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 intell...
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...
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 po...
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-l...
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 informati...
J. Lazar, Evan Komp, I. Martínez et al.· bioRxiv· 1 citation
A new machine-learning framework aims to improve the success rate of computational protein design while moving away from results that reproduce sequences found in nature.
