Kinetic Ensemble of Tau Protein through the Markov State Model and Deep Learning Analysis

Area of Science:

• Neuroscience
• Biochemistry
• Computational Biology

Background:

• Tau protein aggregation into filaments is a hallmark of Alzheimer's and other neurodegenerative diseases.
• Stabilizing Tau protein offers a therapeutic strategy for tauopathies.

Purpose of the Study:

• To investigate the aggregation mechanisms of the K18 fragment of Tau protein.
• To determine the kinetics of K18 conformational changes using molecular simulations.

Main Methods:

• Molecular dynamics simulations were performed to explore K18 conformations.
• Markov state models and deep learning were used to analyze simulation data.
• Analysis covered approximately 150 microseconds of in silico data.

Main Results:

• All four repeat regions (R1-R4) of K18 are highly dynamic with frequent conformational changes.
• The R2 region exhibits greater flexibility compared to R1, R3, and R4.
• Specific residues in R2-R3 and R3 regions form sheet structures, suggesting a broader role for K18.
• Five key conformational states and microsecond-scale transition rates were identified.

Conclusions:

• This study provides molecular insights into Tau pathological aggregation.
• Findings contribute to developing therapeutic strategies for tauopathies and advancing drug discovery.