Dataset Information

Amphipathic lipid packing sensor motifs: probing bilayer defects with hydrophobic residues.

ABSTRACT: Sensing membrane curvature allows fine-tuning of complex reactions that occur at the surface of membrane-bound organelles. One of the most sensitive membrane curvature sensors, the Amphipathic Lipid Packing Sensor (ALPS) motif, does not seem to recognize the curved surface geometry of membranes per se; rather, it recognizes defects in lipid packing that arise from membrane bending. In a companion paper, we show that these defects can be mimicked by introducing conical lipids in a flat lipid bilayer, in agreement with experimental observations. Here, we use molecular-dynamics (MD) simulations to characterize ALPS binding to such lipid bilayers. The ALPS motif recognizes lipid-packing defects by a conserved mechanism: peptide partitioning is driven by the insertion of hydrophobic residues into large packing defects that are preformed in the bilayer. This insertion induces only minor modifications in the statistical distribution of the free packing defects. ALPS insertion is severely hampered when monounsaturated lipids are replaced by saturated lipids, leading to a decrease in packing defects. We propose that the hypersensitivity of ALPS motifs to lipid packing defects results from the repetitive use of hydrophobic insertions along the monotonous ALPS sequence.

SUBMITTER: Vanni S

PROVIDER: S-EPMC3566459 | biostudies-other | 2013 Feb

REPOSITORIES: biostudies-other

ACCESS DATA

Publications

Amphipathic lipid packing sensor motifs: probing bilayer defects with hydrophobic residues.

Vanni Stefano S Vamparys Lydie L Gautier Romain R Drin Guillaume G Etchebest Catherine C Fuchs Patrick F J PF Antonny Bruno B

Biophysical journal 20130201 3

Sensing membrane curvature allows fine-tuning of complex reactions that occur at the surface of membrane-bound organelles. One of the most sensitive membrane curvature sensors, the Amphipathic Lipid Packing Sensor (ALPS) motif, does not seem to recognize the curved surface geometry of membranes per se; rather, it recognizes defects in lipid packing that arise from membrane bending. In a companion paper, we show that these defects can be mimicked by introducing conical lipids in a flat lipid bila ...[more]

PMID: 23442908

Similar Datasets

Project description:In biological systems, proteins can be attracted to curved or stretched regions of lipid bilayers by sensing hydrophobic defects in the lipid packing on the membrane surface. Here, we present an efficient end-state free energy calculation method to quantify such sensing in molecular dynamics simulations. We illustrate that lipid packing defect sensing can be defined as the difference in mechanical work required to stretch a membrane with and without a peptide bound to the surface. We also demonstrate that a peptide's ability to concurrently induce excess leaflet area (tension) and elastic softening─a property we call the "characteristic area of sensing" (CHAOS)─and lipid packing sensing behavior are in fact two sides of the same coin. In essence, defect sensing displays a peptide's propensity to generate tension. The here-proposed mechanical pathway is equally accurate yet, computationally, about 40 times less costly than the commonly used alchemical pathway (thermodynamic integration), allowing for more feasible free energy calculations in atomistic simulations. This enabled us to directly compare the Martini 2 and 3 coarse-grained and the CHARMM36 atomistic force fields in terms of relative binding free energies for six representative peptides including the curvature sensor ALPS and two antiviral amphipathic helices (AH). We observed that Martini 3 qualitatively reproduces experimental trends while producing substantially lower (relative) binding free energies and shallower membrane insertion depths compared to atomistic simulations. In contrast, Martini 2 tends to overestimate (relative) binding free energies. Finally, we offer a glimpse into how our end-state-based free energy method can enable the inverse design of optimal lipid packing defect sensing peptides when used in conjunction with our recently developed evolutionary molecular dynamics (Evo-MD) method. We argue that these optimized defect sensors─aside from their biomedical and biophysical relevance─can provide valuable targets for the development of lipid force fields.

Dataset Information

Amphipathic lipid packing sensor motifs: probing bilayer defects with hydrophobic residues.

Publications

Amphipathic lipid packing sensor motifs: probing bilayer defects with hydrophobic residues.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets