Model-free photon analysis of diffusion-based single-molecule FRET experiments

Ivan Terterov (), Daniel Nettels, Tanya Lastiza-Male, Kim Bartels, Christian Löw, Renee Vancraenenbroeck, Itay Carmel, Gabriel Rosenblum and Hagen Hofmann ()
Additional contact information
Ivan Terterov: Weizmann Institute of Science
Daniel Nettels: University of Zurich
Tanya Lastiza-Male: Weizmann Institute of Science
Kim Bartels: Centre for Structural Systems Biology (CSSB) DESY
Christian Löw: Centre for Structural Systems Biology (CSSB) DESY
Renee Vancraenenbroeck: Weizmann Institute of Science
Itay Carmel: Weizmann Institute of Science
Gabriel Rosenblum: Weizmann Institute of Science
Hagen Hofmann: Weizmann Institute of Science

Nature Communications, 2025, vol. 16, issue 1, 1-16

Abstract: Abstract Photon-by-photon analysis tools for diffusion-based single-molecule Förster resonance energy transfer (smFRET) experiments often describe protein dynamics with Markov models. However, FRET efficiencies are only projections of the conformational space such that the measured dynamics can appear non-Markovian. Model-free methods to quantify FRET efficiency fluctuations would be desirable in this case. Here, we present such an approach. We determine FRET efficiency correlation functions free of artifacts from the finite length of photon trajectories or the diffusion of molecules through the confocal volume. We show that these functions capture the dynamics of proteins from nano- to milliseconds both in simulation and experiment, which provides a rigorous validation of current model-based analysis approaches.

Date: 2025
References: Add references at CitEc
Citations:

Downloads: (external link)
https://www.nature.com/articles/s41467-025-60764-8 Abstract (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:nat:natcom:v:16:y:2025:i:1:d:10.1038_s41467-025-60764-8

Ordering information: This journal article can be ordered from
https://www.nature.com/ncomms/

DOI: 10.1038/s41467-025-60764-8

Access Statistics for this article

Nature Communications is currently edited by Nathalie Le Bot, Enda Bergin and Fiona Gillespie

More articles in Nature Communications from Nature
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().