Understanding how biomolecules couple structural dynamics with function is at the heart of several disciplines and remains an outstanding goal in biology. Linking conformational states and their transitions to biochemical function requires the ability to precisely resolve the structure and dynamics of a biological system, which is often altered upon ligand binding or influenced by the chemical and physical properties of its environment. The most well-established structural biology tools have provided high-resolution ‘snapshots’ of states in a crystallized or frozen form (e.g., X-ray crystallography and single-particle cryo-electron microscopy, cryoEM) or an ensemble average of all contributing conformations (e.g., nuclear magnetic resonance, NMR; small-angle X-ray scattering, SAXS; small-angle neutron scattering, SANS; double electron-electron resonance, DEER; cross-linking mass spectrometry, XL-MS; ensemble-FRET). In recent years, further developments have enabled these conventional structural tools to detect conformational dynamics and reaction intermediates. For example, NMR techniques (Anthis and Clore, 2015; Clore and Iwahara, 2009; Palmer, 2004; Ravera et al., 2014; Sekhar and Kay, 2019) and electron paramagnetic resonance techniques (Jeschke, 2018; Jeschke, 2012; Krstić et al., 2011) have been advanced to study conformational dynamics and capture transient intermediates. Time-resolved crystallographic investigations have been employed to resolve functionally relevant structural displacements associated with a biological function (Kupitz et al., 2014; Moffat, 2001; Schlichting et al., 1990; Schlichting and Chu, 2000; Schotte et al., 2003). Advances in microfluidic mixing and spraying devices have enabled time-resolved cryoEM (Feng et al., 2017; Kaledhonkar et al., 2018) and cross-linking mass spectrometry (XL-MS or CL-MS) (Braitbard et al., 2019; Brodie et al., 2019; Chen et al., 2020; Iacobucci et al., 2019; Murakami et al., 2013; Slavin and Kalisman, 2018). Progress in computational methods has also afforded novel tools for examining biomolecular structure and dynamics. Each of these advances highlights an increased awareness that one needs to directly and continuously track the dynamical properties of individual biomolecules in order to understand their function and regulation.

In this context, FRET (referred to as fluorescence resonance energy transfer or Förster resonance energy transfer [Braslavsky et al., 2008]) studies at the ensemble and single-molecule levels have emerged as important tools for measuring structural dynamics over at least 12 orders of magnitude in time and mapping the conformational and functional heterogeneities of biomolecules under ambient conditions. FRET studies probing fluorescence decays at the ensemble level (Grinvald et al., 1972; Haas et al., 1975; Haas and Steinberg, 1984; Hochstrasser et al., 1992) (time-resolved FRET) permitted already in the early 1970s the study of structural heterogeneities on timescales longer than the fluorescence lifetime (a few ns). This approach is still used nowadays (Becker, 2019; Orevi et al., 2014; Peulen et al., 2017) and has been transferred to single-molecule studies. The ability to measure FRET in single molecules (Deniz et al., 1999; Ha et al., 1996; Lerner et al., 2018a) has made the method even more appealing. The single-molecule FRET (smFRET) approach has been extensively used to study conformational dynamics and biomolecular interactions under steady-state conditions (Dupuis et al., 2014; Larsen et al., 2019; Lerner et al., 2018a; Lipman et al., 2003; Margittai et al., 2003; Mazal and Haran, 2019; Michalet et al., 2006; Orevi et al., 2014; Ray et al., 2019; Sasmal et al., 2016; Schuler et al., 2005; Schuler et al., 2002; Steiner et al., 2008; Zhuang et al., 2000). It is notable that, in many mechanistic studies, it suffices to use FRET for distinguishing different conformations and determining kinetic rates such that absolute FRET efficiencies and thereby distances do not need to be determined. However, the ability to measure accurate distances and kinetics with smFRET has led to its emergence as an important tool in this new era of ‘dynamic structural biology’ for mapping biomolecular heterogeneities and for measuring structural dynamics over a wide range of timescales (Lerner et al., 2018a; Mazal and Haran, 2019; Sanabria et al., 2020; Schuler and Hofmann, 2013; Weiss, 1999).

Single-molecule FRET (smFRET) approaches have many advantages as a structural biology method, including:

• sensitivity to macro-molecular distances (2.5–10 nm),

• the ability to resolve structural and dynamic heterogeneities,

• high-quality measurements with low sample consumption of the molecules of interest (low concentrations and low volumes), as the sample is analyzed one molecule at a time,

• determination of structural transitions in equilibrium, hence without the need for synchronization,

• the ability to detect (very) rare events. Indeed, in biology, the most interesting molecules to study are often the sparse, functionally active ones amidst a sea of inactive molecules,

• high sensitivity and specificity for labeled molecules. As only the labeled molecule uniquely contributes to the detected signal, these tracers can also be applied as FRET-reporters in crowded environments (Dupuis et al., 2014; Soranno et al., 2014; Zosel et al., 2020b) (hence smFRET can be used to validate results determined in isolation or detect the modulation of conformational preferences and/or structural dynamics through so-called quinary interactions [Guin and Gruebele, 2019]), and

• high specificity for residues/domains via specific labeling. Biomolecules can be specifically labeled by a unique dye pair enabling smFRET measurements to be applicable on all sizes of molecules, including large complex assemblies (see Figure 1 [Kilic et al., 2018]), active biological machines (e.g., the ribosomes) (Dunkle et al., 2011) and even on whole native virions (Lu et al., 2019; Munro et al., 2014).

Figure 1

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Several methods have been utilized to determine structural ensembles such as NMR, single-particle cryoEM or XL-MS, and, recently, also smFRET in an integrative/hybrid (I/H) approach with computational modeling to overcome the sparsity of experimental data with respect to an atomistic description (Berman et al., 2019; de Souza and Picotti, 2020; Dimura et al., 2020; Gauto et al., 2019; Koukos and Bonvin, 2020; Na and Paek, 2020; Tang and Gong, 2020; Webb et al., 2018). I/H structural models derived from smFRET experiments using inter-dye distances as restraints were reported for flexible folded proteins (Brunger et al., 2011; Hellenkamp et al., 2017; Margittai et al., 2003; McCann et al., 2012), conformational ensembles of disordered/unstructured and unfolded proteins (Borgia et al., 2018; Holmstrom et al., 2018; Schuler et al., 2020), nucleic acids and protein-nucleic acid complexes (Craggs et al., 2019; Craggs and Kapanidis, 2012; Kalinin et al., 2012; Lerner et al., 2018b; Muschielok et al., 2008; Wozniak et al., 2008).

A further unique aspect of smFRET studies is that structural, kinetic, and spectroscopic information on large and complex systems can be recorded simultaneously in a single measurement. This facilitates linking dynamic and structural information in an integrative approach to (Figure 1A) (Hellenkamp et al., 2017; Kilic et al., 2018; Li et al., 2020b; Sanabria et al., 2020; Wasserman et al., 2016; Yanez Orozco et al., 2018):

• define the number of possible structures consistent with data,

• potentially reduce the ambiguity between different structural models compatible with the experimental data, and

• reveal the dynamic exchange pathways that are structurally allowed.

As an example, Figure 1B shows the outcome of a multimodal smFRET study on the conformational landscape of a 12-mer chromatin array (~2.5 MDa) (Kilic et al., 2018) with dynamics occurring on timescales from nanoseconds to hours. SmFRET experiments could detect the flexible chromatin conformations (Figure 1B, middle panel), revealing their dynamic structural heterogeneity (Figure 1B, bottom panel), in contrast to the well-ordered static structures of chromatin fibers (Figure 1B, top panel). These flexible, partially-open and open conformations that are quite abundant in solution (population of >70%; Figure 1B, bottom panel) were not resolved before, although they are essential for proper gene organization and function. They represent the central interconversion hub for the distinct stacking registers of chromatin and are difficult to detect with other structural techniques. This approach of visualizing biomolecules in action under ambient conditions emphasizes the importance of their dynamic nature by resolving transitions between various conformational states, which, in many cases, promotes function (Aviram et al., 2018; Henzler-Wildman et al., 2007; Iljina et al., 2020; Lerner et al., 2018b; Sanabria et al., 2020; Tassis et al., 2020).

SmFRET measurements are typically performed using two approaches: with surface-immobilized molecules using total internal reflection fluorescence microscopy (TIRFM) and camera-based detection, or with freely diffusing molecules in solution using confocal microscopy and point detectors. Experimental systems are available commercially but are typically home-built. Samples are prepared and the data collected using lab-specific protocols, where data are stored in a variety of file formats and analyzed using an array of increasingly powerful software. For the field in general and for structural studies in particular, it is important to demonstrate that smFRET, as a method, is reproducible and reliable regardless of where and how the sample is measured. To this end, in an effort led by Thorsten Hugel, twenty laboratories joined in measuring smFRET on several dsDNA constructs (Hellenkamp et al., 2018a). Studying six distinct samples with different dyes and varying inter-dye distances, the mean FRET efficiencies obtained by the participating labs exhibited a surprisingly high degree of agreement (a ${\displaystyle \mathrm{\Delta }E}$ between 0.02 and 0.05 depending on the details of the sample). The quantitative assessment and reproducibility of the intensity-based smFRET measurements and discussions about data analysis was an important milestone. These dsDNA FRET standards are now available for every day calibration and are especially useful for new groups joining the community.

Encouraged by the insights gained in the above-mentioned FRET endeavor (Hellenkamp et al., 2018a), new multi-lab blind studies have been initiated. The next comparative FRET study, led by Thorben Cordes, investigates the robustness and reliability of smFRET experiments on proteins undergoing ligand-induced conformational changes (Gebhardt et al., in preparation). This study uses two distinct model proteins to assess the reproducibility and accuracy of protein-based smFRET for inter-dye distance determination measurements. Protein systems bring new challenges, including statistical dye labeling, site-specific dye properties, protein stability, shipping, storage and conformational dynamics. Hence, the study also assesses the ability of smFRET to discover and quantify dynamics on different timescales from microseconds to seconds. Another FRET challenge, initiated by Sonja Schmid, is the kinSoftChallenge (http://www.kinsoftchallenge.com, Götz et al., in preparation), which evaluates existing tools for extracting kinetic information from single-molecule time trajectories. This challenge aims to: (1) demonstrate the ability of smFRET-based kinetic analyses to accurately infer dynamic information and (2) provide the community with the means of evaluating the different available software tools.

One important outcome of the various multi-lab FRET studies was that, although the agreement was good, it could be improved even further. In particular, the data analysis, and specifically corrections, can have an impact on the determined FRET efficiencies and resulting distances. Hence, an open discussion regarding which approaches work most reliably under what conditions is necessary. Access to the primary data and the ability to process them with various analysis approaches is, and will remain, the most transparent way to move the field forward. Currently, this is difficult given the many variations in methods employed, their documentation, file formats and experimental procedures implemented across laboratories establishing the optimal conditions, workflow and best practices even for existing, well-tested methods is challenging since a comparison of these methods is time-consuming and the necessary information is, in many cases, not available. With the increase in open scientific practices and submission of published data to repositories, a consensus is needed regarding what data and metadata should be stored and in which possible formats so that it can be readily utilized by the community.

Due to these considerations and the many opportunities for growth of the smFRET community, several laboratories with expertise in FRET, without pretension to be exhaustive or exclusive, have gathered to endorse these efforts and propose steps to organize the community around consistent and open-science practices. This action translates into general methodological recommendations or suggestions, which we introduce following the typical workflow of a smFRET experiment, including sample preparation and characterization, setup description, data acquisition and preservation, and data analysis. These recommendations on how to ‘practice’ smFRET are not an attempt to regiment the community but rather an initial suggestion that aims at encouraging an open dialog about existing practices in our field and leads to higher reproducibility in the results from smFRET experiments. We then discuss open science practices as well as the first steps that have been taken to form an international FRET community. We end with highlighting a few of the areas where we see smFRET making a big impact in various scientific fields in the near future.

Within the FRET community, considerable know-how and expertise exists for the design, measurement and analysis of FRET experiments. In this section of the paper, we:

• review the workflow of smFRET experiments,

• discuss practical problems and potential pitfalls,

• provide recommendations for good practice, and

• list key scientific challenges that the field faces.

In the following, we consider each of these four aspects at every step of the smFRET workflow, from the choice of instrumentation all the way to the generation of structural and dynamic models.

Experimental approaches: free diffusion or surface immobilization?

The workflow of smFRET studies starts with choosing one of the two most popular smFRET implementations: confocal and TIRF microscopy. Confocal microscopy is especially well-suited for studying freely diffusing molecules (Figure 2A), while TIRF microscopy is typically used for surface-immobilized molecules (Figure 2B; e.g., reviewed in Juette et al., 2014; Roy et al., 2008; Sasmal et al., 2016).

Figure 2

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Compared to most other single-molecule approaches, both smFRET modalities offer relatively high throughput.

• In the confocal modality, the free diffusion of molecules into the observation volume and the short residence times enable the acquisition of many single-molecule events for extended amounts of time at rates of a few events per second. It can offer sub-nanosecond time resolution, yet single molecules are only observed during diffusion through the confocal excitation volume (typically <10 milliseconds). This allows one to obtain snapshots of thousands of individual molecules over the course of hours.

• In the TIRF modality, hundreds to thousands of dye-labeled molecules can be imaged simultaneously in one field of view. This approach reveals ‘motion pictures’ of individual molecules from seconds to minutes until the fluorophores photobleach. It typically has a lower temporal resolution of about a few tens of milliseconds but this is improving with technological advances. TIRF can be performed by illuminating through a high-numerical-aperture objective (Figure 2B) or through a quartz prism (Roy et al., 2008).

When embarking on the investigation of conformational dynamics of a new biological system, the method of choice most often depends on the availability of the proper instrumentation. However, the dynamical aspects (reviewed in section Conformational dynamics) of the biological system under investigation, which are typically not known a priori, will eventually define which of the two methods is best suited. Because the dynamics of biological systems occur over a range of timescales from nanoseconds to seconds (Figure 3), ideally one would like to apply both modalities in parallel to obtain a complete understanding of the system (e.g., as shown in Figure 1).

Figure 3

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Many variations exist with respect to the above-mentioned basic modalities to:

1) maximize the information content of the fluorescence signal.

• The confocal modality equipped with TCSPC and polarization-sensitive detections, so-called multiparameter fluorescence detection (MFD), allows monitoring of the fluorescence lifetime and anisotropy in addition to the fluorescence intensity (Kühnemuth and Seidel, 2001; Rothwell et al., 2003; Sisamakis et al., 2010; Widengren et al., 2006). The simultaneous collection and analysis of multiple parameters provides valuable insights into conformational dynamics, impurities and other spurious fluorophore-related artifacts.

• Alternating laser excitation (ALEX) (Kapanidis et al., 2004) allows for optical sorting of molecules exhibiting fluorescence from a single dye or from the two dyes in the FRET experiment (Figure 2A-iv) and also extract information on dye photophysics. In the TIRF modality, millisecond ALEX (msALEX) (Margeat et al., 2006) is typically used; in the confocal modality microsecond ALEX (μsALEX) (Kapanidis et al., 2005; Kapanidis et al., 2004; Lee et al., 2005) or nanosecond ALEX (nsALEX), aka. pulsed interleaved excitation (PIE) (Kudryavtsev et al., 2012; Laurence et al., 2005; Müller et al., 2005) are used.

• Three or more spectral channels can be used for multi-color smFRET (Clamme and Deniz, 2005; Hohng et al., 2004; Lee et al., 2010c; Lee et al., 2007a; Ratzke et al., 2014; Stein et al., 2011).

2) optimize data collection.

• A confocal microscope equipped with a laser and a sample or laser scanning module is also suited to study immobilized molecules (Chung et al., 2012; Edman et al., 1999; Ha et al., 1999; Ha et al., 1997; Hanson et al., 2007; Rhoades et al., 2003; Sabanayagam et al., 2004; Sturzenegger et al., 2018; Uphoff et al., 2011; Wang and Lu, 2010). It is the ‘best of both worlds’ in terms of timing, that is high time resolution and long observation times. However, it requires localizing and measuring each molecule individually, leading to lower throughput.

• Multi-spot detection, on arrays of single-photon avalanche diode detectors (SPAD arrays) and other state-of-the-art detectors, increases the throughput of confocal-based smFRET measurements and enables the study of non-equilibrium kinetics with higher time resolution (Ingargiola et al., 2016b; Ingargiola et al., 2018a; Segal et al., 2019).

• Objective-type TIRF can be combined with micro-mirrors in the excitation path to reduce background (Larson et al., 2014).

• Novel large-chip sCMOS cameras allow imaging at higher frame rates than their EMCCD counterparts. With the larger chip size, it can detect tens of thousands of molecules simultaneously (Juette et al., 2016) and the time resolution can be pushed into the sub-millisecond time scale (Fitzgerald et al., 2019; Girodat et al., 2020; Pati et al., 2020).

3) control the sample.

• In the confocal modality, the upper limit of the observation time can be pushed by recurrence analysis (Hoffmann et al., 2011) or by conjugating the molecules to large slowly-diffusing particles or liposomes (Diez et al., 2004; Kim et al., 2015a). Alternatively, the Moerner group confined molecules of interest to the observation volume without immobilization by using an anti-Brownian electrokinetic (ABEL) trap (Cohen and Moerner, 2005; Wilson and Wang, 2019).

• The space available for diffusion can be confined by using nanochannel devices (Fontana et al., 2019; Tyagi et al., 2014) or limiting the sectioning of the excited region through highly inclined and laminated optical (HILO) excitation (Gilboa et al., 2019) so that freely diffusing molecules can be tracked with camera detection.

• Microfluidics-based sample handling devices, including various mixers (Gambin et al., 2011; Hellenkamp et al., 2018b; Kim et al., 2011; Lemke et al., 2009; Lipman et al., 2003; Wunderlich et al., 2013; Zijlstra et al., 2017), allow automated sample handling and enable non-equilibrium measurements (Hamadani and Weiss, 2008; Juette et al., 2016).

The many possibilities available in the choice of hardware underscore the importance of precisely describing the components of the experimental setup. This includes optical elements (e.g., lenses, filters, mirrors, dichroics), light sources, optomechanical/optoelectronic devices and their characteristics, and detectors and their associated electronics. These details contribute in many ways to the finally recorded data and cannot, in general, be inferred retrospectively.

With the palette of FRET modalities increasing steadily, we recommend a rigorous comparative study of the different methods using well-characterized model samples. First and foremost, the study should determine the precision and limitations of each method and their complementarity. As one example, potential pitfalls in the determination of data correction factors (described in the section FRET efficiency) could be identified by a side-by-side comparison of fluorescence lifetime and intensity-based FRET methods.

Conformational dynamics

Many users in the FRET community employ the detection and characterization of different subpopulations or measurements of conformational dynamics as a handle to study biomolecules or biomolecular systems. Conformational dynamics are typically defined as:

• conformational transitions between distinct states separated by an activation barrier, typically defined as larger than the thermal energy, ${\displaystyle k_{B}T}$, where ${\displaystyle k_B}$ is Boltzmann’s constant and ${\displaystyle T}$ is the absolute temperature, and

• or conformational fluctuations within states, defined by the shape of the potential wells between activation barriers.

Transitions can occur under equilibrium conditions, can be induced by the addition of substrates, ligands, or interaction partners (de Boer et al., 2019a; Mapa et al., 2010; Mazal et al., 2018; Schluesche et al., 2007); induced by mixing with denaturants (Kuzmenkina et al., 2006; Lindhoud et al., 2015; Maity and Reddy, 2016; Moosa et al., 2018; Nienhaus, 2006; Pirchi et al., 2011; Rieger et al., 2011; Schuler et al., 2002); or triggered by temperature (Ebbinghaus et al., 2010; Holmstrom et al., 2014; Nettels et al., 2009; Zhao et al., 2010a) and pressure modulations (Schneider et al., 2018; Sung and Nesbitt, 2020). Structural transitions can also occur spontaneously.

SmFRET is unique in that it allows the detection and analysis of equilibrium and non-equilibrium conformational dynamics across at least 12 orders of magnitude in time, that is from the nanoseconds to, in principle, thousands of seconds (Figure 3). Notably, it is important to optimize the labeling positions to maximize the distinction between different conformational states based on their FRET efficiencies (Dimura et al., 2020).

Detecting dynamics

Biomolecules are dynamic systems that show conformational flexibility and dynamics on fast time scales (Henzler-Wildman and Kern, 2007). Oftentimes, conformational interconversions occur on a timescale faster than the sampling time of the detection system, for example < 10 ms for TIRF modality or < 0.1 ms for confocal modality, resulting in the observed single-molecule time series or FRET efficiency histogram exhibiting only time-averaged FRET values, weighted by the fractional population of each conformational state. Several groups have developed methods for detecting and analyzing such ‘dynamic averaging’ from confocal-modality data. In general, these methods allow retrieval of dynamics on the milliseconds and sub-millisecond timescales by analyzing the average fluorescence lifetimes and/or photon counting statistics of single-molecule bursts. The precise knowledge of the experimental shot noise separates smFRET from other techniques in structural biology and enables a quantitative analysis of fluctuations caused by biomolecular dynamics. A number of methods have been developed for detecting and quantifying smFRET dynamics, which we discuss in more detail below on slower (section Slow dynamics) and faster time scales (section Faster dynamics). The first step in analyzing smFRET dynamics is the verification that dynamics are present. Popular methods for the visual detection of dynamics include:

• 2D histograms of burst-integrated average donor fluorescence lifetimes versus burst-integrated FRET efficiencies (Gopich and Szabo, 2012; Kalinin et al., 2010b; Rothwell et al., 2003; Schuler et al., 2016),

• burst variance analysis (BVA) (Torella et al., 2011),

• two-channel kernel-based density distribution estimator (2CDE) (Tomov et al., 2012),

• FRET efficiency distribution-width analysis, for example by comparison to the shot noise limit (Antonik et al., 2006; Gopich and Szabo, 2005a; Ingargiola et al., 2018b; Laurence et al., 2005; Nir et al., 2006) or known standards (Geggier et al., 2010; Gregorio et al., 2017; Schuler et al., 2002), and time-window analysis (Chung et al., 2011; Kalinin et al., 2010a; Gopich and Szabo, 2007), and

• direct visualization of the FRET efficiency fluctuations in the trajectories (Campos et al., 2011; Diez et al., 2004; Margittai et al., 2003).

Slow dynamics

For dynamics on the order of 10 ms or slower, transitions between conformational states can be directly observed using TIRF-modality approaches, as have been demonstrated in numerous studies (Blanchard et al., 2004; Deniz, 2016; Juette et al., 2014; Robb et al., 2019; Sasmal et al., 2016; Zhuang et al., 2000). Nowadays, hidden Markov models (HMM) (Figure 4E) are routinely used for a quantitative analysis of smFRET time traces to determine the number of states, the connectivity between them and the individual transition rates (Andrec et al., 2003; Keller et al., 2014; McKinney et al., 2006; Munro et al., 2007; Steffen et al., 2020; Stella et al., 2018; Zarrabi et al., 2018). Below, we list extensions and other approaches for studying slow dynamics.

Figure 4

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• Classical HMM analysis has been extended to Bayesian inference-based approaches such as variational Bayes (Bronson et al., 2009), empirical Bayes (van de Meent et al., 2014), combined with boot-strapping (Hadzic et al., 2018) or modified to infer transition rates that are much faster than the experimental acquisition rate (Kinz-Thompson and Gonzalez, 2018).

• Bayesian non-parametric approaches go beyond classical HMM analysis and also infer the number of states (Sgouralis et al., 2019; Sgouralis and Pressé, 2017).

• Hidden Markov modeling approaches have been extended to detect heterogeneous kinetics in smFRET data (Hon and Gonzalez, 2019; Schmid et al., 2016).

• Concatenation of time traces in combination with HMM can measure kinetic rate constants of conformational transitions that occur on timescales comparable to or longer than the measurement time (Kim et al., 2015b).

• In the confocal modality, slower timescales are accessible by exploiting the reentry of single molecules into the observation volume (recurrence analysis of single particles, RASP) (Hoffmann et al., 2011).

There are still many challenges with respect to the accuracy of the approaches that need to be discussed and improvements made to provide a reliable determination of kinetics.

Faster dynamics

Several methods exist that assist in the quantification of the kinetic parameters governing fast conformational dynamics, as also exemplified in Figure 3A,B.

• Dynamic photon distribution analyses (PDA) that analyze the width of FRET efficiency distributions with respect to photon shot noise and broadening by dynamic exchange (Gopich and Szabo, 2007; Kalinin et al., 2010b; Santoso et al., 2010b).

• Applying hidden Markov models on a photon-by-photon basis extends the achievable time resolution into the microsecond regime (Aviram et al., 2018; Keller et al., 2014; Mazal et al., 2019; Pirchi et al., 2016). More generally, photon-by-photon maximum likelihood analysis (Figure 4D) of diffusing or immobilized molecules has made it possible to extract sub-millisecond transition rates (Chung and Gopich, 2014; Chung et al., 2011; Gopich and Szabo, 2009), transition path times of protein folding (Chung et al., 2012; Chung and Eaton, 2018) and binding of disordered proteins (Kim et al., 2018a; Sturzenegger et al., 2018; Kim and Chung, 2020) on the microsecond timescale.

• Using confocal-modality approaches, numerous studies also directly mapped transitions between conformational states for dynamics on the order of 0.5 ms or slower (Diez et al., 2004; Hanson et al., 2007; Margittai et al., 2003).

• Plasmonic enhancement of the fluorescence signal, reaching count rates in the megahertz regime for single molecules, allows a direct visualization of dynamics on the sub-millisecond timescale without analysis of the photon statistics (Acuna et al., 2012; Bohlen et al., 2019).

• Higher time resolution for TIRF experiments below 10 ms can be achieved using stroboscopic illumination (Farooq and Hohlbein, 2015) or fast sCMOS cameras (Fitzgerald et al., 2019; Girodat et al., 2020; Juette et al., 2016; Pati et al., 2020), reaching into the sub-millisecond domain.

Fluorescence correlation spectroscopy (FCS) (Magde et al., 1972; Rigler et al., 1993) methods have also been widely applied and the observed kinetic rates are model-independent (i.e., unbiased).

• By combining smFRET with FCS (Felekyan et al., 2013; Gurunathan and Levitus, 2010; Margittai et al., 2003; Schuler, 2018; Torres and Levitus, 2007), it is possible to quantify FRET dynamics that are faster than the diffusion timescale (Figure 4C).

• FRET dynamics as fast as a few picoseconds can also be retrieved from a variant of FCS dubbed ‘nanosecond FCS’ (nsFCS) (Nettels et al., 2008; Nettels et al., 2007; Schuler and Hofmann, 2013; Figure 3A).

• Using statistical filters, it is possible to extract species-specific properties and to quantify the exchange rates between different sub-populations (filtered-FCS) (Böhmer et al., 2002; Enderlein et al., 2005; Felekyan et al., 2013; Kapusta et al., 2007).

• Species-specific hydrodynamic radii can be extracted using single-burst FCS (Bravo et al., 2018; Laurence et al., 2008; Laurence et al., 2007). In combination with FRET-FCS or filtered-FCS, this approach simplifies the analysis of kinetics by eliminating the contribution of single-labeled molecules (Barth et al., 2018; Felekyan et al., 2013).

In the analysis of fast dynamics and subpopulations, it is generally important to account for fast photophysical transitions, such as dye photoblinking, and for interactions of the dyes with the biomolecular surface, which may interfere with subpopulation dynamics and result in inaccurate transition rates (Chung and Gopich, 2014; Ingargiola et al., 2018b; Lerner et al., 2018b).

The detection and analysis of fast dynamics is one of the issues addressed in the protein FRET challenge (Gebhardt et al., in preparation). Finally, at the moment of this writing, different analysis algorithms are typically being applied to the data independently from each other (e.g., MFD, BVA, 2CDE, PDA, filtered-FCS in burst analysis) while, in fact, they could corroborate each other or even help in deciding on models. The field could focus on creating global multi-algorithm workflows or tools to test, how a model obtained with one algorithm would influence the results of other analyses.

Detection and characterization of intra-state dynamics

Rapid structural dynamics within a given conformational state, that is within a single energy minimum, can also be studied with smFRET by modeling them as a continuous distribution rather than a state-dependent distribution. In the given example, rigid and flexible conformational states can be distinguished in the measurement. Information regarding the flexibility of a given conformation can be retrieved by:

• describing their kinetic signatures in FRET efficiency vs. lifetime (${\displaystyle E-\tau }$) plots (Figure 4A–B) (Gopich and Szabo, 2012; Kalinin et al., 2010b),

• using the fluorescence lifetime information available with TCSPC, which can be used to analyze sub-ensemble fluorescence decays and retrieve the inter-dye distance distribution and the inter-dye distance diffusion coefficient (Gansen et al., 2018; Lerner et al., 2014; Neubauer et al., 2007; Rahamim et al., 2015; Sisamakis et al., 2010),

• analyzing brightness by sub-ensemble fluorescence intensity distribution analysis (FIDA) (Neubauer et al., 2007),

• analyzing the photon statistics of the time-stamped photon arrival trajectories (Ingargiola et al., 2018b; Ramanathan and Muñoz, 2015), and

• relating the time-averaged FRET efficiencies and subpopulation-specific nsFCS to polymer models or simulated ensembles (Borgia et al., 2018; Holmstrom et al., 2018).

It is, however, important to mention that the distinction between dynamics within a conformational state and dynamics of transitions between different conformational states is still under debate, which highly depends on the definition of an activation barrier for different modes of structural dynamics and on the different smFRET modalities used.

Inter-dye distances

When smFRET experiments are used for structural studies or accurate distance determination is desired, many steps need to be taken to convert the raw data (photons detected and registered by the detectors) into absolute inter-dye distances. In essence, it requires exact knowledge of the Förster distance, ${\displaystyle R_0}$ (also referred to as the Förster radius) and therefore of all parameters required for determining it, as well as knowledge with respect to the flexibility of the attached fluorophores (approximated using a dye-model). In this section, we review the various issues involved.

Förster distance ${\displaystyle R_0}$

In FRET, the excitation energy of the donor fluorophore is transferred to an acceptor fluorophore via weak dipolar coupling. Considering a single donor-acceptor distance, ${\displaystyle R_{DA}}$, the efficiency, ${\displaystyle E}$, of this non-radiative transfer process scales with the sixth power of ${\displaystyle R_{DA}}$ normalized by the Förster distance, ${\displaystyle R_0}$ (Equation 1). In smFRET studies, dyes are usually coupled to the biomolecules via long (ranging typically between 10 and 15 atoms) mostly flexible linkers, which result in an equilibrium distribution of ${\displaystyle R_{DA}}$ values, ${\displaystyle p(R_{DA})}$, caused by the flexibility of the dye linkers. In this case, one may observe a mean FRET efficiency ⟨${\displaystyle E}$⟩ related to the FRET efficiency, averaged over all distances and their probabilities:

(5) ${\displaystyle ⟨E⟩=\underset{0}{\overset{\mathrm{\infty }}{\int }}\frac{p\left(R_{DA}\right)}{1+{\left(\frac{R_{DA}}{R_0}\right)}^6}d{R}_{DA}.}$

It is noteworthy to mention that Equation 5 holds under the assumption that the inter-dye distance remains unchanged during the excited-state lifetime of the donor fluorophore. From the mean FRET efficiency ⟨${\displaystyle E}$⟩, one obtains the FRET-averaged apparent donor-acceptor distance, ${\displaystyle {⟨R_{DA}⟩}_E}$, which differs from the distance between the mean dye positions (Kalinin et al., 2012) and is dependent on the flexibility and dynamics of the dye.

As mentioned before, ${\displaystyle R_0}$ (Equation 1) is the distance at which half of the donor de-excitation events occur via energy transfer to the acceptor fluorophore. ${\displaystyle R_0}$ (in Å) is given by:

(6) ${\displaystyle R_0=0.2108{\left(\frac{{\kappa }^2{\mathrm{\Phi }}_{F,D\left(0\right)}}{n_{im}^4}\int {\stackrel{-}{F}}_D\left(\lambda \right){\epsilon }_A\left(\lambda \right){\lambda }^{4}d\lambda \right)}^{\frac{1}{6}},}$

meaning that it depends on the donor fluorescence quantum yield in the absence of an acceptor, ${\displaystyle {\varphi }_{F,D\left(0\right)}}$, the overlap between the area-normalized donor emission spectrum, ${\stackrel{-}{F}}_D\left(\lambda \right)$, and the acceptor excitation spectrum with extinction coefficient, ${\displaystyle {\epsilon }_A(\lambda )}$ (in M⁻¹cm⁻¹), at the wavelength $\lambda$ (in nm), the relative orientation of the dye dipoles captured by the orientation factor, κ², and the refractive index of the medium, ${\displaystyle n_{im}}$, between and around the dyes. It should be noted that, due to the ${\lambda }^4$ dependence of the overlap integral, small shifts in the spectra can have large effects on the ${\displaystyle R_0}$. The following sections describe the factors that influence ${\displaystyle R_0}$ and the FRET efficiency in more detail.

Extinction coefficient ε

The extinction coefficient of the acceptor dye affects ${\displaystyle R_0}$ and the expected excitation rate in ALEX/PIE experiments. In the absence of an easy or affordable way to measure this parameter (it requires large amounts of dye for gravimetric analysis or FCS with controlled dilution [Fries et al., 1998]), the experimenter typically relies on the value given by the manufacturer, a value that can at times be unreliable. Alternatively, the extinction coefficient of the dyes may be theoretically assessed via the Strickler and Berg, 1962 equation, when ${\displaystyle {\varphi }_{F,D(0)}}$ and the fluorescence lifetime are known. Fortunately, ε is not expected to vary much depending on the environment of the fluorophores, since both the ${\displaystyle {\varphi }_{F,D(0)}}$ and the fluorescence lifetime, in most cases, vary accordingly. Hence, one can conclude that the local environment does not heavily influence the excitation probability (according to the Strickler-Berg equation mentioned above).

Fluorescence quantum yield ${\displaystyle {\varphi }_{\mathit{F}}}$

${\displaystyle {\varphi }_F}$ oftentimes changes upon labeling and can be sensitive to the local environment at the labeling position, to the conformational state of the molecule and to the binding of ligands, substrates or complex partners. Even dyes that are considered relatively insensitive to their local environment have been shown to exhibit a large change in ${\displaystyle {\varphi }_F}$ upon conjugation to nucleic acids or proteins. As an extreme example, the quantum yield of Cy3B ranges from 0.19 to 0.97 at different labeling positions on dsDNA, leading to considerable variation in the value of ${\displaystyle R_0}$ for the pair Cy3B-ATTO 647N between 54.8 Å and 65.9 Å (Lerner et al., 2018b; Craggs et al., 2019). For dyes of the cyanine family, such as Cy3 and Cy5, or its variants Alexa Fluor 555 and Alexa Fluor 647 (Gebhardt et al., in preparation), ${\displaystyle {\varphi }_F}$ is dependent on the excited-state isomerization, which is influenced by viscosity, steric restriction and (stacking) interactions (Hwang and Myong, 2014; Lerner et al., 2016; Levitus and Ranjit, 2011; Sanborn et al., 2007; White et al., 2006; Widengren et al., 2001). In summary, independent determination of ${\displaystyle {\varphi }_F}$ for different labeling positions is strongly recommended. Notably, nsALEX/PIE and MFD experiments can probe the fluorescence lifetime, and thus directly identify changes in ${\displaystyle {\varphi }_F}$. Development of standard procedures for measuring or estimating ${\displaystyle {\varphi }_F}$, for example using an integrating sphere (Gaigalas and Wang, 2008; Pati et al., 2020) or a nanocavity (Chizhik et al., 2013; Chizhik et al., 2011), would benefit the field and should be discussed.

Refractive index ${\displaystyle n_{im}}$

The actual index of refraction to be used for calculation of ${\displaystyle R_0}$ lies somewhere between the index of refraction of an aqueous buffer (1.33) and that for proteins and DNA (~1.5) but the exact value is not known. Robert Clegg recommended using an intermediate value of 1.4, which reduces the maximal error in ${\displaystyle R_0}$ to ~ 4% (Clegg, 1992). However, different values may be more appropriate depending on the geometry and environment of the fluorophores. To date, the refractive index has received very little attention in the field (Knox and van Amerongen, 2002).

Dye transition dipole orientation factor κ²

This parameter describes the relative orientation of the transition dipole moments of the dyes and strongly depends on dye mobility. Since the dyes’ orientations can change randomly on the time scale of typical FRET events, the mean value of <κ²> = 2/3 is typically taken. This well-known dynamic averaging approximation assumes that the rotational diffusion timescale of a FRET pair is much shorter than the fluorescence lifetime of the donor. However, it may well be that one of the dyes is not freely rotating on this timescale (e.g., it may interact with the microenvironment). An extreme example is a FRET system in which non-canonical fluorescent nucleotides were incorporated into dsDNA. The rigid structure and natural helical twist of the DNA caused the relationship between ${\displaystyle E}$ and ${\displaystyle R_{DA}}$ to follow an interesting trend (Ranjit et al., 2009) with ${\displaystyle E}$ being relatively low around ${\displaystyle R_{DA}\sim R_0}$, because of κ² ~ 0 (Wranne et al., 2017). In another smFRET experiment, a DNA molecule was end-labeled with Cy dyes without sulfonic acids groups (Cy3 and Cy5), which have a tendency to stack onto bases at the DNA termini (Iqbal et al., 2008; Ouellet et al., 2011), and the influence of orientational effects on the FRET efficiency was measured. Although an influence of the orientation could be detected, the data showed that orientational effects average-out quite well in most realistic cases (Iqbal et al., 2008). A method to estimate the lower and upper bounds for <κ²> from the donor and acceptor time-resolved anisotropies was proposed in the 1970s (Dale et al., 1979; van der Meer, 2002). In smFRET measurements using the polarization-resolved MFD modality, information on the donor and acceptor fluorescence intensities, lifetimes, and anisotropies (Schaffer et al., 1999) are collected simultaneously and fluorescence anisotropy decays of different single-molecule sub-populations can be used to assess the <κ²> uncertainty per conformational state (Ivanov et al., 2009; Kudryavtsev et al., 2012; Sindbert et al., 2011). It is noteworthy to mention that the majority of fluorophores used as donor and acceptor dyes in smFRET have a mono-exponential fluorescence decay and, hence, have one major emission dipole. In this case, the estimation of <κ²> depends on the orientation of these single transition dipole moments. It has been proposed that the assumption of <κ²> = 2/3 would carry much less uncertainty when the fluorescence signal is emanating from more than one emission dipole, yielding multi-exponential decays (Haas et al., 1978). This is an intriguing idea that could provide a realistic estimation for κ² and thus help simplify the transformation of FRET efficiencies into inter-dye distances. For a review on the dependence of FRET on κ², the reader is referred to (van der Meer, 2002). Finally, we note that there are several routines recommended by experienced members of the community to determine ${\displaystyle R_0}$ accurately. Which approach is the most optimal is still under discussion.

Dye models

The Förster equation (Equation 1) allows the extraction of a distance directly from a FRET efficiency measurement. This distance directly corresponds only to the separation of the FRET fluorophores when the positions of the donor and the acceptor molecules are constant, the dye’s orientations are rapidly averaged and their microenvironment is known. Strictly speaking, this is never the case, even for a stable conformation of the labeled macromolecule, since dye molecules are typically attached to the macromolecules via flexible linkers, and the labeled macromolecule usually restricts the volume accessible to the fluorophore (Best et al., 2007; Hellenkamp et al., 2018a; Ingargiola et al., 2018b). In addition, diffusion of the dyes while the donor is in the excited state can also influence the measured FRET efficiency (Ingargiola et al., 2018b). When the FRET rate is not too high (leading to a FRET efficiency of ${\displaystyle E<0.8}$) and the dyes do not interact with the protein surface, deviations due to dye dynamics are usually negligible (Hellenkamp et al., 2018a; Hellenkamp et al., 2017; Kalinin et al., 2012).

Various groups have developed detailed dye models that account for the translational and rotational flexibility of the dyes and thus allow a more accurate description of the actual distance FRET measures (Figure 5D) (Beckers et al., 2015; Craggs and Kapanidis, 2012; Dimura et al., 2016; Haas et al., 1978; Kalinin et al., 2012; Muschielok et al., 2008; Schuler et al., 2020; Sindbert et al., 2011) and to test them experimentally (Hellenkamp et al., 2018a; Nagy et al., 2018; Peulen et al., 2017; Wozniak et al., 2008). For any given FRET efficiency, different dye models will lead to slightly different extracted ${\displaystyle R_{DA}}$ distributions (deviations ≤ 5%). Choosing an appropriate model is therefore important for the accurate determination of ${\displaystyle R_{DA}}$.

Figure 5

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Changes in ${\displaystyle R_{DA}}$ and the relative orientation of the dyes can occur on many different time scales, including the excited state lifetime, the interphoton time, and the photon burst duration. Averaging over these different distances and orientations is complicated due to the inherent non-linearity of the energy transfer process. However, there are exact analyses that describe the photon statistics in smFRET experiments (Antonik et al., 2006; Gopich and Szabo, 2005a; Nir et al., 2006).

Since the dye linker lengths of the typical dyes used in smFRET experiments are long (ranging typically between 10 and 15 atoms, Figure 5B), translational and rotational diffusion of the dyes within the accessible volumes constrained by their linkers and the macromolecules to which they are conjugated lead to considerable changes in ${\displaystyle R_{DA}}$. Such dynamics can occur on timescales comparable to the fluorescence lifetime, which leads to changes in ${\displaystyle R_{DA}}$, from the moment of donor excitation to the moment of donor de-excitation. This process leads to the well-documented phenomenon termed diffusion-enhanced FRET (Beechem and Haas, 1989; Haas and Steinberg, 1984; Orevi et al., 2014), where the measured FRET efficiencies are higher than expected from a static distribution of ${\displaystyle R_{DA}}$ due to the increase in the probability for FRET to occur when ${\displaystyle R_{DA}}$ shortens while the donor is in the excited state (Eilert et al., 2018; Ingargiola et al., 2018b).

This phenomenon has been treated by incorporating both rotational and translational diffusion of the fluorophores as fluctuations in ${\displaystyle R_{DA}}$ inside a potential well of the reaction coordinate ${\displaystyle R_{DA}}$ (Dingfelder et al., 2018; Haas and Steinberg, 1984; Ingargiola et al., 2018b). Similarly, rotational motions lead to changes in the relative orientation of the donor and the acceptor molecules and therefore to changes in ${\displaystyle R_0}$ via κ². To this end, a complete kinetic theory treating both rotational and translational diffusion has been developed (Eilert et al., 2018). In many cases, a dynamic rotation - static translation model can be used (i.e., ${\displaystyle k_{rotation}>>k_{FRET}>>k_{translation}}$) (Figure 5A). Interestingly, Monte-Carlo simulations show that this often-applied simplification can lead to errors in ${\displaystyle R_{DA}}$ (Hellenkamp et al., 2018a). The magnitude of the uncertainty depends on the donor fluorescence lifetime, the FRET efficiency, and the dye molecules’ diffusion constants and rotational correlation times. So far, no major disagreement of the dynamic rotation – static translation model with experimental data has been reported, thus supporting the use of the isotropic average of <κ²> = 2/3.

To obtain atomistic insights into the behavior of dyes on biomolecules, molecular dynamics simulations have been explored (Best et al., 2007; Deplazes et al., 2011; Spiegel et al., 2016; Girodat et al., 2020; Grotz et al., 2018; Hoefling et al., 2011; Reinartz et al., 2018; Shoura et al., 2014). By simulating the whole system, including the fluorophores, information is obtained about the accessible volume of the fluorophore, its potential interactions with the biomolecular surface and the dynamics of the system. The results of such simulations crucially depend on the parameterization (force field) of the dyes. Different parameter sets have been reported for commonly used dyes and validated against experimental data (Best et al., 2015; Graen et al., 2014; Schepers and Gohlke, 2020; Shaw et al., 2020), but a consensus on the optimal parameterization has not yet been reached.

Structural modeling

By accounting for various uncertainties described in the section Inter-dye distances, precise distances can be calculated from FRET efficiencies. This enables the application of FRET for FRET-based structural studies, which are particularly promising for studying the conformations of large, heterogeneous, flexible, and dynamic biomolecules and their complexes (Brunger et al., 2011; Craggs et al., 2019; Filius et al., 2020; Hellenkamp et al., 2017; Holmstrom et al., 2018; Kilic et al., 2018; Muschielok et al., 2008; Nagy et al., 2015; Sanabria et al., 2020; Treutlein et al., 2012; Yanez Orozco et al., 2018). Such systems are notoriously difficult to study with classical structural biology methods. For structure determination, there are further steps that need to be taken.

Pipeline

Different approaches have been used to derive structural models from FRET distance restraints. The general pipeline consists of the following steps:

• preparation and measurement of multiple donor-acceptor labeled variants with different labeling positions,

• control experiments to assess the activity after labeling or immobilization, the photophysics of the probes, and the rotational freedom of the dyes,

• the non-trivial transformation from proximity ratios (uncorrected ${\displaystyle E}$ values) to absolute FRET efficiencies of the different conformational subpopulations, to inter-dye distance information (or equilibrium distance distributions),

• relating the inter-dye distances to the structure by an appropriate dye model, and

• assessing the quality of the structural model.

The FRET information alone is insufficient to generate an atomistic model de novo. FRET-restrained structural modeling thus relies on prior structural knowledge, from which novel structural models are generated. Different approaches have been used, each with specific advantages and limitations (Figure 6).

Figure 6

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• Rigid body modeling/docking: The different parts or domains of the structure or complex are treated as rigid bodies and arranged in 3D space to satisfy the FRET restraints (Choi et al., 2010; Hellenkamp et al., 2017; Kalinin et al., 2012; Mekler et al., 2002; Peulen et al., 2020).

• Ensemble selection: An ensemble of structures is generated (e.g., based on MD simulations or normal mode analysis), from which the structures that are best explained by the FRET results are selected (Dimura et al., 2016; Kalinin et al., 2012; Sanabria et al., 2020).

• Credible volumes: The relative position of unresolved structural elements with respect to the known structure is estimated from the FRET-derived distances (Muschielok et al., 2008).

• FRET-guided molecular dynamics: To guide the simulation, FRET-restraints can be incorporated into coarse-grained structural modeling and in all-atom MD simulations (Dimura et al., 2020).

Different laboratories use different approaches to analyze their experimental results in context-dependent manners and the steps necessary for the conversion from FRET information to inter-dye distance information are still under debate. Importantly, the overall uncertainties in inter-dye distances need to be determined from the uncertainty in ${\displaystyle R_0}$, the dye model and experimental precision, and to be incorporated into the integrative structural modeling (Dimura et al., 2016; Hellenkamp et al., 2018a; Hellenkamp et al., 2017; Kalinin et al., 2012; Muschielok et al., 2008; Muschielok and Michaelis, 2011; Peulen et al., 2017). As a result, FRET restraints yielded structural models that showed excellent agreement with existing models in benchmark studies (Kalinin et al., 2012; Mekler et al., 2002), resolved the structure of flexible parts and short-lived excited conformational states that were inaccessible in crystallographic studies (Andrecka et al., 2009; Sanabria et al., 2020) and quantified the conformational flexibility of crystallographic states (Hellenkamp et al., 2017). To increase the success of FRET-derived structural models, a protocol has recently been proposed to determine the most informative labeling positions, the number of labeling positions needed to resolve a given conformation and the accuracy that can be expected for the FRET-derived structural models (Dimura et al., 2020).

We should also be aware of the expected uncertainty for the determined structural model, which can be computed from the number of measurements, their average quality and the properties of the underlying computational method. Besides estimating the precision (Dimura et al., 2020), it is useful to introduce a quantitative quality estimate, ${\mathrm{\chi }}_n^2$, for judging the accuracy of FRET-derived structure models based on a cross-validation statistical method in the spirit of ${\displaystyle R_{free}}$ used in X-ray crystallography (Brünger, 1992). Such accuracy estimates are essential for the quality control of I/H structure models, especially those deposited in the PDB-Dev. Dimura et al., 2020 pointed out that the complexity arising from different experiments could be mitigated by using Bayesian hierarchical data processing frameworks, which abstracts the experimental data and propagates the information and uncertainties to enable structural modeling at higher precision and accuracy. Combining the smFRET information with additional constraints provided by complementary methods has the potential to further improve the accuracy of the obtained I/H structural models (Berman et al., 2019).

One of the cornerstones of the scientific method is the ability to reproduce experimental results. As experiments become more sophisticated, a clear description of the experiments is crucial. Recent trends toward Open Science practices call for full transparency of the scientific process, as has been formulated in the FAIR principle that data should be ‘Findable, Accessible, Interoperable, and Re-usable’ (Wilkinson et al., 2016). To this end, the procedures taken to acquire, analyze, and interpret experimental data should be provided. That includes describing each step, the reasons for taking the step and the information associated with the step. To ensure that the analysis remains transparent and tractable, code should generally be openly available and all parameters and settings used in the analysis should be stored.

Funding agencies embracing this philosophy (e.g., https://datascience.nih.gov/strategicplan) expect grantees to publish in open-access (OA) journals (and pay for the corresponding OA fees) or deposit manuscripts on preprint servers (e.g., Pubmed Central, arXiv, bioRxiv, ChemRxiv, medRxiv), and deposit data (sometimes also raw data) in repositories (e.g., Zenodo, the Dryad Digital Repository, FigShare) as well as analyses codes (e.g., GitHub). Open science disseminates knowledge by freely sharing results and the tools developed by independent scientists or teams working as part of a collaborative network. We would like to see the FRET community embrace and be committed to open science. Some tools are already in place, while others still need to be developed to make it easier to communicate the continuously growing knowledge and experience present in the FRET community.

Standard file format

To expedite the exchange of data between different groups and testing of different analysis methods, it would be beneficial to have a minimal number of file formats, and to avoid the multiplication of ad hoc formats developed independently. In fact, the absence of a standard file format and supporting documents has caused issues within individual labs with respect to long-term data storage and re-analysis. Online data deposition in well-documented file formats would therefore save a lot of headaches for many laboratories. Even if standard file formats are carefully designed or developed, it will be inevitable to modify the existing formats or introduce new formats in the future. The list of standard formats should be regularly updated, and the analysis codes should also be kept current and properly maintained to guarantee their compatibility with the new standard formats.

The raw experimental data should be supplied in a universal data file format that can be easily read and scrutinized (Figure 7 left). Ideally, the file should store both raw data and sufficient metadata to specify the measurement, setup, and sample. The metadata should be stored in a human-readable text-based format, while space-efficient storage of the raw photon data should be ensured by lossless compression. There are currently many different file formats for smFRET data, developed by different research groups and companies. In general, such files are hard to access for other laboratories and they are not guaranteed to be perennial, which poses an additional challenge to the community. To promote the adaptation of new file formats, conversion tools for older file formats should be provided so that future software codes can focus on handling one (or at least only a very few) common file formats, such as the phconvert suite of notebooks for transforming many file formats into Photon-HDF5 (see http://photon-hdf5.github.io/phconvert/). Reaching such a consensus is possible, as has been demonstrated by the acceptance of a single file format for the deposition of NMR data (Ulrich et al., 2019).

Figure 7

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File formats for (time-correlated) photon counting with point-detector data (confocal modality)

Several formats have been used and reported for time-correlated single-photon counting data (Brand et al., 1997; Brooks Shera et al., 1990; Eggeling et al., 2001; Felekyan et al., 2005; Rigler et al., 1993; Schaffer et al., 1999; Tellinghuisen et al., 1994; Wahl et al., 2008; Widengren et al., 2006). One example is the time-tagged time-resolved (TTTR) file format given in the left panel of Figure 7. Because of its compactness, this file format has been widely adopted by commercial companies (e.g., Becker and Hickl and PicoQuant) providing TCSPC electronics and point detectors for solution/imaging studies.

The basic formats have been extended in the Photon-HDF5 (Ingargiola et al., 2016a) file format that connects rich metadata with the raw photon information in a single, space-efficient format suitable for sharing and long-term data archival. Moreover, several software programs exist that can easily transform raw data files to the Photon-HDF5 format. Once enough metadata is available in the community, the relative importance of particular metadata entries on the resulting FRET data can be assessed.

File format for camera-based acquisition (wide-field/TIRF modality)

Camera-based data is acquired as a stack of images (e.g., TIFF). To extract time trajectories, several steps are needed to yield the time-binned fluorescence intensity (e.g., spot identification; donor and acceptor spot registration; thresholding; background subtraction, etc.). A file format for TIRF-based smFRET (immobilized) measurements has been proposed (Greenfeld et al., 2015). Alternatively, human-readable plain text files with an agreed format and greatly downsized from the raw TIFF stacks can also work well for TIRF-based smFRET traces. Binary file formats for efficient data compression have also been implemented (Juette et al., 2016).

Exchange file format for processed data

In addition to standardized raw file formats, we recommend defining exchange file formats for different levels of processed data (Figure 7, middle panel). This will allow researchers to establish flexible and modular workflows spanning different software packages and facilitate the adoption of novel analysis approaches. The deposition of processed data in agreed-upon file formats also ensures that published data can be re-used at a later time point, for example, for more elaborate structural modeling approaches (Köfinger et al., 2019). For the storage of processed data, we believe that it is important to retain the connection to the raw data by including the relevant metadata. For example, corrected FRET efficiency histograms should be deposited together with the raw signal intensities in the donor and acceptor channels, the background intensities and the calibration factors.

Repositories and data bank for FRET data and models

Metadata for FRET experiments

To ensure that the reported results are reproducible, raw data must be sufficiently annotated. One important source of inspiration for developing recommendations and standards for the FRET community comes from the world-wide Protein Data Bank (wwPDB) (Berman et al., 2003; Young and Westbrook, 2019). Following the standards of the wwPDB, it is recommended to provide additional information to the models as outlined in Figure 8.

Figure 8

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Archiving FRET experiments

In this respect, we strongly encourage the publication of datasets with detailed descriptions of the acquisition and analysis in scientific journals. Alternatively, or in addition, structural information and raw datasets should immediately be deposited in repositories provided by the publisher or generalist repositories (Table 2) with a Digital Object Identifier or link for access and citation. Online and public repositories also act as a form of data and knowledge backup, which is difficult to achieve and maintain at the scale of a single laboratory. Comparing a repository with a database, it is obvious that a database has significant advantages for the scientific community. While both archive forms provide open access to the data for all users, only a database can fulfill the quality criteria for safe usage of the deposited data by (1) creating standard definitions for the experimental data used for determining kinetic and/or structural models and their features; (2) developing methods to collect the minimum amount of required information for curation and validation of models and data; and (3) building the infrastructure for acquiring, archiving and disseminating the models and the data. To conclude, a database assures appropriate documentation with a defined format and quality control of the results.

Table 2

Repository name	URL	Size limits	Fee/Costs
Dryad Digital Repository	https://datadryad.org/stash	No	$120 USD for first 20 GB, and $50 USD for each additional 10 GB
figshare	https://figshare.com/	1 TB per dataset	It varies with publishers
Harvard Dataverse	https://dataverse.harvard.edu/	2.5 GB per file, 10 GB per dataset	Free
Open Science Framework	https://osf.io/	5 GB per file, multiple files can be uploaded	Free
Zenodo	https://zenodo.org/	50 GB per dataset	Donation
Mendeley Data	https://data.mendeley.com/	10 GB per dataset	Free

Given the importance of integrative structures for advancing life sciences and the significant world-wide investment made to determine them, the wwPDB has proposed a governance structure for federating archives containing structural models (e.g., PDB, Electron Microscopy Data Bank, EMDB Tagari et al., 2002) or experimental data. Moreover, for integrative modeling integrative/hybrid modeling structures (Sali et al., 2015), PDB-Dev (Berman et al., 2019; Vallat et al., 2018) was established as an associated prototype system for archiving multi-state and ensemble structures on multiple spatial scales. In addition, associated kinetic models can be deposited.

To deposit structural models and kinetic networks obtained using FRET experiments in PDB-Dev, a dictionary for FRET data is under development (https://github.com/ihmwg/FLR-dictionary; Vallat et al., 2020), serving as a method-specific extension to the existing integrative/hybrid modeling (IHM) dictionary that contains the data categories described in Figure 8. Presently, three integrative FRET-assisted structural models can be found on PDB-Dev.

In addition to archiving the models themselves, all relevant experimental data and metadata should be archived in a technique-associated data bank similar to the Biological Magnetic Resonance Data Bank, BMRB (Ulrich et al., 2019; Ulrich et al., 2008). Reaching such a consensus is possible, as has been demonstrated by the acceptance of a single file format for the deposition of NMR data (Ulrich et al., 2019). In the future, federated resources for other biophysical techniques are expected to align with the structural model archives of the wwPDB for participating in data exchange. Thus, it would be desirable to establish a federated data bank for archiving FRET and, more generally, fluorescence data that could be referred to as the Fluorescence Data Bank (FLDB).

To better achieve a consensus on the current and the future directions of the smFRET community, an open forum is needed where the current issues, needs, and desires could be discussed. We propose the following tools to organize the community around standardization efforts and open science practices. Some of these tools have already been put in place.

With improved communication and dissemination within the FRET community and agreement on the standard information required for depositing FRET-based or integrative structural data, smFRET will be better positioned to impact the expanding field of dynamic structural biology. We expect integrated approaches such as combining smFRET with NMR (Aznauryan et al., 2016; Liu et al., 2018; Milles et al., 2015; Sottini et al., 2020; Tsytlonok et al., 2019; Voith von Voithenberg et al., 2016), EPR (Boura et al., 2011; Masliah et al., 2018; Peter et al., 2020; Peulen et al., 2020; Sanabria et al., 2020; Vöpel et al., 2014), cross-linking mass spectrometry (Calabrese et al., 2020; Liu et al., 2018; Tyagi and Lemke, 2015), hydrogen/deuterium exchange (Calabrese et al., 2020; Liu et al., 2018; Munro and Lee, 2018), and/or MD simulations (see section Structural modeling and below) will have a big impact in the future.

One example of a major area of interest that is profiting from these developments is the study of intrinsically disordered proteins using smFRET experiments (Gomes and Gradinaru, 2017; Gomes et al., 2020; LeBlanc et al., 2018; Lee et al., 2018; Metskas and Rhoades, 2020; Nasir et al., 2020; Schuler et al., 2016). The dynamic nature of these proteins and their interactions play major roles in numerous cellular processes, including the formation of membrane-less intracellular biomolecular condensates, a new paradigm that presents huge challenges for traditional tools of structural biology (Banani et al., 2017; Choi et al., 2020; van der Lee et al., 2014; Wright and Dyson, 2015). Many IDPs undergo large folding transitions in conjunction with binding to partners, while others remain disordered upon complex formation (Schuler et al., 2020; Wang and Wang, 2019; Wu et al., 2017). SmFRET studies of these systems began more than a decade ago and have tackled increasingly complex systems using more advanced methods, including three-color smFRET or complex labeling schemes (Borgia et al., 2018; Kim and Chung, 2020; Lee et al., 2018; Metskas and Rhoades, 2020; Milles et al., 2015; Nasir et al., 2019; Schuler et al., 2016; Yoo et al., 2020). A recent study that combined smFRET, NMR, and MD simulations to investigate the interaction of H1 with ProTα is highlighted in Figure 9 (Borgia et al., 2018).

Figure 9

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Studies of IDPs are even more challenging in heterogeneous environments such as phase-separated mesoscale structures (e.g., membrane-less organelles) or cells (Nasir et al., 2019). Here, the strengths of smFRET would be especially valuable, while the field, at the same time, will benefit from the methodological developments. The ultimate goal is to combine both the structural and dynamic information in order to reduce the ambiguity in the underlying structures of conformational states and to gain detailed information on kinetic pathways between the associated states.

Although we have focused mostly on kinetic studies, smFRET-based structure determination and structural dynamics in this paper, there are a myriad of other new exciting directions where smFRET will have future impact. A detailed description of the various possibilities is beyond the scope of this report, but it is worth mentioning a few of them below.

• Combining FRET with other fluorescence methods: Several groups have combined smFRET with other fluorescence techniques, including protein-induced fluorescence enhancement (PIFE) (Hwang et al., 2011; Hwang and Myong, 2014; Lerner et al., 2016; Ploetz et al., 2016), photoinduced electron transfer (PET) (Haenni et al., 2013), quenchable FRET (Cordes et al., 2010) and stacking-induced fluorescence increase (SIFI) (Morten et al., 2020). The advantages of combining smFRET with other fluorescence-based rulers with higher sensitivity at short distances are obvious – gaining more spatial information on biomolecular systems being measured as well as information on possible synchronized motions between different parts of the biomolecule or biomolecular complex and between different modes of motion. As an example, single-molecule PIFE was used for probing the local structural stabilization in the intrinsically disordered protein α-Synuclein (Chen et al., 2020), which typically appears globally disordered when measured over larger distances using smFRET experiments. Another possibility is combining FRET with information regarding the shape of biomolecules and their assemblies via their translational (Dertinger et al., 2008; Sherman and Haran, 2006) and rotational diffusion (Möckel et al., 2019; Pieper and Enderlein, 2011; Viegas et al., 2020), determined using various FCS modalities. Recently, fluorescence anisotropy- and polarization-resolved FCS were used in integrative studies with non-FRET methods to probe local flexibilities (Möckel et al., 2019) or identify hinge-regions of a protein (Tsytlonok et al., 2020; Tsytlonok et al., 2019).

• Combining FRET with force spectroscopy. Another popular combination is FRET with various manipulation methods including optical tweezers (Hohng et al., 2007), magnetic tweezers (Lee et al., 2010a; Long et al., 2016; Swoboda et al., 2014), tethered particle motion (May et al., 2014), and force spectroscopy by DNA origami (Nickels et al., 2016). The advantages of combining smFRET with force manipulation techniques are obvious – detecting local structural changes or molecular interactions (via smFRET) as well as the global extension of macromolecules (via bead tracking) synchronously under mechanical control.

• Combining FRET with MD simulations. MD simulations have been widely applied with smFRET experiments to provide atomistic insights into the dynamic behavior of biomolecules and their assemblies, as shown in Figure 9 (Barth et al., 2018; Borgia et al., 2018; Holmstrom et al., 2018; Lehmann et al., 2020; Matsunaga and Sugita, 2018; Tsytlonok et al., 2020; Yanez Orozco et al., 2018; Zhao et al., 2010b). The vast information provided by MD simulations often motivates new hypotheses about the functional mechanism that can be tested experimentally via targeted mutations. MD simulations can also be combined with the information provided by smFRET experiments to steer the simulation from one conformational state to the other using accelerated or enhanced sampling techniques (Dimura et al., 2020). To characterize highly dynamic systems, coarse-grained approaches have been applied to intrinsically disordered proteins (Borgia et al., 2018), nucleic acids (Craggs et al., 2019), large chromatin arrays (Kilic et al., 2018) or large DNA origami nanostructures (Bartnik et al., 2020; Khara et al., 2018), to sample the conformational space of the system more efficiently. Alternatively, discrete MD simulations coupled with replica exchange, where discretized potential energies are employed, also assist in accelerating atomistic MD simulations of the ensemble structure of intrinsically disordered proteins (Brodie et al., 2019; Brodie et al., 2017; Chen et al., 2020; Fay et al., 2016; Hadi-Alijanvand et al., 2016; Popov et al., 2019) and holds great promises for being incorporated with single-molecule fluorescence-based technique.

• Combining FRET with imaging. SmFRET can be combined with super-resolution imaging (STED-FRET, Kim et al., 2018b; Tardif et al., 2019; Szalai et al., 2021) or FRET-DNA-PAINT (Deußner-Helfmann et al., 2018; Filius et al., 2020). The combination of fluorescence imaging with spectroscopy makes it possible to detect more species within a pixel of an image, expanding the information that can be extracted from such an experiment. Correlative imaging with electron microscopy, fluorescence and FRET also has the potential to allow the recognition of different subpopulations in the sample, which can then be separated for single-particle reconstructions (Ando et al., 2018; de Boer et al., 2015; Schirra and Zhang, 2014; Verkade and Collinson, 2019).

• SmFRET in live cells. Genetically-encoded fluorescent proteins are the most widely used fluorophores in live-cell imaging. Their maximal brightness and photon yield are, however, limited by excitation-dependent blinking and photobleaching, respectively (Dickson et al., 1997; Seefeldt et al., 2008). Combined with their large size, this makes them non-ideal for quantitative FRET studies. Nevertheless, considerable efforts have been made to extract quantitative FRET information to live-cell imaging data using the fluorescence intensity (Coullomb et al., 2020; Periasamy et al., 2008) or lifetime and anisotropy information (Hinde et al., 2012; Kravets et al., 2016; Kudryavtsev et al., 2007; Liput et al., 2020; Weidtkamp-Peters et al., 2009), and to develop an appropriate dye model for fluorescent proteins (Greife et al., 2016). Recently, the green fluorescent protein has been used for in-cell smFRET measurements in combination with an organic dye attached via the self-labeling protein tag (HaloTag) (Okamoto et al., 2020). To avoid the drawbacks of fluorescent proteins, several groups have shown that smFRET can be performed in live bacterial and eukaryotic cells by using in vitro labeled biomolecules that can be internalized in cells by several means. Electroporation has been shown to work well for the internalization of ssDNA, dsDNA, tRNA and proteins into bacteria and yeast cells (Craggs et al., 2019; Plochowietz et al., 2017; Plochowietz et al., 2016; Plochowietz et al., 2014; Sustarsic et al., 2014; Volkov et al., 2018). Microinjection of labeled molecules is an alternative approach, especially for smFRET in live eukaryotic cells, and has been demonstrated to yield structural information and dynamics from nanoseconds to milliseconds (König et al., 2015; Sakon and Weninger, 2010). With the further development of probes and labeling strategies, and the engineering of better fluorescent proteins (Shaner et al., 2007), there are many exciting possibilities for investigating cellular processes with unprecedented detail (Sustarsic and Kapanidis, 2015).

• SmFRET studies in crowded environments. SmFRET can be used to investigate the influence of the surrounding environment on biomolecules. Such studies can be performed over a drastic range of measurement conditions: from single molecules isolated in solvent cages to molecular environments equivalent to cellular conditions with millimolar concentrations of crowded biomolecules. An important thermodynamic effect of the limited space is minimization of the excluded volume. This (i) influences the hydrodynamic volume of biomolecules with potential consequences for their internal structure, dynamics and functionality and (ii) favors the association state in binding equilibria leading to phase transitions (Banani et al., 2017; Kuznetsova et al., 2014). The profound experimental impact of crowding was confirmed by computer simulations (Nawrocki et al., 2019; Sugita and Feig, 2020) and experimental studies (Gao et al., 2016; Gnutt et al., 2015; Guin and Gruebele, 2019; Möckel et al., 2019; Nasir et al., 2019; Neubauer et al., 2007; Reinkemeier et al., 2019; Zosel et al., 2020b) using labeled molecules as tracers even down to the single-molecule level. In the context of living cells, biomolecular condensates formed by liquid-liquid phase separation have recently been recognized as an important mechanism to spatially organize complex biochemical reactions in membrane-less organelles within the cytoplasm or nucleus (Banani et al., 2017; Hyman et al., 2014). We envision that smFRET studies, especially in combination with integrative experimental approaches, will play a central role in uncovering the dynamic organization and interactions within phase-separated droplets in vitro and in living cells.

• In vitro smFRET of membrane proteins. One class of proteins that remains understudied by structural biology in general is membrane proteins, owing to the complexity of membrane protein production, stabilization and crystallization. As smFRET requires only low amounts of protein to be produced and is performed under experimental conditions that potentially limit solubility issues, it serves a vital role here. Indeed, in recent years, smFRET is increasingly being used to study a variety of membrane proteins, including G-protein-coupled receptors (Gregorio et al., 2017; Olofsson et al., 2014), transporters (Akyuz et al., 2013; Ciftci et al., 2020; Dyla et al., 2017; Fitzgerald et al., 2019; Husada et al., 2018; Terry et al., 2018), and ion channels (Bavi et al., 2016; Wang et al., 2016; Wang et al., 2014). For some recent reviews, see Husada et al., 2015; Martinac, 2017; Quast and Margeat, 2019. However, membrane proteins in a living cell are surrounded by specific lipids, proteins, ion gradients and an electric membrane potential. In addition to investing in intracellular smFRET assays, an important challenge for in vitro smFRET on membrane proteins is to further develop ‘cell-mimicking’ assays.

• SmFRET between multiple chromophores. By measuring the transfer of excitation energy between three or more spectrally different fluorophores, multiple distances are obtained simultaneously, and the correlation of the distances can be determined. Following early ensemble implementations (Haustein et al., 2003; Horsey et al., 2000; Ramirez-Carrozzi and Kerppola, 2001; Watrob et al., 2003; Yim et al., 2012), three- and four-color smFRET experiments have been applied to various static (Clamme and Deniz, 2005; Lee et al., 2007b; Stein et al., 2011) and dynamic systems (Ferguson et al., 2015; Götz et al., in preparation; Hohng et al., 2004; Lee et al., 2010c; Lee et al., 2010b; Morse et al., 2020; Munro et al., 2010; Ratzke et al., 2014; Vušurović et al., 2017; Wasserman et al., 2016). FRET to many acceptors has also been reported (Krainer et al., 2015; Uphoff et al., 2010). Multi-color FRET experiments, however, remain challenging, in particular for diffusion-based experiments, because of the increased shot-noise, and the more complex FRET efficiency calculations and corrections. Recent advances in this respect include the development of a photon distribution analysis for three-color FRET to extract three-dimensional distance distributions (Barth et al., 2019) and a maximum likelihood approach applied to the study of fast protein folding (Kim and Chung, 2020; Yoo et al., 2020; Yoo et al., 2018). Further progress in multiple-chromophore smFRET will require expanding the useable spectral range to the near infra-red (which requires better fluorophores and detectors in that region) and measurement of the single-molecule spectra (Lacoste et al., 2000; Squires and Moerner, 2017) rather than the use of individual channels.

• SmFRET with nanomaterials. Emerging structurally synthesized and targeted specific nanomaterials such as quantum dots (QDs) (Jamieson et al., 2007), aggregation-induced emission (AIE) nanoparticles (Hong et al., 2011), and nitrogen-vacancy centers in diamond (Schirhagl et al., 2014; Tisler et al., 2011) have made it possible to implement chemically engineered fluorophores for a wide range of applications in structural biology investigations and, more specifically, in FRET-related studies (Börsch et al., 2009; Medintz et al., 2003; Oh et al., 2005; Shi et al., 2006; Soleimaninejad et al., 2017).

• SmFRET with plasmonics. Placing fluorescent dyes close to metallic nanostructures in ‘plasmonic hotspots’ increases the detectable signal of a single molecule into the megahertz region (Acuna et al., 2012; Grabenhorst et al., 2020). Recent work has shown the possibility of plasmon-assisted FRET (Baibakov et al., 2020; Baibakov et al., 2019; Bohlen et al., 2019). Excitingly, it has recently been shown that tryptophan fluorescence of proteins can be detected with single-molecule resolution in zero-mode waveguides (Barulin et al., 2019), paving the way toward studies using intrinsic labels.

In this article, we have summarized current perspectives on the status of the smFRET field, limitations that still need to be overcome, and joint efforts towards the adoption of consistent methodologies and open-science practices. While this article encourages a discussion regarding optimal smFRET practices, it is important to remember that, as scientists, we should value independence of thought and creativity. Hence, our recommendations should be taken as constructive suggestions, and it is important to realize that many biological questions can be answered using multiple approaches. On the one hand, the reproducibility and reliability of smFRET measurements are currently limited by the variety of approaches taken to calculate the FRET efficiency and the resulting inter-dye distance. Combining years of experience from various experts in an open discussion can help us, as a community, to improve the methodology and overcome many of its challenges. On the other hand, it is important to be open to new ideas and approaches. Here is where open scientific practices can help the community to quickly exchange data and analysis approaches to test new ideas. Such a community effort is necessary to consolidate the role of smFRET as a useful tool in various fields and to jointly move the field forward. Our hope is that these efforts will benefit not only the smFRET community, but also the structural biology community and science in general.

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