Bayesian inference of accurate population sizes and FRET efficiencies from single diffusing biomolecules

Area of Science:

• Biophysics
• Single-molecule spectroscopy
• Biomolecular dynamics

Background:

• Accurate intramolecular distance and population information is crucial for understanding biomolecules in solution using single-molecule Förster resonance energy transfer (smFRET).
• Current smFRET data analysis relies on simplistic burst selection and denoising, which introduce inaccuracies in complex systems.
• Existing methods struggle with low-signal or complex datasets, limiting precise biomolecular analysis.

Purpose of the Study:

• To develop a rigorous, model-based Bayesian inference method for analyzing raw smFRET data.
• To overcome the limitations of traditional threshold-based methods in smFRET data analysis.
• To accurately estimate population sizes and intramolecular distances directly from smFRET datasets.

Main Methods:

• Developed a parametric model for the single-molecule Förster resonance energy transfer (smFRET) process.
• Implemented a Monte Carlo Markov chain (MCMC) algorithm for simultaneous estimation of parameters.
• Analyzed raw smFRET data without intermediate event selection or denoising steps.

Main Results:

• The Bayesian inference method accurately estimates population sizes and intramolecular distances from smFRET data.
• The developed Monte Carlo Markov chain (MCMC) algorithm directly processes raw smFRET data.
• Model-based analysis systematically outperforms traditional threshold-based techniques on simulated and experimental data.

Conclusions:

• The novel Bayesian approach provides a more accurate and robust method for analyzing smFRET data from biomolecules in solution.
• This technique enhances the precision of intramolecular distance and population size determination, particularly for complex systems.
• The model-based inference method offers a significant advancement over existing simplistic analysis techniques in biophysical studies.