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Structure-Based Discovery of Small-Molecule Inhibitors of the Autocatalytic Proliferation of α-Synuclein Aggregates

Chia, Sean ; Faidon Brotzakis, Z. ; Horne, Robert I. ; Possenti, Andrea ; Mannini, Benedetta ; Cataldi, Rodrigo ; Nowinska, Magdalena ; Staats, Roxine ; Linse, Sara LU and Knowles, Tuomas P.J. , et al. (2023) In Molecular Pharmaceutics 20(1). p.183-193
Abstract

The presence of amyloid fibrils of α-synuclein is closely associated with Parkinson's disease and related synucleinopathies. It is still very challenging, however, to systematically discover small molecules that prevent the formation of these aberrant aggregates. Here, we describe a structure-based approach to identify small molecules that specifically inhibit the surface-catalyzed secondary nucleation step in the aggregation of α-synuclein by binding to the surface of the amyloid fibrils. The resulting small molecules are screened using a range of kinetic and thermodynamic assays for their ability to bind α-synuclein fibrils and prevent the further generation of α-synuclein oligomers. This study demonstrates that the combination of... (More)

The presence of amyloid fibrils of α-synuclein is closely associated with Parkinson's disease and related synucleinopathies. It is still very challenging, however, to systematically discover small molecules that prevent the formation of these aberrant aggregates. Here, we describe a structure-based approach to identify small molecules that specifically inhibit the surface-catalyzed secondary nucleation step in the aggregation of α-synuclein by binding to the surface of the amyloid fibrils. The resulting small molecules are screened using a range of kinetic and thermodynamic assays for their ability to bind α-synuclein fibrils and prevent the further generation of α-synuclein oligomers. This study demonstrates that the combination of structure-based and kinetic-based drug discovery methods can lead to the identification of small molecules that selectively inhibit the autocatalytic proliferation of α-synuclein aggregates.

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Please use this url to cite or link to this publication:
@article{e8bab957-9e61-46a2-b0f5-832332347990,
  abstract     = {{<p>The presence of amyloid fibrils of α-synuclein is closely associated with Parkinson's disease and related synucleinopathies. It is still very challenging, however, to systematically discover small molecules that prevent the formation of these aberrant aggregates. Here, we describe a structure-based approach to identify small molecules that specifically inhibit the surface-catalyzed secondary nucleation step in the aggregation of α-synuclein by binding to the surface of the amyloid fibrils. The resulting small molecules are screened using a range of kinetic and thermodynamic assays for their ability to bind α-synuclein fibrils and prevent the further generation of α-synuclein oligomers. This study demonstrates that the combination of structure-based and kinetic-based drug discovery methods can lead to the identification of small molecules that selectively inhibit the autocatalytic proliferation of α-synuclein aggregates.</p>}},
  author       = {{Chia, Sean and Faidon Brotzakis, Z. and Horne, Robert I. and Possenti, Andrea and Mannini, Benedetta and Cataldi, Rodrigo and Nowinska, Magdalena and Staats, Roxine and Linse, Sara and Knowles, Tuomas P.J. and Habchi, Johnny and Vendruscolo, Michele}},
  issn         = {{1543-8384}},
  keywords     = {{computational docking; kinetic-based small-molecule discovery; Parkinson's disease, α-synuclein; protein aggregation; structure-based small-molecule discovery}},
  language     = {{eng}},
  month        = {{01}},
  number       = {{1}},
  pages        = {{183--193}},
  publisher    = {{The American Chemical Society (ACS)}},
  series       = {{Molecular Pharmaceutics}},
  title        = {{Structure-Based Discovery of Small-Molecule Inhibitors of the Autocatalytic Proliferation of α-Synuclein Aggregates}},
  url          = {{http://dx.doi.org/10.1021/acs.molpharmaceut.2c00548}},
  doi          = {{10.1021/acs.molpharmaceut.2c00548}},
  volume       = {{20}},
  year         = {{2023}},
}