New antibiotic candidates for drug-resistant bacteria may\u00a0reside\u00a0inside prions, mis-folded protein in the brain best known for rare and fatal\u00a0degenerative\u00a0brain diseases.\u00a0Prion and prion-like proteins may hide short peptides, named “prionins,”\u00a0that can kill bacteria,\u00a0suggesting\u00a0proteins best known for their role in\u00a0neurodegeneration\u00a0may contain molecular features linked to immune defense, according to new research from the Perelman School of Medicine at the University of Pennsylvania.\u00a0<\/p>\n
From fatal brain disease to antibiotic discovery\u00a0<\/p>\n
The findings,\u00a0published today in\u00a0Nature Microbiology, point to a surprising new place to search for antibiotic candidates at a time when drug-resistant infections are narrowing treatment options. The work also raises a broader biological question: whether proteins most often associated with\u00a0neurodegeneration\u00a0may\u00a0contain\u00a0hidden molecular features connected to innate immunity.\u00a0<\/p>\n
Earlier studies had hinted at this link. Researchers had reported that fragments from\u00a0some\u00a0proteins, including amyloid-beta, which is involved in neurodegenerative diseases like Alzheimer’s disease, and the cellular prion protein,\u00a0including amyloid-beta and the cellular prion protein, could fight microbes. But no one had systematically\u00a0searched\u00a0prion and prion-like proteins at scale for hidden antimicrobial peptides. The Penn team used AI to do that.\u00a0<\/p>\n
AI search reveals a hidden class of antimicrobial peptides\u00a0<\/p>\n
The Penn team used a deep-learning platform called APEX 1.1 to scan 19.3 million short peptide fragments from 2,897 prion and prion-like proteins.\u00a0APEX\u00a0can predict\u00a0the antibiotic activity of a given amino acid sequence,\u00a0identifying\u00a01,179 candidate antimicrobial peptides. The researchers named the new class “prionins.”\u00a0<\/p>\n
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This work changes where we think antibiotics might be hiding. Prions have long been seen\u00a0almost entirely\u00a0through the lens of disease, but AI let us ask a different question: whether these proteins also encode useful molecular fragments. The answer appears to be yes.”\u00a0<\/p>\n
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C\u00e9sar de la Fuente, PhD, FRSB, Presidential Associate Professor and director of the Machine Biology Group at the University of Pennsylvania Perelman School of Medicine and senior author of the study<\/p>\n
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Lab and mouse tests\u00a0validate\u00a0promising candidates\u00a0<\/p>\n
The study team selected 75\u00a0of the most promising\u00a0peptides for experimental testing\u00a0based on how well the platform assessed they would perform against 11 different bacterial pathogens, including drug-resistant strains. Of those, 59 inhibited at least one bacterial pathogen, and 42 showed strong activity at low concentrations, a designation especially important for.\u00a0<\/p>\n
Additional\u00a0experiments suggested that many of the active\u00a0prionins\u00a0work by disrupting bacterial membranes, a common strategy used by antimicrobial peptides. Signs of toxicity were limited, and 16 active peptides showed no measurable harm to red blood cells or human cells at the highest concentrations tested.\u00a0<\/p>\n
To verify these findings, researchers tested\u00a0two of the most promising peptides-one from a fungus and one from a roundworm-in mice. They found that\u00a0the\u00a0approach\u00a0reduced bacteria levels in a standard skin infection model caused by\u00a0Acinetobacter\u00a0baumannii, a difficult-to-treat pathogen. Their effects were comparable to polymyxin B, and researchers saw no treatment-related weight loss.\u00a0<\/p>\n
“This is where the story becomes more than a computer screen,”\u00a0said Marcelo D. T. Torres, co-first author of the study.\u00a0“The AI search gave us\u00a0a short list\u00a0of candidates, but the\u00a0important point\u00a0is that many of those molecules worked in the lab, and two worked in an animal infection model. That is what makes this a discovery platform, not just a prediction exercise.”\u00a0<\/p>\n
A new frontier in antibiotic discovery\u00a0<\/p>\n
The findings build on the de la\u00a0Fuente\u00a0Lab’s\u00a0broader effort\u00a0to mine the biological world for\u00a0“encrypted peptides”\u00a0– short, hidden sequences inside larger proteins that can have biological functions when isolated.\u00a0Previous\u00a0work from the group has\u00a0searched\u00a0human proteins, extinct organisms, archaea, microbiomes, and venoms. The\u00a0prion\u00a0study expands that idea into one of biology’s most unexpected protein classes.\u00a0<\/p>\n
The study also raises an intriguing possibility at the intersection of\u00a0neurodegeneration\u00a0and innate immunity. It does not show that\u00a0prionins\u00a0are naturally released during\u00a0infection\u00a0or\u00a0that prion and\u00a0prion-like proteins normally act as antibiotics in the body. It also does not change what is known about the harmful role of\u00a0misfolded\u00a0prions in neurodegenerative disease. Instead, the work suggests that these proteins may be a rich and previously overlooked source of antibiotic candidates,\u00a0and a new place to ask questions about links between protein aggregation and host defense.\u00a0<\/p>\n
“For a long time, drug discovery has been limited not only by what we can test, but by where we choose to look,”\u00a0de la\u00a0Fuente\u00a0said.\u00a0“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.”\u00a0<\/p>\n
Source:<\/p>\n
University of Pennsylvania School of Medicine<\/a><\/p>\n Journal reference:<\/p>\n