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Updated: May 8, 2026

Fabricating Multi-Component Lipid Nanotube Networks Using the Gliding Kinesin Motility Assay
Published on: July 26, 2021
Why do arginine and lysine organize lipids differently? Insights from coarse-grained and atomistic simulations
Zhe Wu1, Qiang Cui, Arun Yethiraj
1Department of Chemistry and Theoretical Chemistry Institute, University of Wisconsin-Madison , 1101 University Avenue, Madison, Wisconsin 53706, United States.
Abstract:
An important puzzle in membrane biophysics is the difference in the behaviors of lysine (Lys) and arginine (Arg) based peptides at the membrane. For example, the translocation of poly-Arg is orders of magnitude faster than that of poly-Lys. Recent experimental work suggests that much of the difference can be inferred from the phase behavior of peptide/lipid mixtures. At similar concentrations, mixtures of phosphatidylethanolamine (PE) and phosphatidylserine (PS) lipids display different phases in the presence of these polypeptides, with a bicontinuous phase observed with poly-Arg peptides and an inverted hexagonal phase observed with poly-Lys peptides. Here we show that simulations with the coarse-grained (CG) BMW-MARTINI model reproduce the experimental results. An analysis using atomistic and CG models reveals that electrostatic and glycerol-peptide interactions play a crucial role in determining the phase behavior of peptide-lipid mixtures, with the difference between Arg and Lys arising from the stronger interactions of the former with lipid glycerols. In other words, the multivalent nature of the guanidinium group allows Arg to simultaneously interact with both phosphate and glycerol groups, while Lys engages solely with phosphate; this feature of amino acid/lipid interactions has not been emphasized in previous studies. The Arg peptides colocalize with PS in regions of high negative Gaussian curvature and stabilize the bicontinuous phase. Decreasing the strength of either the electrostatic interactions or the peptide-glycerol interactions causes the inverted hexagonal phase to become more stable. The results highlight the utility of CG models for the investigation of phase behavior but also emphasize the subtlety of the phenomena, with small changes in specific interactions leading to qualitatively different phases.
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