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Glycolysis-compatible urethanases for polyurethane recycling.

Yanchun Chen1, Jinyuan Sun1,2, Kelun Shi1,2

  • 1AIM Center, College of Life Sciences and Technology, Beijing University of Chemical Technology, Institute of Microbiology, Chinese Academy of Sciences, Beijing, China.

Science (New York, N.Y.)
|October 30, 2025
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This summary is machine-generated.

Researchers developed GRASE, a deep learning framework, to discover efficient urethanase enzymes for polyurethane recycling. A novel enzyme, AbPURase, achieved high activity in harsh conditions, enabling kilogram-scale recycling.

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Area of Science:

  • Biocatalysis
  • Polymer Science
  • Artificial Intelligence

Background:

  • Thermoset polyurethane recycling is challenging due to stable urethane bonds and cross-linked structures.
  • Current chemo-enzymatic methods lack efficient urethanases for industrial glycolysis conditions.

Purpose of the Study:

  • To develop a deep learning framework (GRASE) for identifying efficient and stable urethanases compatible with industrial glycolysis.
  • To discover novel biocatalysts for enhanced polyurethane recycling.

Main Methods:

  • Utilized a graph neural network (GNN)-based framework integrating self-supervised and supervised learning.
  • Screened and identified novel urethanases, including AbPURase.
  • Performed structural analysis of AbPURase to understand its stability and efficiency.

Main Results:

  • The GRASE framework successfully identified efficient urethanases.
  • AbPURase demonstrated two orders of magnitude greater activity than known enzymes in 6 M diethylene glycol.
  • Near-complete depolymerization of commercial polyurethane at kilogram scale was achieved within 8 hours.
  • Structural features like a hydrophobic core and proline-stabilized lid loop contribute to AbPURase's performance.

Conclusions:

  • Deep learning accelerates biocatalyst discovery for industrial applications.
  • AbPURase represents a significant advancement in polyurethane recycling, overcoming a critical barrier.
  • The GRASE framework shows promise for discovering industrial-scale enzymes.