Photon-by-Photon Hidden Markov Model Analysis for Microsecond Single-Molecule FRET Kinetics.

Area of Science:

• Biophysics
• Biochemistry
• Computational Biology

Background:

• Biological macromolecules exhibit conformational dynamics crucial for function, spanning microseconds to seconds.
• Allosteric regulation heavily relies on these large-scale motions, necessitating methods to probe fast dynamics.
• Current single-molecule fluorescence analysis methods struggle to extract information at microsecond timescales.

Purpose of the Study:

• To introduce H²MM, a novel maximum likelihood estimation algorithm for photon-by-photon analysis of single-molecule FRET data.
• To enable the study of ultrafast conformational dynamics in biomolecules.
• To overcome limitations in current analysis methods for microsecond timescale dynamics.

Main Methods:

• Development of H²MM, a maximum likelihood estimation algorithm based on the Baum-Welch algorithm.
• Analytical estimation of model parameters for efficient data analysis.
• Application to simulated and experimental single-molecule FRET data.

Main Results:

• H²MM accurately retrieves reaction times from seconds down to microseconds, and even faster.
• The algorithm successfully analyzed experimental FRET data from Holliday junction molecules.
• Demonstrated kinetics as fast as approximately 10⁴ s⁻¹ at low magnesium concentrations.

Conclusions:

• H²MM significantly advances the analysis of single-molecule FRET data, particularly for freely diffusing molecules.
• The algorithm facilitates the study of ultrafast functional dynamics in biomolecules.
• H²MM is readily integrable into existing analysis workflows, broadening accessibility.