Cooperative membrane association unifies mechanistic understanding of synergistic antimicrobial peptide activity

The escalating crisis of antimicrobial resistance necessitates novel therapeutic strategies beyond conventional antibiotics. Antimicrobial peptides (AMPs), key components of innate immunity, offer a promising alternative due to their broad-spectrum activity and reduced susceptibility to resistance mechanisms. While combinations of AMPs are known to act synergistically, achieving enhanced efficacy at lower concentrations, a unifying mechanistic and predictive framework for this synergy has been lacking, hindering rational design and clinical translation.

This review synthesizes the latest theoretical, computational, and experimental advances to present a novel quantitative framework for AMP synergy. Researchers employed chemical-kinetic models to analyze AMP association with bacterial membranes, focusing on intermolecular interactions between different AMP species. Complementary statistical and machine-learning analyses were utilized to identify physicochemical features derived from AMP sequences that characterize synergistic combinations, aiming to establish a predictive model for AMP activity.

The proposed framework posits that AMP synergy originates from cooperative membrane association, where favorable intermolecular interactions between different AMP species accelerate their binding to bacterial membranes, thereby enhancing antibacterial activity. A key finding is the emergence of an effective interaction parameter, ΔE, which quantitatively describes cooperativity and links microscopic interactions to macroscopic synergy metrics, such as minimal inhibitory concentrations (MIC). This approach also rationalizes the enhanced efficacy observed in heterogeneous multi-AMP mixtures and clarifies specific mechanisms like hetero-oligomer formation before membrane binding.

Complementary statistical and machine-learning analyses reveal that synergistic AMP combinations are characterized by physicochemical complementarity rather than similarity, enabling prediction of synergy from sequence-derived features. This comprehensive model provides a foundation for rational design.