind中筛选整理而成。在此基础上，我们提出EnzyControl方法，实现酶骨架生成过程中的功能性与底物特异性控制。该方法通过从整理的酶-底物数据中自动提取MSA注释的催化位点及其对应底物作为条件，生成酶骨架结构。EnzyControl的核心是集成于预训练基序支架模型的EnzyAdapter模块，这个轻量级可插拔组件使模型具备底物识别能力。采用两阶段训练范式进一步优化模型生成精确功能性酶结构的能力。实验表明，我们的EnzyControl在EnzyBind和EnzyBench基准测试的结构与功能指标上均取得最优性能，其中可设计性指标提升13%，催化效率较基线模型提高13%。代码已发布于https://github.com/Vecteur-libre/EnzyControl。

English

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.