In a recent study published in the journal Nature Biomedical Engineering , a group of researchers demonstrated the use of deep learning to resurrect antibiotic peptides from extinct organisms, providing new solutions for antibiotic resistance and other biomedical challenges. Molecular de-extinction of antibiotics from ancient proteomes using deep learning. All available proteomes of extinct organisms were mined by APEX, our deep learning algorithm.
Amino acid sequences ranging from 8 to 50 amino acid residues within proteins from extinct organisms were inputted into multitask deep learning models that trained on both public and in-house peptide data to evaluate the potential antimicrobial activity. The highest-ranked peptides based on predicted antimicrobial activities were then selected and thoroughly characterized against clinically relevant pathogens both in vitro and in animal models. The mechanism of action, physicochemical features, and synergistic interactions of these peptides were also assayed.
The dates report the approximate extinction date or period for the organisms studied. The protein and peptide structures shown in the figure were created with PyMOL Molecular Graphics System, version 2.1 Schrödinger, LLC.
Study: Deep-learning-enabled antibiotic discovery through molecular de-extinction . With antimicrobial-resistant infections causing approximately 1.27 million deaths annually worldwide and projections indicating a potential 10 million annual fatalities by 2.
