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Machine learning-aided engineering of hydrolases for PET depolymerization

Abstract

Plastic waste poses an ecological challenge1,2,3 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 products6,7,8,9,10. 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.

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Fig. 1: Machine learning guided predictions improve enzyme performance across PETase scaffolds.
Fig. 2: The thermostability and PET-hydrolytic activity of WT and lysine mutation enzymes.
Fig. 3: The superior performance of FAST-PETase in enzymatic depolymerization of thermoformed pc-PET products.
Fig. 4: Depolymerization of PET water bottles and polyester products by FAST-PETase and application of FAST-PETase in enzymatic-chemical recycling of PET.

Data availability

The authors declare that all data supporting the findings of this study are available in the article, its Extended Data, its Source Data or from the corresponding authors upon request. The complete data set of MutCompute predictions used in this study can be acquired at https://mutcompute.com. Coordinates for the FAST-PETase structure have been deposited into the PDB with accession code 7SH6. Interactive visualizations of MutCompute for Fig. 1 are available at https://www.mutcompute.com/petase/5xjh and https://www.mutcompute.com/petase/6ij6Source data are provided with this paper.

Code availability

MutCompute and MutCompute-View are publicly available at https://mutcompute.com and https://mutcompute.com/view for academic research.

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Acknowledgements

This work was financed under research agreement no. EM10480.26/UTA16-000509 between the ExxonMobil Research and Engineering Company and The University of Texas at Austin. Sequencing was conducted at the Genomic Sequencing and Analysis Facility (RRID no. SCR_021713), SEM was conducted at the Microscopy and Imaging Facility (RRID no. SCR_021756) at the UT Austin Center for Biomedical Research Support, and AFM analysis was conducted at the Texas Materials Institute at UT Austin. N.A.L. and C.Z. thank the Welch Foundation for partial support of this research (Grant #F-1904). The crystallography study is supported by a grant from the National Institutes of Health (no. GM104896 to Y.J.Z.). Crystallographic data collections were conducted at Advanced Photon Sources (BL23-ID-B), Department of Energy national user facility. We acknowledge the Texas Advanced Computing Center at The University of Texas at Austin for providing deep learning resources for neural network predictions and analysis that have contributed to the research results reported in this paper.

Author information

Authors and Affiliations

Authors

Contributions

H.S.A., A.D.E., N.A.L. and H.L. designed and directed the research. In investigation and validation, R.S. and D.J.D. performed neural network analysis. H.L. performed enzyme engineering, purification and the depolymerization experiments of both model and pc-PET substrates. H.L., N.J.C., C.Z., D.J.A. and H.O.C. carried out structural and physical characterization of variants. H.L., C.Z. and N.J.C. performed physical characterization of the treated and untreated commercial PET materials. C.Z carried out experiments for purifying TPA and regenerating virgin PET and plastics films. D.J.D. and B.R.A. developed MutCompute-View for visualizing predictions from the neural network model. W.K. and Y.J.Z. performed protein crystallization and structural analysis of the engineered enzyme. H.S.A. and H.L. wrote the original draft of the manuscript. H.S.A., A.D.E., N.A.L. and H.L. revised the manuscript. H.S.A. and A.D.E. conceived the project idea. All authors reviewed and accepted the manuscript.

Corresponding author

Correspondence to Hal S. Alper.

Ethics declarations

Competing interests

A 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 cofounder of Aperiam, a company that applies machine learning to protein engineering. R.S. and A.D.E. are inventors on a patent for applying machine learning to protein engineering that has been licensed to Aperiam.

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Nature thanks Gregory Bowman, Ulphard Thoden van Velzen and the other, anonymous, reviewer(s) for their contribution to the peer review of this work.

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Extended data figures and tables

Extended Data Fig. 1 Top 10 MutCompute predictions ranked by fold change in the probabilities between the predicted and the wild-type amino acid.

The top 10 mutations predicted using the wild-type PETase (a) and ThermoPETase (b) as scaffolds are presented. MutCompute is an ensembled model that consist of three individually trained 3-dimensional convolutional neural network (3DCNN) models. Thus, the avg_log_ratio column is the average of the three log ratio values obtained from the three 3DCNN models, rather than being the log ratio of the average probability assigned to the wild type and predicted amino acid across the three 3DCNN models.

Extended Data Fig. 2 Thermostability and protein yield of the PETase variants incorporating the predicted mutations and their respective scaffolds—wild-type PETase (WT), ThermoPETase (Thermo), DuraPETase (Dura).

Tm of each enzyme (left) was determined by DSC. The protein yield of each enzyme (right) from P. putida purification experiments was evaluated using a Bradford protein assay. All measurements were conducted in triplicate (n = 3). The bars shown represent the average numbers.

Extended Data Fig. 3 X-ray crystal structure of FAST-PETase.

a, Overall crystal structure of FAST-PETase. Catalytic triads (S160, D206, H237) are shown in blue sticks. Mutations originating from or shared with ThermoPETase (S121E, D186H, R280A) are shown in pink sticks, and completely novel mutations predicted by MutCompute are shown in green-yellow sticks. b, c, 2Fo-Fc map (contoured at 1.5 σ) shown as grey mesh superimposed on the stick models of novel mutation sites (b) R224Q, (c) N233K.

Extended Data Fig. 4 Location of the LM site in the crystal structures of homologous PHEs.

On wild-type PETase from I. Sakaiensis (white ribbon), catalytic residues are shown as blue sticks. LM site is shown as gray sticks on top of cartoon representation. LM site is zoomed in to show superimposed structures of four different homologous PHEs (right top panel: WT): I. Sakaiensis wild-type PETase (gray sticks, PDB code 5XJH), LCC (yellow sticks, PDB code 4EB0), LCCF243I/D238C/S283C/N246M (ICCM) (green sticks, PDB code 6THT - LCCF243I/D238C/S283C/Y127G (ICCG) variant structure), and S. viridis Cut190 (pink sticks, PDB code 4WFI - Cut190S226P variant structure). Based on FAST-PETase structure, the structure of homologous PHEs with LM is modelled (right bottom panel: LM) with residues shown as blue-colored sticks.

Extended Data Fig. 5 Time-course of mass loss and PET monomers released from hydrolyzing the hole-punched films of six representative pc-PET products with FAST-PETase.

The six pc-PET products represent PET #2, 6, 8, 25, 29, 32 that were randomly selected from the 51 pc-PET products (Supplementary Table 3 and Supplementary Fig. 4). The pc-PET films hole-punched from these PET products were hydrolysed by serial treatment with FAST-PETase at 50 °C until the films were completely degraded (film disappeared). Enzyme solution (200 nM of FAST-PETase in 100 mM KH2PO4-NaOH (pH 8.0) buffer) was replenished every 22 h. All measurements were conducted in triplicate (n = 3). The squares (mass loss) and circles (PET monomers released) shown represent the individual numbers. The line connects mean values of the timepoints.

Source data

Extended Data Fig. 6 Scatterplot of time needed for complete degradation versus initial mass of the hole-punched films from 51 different pc-PET products.

Degradation time was found to be corelated with the thickness (as thickness and mass are related) of the hole-punched films from various plastic products.

Source data

Extended Data Fig. 7 Scatterplot of degradation rate versus (a.) initial mass, (b.) crystallinity%, (c.) weight average molecular weight (Mw), (d.) number average molecular weight (Mn), or (e.) polydispersity indices of the hole-punched films from 51 different pc-PET products.

Degradation rate was not found to be dependent on any one metric across these various pc-PET plastics.

Source data

Extended Data Fig. 8 Scanning electron microscopic analysis of the pc-PET films.

The hole-punched PET films from a bean cake PET container were treated with FAST-PETase for 0 h, 8 h, 16 h in 100 mM KH2PO4-NaOH (pH 8.0) buffer at 50 °C.

Extended Data Fig. 9 Depolymerization of the Finish/Neck, Body and Base Center fragments of an untreated water bottle.

Depolymerization was tested by FAST-PETase, wild-type PETase (WT), ThermoPETase (Thermo), DuraPETase (Dura), LCC and ICCM at (a) 50 ºC, (b) 60 ºC, and (c) 72 ºC. All measurements were conducted in triplicate (n = 3). The bars and circles shown for each enzyme represent the average and individual numbers, respectively. This comparative analysis provides two main conclusions. First, although higher reaction temperatures do promote the hydrolytic activity of the thermophilic LCC and ICCM against the amorphous parts of the bottle (base center and finish), the highly crystalline body part still cannot be efficiently depolymerized by any tested enzymes and temperatures. Second, FAST-PETase at 50 ºC exhibited the highest overall depolymerization rate seen in these experiments releasing 42, 0.14 and 15 mM of PET monomers within 24 h against the finish, body, and bottom center of the bottle respectively. These values are 25%, 43% and 20% higher, respectively, than that of ICCM at 72 ºC.

Source data

Extended Data Table 1 Statistics of the crystal structural determination of FAST-PETase

Supplementary information

Supplementary Information

This file contains Supplementary Methods, Discussion; Tables 1–3 and Figs. 1–13.

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Lu, H., Diaz, D.J., Czarnecki, N.J. et al. Machine learning-aided engineering of hydrolases for PET depolymerization. Nature 604, 662–667 (2022). https://doi.org/10.1038/s41586-022-04599-z

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