Computer-generated Antibiotics, Biosensor Band-Aids, and the Quest to Beat Antibiotic Resistance

For Penn synthetic biologist César de la Fuente and his team, these concepts aren’t some far-off ideal. They’re projects already in progress, and they have huge real-world implications should they succeed.

Penn Engineering
Dec 13, 2019 · 3 min read
Postdoc Esther Broset pipettes at a bench.
Postdoc Esther Broset pipettes at a bench.
Postdoc Esther Broset is working on a project that will eventually encode therapeutic proteins and peptides into the DNA of specific bacteria called probiotics, the “good” bacteria found in foods like yogurt. Under the right circumstances, the probiotic will then notice the presence of a dangerous pathogen and automatically secrete the peptide treatment to counter it.

By Michele W. Berger

magine if a computer could learn from molecules found in nature and use an algorithm to generate new ones. Then imagine those molecules could get printed and tested in a lab against some of the nastiest, most dangerous bacteria out there — bacteria quickly becoming resistant to our current antibiotic options.

Or consider a bandage that can sense an infection with fewer than 100 bacterial cells present in an open wound. What if that bandage could then send a signal to your phone letting you know an infection had started and asking you to press a button to trigger the release of the treatment therapy it contained?

These ideas aren’t science fiction. They’re projects happening right now, in various stages, in the lab of Penn synthetic biologist César de la Fuente, who joined the University as a Presidential Professor in May 2019. His ultimate goal is to develop the first computer-made antibiotics. But beyond that, his lab — which includes three postdoctoral fellows, a visiting professor, and a handful of graduate students and undergrads — has many other endeavors that sit squarely at the intersection of computer science and microbiology.

Computer-generated antibiotics

Antibiotic resistance is becoming a dangerous problem, both in the United States and worldwide. According to the Centers for Disease Control and Prevention, each year in the U.S., at least 2.8 million people get infections that antibiotics can’t help, and more than 35,000 die from those infections. Around the world, common ailments like pneumonia and food-borne illness are getting harder to treat.

De la Fuente poses near Penn’s “Biopond”
De la Fuente poses near Penn’s “Biopond”
De la Fuente earned his bachelor’s degree in biotechnology, then a doctorate in microbiology and immunology and a postdoc in synthetic biology and computational biology. Combining these fields led him to the innovative work his lab does today.

New antibiotics are needed, and according to de la Fuente, it’s time to look beyond the traditional approach.

“We’ve relied on nature as a source of antibiotics for many, many years. My whole hypothesis is that nature has perhaps run out of inspiration,” says de la Fuente, who has appointments in the Perelman School of Medicine and the School of Engineering and Applied Science. “We haven’t been able to discover any new scaffolds for many years. Can we digitize that information, nature’s chemistry, to be able to create and discover new molecules?”

To do that, his team turned to amino acids, the building blocks of protein molecules. The 20 that occur naturally bond in countless sequences and lengths, then fold to form different proteins. The sequencing possibilities are expansive, more than the number of stars in the universe. “We could never synthesize all of them and just see what happens,” says postdoc Marcelo Melo. “We have to combine the chemical knowledge — decades of chemistry on these tell us how they behave — with the computational side, because a computer can find patterns unlike any human could.”

Using machine learning, the researchers provide the computer with natural molecules that successfully work against bacteria. The computer learns from those examples, then generates new, artificial molecules. “We try this back and forth and hopefully we find patterns, new patterns that we can explore, instead of blindly searching,” Melo says.

The computer can then test each artificial sequence virtually, setting aside the most successful components and tossing the rest, in a form of computational natural selection. Those pieces with the highest potential get used to create new sequences, theoretically producing better and better ones each time.

De la Fuente’s team has seen some promising results already: “A lot of the molecules we’ve synthesized have worked,” he says. “The best ones worked in animal models. They were able to reduce infections in mice — which was pretty cool, given that the computer generated the whole thing.” Still, de la Fuente says the work is years away from producing anything close to a shelf-ready antibiotic.

Continue reading on Penn Today.

Penn Engineering

University of Pennsylvania’s School of Engineering and Applied Science

Penn Engineering

Written by

Penn Engineering

University of Pennsylvania’s School of Engineering and Applied Science

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