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Sourcing thermotolerant poly(ethylene terephthalate) hydrolase scaffolds from natural diversity

Author

Listed:
  • Erika Erickson

    (National Renewable Energy Laboratory
    BOTTLE Consortium)

  • Japheth E. Gado

    (National Renewable Energy Laboratory
    BOTTLE Consortium)

  • Luisana Avilán

    (University of Portsmouth)

  • Felicia Bratti

    (National Renewable Energy Laboratory
    BOTTLE Consortium)

  • Richard K. Brizendine

    (National Renewable Energy Laboratory
    BOTTLE Consortium)

  • Paul A. Cox

    (University of Portsmouth)

  • Raj Gill

    (University of Portsmouth)

  • Rosie Graham

    (University of Portsmouth)

  • Dong-Jin Kim

    (BOTTLE Consortium
    Montana State University)

  • Gerhard König

    (University of Portsmouth)

  • William E. Michener

    (National Renewable Energy Laboratory
    BOTTLE Consortium)

  • Saroj Poudel

    (Montana State University)

  • Kelsey J. Ramirez

    (National Renewable Energy Laboratory
    BOTTLE Consortium)

  • Thomas J. Shakespeare

    (University of Portsmouth)

  • Michael Zahn

    (University of Portsmouth)

  • Eric S. Boyd

    (Montana State University)

  • Christina M. Payne

    (National Science Foundation)

  • Jennifer L. DuBois

    (BOTTLE Consortium
    Montana State University)

  • Andrew R. Pickford

    (BOTTLE Consortium
    University of Portsmouth)

  • Gregg T. Beckham

    (National Renewable Energy Laboratory
    BOTTLE Consortium)

  • John E. McGeehan

    (BOTTLE Consortium
    University of Portsmouth
    World Plastics Association)

Abstract

Enzymatic deconstruction of poly(ethylene terephthalate) (PET) is under intense investigation, given the ability of hydrolase enzymes to depolymerize PET to its constituent monomers near the polymer glass transition temperature. To date, reported PET hydrolases have been sourced from a relatively narrow sequence space. Here, we identify additional PET-active biocatalysts from natural diversity by using bioinformatics and machine learning to mine 74 putative thermotolerant PET hydrolases. We successfully express, purify, and assay 51 enzymes from seven distinct phylogenetic groups; observing PET hydrolysis activity on amorphous PET film from 37 enzymes in reactions spanning pH from 4.5–9.0 and temperatures from 30–70 °C. We conduct PET hydrolysis time-course reactions with the best-performing enzymes, where we observe differences in substrate selectivity as function of PET morphology. We employed X-ray crystallography and AlphaFold to examine the enzyme architectures of all 74 candidates, revealing protein folds and accessory domains not previously associated with PET deconstruction. Overall, this study expands the number and diversity of thermotolerant scaffolds for enzymatic PET deconstruction.

Suggested Citation

  • Erika Erickson & Japheth E. Gado & Luisana Avilán & Felicia Bratti & Richard K. Brizendine & Paul A. Cox & Raj Gill & Rosie Graham & Dong-Jin Kim & Gerhard König & William E. Michener & Saroj Poudel &, 2022. "Sourcing thermotolerant poly(ethylene terephthalate) hydrolase scaffolds from natural diversity," Nature Communications, Nature, vol. 13(1), pages 1-15, December.
    • Handle: RePEc:nat:natcom:v:13:y:2022:i:1:d:10.1038_s41467-022-35237-x
    DOI: 10.1038/s41467-022-35237-x
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    1. Kathryn Tunyasuvunakool & Jonas Adler & Zachary Wu & Tim Green & Michal Zielinski & Augustin Žídek & Alex Bridgland & Andrew Cowie & Clemens Meyer & Agata Laydon & Sameer Velankar & Gerard J. Kleywegt, 2021. "Highly accurate protein structure prediction for the human proteome," Nature, Nature, vol. 596(7873), pages 590-596, August.
    2. John Jumper & Richard Evans & Alexander Pritzel & Tim Green & Michael Figurnov & Olaf Ronneberger & Kathryn Tunyasuvunakool & Russ Bates & Augustin Žídek & Anna Potapenko & Alex Bridgland & Clemens Me, 2021. "Highly accurate protein structure prediction with AlphaFold," Nature, Nature, vol. 596(7873), pages 583-589, August.
    3. Alex Hawkins-Hooker & Florence Depardieu & Sebastien Baur & Guillaume Couairon & Arthur Chen & David Bikard, 2021. "Generating functional protein variants with variational autoencoders," PLOS Computational Biology, Public Library of Science, vol. 17(2), pages 1-23, February.
    4. Andrew W. Senior & Richard Evans & John Jumper & James Kirkpatrick & Laurent Sifre & Tim Green & Chongli Qin & Augustin Žídek & Alexander W. R. Nelson & Alex Bridgland & Hugo Penedones & Stig Petersen, 2020. "Improved protein structure prediction using potentials from deep learning," Nature, Nature, vol. 577(7792), pages 706-710, January.
    5. Xu Han & Weidong Liu & Jian-Wen Huang & Jiantao Ma & Yingying Zheng & Tzu-Ping Ko & Limin Xu & Ya-Shan Cheng & Chun-Chi Chen & Rey-Ting Guo, 2017. "Structural insight into catalytic mechanism of PET hydrolase," Nature Communications, Nature, vol. 8(1), pages 1-6, December.
    6. Jung-Eun Shin & Adam J. Riesselman & Aaron W. Kollasch & Conor McMahon & Elana Simon & Chris Sander & Aashish Manglik & Andrew C. Kruse & Debora S. Marks, 2021. "Protein design and variant prediction using autoregressive generative models," Nature Communications, Nature, vol. 12(1), pages 1-11, December.
    7. Seongjoon Joo & In Jin Cho & Hogyun Seo & Hyeoncheol Francis Son & Hye-Young Sagong & Tae Joo Shin & So Young Choi & Sang Yup Lee & Kyung-Jin Kim, 2018. "Structural insight into molecular mechanism of poly(ethylene terephthalate) degradation," Nature Communications, Nature, vol. 9(1), pages 1-12, December.
    8. V. Tournier & C. M. Topham & A. Gilles & B. David & C. Folgoas & E. Moya-Leclair & E. Kamionka & M.-L. Desrousseaux & H. Texier & S. Gavalda & M. Cot & E. Guémard & M. Dalibey & J. Nomme & G. Cioci & , 2020. "An engineered PET depolymerase to break down and recycle plastic bottles," Nature, Nature, vol. 580(7802), pages 216-219, April.
    9. Gottfried J. Palm & Lukas Reisky & Dominique Böttcher & Henrik Müller & Emil A. P. Michels & Miriam C. Walczak & Leona Berndt & Manfred S. Weiss & Uwe T. Bornscheuer & Gert Weber, 2019. "Structure of the plastic-degrading Ideonella sakaiensis MHETase bound to a substrate," Nature Communications, Nature, vol. 10(1), pages 1-10, December.
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    1. 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.
    2. Elizabeth L. Bell & Gloria Rosetto & Morgan A. Ingraham & Kelsey J. Ramirez & Clarissa Lincoln & Ryan W. Clarke & Japheth E. Gado & Jacob L. Lilly & Katarzyna H. Kucharzyk & Erika Erickson & Gregg T. , 2024. "Natural diversity screening, assay development, and characterization of nylon-6 enzymatic depolymerization," Nature Communications, Nature, vol. 15(1), pages 1-17, December.
    3. 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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