EnzyControl：为酶骨架生成添加功能与底物特异性控制

摘要

在計算蛋白質工程領域，如何設計具有受質特異性功能的酶骨架結構是一項關鍵挑戰。現有生成模型雖在蛋白質設計方面表現卓越，但在結合數據、受質特異性控制及從頭生成酶骨架的靈活性方面仍存在局限。為此，我們基於PDBbind數據庫精心篩選出11,100個經實驗驗證的酶-受質對，構建了EnzyBind數據集。在此基礎上，我們提出EnzyControl方法，實現酶骨架生成過程中的功能與受質特異性控制。該方法通過從酶-受質數據中自動提取MSA標註的催化位點及其對應受質作為條件約束，核心組件EnzyAdapter作為輕量級模塊集成於預訓練的基序支架模型中，使其具備受質感知能力。採用兩階段訓練範式進一步優化模型生成精確功能酶結構的能力。實驗表明，EnzyControl在EnzyBind和EnzyBench基準測試中均取得結構與功能指標的最佳性能，相較基準模型在可設計性上提升13%，催化效率提高13%。代碼已開源於：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.

EnzyControl：为酶骨架生成添加功能与底物特异性控制

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

摘要

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