2020
DOI: 10.1021/acs.jpcb.0c02962
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Sphingomyelin Effects in Caveolin-1 Mediated Membrane Curvature

Abstract: The caveolin-1 (cav-1) protein is an integral component of caveolae and has been reported to colocalize with cholesterol and sphingomyelin-rich curved membrane domains. Here, we analyze the molecular interactions between cav-1 and sphingomyelin containing bilayers using a series of coarse-grain simulations, focusing on lipid clustering and membrane curvature. We considered a palmitoylated-cav-1 construct interacting with phospholipid/cholesterol membranes with asymmetrically distributed sphingomyelin, varying … Show more

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Cited by 15 publications
(16 citation statements)
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“…The lateral and rotational freedom of molecules are increased in less ordered membrane. Supporting this notion, sphingomyelin was shown to be induced in DBT, which could induce the distribution of nanodomains in membrane ( Krishna et al, 2020 ). Second, calycosin can collaborate with other compounds in interacting with their corresponding receptors.…”
Section: Discussionmentioning
confidence: 94%
“…The lateral and rotational freedom of molecules are increased in less ordered membrane. Supporting this notion, sphingomyelin was shown to be induced in DBT, which could induce the distribution of nanodomains in membrane ( Krishna et al, 2020 ). Second, calycosin can collaborate with other compounds in interacting with their corresponding receptors.…”
Section: Discussionmentioning
confidence: 94%
“…8−11 Computational studies furthermore show how specific lipids can effect protein dimerization 12 and modulate protein induced membrane curvature. 13,14 These are supported by experimental investigations of diffusion constraints and peptide-binding in models of mammalian 15 and bacterial 16 membranes.…”
mentioning
confidence: 77%
“…The power of combining experimental and computational assays is exemplified by contributions from various groups on the structural characterization of membranes, e.g., revealing the effect of Ca 2+ on PS-containing membranes, characterizing the order of SM bilayers, and revealing the special roles of plasmalogen lipids and a particular class of glycolipids found in bacterial membranes . In addition, a number of studies address protein–lipid interplay, revealing novel binding sites from in silico predictions, often corroborated or validated with experimental assays. Computational studies furthermore show how specific lipids can effect protein dimerization and modulate protein induced membrane curvature. , These are supported by experimental investigations of diffusion constraints and peptide-binding in models of mammalian and bacterial membranes.…”
mentioning
confidence: 99%
“…The Martini model enables these long simulation timescales and the longest simulation time reported is 320 μs, a cumulative time of 0.2 s with an integrated approach . Although the Martini model is reduced in resolution (due to its coarse-grain nature), its ability to distinguish the chemical and biophysical nature of numerous lipid types and their differential effects have been successfully elucidated. ,, In addition, the Martini model has been used to successfully probe protein–protein interactions, ,,, lipid–lipid interactions, and diverse lipid effects underlying the protein–membrane interactions. , However, the conformational dynamics of the receptor is not well sampled in the Martini model and the effect of these lipid interactions on GPCR conformational dynamics remains unclear.…”
Section: Discussionmentioning
confidence: 99%