Generative latent diffusion language modeling yields anti-infective synthetic peptides
Abstract
Generative artificial intelligence (AI) offers a powerful avenue for peptide design, yet this process remains challenging due to vast sequence space, complex structure-activity relationships, and the need to balance antimicrobial potency with low toxicity. Here, we introduce AMP-Diffusion, a latent diffusion model fine-tuned on antimicrobial peptide (AMP) sequences using embeddings from protein language models (pLMs). AMP-Diffusion enables the rapid discovery of antibiotic candidates by systematically exploring sequence space. We generated 50,000 candidate sequences, filtered and ranked them using our APEX deep learning (DL) model, and synthesized 46 top candidates. These peptides showed broad-spectrum antibacterial activity, including against multidrug-resistant strains, while exhibiting low cytotoxicity. Mechanistic studies revealed membrane permeabilization and depolarization as primary modes of action. In a preclinical mouse model, lead peptides reduced bacterial loads with efficacy comparable to polymyxin B and levofloxacin, with no detectable adverse effects. AMP-Diffusion thus presents a robust platform for designing antibiotics. • AMP-Diffusion designs potent peptides using latent diffusion modeling • 76% of tested peptides kill bacteria with low toxicity • Lead peptides reduce infections in vivo with no adverse effects • Diffusion AI generates diverse, bioactive peptides de novo Antibiotic resistance is accelerating faster than our ability to discover new drugs.…
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