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Deep learning-guided discovery and engineering of binding peptides for accelerated enzymatic degradation of polyethylene terephthalate
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Researchers used a deep learning model trained on genomic data from Ideonella sakaiensis to discover and engineer PET-binding peptides, then fused the optimized peptides with a PETase mutant to boost PET hydrolysis by up to 24.8-fold, with molecular dynamics simulations explaining how closer substrate docking drives the catalytic improvement.
Enzymatic polyethylene terephthalate (PET) degradation holds promise for environmental restoration. However, limited substrate catalytic capacity hinders its application in addressing PET plastic contamination. To enhance enzyme-substrate interaction, effective anchoring strategies are essential. This study presents a novel deep learning approach to identify and engineer high-performance PET-binding peptides from genomic data. Utilizing this approach, we discovered promising PET-binding peptides from the Ideonella sakaiensis genome and fused them with an optimized Ideonella sakaiensis PETase mutant to enhance PET hydrolysis. Remarkably, the Efficient Attention-Based Model for Computational Protein Design-optimized fusion proteins achieved a 2.0- to 24.8-fold increase in PET hydrolysis compared with the enzyme without an anchor. Importantly, we elucidated the mechanism by which the binding peptide domain enhances the catalytic activity of the enzyme against PET substrates, supported by comprehensive molecular dynamics simulations. This work establishes a robust deep learning framework for biocatalyst design and provides potent enzymatic solutions to address global PET plastic pollution.
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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-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.
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Researchers used computational molecular docking and machine learning to show that the enzyme PHL7 degrades PET nanoplastics most efficiently at shorter chain lengths, with binding affinity becoming unfavorable once PET oligomer chains exceed six repeat units in length.
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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.