Molecular-Level Mechanistic Insights into PETase-Catalyzed Plastics Hydrolysis from Accurate QM/MM Free Energy Calculations

The enzyme PETase is capable of depolymerizing plastics such as polyethylene terephthalate (PET) at moderate temperatures, and demonstrated even higher activity toward polyethylene-2,5-furan dicarboxylate (PEF), opening promising routes for the efficient upcycling of plastic wastes. To fully exploit the potential of these biocatalytic systems, an understanding of the mechanism of their activity at the atomistic level is pivotal. To this end, this study investigates two fundamental stages of the catalytic cycle of PET and PEF hydrolysis by PETase-acylation and deacylation-using hybrid QM/MM enhanced sampling molecular dynamics simulations to capture all relevant dynamic effects. Well-tempered metadynamics simulations at the DFTB3 level are performed along collective variables optimized via linear discriminant analysis, a supervised learning-assisted approach that accounts for the contributions of each potentially relevant degree of freedom. The free energy (FE) profiles indicate that the acylation stage is the rate-limiting step for both PET and PEF degradation, with barriers approximate to 8 kcal/mol and approximate to 4 kcal/mol higher than those obtained for the deacylation step, respectively. Remarkably, substantial mechanistic differences are found. While PET acylation occurs in a concerted manner, with a single energy barrier of approximate to 21 kcal/mol, PEF acylation follows a two-step mechanism where after the first barrier, approximate to 10 kcal/mol high, a metastable intermediate state is formed, which then evolves toward the product once a second barrier of approximate to 2 kcal/mol is overcome. This mechanistic description is consistent with the FE profiles obtained at higher levels of theory (PBE, B3LYP, RI-MP2) via FE perturbation, thus validating the key insights elucidated by metadynamics simulations. Finally, both global and local reactivity descriptors derived from conceptual density functional theory suggest that PEF is more electrophilic and susceptible to nucleophilic attack than PET. The results obtained by means of the robust computational protocol adopted here offer thermodynamic and mechanistic insights into PET and PEF hydrolysis by PETase at the molecular level, corroborating the experimentally observed enhanced activity of this enzyme toward PEF. The distinctive hallmarks of PETase depolymerization uncovered in this work provide valuable foundations for enzyme engineering efforts aimed at developing universal biocatalysts for semiaromatic plastic recycling, ultimately paving the way for efficient application in industrial settings.

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Publication Details

Journal
ACS Catalysis
Published
2025-06-06
DOI
https://doi.org/10.1021/acscatal.5c01263
Citations
6
Primary Topic
Machine Learning in Materials Science
Type
article
Field-Weighted Citation Impact
3.11

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article

Molecular-Level Mechanistic Insights into PETase-Catalyzed Plastics Hydrolysis from Accurate QM/MM Free Energy Calculations

Alessandro Berselli, Maria Cristina Menziani, Francesco Muniz‐Miranda, GiovanniMaria Piccini
6 citations
ACS Catalysis
Machine Learning in Materials Science
3.11
article

Molecular-Level Mechanistic Insights into PETase-Catalyzed Plastics Hydrolysis from Accurate QM/MM Free Energy Calculations

Alessandro Berselli, Maria Cristina Menziani, Francesco Muniz‐Miranda, GiovanniMaria Piccini
article en
6 citations

Abstract

The enzyme PETase is capable of depolymerizing plastics such as polyethylene terephthalate (PET) at moderate temperatures, and demonstrated even higher activity toward polyethylene-2,5-furan dicarboxylate (PEF), opening promising routes for the efficient upcycling of plastic wastes. To fully exploit the potential of these biocatalytic systems, an understanding of the mechanism of their activity at the atomistic level is pivotal. To this end, this study investigates two fundamental stages of the catalytic cycle of PET and PEF hydrolysis by PETase-acylation and deacylation-using hybrid QM/MM enhanced sampling molecular dynamics simulations to capture all relevant dynamic effects. Well-tempered metadynamics simulations at the DFTB3 level are performed along collective variables optimized via linear discriminant analysis, a supervised learning-assisted approach that accounts for the contributions of each potentially relevant degree of freedom. The free energy (FE) profiles indicate that the acylation stage is the rate-limiting step for both PET and PEF degradation, with barriers approximate to 8 kcal/mol and approximate to 4 kcal/mol higher than those obtained for the deacylation step, respectively. Remarkably, substantial mechanistic differences are found. While PET acylation occurs in a concerted manner, with a single energy barrier of approximate to 21 kcal/mol, PEF acylation follows a two-step mechanism where after the first barrier, approximate to 10 kcal/mol high, a metastable intermediate state is formed, which then evolves toward the product once a second barrier of approximate to 2 kcal/mol is overcome. This mechanistic description is consistent with the FE profiles obtained at higher levels of theory (PBE, B3LYP, RI-MP2) via FE perturbation, thus validating the key insights elucidated by metadynamics simulations. Finally, both global and local reactivity descriptors derived from conceptual density functional theory suggest that PEF is more electrophilic and susceptible to nucleophilic attack than PET. The results obtained by means of the robust computational protocol adopted here offer thermodynamic and mechanistic insights into PET and PEF hydrolysis by PETase at the molecular level, corroborating the experimentally observed enhanced activity of this enzyme toward PEF. The distinctive hallmarks of PETase depolymerization uncovered in this work provide valuable foundations for enzyme engineering efforts aimed at developing universal biocatalysts for semiaromatic plastic recycling, ultimately paving the way for efficient application in industrial settings.

ACS CatalysisVol. 15(12)
University of Modena and Reggio Emilia (IT)
Università Degli Studi di Modena e Reggio Emila, Ministero dell'Università e della Ricerca
Affordable and clean energy
Openalex Percentile: Top 8%
Machine Learning in Materials Science
3.11
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