The Levinthal Problem in Amyloid Aggregation: Sampling of a Flat Reaction Space

Area of Science:

• Biophysics
• Computational Chemistry
• Neuroscience

Background:

• Amyloid fibril formation is linked to neurodegenerative diseases, but aggregation mechanisms are unclear due to long timescales.
• Previous microscopic theory identified conformational search as the rate-limiting step in fibril elongation.
• Simulating these slow processes requires advanced computational approaches beyond standard atomistic methods.

Purpose of the Study:

• To develop and apply a multiscale computational algorithm for simulating amyloid fibril growth.
• To investigate the fibril growth mechanism and kinetics of the Aβ16-22 peptide and its mutants.
• To validate the computational approach against experimental observations.

Main Methods:

• Developed a multiscale algorithm combining short atomistic simulations with Markov state models (MSMs).
• Computed system diffusion tensor in reaction coordinate space based on analytic theory.
• Generated ensemble aggregation pathways and kinetics from MSM trajectories.

Main Results:

• The multiscale algorithm successfully predicted the relative growth rates of wild-type Aβ16-22 and two single mutants (CHA19, CHA20), aligning with experimental findings.
• The simulation accurately predicted a reduced growth rate for the double mutant (CHA19/CHA20).
• Observed trends in growth rates emerged from the ensemble of MSM trajectories, not individual kinetic parameters.

Conclusions:

• The developed multiscale computational method provides a viable approach to study slow amyloid aggregation kinetics.
• The study elucidates the role of conformational search in fibril elongation kinetics.
• The findings highlight the importance of ensemble dynamics in understanding complex aggregation pathways.