Food-derived antimicrobial peptides: advances in sources, mechanisms, structure-activity relationships, and AI-assisted design.

The persistent issues of food spoilage caused by microorganisms and the escalating challenge of antimicrobial resistance drive the need for novel, safe, and sustainable preservatives. Food-derived antimicrobial peptides (AMPs) have attracted considerable attention due to their natural origin, multifunctional properties, and low propensity for inducing resistance. This review offers a comprehensive and systematic analysis of food-derived AMPs, encompassing their diverse sources, preparation methods, mechanisms of action, and complex structure-activity relationships. It critically examines how these peptides disrupt microbial membranes, interfere with intracellular functions, modulate immunity, and combat biofilms. Furthermore, the review highlights the transformative role of artificial intelligence (AI) in overcoming the limitations of traditional research and development approaches, detailing AI-driven progress in virtual screening, activity prediction, de novo design, and mechanistic interpretation. Food-derived AMPs thus represent a promising, safe, and sustainable class of preservatives. They act through multiple mechanisms, including membrane disruption, intracellular targeting, immunomodulation, and biofilm inhibition. Their activity is governed by key structural determinants, such as net charge, hydrophobicity, amphipathicity, and specific amino acid residues, which define their structure-activity relationships. The integration of AI significantly accelerates the discovery and rational design of AMPs by deciphering these complex relationships. When combined with experimental methods, AI provides a powerful framework for developing next-generation intelligent preservatives and functional ingredients, thus ultimately enhancing food safety and health.