Newly Discovered ‘Encrypted Peptides’ Found in Human Plasma Exhibit Antibiotic Properties

by Melissa Pappas

The antimicrobial peptides the researchers studied are “encrypted” in that they are contained within Apolipoprotein B, a blood plasma protein that is not directly involved in the immune response, but are not normally expressed on their own.

The rise of drug-resistant bacteria infections is one of the world’s most severe global health issues, estimated to cause 10 million deaths annually by the year 2050. Some of the most virulent and antibiotic-resistant bacterial pathogens are the leading cause of life-threatening, hospital-acquired infections, particularly dangerous for immunocompromised and critically ill patients. Traditional and continual synthesis of antibiotics will simply not be able to keep up with bacteria evolution.

To avoid the continual process of synthesizing new antibiotics to target bacteria as they evolve, Penn Engineers have looked at a new, natural resource for antibiotic molecules.

César de la Fuente, Ph.D.

A recent study on the search for encrypted peptides with antimicrobial properties in the human proteome has located naturally occurring antibiotics within our own bodies. By using an algorithm to pinpoint specific sequences in our protein code, a team of Penn researchers along with collaborators, led by César de la Fuente, Presidential Assistant Professor in Psychiatry, Bioengineering, Microbiology, and Chemical and Biomolecular Engineering, and Marcelo Torres, a post doc in de la Fuente’s lab, were able to locate novel peptides, or amino acid chains, that when cleaved, indicated their potential to fend off harmful bacteria.

Now, in a new study published in ACS Nano, the team along with Angela Cesaro, the lead author and post doc in de la Fuente’s lab, have identified three distinct antimicrobial peptides derived from a protein in human plasma and demonstrate their abilities in mouse models. Angela Cesaro performed a great part of the activities during her PhD under the supervision of corresponding author, Professor Angela Arciello, from the University of Naples Federico II. The collaborative study also includes Utrecht University in the Netherlands.

“We identified the cardiovascular system as a hot spot for potential antimicrobials using an algorithmic approach,” says de la Fuente. “Then we looked closer at a specific protein in the plasma.”

Read the full story in Penn Engineering Today.

César de La Fuente Uses AI to Discover Germ-fighting Peptides

César de la Fuente, PhD

The impending danger of bacterial resistance to antibiotics is well-documented within the scientific community. Bacteria are the most efficient evolvers, and their ability to develop tolerance to drugs, in addition to antibiotic overuse and misuse, means that researchers have had to get particularly resourceful to ensure the future of modern medicine.  

Presidential Assistant Professor in Bioengineering, Microbiology, Psychiatry, and Chemical and Biomolecular Engineering César de la Fuente and his team are using an algorithm to search the human genome for microbe-fighting peptides. So far, the team has synthesized roughly 55 peptides that, when tested against popular drug-resistant microbes such as the germ responsible for staph infections, have proven to prevent bacteria from replicating.  

WIRED’s Max G. Levy recently spoke with de la Fuente and postdoctoral researcher and study collaborator Marcelo Torres about the urgency of the team’s work, and why developing these solutions is critical to the survival of civilization as we know it. The team’s algorithm, based on pattern recognition software used to analyze images, makes an otherwise insurmountable feat tangible.  

De la Fuente’s lab specializes in using AI to discover and design new drugs. Rather than making some all-new peptide molecules that fit the bill, they hypothesized that an algorithm could use machine learning to winnow down the huge repository of natural peptide sequences in the human proteome into a select few candidates.

“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.”

Read Max G. Levy’s An AI Finds Superbug-Killing Potential in Human Proteins” at WIRED. 

This story previously appeared in Penn Engineering Today.