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Published in final edited form as: Curr Opin Struct Biol. 2026 May 22;98:103277. doi: 10.1016/j.sbi.2026.103277

Advancements in single-molecule fluorescence spectroscopy for probing conformations, dynamics, and interactions in disordered protein regions

Drake Jensen 1,2, Jasmine Cubuk 1,2, Nirnay Samanta 1,2, Melissa D Stuchell-Brereton 1,2, Andrea Soranno 1,2
PMCID: PMC13359366  NIHMSID: NIHMS2193511  PMID: 42172735

Abstract

Single-molecule fluorescence spectroscopy encompasses a range of techniques to probe dynamic conformational ensembles of intrinsically disordered regions (IDRs). By resolving subpopulation-specific properties and spanning timescales from nanoseconds to seconds, these measurements provide observables often inaccessible with ensemble measurements. Here, we contextualize key challenges and recent advances, including integrating experiments, simulations, and theory to quantify sequence-encoded effects, as well as photon statistic analyses to characterize protein dynamics. We also summarize extensions of single-molecule fluorescence approaches to decode IDR conformations, dynamics, and spatial organizations within biomolecular condensates. Together, these technical advances make single-molecule fluorescence spectroscopy increasingly accessible for studying IDR function, while the complexity of IDR biology and physics continues to drive further methodological innovation.

Introduction

Single-molecule Förster resonance energy transfer (smFRET) and fluorescence correlation spectroscopy (FCS) have been essential for advancing our understanding of biomolecular structural dynamics [1]. Here, we review recent applications and methodological advances in single-molecule fluorescence spectroscopy to study intrinsically disordered regions (IDRs), which lack stable three-dimensional structures yet play key roles in cellular function [2]. These approaches have revealed how sequence properties influence conformations, dynamics, and intermolecular interactions, from ligand binding to inclusion in biomolecular condensates [3,4], across a broad range of length- and time-scales (Figure 1). The application of these techniques has informed underlying polymer physics models and improved computational methods that describe IDR properties. Despite increasing standardization and routine application, recent work highlights that substantial opportunities remain for further methodological development to uncover new aspects of IDR behavior.

Figure 1. Single-molecule fluorescence techniques to study IDRs.

Figure 1

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(Middle) Physical properties of IDRs including dynamics (light blue) and conformational ensemble (dark blue), followed by properties associated with IDR intermolecular interactions including fraction bound (light green), stoichiometry (green), and kinetics (dark green). (Left) Length-scale pertinent to different single-molecule fluorescence approaches and (right) associated time-scales. Colored bars represent the IDR property that can be measured with the corresponding technique. Solid-line boxes (gray) represent the length-scale and time-scale span at which the technique/analytical tool is used. Dashed boxes provide grouping of techniques/analytical tools. Dotted box represents application time-scales for IDR measurements. Acronyms: ALEX, alternating-laser excitation; BVA, burst variance analysis; FRET, Förster resonance energy transfer; FCS, fluorescence correlation spectroscopy; HMM, hidden Markov modeling; IDRs, intrinsically disordered regions; MLE, maximum likelihood estimation; PDA, photon distribution analysis; PET, photon-induced electron transfer; PIE, pulsed interleaved excitation.

Integration of single-molecule FRET and simulations to probe IDR conformations and interactions

smFRET has become an important method for characterizing protein conformations, offering highly precise and accurate inter-dye distance measurements as demonstrated by recent multi-laboratory blind studies [5,6]. In the context of IDRs, integration of smFRET measurements with simulations provides consistency checks for simulated results and enables interpolation of FRET distance-pair information. Improvements in all-atom and coarse-grained force fields have narrowed the gap between experiments and computation. For example, using multiple FRET labeling positions, excellent conformational agreement between simulations and experiments has been reported for the transcription factor Sox2 [7] (Figure 2a), the translation factor eIF4B [8] (Figure 2b), alpha synuclein [9], and the linker histone H1 bound to prothymosin α [10] and to nucleosomes [11]. The narrowing gap between experiments and simulations, and now between experiments and machine learning predictors [12–15], might suggest reliance on computational tools alone to describe IDR conformations. However, because the IDR sequence-conformation space remains vastly unexplored experimentally compared to structured proteins, computational models are only as reliable as the sequences used for benchmarking. Indeed, recent experiments probing sequence properties and solution conditions highlight challenges as sequence space broadens. In the most comprehensive experimental study to date examining how sequence encodes IDR conformational behavior, Holla et al. analyzed 16 equal-length IDRs corresponding to naturally occurring linker regions between folded RNA-binding domains [16]. These IDR linkers varied in hydrophobicity, net charge, charge segregation, and enrichment of specific amino acids (Figure 2e). smFRET revealed large differences in transfer efficiencies (Figure 2c), with chain dimensions not explained by any single-sequence feature (Figure 2d), underscoring the complexity of sequence-to-ensemble relationships.

Figure 2. Integration of smFRET and simulations to probe IDR conformational changes.

Figure 2

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(a) Comparison between measured and simulated transfer efficiencies (<E>) of the eukaryotic transcription factor Sox2 across 11 distinct labeling positions in absence (blue) and presence (red) of a 15bp DNA oligo with corresponding correlation. (b) Comparison between measured and simulated transfer efficiencies for seven distinct labeling positions in the C-terminal region (CTR) of the eukaryotic translation factor eIF4B, in presence (red) and absence (blue) of the DRYG-rich region with corresponding correlation. (c, left) Transfer efficiency histograms for 16 IDRs selected from linker regions in RNA-binding proteins. (c, right) Distance distributions calculated from the self-avoiding walk scaling exponent (SAW-ν) model (colored lines), reweighted ABSINTH simulations (gray lines), and optimized coarse-grained simulations (black lines). Values for the average root-mean-squared distances (R) and the average scaling exponents (ν) are shown for each IDR. (d) Measured mean transfer efficiencies in (c) as a function of sequence hydrophobicity, κ (charge segregation) and the net charge per residue (NCPR). (e) Sequences of 16 IDRs colored-coded for acidic (red), basic (blue), or proline (brown) residues. (f) Correlation of measured mean transfer efficiencies in (c) with coarse-grained and all-atom molecular dynamics simulations performed with parametrized dyes. (g) Comparison of analytical polymer models used to fit root-mean-squared end-to-end distances (Ree) from two representative IDRs in (e) as a function of salt. Models include the mean-field Higgs and Joanny model (H-J, dotted black lines), full ionization model accounting for charge patterning (M0, blue lines), and counter-ion condensation models accounting for sequence specific effects (M1, orange lines) and calibrated on low-salt measured Ree (M2, green lines). Figures are adapted from Refs. (a) [7], (b) [8], (c–e) [16], (f, left) [16], (f, right) [19], (g–h) [20]. IDRs, intrinsically disordered regions.

Multiple computational and theoretical models were evaluated and benchmarked against this smFRET dataset. All-atom force fields commonly employed to describe IDRs, such as ABSINTH [17] and AMBER ff99sbws [18], captured most experimental observations but showed distinct limitations, with ABSINTH showing large deviations for glycine- and asparagine-rich sequences [16] and AMBER improving when the number of salt bridges were reweighted [19]. Additionally, end-to-end distance distributions generated by the deep learning model STARLING, which is trained on coarse-grained simulations, showed comparable agreement with all-atom simulations [13]. Interestingly, explicit inclusion of dyes in both coarse-grained and all-atom simulations modestly improved agreement [16,19] (Figure 2f). These results help contextualize dye effects on sequence dimensions—a common criticism of any dye-based measurement—and underscore the importance of explicit dye incorporation to account for photophysical effects and conformational biases. Finally, the same 16 sequences (along with highly negatively charged IDRs of different sequence lengths) served as benchmarks for polymer theories, helping to derive new predictive models that included counterion condensation effects [20] (Figure 2g,h). One could argue based on the success of simulations that analytical models are surpassed, but such models remain essential for uncovering and generalizing the principles that govern IDR conformations and interactions. Beyond counterion condensation, additional factors such as charge patterning [21,22] and crowding [23,24] also influence IDR conformations and binding. While simulations can reliably capture these contributions and generative models can extend patterns to make predictive inferences, analytical models provide a unifying framework for interpreting underlying mechanisms across diverse sequences.

smFRET provides unique information relative to complementary ensemble approaches such as NMR and SAXS by yielding long-range, position-dependent information. Additionally, the ability of revealing sub-populations arising from conformational biases and/or transient interactions with neighboring domains or binding of ligands is a key advantage of single-molecule detection and is one of the next challenges in improving computational modeling. In this respect, recent smFRET studies have leveraged IDR conformational changes to monitor IDR binding equilibria with other IDRs, with folded protein domains, and with nucleic acids, examining roles of electrostatics, phosphorylation, and chirality [7,8,11,25–30]. Overall, these results highlight the versatility of smFRET, while emphasizing the need to explore broader sequence-conformation and solution-condition spaces to fully capture IDR physics.

Technical and analytical improvements for studying IDR dynamics

Measurements of dynamics are essential for understanding IDR functional mechanisms. IDRs interact through diverse modes, ranging from folding upon binding to forming of highly dynamic complexes, in which conformational flexibility facilitates recognition of specific interaction motifs. Single-molecule fluorescence provides a powerful toolset to study protein dynamics across many time-scales [31] (Figure 1), which is particularly important for IDRs, where intrachain dynamics often occur on nanosecond time-scales but can be slowed down by intermolecular interactions. A common approach to detect dynamics in fluorescence spectroscopy is signal correlation. Nanosecond-FCS (nsFCS) enables measurements of IDR dynamics using both photon-induced electron transfer (PET-nsFCS) and FRET (FRET-nsFCS). PET-nsFCS relies on fluorescence fluctuations associated with formation of a dark, static complex between the fluorophore and a quencher, often an aromatic residue in the protein or another fluorophore [32]. FRET-nsFCS probes inter-dye distance fluctuations through anti-correlated donor–acceptor emission probabilities [33]. However, both methods require extensive data collection to attain sufficient photon statistics. For example, FRET-nsFCS can require ~10 h per measurement, and detection of very fast dynamics may be masked by antibunching from the fluorophore lifetime. Zero-mode wave guides (ZMWs) increase the photon budget, shorten the effective lifetime, and effectively increase the Förster radius, reducing acquisition times by orders of magnitude while extending the accessible time-scale down to a few nanoseconds [34] (Figure 3a), and allowing better resolution of larger distances [35]. However, these same advantages introduce new challenges for quantitative data interpretation, requiring careful calibration of the lifetimes and Förster radius. ZMWs were recently employed to measure the intrachain dynamics of some of the 16 IDR sequences depicted in Figure 2e, where a surprising lack of correlation between chain dimensions, and by extension sequence, with the corresponding reconfiguration times was observed [19]. Importantly, these reconfiguration times agreed well with simulations, underscoring advances in modeling not only conformational ensembles but also their dynamics [19]. As IDR function is dictated by a combination of its conformational ensemble and the associated time-scales on which that ensemble is sampled, this marks another important technical advancement. Understanding how the sequence composition affects dynamics remains an open challenge, in particular with respect to the coupling between internal friction and chain dimensions [36].

Figure 3. New technical and analytical improvements to measure IDR dynamics.

Figure 3

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(a,b) Fluorescence enhancement for single-molecule spectroscopy via nanophotonics. (a, top) Schematic of a zero-mode waveguide (ZMW) with a confocal-based setup and a FRET-labeled peptide. (a, bottom) FRET-nsFCS measurements with donor–donor (green), acceptor–acceptor (red), and donor–acceptor (blue) correlations overlayed from 7 h of acquisition time without (left) and with ZMW (right). Insets depict correlation functions for 20 and 0.2 h of data acquisition for without and with ZMWs, respectively. (b, top) DNA origami nanoantenna attached to gold nanoparticles that enable positioning of a fluorescently labeled single-stranded-DNA-tethered ACTR (yellow) with a freely diffusing fluorescently labeled NCBD (green). (b, bottom) (Left) Comparison of models containing either an instantaneous transition or intermediate step with a distinct FRET efficiency used to estimate the transition path time. (Right) Fluorescence time traces (donor: blue, acceptor: red, FRET efficiency: green) binned at 40 and 20 μs, where inset detects a potential intermediate state. (c–e) Analytical tools used to obtain IDR dynamics and reaction kinetics from fluorescence bursts (donor and acceptor photons in green and red, respectively). (c, top) In time-resolved burst variance analysis (BVA), photons are binned based on varying segment lengths m. FRET efficiency and resulting variance is computed for each m. In absence of dynamics, the variance coincides with the shot-noise width (light gray, left column), whereas in presence of dynamics, the variance can exceed the shot-noise width (dark gray, right column). The excess variance is the differential from shot-noise. (c, bottom) Excess variance as a function of m for simulated data of a freely diffusing particle that undergoes a conformational change between two distinct FRET states. Observed rates of conformational transitions can be obtained from fits to the data (solid lines). (d, top) In photon-pair FRET efficiency correlation spectroscopy (FECS) analysis, a histogram of lag times (τ) between any given pair of photons (donor–donor, acceptor–acceptor, donor–acceptor and acceptor–donor) is normalized by the sum of the four histograms, providing a correlation decay that removes the diffusion component. (d, bottom) Intensity-based and FRET efficiency correlations, obtained from FRET-nsFCS experiments for the IDR ΔMyc, reveal that similar chain dynamics can be computed. (e, top). In maximum likelihood estimation (MLE) methods, interphoton times are used to constrain kinetic models. (e, bottom) Application of MLE to oligomer formation during Аβ42 aggregation. By accounting for brightness and diffusion biases in burst selection criteria, photon count rates and diffusion times enable differentiating monomers from oligomeric species. Figures are adapted from Refs. (a) [34], (b) [56], (c) [44], (d) [45], (e) [52]. FCS, fluorescence correlation spectroscopy; IDR, intrinsically disordered region.

Beyond nsFCS, confocal smFREToffers complementary access to microsecond and slower dynamics using lifetime information [37] and photon statistics. Comparisons of burst lifetimes and transfer efficiencies can report on conformational and interstate dynamics [38,39]. In fluorescence lifetime correlation spectroscopy, lifetime decay functions are correlated [40] and in filtered FCS (fFCS), lifetime decay functions are used to filter the FRET-related correlation functions [41]. Dynamic photon distribution analysis (PDA) enables modeling FRET efficiency distributions, allowing for separation of the shot noise from underlying conformational heterogeneity and dynamic exchange [42]. Other photon stream approaches, such as burst variance analysis [43] characterize dynamics by partitioning bursts into fixed-photon segments. The variance in transfer efficiency within these segments is then compared to the shot -noise variance to determine whether excess width in the transfer efficiency histogram reflects the underlying dynamics. In a new, updated time-resolved form [44], the segment size is systematically varied over the burst duration, and kinetic rates on microsecond to millisecond time-scales are derived from the decay of FRET variance (Figure 3c). This approach enables a more robust identification of multistate dynamic systems by avoiding arbitrary choices of photon counts per segment and by mitigating discrepancies from inconsistent choices of interphoton times. Building on this framework, Terterov et al. introduced FRET efficiency correlation spectroscopy (FECS), a model-independent method to measure dynamics spanning from nanoseconds to milliseconds [45]. In FECS, FRET correlations are generated by sorting photons pairs specific to their type (donor–donor, acceptor–acceptor, acceptor–donor, donor–acceptor) and building a histogram of photon-pair detections lag times, irrespective of the burst from which photons originate (Figure 3d). Normalizing these histograms by those obtained from all photon pairs provides a correlation that removes the diffusion component measured in conventional FCS (Figure 3d), enormously simplifying the readout of multistate interconversion dynamics at slow time-scales that otherwise may overlap with diffusion, while simultaneously providing diagnostics for photophysical effects such as photobleaching.

Additional photon stream analyses include photon-by-photon hidden Markov modeling [46] and maximum likelihood estimation (MLE), which have been extensively tested for surfaced-immobilized systems [47]. These approaches permit quantification of rates faster than the inverse interphoton times and slower than one transition per burst for static and interconverting species alike. Of note are recent advances made for freely diffusing molecules [48–51], which have been applied to IDRs in the context of resolving Аβ42 oligomerization [52] (Figure 3e). One of the most interesting applications of MLE is the quantification of transition path times—the characteristic duration in a successful barrier crossing between two conformational states—typically occurring on microsecond time-scales [53]. Transition paths become relevant in the context of IDRs when characterizing interactions with ligands [54,55]. While these measurements are challenging due to the limited photon budget, plasmonic enhancement using DNA origami nanoantennas was demonstrated to boost the photon count rate [56] (Figure 3b).

Use and challenges of single-molecule fluorescence in biomolecular condensates

Over the past fifteen years, biomolecular condensates—dense cellular assemblies thought to arise through phase separation or percolation of cellular components—have attracted considerable attention [57]. While IDRs have emerged as key elements of these assemblies, linking molecular IDR behavior to mesoscale condensate properties remains experimentally demanding. For single-molecule fluorescence approaches, major challenges for condensate-based measurements arise from: i) high fluorescence background due to large concentrations of unlabeled molecules and accumulated impurities; ii) slow diffusion (if any) of fluorescent molecules, resulting in longer bursts which increase the probability of photo-bleaching; and iii) heterogeneity within the dense phase, which complicates data interpretation. Nevertheless, continuous technical progress has been made within the past 10 years. For instance, FCS has been used to measure molecular partitioning and diffusion in dilute and dense phases and to assess salt effects and aging/aggregation kinetics within condensates [58–63]. These measurements require careful calibration in order to extract absolute concentrations, accounting for refractive index changes, confocal volume distortion, and the background signal [64]; challenges that can be mitigated when using scanning FCS [59,60]. As in the case of in-cell single-molecule detection, use of dyes with wavelengths longer than 530 nm reduces background autofluorescence from molecules in the condensate as shown for nucleophosmin and prothymosin ɑ condensates [65–67] (Figure 4a). FRET-nsFCS has also been applied to quantify the conformational dynamics of prothymosin ɑ within condensates formed by polycationic sequences. By capturing nanoscale conformational fluctuations (via FRET) and microscale translational diffusion (via FCS), these measurements established a mechanistic link between molecular and mesoscopic transport properties within the condensate [67] (Figure 4a). Finally, advances in single-molecule fluorescence localization and particle tracking techniques have enabled assessment of diffusion and heterogeneity within condensates. For instance, point accumulation for imaging in nanoscale topography (PAINT) has been used to measure diffusive dynamics and the spatial orientation of fused in sarcoma (FUS) nanodomains within condensates [68] (Figure 4b). Additionally, single-molecule orientation-localization microscopy, which infers molecular orientations from polarized dipolar emission, resolved nanoscale and microscale heterogeneities within hnRNPA1 and DDX4 biomolecular condensates [69] (Figure 4c).

Figure 4. Single-molecule fluorescence approaches within condensates.

Figure 4

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(a, left) Fluorescence time traces acquired for double-labeled prothymosin ɑ (from top to bottom) as a monomer in solution or in condensates formed in the presence of histone H1, K50, protamine, and R50 polycationic peptides. The diffusion time, τD, is detailed with black lines, representing the average time it takes for a single prothymosin ɑ molecule to pass through the confocal volume. (a, middle) Corresponding transfer efficiency histograms where shaded out regions represent the contribution of donor-only molecules excluded from the correlation analyses. Full FCS curves are depicted for the donor–acceptor cross-correlation, where τrot is the segmental rotational correlation time, τr is the chain reconfiguration time, and τD is the translational diffusion time. (a, right) Plot of τr, τD, and viscosity obtained for prothymosin ɑ within condensates measured across various KCl concentrations. (b) Time-lapse single-molecule tracking point accumulation for imaging in nanoscale topography (SMT-PAINT) reconstructions of individual FUS molecules (left) and of FUS nanodomains formed within a condensate (right) permit spatial orientations and diffusive behavior to be measured. (c) Comparison of single-molecule localization microscopy (SMLM) (top) and single-molecule orientation-localization microscopy (SMOLM) (bottom) images obtained for hnRNPA1 condensates, showing localization of the merocyanine 540 (MC540) dye, which selectively partitions into hydrophobic environments. SMOLM indicates a preference for MC540 to orient parallel to the condensate interface. Figures are adapted from Refs. (a) [67], (b) [68], (c) [69]. FCS, fluorescence correlation spectroscopy.

Forthcoming methodologies to study IDRs

Recent technical advances are poised to make single-molecule fluorescence measurements of IDRs more accessible by expanding detection range and sensitivity. Open-source hardware platforms [70,71] provide cost-effective instrumentation, while automation can increase measurement throughput [72]. Among the ever-growing class of super-resolution microscopy, MINFLUX (MINimal fluorescence photon FLUX microscopy) [73] has been shown to quantify distance distributions with Angstrom resolution, below and above the typical detection limits of standard FRET experiments, while only needing minimal photon counts [74,75]. New applications of FRET-fluorescence lifetime imaging can further help in resolving intermolecular interactions with 20–30 nm resolution [76]. Use of Anti-Brownian ELectrophoretic (ABEL) traps extend observation times of freely diffusing molecules from milliseconds to seconds, permitting deconvolution of closely spaced FRET populations [77,78] and simultaneous measurements of the hydrodynamic radius from a molecule’s Brownian motion within the trap [78,79]. While these experimental approaches have yet to be applied to IDRs directly, their proven feasibility in nucleic-acid polymer systems suggests that they will become increasingly valuable for future studies.

Despite the progress in understanding the biophysics and biochemistry of IDRs, many unresolved aspects remain to be addressed, such as resolving conformations and dynamics in both bistable structural ensembles and micro-phase and micellar states of IDRs, in vitro and in cell. While in cell IDR measurements remain challenging, requiring the use of large fluorescent proteins or microinjection of synthetically labeled proteins, recent advances demonstrate feasibility of in situ-specific labeling with synthetic dyes for characterizing IDRs [80]. With current and forthcoming technical advances, single-molecule fluorescence approaches continue to be well suited to address the complexities of IDR biology across large spatial and temporal ranges, addressing how conformational ensembles and dynamics inform function.

Acknowledgements

Research reported in this publication was supported by the National Institute of Allergy and Infectious Diseases of the National Institutes of Health under Award Numbers F32AI186575 to Drake Jensen and R01AI163142 to Andrea Soranno and by the National Heart Lung and Blood Institute under Award Number R01HL179124 to Andrea Soranno. The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health.

Declaration of competing interest

The authors declare the following financial interests/personal relationships which may be considered as potential competing interests: Andrea Soranno and Drake Jensen report that financial support was provided by the National Institutes of Health. Andrea Soranno and Drake Jensen report relationships with the National Institutes of Health that include funding grants. If there are other authors, they declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

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