Analysis of single-molecule FRET trajectories using hidden Markov modeling

Area of Science:

• Biophysics
• Single-molecule biophysics
• Computational biology

Background:

• Single-molecule fluorescence resonance energy transfer (FRET) is crucial for studying molecular dynamics.
• Traditional methods for analyzing FRET trajectories struggle with complex systems and noise.
• Accurate determination of transition rates and states is essential for understanding biological processes.

Purpose of the Study:

• To develop a robust computational method for analyzing complex single-molecule FRET data.
• To accurately determine the number of states, their FRET values, and interconversion rates.
• To provide a more reliable approach for deducing underlying dynamics from FRET trajectories.

Main Methods:

• Developed an analysis scheme casting FRET trajectories as Hidden Markov Processes (HMPs).
• Utilized probability-based methods to identify FRET states and transition rates.
• Employed transition density plots and Bayesian Information Criterion (BIC) to determine the number of states.

Main Results:

• The HMP approach accurately identifies FRET states and their interconversion rates.
• The method successfully determines the number of states and state-to-state transition probabilities.
• Algorithm validated with simulated data and applied to Holliday junction and RecA-DNA binding studies.

Conclusions:

• The Hidden Markov Model approach offers a significant advancement in analyzing complex single-molecule FRET data.
• This method provides a more accurate and reliable way to extract dynamic information from FRET trajectories.
• The algorithm is applicable to various biological systems, including protein-DNA interactions.