Research Data Leeds Repository

Dataset associated with 'The effect of mutation on an aggregation-prone protein: An in vivo, in vitro and in silico analysis'.

Guthertz, Nicolas and Van der Kant, Rob and Smith, Hugh and Maya Martinez, Roberto and Xu, Yong and Trinh, Chi H. and Iorga, Bogdan I. and Rousseau, Frederic and Schymkowitz, Joost and Brockwell, David J. and Radford, Sheena E. (2022) Dataset associated with 'The effect of mutation on an aggregation-prone protein: An in vivo, in vitro and in silico analysis'. University of Leeds. [Dataset] https://doi.org/10.5518/1073

Dataset description

Aggregation of initially stably structured proteins is involved in more than 20 human amyloid diseases. Despite intense research, however, how this class of proteins assembles into amyloid fibrils remains poorly understood. We address this question using β2-microglobulin (β2m) as a model system, focusing on D76N-β2m that is involved in hereditary amyloidosis. Here, we identify the residues key to protect β2m from aggregation and we show that residue 76 has a unique ability to drive β2m aggregation in vivo and in vitro.

Keywords:	Amyloid, protein aggregation, β2-microglobulin, aggregation prone region
Subjects:	C000 - Biological sciences > C700 - Molecular biology, biophysics & biochemistry > C790 - Molecular biology, biophysics & biochemistry not elsewhere classified
Divisions:	Faculty of Biological Sciences > School of Biology Faculty of Biological Sciences > School of Molecular and Cellular Biology
Related resources:	Location Type https://doi.org/10.1073/pnas.2200468119 Publication https://eprints.whiterose.ac.uk/186372/ Publication
License:	Creative Commons Attribution 4.0 International (CC BY 4.0)
Date deposited:	24 May 2022 17:03
URI:	https://archive.researchdata.leeds.ac.uk/id/eprint/967
Additional details Additional details Creators: Creators ORCID Other ID Email Primary affiliation Primary affiliation ID Primary affiliation ID type Secondary affiliation Secondary affiliation ID Secondary affiliation ID type Guthertz, Nicolas https://orcid.org/0000-0001-7247-1176 University of Leeds https://ror.org/024mrxd33 ror Van der Kant, Rob https://orcid.org/0000-0003-3639-9749 KU Leuven https://ror.org/05f950310 ror Smith, Hugh https://orcid.org/0000-0003-3049-9810 University of Leeds https://ror.org/024mrxd33 ror Maya Martinez, Roberto https://orcid.org/0000-0002-7371-8051 University of Leeds https://ror.org/024mrxd33 ror Xu, Yong https://orcid.org/0000-0002-8389-4120 University of Leeds https://ror.org/024mrxd33 ror Trinh, Chi H. https://orcid.org/0000-0002-5087-5011 University of Leeds https://ror.org/024mrxd33 ror Iorga, Bogdan I. https://orcid.org/0000-0003-0392-1350 Université Paris-Saclay https://ror.org/03xjwb503 ror Rousseau, Frederic https://orcid.org/0000-0002-9189-7399 KU Leuven https://ror.org/05f950310 ror Schymkowitz, Joost https://orcid.org/0000-0003-2020-0168 KU Leuven https://ror.org/05f950310 ror Brockwell, David J. https://orcid.org/0000-0002-0802-5937 University of Leeds https://ror.org/024mrxd33 ror Radford, Sheena E. https://orcid.org/0000-0002-3079-8039 University of Leeds https://ror.org/024mrxd33 ror Type of data: Dataset Research funders: Wellcome Trust Grant numbers: 204963 Publication date: 2022 Resource language: English Metadata language: English Publisher: University of Leeds Contact Email Address: n.guthertz@leeds.ac.uk Last Modified: 18 Jun 2024 08:44

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Data

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Supplementary Figure 15 data [211kB] Supplementary Figure 15 data [211kB] Supplementary Figure 15 data [211kB] Visible to: Anyone File name: SupFigure_15.zip Content type: Data Mime-Type: application/zip License: Creative Commons Attribution 4.0 International (CC BY 4.0) File size: 211kB