Cesar de la Fuente, Presidential Assistant Professor with appointments in the Perelman School of Medicine, School of Engineering and School of Arts & Sciences (Image: Eric Sucar)
In a significant advance against the growing threat of antibiotic-resistant bacteria, researchers have identified a novel class of antimicrobial agents known as encrypted peptides, which may expand the immune system’s arsenal of tools to fight infection. The findings, published in Trends in Biotechnology by Cell Press, reveal that many antimicrobial molecules originate from proteins not traditionally associated with immune responses.
Unlike conventional antibiotics that target specific bacterial processes, these newly discovered peptides disrupt the protective membranes surrounding bacterial cells. By inserting themselves into these membranes—much like breaching a fortress wall—the peptides destabilize and ultimately destroy the bacteria.
“Our findings suggest that these previously overlooked molecules could be key players in the immune system’s response to infection,” says César de la Fuente, presidential assistant professor in bioengineering and in chemical and biomolecular engineering in the School of Engineering and Applied Science, in psychiatry and microbiology in the Perelman School of Medicine, and in chemistry in the School of Arts & Sciences, who led the research team. “This may not only redefine how we understand immunity but also opens up new possibilities for treating drug-resistant infections.”
Penn Engineering and Stanford researchers leveraged AI to discover dozens of potential new antibiotics in the human gut microbiome. (ChrisChrisW via Getty Images)
The average human gut contains roughly 100 trillion microbes, many of which are constantly competing for limited resources. “It’s such a harsh environment,” says César de la Fuente, Presidential Assistant Professor in Bioengineering and in Chemical and Biomolecular Engineering within the School of Engineering and Applied Science, in Psychiatry and Microbiology within the Perelman School of Medicine, and in Chemistry within the School of Arts & Sciences. “You have all these bacteria coexisting, but also fighting each other. Such an environment may foster innovation.”
In that conflict, de la Fuente’s lab sees potential for new antibiotics, which may one day contribute to humanity’s own defensive stockpile against drug-resistant bacteria. After all, if the bacteria in the human gut have to develop new tools in the fight against one another to survive, why not use their own weapons against them?
In a new paper in Cell, the labs of de la Fuente and Ami S. Bhatt, Professor in Medicine (Hematology) and Genetics at Stanford, surveyed the gut microbiomes of nearly 2,000 people, discovering dozens of potential new antibiotics. “We think of biology as an information source,” says de la Fuente. “Everything is just code. And if we can come up with algorithms that can sort through that code, we can dramatically accelerate antibiotic discovery.”
Enamored by the chemical processes of life, Yihui Shen, J. Peter and Geri Skirkanich Assistant Professor of Innovation in Bioengineering, started her research career as a chemist studying the way that proteins fold and the intricate dynamics underlying life processes.
“As an undergraduate, I studied physical chemistry, thinking that one day I’d be addressing challenges in hardcore STEM fields,” she says. “It wasn’t until I observed the dynamics of a single protein molecule that I fell in love with microscopy. I realized that this imaging tool could not only help us observe biological processes on a small scale, but it could also provide new insight at the interface of engineering, chemistry and physics and solve problems on a large scale.”
When Shen turned her attention to microscopy, the field itself was advancing quickly, with improvements being made and new techniques being released every month. Without missing a beat, Shen dove deeper into the most current tools available when she joined Dr. Wei Min’s lab at Columbia University as a doctoral student.
“Professor Wei Min is a pioneer in a new imaging technique called coherent Raman imaging,” says Shen. “In this type of microscopy, we focus light on a very specific point in the cell and measure the amount of scattered light that comes back after exchanging energy with the molecular vibration. This approach allows us to visualize the spatial distribution of different molecules, the very chemistry of life I had studied as an undergraduate, at a high enough resolution to gain insights into biological processes, such as tissue organization, drug distribution and cellular metabolism.”
With this new tool under her belt, Shen was able to ask the kinds of questions that could connect the use of this observation tool to practical applications for real-world challenges.
“I started thinking outside the box,” says Shen. “What if we could observe the chemical exchanges involved in metabolism as they are happening on the scale of a single cell, and then use that insight to pinpoint the exact metabolic pathways and molecules that facilitate tumor growth and disease?”
Cesar de la Fuente (left), Fangping Wan (center), and Marcelo der Torossian Torres (right). Fangping holds a 3D model of a unique ATP synthase fragment, identified by their lab’s deep learning model, APEX, as having potent antibiotic properties.
“Make sure you finish your antibiotics course, even if you start feeling better’ is a medical mantra many hear but ignore,” says Cesar de la Fuente of the University of Pennsylvania.
He explains that this phrase is, however, crucial as noncompliance could hamper the efficacy of a key 20th century discovery, antibiotics. “And in recent decades, this has led to the rise of drug-resistant bacteria, a growing global health crisis causing approximately 4.95 million deaths per year and threatens to make even common infections deadly,” he says.
De la Fuente, a Presidential Assistant Professor, and a team of interdisciplinary researchers have been working on biomedical innovations tackling this looming threat. In a new study, published in Nature Biomedical Engineering, they developed an artificial intelligence tool to mine the vast and largely unexplored biological data—more than 10 million molecules of both modern and extinct organisms— to discover new candidates for antibiotics.
“With traditional methods, it takes around six years to develop new preclinical drug candidates to treat infections and the process is incredibly painstaking and expensive,” de la Fuente says. “Our deep learning approach can dramatically reduce that time, driving down costs as we identified thousands of candidates in just a few hours, and many of them have preclinical potential, as tested in our animal models, signaling a new era in antibiotic discovery.” César de la Fuente holds a 3D model of a unique ATP synthase fragment, identified by his lab’s deep learning model, APEX, as having potent antibiotic properties. This molecular structure, resurrected from ancient genetic data, represents a promising lead in the fight against antibiotic-resistant bacteria.
These latest findings build on methods de la Fuente has been working on since his arrival at Penn in 2019. The team asked a fundamental question: Can machines be used to accelerate antibiotic discovery by mining the world’s biological information? He explains that this idea is based on the notion that biology, at its most basic level, is an information source, which could theoretically be explored with AI to find new useful molecules.
Almost a century ago, the discovery of antibiotics like penicillin revolutionized medicine by harnessing the natural bacteria-killing abilities of microbes. Today, a new study co-led by researchers at the Perelman School of Medicine at the University of Pennsylvania suggests that natural-product antibiotic discovery is about to accelerate into a new era, powered by artificial intelligence (AI).
The study, published in Cell, the researchers used a form of AI called machine learning to search for antibiotics in a vast dataset containing the recorded genomes of tens of thousands of bacteria and other primitive organisms. This unprecedented effort yielded nearly one million potential antibiotic compounds, with dozens showing promising activity in initial tests against disease-causing bacteria.
“AI in antibiotic discovery is now a reality and has significantly accelerated our ability to discover new candidate drugs. What once took years can now be achieved in hours using computers” said study co-senior author César de la Fuente, PhD, a Presidential Assistant Professor in Psychiatry, Microbiology, Chemistry, Chemical and Biomolecular Engineering, and Bioengineering.
Nature has always been a good place to look for new medicines, especially antibiotics. Bacteria, ubiquitous on our planet, have evolved numerous antibacterial defenses, often in the form of short proteins (“peptides”) that can disrupt bacterial cell membranes and other critical structures. While the discovery of penicillin and other natural-product-derived antibiotics revolutionized medicine, the growing threat of antibiotic resistance has underscored the urgent need for new antimicrobial compounds.
In recent years, de la Fuente and colleagues have pioneered AI-powered searches for antimicrobials. They have identified preclinical candidates in the genomes of contemporary humans, extinct Neanderthals and Denisovans, woolly mammoths, and hundreds of other organisms. One of the lab’s primary goals is to mine the world’s biological information for useful molecules, including antibiotics.
The growing threat of antimicrobial resistance demands innovative solutions in drug discovery. Scientists are turning to artificial intelligence (AI) and machine learning (ML) to accelerate the discovery and development of antimicrobial peptides (AMPs). These short strings of amino acids are promising for combating bacterial infections, yet transitioning them into clinical use has been challenging. Leveraging novel AI-driven models, researchers aim to overcome these obstacles, heralding a new era in antimicrobial therapy.
A new article in Nature Reviews Bioengineering illuminates the promises and challenges of using AI for antibiotic discovery. Cesar de la Fuente, Presidential Assistant Professor in Microbiology and Psychiatry in the Perelman School of Medicine, in Bioengineering and Chemical and Biomolecular Engineering in the School of Engineering and Applied Science, and Adjunct Assistant Professor in Chemistry in the School of Arts and Sciences, collaborated with James J. Collins, Termeer Professor of Medical Engineering and Science at MIT, to provide an introduction to this emerging field, outlining both its current limitations and its massive potential.
In the past five years, groundbreaking work in the de la Fuente Lab has dramatically accelerated the discovery of new antibiotics, reducing the timeline from years to mere hours. AI-driven approaches employed in his laboratory have already yielded numerous preclinical candidates, showcasing the transformative potential of AI in antimicrobial research and offering new potential solutions against currently untreatable infections.
Recent advancements in AI and ML are revolutionizing drug discovery by enabling the precise prediction of biomolecular properties and structures. By training ML models on high-quality datasets, researchers can accurately forecast the efficacy, toxicity and other crucial attributes of novel peptides. This predictive power expedites the screening process, identifying promising candidates for further evaluation in a fraction of the time required by conventional methods.
Traditional approaches to AMP development have encountered hurdles such as toxicity and poor stability. AI models help overcome these challenges by designing peptides with enhanced properties, improving stability, efficacy and safety profiles, and fast-tracking the peptides’ clinical application.
While AI-driven drug discovery has made significant strides, challenges remain. The availability of high-quality data is a critical bottleneck, necessitating collaborative efforts to curate comprehensive datasets to train ML models. Furthermore, ensuring the interpretability and transparency of AI-generated results is essential for fostering trust and wider adoption in clinical settings. However, the future is promising, with AI set to revolutionize antimicrobial therapy development and address drug resistance.
Integrating AI and ML into antimicrobial peptide development marks a paradigm shift in drug discovery. By harnessing these cutting-edge technologies, researchers can address longstanding challenges and accelerate the discovery of novel antimicrobial therapies. Continuous innovation in AI-driven approaches is likely to spearhead a new era of precision medicine, augmenting our arsenal against infectious diseases.
The de la Fuente Lab uses use the power of machines to accelerate discoveries in biology and medicine. The lab’s current projects include using AI for antibiotic discovery, molecular de-extinction, reprogramming venom-derived peptides to discover new antibiotics, and developing low-cost diagnostics for bacterial and viral infections. Read more posts featuring de la Fuente’s work in the BE Blog.
The bright white spots represent tiny clusters of proteins detected by CluMPS. (Photo by: Thomas Mumford)
Penn Engineers have pioneered a new way to visualize the smallest protein clusters, skirting the physical limitations of light-powered microscopes and opening new avenues for detecting the proteins implicated in diseases like Alzheimer’s and testing new treatments.
In a paper in Cell Systems, Lukasz Bugaj, Assistant Professor in Bioengineering, describes the creation of CluMPS, or Clusters Magnified by Phase Separation, a molecular tool that activates by forming conspicuous blobs in the presence of target protein clusters as small as just a few nanometers. In essence, CluMPS functions like an on/off switch that responds to the presence of clusters of the protein it is programmed to detect.
Normally, says Bugaj, detecting such clusters requires laborious techniques. “With CluMPS, you don’t need anything beyond the standard lab microscope.” The tool fuses with the target protein to form condensates orders of magnitude larger than the protein clusters themselves that resemble the colorful blobs in a lava lamp. “We think the simplicity of the approach is one of its main benefits,” says Bugaj. “You don’t need specialized skills or equipment to quickly see whether there are small clusters in your cells.”
René Vidal, at the podium, introduces the event “ChatGPT turns one: How is generative AI reshaping science?” Bhuvnesh Jain, left at the table, moderated the discussion with Sudeep Bhatia, Konrad Kording, Andrew Zahrt, and Nick Pangakis.
As a neuroscientist surveying the landscape of generative AI—artificial intelligence capable of generating text, images, or other media—Konrad Kording cites two potential directions forward: One is the “weird future” of political use and manipulation, and the other is the “power tool direction,” where people use ChatGPT to get information as they would use a drill to build furniture.
“I’m not sure which of those two directions we’re going but I think a lot of the AI people are working to move us into the power tool direction,” says Kording, a Penn Integrates Knowledge (PIK) University professor with appointments in the Perelman School of Medicine and School of Engineering and Applied Science. Reflecting on how generative AI is shifting the paradigm of science as a discipline, Kording said he thinks “it will push science as a whole into a much more collaborative direction,” though he has concerns about ChatGPT’s blind spots.
Kording joined three University of Pennsylvania researchers from the chemistry, political science, and psychology departments sharing their perspectives in the recent panel “ChatGPT turns one: How is generative AI reshaping science?” PIK Professor René Vidal opened the event, which was hosted by the School of Arts & Sciences’ Data Driven Discovery Initiative (DDDI), and Bhuvnesh Jain, physics and astronomy professor and co-faculty director of DDDI, moderated the discussion.
“Generative AI is moving so rapidly that even if it’s a snapshot, it will be very interesting for all of us to get that snapshot from these wonderful experts,” Jain said. OpenAI launched ChatGPT, a large language model (LLM)-based chatbot, on Nov. 30, 2022, and it rapidly ascended to ubiquity in news reports, faculty discussions, and research papers. Colin Twomey, interim executive director of DDDI, told Penn Today that it’s an open question as to how it will change the landscape of scientific research, and the` idea of the event was to solicit colleagues’ opinions on interesting directions in their fields.
Konrad Paul Kording is Nathan Francis Mossell University Professor in Bioengineering and Computer and Information Science in Penn Engineering and in Neuroscience in the Perelman School of Medicine.
Daeyeon Lee (left), Oren Friedman (center) and Sergei Vinogradov (right)
Each year, the Nemirovsky Engineering and Medicine Opportunity (NEMO) Prize, funded by Penn Health-Tech, awards $80,000 to a collaborative team of researchers from the University of Pennsylvania’s Perelman School of Medicine and the School of Engineering and Applied Science for early-stage, interdisciplinary ideas.
This year, the NEMO Prize has been awarded to Penn Engineering’s Daeyeon Lee, Russel Pearce and Elizabeth Crimian Heuer Professor in Chemical and Biomolecular Engineering, Oren Friedman, Associate Professor of Clinical Otorhinolaryngology in the Perelman School of Medicine, and Sergei Vinogradov, Professor in the Department of Biochemistry and Biophysics in the Perelman School of Medicine and the Department of Chemistry in the School of Arts & Sciences. Together, they are developing a new therapy that improves the survival and success of soft-tissue grafts used in reconstructive surgery.
More than one million people receive soft-tissue reconstructive surgery for reasons such as tissue trauma, cancer or birth defects. Autologous tissue transplants are those where cells and tissue such as fat, skin or cartilage are moved from one part of a patient’s body to another. As the tissue comes from the patient, there is little risk of transplant rejection. However, nearly one in four autologous transplants fail due to tissue hypoxia, or lack of oxygen. When transplants fail the only corrective option is more surgery. Many techniques have been proposed and even carried out to help oxygenate soft tissue before it is transplanted to avoid failures, but current solutions are time consuming and expensive. Some even have negative side effects. A new therapy to help oxygenate tissue quickly, safely and cost-effectively would not only increase successful outcomes of reconstructive surgery, but could be widely applied to other medical challenges.
The therapy proposed by this year’s NEMO Prize recipients is a conglomerate or polymer of microparticles that can encapsulate oxygen and disperse it in sustainable and controlled doses to specific locations over periods of time up to 72 hours. This gradual release of oxygen into the tissue from the time it is transplanted to the time it functionally reconnects to the body’s vascular system is essential to keeping the tissue alive.
“The microparticle design consists of an oxygenated core encapsulated in a polymer shell that enables the sustained release of oxygen from the particle,” says Lee. “The polymer composition and thickness can be controlled to optimize the release rate, making it adaptable to the needs of the hypoxic tissue.”
These life-saving particles are designed to be integrated into the tissue before transplantation. However, because they exist on the microscale, they can also be applied as a topical cream or injected into tissue after transplantation.
“Because the microparticles are applied directly into tissues topically or by interstitial injection (rather than being administered intravenously), they surpass the need for vascular channels to reach the hypoxic tissue,” says Friedman. “Their micron-scale size combined with their interstitial administration, minimizes the probability of diffusion away from the injury site or uptake into the circulatory system. The polymers we plan to use are FDA approved for sustained-release drug delivery, biocompatible and biodegrade within weeks in the body, presenting minimal risk of side effects.”
The research team is currently testing their technology in fat cells. Fat is an ideal first application because it is minimally invasive as an injectable filler, making it versatile in remodeling scars and healing injury sites. It is also the soft tissue type most prone to hypoxia during transplant surgeries, increasing the urgency for oxygenation therapy in this particular tissue type.
Bella Mirro, a fourth year student in Bioengineering who also minors in Chemistry, spoke with 34th Street Magazine about her many roles at Penn, including being Co–President of Shelter Health Outreach Program (SHOP), a Research Assistant in lab of Michal A. Elovitz, the Hilarie L. Morgan and Mitchell L. Morgan President’s Distinguished Professor in Women’s Health at Penn Medicine, and a Penn Engineering Council Marketing Team Member. In this Q&A, she discusses her research in women’s health and her passions for accessible healthcare, serving Philadelphia’s homeless community, and good food.
