ArticleBiophysical journal2025
Three-color single-molecule maximum likelihood analysis of folding and binding of diffusing molecules.
Article in Biophysical journal, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
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Who cites it
2 citing papers in PubMed.
- Advancements in single-molecule fluorescence spectroscopy for probing conformations, dynamics, and interactions in disordered protein regions.Current opinion in structural biology · 2026Review
- Quantitative analysis methods for free diffusion single-molecule FRET experiments.Current opinion in structural biology · 2025Review
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Authors and funding
4 authors.
Funding
Abstract
Three-color single-molecule Förster resonance energy transfer (FRET) is a valuable tool to study conformational dynamics of macromolecules. In this work, we present a maximum likelihood method for analyzing three-color fluorescence bursts collected from freely diffusing molecules in confocal microscopy. In three-color single-molecule FRET measurements, the third dye with the longest wavelength typically has a much lower quantum yield than the other two dyes, which leads to significantly reduced brightness, particularly for molecular states with high energy transfer to the third dye. This results in biased detection of bursts and inaccurate estimation of kinetic parameters. We extend the previously developed two-color maximum likelihood method (burstML) to the analysis of three-color data, rigorously accounting for burst selection criteria and background noise. The analyses of both experimental and simulated data show that brightness, fractions of acceptor photons related to two-color FRET efficiencies, diffusivity, populations of different molecular states, and transition rates between them can be accurately determined from widely used single-molecule free diffusion experiments without immobilization. BurstML is especially important for the analyses of molecular states with unequal brightness or in the presence of high background noise, outperforming conventional methods that do not explicitly account for burst selection bias and background contributions.
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