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Artificial intelligence-assisted mining of polyethylene terephthalate hydrolases
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Researchers reviewed how artificial intelligence is accelerating the discovery and engineering of PET-degrading enzymes, covering AI-driven high-throughput screening from biological databases, structure-function prediction, directed molecular evolution, and emerging de novo enzyme design — proposing an integrated framework to overcome the throughput bottlenecks of traditional enzyme mining approaches.
Polyethylene terephthalate (PET) hydrolases efficiently hydrolyze the ester bonds in PET, converting it into valuable monomers or oligomers, offering a sustainable biological solution to global PET plastic pollution. However, the large-scale development of high-performance PET hydrolases remains challenging due to limitations in traditional enzyme resource mining methods, including low throughput and lengthy cycles. Recent advances in artificial intelligence (AI) provide novel methodologies to overcome these challenges. This review systematically summarizes how AI empowers the high-throughput screening of PET hydrolases from massive biological databases, while allowing the accurate prediction of enzyme structures and functions. Furthermore, it critically analyzes AI-driven strategies for enzyme molecular engineering and highlights the emerging frontier of AI-assisted de novo enzyme design. By systematically evaluating the advantages and challenges of AI models in the research of PET hydrolases, this review provides an integrated technical framework and theoretical foundation to guide future innovation in enzyme mining and plastic biodegradation.
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Recent advances in screening and identification of PET-degrading enzymes
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Researchers reviewed recent advances in discovering and engineering enzymes capable of breaking down PET plastic, one of the most widely produced and persistent plastic types. They examined screening methods including metagenomic mining and machine learning approaches that have accelerated the identification of promising PET-degrading enzymes. The study suggests that enzymatic recycling could become a viable, environmentally friendly alternative to traditional PET disposal methods.
Deep Learning‐Driven Discovery and Engineering of an Efficient PETase for Depolymerization and Detoxification of PET Microplastics Under Physiological Conditions
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Scientists used artificial intelligence to discover a new enzyme, called AhPETase, that breaks down a common plastic (PET, found in bottles and packaging) at body temperature, something most enzymes struggle to do efficiently. In lab tests, an improved version of this enzyme not only degraded real-world plastic waste much faster than a similar human enzyme, but also reversed the toxic effects of plastic particles on human lung and colon cells. While still early-stage research, this points toward a possible future tool for breaking down microplastics and protecting the body from their harmful effects.
Deep Learning-driven synergistic engineering of PET hydrolase for post-consumer PET depolymerization.
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Scientists used AI to redesign an enzyme that breaks down PET plastic (the kind used in water bottles), making it nearly twice as effective at digesting real-world plastic waste, even the dirty, previously-used kind. This matters because better plastic-eating enzymes could help recycle plastic more efficiently instead of it piling up in landfills or breaking down into microplastics that end up in our water, food, and bodies.
Deep learning-guided design of hydrolases for crystalline PET depolymerization
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Scientists used AI to design a super-enzyme that can break down PET plastic (the material in water bottles and clothing) into its original building blocks, even without needing to pre-treat or heat-soften the plastic first—something that's been a major roadblock for plastic recycling. This matters because PET plastic pollution takes centuries to break down naturally and sheds microplastics into our food, water, and bodies; this enzyme could enable truly circular recycling where old plastic becomes new, high-quality plastic again instead of ending up in landfills, oceans, or our bloodstreams.
Molecular Engineering of PET-Degrading Enzymes: Opportunities and Challenges
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Researchers reviewed advances in engineering PET-degrading enzymes for closed-loop plastic recycling, highlighting how directed evolution, rational design, and machine learning are pushing catalytic performance forward, while noting persistent barriers including poor activity at low temperatures and limited depolymerization of highly crystalline PET.
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When a large batch of papers lands in the Atlas, we read through it and send a short write-up of what stood out.