“Our main goal is to have a computer design an antibiotic with very minimal human intervention that will be able to enter clinical trials.”
Torres lifted the clear plastic cover off of a petri dish one morning last June. The dish, still warm from its sleepover in the incubator, smelled of rancid broth. Inside it sat a rubbery bed of amber-colored agar, and on that bed lay neat rows of pinpricks—dozens of colonies of drug-resistant bacteria sampled from the skin of a lab mouse.
The team tested 55 of those candidates in tiny vials, and a majority of them eliminated bacteria. Then, Torres tested two of them in lab mice and found that they stopped infections from growing. “The results are compelling,” says Daria Van Tyne, an expert in bacterial evolution at the University of Pittsburgh School of Medicine, who was not involved in the work. “It's certainly opening a new class of antimicrobial peptides, and finding them in an unexpected place.
Governments, philanthropies, and pharma companies have pledged billions of dollars to get new drugs approved by 2030. And the natural world has already inspired new ways to kill drug-resistant germs. In 2019, one genetically-altered virus helped save a teen from a deadly infection. But Torres and de la Fuente turned their attention to somewhere even more natural to us: our own bodies
“We know those patterns—the multiple patterns—that we're looking for,” says de la Fuente. “So that allows us to use the algorithm as a search function.” A few peptides stood out, including SCUB1-SKE25 and SCUB3-MLP22. These peptides live along regions called “CUB domains” that exist in proteins involved in a long list of functions like fertilization, making new blood vessels, and suppressing tumors. The SCUBs are only pieces of the whole. But on their own, they seemed shockingly adept at killing germs. So Torres promoted these two SCUBs to trials in mice.
The team thinks life evolved this way to pack as much of a punch as possible into the genome. “One gene codes for one protein, but that protein has multiple functions,” de la Fuente says. “This is a really, I think, clever way for evolution to just keep the genomic information at a minimum.”
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