Breaking down polyethylene terephthalate (PET) into its constituent monomers under mild conditions is a key goal of plastics recycling. Knowledge of how many naturally occurring enzymes are capable of breaking down PET, and which would be most effective in industrial settings, is limited, however. Researchers at Kyungpook National University (KNU; Daegu, South Korea; en.knu.ac.kr) have developed a method for identifying and characterizing the potential of microbial enzymes to degrade PET, with the objective of improving the effectiveness and efficiency of chemical recycling processes.
The researchers developed what they call a “landscaping-and-sampling framework” for connecting data on protein sequences to enzyme activity in the polyesterase-lipase-cutinase family. They screened close to 2,000 candidate microbial enzymes for the best targets for further exploration and development as biocatalysts. 
The analysis yielded two enzymes with especially high fitness for PET-degrading activity and thermal stability, which is required for industrial recycling. The two most promising enzymes, known as Kubu-P and Mipa-P, were genetically engineered to produce variants that outperformed benchmarks for PET decomposition under industrially relevant conditions, the researchers said. “Kubu-PM12 even endured ethylene glycol as the solvent and reactant in enzyme-catalyzed PET glycolysis, yielding bis(2-hydroxyethyl) terephthalate as the main product at concentrations reaching tens of millimolar,” they wrote.
“We were particularly excited to observe that Kubu-PM12 thrives under industrial conditions. Its ability to handle high PET loads and withstand elevated temperatures, coupled with its impressive activity at lower temperatures, is highly advantageous. This makes it a promising candidate for real-world applications,” KNU professor Kyung-Jin Kim remarks.
“The novel enzymes discovered through this method will enable the continuous recycling process of PET at lower temperatures and higher productivity. Furthermore, this landscaping method of protein sequences proposed in this study may allow for the classification categories of the chaotic enzyme family to be established in detail, which will allow scientists to more easily predict and understand enzyme function,” Kim explains.
The study is published in a recent issue of the journal Science.