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

2025-10-28

AI Summary

Existing generative models for enzyme design struggle to simultaneously control substrate specificity and generate de novo protein backbones. This work introduces EnzyBind, a curated dataset comprising 11,100 experimentally validated enzyme–substrate pairs, and proposes EnzyControl—a novel framework featuring the lightweight EnzyAdapter module—to enable substrate-conditioned, controllable backbone generation from pretrained models for the first time. The method integrates multiple key techniques: MSA-driven catalytic site identification, substrate structural encoding, and two-stage fine-tuning of a motif-scaffold model. Evaluated on both the EnzyBind and EnzyBench benchmarks, EnzyControl achieves state-of-the-art performance, with designed enzymes exhibiting 13% higher functionality and catalytic efficiency over baseline methods. This advance significantly progresses programmable, function-driven de novo enzyme design.

Technology Category

• Natural Language Processing: Code Generation / Program Synthesis from Natural Language
• Machine Learning: Deep Generative Models & Autoencoders
• Cognitive Modeling & Cognitive Systems: Computational Creativity

Application Category

• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs
• Economics, Online Markets and Human Computation: Incentives in network design for Web infrastructures and ecosystems

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.

Problem

• Generating enzyme backbones with substrate-specific functional control
• Addressing limitations in binding data and flexibility for de novo enzymes
• Improving designability and catalytic efficiency in enzyme structure generation

Innovation

• Generates enzyme backbones using catalytic sites and substrates
• Integrates lightweight adapter into pretrained scaffolding model
• Employs two-stage training for accurate functional structures