ArticleAdvanced science (Weinheim, Baden-Wurttemberg, Germany)2026
Deep Learning-Driven Discovery and Engineering of an Efficient PETase for Depolymerization and Detoxification of PET Microplastics Under Physiological Conditions.
Article in Advanced science (Weinheim, Baden-Wurttemberg, Germany), 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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10 authors.
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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
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