Deep Learning-Driven Discovery and Engineering of an Efficient PETase for Depolymerization and Detoxification of PET Microplastics Under Physiological Conditions.

Authors

Yuxuan Wang, Shijie He, Yuheng Chang, Sheng Mao, Jianan Canal Li, Binbin Chen, Hongxun Gao, Mingchun Xu, Chenxu Liu, Yajie Wang

Published in

Advanced science (Weinheim, Baden-Wurttemberg, Germany). Pages e77163. Aug 18, 2026. Epub Aug 18, 2026.

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 AhPETase^M1 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.

PMID:
42610517
Bibliographic data and abstract were imported from PubMed on 18 Aug 2026.

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