A distribution-based method to resolve single-molecule Förster resonance energy transfer observations

Area of Science:

• Biophysics
• Chemical Physics
• Molecular Dynamics

Background:

• Traditional ensemble methods for Förster resonance energy transfer (FRET) analysis assume observable FRET efficiencies, which are unachievable for single molecules.
• Existing single-molecule FRET analysis often relies on assumptions about the number and distribution of underlying FRET states.

Purpose of the Study:

• To develop a new analytical approach for single-molecule FRET data that overcomes limitations of traditional methods.
• To predict distributions of FRET parameters directly from experimental data, enhancing information extraction.
• To provide a more comprehensive understanding of molecular dynamics by analyzing joint parameter distributions.

Main Methods:

• Utilized Bayesian inference to precisely define distributions of FRET rates from single-molecule FRET data.
• Developed a method to predict FRET parameter distributions without prior assumptions on the number or nature of FRET states.
• Benchmarked the new method's response time against traditional averaging techniques.

Main Results:

• The new Bayesian approach significantly increases the information derived from single-molecule FRET data.
• Demonstrated superior response time compared to traditional ensemble averaging methods.
• The method successfully identified multiple dynamical states in a pilot study of calmodulin molecules.

Conclusions:

• The proposed Bayesian method offers a more powerful and flexible framework for single-molecule FRET data analysis.
• This approach provides deeper insights into molecular dynamics, including joint parameter distributions, surpassing conventional FRET analysis.
• The method's ability to reveal complex dynamical behaviors has significant implications for biophysical studies.