Drift-diffusion (DrDiff) framework determines kinetics and thermodynamics of two-state folding trajectory and tunes...

Area of Science:

• Computational Biology
• Biophysics
• Statistical Mechanics

Background:

• Characterizing dynamical properties of simulation data is crucial for understanding biomolecular systems.
• Existing methods for analyzing transition times can be computationally intensive and may require further refinement.

Purpose of the Study:

• To introduce and validate the stochastic drift-diffusion (DrDiff) theory for analyzing simulation data.
• To assess the DrDiff framework's ability to determine kinetic and thermodynamic properties of biomolecular systems, specifically the prion protein (PrP).

Main Methods:

• The DrDiff theory was applied to analyze trajectory time traces, determining coordinate-dependent drift-velocity [v(Q)] and diffusion [D(Q)] coefficients.
• Numerical integration of the Langevin equation was performed to test and tune the DrDiff approach against known parameters.
• Coarse-grained Cα-level simulations using a protein structure-based model (Go¯-model) were employed for prion protein folding/unfolding studies.

Main Results:

• The DrDiff approach successfully recovered inputted drift and diffusion coefficients from numerical simulations.
• DrDiff accurately predicted the prion protein's thermodynamic double-well free-energy profile [F(Q)], folding time [τf(T)], and transition path time [τTP(T)].
• The predicted τf(T) exhibited an "X" shape, and τTP(T) showed a linear shape, consistent with theoretical expectations.

Conclusions:

• The DrDiff theory provides a robust framework for characterizing the dynamical properties of simulation data.
• This method offers a valuable tool for determining kinetic and thermodynamic properties of biomolecular systems, particularly when analyzing time observables.
• The DrDiff framework shows promise for advancing the study of complex biological processes like prion misfolding and aggregation.