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A minimal conformational switching-dependent model for amyloid self-assembly.

Srivastav Ranganathan1, Dhiman Ghosh1, Samir K Maji1

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Summary

This study presents a new model for amyloid filament growth, revealing distinct kinetic regimes and predicting a phase diagram. The findings offer insights into amyloid aggregation dynamics and heterogeneity.

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Area of Science:

  • Biophysics
  • Biochemistry
  • Materials Science

Background:

  • Amyloid formation is linked to diseases like Alzheimer's and Parkinson's, characterized by protein conformational changes into beta-sheet rich filaments.
  • Understanding these conformational transitions is crucial for both disease mechanisms and functional amyloid roles.

Purpose of the Study:

  • To develop an analytically solvable model for probing the dynamics of single amyloid filaments.
  • To investigate the kinetic regimes of self-assembling filament growth and their dependence on protein concentration.
  • To provide a physical understanding of amyloid aggregation, including heterogeneity and force generation.

Main Methods:

  • Development of an analytically solvable theoretical model for amyloidogenic proteins.
  • Utilizing Monte Carlo simulations to analyze filament dynamics.
  • Computing filament length fluctuations to assess aggregate heterogeneity.

Main Results:

  • Identification of two kinetic growth regimes: switching-dependent and switching-independent.
  • Observation of saturated fibril elongation velocities at higher concentrations, explaining experimental findings.
  • Prediction of a kinetic phase diagram with three distinct phases: monomers/oligomers, disordered aggregates, and beta-rich filaments.
  • Estimation of conformational switching rates from early growth velocities and length fluctuations.

Conclusions:

  • The model offers a novel explanation for concentration-independent growth velocities in amyloid fibrils.
  • It provides a framework for characterizing aggregate heterogeneity and estimating conformational switching rates.
  • The predicted kinetic phase diagram and force generation potential enhance the physical understanding of amyloid aggregation processes.