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Artificial intelligence-assisted mining of polyethylene terephthalate hydrolases
Summary
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.