Diffusion Enhanced Photon Inference DEPI for accurate retrieval of distance distributions in single molecule FRET experiments

by Eitan Lerner

at Condensed Matter Theory Seminar

Wed, 07 Nov 2018, 13:30
Physics building (#54) room 207


Single molecule F rster Resonance Energy Transfer smFRET is gaining ever growing popularity in the study of the structure and structural dynamics of bio macromolecules and their complexes The structural assessment is based on the F rster relation between the efficiency of energy transfer between two dyes and the distance between them smFRET analysis via photon distribution analysis PDA takes into account photon shot noise inter dye distance distribution and interconversion between states to extract accurate distance information Yet inter dye distance fluctuations on the timescale of the fluorescence lifetime or shorter can increase the observed FRET efficiency and thus give the impression of an overall decreased inter dye distance Although the information on diffusion enhancement of FRET could in principle be retrieved from model fitting to fluorescence decays in single molecule fluorescence measurements fluorescence decays are too noisy to be accurately fitted with such complex models Here we introduce a PDA approach dubbed Monte Carlo diffusion enhanced photon inference MC DEPI MC DEPI recolors photons of smFRET measurements taking into account dynamics of inter dye distance fluctuations multiple interconverting states and photoblinking Using this approach we introduce a global fitting approach for retrieving the underlying inter dye distance distribution decoupled from the effects of rapid inter dye distance fluctuations and photoblinking on FRET We show that distance interpretation of smFRET experiments of molecules as simple as doubly labeled dsDNA normally used for calibration of smFRET distance measurements is nontrivial and requires decoupling the effects of rapid inter dye distance fluctuations on FRET in order to avoid systematic biases in the estimation of the inter dye distance distribution

Created on 02-11-2018 by Bar Lev, Yevgeny (ybarlev)
Updaded on 02-11-2018 by Bar Lev, Yevgeny (ybarlev)