01875nas a2200181   4500008004100000022001400041245010400055210006900159260000800228300001700236490000700253520121700260100002701477700002101504700002401525700002001549856012401569       2026                        eng d  a1359-734500aMechanistic principles of antimicrobial peptides uncovered by charge density-based machine learning0 aMechanistic principles of antimicrobial peptides uncovered by ch  cFEB  aPMID 96108380 v623 a<p>
	Antimicrobial peptides (AMPs) are emerging as potent alternatives to conventional antibiotics, yet their diverse nature due to divergent mechanisms of action hinders rational design. Here, we present an electrostatics-stratified computational framework that uncovers key physicochemical principles governing AMP activity. Experimentally validated peptides were grouped by average charge per residue (i.e., the charge/length of the peptide) and analyzed through integrated sequence-, structure-, and chemistry-based descriptors. Distinct molecular signatures emerged across electrostatic regimes: low-charge/length peptides rely on amphipathic organization via structural compactness, whereas the intermediate-charge/length peptides exhibit balanced hydrophobicity and electrostatics. The high-charge peptides couple strong cationic attraction with lipophilicity and tryptophan anchoring to mainly disrupt membranes. Interestingly, hydrophobic moment, which is a measure of the amphipathicity, is found to be important in all three classes of AMPs. This study identifies distinguishing features of AMP sub-groups and suggests design guidelines for developing selective and potent next-generation AMPs.</p>
1 aMalshikare, Hrushikesh1 aPriyakumar, Deva1 aChatterjee, Prathit1 aSengupta, Durba  uhttps://library.ncl.res.in/content/mechanistic-principles-antimicrobial-peptides-uncovered-charge-density-based-machine