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
Abstract
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.