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Prions are best known for their role in rare, fatal neurodegenerative diseases. But a new study by researchers at the University of Pennsylvania suggests that proteins in this family may also conceal molecular fragments that can kill bacteria, including drug-resistant strains.
The Penn scientists used a deep learning platform called APEX 1.1 to scan millions of short protein fragments derived from nearly 3,000 prion and prion-like proteins. The search identified more than a thousand candidate antimicrobial peptides, which they called “prionins.” In tests 59 synthesized prionins inhibited bacterial pathogens, and two reduced Acinetobacter baumannii burden in mice.

The discovery is unexpected because prions are usually discussed in the context of misfolding, aggregation, and brain disease—not immunity or antibiotic discovery. The new findings suggest that useful biological activities may be hidden inside proteins whose known roles have little to do with infection, and that artificial intelligence can help reveal them.
“Prions have long been seen almost entirely through the lens of disease,” said César de la Fuente, PhD, associate professor and director of the Machine Biology Group at the University of Pennsylvania. “Our work shows that when AI looks across biology at scale, even proteins with a dark reputation can contain useful molecular instructions. In this case, those instructions point to possible new antibiotics.”
De la Fuente is senior and corresponding author of the researchers’ published paper in Nature Microbiology, titled “Deep learning reveals antimicrobial peptides within prions.”

Antibiotic resistance is among the most urgent challenges in medicine, and many existing antibiotics were discovered by searching traditional natural sources. The new study takes a different route: instead of asking where antibiotics usually come from, it asks whether biology has hidden antimicrobial molecules in places scientists would not normally look.
Certain amyloid-associated protein sequences may participate in host defense, the authors wrote. “Several amyloid-associated proteins, including amyloid-β and the cellular prion protein, have been reported to display antimicrobial or host-protective activities, raising the possibility that aggregation-prone proteins may encode cryptic antimicrobial fragments within their primary sequence.” But until now, scientists had not systematically searched prion and prion-like proteins at scale to ask whether they broadly encode hidden antimicrobial peptides.
“Whether such encrypted peptides are broadly embedded across prion and prion-like proteins has not been systematically examined,” the researchers continued. The Penn team took on that task, using AI to move from scattered observations to a global search across millions of possible protein fragments. They mined prion-related proteins with APEX1.1, a deep learning platform for antimicrobial peptide (AMP) discovery. “… using deep learning, we screened 19.3 million fragments from 2,897 curated prion-related proteins and identified 1,179 candidate antimicrobial peptides, which we term prionins,” they stated.
To test the predictions, the researchers synthesized 75 prionins and evaluated them against a panel of clinically relevant bacterial pathogens, including multidrug-resistant strains. Fifty-nine inhibited at least one pathogen, and 42 showed potent activity at concentrations of 16 micromolar or lower against at least one pathogen.
The team then examined how the molecules worked. Many active prionins damaged bacterial membranes, a common mechanism used by antimicrobial peptides. Importantly, several candidates also showed early signs of selectivity: hemolysis was rare, and 16 active peptides showed neither measurable hemolysis nor cytotoxicity at the highest concentrations tested.
Two of the strongest candidates were tested in a mouse skin-infection model caused by Acinetobacter baumannii, a difficult-to-treat pathogen. A single topical dose of each peptide significantly lowered the bacterial burden, with effects comparable to the antibiotic polymyxin B in the model tested. The researchers observed no treatment-associated weight loss. In summary, they wrote, “What makes this exciting is that the predictions held up experimentally,” said Marcelo D T Torres, PhD, co-first author of the study. “We went from millions of hidden protein fragments to synthesized molecules that killed bacteria in the lab, and then to candidates that worked in an animal infection model. That is the difference between an AI screen and a true discovery platform.”

The findings build on the de la Fuente lab’s broader effort to mine the biological world for “encrypted peptides”—short, hidden sequences within larger proteins that can have biological functions when isolated. Previous work from the group has searched human proteins, extinct organisms, archaea, microbiomes, and venoms. The prion study expands that concept into one of biology’s most unexpected protein classes.
The study also raises a provocative possibility at the intersection of neurodegeneration and innate immunity. It does not establish that these peptides are naturally released during infection or function physiologically in host defense, they stated. But it suggests that prion and prion-like proteins may contain cryptic antimicrobial sequences, opening a new way to think about prion biology and its possible links to immunity. “… it establishes prion-related proteins as a productive source space for antibiotic discovery and provides a framework for testing whether cryptic peptides contribute to defense in specific biological contexts.”
The researchers emphasize that this is an early discovery, not a new treatment ready for patients. The study does not change the established role of misfolded prions in devastating neurodegenerative disease. Instead, it suggests prion and prion-like proteins as a rich and previously overlooked source space for antibiotic discovery. “Our findings identify prion and prion-like proteins as an unexpectedly rich reservoir of encrypted AMPs,” the authors concluded. “This expands a growing view that antimicrobial activity can be hidden within proteins not canonically annotated as immune effectors and extends that concept to prion biology … These results connect prion-related sequence space to antimicrobial function and highlight unconventional protein classes as sources of antibiotic leads.”
“For a long time, drug discovery has been limited not only by what we can test, but by where we choose to look,” de la Fuente said. “AI is changing that. It gives us a way to search the hidden layers of biology and ask whether molecules associated with one story—in this case, disease—may also carry another story with therapeutic potential.”
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