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The DOI matches its Crossref record; 2 of the 2 references it cites were found in a registry or an index.

Mapping Long-Range Interactions in α-Synuclein using Spin-Label NMR and Ensemble Molecular Dynamics Simulations

Dedmon, Matthew M., Lindorff-Larsen, Kresten, Christodoulou, John, Vendruscolo, Michele, Dobson, Christopher M.·2005·Journal of the American Chemical Society

Abstract

The intrinsically disordered protein alpha-synuclein plays a key role in the pathogenesis of Parkinson's disease (PD). We show here that the native state of alpha-synuclein consists of a broad distribution of conformers with an ensemble-averaged hydrodynamic radius significantly smaller than that expected for a random coil structure. This partial condensation is driven by interactions between the highly charged C-terminus and a large hydrophobic central region of the protein sequence. We suggest that this structure could inhibit the formation of alpha-synuclein aggregates, which are thought to be the cytotoxic species responsible for neurodegeneration in PD.

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Metadata

Authors
Dedmon, M. M., Lindorff-Larsen, K., Christodoulou, J., Vendruscolo, M., & Dobson, C. M.
Venue
Journal of the American Chemical Society
Published
2004-12-21
Volume
127
Issue
2
Pages
476-477
ISSN
  • 0002-7863 (ISSN-L)
  • 1520-5126
OpenAlex
W2052660967
Cited by
716
References
2
FWCI
5.90
Type
journal article
Language
en
Open access
closed

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CoChat. (2026). Citation check: Mapping Long-Range Interactions in α-Synuclein using Spin-Label NMR and Ensemble Molecular Dynamics Simulations. https://app.cochat.ai/papers/10.1021/ja044834j
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Dedmon, M. M., Lindorff-Larsen, K., Christodoulou, J., Vendruscolo, M., & Dobson, C. M. (2005). Mapping Long-Range Interactions in α-Synuclein using Spin-Label NMR and Ensemble Molecular Dynamics Simulations. Journal of the American Chemical Society. https://doi.org/10.1021/ja044834j

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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).

Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.