Glycolysis-compatible urethanases for polyurethane recycling
- Yanchun Chen
- Jinyuan Sun
- Kelun Shi
- Tong Zhu
- Ruifeng Li
- Ruiqiao Li
- Xiaomeng Liu
- Xinying Xie
- Chao Ding
- Wen-Chao Geng
- Jinwei Ren
- Wenyu Shi
- Yinglu Cui
- Bian Wu
2025-10-30
Recycling thermoset polyurethanes is hindered by their cross-linked structures and chemically stable urethane bonds. Although chemo-enzymatic approaches offer promise, known urethanases remain inefficient under industrial glycolysis conditions. Here, we present GRASE [graph neural network (GNN)–based recommendation of active and stable enzymes], a GNN-based framework that integrates self-supervised and supervised learning to identify efficient, glycolysis-compatible urethanases. Among these, Ab PURase exhibited two orders of magnitude greater activity than previously known enzymes in 6 molar diethylene glycol, enabling near-complete depolymerization of commercial polyurethane at kilogram scale within 8 hours. Structural analysis revealed that a tightly packed hydrophobic core and proline-stabilized lid loop may confer Ab PURase’s stability and efficiency in harsh solvents. This work highlights how deep learning accelerates the discovery of biocatalysts with industrial potential and addresses a critical barrier in polyurethane recycling.