US scientists have built a high-throughput biological platform that can speed up the search for rare earth-separating materials, a key step in strengthening supply chains for electronics, batteries and magnets.
Researchers at Lawrence Livermore National Laboratory (LLNL) are using naturally occurring bacterial proteins to isolate and study rare earth elements.
These proteins, known as ranmodulins, evolved in microorganisms that use rare earth elements for their metabolism.
The goal is to turn this biological capability into a scalable tool for industrial metal separation.
However, traditional protein screening methods are time-consuming. Scientists typically test candidates one by one, making large-scale discoveries impractical.
A new platform developed at LLNL changes that process by enabling parallel screening of hundreds of protein variants in a single run.
spicy rum type
The method, published in Nature Chemical Biology, is called SpyTag-Catcher Immobilization of Lanmodulin to Assay Metal Binding Selectivity (SpyCI-LAMBS) and has been nicknamed “spicy lamb.”
“This new assay took just one month to collect the equivalent of 600 proteins,” said LLNL scientist and lead author Patrick Dieppe. “A normal process would have taken three to five years.”
This system works by adding artificial tags to proteins, allowing them to automatically bind to solid surfaces, eliminating the need for complex purification steps that typically slow down experiments.
Previous methods required researchers to extract proteins from bacterial mixtures containing thousands of other molecules, a process that severely limited throughput.
“We started by just saying, ‘Let’s go through and test these ranmodulin proteins one by one.’ We got through a handful of them, but we realized it would take us the rest of our lives to effectively characterize them all,” said Dan Park, a scientist at LLNL and senior author.
With the new platform, researchers can avoid that bottleneck entirely.
large protein
Using 96-well plates, scientists can now test dozens of ranmodulin variants against rare earth elements in parallel. Multiple plates can also be run simultaneously, allowing for large-scale mapping of metal bonding behavior.
The research team identified eight different protein clusters with different rare earth selectivity patterns.
One group of more than 200 variants demonstrated improved separation performance for light rare earth elements, a key challenge when purifying critical materials.
Some engineered proteins could complete their separation in a single step, reducing the complexity of the process.
The data generated is also used to train machine learning models that predict how proteins will behave before they are physically tested.
Shift to machine learning
By combining biology and data science, researchers aim to engineer proteins with targeted metal-binding properties, rather than discovering them through trial and error.
“This approach opens the door to predictive, data-driven design of metal-selective proteins by transforming metalloprotein characterization from a low-throughput bottleneck to a scalable data-generating platform,” said LLNL scientist and co-author Yongqin Jiao.
The platform could also be extended beyond rare earth elements, with researchers exploring applications to other important metals.
“What I like about this approach is that it reduces the cost of failure, so we can try some pretty wild ideas,” Park says. “We often throw in all kinds of different designs and concepts, and sometimes they work, and sometimes they don’t, but the important thing is that we have the luxury of testing these ideas now.”
The findings demonstrate how automation, biology, and machine learning are converging to reshape materials discovery at scale.
This research Natural chemical biology.
With over a decade of career in journalism, Neetika Walter has worked with Economic Times, ANI and Hindustan Times covering politics, business, technology and clean energy sectors. Passionate about contemporary culture, books, poetry, and stories, she brings depth and insight to her writing. When she’s not chasing a story, she’s probably engrossed in a book or enjoying the company of her dog.
