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Analysis and modeling of SDS and DPC micelle SAXS data for membrane protein solution structure characterization
Herein, we present analysis and analytical modeling of Small Angle X-ray Scattering (SAXS) data on two surfactants forming micelles (i.e., sodium dodecyl sulfate and dodecyl phosphocholine) and used for the study in solution of mTSPO, the translocator membrane protein from Mus musculus, as supportin...
Autores principales: | , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Elsevier
2023
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9898585/ https://www.ncbi.nlm.nih.gov/pubmed/36747984 http://dx.doi.org/10.1016/j.dib.2023.108915 |
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author | Pozza, Alexandre Bonneté, Françoise |
author_facet | Pozza, Alexandre Bonneté, Françoise |
author_sort | Pozza, Alexandre |
collection | PubMed |
description | Herein, we present analysis and analytical modeling of Small Angle X-ray Scattering (SAXS) data on two surfactants forming micelles (i.e., sodium dodecyl sulfate and dodecyl phosphocholine) and used for the study in solution of mTSPO, the translocator membrane protein from Mus musculus, as supporting data of the research article published in Biochimie (Combet et al., 2022). For both surfactants, concentration series were measured at two Synchrotron SAXS-beamlines. After reduction, buffer subtraction and water calibration of the data, SAXS curves were normalized to surfactant concentration to highlight possible changes in micelle shape or presence of inter-micellar weak interactions. They were then analyzed in terms of radius of gyration (R(G)), absolute forward intensity (I0) to access the surfactant aggregation number (N(a)) and pair-distance distribution function (P(r)), which gives information on the shape and dimensions of the micelles. Finally, an analytical modeling using SasView - a SAS analysis software package (https://www.sasview.org/) - was performed to describe structural features of the two surfactant micelles at a concentration at which no change in the micelle shape nor weak interactions are observed. A core-shell ellipsoidal model was used to fit the SAXS curves, which provided geometrical parameters of the micelles (equatorial and polar radii, shell thickness) and also scattering length densities (SLD) of both the hydrophobic core and the hydrophilic shell. Hydration of polar heads into the micelle shell could be estimated from micelle volume calculations (V(core) and V(shell)). These parameters are particularly useful when modeling SAXS curves of membrane protein-surfactant complexes as described in Combet et al. (2022). |
format | Online Article Text |
id | pubmed-9898585 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | Elsevier |
record_format | MEDLINE/PubMed |
spelling | pubmed-98985852023-02-05 Analysis and modeling of SDS and DPC micelle SAXS data for membrane protein solution structure characterization Pozza, Alexandre Bonneté, Françoise Data Brief Data Article Herein, we present analysis and analytical modeling of Small Angle X-ray Scattering (SAXS) data on two surfactants forming micelles (i.e., sodium dodecyl sulfate and dodecyl phosphocholine) and used for the study in solution of mTSPO, the translocator membrane protein from Mus musculus, as supporting data of the research article published in Biochimie (Combet et al., 2022). For both surfactants, concentration series were measured at two Synchrotron SAXS-beamlines. After reduction, buffer subtraction and water calibration of the data, SAXS curves were normalized to surfactant concentration to highlight possible changes in micelle shape or presence of inter-micellar weak interactions. They were then analyzed in terms of radius of gyration (R(G)), absolute forward intensity (I0) to access the surfactant aggregation number (N(a)) and pair-distance distribution function (P(r)), which gives information on the shape and dimensions of the micelles. Finally, an analytical modeling using SasView - a SAS analysis software package (https://www.sasview.org/) - was performed to describe structural features of the two surfactant micelles at a concentration at which no change in the micelle shape nor weak interactions are observed. A core-shell ellipsoidal model was used to fit the SAXS curves, which provided geometrical parameters of the micelles (equatorial and polar radii, shell thickness) and also scattering length densities (SLD) of both the hydrophobic core and the hydrophilic shell. Hydration of polar heads into the micelle shell could be estimated from micelle volume calculations (V(core) and V(shell)). These parameters are particularly useful when modeling SAXS curves of membrane protein-surfactant complexes as described in Combet et al. (2022). Elsevier 2023-01-20 /pmc/articles/PMC9898585/ /pubmed/36747984 http://dx.doi.org/10.1016/j.dib.2023.108915 Text en © 2023 The Author(s) https://creativecommons.org/licenses/by-nc-nd/4.0/This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/). |
spellingShingle | Data Article Pozza, Alexandre Bonneté, Françoise Analysis and modeling of SDS and DPC micelle SAXS data for membrane protein solution structure characterization |
title | Analysis and modeling of SDS and DPC micelle SAXS data for membrane protein solution structure characterization |
title_full | Analysis and modeling of SDS and DPC micelle SAXS data for membrane protein solution structure characterization |
title_fullStr | Analysis and modeling of SDS and DPC micelle SAXS data for membrane protein solution structure characterization |
title_full_unstemmed | Analysis and modeling of SDS and DPC micelle SAXS data for membrane protein solution structure characterization |
title_short | Analysis and modeling of SDS and DPC micelle SAXS data for membrane protein solution structure characterization |
title_sort | analysis and modeling of sds and dpc micelle saxs data for membrane protein solution structure characterization |
topic | Data Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9898585/ https://www.ncbi.nlm.nih.gov/pubmed/36747984 http://dx.doi.org/10.1016/j.dib.2023.108915 |
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