New plastic-eating enzyme has potential to degrade billions of tonnes of landfill waste

landfill plasticEngineers and scientists at The University of Texas at Austin have created an enzyme variant that can break down plastics in hours or days which take years to degrade naturally.

Professor in the McKetta Department of Chemical Engineering at UT Austin, Hal Alper, said: “The possibilities are endless across industries to leverage this leading-edge recycling process,

“beyond the obvious waste management industry, this also provides corporations from every sector the opportunity to take a lead in recycling their products. Through these more sustainable enzyme approaches, we can begin to envision a true circular plastics economy.”

The enzyme targets polyethylene terephthalate (PET), which is found in most consumer packaging and certain textiles and fibres.

The enzyme breaks down plastic into smaller parts through depolymerization before repolymerization occurs which chemically puts it back together. There are examples of these plastics being fully broken down into monomers in 24 hours.

As part of the enzyme’s development, a machine learning model was employed by researchers at the Cockrell School of Engineering and College of Natural Sciences to develop unique mutations in PETase, a natural enzyme that allows bacteria to digest PET polymers. The model predicts which enzyme mutations would achieve the goal of depolymerizing post-consumer waste plastic quickly and at low temperatures.

The enzyme is being called FAST-PETase (functional, active, stable and tolerant PETase.

The researchers proved the enzyme’s effectiveness by studying 51 different post-consumer plastic containers, five different polyester fibres and fabrics and water bottles all made from PET.

As part of the next stage of development, the team plans to work on scaling enzyme production for industrial and environmental applications.

Professor in the Centre for Systems and Synthetic Biology, Andrew Ellington, whose team led the development of the machine learning model, said: “This work really demonstrates the power of bringing together different disciplines, from synthetic biology to chemical engineering to artificial intelligence.”

Send this to a friend