Summary
A multidisciplinary ORNL team used expertise in synthetic biology, AI-driven analysis, chemistry, neutrons and materials science to identify new members of a family of enzymes with a natural affinity for degrading synthetic nylon polymers.
Objective
Scientists sought to identify and characterize new enzymes that can be used for a more efficient method of breaking down and reusing the synthetic polymer nylon 66, a product widely used to manufacture lightweight, strong composite materials for applications ranging from vehicles to robotics, aerospace and sporting goods. When nylon 66 is mixed with carbon fiber, for instance, the resulting filament is ideal for additive manufacturing.
The project focused on developing better tools for enzymatic depolymerization, a technique using engineered enzymes—biological catalysts that trigger chemical reactions—to break down nylon 66 in water, requiring less energy and fewer chemical inputs versus traditional methods. The process, called nylon hydrolysis, also breaks fewer bonds in target polymers, making it easier to reassemble the nylon for reuse, reducing waste and providing a new, domestic source of primary materials for the U.S. manufacturing sector.
Approach
Scientists leveraged ORNL capabilities in synthetic biology, AI, high-performance computing, and chemical, materials and neutron sciences to identify, analyze and confirm the function of new nylon 66-selective enzymes. The team started with a set of 95 enzymes, finding that 40% had the natural ability to degrade nylon. They selected the top performers to purify and characterize further, revealing that these best-in-class enzymes had a strong preference for nylon 66.
AI and the lab’s high-performance computing resources were used to determine an initial panel of 95 candidate enzymes. The team performed molecular dynamic simulations to explore their structure, interactions and behavior. Molecular biologists expressed and characterized the enzymes, and analytical chemists leveraged high-throughput sampling and mass spectrometry capabilities to experiment with the catalysts.
Researchers measured the outcome of physical experiments using techniques such as ORNL’s high-throughput rapid droplet/open port sampling interface to confirm the enzymes’ functionality in targeting nylon 66, as well as to measure new chemicals generated as the nylon breaks down. The project used X-ray crystallography to create crystalline structures of the enzymes in order to better map their atomic structure and function, including the identification of active sites that can be augmented to enhance performance.
Results and Impact
Researchers identified and confirmed the function of 38 new enzymes, several of which were found to selectively hydrolyze nylon 66. These enzymes could not only selectively remove nylon 66 from mixed materials, but also generate a product that is easily repolymerized for new uses.
With further enhancement of the enzymes, manufacturers could selectively remove nylon 66 for reuse in a variety of commercial products, or recover valuable materials such as carbon fiber from composites. The discovery paves the way for a cost-effective domestic supply chain for primary materials, benefiting the manufacturing sector and strengthening global competitiveness.
Team Insights
“Artificial intelligence-based modeling of enzyme structure and molecular dynamics simulation gave us insight into the atomic-level interactions that occur in enzymes when they are active, for instance how enzyme molecules may work together to become more stable, control chemical reactions, and target nylon polymers more effectively,” said project co-lead Serena Chen of the Computational Sciences and Engineering Division.
“Biology can do many things that are harder to accomplish with chemistry alone,” said project co-lead Josh Michener of the Biosciences Division. “Biotechnology can make an enormous difference in challenges like materials science. Targeting these selective substates like nylon 66 with enzymes is a great example.”
Publication
Erin E. Drufva, John F. Cahill, Serena H. Chen, Joshua K. Michener, et al. (2025). Identification and characterization of substrate- and product-selective nylon hydrolases. Chem Catalysis, ISSN 2667-1093.
Funding
The research was supported by ORNL’s Laboratory-Directed Research and Development Program.
