Machine learning-aided engineering of hydrolases for PET depolymerization.

Journal: Nature
Published Date: Apr 1, 2022

Abstract

Plastic waste poses an ecological challenge and enzymatic degradation offers one, potentially green and scalable, route for polyesters waste recycling. Poly(ethylene terephthalate) (PET) accounts for 12% of global solid waste, and a circular carbon economy for PET is theoretically attainable through rapid enzymatic depolymerization followed by repolymerization or conversion/valorization into other products. Application of PET hydrolases, however, has been hampered by their lack of robustness to pH and temperature ranges, slow reaction rates and inability to directly use untreated postconsumer plastics. Here, we use a structure-based, machine learning algorithm to engineer a robust and active PET hydrolase. Our mutant and scaffold combination (FAST-PETase: functional, active, stable and tolerant PETase) contains five mutations compared to wild-type PETase (N233K/R224Q/S121E from prediction and D186H/R280A from scaffold) and shows superior PET-hydrolytic activity relative to both wild-type and engineered alternatives between 30 and 50 °C and a range of pH levels. We demonstrate that untreated, postconsumer-PET from 51 different thermoformed products can all be almost completely degraded by FAST-PETase in 1 week. FAST-PETase can also depolymerize untreated, amorphous portions of a commercial water bottle and an entire thermally pretreated water bottle at 50 ºC. Finally, we demonstrate a closed-loop PET recycling process by using FAST-PETase and resynthesizing PET from the recovered monomers. Collectively, our results demonstrate a viable route for enzymatic plastic recycling at the industrial scale.

Authors

  • Hongyuan Lu
    McKetta Department of Chemical Engineering, The University of Texas at Austin, Austin, TX, USA.
  • Daniel J Diaz
    Center for Systems and Synthetic Biology, Department of Chemistry, and Department of Molecular Biosciences, The University of Texas at Austin, Austin, TX, USA.
  • Natalie J Czarnecki
    McKetta Department of Chemical Engineering, The University of Texas at Austin, Austin, TX, USA.
  • Congzhi Zhu
    McKetta Department of Chemical Engineering, The University of Texas at Austin, Austin, TX, USA.
  • Wantae Kim
    McKetta Department of Chemical Engineering, The University of Texas at Austin, Austin, TX, USA.
  • Raghav Shroff
    CCDC Army Research Lab, Austin, Texas, USA.
  • Daniel J Acosta
    Department of Molecular Biosciences, The University of Texas at Austin, Austin, TX, USA.
  • Bradley R Alexander
    Department of Molecular Biosciences, The University of Texas at Austin, Austin, TX, USA.
  • Hannah O Cole
    McKetta Department of Chemical Engineering, The University of Texas at Austin, Austin, TX, USA.
  • Yan Zhang
    Affiliated Hospital of Liaoning University of Traditional Chinese Medicine, Shenyang, 110032, China.
  • Nathaniel A Lynd
    McKetta Department of Chemical Engineering, The University of Texas at Austin, Austin, TX, USA.
  • Andrew D Ellington
    Center for Systems and Synthetic Biology and Department of Molecular Biosciences, The University of Texas at Austin, Austin, TX, USA.
  • Hal S Alper
    McKetta Department of Chemical Engineering, The University of Texas at Austin, 200 E Dean Keeton St. Stop C0400, Austin, TX, 78712, USA.

Keywords

External Resources

Popular Topics
Recent Journals