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Single-photon smFRET: II. Application to continuous illumination.

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This study introduces BNP-FRET, a Bayesian method to analyze single-molecule kinetics from smFRET data. It accurately determines the number of states and escape rates, even for fast processes.

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Area of Science:

  • Biophysics
  • Computational Biology
  • Chemical Physics

Background:

  • Single-molecule Förster resonance energy transfer (smFRET) is crucial for studying molecular dynamics.
  • Analyzing smFRET kinetics, especially from continuous illumination, presents computational challenges.
  • Understanding protein dynamics, particularly for intrinsically disordered proteins, requires robust kinetic analysis.

Purpose of the Study:

  • To adapt the Bayesian nonparametrics (BNP) framework for analyzing smFRET kinetics under continuous illumination.
  • To develop a computational tool (BNP-FRET) for learning system states and escape rates from smFRET traces.
  • To apply and validate the method for studying intrinsically disordered proteins.

Main Methods:

  • Adaptation of the Bayesian nonparametrics (BNP) framework.
  • Development and application of the BNP-FRET sampler for analyzing single-photon, single-molecule FRET traces.
  • Benchmarking using synthetic and experimental data, including two-color FRET for intrinsically disordered proteins.

Main Results:

  • The BNP-FRET sampler successfully learns escape rates and the number of system states from smFRET data.
  • The method accurately analyzes kinetics for intrinsically disordered proteins under varying conditions.
  • Demonstrated ability to deduce the number of system states even for kinetics occurring at interphoton timescales.

Conclusions:

  • BNP-FRET provides a robust computational approach for kinetic analysis of smFRET data.
  • The method is effective for characterizing complex systems like intrinsically disordered proteins.
  • This framework advances the analysis of single-molecule dynamics, particularly under continuous illumination.