Artificial intelligence can help to find solutions for environmental protection – especially in the field of recycling and the decomposition of plastics. Engineers and scientists at the University of Texas at Austin have developed an enzyme with “supernatural” powers. Developed in the lab using machine learning, the protein is able to almost completely break down PET packaging in just a week. It usually takes centuries for the material to decompose.
Reuse of plastics at the molecular level
In their article published in the journal Nature, the researchers explain that their enzyme, called FAST-PETase, is also able to carry out a circular process in which the plastic is broken down into smaller components (depolymerization) and then chemically reassembled (repolymerization). ). The whole thing happens at a temperature of less than 50 degrees Celsius. “Until now, no one has been able to figure out how to make enzymes that can work efficiently at low temperatures and then make them both portable and affordable on a large industrial scale,” the University of Texas at Austin said in a press release. The new substance catalyzes chemical reactions and thus has the potential to boost large-scale recycling by recovering and reusing plastics at the molecular level. FAST-PETase also shows promise for conducting environmental remediation. The research team is now investigating a number of ways to use this artificial protein to clean up polluted sites.
Five mutations of a natural enzyme
FAST-PETase is a mutated version (consisting of five mutations) of the naturally occurring enzyme PETase, which is also able to break down PET – but not as quickly. FAST-PETase was created using a machine-learning algorithm that selected mutations of the natural enzyme and predicted which mutations would achieve the goal of fast depolymerization at low temperatures. “This work really shows the potential of bringing together different disciplines, from synthetic biology to chemical engineering to artificial intelligence,” said Andrew Ellington, a professor at the Center for Systems and Synthetic Biology, whose team led the development of the machine learning model.
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