Food-derived antimicrobial peptides emerge as sustainable preservatives, with AI accelerating their discovery and rational design.

The global challenges of food spoilage and escalating antimicrobial resistance (AMR) necessitate the urgent development of novel, safe, and sustainable preservation strategies. Traditional chemical preservatives often raise consumer concerns, while conventional antibiotics face diminishing efficacy. Food-derived antimicrobial peptides (AMPs) present a compelling alternative due to their natural origin, broad-spectrum activity, and significantly lower propensity for inducing microbial resistance. These peptides act through various mechanisms, making them attractive candidates to address critical gaps in both food safety and public health.

This comprehensive review systematically analyzed existing literature on food-derived antimicrobial peptides (AMPs), synthesizing information across their diverse sources, preparation methods, and mechanisms of action. It critically examined their complex structure-activity relationships (SARs), identifying key structural determinants influencing efficacy. Furthermore, the review highlighted the transformative role of artificial intelligence (AI) in overcoming traditional research and development limitations. The authors integrated findings on AI-driven progress in virtual screening, activity prediction, de novo design, and mechanistic interpretation to provide a holistic view of AMPs' potential as next-generation intelligent preservatives and functional ingredients.

The review elucidated that food-derived AMPs exert their antimicrobial effects through multiple mechanisms, including direct membrane disruption, interference with intracellular functions, immunomodulation, and biofilm inhibition. Their activity is critically governed by structural determinants such as net charge, hydrophobicity, amphipathicity, and the presence of specific amino acid residues, which collectively define their SARs. A significant finding was the accelerating impact of artificial intelligence (AI) in the AMP discovery pipeline. AI tools facilitate rapid virtual screening of vast peptide libraries, accurately predicting activity, and enabling de novo design of novel peptides with enhanced properties. AI also aids in deciphering complex mechanistic interpretations of AMP action, streamlining the development process.

Food-derived AMPs represent a promising, safe, and sustainable class of preservatives, acting through multiple mechanisms and offering a low propensity for inducing resistance, with AI significantly accelerating their discovery and rational design.