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<p>Defensins function as critical effectors of innate immunity, displaying broad-spectrum antimicrobial activity against bacteria, fungi, and viruses. While experimental studies have extensively characterized these peptides, the molecular mechanisms governing their membrane interactions remain poorly understood. This investigation employed comprehensive molecular dynamics simulations to analyze six defensins (human and rabbit α-defensins HNP1, HNP3, rabbit NP4 and human β-defensins HBD1, HBD2, HBD3) using the IMM1 implicit membrane model for studying interactions with anionic bacterial membranes. Both monomeric and dimeric forms were examined to elucidate oligomerization effects on membrane binding and penetration mechanisms. Transfer energy calculations revealed distinct binding patterns and orientations among different defensin subtypes, with β-defensins demonstrating superior membrane affinity compared to α-defensins. HBD3 exhibited the most favorable membrane interactions, correlating with its exceptional antimicrobial potency. Binding orientation analysis demonstrated that defensins adopt specific membrane-bound configurations that optimize electrostatic interactions with anionic membrane surfaces while strategically positioning hydrophobic regions for effective membrane insertion. These findings provide molecular level insights into defensin selectivity mechanisms for bacterial membranes and establish a foundation for defensin-based therapeutic development. The results validate that computational approaches effectively complement experimental studies in elucidating complex antimicrobial peptide mechanisms. This modeling framework supports rational design of next generation defensin therapeutics with optimized antimicrobial activity and pathogen selectivity.</p> Graphical abstract <p></p>

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Mechanistic Insights into Human Defensin Antimicrobial Activity from Membrane Simulations

  • 期刊论文

  • 发表日期：

    2026年3月4日

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  • Anna K. Manukyan

摘要

Defensins function as critical effectors of innate immunity, displaying broad-spectrum antimicrobial activity against bacteria, fungi, and viruses. While experimental studies have extensively characterized these peptides, the molecular mechanisms governing their membrane interactions remain poorly understood. This investigation employed comprehensive molecular dynamics simulations to analyze six defensins (human and rabbit α-defensins HNP1, HNP3, rabbit NP4 and human β-defensins HBD1, HBD2, HBD3) using the IMM1 implicit membrane model for studying interactions with anionic bacterial membranes. Both monomeric and dimeric forms were examined to elucidate oligomerization effects on membrane binding and penetration mechanisms. Transfer energy calculations revealed distinct binding patterns and orientations among different defensin subtypes, with β-defensins demonstrating superior membrane affinity compared to α-defensins. HBD3 exhibited the most favorable membrane interactions, correlating with its exceptional antimicrobial potency. Binding orientation analysis demonstrated that defensins adopt specific membrane-bound configurations that optimize electrostatic interactions with anionic membrane surfaces while strategically positioning hydrophobic regions for effective membrane insertion. These findings provide molecular level insights into defensin selectivity mechanisms for bacterial membranes and establish a foundation for defensin-based therapeutic development. The results validate that computational approaches effectively complement experimental studies in elucidating complex antimicrobial peptide mechanisms. This modeling framework supports rational design of next generation defensin therapeutics with optimized antimicrobial activity and pathogen selectivity.

Graphical abstract