Snake, scorpion, and spider venom are most frequently associated with poisonous bites, but with the help of artificial intelligence, they may be able to help fight antibiotic resistance.
RT’s Three Key Takeaways
- Ai-Powered Antibiotic Discovery: Researchers used an Ai system called APEX to screen over 40 million venom-derived peptides, identifying 386 with strong potential as next-generation antibiotics.
- High Success in Lab Testing: Of the 58 synthesized peptides, 53 successfully killed drug-resistant bacteria without harming human red blood cells.
- New Antibacterial Blueprints: The platform uncovered over 2,000 novel antibacterial motifs, offering a rich foundation for developing innovative, venom-based antibiotics.
In a study published in Nature Communications, researchers at the University of Pennsylvania used a deep-learning system called APEX to sift through a database of more than 40 million venom encrypted peptides (VEPs), tiny proteins evolved by animals for attack or as a defense mechanism. In a matter of hours, the algorithm flagged 386 compounds with the molecular hallmarks of next-generation antibiotics.
“Venoms are evolutionary masterpieces, yet their antimicrobial potential has barely been explored,” said senior author César de la Fuente, PhD, a Presidential Associate Professor of Psychiatry, Microbiology, Bioengineering, Chemical and Biomolecular Engineering, and Chemistry. “APEX lets us scan an immense chemical space in just hours and identify peptides with exceptional potential to fight the world’s most stubborn pathogens.”
From the AI-selected shortlist, the team synthesized 58 venom peptides for laboratory testing. 53 killed drug-resistant bacteria—including Escherichia coli and Staphylococcus aureus—at doses that were harmless to human red blood cells.
“By pairing computational triage with traditional lab experimentation, we delivered one of the most comprehensive investigations of venom derived antibiotics to date,” added co-author Marcelo Torres, PhD, a research associate at Penn. Changge Guan, PhD, a postdoctoral researcher in the De la Fuente Lab and co-author, noted that the platform mapped more than 2,000 entirely new antibacterial motifs—short, specific sequences of amino acids within a protein or peptide responsible for their ability to kill or inhibit bacterial growth.
The team is now taking the top peptide candidates which could lead to new antibiotics and improving them through medicinal-chemistry tweaks.
