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Machine learning-aided engineering of hydrolases for PET depolymerization

Author

Listed:
  • Hongyuan Lu

    (The University of Texas at Austin)

  • Daniel J. Diaz

    (The University of Texas at Austin)

  • Natalie J. Czarnecki

    (The University of Texas at Austin)

  • Congzhi Zhu

    (The University of Texas at Austin)

  • Wantae Kim

    (The University of Texas at Austin)

  • Raghav Shroff

    (The University of Texas at Austin
    DEVCOM ARL-South)

  • Daniel J. Acosta

    (The University of Texas at Austin)

  • Bradley R. Alexander

    (The University of Texas at Austin)

  • Hannah O. Cole

    (The University of Texas at Austin
    The University of Texas at Austin)

  • Yan Zhang

    (The University of Texas at Austin)

  • Nathaniel A. Lynd

    (The University of Texas at Austin)

  • Andrew D. Ellington

    (The University of Texas at Austin)

  • Hal S. Alper

    (The University of Texas at Austin)

Abstract

Plastic waste poses an ecological challenge1–3 and enzymatic degradation offers one, potentially green and scalable, route for polyesters waste recycling4. Poly(ethylene terephthalate) (PET) accounts for 12% of global solid waste5, and a circular carbon economy for PET is theoretically attainable through rapid enzymatic depolymerization followed by repolymerization or conversion/valorization into other products6–10. 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 plastics11. 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 alternatives12 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.

Suggested Citation

  • Hongyuan Lu & Daniel J. Diaz & Natalie J. Czarnecki & Congzhi Zhu & Wantae Kim & Raghav Shroff & Daniel J. Acosta & Bradley R. Alexander & Hannah O. Cole & Yan Zhang & Nathaniel A. Lynd & Andrew D. El, 2022. "Machine learning-aided engineering of hydrolases for PET depolymerization," Nature, Nature, vol. 604(7907), pages 662-667, April.
  • Handle: RePEc:nat:nature:v:604:y:2022:i:7907:d:10.1038_s41586-022-04599-z
    DOI: 10.1038/s41586-022-04599-z
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    Cited by:

    1. Youying Mu & Chengzhuo Duan & Xin Li & Yongbo Wu, 2023. "A Monitoring Method for Corporate Environmental Performance Based on Data Fusion in China under the Double Carbon Target," Sustainability, MDPI, vol. 15(12), pages 1-16, June.
    2. Hwaseok Hong & Dongwoo Ki & Hogyun Seo & Jiyoung Park & Jaewon Jang & Kyung-Jin Kim, 2023. "Discovery and rational engineering of PET hydrolase with both mesophilic and thermophilic PET hydrolase properties," Nature Communications, Nature, vol. 14(1), pages 1-13, December.
    3. Javier M. Hernández-Sancho & Arnaud Boudigou & Maria V. G. Alván-Vargas & Dekel Freund & Jenny Arnling Bååth & Peter Westh & Kenneth Jensen & Lianet Noda-García & Daniel C. Volke & Pablo I. Nikel, 2024. "A versatile microbial platform as a tunable whole-cell chemical sensor," Nature Communications, Nature, vol. 15(1), pages 1-18, December.
    4. Katarzyna Świderek & Susana Velasco-Lozano & Miquel À. Galmés & Ion Olazabal & Haritz Sardon & Fernando López-Gallego & Vicent Moliner, 2023. "Mechanistic studies of a lipase unveil effect of pH on hydrolysis products of small PET modules," Nature Communications, Nature, vol. 14(1), pages 1-10, December.
    5. Zhuozhi Chen & Rongdi Duan & Yunjie Xiao & Yi Wei & Hanxiao Zhang & Xinzhao Sun & Shen Wang & Yingying Cheng & Xue Wang & Shanwei Tong & Yunxiao Yao & Cheng Zhu & Haitao Yang & Yanyan Wang & Zefang Wa, 2022. "Biodegradation of highly crystallized poly(ethylene terephthalate) through cell surface codisplay of bacterial PETase and hydrophobin," Nature Communications, Nature, vol. 13(1), pages 1-17, December.
    6. Anni Li & Yijie Sheng & Haiyang Cui & Minghui Wang & Luxuan Wu & Yibo Song & Rongrong Yang & Xiujuan Li & He Huang, 2023. "Discovery and mechanism-guided engineering of BHET hydrolases for improved PET recycling and upcycling," Nature Communications, Nature, vol. 14(1), pages 1-16, December.
    7. Shunshi Kohyama & Béla P. Frohn & Leon Babl & Petra Schwille, 2024. "Machine learning-aided design and screening of an emergent protein function in synthetic cells," Nature Communications, Nature, vol. 15(1), pages 1-14, December.
    8. Mark J. G. Bakkers & Tina Ritschel & Machteld Tiemessen & Jacobus Dijkman & Angelo A. Zuffianò & Xiaodi Yu & Daan Overveld & Lam Le & Richard Voorzaat & Marlies M. Haaren & Martijn Man & Sem Tamara & , 2024. "Efficacious human metapneumovirus vaccine based on AI-guided engineering of a closed prefusion trimer," Nature Communications, Nature, vol. 15(1), pages 1-17, December.
    9. Xinlei Wei & Xue Yang & Congcong Hu & Qiangzi Li & Qianqian Liu & Yue Wu & Leipeng Xie & Xiao Ning & Fei Li & Tao Cai & Zhiguang Zhu & Yi-Heng P. Job Zhang & Yanfei Zhang & Xuejun Chen & Chun You, 2024. "ATP-free in vitro biotransformation of starch-derived maltodextrin into poly-3-hydroxybutyrate via acetyl-CoA," Nature Communications, Nature, vol. 15(1), pages 1-14, December.
    10. Teng Bao & Yuanchao Qian & Yongping Xin & James J. Collins & Ting Lu, 2023. "Engineering microbial division of labor for plastic upcycling," Nature Communications, Nature, vol. 14(1), pages 1-13, December.
    11. Simon d’Oelsnitz & Daniel J. Diaz & Wantae Kim & Daniel J. Acosta & Tyler L. Dangerfield & Mason W. Schechter & Matthew B. Minus & James R. Howard & Hannah Do & James M. Loy & Hal S. Alper & Y. Jessie, 2024. "Biosensor and machine learning-aided engineering of an amaryllidaceae enzyme," Nature Communications, Nature, vol. 15(1), pages 1-14, December.
    12. P. Konstantin Richter & Paula Blázquez-Sánchez & Ziyue Zhao & Felipe Engelberger & Christian Wiebeler & Georg Künze & Ronny Frank & Dana Krinke & Emanuele Frezzotti & Yuliia Lihanova & Patricia Falken, 2023. "Structure and function of the metagenomic plastic-degrading polyester hydrolase PHL7 bound to its product," Nature Communications, Nature, vol. 14(1), pages 1-11, December.
    13. Noelia Ferruz & Steffen Schmidt & Birte Höcker, 2022. "ProtGPT2 is a deep unsupervised language model for protein design," Nature Communications, Nature, vol. 13(1), pages 1-10, December.
    14. Daniel J. Diaz & Chengyue Gong & Jeffrey Ouyang-Zhang & James M. Loy & Jordan Wells & David Yang & Andrew D. Ellington & Alexandros G. Dimakis & Adam R. Klivans, 2024. "Stability Oracle: a structure-based graph-transformer framework for identifying stabilizing mutations," Nature Communications, Nature, vol. 15(1), pages 1-15, December.
    15. Amelia R. Bergeson & Ashli J. Silvera & Hal S. Alper, 2024. "Bottlenecks in biobased approaches to plastic degradation," Nature Communications, Nature, vol. 15(1), pages 1-9, December.
    16. Yinglu Cui & Yanchun Chen & Jinyuan Sun & Tong Zhu & Hua Pang & Chunli Li & Wen-Chao Geng & Bian Wu, 2024. "Computational redesign of a hydrolase for nearly complete PET depolymerization at industrially relevant high-solids loading," Nature Communications, Nature, vol. 15(1), pages 1-12, December.

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