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Machine learning-aided engineering of hydrolases for PET depolymerization

Nature (London), 2022-04, Vol.604 (7907), p.662-667 [Peer Reviewed Journal]

2022. The Author(s), under exclusive licence to Springer Nature Limited. ;Copyright Nature Publishing Group Apr 28, 2022 ;ISSN: 0028-0836 ;EISSN: 1476-4687 ;DOI: 10.1038/s41586-022-04599-z ;PMID: 35478237

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Title:
Machine learning-aided engineering of hydrolases for PET depolymerization
Author: Lu, Hongyuan ; Diaz, Daniel J ; Czarnecki, Natalie J ; Zhu, Congzhi ; Kim, Wantae ; Shroff, Raghav ; Acosta, Daniel J ; Alexander, Bradley R ; Cole, Hannah O ; Zhang, Yan ; Lynd, Nathaniel A ; Ellington, Andrew D ; Alper, Hal S
Subjects: Algorithms ; Amino acids ; Crystal structure ; Depolymerization ; Enzymes ; Hydrolase ; Hydrolases - genetics ; Hydrolases - metabolism ; Hydrolysis ; Learning algorithms ; Machine Learning ; Monomers ; Mutation ; Neural networks ; pH effects ; Plastic debris ; Plastics ; Polyester resins ; Polyesters ; Polyethylene terephthalate ; Polyethylene Terephthalates - metabolism ; Protein Engineering ; Proteins ; Recycling ; Scaffolds
Is Part Of: Nature (London), 2022-04, Vol.604 (7907), p.662-667
Description: 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.
Publisher: England: Nature Publishing Group
Language: English
Identifier: ISSN: 0028-0836
EISSN: 1476-4687
DOI: 10.1038/s41586-022-04599-z
PMID: 35478237
Source: ProQuest One Psychology
MEDLINE
ProQuest Central

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Machine learning-aided engineering of hydrolases for PET depolymerization

2022. The Author(s), under exclusive licence to Springer Nature Limited. ;Copyright Nature Publishing Group Apr 28, 2022 ;ISSN: 0028-0836 ;EISSN: 1476-4687 ;DOI: 10.1038/s41586-022-04599-z ;PMID: 35478237

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