About This Project
PET is notorious for its environmental persistence and contribution to CO2 emissions. Therefore, the project involves designing highly active and thermostable mutants of the hydrolyzing enzyme MHETase using cutting-edge computational methods and validating the predictions using in vitro techniques. This research will advance the development of green technologies for plastic recycling and thereby help in moving a step forward towards lowering CO2 emissions.
Ask the ScientistsJoin The Discussion
What is the context of this research?
The need for innovative solutions to mitigate plastic accumulation in the environment has never been more pressing1. Ubiquitous and persistent plastics such as polyethylene terephthalate (PET)2, pose a profound and enduring threat to our ecosystems3 and contribute to increased greenhouse gas emission during production and disposal process. Current PET waste methods, such as thermal, mechanical, or chemical processes are energy-intensive and ecologically unsustainable. To address this challenge, biodegradation using PET-hydrolyzing enzymes offers an eco-friendly alternative and regenerating recyclable monomers4. However, it is crucial to optimize the enzymes to industry-relevant conditions to ensure efficient implementation of PET degradation processes5.
What is the significance of this project?
PET (polyethylene terephthalate) breakdown into essential molecules, terephthalic acid (TPA) and ethylene glycol (EG)7, is vital for recycling and can be carried out in an eco-friendly manner using two key enzymes, PETase and MHETase1,6. They synergistically convert PET into TPA and EG4,6, which can help in the growth of microorganisms and can be reutilized to create new plastics8,9, thereby fostering a circular plastic economy. Improving the thermostability of the enzymes over PET's glass transition temperature (Tg) has shown to be beneficial. While efforts have improved PETase thermostability (eg. BhrPETase, DuraPETase), MHETase has been overlooked. Thus, our research focuses on enhancing MHETase's thermostability and efficiency in PET hydrolysis.
What are the goals of the project?
The overarching goal of our research is to create innovative biocatalysts to tackle environmental challenges. To achieve this, we intend to enhance the thermostability and catalytic efficiency of MHETase-like enzymes through rational engineering. With our improved MHETase-variants, we will explore the potential synergy with BhrPETase, the most thermostable and highly active PET hydrolase. The wildtype MHETase with a Km range of 18-23 µM and a Tm of 50°C, presents opportunities for improvement. Ideally, reducing Km to <10 µM and raising the Tm closer to the Tg, around 80°C, might help in achieving a better synergistic effect.
Research personnel salary and material cost involved in the research
Sep 30, 2025
Improve the thermostability and activity of MHETase
Meet the Team
Dr. Subha Kalyaanamoorthy is an early career Assistant Professor in the Department of Chemistry at the University of Waterloo. Her academic journey began with a Ph.D. in Bioinformatics from La Trobe University, Australia, followed by enriching post-doctoral fellowships at esteemed institutions, including CSIRO (Office of Chief Executive PDF) in Australia and the University of Alberta (NSERC PDF) in Canada.
Dr. Kalyaanamoorthy's research pursuits are dedicated to harnessing the power of computational methods to confront pressing challenges in the realms of biology, health, and the environment. Her multidisciplinary approach spans a wide spectrum of scientific disciplines, encompassing molecular modelling, molecular dynamics simulations, quantum modelling, protein biochemistry, machine learning, phylogenetic inference, and bioinformatics. Through these diverse lenses, she delves deep into the intricacies of protein structures, functions, dynamics, and evolutionary trajectories and develops peptides, small molecules, and nanoparticles for modulating the biological functions of proteins.
Dr. Kalyaanamoorthy has authored over 35 peer-reviewed articles, making impactful contributions to prestigious journals such as Nature Methods, Circulation, and Bioinformatics. Her ground-breaking work has garnered support from renowned funding bodies, including the New Frontiers in Research Fund, the Natural Sciences and Engineering Research Council of Canada (NSERC), as well as pivotal organizations in cancer research, such as the Cancer Research Society, Breast Cancer Society of Canada, and Mitacs. Beyond her publications and grants, Dr. Kalyaanamoorthy's innovative spirit shines through her role as a co-inventor in two patent applications and third one in progress, exemplifying her dedication to advancing scientific knowledge.
Dr. Subha Kalyaanamoorthy's work embodies the spirit of scientific exploration and its transformative potential to shape the future of biology and healthcare.
(1) Austin, H. P.; Allen, M. D.; Donohoe, B. S.; Rorrer, N. A.; Kearns, F. L.; Silveira, R. L.; Pollard, B. C.; Dominick, G.; Duman, R.; El Omari, K.; Mykhaylyk, V.; Wagner, A.; Michener, W. E.; Amore, A.; Skaf, M. S.; Crowley, M. F.; Thorne, A. W.; Johnson, C. W.; Woodcock, H. L.; McGeehan, J. E.; Beckham, G. T. Characterization and Engineering of a Plastic-Degrading Aromatic Polyesterase. Proc. Natl. Acad. Sci. 2018, 115 (19), E4350–E4357. https://doi.org/10.1073/pnas.1718804115.
(2) Montazer, Z.; Habibi Najafi, M. B.; Levin, D. B. Challenges with Verifying Microbial Degradation of Polyethylene. Polymers 2020, 12 (1), 123. https://doi.org/10.3390/polym12010123.
(3) Sagong, H.-Y.; Seo, H.; Kim, T.; Son, H. F.; Joo, S.; Lee, S. H.; Kim, S.; Woo, J.-S.; Hwang, S. Y.; Kim, K.-J. Decomposition of the PET Film by MHETase Using Exo-PETase Function. ACS Catal. 2020, 10 (8), 4805–4812. https://doi.org/10.1021/acscatal.9b05604.
(4) Taniguchi, I.; Yoshida, S.; Hiraga, K.; Miyamoto, K.; Kimura, Y.; Oda, K. Biodegradation of PET: Current Status and Application Aspects. ACS Catal. 2019, 9 (5), 4089–4105. https://doi.org/10.1021/acscatal.8b05171.
(5) Yan, F.; Wei, R.; Cui, Q.; Bornscheuer, U. T.; Liu, Y.-J. Thermophilic Whole-Cell Degradation of Polyethylene Terephthalate Using Engineered Clostridium Thermocellum. Microb. Biotechnol. 2021, 14 (2), 374–385. https://doi.org/10.1111/1751-7915.13580.
(6) Joo, S.; Cho, I. J.; Seo, H.; Son, H. F.; Sagong, H.-Y.; Shin, T. J.; Choi, S. Y.; Lee, S. Y.; Kim, K.-J. Structural Insight into Molecular Mechanism of Poly(Ethylene Terephthalate) Degradation. Nat. Commun. 2018, 9 (1), 382. https://doi.org/10.1038/s41467-018-02881-1.
(7) Knott, B. C.; Erickson, E.; Allen, M. D.; Gado, J. E.; Graham, R.; Kearns, F. L.; Pardo, I.; Topuzlu, E.; Anderson, J. J.; Austin, H. P.; Dominick, G.; Johnson, C. W.; Rorrer, N. A.; Szostkiewicz, C. J.; Copié, V.; Payne, C. M.; Woodcock, H. L.; Donohoe, B. S.; Beckham, G. T.; McGeehan, J. E. Characterization and Engineering of a Two-Enzyme System for Plastics Depolymerization. Proc. Natl. Acad. Sci. U. S. A. 2020, 117 (41), 25476–25485. https://doi.org/10.1073/pnas.2006753117.
(8) Glaser, J. A. Biological Degradation of Polymers in the Environment. In Plastics in the Environment; IntechOpen, 2019. https://doi.org/10.5772/intechopen.85124.
(9) Karunatillaka, I.; Jaroszewski, L.; Godzik, A. Novel Putative Polyethylene Terephthalate (PET) Plastic Degrading Enzymes from the Environmental Metagenome. Proteins 2022, 90 (2), 504–511. https://doi.org/10.1002/prot.26245.
(10) Zumstein, M. T.; Rechsteiner, D.; Roduner, N.; Perz, V.; Ribitsch, D.; Guebitz, G. M.; Kohler, H.-P. E.; McNeill, K.; Sander, M. Enzymatic Hydrolysis of Polyester Thin Films at the Nanoscale: Effects of Polyester Structure and Enzyme Active-Site Accessibility. Environ. Sci. Technol. 2017, 51 (13), 7476–7485. https://doi.org/10.1021/acs.est.7b01330.
(11) Demi̇Rel, B.; Yara, A.; Elç, H. Crystallization Behavior of PET Materials. 2011.
(12) Palm, G. J.; Reisky, L.; Böttcher, D.; Müller, H.; Michels, E. A. P.; Walczak, M. C.; Berndt, L.; Weiss, M. S.; Bornscheuer, U. T.; Weber, G. Structure of the Plastic-Degrading Ideonella Sakaiensis MHETase Bound to a Substrate. Nat. Commun. 2019, 10 (1), 1717. https://doi.org/10.1038/s41467-019-09326-3.
(13) Carniel, A.; Valoni, É.; Nicomedes, J.; Gomes, A. da C.; Castro, A. M. de. Lipase from Candida Antarctica (CALB) and Cutinase from Humicola Insolens Act Synergistically for PET Hydrolysis to Terephthalic Acid. Process Biochem. 2017, 59, 84–90. https://doi.org/10.1016/j.procbio.2016.07.023.
(14) Barth, M.; Honak, A.; Oeser, T.; Wei, R.; Belisário-Ferrari, M. R.; Then, J.; Schmidt, J.; Zimmermann, W. A Dual Enzyme System Composed of a Polyester Hydrolase and a Carboxylesterase Enhances the Biocatalytic Degradation of Polyethylene Terephthalate Films. Biotechnol. J. 2016, 11 (8), 1082–1087. https://doi.org/10.1002/biot.201600008.
(15) Aboelnga, M. M.; Kalyaanamoorthy, S. QM/MM Investigation to Identify the Hallmarks of Superior PET Biodegradation Activity of PETase over Cutinase. ACS Sustain. Chem. Eng. 2022, 10 (48), 15857–15868. https://doi.org/10.1021/acssuschemeng.2c04913.
(16) Lu, H.; Diaz, D. J.; Czarnecki, N. J.; Zhu, C.; Kim, W.; Shroff, R.; Acosta, D. J.; Alexander, B. R.; Cole, H. O.; Zhang, Y.; Lynd, N. A.; Ellington, A. D.; Alper, H. S. Machine Learning-Aided Engineering of Hydrolases for PET Depolymerization. Nature 2022, 604 (7907), 662–667. https://doi.org/10.1038/s41586-022-04599-z.
(17) Mazurenko, S.; Prokop, Z.; Damborsky, J. Machine Learning in Enzyme Engineering. ACS Catal. 2020, 10 (2), 1210–1223. https://doi.org/10.1021/acscatal.9b04321.
(18) Feehan, R.; Montezano, D.; Slusky, J. S. G. Machine Learning for Enzyme Engineering, Selection and Design. Protein Eng. Des. Sel. 2021, 34, gzab019. https://doi.org/10.1093/protein/gzab019.
(19) Sampaio, P. S.; Fernandes, P. Machine Learning: A Suitable Method for Biocatalysis. Catalysts 2023, 13 (6), 961. https://doi.org/10.3390/catal13060961.
(20) Reeves, S.; Kalyaanamoorthy, S. Zero-Shot Transfer of Protein Sequence Likelihood Models to Thermostability Prediction. bioRxiv July 19, 2023, p 2023.07.17.549396. https://doi.org/10.1101/2023.07.17.549396.
(21) Hsu, C.; Nisonoff, H.; Fannjiang, C.; Listgarten, J. Learning Protein Fitness Models from Evolutionary and Assay-Labeled Data. Nat. Biotechnol. 2022, 40 (7), 1114–1122. https://doi.org/10.1038/s41587-021-01146-5.
(22) Dauparas, J.; Anishchenko, I.; Bennett, N.; Bai, H.; Ragotte, R. J.; Milles, L. F.; Wicky, B. I. M.; Courbet, A.; de Haas, R. J.; Bethel, N.; Leung, P. J. Y.; Huddy, T. F.; Pellock, S.; Tischer, D.; Chan, F.; Koepnick, B.; Nguyen, H.; Kang, A.; Sankaran, B.; Bera, A. K.; King, N. P.; Baker, D. Robust Deep Learning–Based Protein Sequence Design Using ProteinMPNN. Science 2022, 378 (6615), 49–56. https://doi.org/10.1126/science.add2187.
(23) Yang, K. K.; Zanichelli, N.; Yeh, H. Masked Inverse Folding with Sequence Transfer for Protein Representation Learning. bioRxiv May 28, 2022, p 2022.05.25.493516. https://doi.org/10.1101/2022.05.25.493516.
(24) Rao, R. M.; Liu, J.; Verkuil, R.; Meier, J.; Canny, J.; Abbeel, P.; Sercu, T.; Rives, A. MSA Transformer. In Proceedings of the 38th International Conference on Machine Learning; PMLR, 2021; pp 8844–8856.
(25) Notin, P. et al. Tranception: protein fitness prediction with autoregressive transformers and inference-time retrieval (2022). ArXiv:2205.13760 [cs] - Google Search. https://www.google.com/search?... (accessed 2023-10-17).
(26) Saridakis, E.; Coste, F. Thermal Shift Assay for Characterizing the Stability of RNA Helicases and Their Interaction with Ligands. Methods Mol. Biol. Clifton NJ 2021, 2209, 73–85. https://doi.org/10.1007/978-1-0716-0935-4_5.
(27) Elgert, C.; Rühle, A.; Sandner, P.; Behrends, S. Thermal Shift Assay: Strengths and Weaknesses of the Method to Investigate the Ligand-Induced Thermostabilization of Soluble Guanylyl Cyclase. J. Pharm. Biomed. Anal. 2020, 181, 113065. https://doi.org/10.1016/j.jpba.2019.113065.
(28) Loll-Krippleber, R.; Sajtovich, V. A.; Ferguson, M. W.; Ho, B.; Burns, A. R.; Payliss, B. J.; Bellissimo, J.; Peters, S.; Roy, P. J.; Wyatt, H. D. M.; Brown, G. W. Development of a Yeast Whole-Cell Biocatalyst for MHET Conversion into Terephthalic Acid and Ethylene Glycol. Microb. Cell Factories 2022, 21 (1), 280. https://doi.org/10.1186/s12934-022-02007-9.
(29) Meyer-Cifuentes, I. E.; Öztürk, B. Mle046 Is a Marine Mesophilic MHETase-Like Enzyme. Front. Microbiol. 2021, 12.
(30) Barth, M.; Oeser, T.; Wei, R.; Then, J.; Schmidt, J.; Zimmermann, W. Effect of Hydrolysis Products on the Enzymatic Degradation of Polyethylene Terephthalate Nanoparticles by a Polyester Hydrolase from Thermobifida Fusca. Biochem. Eng. J. 2015, 93, 222–228. https://doi.org/10.1016/j.bej.2014.10.012.
(31) Barth, M.; Wei, R.; Oeser, T.; Then, J.; Schmidt, J.; Wohlgemuth, F.; Zimmermann, W. Enzymatic Hydrolysis of Polyethylene Terephthalate Films in an Ultrafiltration Membrane Reactor. J. Membr. Sci. 2015, 494, 182–187. https://doi.org/10.1016/j.memsci.2015.07.030.
(32) Wei, R.; Oeser, T.; Schmidt, J.; Meier, R.; Barth, M.; Then, J.; Zimmermann, W. Engineered Bacterial Polyester Hydrolases Efficiently Degrade Polyethylene Terephthalate Due to Relieved Product Inhibition. Biotechnol. Bioeng. 2016, 113 (8), 1658–1665. https://doi.org/10.1002/bit.25941.
(33) Zhong-Johnson, E. Z. L.; Voigt, C. A.; Sinskey, A. J. An Absorbance Method for Analysis of Enzymatic Degradation Kinetics of Poly(Ethylene Terephthalate) Films. Sci. Rep. 2021, 11 (1), 928. https://doi.org/10.1038/s41598-020-79031-5.
- $0Total Donations
- $0Average Donation