Dataset Information

Evidence for Surface Recognition by a Cholesterol-Recognition Peptide.

ABSTRACT: Two cholesterol recognition/interaction amino-acid consensus peptides, N-acetyl-LWYIKC-amide, and N-acetyl-CLWYIK-amide, have been coupled to exchangeable mimics of Chol (cholesterol) and Phos (1,2-dipalmitoyl-sn-glycerol-3-phospho-(1'rac-glycerol)) via disulfide bond formation. Equilibration between Chol and Phos via thiolate-disulfide interchange reactions has revealed that both peptides favor Chol as a nearest-neighbor in liquid-disordered (ld) bilayers to the same extent. In contrast, no Chol- or Phos-recognition could be detected by these peptides in analogous liquid-ordered (lo) bilayers. Fluorescence measurements of the tryptophan moiety have shown that both peptides favor the membrane-water interface. Taken together, these results provide strong evidence that the recognition behavior of the LWYIK motif is, fundamentally, a surface phenomenon but that partial penetration into the bilayer is also necessary.

SUBMITTER: Mukai M

PROVIDER: S-EPMC4919587 | biostudies-literature | 2016 Jun

REPOSITORIES: biostudies-literature

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Evidence for Surface Recognition by a Cholesterol-Recognition Peptide.

Mukai Masaru M Glover Kerney Jebrell KJ Regen Steven L SL

Biophysical journal 20160606 12

Two cholesterol recognition/interaction amino-acid consensus peptides, N-acetyl-LWYIKC-amide, and N-acetyl-CLWYIK-amide, have been coupled to exchangeable mimics of Chol (cholesterol) and Phos (1,2-dipalmitoyl-sn-glycerol-3-phospho-(1'rac-glycerol)) via disulfide bond formation. Equilibration between Chol and Phos via thiolate-disulfide interchange reactions has revealed that both peptides favor Chol as a nearest-neighbor in liquid-disordered (ld) bilayers to the same extent. In contrast, no Cho ...[more]

PMID: 27283494

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Project description:Membrane lipids play a pivotal role in the pathogenesis of Alzheimer's disease, which is associated with conformational changes, oligomerization and/or aggregation of Alzheimer's beta-amyloid (Abeta) peptides. Yet conflicting data have been reported on the respective effect of cholesterol and glycosphingolipids (GSLs) on the supramolecular assembly of Abeta peptides. The aim of the present study was to unravel the molecular mechanisms by which cholesterol modulates the interaction between Abeta(1-40) and chemically defined GSLs (GalCer, LacCer, GM1, GM3). Using the Langmuir monolayer technique, we show that Abeta(1-40) selectively binds to GSLs containing a 2-OH group in the acyl chain of the ceramide backbone (HFA-GSLs). In contrast, Abeta(1-40) did not interact with GSLs containing a nonhydroxylated fatty acid (NFA-GSLs). Cholesterol inhibited the interaction of Abeta(1-40) with HFA-GSLs, through dilution of the GSL in the monolayer, but rendered the initially inactive NFA-GSLs competent for Abeta(1-40) binding. Both crystallographic data and molecular dynamics simulations suggested that the active conformation of HFA-GSL involves a H-bond network that restricts the orientation of the sugar group of GSLs in a parallel orientation with respect to the membrane. This particular conformation is stabilized by the 2-OH group of the GSL. Correspondingly, the interaction of Abeta(1-40) with HFA-GSLs is strongly inhibited by NaF, an efficient competitor of H-bond formation. For NFA-GSLs, this is the OH group of cholesterol that constrains the glycolipid to adopt the active L-shape conformation compatible with sugar-aromatic CH-pi stacking interactions involving residue Y10 of Abeta(1-40). We conclude that cholesterol can either inhibit or facilitate membrane-Abeta interactions through fine tuning of glycosphingolipid conformation. These data shed some light on the complex molecular interplay between cell surface GSLs, cholesterol and Abeta peptides, and on the influence of this molecular ballet on Abeta-membrane interactions.

Dataset Information

Evidence for Surface Recognition by a Cholesterol-Recognition Peptide.

Publications

Evidence for Surface Recognition by a Cholesterol-Recognition Peptide.

Similar Datasets

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