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Synuclein Structure and Function in Parkinson’s Disease

  • David Eliezer
Chapter
Part of the Focus on Structural Biology book series (FOSB, volume 7)

Abstract

The protein alpha-synuclein is implicated in the etiology of both sporadic and hereditary Parkinson’s disease. Structural studies of both the intrinsically disordered free state of the protein and of more ordered states, adopted when alpha-synuclein self assembles into fibrils or binds to lipid membranes or detergent micelles, have begun to provide insights into factors that likely influence both the pathological aggregation of the protein and its normal functions. Residual secondary structure and transient long-range interactions within the free state can be detected and may influence alpha-synuclein aggregation pathways. Structure within the amyloid fibril form of alpha-synuclein can also provide clues regarding the assembly pathways of the protein. Alpha-synuclein folds upon binding to lipid membranes and the experimentally determined topology of the bound protein likely mediates its physiological functions. The influence of disease-linked mutations on the structural properties of the free, fibrillar, and bound states has also been evaluated in order to examine the basis for altered aggregation kinetics and possible functional impairments of the mutant proteins. Comparative structural studies of the other human synuclein family members, β-synuclein and γ-synuclein have also been performed to clarify features that differentiate them from alpha-synuclein in both pathological and functional contexts. This chapter provides an up to date review of structural studies of the human synuclein family and of the implications of these studies for understanding synuclein pathways in biology and disease.

Keywords

Electron Spin Resonance Synaptic Vesicle Residual Dipolar Coupling Electron Spin Resonance Data Paramagnetic Relaxation Enhancement 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer Science+Business Media B.V. 2009

Authors and Affiliations

  • David Eliezer
    • 1
  1. 1.Weill Cornell Medical CollegeNew YorkUSA

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