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Snake, scorpion, and spider venom are best known for their sting. But with the help of artificial intelligence, they could also help fight antibiotic resistance, which contributes to more than one million deaths worldwide each year.
In a study published in Nature Communications, researchers at Penn’s School of Engineering and Applied Science (SEAS) and Perelman School of Medicine have used a deep-learning system called APEX to sift through a database of more than 40 million venom-encrypted peptides, 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,” says César de la Fuente, a Presidential Associate Professor of psychiatry, microbiology, bioengineering, chemical and biomolecular engineering, and chemistry in the school of Medicine, Engineering and Arts & Sciences. “APEX lets us scan an immense chemical space in just hours and identify molecules with exceptional potential to fight the world’s most stubborn pathogens.”
“By pairing computational triage with traditional lab experimentation, we delivered one of the most comprehensive investigations of venom derived antibiotics to date,” adds co-author Marcelo Der Torossian Torres, a research associate at Penn. Changge Guan, a postdoctoral researcher in the de la Fuente Lab and co-author, notes 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—work that could, if successful, expand the antibiotic toolkit at a time when many drugs are losing their punch.
Read more at Penn Medicine News.
Eric Horvath
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