bioRxivBiophysicsPreprint
Membrane Lipid Composition and Asymmetry Act as a Switch for Cell-Penetrating Peptide Translocation versus Leakage
Èric Catalina-HernándeziD, Mateo Calle-Velasquez, Mohsen HabibniaiD, Marcel Aguilella‐ArzoiD, Ramon Barnadas‐RodrígueziD, V. LorenziD, Alex Perálvarez‐MaríniD
Abstract
Cell-penetrating peptides (CPPs) can enable intracellular access while avoiding cytotoxicity, yet their behavior is highly sensitive to membrane composition. Here we show that lipid composition and leaflet asymmetry act as a switch that determines whether the amphipathic CPP MAP undergoes non-disruptive translocation or stabilizes membrane pores that drive leakage. Using computational electrophysiology (CompEL) simulations in membranes of increasing physiological relevance, we find that symmetric anionic bilayers favor MAP insertion coupled to transmembrane pore stabilization in POPC:POPG membranes, reproducing progressive dye release in liposome leakage assays. Incorporation of cholesterol reduces the number of inserted peptides yet enhances pore stabilization, consistent with faster leakage kinetics in cholesterol-containing vesicles. In contrast, an asymmetric membrane model mimicking the eukaryotic plasma membrane (POPC outer leaflet; POPC:POPS inner leaflet) supports MAP translocation without sustained membrane disruption. This delivery-relevant mechanism is supported by efficient MAP internalization in HEK293 cells while maintaining high viability. Polarized ATR-FTIR provides structural context, indicating predominantly membrane-associated -helical conformations across lipid compositions. Together, these results establish lipid asymmetry and composition as actionable biointerface parameters that tune CPP function between translocation and leakage and demonstrate an experimentally benchmarked framework for predicting membrane outcomes across complex lipid environments.
- organism: human
- assay: electrophysiology
- research focus: cell type annotation
- research focus: cell signaling & trafficking
- approach: deep learning
- approach: statistical model
- Citations
- 0
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