TY - JOUR
T1 - Machine learning-aided engineering of hydrolases for PET depolymerization
AU - Lu, Hongyuan
AU - Diaz, Daniel J.
AU - Czarnecki, Natalie J.
AU - Zhu, Congzhi
AU - Kim, Wantae
AU - Shroff, Raghav
AU - Acosta, Daniel J.
AU - Alexander, Bradley R.
AU - Cole, Hannah O.
AU - Zhang, Yan
AU - Lynd, Nathaniel A.
AU - Ellington, Andrew D.
AU - Alper, Hal S.
N1 - Publisher Copyright:
© 2022, The Author(s), under exclusive licence to Springer Nature Limited.
PY - 2022/4/28
Y1 - 2022/4/28
N2 - 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.
AB - 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.
KW - Hydrolases/genetics
KW - Hydrolysis
KW - Machine Learning
KW - Plastics
KW - Polyethylene Terephthalates/metabolism
KW - Protein Engineering
UR - http://www.scopus.com/inward/record.url?scp=85128944935&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85128944935&partnerID=8YFLogxK
U2 - 10.1038/s41586-022-04599-z
DO - 10.1038/s41586-022-04599-z
M3 - Article
C2 - 35478237
AN - SCOPUS:85128944935
SN - 0028-0836
VL - 604
SP - 662
EP - 667
JO - Nature
JF - Nature
IS - 7907
ER -