Fast three-color single-molecule FRET using statistical inference

Abstract

We describe theory, experiments, and analyses of three-color Förster resonance energy transfer (FRET) spectroscopy for probing sub-millisecond conformational dynamics of protein folding and binding of disordered proteins. We devise a scheme that uses single continuouswave laser excitation of the donor instead of alternating excitation of the donor and one of the acceptors. This scheme alleviates photophysical problems of acceptors such as rapid photobleaching, which is crucial for high time resolution experiments with elevated illumination intensity. Our method exploits the molecular species with one of the acceptors absent or photobleached, from which two-color FRET data is collected in the same experiment. We show that three FRET efficiencies and kinetic parameters can be determined without alternating excitation from a global maximum likelihood analysis of two-color and three-color photon trajectories. We implement co-parallelization of CPU-GPU processing, which leads to a significant reduction of the likelihood calculation time for efficient parameter determination.

Extraction of rapid kinetics from smFRET measurements using integrative detectors

“…HMJPs, just like HMMs, do not, however, pertain to the analysis of single-photon arrival in the smFRET, 1 , 9 , 42 , 43 and, for that reason, limitations of the HMM are not intrinsic to single-photon-arrival analysis methods. However, because existing smFRET analysis, starting from single photon arrival, operates within a maximum-likelihood paradigm, it can, in principle, be reframed within a Bayesian framework, 67 such as the HMJP.…”

“…We mention in passing that alternate approaches have been developed to analyze fast kinetics by smFRET, 1 , 9 , 42 , 43 although they have been tied to the analysis of single-photon arrivals. Such methods do not rely on the HMM paradigm because they do not analyze binned data collected by integrative detectors, even though those methods are also limited by the photon detection rate.…”

“…However, all the aforementioned methods, whether in the analysis of single-photon data or binned data, 1 , 3 , 9 , 42 , 43 rely on maximum likelihood, 44 often with assumptions required to render computations analytically tractable (such as assuming there are only transitions between two linearly arranged conformational states). 45 Because of working within a maximum-likelihood approach, only point estimates of the quantities of interest can be directly drawn from the data with additional statistical strategies, such as Fisher information or bootstrapping, used to extract error bars.…”

DeepFRET, a software for rapid and automated single-molecule FRET data classification using deep learning

“…DeepFRET is currently trained and thoroughly tested to operate on 2-color ALEX and non-ALEX smFRET data. Other experimental techniques such as 3- and 4-color FRET ( Stein et al, 2011 ; Yoo et al, 2020 ), protein-induced fluorescence enhancement (PIFE; Hwang and Myong, 2014 ), metal-induced energy transfer (MIET; Chizhik et al, 2014 ) or intraparticle surface energy transfer (i-SET; Zhou et al, 2020 ) could be implemented in the future through additional simulation of training data with subsequent DNN re-training and software optimization (see Materials and methods and data availability for instructions). The simulation of data in DeepFRET is based on the assumption that protein dynamics as observed by smFRET follows a Markov process.…”

A Combined Acceptor Photobleaching and Donor Fluorescence Lifetime Imaging Microscopy Approach to Analyze Multi-Protein Interactions in Living Cells

“…In recent years, however, FRET analysis techniques have been further developed to enable the simultaneous measurement of multiple protein interactions using detection methods with advanced FRET corrections for up to three fluorophores (Galperin et al, 2004;Pauker et al, 2012;Fried et al, 2014) or linear unmixing of spectral components of the multiple fluorophores involved (Sun et al, 2010;Wallrabe et al, 2012;Hoppe et al, 2013;Scott and Hoppe, 2016). In addition to fluorescence microscopy, fluorescence spectroscopy (Lee et al, 2007;Aneja et al, 2008;Chung et al, 2017;Yoo et al, 2020) and flow cytometry (Fábián et al, 2013) have been used to analyze multi-FRET scenarios. So far, most microscopy studies have focused on detecting sensitized emission of the acceptor to study FRET among multiple fluorophores.…”