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Contact-Based Analysis of Aggregation of Intrinsically Disordered Proteins.

Marek Cieplak1, Łukasz Mioduszewski2, Mateusz Chwastyk2

  • 1Institute of Physics, Polish Academy of Sciences, Warsaw, Poland. mc@ifpan.edu.pl.

Methods in Molecular Biology (Clifton, N.J.)
|February 15, 2022
PubMed
Summary

The number of protein chain association events effectively measures aggregation propensity for intrinsically disordered proteins. This finding holds true across different protein types and temperatures, independent of concentration.

Keywords:
Aggregation of proteinsCoarse-grained modelsContact mapIntrinsically disordered proteinsMolecular dynamicsPolyQProtein tauα-Synuclein

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Area of Science:

  • Biophysics
  • Computational Biology
  • Protein Science

Background:

  • Intrinsically disordered proteins (IDPs) aggregate, a process implicated in various diseases.
  • Understanding IDP aggregation requires accurate modeling approaches.
  • Contact-based descriptions are crucial for simulating protein associations.

Purpose of the Study:

  • To review and analyze contact-based descriptions of IDP aggregation in computational models.
  • To evaluate the utility of association events as a measure of aggregation propensity.
  • To investigate the aggregation behavior of specific IDPs like polyglutamines, polyalanines, alpha-synuclein, and tau protein segments.

Main Methods:

  • Review of coarse-grained and all-atom molecular dynamics simulations.
  • Analysis of protein-protein association events in simulated aggregates.
  • Statistical analysis of association event distributions and their dependence on system parameters.

Main Results:

  • The total number of two-chain association events serves as a reliable indicator of aggregation propensity.
  • This measure aligns with experimental data, such as mass spectrometry.
  • Association event distributions follow a power law related to event duration.
  • The power law exponent is protein and temperature-dependent, but concentration-independent.

Conclusions:

  • Contact-based modeling provides a robust framework for studying IDP aggregation.
  • Quantifying association events offers a valuable metric for predicting aggregation behavior.
  • The observed power-law behavior reveals fundamental aspects of protein association dynamics.