RT Journal Article
SR Electronic
T1 Deep learning redesign of PETase for practical PET degrading applications
JF bioRxiv
FD Cold Spring Harbor Laboratory
SP 2021.10.10.463845
DO 10.1101/2021.10.10.463845
A1 Hongyuan Lu
A1 Daniel J. Diaz
A1 Natalie J. Czarnecki
A1 Congzhi Zhu
A1 Wantae Kim
A1 Raghav Shroff
A1 Daniel J. Acosta
A1 Brad Alexander
A1 Hannah Cole
A1 Yan Jessie Zhang
A1 Nathaniel Lynd
A1 Andrew D. Ellington
A1 Hal S. Alper
YR 2021
UL http://biorxiv.org/content/early/2021/10/12/2021.10.10.463845.abstract
AB Plastic waste poses an ecological challenge1. While current plastic waste management largely relies on unsustainable, energy-intensive, or even hazardous physicochemical and mechanical processes, enzymatic degradation offers a green and sustainable route for plastic waste recycling2. Poly(ethylene terephthalate) (PET) has been extensively used in packaging and for the manufacture of fabrics and single-used containers, accounting for 12% of global solid waste3. The practical application of PET hydrolases has been hampered by their lack of robustness and the requirement for high processing temperatures. Here, we use a structure-based, deep learning algorithm to engineer an extremely robust and highly active PET hydrolase. Our best resulting mutant (FAST-PETase: Functional, Active, Stable, and Tolerant PETase) exhibits superior PET-hydrolytic activity relative to both wild-type and engineered alternatives, (including a leaf-branch compost cutinase and its mutant4) and possesses enhanced thermostability and pH tolerance. We demonstrate that whole, untreated, post-consumer PET from 51 different plastic products can all be completely degraded by FAST-PETase within one week, and in as little as 24 hours at 50 °C. Finally, we demonstrate two paths for closed-loop PET recycling and valorization. First, we re-synthesize virgin PET from the monomers recovered after enzymatic depolymerization. Second, we enable in situ microbially-enabled valorization using a Pseudomonas strain together with FAST-PETase to degrade PET and utilize the evolved monomers as a carbon source for growth and polyhydroxyalkanoate production. Collectively, our results demonstrate the substantial improvements enabled by deep learning and a viable route for enzymatic plastic recycling at the industrial scale.Competing Interest StatementA patent has been filed in 2020, Mutations for improving activity and thermostability of PETase enzymes relating to the mutants and applications developed in this study. R.S. is a co-founder of Aperiam, a company that applies machine learning to protein engineering. R.S. and A.E. are inventors on a patent for applying machine learning to protein engineering, that has been licensed to Aperiam.