EnzyControl: Adding Functional and Substrate-Specific Control for Enzyme Backbone Generation
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| Main Authors: | , , , , , , , , |
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| Format: | Preprint |
| Published: |
2025
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| _version_ | 1866911239104364544 |
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| author | Song, Chao Liu, Zhiyuan Huang, Han Wang, Liang Wang, Qiong Shi, Jianyu Yu, Hui Zhou, Yihang Zhang, Yang |
| author_facet | Song, Chao Liu, Zhiyuan Huang, Han Wang, Liang Wang, Qiong Shi, Jianyu Yu, Hui Zhou, Yihang Zhang, Yang |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2510_25132 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | EnzyControl: Adding Functional and Substrate-Specific Control for Enzyme Backbone Generation Song, Chao Liu, Zhiyuan Huang, Han Wang, Liang Wang, Qiong Shi, Jianyu Yu, Hui Zhou, Yihang Zhang, Yang Biomolecules Machine Learning 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. |
| title | EnzyControl: Adding Functional and Substrate-Specific Control for Enzyme Backbone Generation |
| topic | Biomolecules Machine Learning |
| url | https://arxiv.org/abs/2510.25132 |