• Plastic-eating enzyme could eliminate bi

    From ScienceDaily@1:317/3 to All on Wednesday, April 27, 2022 22:30:48
    Plastic-eating enzyme could eliminate billions of tons of landfill waste


    Date:
    April 27, 2022
    Source:
    University of Texas at Austin
    Summary:
    An enzyme variant created by engineers and scientists can break
    down environment-throttling plastics that typically take centuries
    to degrade in just a matter of hours to days.



    FULL STORY ==========================================================================
    An enzyme variant created by engineers and scientists at The University
    of Texas at Austin can break down environment-throttling plastics that typically take centuries to degrade in just a matter of hours to days.


    ==========================================================================
    This discovery, published today in Nature, could help solve one of
    the world's most pressing environmental problems: what to do with the
    billions of tons of plastic waste piling up in landfills and polluting
    our natural lands and water.

    The enzyme has the potential to supercharge recycling on a large scale
    that would allow major industries to reduce their environmental impact
    by recovering and reusing plastics at the molecular level.

    "The possibilities are endless across industries to leverage this
    leading-edge recycling process," said Hal Alper, professor in the McKetta Department of Chemical Engineering at UT Austin. "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 project focuses on polyethylene terephthalate
    (PET), a significant polymer found in most consumer packaging, including
    cookie containers, soda bottles, fruit and salad packaging, and certain
    fibers and textiles. It makes up 12% of all global waste.

    The enzyme was able to complete a "circular process" of breaking down the plastic into smaller parts (depolymerization) and then chemically putting
    it back together (repolymerization). In some cases, these plastics can
    be fully broken down to monomers in as little as 24 hours.

    Researchers at the Cockrell School of Engineering and College of Natural Sciences used a machine learning model to generate novel mutations to
    a natural enzyme called PETase that allows bacteria to degrade PET
    plastics. The model predicts which mutations in these enzymes would
    accomplish the goal of quickly depolymerizing post-consumer waste plastic
    at low temperatures.



    ========================================================================== Through this process, which included studying 51 different post-consumer plastic containers, five different polyester fibers and fabrics and
    water bottles all made from PET, the researchers proved the effectiveness
    of the enzyme, which they are calling FAST-PETase (functional, active,
    stable and tolerant PETase).

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

    Recycling is the most obvious way to cut down on plastic waste. But
    globally, less than 10% of all plastic has been recycled. The most common method for disposing of plastic, besides throwing it in a landfill,
    is to burn it, which is costly, energy intensive and spews noxious
    gas into the air. Other alternative industrial processes include very energy-intensive processes of glycolysis, pyrolysis, and/or methanolysis.

    Biological solutions take much less energy. Research on enzymes for
    plastic recycling has advanced during the past 15 years. However, until
    now, no one had been able to figure out how to make enzymes that could
    operate efficiently at low temperatures to make them both portable and affordable at large industrial scale. FAST-PETase can perform the process
    at less than 50 degrees Celsius.

    Up next, the team plans to work on scaling up enzyme production to prepare
    for industrial and environmental application. The researchers have filed
    a patent application for the technology and are eying several different
    uses. Cleaning up landfills and greening high waste-producing industries
    are the most obvious.

    But another key potential use is environmental remediation. The team
    is looking at a number of ways to get the enzymes out into the field to
    clean up polluted sites.

    "When considering environmental cleanup applications, you need an enzyme
    that can work in the environment at ambient temperature. This requirement
    is where our tech has a huge advantage in the future," Alper said.

    Alper, Ellington, associate professor of chemical engineering Nathaniel
    Lynd and Hongyuan Lu, a postdoctoral researcher in Alper's lab, led
    the research.

    Danny Diaz, a member of Ellington's lab, created the machine learning
    model.

    Other team members include from chemical engineering: Natalie Czarnecki, Congzhi Zhu and Wantae Kim; and from molecular biosciences: Daniel Acosta,
    Brad Alexander, Yan Jessie Zhang and Raghav Shroff. The work was funded
    by ExxonMobil's research and engineering division as part of an ongoing research agreement with UT Austin.

    Video: https://youtu.be/jXVSpuclZt4

    ========================================================================== Story Source: Materials provided by University_of_Texas_at_Austin. Note: Content may be edited for style and length.


    ========================================================================== Journal Reference:
    1. Hongyuan Lu, Daniel J. Diaz, Natalie J. Czarnecki, Congzhi Zhu,
    Wantae
    Kim, Raghav Shroff, Daniel J. Acosta, Bradley R. Alexander,
    Hannah O.

    Cole, Yan Zhang, Nathaniel A. Lynd, Andrew D. Ellington, Hal
    S. Alper.

    Machine learning-aided engineering of hydrolases for
    PET depolymerization. Nature, 2022; 604 (7907): 662 DOI:
    10.1038/s41586-022- 04599-z ==========================================================================

    Link to news story: https://www.sciencedaily.com/releases/2022/04/220427115722.htm

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