Engineering Bacteria-Killing Molecules from Wasp Venom

César de la Fuente, PhD

César de la Fuente a Presidential Assistant Professor in the Perelman School of Medicine’s departments of Psychiatry and Microbiology and Engineering’s department of Bioengineering, has racked up accolades for his innovative, computational approach to discovering new antibiotics.

Now, in his most recent study, de la Fuente has shown how these vital drugs might be derived from wasp venom.

The study, published in The Proceedings of the National Academy of Sciences, involved altering a highly toxic small protein from a common Asian wasp species, Vespula lewisii, the Korean yellow-jacket wasp. The alterations enhanced the molecule’s ability to kill bacterial cells while greatly reducing its ability to harm human cells. In animal models, de la Fuente and his colleagues showed that this family of new antimicrobial molecules made with these alterations could protect mice from otherwise lethal bacterial infections.

There is an urgent need for new drug treatments for bacterial infections, as many circulating bacterial species have developed a resistance to older drugs. The U.S. Centers for Disease Control & Prevention has estimated that each year nearly three million Americans are infected with antibiotic-resistant microbes and more than 35,000 die of them. Globally the problem is even worse: Sepsis, an often-fatal inflammatory syndrome triggered by extensive bacterial infection, is thought to have accounted for about one in five deaths around the world as recently as 2017.

“New antibiotics are urgently needed to treat the ever-increasing number of drug-resistant infections, and venoms are an untapped source of novel potential drugs. We think that venom-derived molecules such as the ones we engineered in this study are going to be a valuable source of new antibiotics,” says de la Fuente.

De la Fuente and his team started with a small protein, or “peptide,” called mastoparan-L, a key ingredient in the venom of Vespula lewisii wasps. Mastoparan-L-containing venom is usually not dangerous to humans in the small doses delivered by wasp stings, but it is quite toxic. It destroys red blood cells, and triggers a type of allergic/inflammatory reaction that in susceptible individuals can lead to a fatal syndrome called anaphylaxis—in which blood pressure drops and breathing becomes difficult or impossible.

Mastoparan-L (mast-L) also is known for its moderate toxicity to bacterial species, making it a potential starting point for engineering new antibiotics. But there are still some unknowns, including how to enhance its anti-bacterial properties, and how to make it safe for humans.

Continue reading at Penn Medicine News.

César de la Fuente on AIChE’s 35 Under 35 List

César de la Fuente, PhD

César de la Fuente, Presidential Assistant Professor in Psychiatry, Microbiology, and Bioengineering, was named one of the American Institute of Chemical Engineers’ (AIChE) 35 members under 35 for 2020.

“The AIChE 35 Under 35 Award was founded to recognize young chemical engineers who have achieved greatness in their fields,” reads the 2020 award announcement. “The winners are a group of driven, engaged, and socially active professionals, representing the breadth and diversity that chemical engineering exemplifies.”

De la Fuente was named in the list’s “Bioengineering” category for his his lab’s work in machine biology. Their goal is to develop computer-made tools and medicines that will combat antibiotic resistance. De la Fuente has already been featured on several other young innovators lists, including MIT Technology Review’s 35 under 35 and GEN’s Top 10 under 40, both in 2019. His research in antibiotic resistance has been profiled in Penn Today and Penn Engineering Today, and he was recently awarded Penn Health-Tech’s inaugural NEMO Prize for his proposal to develop paper-based COVID diagnostic system that could capture viral particles on a person’s breath.

In addition to being named on the 2020 list, the honorees will receive a $500 prize and will be celebrated at the 2020 AIChE Annual Meeting this November.

Learn more about de la Fuente’s pioneering research on his lab website.

Machine Learning Reveals New Antibiotics for Resistant Bacteria

Cesar de la Fuente-Nunez, PhD

Once hailed as medical miracles, antibiotics are losing their effectiveness due to the rapid increase of bacterial immunity.

Researchers are scrambling to keep up with evolution, and they are currently exploring how machine learning can be applied to microbiology to develop more effective treatments.

In the past, researchers have studied bacteria behavior and used their findings to work against the natural patterns of bacterial life. In the 1980s, computer-assisted screening methods helped researchers in their efforts but few developments surfaced from their work. It seemed that there were no new antibiotics to be found using traditional methods, and pharmaceutical companies stepped away from funding antibiotic development in favor of more profitable drugs used to treat chronic conditions. But a new field of research shows a way forward, thanks to the massive advances in computing that have occurred over the intervening decades.

Among the pioneering researchers in this field is César de La Fuente, Presidential Assistant Professor in Psychiatry, Microbiology and Bioengineering. De La Fuente is accelerating the discovery of new antibiotics with his Drug Repurposing Hub, a library of more than 6,000 compounds that is using machine learning algorithms to seek out possible solutions for human disease. With his compound library, de La Fuente is able to examine drugs already approved by the FDA and hunt for new, more effective applications.

In addition to this work, de La Fuente and his colleagues are interested in using machine learning to innovate drug design itself. His lab uses a machine learning platform to generate new molecules in silico and perform experiments on them. Once the results of the experiments come in, they are fed back into the computer so the machine learning platform can continuously learn and improve its findings from the data.

In a recent interview with Katherine Harmon Courage in Quanta Magazine, de La Fuente said:

“The hypothesis is that nature has run out of inspiration in terms of providing us with new antibiotics. That’s why we think that machines … could diversify natural molecules to convert them to synthetic versions that would be much more effective.”

Originally posted on the Penn Engineering blog. Read more about de La Fuente’s work and other researchers exploring the computational design of new antibiotics in Quanta Magazine or The Atlantic.

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

By Michele W. Berger

Imagine 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 synthetic biologist , 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 , 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 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 and the . “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.

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