Deep Learning‐Driven Discovery and Engineering of an Efficient PETase for Depolymerization and Detoxification of PET Microplastics Under Physiological Conditions
Aug 2026· Advancement of science· 0 citations· 77 references
Medicine
Abstract
ABSTRACT Microplastics (MPs) accumulation in ecosystem and human organs poses urgent environmental and health risks, yet few enzymes efficiently degrade polyethylene terephthalate (PET) under physiological conditions. We leveraged deep learning to mine unexplored sequence space across 246 million proteins, discovering AhPETase, an evolutionarily distinct hydrolase with low homology (<50% sequence identity) to known PET‐degrading enzymes. This noncanonical biocatalyst efficiently depolymerizes PET at 37°C, outperforming all typical PETases and achieving a 7.76‐fold enhancement over IsPETase, one of the most representative mesophilic PETases. Additionally, engineered variant AhPETaseM1 retains functional activity for over 20 days under physiological conditions and can degrade post‐consumer PET MPs 34‐fold faster than recombinant human‐derived enzyme MG8 (rMG8) under equal enzyme loading. Critically, it reversed PET‐induced toxicity in human lung and colon cells, establishing the first proof‐of‐concept for enzymatic MPs detoxification.
Enzymatic depolymerization of polyethylene terephthalate (PET) offers a promising route to mitigate the increasingly severe problem of plastic pollution. However, the development of highly efficient PET hydrolases capable of processing post-consumer PET remains a critical challenge. Recent advances in artificial intelligence (AI) provide new opportunities to accelerate the enzyme engineering of PET hydrolases. Here, we report a systematic computational redesign of the PET hydrolase NI (ThcCut1-AICCG-H185N/F189I) using the deep-learning framework, EITLEM-Kinetics. By integrating mutation free-energy constraints with kinetic parameter prediction, the framework enables simultaneous optimization of catalytic activity and thermostability. A total of nine beneficial substitution sites were identified and experimentally validated that overcoming the activity–stability trade-off. Combinatorial iteration yielded an optimal variant, NI-E65K/H107Y/A2R/L33F (NI-KYRF), which exhibited an 80% and 90% increase in depolymerization activity toward Gf-PET film and one-step pretreated post-consumer PET (pc-PET powder), respectively, along with a 2.61 °C increase in melting temperature (Tm). NI-KYRF displayed a specific activity of 665 μmolTPAeq h−1 mgenzyme−1, representing 2.0- and 1.48-fold improvements over representative high-performance PET hydrolases ICCG and TurboPETase. The depolymerization performance of NI-KYRF towards various post-consumer PET wastes and polyester plastics was significantly improved. At 70 °C, it achieved a 95.5% depolymerization conversion of pc-PET powder within 24 h, and for untreated post-consumer PET film, the conversion reached 80.1%, representing a 2.1-fold improvement in depolymerization efficiency over the parental NI. In addition, enhanced activity toward polyesters plastics, including PBAT and PBT, indicates an expanded substrate scope. Molecular dynamics simulations and structural analysis reveal that these mutations enhance enzyme performance through synergistic mechanisms. Overall, this study establishes a deep learning–guided computational design framework based on EITLEM-Kinetics and demonstrates its effectiveness and broad potential for engineering high-performance PET hydrolases.
Chaofeng Shao, Xiaowei Shen, Jianyu Long et al.· Synthetic and Systems Biotec...· 0 citations
A targeted mining workflow is developed that screens exclusively plastic-associated datasets through multi-step bioinformatic filtering—integrating catalytic-motif screening, disulfide-topology validation, structural-similarity scoring, and phylogenetic profiling—to recover high-confidence PETase candidates, resulting in a thermostable enzyme that depolymerizes PET across a broad temperature range.
Konstantinos Rigkos, Dimitra S Bezantakou, Kyriakos Antoniadis et al.· bioRxiv· 0 citations
The “Motif‐to‐Market” framework is proposed, using the M5 motif as a fingerprint to curate functional PETases for efficiency and stability, with distinct pathways for industrial and marine applications.
Polyethylene terephthalate (PET) hydrolase-based biodegradation offers a promising route for plastic waste remediation, yet the dynamic origins of high activity and their linkage across catalytic stages still require further elucidation. Here, we focus on the Leaf-Branch Compost Cutinase (LCC) system to reveal the possible mechanistic origins underlying the elevated activity of the LCC variants. By integrating molecular dynamics simulations, enhanced sampling, and deep learning-assisted network analysis, we systematically investigate the critical prereactive stage of amorphous PET adsorption and substrate binding. We identify three mechanistic origins underlying the high activity of LCC-LANL in the prereactive state and clarify its structure–dynamics–function relationship: (i) enhanced interaction strength coupled with an active-pocket orientation that, despite not directly facing the amorphous PET surface, maintains closer proximity to it than LCC-WT, thus promoting substrate recruitment; (ii) higher occupancy of the PET ester bond near the catalytic triad, which forms the basis for catalysis, coupled with the efficient dynamic interchange between the “W” and coiled conformations near the catalytic triad; and (iii) the enhancement of prereactive organization through long-range allosteric communication by distal mutations in LCC-LANL. Additionally, we propose a region-specific cooperative optimization strategy tailored to domain-specific functional roles and distill six design principles for efficient PETases. In summary, this work elucidates the prereactive origins underlying the high activity of LCC-LANL, paving the way for future studies on actual catalytic PET hydrolysis.
Jiawen Wang, Haozhe Pan, Huilong Dong et al.· Journal of Chemical Informat...· 0 citations
The enzymatic degradation of poly(ethylene terephthalate) (PET) offers a sustainable route for plastic recycling but is often hindered by limited enzyme adsorption on hydrophobic surfaces. Inspired by carbohydrate-binding modules (CBMs), which enhance enzyme performance on insoluble substrates, we developed a machine-learning-assisted pipeline to discover PET-binding modules from natural protein architectures. Integration of profile hidden Markov model-based homology searching with a supervised PET hydrolase machine-learning model (PETML) revealed that CBMs belonging to family 13, typically known for glycan recognition, were the most abundant CBMs associated with putative PET hydrolase homologs in the screened dataset. From 197 non-redundant candidates, high-throughput docking and molecular dynamics simulations prioritized tCBM13-1 (WP_357125140.1), which exhibited stable interfacial binding via cooperative aromatic and polar interactions. When fused to sfGFP, tCBM13-1 demonstrated superior adsorption (∼70%) and surface retention (∼90%) on PET powder at 37 °C and 45 °C, outperforming a benchmark CBM2. Co-displayed with FAST-PETase on the Escherichia coli (E. coli) surface using a dual-anchor system (OmpA and EhaA), tCBM13-1 enhanced PET film depolymerization by ∼ 43%, achieving a rate of 2793 μg/(d·cm2). The whole-cell catalyst retained > 64% of its initial activity after 10 cycles, indicating robust recyclability. This work integrates machine-learning-guided module mining with synthetic biology to engineer efficient, reusable biocatalysts for PET degradation, offering a scalable strategy for polymer biorecycling.
Rui Long, Yaxin Tang, Chengyong Wang et al.· Bioresource Technology· 1 citation
This work demonstrates that integrating cell-free enzyme engineering with machine learning enables opportunities for high-throughput experimental measurements to benchmark and improve protein language models, accelerate design loops, and expand functional exploration within protein families where experimental information is limited.
J. Lazar, Evan Komp, I. Martínez et al.· bioRxiv· 1 citation