Authors
Priyanka Banerjee, Iddo Friedberg, Britta Rued, Oliver Eulenstein
Published in
bioRxiv : the preprint server for biology. Feb 23, 2026. Epub Feb 23, 2026.
Abstract
Antibiotic-resistant infections in humans and animals are rising, creating an urgent need for new antimicrobial strategies. This challenge extends beyond clinical settings to food production systems; the Centers for Disease Control and Prevention estimates that foodborne pathogens cause over 48 million illnesses annually in the U.S. alone. Antimicrobial peptides (AMPs) are a promising alternative, with broad activity and lower risk of resistance. However, rational design remains challenging, especially when simultaneously controlling sequence, function, and peptide length.
We introduce Peptide modeLs for Understanding and engineering antiMicrobial therapeutics (PLUM), a structured conditional Variational Autoencoder for controlled AMP generation. PLUM disentangles sequence, function, and length in its latent space, enabling de novo and prototype-conditioned generation of peptides 5-35 amino acids long, allowing capture of larger functional domains. Across 45,000 generated peptides, PLUM achieved the highest AMP yield (0.885, 7% higher than HydrAMP) and increased AMP diversity (14% higher than HydrAMP), while maintaining the highest non-AMP sequence yield 0.895 (19% higher than HydrAMP). For prototype-conditioned generation, PLUM produced 37% more AMPs than HydrAMP, generating sequences that closely matched real peptide compositions with low predicted toxicity. Integrated AMP classifiers enabled robust evaluation of identity and potency across diverse bacteria. These results establish PLUM as a scalable, versatile platform for designing AMPs and next-generation therapeutics.
https://github.com/priyamayur/PLUM.
PMID:
42124643
Bibliographic data and abstract were imported from PubMed on 14 Sep 2026.
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