ArticleSynthetic and systems biotechnology2027
Deep Learning-driven synergistic engineering of PET hydrolase for post-consumer PET depolymerization.
Article in Synthetic and systems biotechnology, 2027. 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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6 authors.
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Abstract
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 (
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