Authors
Sihan Chen, Xiaoyu Wang, Fuyi Li, Octavio L Franco, Jiangning Song, Cesar de la Fuente-Nunez
Published in
Annual review of microbiology. Volume 80. Issue 1. Pages 455-478.
Abstract
Peptides are among the smallest functional biomolecules. Learning to systematically design their activity would revolutionize biology and medicine. Yet achieving this goal remains an open challenge. Recent advances in machine learning enable the discovery and design of peptides at scale, not only yielding new antibiotic agents effective against priority pathogens but also revealing unexpected roles for peptides in host immunity. In this review, we survey how artificial intelligence (AI) methods can be used to mine genomes, proteomes, and even ancient sequence data to uncover "encrypted" peptide antibiotics across the tree of life and over evolutionary time. We highlight emerging approaches for activity prediction, structure-activity modeling, sequence optimization, and de novo peptide generation, and we discuss workflows that tightly couple in silico design with high-throughput experimental validation. We further introduce the concept of "encrypted immunity," in which ordinary proteins encode latent antimicrobial fragments, and we outline translational opportunities as well as remaining challenges on the path to bringing AI-designed peptides to the clinic.
PMID:
42919804
Bibliographic data and abstract were imported from PubMed on 24 Oct 2026.
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