Submitted:
04 November 2026
Posted:
05 November 2026
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Abstract
Antimicrobial peptides (AMPs) are short, mostly cationic and amphipathic molecules that constitute an evolutionarily ancient arm of innate immunity in organisms ranging from bacteria to humans, and they have re-emerged as leading candidates against antimicrobial resistance (AMR). This review integrates the biosynthesis, structure–function relationships, molecular mechanisms, pharmacology and biomedical translation of AMPs. AMPs can be classified according their biosynthesis and structures: Ribosomally synthesized peptides—including classical host-defence peptides, ribosomally synthesized and post-translationally modified peptides (RiPPs), bacteriocins and non-ribosomal peptides assembled by modular megaenzymes (non-ribosomal peptide synthetases -NRPS. Activity is governed less by a fixed sequence than by a tunable balance of cationicity, hydrophobicity and amphipathicity that jointly determine potency and selectivity. Mechanistically, AMPs act through membrane permeabilization (barrel-stave, toroidal-pore and carpet models) and, as is increasingly recognized, through intracellular targets such as lipid II, nucleic acids, the ribosome and chaperones, as well as through host immunomodulation. The principal barrier to systemic use is due to unfavorable pharmacokinetics, proteolytic lability, rapid clearance and serum sequestration. Therefore, extensive peptide engineering (D-amino acids, cyclisation, lipidation, peptidomimetics) and most recently, machine learning and generative artificial intelligence (AI) to navigate an astronomically large sequence space toward multi-objective optima are used to develop new AMPs. Clinically validated AMPs (nisin, daptomycin, polymyxins, gramicidin, bacitracin) cluster where pharmacokinetic constraints are least binding, while antifungal, antiviral, antibiofilm, immunomodulatory, anticancer and antibiotic-resensitizing applications are advancing. With a comparatively high genetic barrier to resistance, AMPs are positioned as a complementary—not a replacement—class within the response to the AMR.
Keywords:
antimicrobial peptides (AMPs)
; peptide antibiotics
; bacteriocins
; antimicrobial resistance
; mechanism of action
; structure–function relationship
; artificial intelligence in peptide design
; peptide engineering
; pharmacokinetics
; pharmacodynamics
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