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Plastic pollution remains a significant environmental challenge, with conventional waste management strategies proving insufficient in addressing the problem. Enzymatic degradation has emerged as a promising alternative, with LCCICCG, an engineered metagenome-derived cutinase, being the most effective in degrading polyethylene terephthalate (PET), the most commonly produced and discarded polyester. However, more efficient PET-hydrolases are needed for the upscaling of a PET-waste biorefinery. In this regard, the study reports the characterization of a novel, phylogenetically distinct, thermophilic polyesterase from Deinococcus maricopensis (DmPETase) and its comparison to LCCICCG. DmPETase is capable of degrading various synthetic polymers, including PET, polyurethane, as well as four semi- crystalline aliphatic polyesters. DmPETase was found to be comparable to LCCICCG at 50 °C in degrading semi-crystalline sections of post-consumer PET bottles, but it appeared to be less sensitive to crystallinity degree increase. This property makes DmPETase a new template for protein engineering endeavors to create an efficient biocatalyst to be integrated into the bio-recycling process of PET waste, without the need for amorphization of the materials.
Polyethylene Terephthalates, Hydrolases, Deinococcus, Environmental Pollution, PETase; LCC; post-consumer PET degradation; plastic biodegradation; PET crystallinity, Plastics
Polyethylene Terephthalates, Hydrolases, Deinococcus, Environmental Pollution, PETase; LCC; post-consumer PET degradation; plastic biodegradation; PET crystallinity, Plastics
| selected citations These citations are derived from selected sources. This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically). | 33 | |
| popularity This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network. | Top 10% | |
| influence This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically). | Average | |
| impulse This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network. | Top 1% |
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