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In-Cell NMR Characterization of the Secondary Structure Populations of a Disordered Conformation of α-Synuclein within E. coli Cells

Overview of attention for article published in PLOS ONE, August 2013
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Title
In-Cell NMR Characterization of the Secondary Structure Populations of a Disordered Conformation of α-Synuclein within E. coli Cells
Published in
PLOS ONE, August 2013
DOI 10.1371/journal.pone.0072286
Pubmed ID
Authors

Christopher A. Waudby, Carlo Camilloni, Anthony W. P. Fitzpatrick, Lisa D. Cabrita, Christopher M. Dobson, Michele Vendruscolo, John Christodoulou

Abstract

α-Synuclein is a small protein strongly implicated in the pathogenesis of Parkinson's disease and related neurodegenerative disorders. We report here the use of in-cell NMR spectroscopy to observe directly the structure and dynamics of this protein within E. coli cells. To improve the accuracy in the measurement of backbone chemical shifts within crowded in-cell NMR spectra, we have developed a deconvolution method to reduce inhomogeneous line broadening within cellular samples. The resulting chemical shift values were then used to evaluate the distribution of secondary structure populations which, in the absence of stable tertiary contacts, are a most effective way to describe the conformational fluctuations of disordered proteins. The results indicate that, at least within the bacterial cytosol, α-synuclein populates a highly dynamic state that, despite the highly crowded environment, has the same characteristics as the disordered monomeric form observed in aqueous solution.

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Geographical breakdown

Country Count As %
France 2 1%
Israel 2 1%
India 1 <1%
Japan 1 <1%
United States 1 <1%
Unknown 130 95%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 34 25%
Student > Master 19 14%
Researcher 18 13%
Professor 13 9%
Student > Bachelor 11 8%
Other 27 20%
Unknown 15 11%
Readers by discipline Count As %
Biochemistry, Genetics and Molecular Biology 38 28%
Chemistry 32 23%
Agricultural and Biological Sciences 32 23%
Engineering 4 3%
Physics and Astronomy 3 2%
Other 7 5%
Unknown 21 15%