Stochastic diffusion framework determines the free-energy landscape and rate from single-molecule trajectory.

Area of Science:

• Computational Physics
• Biophysics
• Theoretical Chemistry

Background:

• Analyzing single-molecule dynamics requires characterizing position-dependent diffusion coefficients and drift velocities.
• Existing computational tools are often resource-intensive, difficult to implement, and not readily accessible.
• Accurate reconstruction of free-energy landscapes is crucial for understanding molecular dynamics.

Purpose of the Study:

• To develop a simplified, fast, and accessible stochastic diffusion framework for analyzing molecular dynamics.
• To enable the reconstruction of free-energy landscapes and prediction of rates without complex inference methods or sampling bias.
• To provide a computationally efficient tool for researchers studying biological and condensed-phase systems.

Main Methods:

• Developed a theoretical stochastic diffusion framework to analyze single-molecule time traces (Q(t)).
• Reconstructed free-energy landscapes (F(Q)) using calculated diffusion coefficients (D(Q)) and drift velocities.
• Applied the framework to a protein-like lattice model using Monte Carlo simulations and Kramers' theory.

Main Results:

• The framework accurately predicted folding rates in a protein-like model.
• Results showed good agreement when compared with Bayesian analysis and the fep1D algorithm.
• Demonstrated the framework's ability to determine free-energy landscapes and rates from time-series data.

Conclusions:

• The developed stochastic diffusion framework offers a computationally efficient and accessible method for analyzing molecular dynamics.
• It provides a valid approach for reconstructing free-energy landscapes and predicting rates in biological and condensed-phase systems.
• The freely available Python code facilitates broader application and extension of this methodology.