{"paper_id":"9753a3b2-d0d3-4c03-b691-bd5452709d8c","body_text":"Label-free optical observation of disordered-to-ordered transitions in single intrinsically disordered proteins | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Article Label-free optical observation of disordered-to-ordered transitions in single intrinsically disordered proteins Saaman Zargarbashi, Cyril Dominguez, Matthew Peters, Arman Yousefi, and 9 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8222117/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 11 You are reading this latest preprint version Abstract Intrinsically disordered proteins (IDPs) and structured proteins with intrinsically disordered regions (IDRs) lack a definitive tertiary structure and contribute to the onset of diseases such as Alzheimer’s and cancer. To date, experimental observation of single, label-free IDPs/IDRs poses a significant challenge due to their structural heterogeneity limiting ensemble techniques from fully capturing their properties, whilst single-molecule measurements require site-specific modifications or non-physiological conditions, perturbing their native biophysics. Here, we demonstrate the first experimental observation of unmodified IDP/IDR conformational dynamics at the single-molecule level, achieved by optical trapping and investigation of individual IDPs/IDRs using nanoaperture optical tweezers. Our results reveal that IDPs/IDRs exhibit significantly larger conformational variations compared to globular proteins of similar size. We demonstrate that phosphorylation of native tau-441 by glycogen synthase kinase 3-beta (GSK3β-tau) induces compaction and reduced conformational dynamics. We further observed a disorder-to-order transition during binding of the N-terminal region of Src-associated protein in mitosis of 68 kDa (Sam68) to G8.5 RNA. These findings present nanoaperture optical tweezers as a powerful approach to advance our understanding of IDPs/IDRs and further decode their roles in associated diseases. Biological sciences/Biochemistry Biological sciences/Biophysics Biological sciences/Structural biology Figures Figure 1 Figure 2 Figure 3 Figure 4 1. Introduction Intrinsically disordered proteins (IDPs) and intrinsically disordered regions (IDRs) in structured proteins, which constitute approximately 70% of the human proteome 1 , play critical roles in biological processes including neurotransmitter regulation 2 , microtubule regulation 3 , and transcription 4 . Their significant prevalence and roles in the development of various diseases, many of which lack effective treatments and reliable early-stage diagnosis, make IDPs/IDRs important targets for research. Example IDPs include tubulin associated unit (tau) protein and alpha synuclein, which are implicated in neurodegenerative disorders 5 such as Alzheimer’s disease 6 and Parkinson’s disease 7 , respectively. IDP/IDR related diseases also extend to cancer 8 such as the IDR Sam68 (Src-associated protein in mitosis of 68 kDa), which has implicated involvement in ovarian, kidney and lung cancers 9 . IDPs/IDRs are conformationally heterogenous, dynamically fluctuating between different shapes with variable structure, known as a conformational ensemble 1 . This structural flexibility is often integral to the functions of IDPs, where binding to select targets can induce a particular structure necessary for biological activity. These structures present varying levels of disorder, such as an IDP transitioning to a structured conformation, as seen in disorder-to-order transitions 10 , 11 , or the IDP/IDR retaining partial or complete disorder upon binding to form fuzzy complexes 12 . Their heterogeneity and absence of a defined folded state renders many experimental and computational approaches 13 , which were developed for structured proteins, largely inadequate. Understanding their conformational ensemble and its link to their functions is key to understanding their biophysics. Ensemble measurements, such as nuclear magnetic resonance 14 , small angle x-ray scattering 15 , and dynamic light scattering 16 , while very informative, cannot entirely capture the heterogeneity among individual copies of the protein. Structural protein characterisation techniques such as cryogenic electron microscopy (cryo-EM) and x-ray crystallography are suitable for globular proteins, but these techniques yield poor resolution of conformational heterogeneity, leading to low or an absence of electron density in micrographs 17 , 18 . Additionally, such techniques only capture a snapshot of the protein motion on its conformational landscape, losing dynamic information, which are integral to IDPs/IDRs. Notably, cryo-EM is progressing towards overcoming some of these limitations, such as the advent of single particle cryo-EM 19 and use of artificial intelligence to map flexible areas 20 . Single-molecule fluorescence resonance energy transfer (smFRET) and single-molecule force spectroscopy (smFS) can provide excellent information such as free-energy landscapes 21 , tracking intramolecular displacement 22 , and conformational dynamics 23 , and have even provided insight into labelled IDPs over a decade ago 24 , 25 . However, the requirement of labelling the protein with an extrinsic fluorophore for smFRET or tethering it to a surface for smFS can perturb the structure and dynamics of the protein 26 , 27 , particularly for IDPs/IDRs where many are observed to undergo disorder-to-order transitions upon binding 10 , 11 , 28 . Currently, no established protein characterisation technique can capture the conformational dynamics of label-free IDPs/IDRs at the single-molecule level. Nanoaperture optical tweezers (NOTs) utilise localised surface plasmon resonance to trap a single protein molecule and observe its conformational changes in solution without any chemical modifications 29 , 30 . In recent years, NOTs have provided information on single, label-free proteins including protein-binding interactions 31 , conformational transitions 32 , disassembly kinetics 33 , and energy landscapes 34 . Here, we utilise NOTs to monitor the conformational dynamics of label-free IDPs/IDRs in solution, elucidate their free-energy landscapes, and observe a disorder-to-order transition of an IDR upon binding to RNA. We focus on three different IDPs/IDRs associated with disease progression: native tau-441, tau-441 phosphorylated by glycogen synthase kinase 3-beta (GSK3β-tau) and the N-terminal region of Sam68. This work reveals differences in structural flexibility between single IDPs and globular proteins of similar size, provides experimental evidence of structural changes due to phosphorylation of IDPs, and the trajectory of a single IDR transitioning between disordered and ordered structures upon binding and unbinding of a nucleic acid binding partner, with well-resolved binding kinetics. All the above insights into the conformational dynamics of label-free IDPs/IDRs were previously inaccessible to any single-molecule approach. 2. Results and Discussion 2.1 Transmission traces distinguish ordered and disordered structures Trapping of either an IDP/IDR or a globular protein is achieved using a gold double-nanohole (DNH) structure (Fig. 1 a; full setup in Fig. S1 ). When a molecule enters the trapping region, the disparity in the refractive index between the trapped molecule and the surrounding media affects the light scattered by the nanoaperture. This scattering directly correlates to the polarisability of the trapped protein, which is determined by its volume, conformation, and dielectric constant 35 , 36 . The forward-scattered light is collected by an objective (NA = 0.1) and is then recorded as transmission intensity ( I ) by an avalanche photodiode (APD) 37 (Fig. S1 ). We analyse protein dynamics using the normalised transmission intensity change, Δ I/I 0 , where I 0 is the baseline intensity of an unoccupied DNH, and Δ I = I - I 0 represents the change in transmission intensity from this baseline (Fig. 1 b). In this work, we trap all proteins with the laser power that produces a local temperature of ~ 37°C (i.e., 20 mW, see Fig. S2) at the trapping site. Comparing representative trapping trajectories for an IDP and a globular protein reveals distinct dynamic behaviours (Fig. 1 b). The initial increase in Δ I/I 0 corresponds to the protein entering the DNH trap, with the signal magnitude linearly scaling with protein size (Fig. S3d) 33 . Due to the lateral and rotational movements of the trapped proteins within the potential well, we observed a significant increase in signal fluctuation (Fig. 1 b). For the globular protein BSA, these motions account for most of the signal variations, whereas the IDP GSK3β-tau displays substantially larger fluctuations, reflecting additional conformational dynamics superimposed on its translational and rotational movements. This trend is observed across multiple trapping traces involving various IDPs/IDRs and globular proteins (Figs. S4a and S4b) rather than being specific to tau and haemoglobin. Figure S4c confirms that IDPs/IDRs demonstrate higher normalised root-mean-square (NRMS) values, and therefore are more dynamic, than globular proteins of similar molecular weight. These enhanced fluctuations, reflecting the highly dynamic structural transitions of IDPs/IDRs, occur predominantly below 1 kHz, as shown in the power spectral density (PSD) plot (Fig. 1 c). Further analysis of the trapping dynamics using autocorrelation (Fig. 1 d) reveals that, in addition to a fast component (~ 3.6 ms) associated with trap relaxation, GSK3β-tau exhibits a second, slower exponential decay component with a time constant of ~ 30 ms, corresponding to its intrinsic conformational transitions. Two factors may contribute to the large optical signal variations observed in IDPs. First, the loose, extended conformation of IDPs exposes a greater surface area to the solvent, resulting in a larger hydration shell with higher water density than that of globular proteins 38 , 39 . This hydration effect increases the local refractive index around the protein in its elongated states, leading to greater transmission changes. Second, elongated particles of equivalent volume produce orientation-dependent Δ I/I 0 signals, as demonstrated by our simulations (Fig. 1 e, see SI-3 for details of FDTD simulation). IDPs/IDRs continuously switch between different extended conformations on microsecond-to-second timescales 40 , producing intrinsic Δ I/I 0 fluctuations that reflect their conformational sampling and orientations. We hypothesise that elongated globular proteins may preferentially adopt a trapping orientation that maximises the local electric field through the self-induced back action (SIBA) trapping mechanism, and consequently, results in a higher average Δ I/I 0 value than their spherical counterparts, as previously reported 31 , 34 , 41 . As detailed in the supplementary information (SI-3 and SI-5), Δ I/I 0 depends critically on the refractive index, shape, and orientation of the protein within the DNH gap. These dependencies enable Δ I / I 0 to serve as an indicator for the global compactness of the trapped protein. 2.2 Phosphorylation induced order of tau by GSK3β Figures 2 a- 2 c compare the transmission-time traces corresponding to the trapping of the IDP native tau-441 and its phosphorylated variant GSK3β-tau, with those of the globular protein haemoglobin. Trapping these proteins using DNHs with similar dimensions resulted in comparable Δ I / I 0 of approximately 0.1 (Figs. 2 a–c), due to their similar molecular weight (native tau-441, 45.9 kDa, GSK3β-tau, ~ 46–48 kDa, and haemoglobin, 64.5 kDa). The extended conformations of IDPs result in higher polarisability than globular proteins of similar molecular weight, which explains why native tau-441 and GSK3β-tau exhibit Δ I/I 0 values comparable to haemoglobin despite their lower molecular weight. The trace segments in Fig. 2 d reveal substantially different fluctuations in Δ I/I 0 between the three proteins. Native tau-441 and GSK3β-tau resulted in larger fluctuations compared to haemoglobin, consistent with the higher flexibility of IDPs relative to globular proteins. When the laser is turned off for several seconds then turned back on, the transmission intensity returns to the baseline level (Figs. 2 a- 2 c), indicating that the protein molecule diffused away from the trap without surface adsorption. These results demonstrate the effectiveness of the PEG-thiol coating in minimising nonspecific protein adsorption, a major challenge when studying IDPs/IDRs, particularly under conditions where the trapping force retains them close to the gold surface for extended periods. Occasionally, however, the protein did not diffuse away after the laser was turned off, as shown in Fig. S6. These events were excluded from subsequent analysis and discussion in this work. The results shown in Figs. 2 a- 2 d suggest that GSK3β phosphorylation of native tau-441 induces increased order and compaction. Two mechanisms likely underlie these changes: electrostatic interactions and secondary structure formation. First, native tau-441 has a theoretical isoelectric point of ~ 8.24, carrying a net positive charge at pH 7.2. Phosphorylation introduces negatively charged phosphate groups, subsequently reducing the net charge and promoting compaction, consistent with previous reports 44 , 45 . Second, phosphorylation may shift local secondary structure. GSK3β phosphorylation has been shown to increase α-helix propensity at the expense of polyproline type II (PPII) helices within the proline-rich domain of tau 46 (Fig. S7). As α-helices are shorter than PPII helices (5.4 Å/turn vs. 9.3 Å/turn) 47 , this structural transition would also induce compaction in native tau-441. Supplementary SI-8 provides an in-depth discussion of potential phosphorylation effects. Analysis of the autocorrelation functions for native tau-441 and GSK3β-tau (Fig. 2 e) revealed an additional secondary decay (~ 26 ms) in the phosphorylated variant. In contrast, native tau-441 exhibited only a single exponential component (~ 10 ms), likely arising from the similar timescales of confined trapping dynamics and conformational fluctuations. To confirm that GSK3β phosphorylation shifts native tau-441 towards more compact and ordered conformations, we performed additional trapping experiments shown in Figs. 2 f- 2 h (five second variants of these traces are shown in Figs. S8a-c). GSK3β-tau consistently exhibited reduced dynamic behaviour compared to native tau-441, demonstrated by less variation in Δ I/I 0 . While the ACF for native tau-441 could sometimes be fit with a double-exponential decay, suggesting separable conformational and trapping dynamics, GSK3β-tau consistently exhibited a slower time constant. Compared to native tau-441, the PSD of GSK3β-tau (Fig. S8d) displays lower power fluctuations in the 1 Hz–1 kHz range, indicating its increased order and restricted large-scale dynamics occurring on the second–millisecond timescale. These results suggest that GSK3β phosphorylation introduces structural order into tau. Rather than fluctuating continuously among disordered states on millisecond timescales, the phosphorylated variant samples more confined conformations that fluctuate over tens of milliseconds. We note that the number and location of phosphorylated residues in GSK3β-tau may vary from between each molecule. Consequently, this may contribute to the heterogeneity observed in the trapping trace patterns among individual GSK3β-tau molecules. Probability density functions (PDFs) of the transmitted intensity reveal how frequently a protein samples different conformations, providing valuable insight into its accessible structural states. Deconvolution of the PDFs using a point spread function removes the translational and rotational motion of proteins and allows extraction of the PDFs associated with the protein’s true conformational changes. Figure 3 a shows that the deconvoluted PDF for haemoglobin reveals a single, sharp peak, consistent with it existing predominantly in a single conformational state, characteristic of a globular protein. In contrast, the deconvoluted PDFs of both GSK3β-tau and native tau-441 exhibit broader Δ I/I 0 distributions, indicating greater conformational variability and a more dynamic behaviour than haemoglobin. Native tau-441 displays the broadest distribution, suggesting it samples a wider conformational landscape and possess greater structural flexibility than GSK3β-tau. Converting the PDF of single-molecule folding trajectories into free-energy landscapes is well established in techniques such as FRET 48 and smFS 49 . These landscapes provide valuable insights into protein folding, including the number of distinct conformational states, their relative free-energy differences, and how these are altered by binding interactions 50 . Recently, NOTs have been used to resolve the energy landscape of a single unmodified protein 34 . For label-free monomeric IDPs/IDRs, experimental derivation of energy landscapes is particularly important for understanding their conformational dynamics, molecular interactions, and biological functions 50 . Until now, this information has been accessible only through computational modelling 51 , 52 , due to the challenges of experimentally measuring label-free IDPs/IDRs at the single-molecule level. Here, we present the first experimentally derived measurement of the free-energy landscape of label-free IDPs at the single-molecule level. 2D energy landscapes were calculated by taking the negative logarithm of the deconvoluted PDFs, as described by Eq. S3, and are shown in Figs. 3 a and 3 c (black curves). Relative free-energy values are expressed in units of k B T , where k B is Boltzmann’s constant and T is temperature, representing the energy available to the protein. As expected, haemoglobin exhibits a sharp funnel-like landscape characteristic of globular proteins 53 , whereas native tau-441 and GSK3β-tau display multiple shallow minima with lower energy barriers between them, consistent with a broad ensemble of conformational states typical of IDPs 53 . Across all experiments, the deconvoluted PDFs and energy landscapes of GSK3β-tau revealed more compact conformational ensembles, consistent with their smaller Δ I / I 0 fluctuations. To better visualise the complex conformational dynamics of these IDPs, we converted the 2D energy landscapes into 3D plots. First, we applied multi-peak Gaussian fits to the deconvoluted PDFs to identify predominant conformational states (black dashed curves in Fig. 3 a, with individual peaks presented in Fig. S10a). These fitted PDFs were then used to reconstruct the corresponding energy landscapes (black dashed curves, Fig. 3 a), which were further converted to 3D plots (Fig. 3 b) by mapping the peak positions onto a polar coordinate system (Fig. S11). In these plots, angular coordinates reflect the relative spatial distribution of conformational states, while the peak amplitudes correspond to the negative logarithm of the fitted PDF, such that a higher probability indicates a lower free energy. Haemoglobin, as expected for a globular protein, displays a single, funnel-shaped energy landscape consistent with a single, stable folded conformation (Fig. S12). For native tau-441, the 3D energy landscapes reveal multiple shallow troughs with low energy barriers, consistent with the conformational heterogeneity expected in IDPs. In contrast, alongside shallow troughs similar to those observed in native tau-441, GSK3β-tau exhibits several deep, well-defined troughs, indicating the presence of distinct and stable conformational states (Fig. 3 a). These states are also apparent in the top-down projection shown in Fig. S13. The continuous energy landscapes over 120 seconds from native tau-441 and GSK3β-tau confirm conformational dynamics consistent to those observed in the 20-s intervals (Fig. S14). 2.3 Disorder to order transition of the Sam68 N-terminal Our results demonstrate that NOTs enable the direct and prolonged observation of conformational dynamics in single, unmodified IDPs, and allow reconstruction of their free-energy landscapes. To illustrate the broader utility of this approach in capturing disorder-to-order transitions at the single-molecule level, we investigated the RNA binding kinetics of Sam68, a system not previously studied using single-molecule methods. Sam68 is an RNA-binding protein composed of three main regions: the N-terminal (residues 1–96), the central STAR binding domain (residues 97–260), and the C-terminal (residues 261–443) 54 . It binds to G8.5 RNA, a 40-nt RNA sequence, with a dissociation constant ( K d ) of ~ 12 nM determined from an electrophoretic mobility shift assay 55 . This affinity, however, is significantly reduced to around 36.1 µM when only the central STAR domain is present 56 , suggesting that the intrinsically disordered N- and C-terminal regions contribute to strong binding. NMR experiments later confirmed that both termini independently bind G8.5 RNA, with affinities of 1–10 µM for the N-terminal and 30–70 µM for the C-terminal 57 . Here, we focus on the Sam68 N-terminal IDR and its interaction with G8.5 RNA, where the RNA-binding activity is attributed to the two arginine/glycine (R/G)-rich motifs ( 45 RGGGGG 50 and 52 RGG 54 ) on the Sam68 N-terminal, and the adenosine/uracil (A/U) rich motif ( 22 AUUAAAA 28 ) in G8.5 RNA 58 (full sequences are presented in Figs. S15a and S15b). We trapped the N-terminal of Sam68 for ~ 20 minutes before introducing 1 µM G8.5 RNA to monitor binding dynamics (Fig. 4 a). Upon RNA arrival at the trapping site (~ 30 min after initial trapping), a sharp increase in transmission occurs, indicative of a higher polarisability for the RNA-bound complex compared to the unbound protein. This increase is accompanied by a significant reduction in signal fluctuations (Figs. 4 a and 4 b), consistent with a transition to a more stable and ordered structure, representing the first direct observation of a disorder-to-order transition in the Sam68 N-terminal IDR. Figure 4 c compares PSDs of the RNA-bound and unbound states of N-terminal region. The RNA-bound state exhibits reduced signal fluctuations compared to the unbound state, particularly at frequencies below 1 kHz (> 1 ms), suggesting restricted dynamics and enhanced structural stability induced by RNA binding. This binding is reversible, with the signal returning to pre-binding levels upon RNA dissociation. The transmission trajectory during binding/unbinding shows three discrete transmission levels (Fig. 4 d), with PDF peaks corresponding to: (i) the unbound Sam68 N-terminal, (ii) the RNA-bound state, and (iii) an intermediate level likely reflecting co-localisation of Sam68 and G8.5 RNA within the trap without complex formation. To quantify these binding kinetics, we applied a three-step fitting model, assigning levels to the bound and unbound states (Fig. 4 e). Exponential decay fits applied to the histogram of association time ( \\(\\:{\\tau\\:}_{\\text{o}\\text{n}}\\) ) and dissociation time ( \\(\\:{\\tau\\:}_{\\text{o}\\text{f}\\text{f}}\\) ) yielded average \\(\\:{\\tau\\:}_{\\text{o}\\text{n}}\\) and \\(\\:{\\tau\\:}_{\\text{o}\\text{f}\\text{f}}\\) values of 1416 ms and 177 ms, respectively (Figs. 4 f and 4 g). From these values, we estimated a dissociation constant ( K d ) of ~ 8 µM, consistent with previous NMR results (1–10 µM) 57 . Details of the K d calculation are available in Supplementary SI-14. Notably, in some instances, RNA binding induced a transition to a persistent ordered conformation (Fig. S17), likely due to the relatively high affinity. While such traces cannot quantify the dissociation constant, they consistently showed attenuated signal fluctuations in the RNA-bound state, with dynamics reduced on timescales of hundreds of microseconds to seconds. Additional context for this observation is provided in Supplementary SI-15. Conclusions This work demonstrates a key strategy for directly probing the conformational dynamics of single, label-free intrinsically disordered proteins (IDPs) and intrinsically disordered regions in structured proteins (IDRs) in solution. The trapping signals from nanoaperture optical tweezers (NOTs) reveal how IDP/IDR structural disorder manifests through distinct intensity fluctuations, energy states, and dynamic timescales, compared to globular proteins. At single-molecule resolution, we provide experimental evidence that phosphorylation of native tau-441 by GSK3β reduces conformational heterogeneity, with suppressed dynamics observed on the millisecond–second timescale. We further demonstrate the real-time observation of a disorder-to-order transition in the Sam68 N-terminal region upon RNA binding, with dissociation kinetics consistent with ensemble measurements. These findings provide unique insights into the conformational dynamics of disordered proteins that are inaccessible to conventional single-molecule techniques, expanding the current experimental toolkit for studying protein disorder and its role in biological function and disease. Materials and Methods Fabrication of double nanohole structures in gold film. The double nanohole (DNH) structures used in this work were fabricated as previously described 31 , 32 , 37 . A 550 µm thick fused silica wafer was coated with a 30 nm silicon nitride layer using low-pressure chemical vapor deposition (LPCVD). Subsequently, a 5 nm titanium layer and a 100 nm gold layer were deposited using electron-beam evaporation at 190°C. The wafers were diced to 10 mm × 10 mm chips for further use. Two nanoholes, each with a depth of 90 nm, diameter of 160 nm and a centre-to-centre distance of 200 nm were etched into the gold layer using a focused ion beam (FIB, Zeiss Crossbeam) with a gallium ion source. A rectangle of 3 nm in height was then etched to connect the edges of the two holes to form the DNH gap. The FIB was operated at 30 kV with a beam current of 1 pA. Double nanohole surface passivation. Double nanohole structures were passivated using polyethylene glycol methyl ether thiol (PEG-thiol, average MW 800 Da, 729108, Sigma Aldrich) as previously described 31 , 37 . The double nanohole samples were immersed in a solution comprised of 2 mM PEG-thiol in ethanol and left overnight (~ 18 hours) before being rinsed thoroughly with ethanol and dried using an air gun. Solutions were freshly prepared before each use. Nanoaperture optical tweezers setup. Optical components were purchased from Thorlabs as previously described 31 , 32 , 37 . A half-wave plate adjusts the polarisation of the 852 nm laser (Thorlabs, FPL852) to be across the pointed edges of the gap between the two nanoholes ( y- axis, Fig. 1 a) 31 , 59 . The laser was collimated and expanded to 5 mm in diameter and then focused onto the DNH using a 100× objective (1.25 NA PLN100XO, Olympus). The laser power on the DNH was around 20 mW correlating to around 37 o C at the focusing point due to laser heating (Fig. S2). Light passing through the sample was collected with a 4× objective (0.1 NA PLN4XP, Olympus), then was focused onto an avalanche photodiode (APD120A/M, Thorlabs), which converted the light intensity to a voltage signal. Data acquisition. The avalanche photodiode (APD120A/M, Thorlabs) has a bandwidth of 50 MHz. However, considering the signal bandwidth of the system (~ 10 kHz) and to optimise the file size, the voltage signal was recorded at a sampling rate of 1 MHz using a data acquisition card (USB-6361, NI) controlled by a custom LabVIEW program. Based on the Nyquist frequency, this system provides a theoretical time resolution of 2 µs. Microfluidics system. Flow cells were printed using a FormLab 2 printer with Clear V4 resin at a resolution of 50 µm (FormLabs Inc, USA), as previously described 31 , 32 , 37 . Two component silicone glue (Twinsil, Picodent, Germany) was used to seal the DNH structure within the flow cell with a 0.17 mm thick glass coverslip. A 50 µm thick piece of double-sided tape (Arcare 92712, Adhesive Research, Inc) was used to separate the DNH and glass coverslip, creating a chamber with a volume of 3.5 µL. Flow rate and flow direction were controlled using a syringe pump (Harvard Apparatus, US) through a 12-valve distributor (MUX Distributor, Elveflow, France). For Sam68 N-terminal experiments, the RNA solution is infused to the chamber at a flow rate of ~ 1–2 µL/min while Sam68 N-terminal was trapped. Given the ~ 10 µL dead volume of the intake tubing, this flow rate required ~ 5–10 minutes for RNA solution to arrive at the trapping site. Protein and RNA preparation. Human haemoglobin (H7379, Sigma Aldrich), human native tau-441 (T0576, Sigma Aldrich), human GSK3β-tau-441 (SRP0689, Sigma Aldrich), bovine ribonuclease A (R5500, Sigma Aldrich), and bovine serum albumin (A8531, Sigma Aldrich) were prepared in a filtered buffer solution of 0.1 M bis-tris propane, 150 mM NaCl and 20% glycerol at pH 7.2. Bovine actin (A3653, Sigma Aldrich) was prepared in 0.1 M bis-tris propane, 0.2 mM CaCl 2 , 0.2 mM ATP and 20% glycerol at pH 7.2. Human Sam68 N-terminal (amino acids 1–96) and C-terminal (amino acids 267–368) sequences were cloned and produced at the University of Leicester and comprised as previously described 57 (sequence also available in Fig. S15a). G8.5 RNA sequence was bought from Dharmacon, Horizon Discovery and is the same as previously described 55 (sequence also available in Fig. S15b). Both protein solutions were prepared in a filtered solution containing 50 mM sodium phosphate and 150 mM NaCl at pH 6.8. Proteins were aliquoted into 1 µM aliquots of 100 µL volume and immediately flash frozen in liquid nitrogen before being stored at -20°C, except for GSK3β-tau which was stored at -80 o C. The proteins were slow thawed on wet ice on the day of use for an experiment. Data analysis. Custom MATLAB scripts were used to analyse all the data in this work. Data filtering . Raw data were filtered using a zero-phase Gaussian low-pass filter to the desired cut-off frequency by using the filtfilt.m function. Normalisation of optical transmission traces . We used normalised transmission intensity, Δ I/I 0 , to quantify the relative transmission change upon trapping a single protein. Since the optical signal was recorded by the avalanche photodiode as voltage (V), with Δ I/I 0 calculated as Δ I/I 0 = ( V – V 0 )/ V 0 . For trapping traces shown in Figs. 2a-c, 4a, S4a-b, S9a-c, and S17a, V 0 is the mean value of the baseline, whilst for the trapped transmission traces (Figs. 2 f-h, S8a-c, and S17b-c), V 0 corresponds to the mean value of the trace. Probability density function (PDF) . We filtered the 20-s trace with a cutoff frequency of 10 kHz and calculated the PDF by estimating the kernel density using the ksdensity.m function with 300 points. This cutoff frequency was chosen based on the PSD analysis (Figs. 4 c, S8d, and S17d), which shows that protein-induced signal variations remained distinguishable from empty DNH noise up to 10 kHz. Trace detrending . To allow well-assigned levels for step fitting, we removed the linear drift of the whole trace using the detrend.m function from MATLAB 2022b, as shown in Fig. S16. Deconvolution of PDF and energy landscapes . See details in Supplementary SI-11. Power spectral density (PSD) . To estimate the PSD of the time-domain signal across the trapping trace, we used the sampling frequency ( f ) and the signal vector ( XXX ). The frequency vector was taken over half of the frequency spectrum, from 0 to f /2 for the Nyquist frequency using a linearly spaced grid with the number of data points ( N )/2 size. The power spectrum was then computed using the Fast Fourier Transform (FFT) and the squared magnitude was obtained using |FFT( XXX )| 2 before normalisation by N multiplied by f as shown in Eq. 1 : $$\\:{Pxx}_{\\text{t}\\text{e}\\text{m}\\text{p}}=\\:\\frac{FFT\\left(XXX\\right)\\times\\:conj\\left(FFT\\left(XXX\\right)\\right)}{N\\times\\:f}$$ 1 To remove negative frequency components and conserve the total power in the spectrum, the power spectrum was truncated to N /2 points and multiplied by 2 as shown in Eq. 2: \\(\\:Pxx=2\\times\\:{Pxx}_{\\text{t}\\text{e}\\text{m}\\text{p}}(1:[\\frac{N}{2}]\\) ) (2) Finite-difference time-domain simulations. The transmission of DNH structures were modelled based on finite-difference time-domain (FDTD) using a commercial software (Lumerical, Ansys). See Tables S1 and S2 in Supplementary SI-3 for parameters. Volume versus transmission changes . The relationship between particle volume and the change in transmission (Fig. S3d) were simulated by placing a spherical particle (refractive index n = 2.0) with radii ranging from 2 to 6 nm in the centre of the DNH ( x = 0, y = 0, z = 8) (Table S1 ). Laser heating Simulation. The laser heating simulation is similar to that described previously 32 , with details listed in SI-2. Declarations Disclosures The authors declare that they have no competing interests. Code and Data Availability The code and data supporting the presented findings are available from the corresponding author upon reasonable request. Funding This research work was supported by the UK-India Education Research Initiative (UKIERI), Scheme for Promotion of Academic and Research Collaboration (SPARC), and the Academy of Medical Sciences Springboard Award (SBF0010\\1008). S.Z. acknowledges support from the Biotechnology and Biological Sciences Research Council Doctoral Training Partnerships (BBSRC DTP) (BB/T0083690/1). M.R. appreciates the support from the Royal Society, the Royal Society Yusuf Hamied Visiting Fellowship, and the Wolfson Foundation. C.D and A.H. acknowledge support from the BBSRC sLoLa grant (BB/T000627/1). Author Contribution C.Y. conceived the project. S.Z. and Y.W. performed trapping experiments for all proteins and prepared all non-Sam68 protein buffers. S.M. prepared Sam68 constructs. S.Z. and A.Y. performed nanofabrication of DNHs. A.Y. provided support for trapping experiments and nanofabrication. C.D and A.H. provided support for Sam68 content. C.Y. and M.P. conducted energy landscape calculations. S.Z. and C.Y. performed the data analysis. S.C. provided guidance on native tau-441 and GSK3β-tau. R.G. provided guidance on plasmonic nanotweezers and data interpretation. C.J.M., L.X., M.R., and C.Y. provided S.Z. supervisory guidance. The manuscript was written by S.Z., with assistance from C.D., M.P., A.Y., S.C., A.H., R.G., C.J.M., L.X., M.R., and C.Y. Data Availability The code and data supporting the presented findings are available from the corresponding author upon reasonable request. References Holehouse, A. S. & Kragelund, B. B. The molecular basis for cellular function of intrinsically disordered protein regions. Nat Rev Mol Cell Biol 25, 187–211 (2024). Snead, D. & Eliezer, D. Intrinsically disordered proteins in synaptic vesicle trafficking and release. Journal of Biological Chemistry 294, 3325–3342 (2019). Melo, A. M. et al. A functional role for intrinsic disorder in the tau-tubulin complex. 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18:53:55\",\"extension\":\"html\",\"order_by\":13,\"title\":\"\",\"display\":\"\",\"copyAsset\":false,\"role\":\"acdc-reference\",\"size\":148450,\"visible\":true,\"origin\":\"\",\"legend\":\"\",\"description\":\"\",\"filename\":\"earlyproof.html\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-8222117/v1/bc1d1f903185a98ed4778e00.html\"},{\"id\":98457299,\"identity\":\"45d31d36-e276-4638-b758-bac4e6ac6c3c\",\"added_by\":\"auto\",\"created_at\":\"2025-12-17 18:53:55\",\"extension\":\"png\",\"order_by\":1,\"title\":\"Figure 1\",\"display\":\"\",\"copyAsset\":false,\"role\":\"figure\",\"size\":267209,\"visible\":true,\"origin\":\"\",\"legend\":\"\\u003cp\\u003e\\u003cstrong\\u003eSingle-molecule protein trapping using nanoaperture optical tweezers. a\\u003c/strong\\u003e, Schematic of label-free trapping of either a globular protein (top) or an IDP (bottom) within a DNH structure. The DNH was fabricated in a 100-nm gold film and passivated with PEG-thiol (see materials and methods for detailed parameters). SEM images of representative DNH geometries are shown in Fig. S5. IDP and globular protein structures were generated using AlphaFold 3\\u003csup\\u003e42\\u003c/sup\\u003e and edited with ChimeraX\\u003csup\\u003e43\\u003c/sup\\u003e. \\u003cstrong\\u003eb\\u003c/strong\\u003e, Representative trapping trajectories for a globular protein (BSA) and an IDP (GSK3β-tau), shown as raw data (sampling rate: 1 MHz) and after low-pass filtering (1 kHz). The schematics illustrate the trapping potential with a protein confined within the well. The globular protein is restricted to lateral and rotational motions within the trap, whereas the IDP undergoes additional conformational fluctuations superimposed on these movements. \\u003cstrong\\u003ec\\u003c/strong\\u003e, Power spectral density plots of trapping traces for BSA and GSK3β-tau.\\u003cstrong\\u003e d\\u003c/strong\\u003e, Autocorrelation functions (ACF) of trapped BSA and GSK3β-tau traces, with exponential decay fits shown as dashed curves. BSA traces follow a single-exponential decay with a time constant of 3.67 ± 0.10 ms, whereas GSK3β-tau traces require a double-exponential fit with time constants of 3.56 ± 1.0 ms and 30.24 ± 3.80 ms.\\u003cstrong\\u003e e\\u003c/strong\\u003e, Dependence of normalised current change (Δ\\u003cem\\u003eI/I\\u003c/em\\u003e\\u003csub\\u003e\\u003cem\\u003e0\\u003c/em\\u003e\\u003c/sub\\u003e) on the orientation and aspect ratios (\\u003cem\\u003eA\\u003c/em\\u003e/\\u003cem\\u003eB\\u003c/em\\u003e) of ellipsoids with axes (\\u003cem\\u003eA\\u003c/em\\u003e, \\u003cem\\u003eB\\u003c/em\\u003e, \\u003cem\\u003eB\\u003c/em\\u003e). Further details for the simulation parameters are provided in the supplementary information (SI-3 and Table S2).\\u003c/p\\u003e\",\"description\":\"\",\"filename\":\"floatimage1.png\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-8222117/v1/82d4ad72247059e9222424eb.png\"},{\"id\":98457301,\"identity\":\"26c9390f-f770-4453-b6e8-2d6c172ce131\",\"added_by\":\"auto\",\"created_at\":\"2025-12-17 18:53:55\",\"extension\":\"png\",\"order_by\":2,\"title\":\"Figure 2\",\"display\":\"\",\"copyAsset\":false,\"role\":\"figure\",\"size\":915590,\"visible\":true,\"origin\":\"\",\"legend\":\"\\u003cp\\u003e\\u003cstrong\\u003eEffect of GSK3β phosphorylation on tau-441.\\u003c/strong\\u003e \\u003cstrong\\u003ea–c\\u003c/strong\\u003e, Optical transmission traces over 6 minutes through a double nanohole (DNH) for trapped proteins: \\u003cstrong\\u003ea\\u003c/strong\\u003e, native tau-441; \\u003cstrong\\u003eb\\u003c/strong\\u003e, GSK3β-tau; and \\u003cstrong\\u003ec\\u003c/strong\\u003e, haemoglobin. Transmission intensities were sampled at 1 MHz and digitally filtered at 1 kHz (grey), 10 Hz (light coloured) and 2 Hz (dark coloured). Raw traces are shown in Fig. S9. Asterisks indicate the following events: baseline before trapping (*), protein trapped (**), and protein released (***). \\u003cstrong\\u003ed\\u003c/strong\\u003e, Zoomed segments taken from \\u003cstrong\\u003ea–c\\u003c/strong\\u003e, comparing native tau-441 (blue), GSK3β-tau (green), haemoglobin (red), and baseline (black). Data are shown in raw (1 MHz) and filtered (1 kHz). Inset: Schematic illustration showing that phosphorylation reduces the degree of disorder in the tau protein. The depicted protein structures are shown only for illustrative purposes. IDP structures were generated using Alphafold 3\\u003csup\\u003e42\\u003c/sup\\u003e and edited with ChimeraX\\u003csup\\u003e43\\u003c/sup\\u003e. \\u003cstrong\\u003ee\\u003c/strong\\u003e, Autocorrelation functions (ACF) of trapping traces for native tau (blue) and GSK3β-tau (green) proteins, along with their exponential decay fits, with single-exponential fits shown as black dashed curves and double-exponential fits as red dashed curves. Inset: Time constants from exponential fits to the autocorrelation curves. \\u003cstrong\\u003ef-h\\u003c/strong\\u003e, Top: one-second transmission traces from three independent experiments comparing native tau (blue) and GSK3β-tau (green), shown as both raw (1 MHz,) and filtered (1 kHz). Bottom: Corresponding ACF plots for each experiment, along with their exponential decay fitting (dashed curves) and time constants. Dataset #1 was obtained using a third-party fabricated DNH with a reduced gap size (see red box in Fig. S5 for SEM images.\\u003c/p\\u003e\",\"description\":\"\",\"filename\":\"floatimage2.png\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-8222117/v1/d8270e6a7a3f0477eb4cb1b3.png\"},{\"id\":98623888,\"identity\":\"5f530802-98eb-4140-b133-135c66c9eed6\",\"added_by\":\"auto\",\"created_at\":\"2025-12-19 17:07:45\",\"extension\":\"png\",\"order_by\":3,\"title\":\"Figure 3\",\"display\":\"\",\"copyAsset\":false,\"role\":\"figure\",\"size\":608148,\"visible\":true,\"origin\":\"\",\"legend\":\"\\u003cp\\u003e\\u003cstrong\\u003eRevealing protein dynamics and energy landscapes using nanoaperture optical tweezers.\\u003c/strong\\u003e \\u003cstrong\\u003ea\\u003c/strong\\u003e, Probability density functions (PDFs) of Δ\\u003cem\\u003eI/I\\u003c/em\\u003e\\u003csub\\u003e\\u003cem\\u003e0\\u003c/em\\u003e\\u003c/sub\\u003e over 20 s of 10 kHz digital filtered data for each protein (colour shaded regions), with deconvoluted PDFs fitted using multi-peak Gaussian models (black dashed curves). PDFs were deliberately overfitted to maximise accuracy in reconstructing the free-energy landscapes (Figs. S10a and S10b). 2D energy landscapes were derived from the deconvoluted PDFs for haemoglobin (red line), GSK3β-tau (green line), and native tau-441 (blue line). \\u003cstrong\\u003eb\\u003c/strong\\u003e, 3D energy landscapes over 20 s for GSK3β-tau and native tau-441, constructed from the deconvoluted PDFs in \\u003cstrong\\u003ea\\u003c/strong\\u003e. Distance from the origin (0, 0, 0) reflects conformational extension (Δ\\u003cem\\u003eI/I\\u003c/em\\u003e\\u003csub\\u003e\\u003cem\\u003e0 \\u003c/em\\u003e\\u003c/sub\\u003emagnitude, \\u003cem\\u003ex\\u003c/em\\u003e- and \\u003cem\\u003ey-\\u003c/em\\u003eaxis) and thermodynamic stability (\\u003cem\\u003ek\\u003c/em\\u003e\\u003csub\\u003eB\\u003c/sub\\u003e\\u003cem\\u003eT, z-\\u003c/em\\u003eaxis). See Supplementary SI-11 for details on PDF deconvolution and the calculation of energy landscapes. The protein structures were added for illustration purposes. IDP structures were generated using Alphafold 3\\u003csup\\u003e42\\u003c/sup\\u003e and edited with ChimeraX\\u003csup\\u003e43\\u003c/sup\\u003e. \\u003cstrong\\u003ec\\u003c/strong\\u003e, PDFs of Δ\\u003cem\\u003eI/I\\u003c/em\\u003e\\u003csub\\u003e\\u003cem\\u003e0\\u003c/em\\u003e\\u003c/sub\\u003e over 20 s of 10 kHz digital filtered data for three additional datasets (colour shaded regions), along with deconvoluted PDFs (coloured curves) and derived free energy landscapes.\\u003c/p\\u003e\",\"description\":\"\",\"filename\":\"floatimage3.png\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-8222117/v1/8001b47ff7f06d509f4d1170.png\"},{\"id\":98457303,\"identity\":\"d65ad10e-94f2-4fc3-bee5-3a1e876c5c87\",\"added_by\":\"auto\",\"created_at\":\"2025-12-17 18:53:55\",\"extension\":\"png\",\"order_by\":4,\"title\":\"Figure 4\",\"display\":\"\",\"copyAsset\":false,\"role\":\"figure\",\"size\":791794,\"visible\":true,\"origin\":\"\",\"legend\":\"\\u003cp\\u003e\\u003cstrong\\u003eBinding of G8.5 RNA to the Sam68 N-terminal.\\u003c/strong\\u003e \\u003cstrong\\u003ea\\u003c/strong\\u003e, Transmission trace of optical trapping of the Sam68 N-terminal followed by binding/unbinding of RNA. Data was filtered to 1 kHz (light purple) and 10 Hz (dark purple). Raw trace available in Fig. S16. \\u003cstrong\\u003eb\\u003c/strong\\u003e, Zoomed segments from \\u003cstrong\\u003ea\\u003c/strong\\u003e depicting the baseline, Sam68 N-terminal before RNA binding (orange, from orange dashed box) and after RNA binding (blue, from blue dashed box). Traces show raw data (1 MHz, light colours) and filtered data (1 kHz, dark colours). \\u003cstrong\\u003ec,\\u003c/strong\\u003e PSD of the Sam68 N-terminal before RNA binding (orange), after RNA binding (blue) and baseline (black) for comparison. \\u003cstrong\\u003ed\\u003c/strong\\u003e, 7-minute segment from \\u003cstrong\\u003ea\\u003c/strong\\u003e (black dashed box) depicting conformational fluctuations of the Sam68 N-terminal between the unbound (orange) and bound (blue) states to RNA, in addition to a condition in which both components are not associated in the optical trap (green). Right inset: PDFs for the 7-min trace filtered at 10 Hz. \\u003cstrong\\u003ee\\u003c/strong\\u003e, Eight-second zoomed trace from \\u003cstrong\\u003ed\\u003c/strong\\u003e (red asterisk) using a 3-step fit to assign levels to the RNA-bound and two intermediate states for cohabitation of the unbound Sam68 N-terminal and RNA. \\u003cimg width=\\\"18\\\" height=\\\"17\\\" src=\\\"data:image/png;base64,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\\\"/\\u003e\\u0026nbsp;and \\u003cimg width=\\\"19\\\" height=\\\"17\\\" src=\\\"data:image/png;base64,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\\\"/\\u003e\\u0026nbsp;were assigned for association time and dissociation time, respectively. \\u003cstrong\\u003ef\\u003c/strong\\u003e, \\u003cstrong\\u003eg\\u003c/strong\\u003e, Histograms of \\u003cimg width=\\\"18\\\" height=\\\"17\\\" src=\\\"data:image/png;base64,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\\\"/\\u003e\\u0026nbsp;and \\u003cimg width=\\\"19\\\" height=\\\"17\\\" src=\\\"data:image/png;base64,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\\\"/\\u003e\\u0026nbsp;obtained from the whole trace in \\u003cstrong\\u003ed, \\u003c/strong\\u003eoverlaid with single exponential decay fits (blue dashed curves). The dissociation constant (\\u003cem\\u003eK\\u003c/em\\u003e\\u003csub\\u003ed\\u003c/sub\\u003e) for the binding interaction were calculated using the average \\u003cimg width=\\\"18\\\" height=\\\"17\\\" src=\\\"data:image/png;base64,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\\\"/\\u003e\\u0026nbsp;and \\u003cimg width=\\\"19\\\" height=\\\"17\\\" src=\\\"data:image/png;base64,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\\\"/\\u003e\\u0026nbsp;with details provided in Supplementary SI-14.\\u003c/p\\u003e\",\"description\":\"\",\"filename\":\"floatimage4.png\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-8222117/v1/0768f735bee10a5c274814dd.png\"},{\"id\":98631561,\"identity\":\"ee83701f-1c4b-4389-af4d-a742db01b917\",\"added_by\":\"auto\",\"created_at\":\"2025-12-19 17:20:11\",\"extension\":\"pdf\",\"order_by\":0,\"title\":\"\",\"display\":\"\",\"copyAsset\":false,\"role\":\"manuscript-pdf\",\"size\":3590957,\"visible\":true,\"origin\":\"\",\"legend\":\"\",\"description\":\"\",\"filename\":\"manuscript.pdf\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-8222117/v1/8feb481b-14b6-44e7-a046-6bbfc0bdea28.pdf\"},{\"id\":98457313,\"identity\":\"8d6ed22c-54c9-41bf-a670-2ed388626d1f\",\"added_by\":\"auto\",\"created_at\":\"2025-12-17 18:53:55\",\"extension\":\"docx\",\"order_by\":0,\"title\":\"\",\"display\":\"\",\"copyAsset\":false,\"role\":\"supplement\",\"size\":3701967,\"visible\":true,\"origin\":\"\",\"legend\":\"\",\"description\":\"\",\"filename\":\"IDPPaperSI.docx\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-8222117/v1/ed6a0646af2390af8affc78d.docx\"}],\"financialInterests\":\"No competing interests reported.\",\"formattedTitle\":\"Label-free optical observation of disordered-to-ordered transitions in single intrinsically disordered proteins\",\"fulltext\":[{\"header\":\"1. Introduction\",\"content\":\"\\u003cp\\u003eIntrinsically disordered proteins (IDPs) and intrinsically disordered regions (IDRs) in structured proteins, which constitute approximately 70% of the human proteome\\u003csup\\u003e\\u003cspan citationid=\\\"CR1\\\" class=\\\"CitationRef\\\"\\u003e1\\u003c/span\\u003e\\u003c/sup\\u003e, play critical roles in biological processes including neurotransmitter regulation\\u003csup\\u003e\\u003cspan citationid=\\\"CR2\\\" class=\\\"CitationRef\\\"\\u003e2\\u003c/span\\u003e\\u003c/sup\\u003e, microtubule regulation\\u003csup\\u003e\\u003cspan citationid=\\\"CR3\\\" class=\\\"CitationRef\\\"\\u003e3\\u003c/span\\u003e\\u003c/sup\\u003e, and transcription\\u003csup\\u003e\\u003cspan citationid=\\\"CR4\\\" class=\\\"CitationRef\\\"\\u003e4\\u003c/span\\u003e\\u003c/sup\\u003e. Their significant prevalence and roles in the development of various diseases, many of which lack effective treatments and reliable early-stage diagnosis, make IDPs/IDRs important targets for research. Example IDPs include tubulin associated unit (tau) protein and alpha synuclein, which are implicated in neurodegenerative disorders\\u003csup\\u003e\\u003cspan citationid=\\\"CR5\\\" class=\\\"CitationRef\\\"\\u003e5\\u003c/span\\u003e\\u003c/sup\\u003e such as Alzheimer\\u0026rsquo;s disease\\u003csup\\u003e\\u003cspan citationid=\\\"CR6\\\" class=\\\"CitationRef\\\"\\u003e6\\u003c/span\\u003e\\u003c/sup\\u003e and Parkinson\\u0026rsquo;s disease\\u003csup\\u003e\\u003cspan citationid=\\\"CR7\\\" class=\\\"CitationRef\\\"\\u003e7\\u003c/span\\u003e\\u003c/sup\\u003e, respectively. IDP/IDR related diseases also extend to cancer\\u003csup\\u003e\\u003cspan citationid=\\\"CR8\\\" class=\\\"CitationRef\\\"\\u003e8\\u003c/span\\u003e\\u003c/sup\\u003e such as the IDR Sam68 (Src-associated protein in mitosis of 68 kDa), which has implicated involvement in ovarian, kidney and lung cancers\\u003csup\\u003e\\u003cspan citationid=\\\"CR9\\\" class=\\\"CitationRef\\\"\\u003e9\\u003c/span\\u003e\\u003c/sup\\u003e.\\u003c/p\\u003e\\u003cp\\u003eIDPs/IDRs are conformationally heterogenous, dynamically fluctuating between different shapes with variable structure, known as a conformational ensemble\\u003csup\\u003e\\u003cspan citationid=\\\"CR1\\\" class=\\\"CitationRef\\\"\\u003e1\\u003c/span\\u003e\\u003c/sup\\u003e. This structural flexibility is often integral to the functions of IDPs, where binding to select targets can induce a particular structure necessary for biological activity. These structures present varying levels of disorder, such as an IDP transitioning to a structured conformation, as seen in disorder-to-order transitions\\u003csup\\u003e\\u003cspan citationid=\\\"CR10\\\" class=\\\"CitationRef\\\"\\u003e10\\u003c/span\\u003e,\\u003cspan citationid=\\\"CR11\\\" class=\\\"CitationRef\\\"\\u003e11\\u003c/span\\u003e\\u003c/sup\\u003e, or the IDP/IDR retaining partial or complete disorder upon binding to form fuzzy complexes\\u003csup\\u003e\\u003cspan citationid=\\\"CR12\\\" class=\\\"CitationRef\\\"\\u003e12\\u003c/span\\u003e\\u003c/sup\\u003e. Their heterogeneity and absence of a defined folded state renders many experimental and computational approaches\\u003csup\\u003e\\u003cspan citationid=\\\"CR13\\\" class=\\\"CitationRef\\\"\\u003e13\\u003c/span\\u003e\\u003c/sup\\u003e, which were developed for structured proteins, largely inadequate. Understanding their conformational ensemble and its link to their functions is key to understanding their biophysics.\\u003c/p\\u003e\\u003cp\\u003eEnsemble measurements, such as nuclear magnetic resonance\\u003csup\\u003e\\u003cspan citationid=\\\"CR14\\\" class=\\\"CitationRef\\\"\\u003e14\\u003c/span\\u003e\\u003c/sup\\u003e, small angle x-ray scattering\\u003csup\\u003e\\u003cspan citationid=\\\"CR15\\\" class=\\\"CitationRef\\\"\\u003e15\\u003c/span\\u003e\\u003c/sup\\u003e, and dynamic light scattering\\u003csup\\u003e\\u003cspan citationid=\\\"CR16\\\" class=\\\"CitationRef\\\"\\u003e16\\u003c/span\\u003e\\u003c/sup\\u003e, while very informative, cannot entirely capture the heterogeneity among individual copies of the protein. Structural protein characterisation techniques such as cryogenic electron microscopy (cryo-EM) and x-ray crystallography are suitable for globular proteins, but these techniques yield poor resolution of conformational heterogeneity, leading to low or an absence of electron density in micrographs\\u003csup\\u003e\\u003cspan citationid=\\\"CR17\\\" class=\\\"CitationRef\\\"\\u003e17\\u003c/span\\u003e,\\u003cspan citationid=\\\"CR18\\\" class=\\\"CitationRef\\\"\\u003e18\\u003c/span\\u003e\\u003c/sup\\u003e. Additionally, such techniques only capture a snapshot of the protein motion on its conformational landscape, losing dynamic information, which are integral to IDPs/IDRs. Notably, cryo-EM is progressing towards overcoming some of these limitations, such as the advent of single particle cryo-EM\\u003csup\\u003e19\\u003c/sup\\u003e and use of artificial intelligence to map flexible areas\\u003csup\\u003e\\u003cspan citationid=\\\"CR20\\\" class=\\\"CitationRef\\\"\\u003e20\\u003c/span\\u003e\\u003c/sup\\u003e.\\u003c/p\\u003e\\u003cp\\u003eSingle-molecule fluorescence resonance energy transfer (smFRET) and single-molecule force spectroscopy (smFS) can provide excellent information such as free-energy landscapes\\u003csup\\u003e\\u003cspan citationid=\\\"CR21\\\" class=\\\"CitationRef\\\"\\u003e21\\u003c/span\\u003e\\u003c/sup\\u003e, tracking intramolecular displacement\\u003csup\\u003e\\u003cspan citationid=\\\"CR22\\\" class=\\\"CitationRef\\\"\\u003e22\\u003c/span\\u003e\\u003c/sup\\u003e, and conformational dynamics\\u003csup\\u003e\\u003cspan citationid=\\\"CR23\\\" class=\\\"CitationRef\\\"\\u003e23\\u003c/span\\u003e\\u003c/sup\\u003e, and have even provided insight into labelled IDPs over a decade ago\\u003csup\\u003e\\u003cspan citationid=\\\"CR24\\\" class=\\\"CitationRef\\\"\\u003e24\\u003c/span\\u003e,\\u003cspan citationid=\\\"CR25\\\" class=\\\"CitationRef\\\"\\u003e25\\u003c/span\\u003e\\u003c/sup\\u003e. However, the requirement of labelling the protein with an extrinsic fluorophore for smFRET or tethering it to a surface for smFS can perturb the structure and dynamics of the protein\\u003csup\\u003e\\u003cspan citationid=\\\"CR26\\\" class=\\\"CitationRef\\\"\\u003e26\\u003c/span\\u003e,\\u003cspan citationid=\\\"CR27\\\" class=\\\"CitationRef\\\"\\u003e27\\u003c/span\\u003e\\u003c/sup\\u003e, particularly for IDPs/IDRs where many are observed to undergo disorder-to-order transitions upon binding\\u003csup\\u003e\\u003cspan citationid=\\\"CR10\\\" class=\\\"CitationRef\\\"\\u003e10\\u003c/span\\u003e,\\u003cspan citationid=\\\"CR11\\\" class=\\\"CitationRef\\\"\\u003e11\\u003c/span\\u003e,\\u003cspan citationid=\\\"CR28\\\" class=\\\"CitationRef\\\"\\u003e28\\u003c/span\\u003e\\u003c/sup\\u003e. Currently, no established protein characterisation technique can capture the conformational dynamics of label-free IDPs/IDRs at the single-molecule level.\\u003c/p\\u003e\\u003cp\\u003eNanoaperture optical tweezers (NOTs) utilise localised surface plasmon resonance to trap a single protein molecule and observe its conformational changes in solution without any chemical modifications\\u003csup\\u003e\\u003cspan citationid=\\\"CR29\\\" class=\\\"CitationRef\\\"\\u003e29\\u003c/span\\u003e,\\u003cspan citationid=\\\"CR30\\\" class=\\\"CitationRef\\\"\\u003e30\\u003c/span\\u003e\\u003c/sup\\u003e. In recent years, NOTs have provided information on single, label-free proteins including protein-binding interactions\\u003csup\\u003e\\u003cspan citationid=\\\"CR31\\\" class=\\\"CitationRef\\\"\\u003e31\\u003c/span\\u003e\\u003c/sup\\u003e, conformational transitions\\u003csup\\u003e\\u003cspan citationid=\\\"CR32\\\" class=\\\"CitationRef\\\"\\u003e32\\u003c/span\\u003e\\u003c/sup\\u003e, disassembly kinetics\\u003csup\\u003e\\u003cspan citationid=\\\"CR33\\\" class=\\\"CitationRef\\\"\\u003e33\\u003c/span\\u003e\\u003c/sup\\u003e, and energy landscapes\\u003csup\\u003e\\u003cspan citationid=\\\"CR34\\\" class=\\\"CitationRef\\\"\\u003e34\\u003c/span\\u003e\\u003c/sup\\u003e. Here, we utilise NOTs to monitor the conformational dynamics of label-free IDPs/IDRs in solution, elucidate their free-energy landscapes, and observe a disorder-to-order transition of an IDR upon binding to RNA. We focus on three different IDPs/IDRs associated with disease progression: native tau-441, tau-441 phosphorylated by glycogen synthase kinase 3-beta (GSK3β-tau) and the N-terminal region of Sam68. This work reveals differences in structural flexibility between single IDPs and globular proteins of similar size, provides experimental evidence of structural changes due to phosphorylation of IDPs, and the trajectory of a single IDR transitioning between disordered and ordered structures upon binding and unbinding of a nucleic acid binding partner, with well-resolved binding kinetics. All the above insights into the conformational dynamics of label-free IDPs/IDRs were previously inaccessible to any single-molecule approach.\\u003c/p\\u003e\"},{\"header\":\"2. Results and Discussion\",\"content\":\"\\u003cdiv id=\\\"Sec3\\\" class=\\\"Section2\\\"\\u003e\\u003ch2\\u003e2.1 Transmission traces distinguish ordered and disordered structures\\u003c/h2\\u003e\\u003cp\\u003eTrapping of either an IDP/IDR or a globular protein is achieved using a gold double-nanohole (DNH) structure (Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig1\\\" class=\\\"InternalRef\\\"\\u003e1\\u003c/span\\u003ea; full setup in Fig. \\u003cspan refid=\\\"MOESM1\\\" class=\\\"InternalRef\\\"\\u003eS1\\u003c/span\\u003e). When a molecule enters the trapping region, the disparity in the refractive index between the trapped molecule and the surrounding media affects the light scattered by the nanoaperture. This scattering directly correlates to the polarisability of the trapped protein, which is determined by its volume, conformation, and dielectric constant\\u003csup\\u003e\\u003cspan citationid=\\\"CR35\\\" class=\\\"CitationRef\\\"\\u003e35\\u003c/span\\u003e,\\u003cspan citationid=\\\"CR36\\\" class=\\\"CitationRef\\\"\\u003e36\\u003c/span\\u003e\\u003c/sup\\u003e. The forward-scattered light is collected by an objective (NA\\u0026thinsp;=\\u0026thinsp;0.1) and is then recorded as transmission intensity (\\u003cem\\u003eI\\u003c/em\\u003e) by an avalanche photodiode (APD)\\u003csup\\u003e\\u003cspan citationid=\\\"CR37\\\" class=\\\"CitationRef\\\"\\u003e37\\u003c/span\\u003e\\u003c/sup\\u003e (Fig. \\u003cspan refid=\\\"MOESM1\\\" class=\\\"InternalRef\\\"\\u003eS1\\u003c/span\\u003e). We analyse protein dynamics using the normalised transmission intensity change, Δ\\u003cem\\u003eI/I\\u003c/em\\u003e\\u003csub\\u003e\\u003cem\\u003e0\\u003c/em\\u003e\\u003c/sub\\u003e, where \\u003cem\\u003eI\\u003c/em\\u003e\\u003csub\\u003e\\u003cem\\u003e0\\u003c/em\\u003e\\u003c/sub\\u003e is the baseline intensity of an unoccupied DNH, and Δ\\u003cem\\u003eI\\u003c/em\\u003e\\u0026thinsp;=\\u0026thinsp;\\u003cem\\u003eI\\u003c/em\\u003e - \\u003cem\\u003eI\\u003c/em\\u003e\\u003csub\\u003e\\u003cem\\u003e0\\u003c/em\\u003e\\u003c/sub\\u003e represents the change in transmission intensity from this baseline (Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig1\\\" class=\\\"InternalRef\\\"\\u003e1\\u003c/span\\u003eb). In this work, we trap all proteins with the laser power that produces a local temperature of ~\\u0026thinsp;37\\u0026deg;C (i.e., 20 mW, see Fig. S2) at the trapping site.\\u003c/p\\u003e\\u003cp\\u003eComparing representative trapping trajectories for an IDP and a globular protein reveals distinct dynamic behaviours (Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig1\\\" class=\\\"InternalRef\\\"\\u003e1\\u003c/span\\u003eb). The initial increase in Δ\\u003cem\\u003eI/I\\u003c/em\\u003e\\u003csub\\u003e\\u003cem\\u003e0\\u003c/em\\u003e\\u003c/sub\\u003e corresponds to the protein entering the DNH trap, with the signal magnitude linearly scaling with protein size (Fig. S3d)\\u003csup\\u003e\\u003cspan citationid=\\\"CR33\\\" class=\\\"CitationRef\\\"\\u003e33\\u003c/span\\u003e\\u003c/sup\\u003e. Due to the lateral and rotational movements of the trapped proteins within the potential well, we observed a significant increase in signal fluctuation (Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig1\\\" class=\\\"InternalRef\\\"\\u003e1\\u003c/span\\u003eb). For the globular protein BSA, these motions account for most of the signal variations, whereas the IDP GSK3β-tau displays substantially larger fluctuations, reflecting additional conformational dynamics superimposed on its translational and rotational movements. This trend is observed across multiple trapping traces involving various IDPs/IDRs and globular proteins (Figs. S4a and S4b) rather than being specific to tau and haemoglobin. Figure S4c confirms that IDPs/IDRs demonstrate higher normalised root-mean-square (NRMS) values, and therefore are more dynamic, than globular proteins of similar molecular weight. These enhanced fluctuations, reflecting the highly dynamic structural transitions of IDPs/IDRs, occur predominantly below 1 kHz, as shown in the power spectral density (PSD) plot (Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig1\\\" class=\\\"InternalRef\\\"\\u003e1\\u003c/span\\u003ec). Further analysis of the trapping dynamics using autocorrelation (Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig1\\\" class=\\\"InternalRef\\\"\\u003e1\\u003c/span\\u003ed) reveals that, in addition to a fast component (~\\u0026thinsp;3.6 ms) associated with trap relaxation, GSK3β-tau exhibits a second, slower exponential decay component with a time constant of ~\\u0026thinsp;30 ms, corresponding to its intrinsic conformational transitions.\\u003c/p\\u003e\\u003cp\\u003eTwo factors may contribute to the large optical signal variations observed in IDPs. First, the loose, extended conformation of IDPs exposes a greater surface area to the solvent, resulting in a larger hydration shell with higher water density than that of globular proteins\\u003csup\\u003e\\u003cspan citationid=\\\"CR38\\\" class=\\\"CitationRef\\\"\\u003e38\\u003c/span\\u003e,\\u003cspan citationid=\\\"CR39\\\" class=\\\"CitationRef\\\"\\u003e39\\u003c/span\\u003e\\u003c/sup\\u003e. This hydration effect increases the local refractive index around the protein in its elongated states, leading to greater transmission changes. Second, elongated particles of equivalent volume produce orientation-dependent Δ\\u003cem\\u003eI/I\\u003c/em\\u003e\\u003csub\\u003e\\u003cem\\u003e0\\u003c/em\\u003e\\u003c/sub\\u003e signals, as demonstrated by our simulations (Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig1\\\" class=\\\"InternalRef\\\"\\u003e1\\u003c/span\\u003ee, see SI-3 for details of FDTD simulation). IDPs/IDRs continuously switch between different extended conformations on microsecond-to-second timescales\\u003csup\\u003e\\u003cspan citationid=\\\"CR40\\\" class=\\\"CitationRef\\\"\\u003e40\\u003c/span\\u003e\\u003c/sup\\u003e, producing intrinsic Δ\\u003cem\\u003eI/I\\u003c/em\\u003e\\u003csub\\u003e\\u003cem\\u003e0\\u003c/em\\u003e\\u003c/sub\\u003e fluctuations that reflect their conformational sampling and orientations. We hypothesise that elongated globular proteins may preferentially adopt a trapping orientation that maximises the local electric field through the self-induced back action (SIBA) trapping mechanism, and consequently, results in a higher average Δ\\u003cem\\u003eI/I\\u003c/em\\u003e\\u003csub\\u003e\\u003cem\\u003e0\\u003c/em\\u003e\\u003c/sub\\u003e value than their spherical counterparts, as previously reported\\u003csup\\u003e\\u003cspan citationid=\\\"CR31\\\" class=\\\"CitationRef\\\"\\u003e31\\u003c/span\\u003e,\\u003cspan citationid=\\\"CR34\\\" class=\\\"CitationRef\\\"\\u003e34\\u003c/span\\u003e,\\u003cspan citationid=\\\"CR41\\\" class=\\\"CitationRef\\\"\\u003e41\\u003c/span\\u003e\\u003c/sup\\u003e. As detailed in the supplementary information (SI-3 and SI-5), Δ\\u003cem\\u003eI/I\\u003c/em\\u003e\\u003csub\\u003e\\u003cem\\u003e0\\u003c/em\\u003e\\u003c/sub\\u003e depends critically on the refractive index, shape, and orientation of the protein within the DNH gap. These dependencies enable Δ\\u003cem\\u003eI\\u003c/em\\u003e/\\u003cem\\u003eI\\u003c/em\\u003e\\u003csub\\u003e\\u003cem\\u003e0\\u003c/em\\u003e\\u003c/sub\\u003e to serve as an indicator for the global compactness of the trapped protein.\\u003c/p\\u003e\\u003cp\\u003e\\u003c/p\\u003e\\u003c/div\\u003e\\u003cdiv id=\\\"Sec4\\\" class=\\\"Section2\\\"\\u003e\\u003ch2\\u003e2.2 Phosphorylation induced order of tau by GSK3β\\u003c/h2\\u003e\\u003cp\\u003eFigures \\u003cspan refid=\\\"Fig2\\\" class=\\\"InternalRef\\\"\\u003e2\\u003c/span\\u003ea-\\u003cspan refid=\\\"Fig2\\\" class=\\\"InternalRef\\\"\\u003e2\\u003c/span\\u003ec compare the transmission-time traces corresponding to the trapping of the IDP native tau-441 and its phosphorylated variant GSK3β-tau, with those of the globular protein haemoglobin. Trapping these proteins using DNHs with similar dimensions resulted in comparable Δ\\u003cem\\u003eI\\u003c/em\\u003e/\\u003cem\\u003eI\\u003c/em\\u003e\\u003csub\\u003e\\u003cem\\u003e0\\u003c/em\\u003e\\u003c/sub\\u003e of approximately 0.1 (Figs.\\u0026nbsp;\\u003cspan refid=\\\"Fig2\\\" class=\\\"InternalRef\\\"\\u003e2\\u003c/span\\u003ea\\u0026ndash;c), due to their similar molecular weight (native tau-441, 45.9 kDa, GSK3β-tau, ~\\u0026thinsp;46\\u0026ndash;48 kDa, and haemoglobin, 64.5 kDa). The extended conformations of IDPs result in higher polarisability than globular proteins of similar molecular weight, which explains why native tau-441 and GSK3β-tau exhibit Δ\\u003cem\\u003eI/I\\u003c/em\\u003e\\u003csub\\u003e\\u003cem\\u003e0\\u003c/em\\u003e\\u003c/sub\\u003e values comparable to haemoglobin despite their lower molecular weight. The trace segments in Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig2\\\" class=\\\"InternalRef\\\"\\u003e2\\u003c/span\\u003ed reveal substantially different fluctuations in Δ\\u003cem\\u003eI/I\\u003c/em\\u003e\\u003csub\\u003e\\u003cem\\u003e0\\u003c/em\\u003e\\u003c/sub\\u003e between the three proteins. Native tau-441 and GSK3β-tau resulted in larger fluctuations compared to haemoglobin, consistent with the higher flexibility of IDPs relative to globular proteins. When the laser is turned off for several seconds then turned back on, the transmission intensity returns to the baseline level (Figs.\\u0026nbsp;\\u003cspan refid=\\\"Fig2\\\" class=\\\"InternalRef\\\"\\u003e2\\u003c/span\\u003ea-\\u003cspan refid=\\\"Fig2\\\" class=\\\"InternalRef\\\"\\u003e2\\u003c/span\\u003ec), indicating that the protein molecule diffused away from the trap without surface adsorption. These results demonstrate the effectiveness of the PEG-thiol coating in minimising nonspecific protein adsorption, a major challenge when studying IDPs/IDRs, particularly under conditions where the trapping force retains them close to the gold surface for extended periods. Occasionally, however, the protein did not diffuse away after the laser was turned off, as shown in Fig. S6. These events were excluded from subsequent analysis and discussion in this work.\\u003c/p\\u003e\\u003cp\\u003eThe results shown in Figs.\\u0026nbsp;\\u003cspan refid=\\\"Fig2\\\" class=\\\"InternalRef\\\"\\u003e2\\u003c/span\\u003ea-\\u003cspan refid=\\\"Fig2\\\" class=\\\"InternalRef\\\"\\u003e2\\u003c/span\\u003ed suggest that GSK3β phosphorylation of native tau-441 induces increased order and compaction. Two mechanisms likely underlie these changes: electrostatic interactions and secondary structure formation. First, native tau-441 has a theoretical isoelectric point of ~\\u0026thinsp;8.24, carrying a net positive charge at pH 7.2. Phosphorylation introduces negatively charged phosphate groups, subsequently reducing the net charge and promoting compaction, consistent with previous reports\\u003csup\\u003e\\u003cspan citationid=\\\"CR44\\\" class=\\\"CitationRef\\\"\\u003e44\\u003c/span\\u003e,\\u003cspan citationid=\\\"CR45\\\" class=\\\"CitationRef\\\"\\u003e45\\u003c/span\\u003e\\u003c/sup\\u003e. Second, phosphorylation may shift local secondary structure. GSK3β phosphorylation has been shown to increase α-helix propensity at the expense of polyproline type II (PPII) helices within the proline-rich domain of tau\\u003csup\\u003e\\u003cspan citationid=\\\"CR46\\\" class=\\\"CitationRef\\\"\\u003e46\\u003c/span\\u003e\\u003c/sup\\u003e (Fig. S7). As α-helices are shorter than PPII helices (5.4 \\u0026Aring;/turn vs. 9.3 \\u0026Aring;/turn)\\u003csup\\u003e47\\u003c/sup\\u003e, this structural transition would also induce compaction in native tau-441. Supplementary SI-8 provides an in-depth discussion of potential phosphorylation effects.\\u003c/p\\u003e\\u003cp\\u003eAnalysis of the autocorrelation functions for native tau-441 and GSK3β-tau (Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig2\\\" class=\\\"InternalRef\\\"\\u003e2\\u003c/span\\u003ee) revealed an additional secondary decay (~\\u0026thinsp;26 ms) in the phosphorylated variant. In contrast, native tau-441 exhibited only a single exponential component (~\\u0026thinsp;10 ms), likely arising from the similar timescales of confined trapping dynamics and conformational fluctuations. To confirm that GSK3β phosphorylation shifts native tau-441 towards more compact and ordered conformations, we performed additional trapping experiments shown in Figs.\\u0026nbsp;\\u003cspan refid=\\\"Fig2\\\" class=\\\"InternalRef\\\"\\u003e2\\u003c/span\\u003ef-\\u003cspan refid=\\\"Fig2\\\" class=\\\"InternalRef\\\"\\u003e2\\u003c/span\\u003eh (five second variants of these traces are shown in Figs. S8a-c). GSK3β-tau consistently exhibited reduced dynamic behaviour compared to native tau-441, demonstrated by less variation in Δ\\u003cem\\u003eI/I\\u003c/em\\u003e\\u003csub\\u003e\\u003cem\\u003e0\\u003c/em\\u003e\\u003c/sub\\u003e. While the ACF for native tau-441 could sometimes be fit with a double-exponential decay, suggesting separable conformational and trapping dynamics, GSK3β-tau consistently exhibited a slower time constant. Compared to native tau-441, the PSD of GSK3β-tau (Fig. S8d) displays lower power fluctuations in the 1 Hz\\u0026ndash;1 kHz range, indicating its increased order and restricted large-scale dynamics occurring on the second\\u0026ndash;millisecond timescale. These results suggest that GSK3β phosphorylation introduces structural order into tau. Rather than fluctuating continuously among disordered states on millisecond timescales, the phosphorylated variant samples more confined conformations that fluctuate over tens of milliseconds. We note that the number and location of phosphorylated residues in GSK3β-tau may vary from between each molecule. Consequently, this may contribute to the heterogeneity observed in the trapping trace patterns among individual GSK3β-tau molecules.\\u003c/p\\u003e\\u003cp\\u003e\\u003c/p\\u003e\\u003cp\\u003eProbability density functions (PDFs) of the transmitted intensity reveal how frequently a protein samples different conformations, providing valuable insight into its accessible structural states. Deconvolution of the PDFs using a point spread function removes the translational and rotational motion of proteins and allows extraction of the PDFs associated with the protein\\u0026rsquo;s true conformational changes. Figure\\u0026nbsp;\\u003cspan refid=\\\"Fig3\\\" class=\\\"InternalRef\\\"\\u003e3\\u003c/span\\u003ea shows that the deconvoluted PDF for haemoglobin reveals a single, sharp peak, consistent with it existing predominantly in a single conformational state, characteristic of a globular protein. In contrast, the deconvoluted PDFs of both GSK3β-tau and native tau-441 exhibit broader Δ\\u003cem\\u003eI/I\\u003c/em\\u003e\\u003csub\\u003e\\u003cem\\u003e0\\u003c/em\\u003e\\u003c/sub\\u003e distributions, indicating greater conformational variability and a more dynamic behaviour than haemoglobin. Native tau-441 displays the broadest distribution, suggesting it samples a wider conformational landscape and possess greater structural flexibility than GSK3β-tau.\\u003c/p\\u003e\\u003cp\\u003eConverting the PDF of single-molecule folding trajectories into free-energy landscapes is well established in techniques such as FRET\\u003csup\\u003e\\u003cspan citationid=\\\"CR48\\\" class=\\\"CitationRef\\\"\\u003e48\\u003c/span\\u003e\\u003c/sup\\u003e and smFS\\u003csup\\u003e\\u003cspan citationid=\\\"CR49\\\" class=\\\"CitationRef\\\"\\u003e49\\u003c/span\\u003e\\u003c/sup\\u003e. These landscapes provide valuable insights into protein folding, including the number of distinct conformational states, their relative free-energy differences, and how these are altered by binding interactions\\u003csup\\u003e\\u003cspan citationid=\\\"CR50\\\" class=\\\"CitationRef\\\"\\u003e50\\u003c/span\\u003e\\u003c/sup\\u003e. Recently, NOTs have been used to resolve the energy landscape of a single unmodified protein\\u003csup\\u003e\\u003cspan citationid=\\\"CR34\\\" class=\\\"CitationRef\\\"\\u003e34\\u003c/span\\u003e\\u003c/sup\\u003e. For label-free monomeric IDPs/IDRs, experimental derivation of energy landscapes is particularly important for understanding their conformational dynamics, molecular interactions, and biological functions\\u003csup\\u003e\\u003cspan citationid=\\\"CR50\\\" class=\\\"CitationRef\\\"\\u003e50\\u003c/span\\u003e\\u003c/sup\\u003e. Until now, this information has been accessible only through computational modelling\\u003csup\\u003e\\u003cspan citationid=\\\"CR51\\\" class=\\\"CitationRef\\\"\\u003e51\\u003c/span\\u003e,\\u003cspan citationid=\\\"CR52\\\" class=\\\"CitationRef\\\"\\u003e52\\u003c/span\\u003e\\u003c/sup\\u003e, due to the challenges of experimentally measuring label-free IDPs/IDRs at the single-molecule level.\\u003c/p\\u003e\\u003cp\\u003eHere, we present the first experimentally derived measurement of the free-energy landscape of label-free IDPs at the single-molecule level. 2D energy landscapes were calculated by taking the negative logarithm of the deconvoluted PDFs, as described by Eq. S3, and are shown in Figs.\\u0026nbsp;\\u003cspan refid=\\\"Fig3\\\" class=\\\"InternalRef\\\"\\u003e3\\u003c/span\\u003ea and \\u003cspan refid=\\\"Fig3\\\" class=\\\"InternalRef\\\"\\u003e3\\u003c/span\\u003ec (black curves). Relative free-energy values are expressed in units of \\u003cem\\u003ek\\u003c/em\\u003e\\u003csub\\u003eB\\u003c/sub\\u003e\\u003cem\\u003eT\\u003c/em\\u003e, where \\u003cem\\u003ek\\u003c/em\\u003e\\u003csub\\u003eB\\u003c/sub\\u003e is Boltzmann\\u0026rsquo;s constant and \\u003cem\\u003eT\\u003c/em\\u003e is temperature, representing the energy available to the protein. As expected, haemoglobin exhibits a sharp funnel-like landscape characteristic of globular proteins\\u003csup\\u003e\\u003cspan citationid=\\\"CR53\\\" class=\\\"CitationRef\\\"\\u003e53\\u003c/span\\u003e\\u003c/sup\\u003e, whereas native tau-441 and GSK3β-tau display multiple shallow minima with lower energy barriers between them, consistent with a broad ensemble of conformational states typical of IDPs\\u003csup\\u003e\\u003cspan citationid=\\\"CR53\\\" class=\\\"CitationRef\\\"\\u003e53\\u003c/span\\u003e\\u003c/sup\\u003e. Across all experiments, the deconvoluted PDFs and energy landscapes of GSK3β-tau revealed more compact conformational ensembles, consistent with their smaller Δ\\u003cem\\u003eI\\u003c/em\\u003e/\\u003cem\\u003eI\\u003c/em\\u003e\\u003csub\\u003e0\\u003c/sub\\u003e fluctuations.\\u003c/p\\u003e\\u003cp\\u003eTo better visualise the complex conformational dynamics of these IDPs, we converted the 2D energy landscapes into 3D plots. First, we applied multi-peak Gaussian fits to the deconvoluted PDFs to identify predominant conformational states (black dashed curves in Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig3\\\" class=\\\"InternalRef\\\"\\u003e3\\u003c/span\\u003ea, with individual peaks presented in Fig. S10a). These fitted PDFs were then used to reconstruct the corresponding energy landscapes (black dashed curves, Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig3\\\" class=\\\"InternalRef\\\"\\u003e3\\u003c/span\\u003ea), which were further converted to 3D plots (Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig3\\\" class=\\\"InternalRef\\\"\\u003e3\\u003c/span\\u003eb) by mapping the peak positions onto a polar coordinate system (Fig. S11). In these plots, angular coordinates reflect the relative spatial distribution of conformational states, while the peak amplitudes correspond to the negative logarithm of the fitted PDF, such that a higher probability indicates a lower free energy. Haemoglobin, as expected for a globular protein, displays a single, funnel-shaped energy landscape consistent with a single, stable folded conformation (Fig. S12). For native tau-441, the 3D energy landscapes reveal multiple shallow troughs with low energy barriers, consistent with the conformational heterogeneity expected in IDPs. In contrast, alongside shallow troughs similar to those observed in native tau-441, GSK3β-tau exhibits several deep, well-defined troughs, indicating the presence of distinct and stable conformational states (Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig3\\\" class=\\\"InternalRef\\\"\\u003e3\\u003c/span\\u003ea). These states are also apparent in the top-down projection shown in Fig. S13. The continuous energy landscapes over 120 seconds from native tau-441 and GSK3β-tau confirm conformational dynamics consistent to those observed in the 20-s intervals (Fig. S14).\\u003c/p\\u003e\\u003cp\\u003e\\u003c/p\\u003e\\u003c/div\\u003e\\u003cdiv id=\\\"Sec5\\\" class=\\\"Section2\\\"\\u003e\\u003ch2\\u003e2.3 Disorder to order transition of the Sam68 N-terminal\\u003c/h2\\u003e\\u003cp\\u003eOur results demonstrate that NOTs enable the direct and prolonged observation of conformational dynamics in single, unmodified IDPs, and allow reconstruction of their free-energy landscapes. To illustrate the broader utility of this approach in capturing disorder-to-order transitions at the single-molecule level, we investigated the RNA binding kinetics of Sam68, a system not previously studied using single-molecule methods.\\u003c/p\\u003e\\u003cp\\u003eSam68 is an RNA-binding protein composed of three main regions: the N-terminal (residues 1\\u0026ndash;96), the central STAR binding domain (residues 97\\u0026ndash;260), and the C-terminal (residues 261\\u0026ndash;443)\\u003csup\\u003e\\u003cspan citationid=\\\"CR54\\\" class=\\\"CitationRef\\\"\\u003e54\\u003c/span\\u003e\\u003c/sup\\u003e. It binds to G8.5 RNA, a 40-nt RNA sequence, with a dissociation constant (\\u003cem\\u003eK\\u003c/em\\u003e\\u003csub\\u003ed\\u003c/sub\\u003e) of ~\\u0026thinsp;12 nM determined from an electrophoretic mobility shift assay\\u003csup\\u003e\\u003cspan citationid=\\\"CR55\\\" class=\\\"CitationRef\\\"\\u003e55\\u003c/span\\u003e\\u003c/sup\\u003e. This affinity, however, is significantly reduced to around 36.1 \\u0026micro;M when only the central STAR domain is present\\u003csup\\u003e\\u003cspan citationid=\\\"CR56\\\" class=\\\"CitationRef\\\"\\u003e56\\u003c/span\\u003e\\u003c/sup\\u003e, suggesting that the intrinsically disordered N- and C-terminal regions contribute to strong binding. NMR experiments later confirmed that both termini independently bind G8.5 RNA, with affinities of 1\\u0026ndash;10 \\u0026micro;M for the N-terminal and 30\\u0026ndash;70 \\u0026micro;M for the C-terminal\\u003csup\\u003e\\u003cspan citationid=\\\"CR57\\\" class=\\\"CitationRef\\\"\\u003e57\\u003c/span\\u003e\\u003c/sup\\u003e.\\u003c/p\\u003e\\u003cp\\u003eHere, we focus on the Sam68 N-terminal IDR and its interaction with G8.5 RNA, where the RNA-binding activity is attributed to the two arginine/glycine (R/G)-rich motifs (\\u003csup\\u003e45\\u003c/sup\\u003eRGGGGG\\u003csup\\u003e50\\u003c/sup\\u003e and \\u003csup\\u003e52\\u003c/sup\\u003eRGG\\u003csup\\u003e\\u003cspan citationid=\\\"CR54\\\" class=\\\"CitationRef\\\"\\u003e54\\u003c/span\\u003e\\u003c/sup\\u003e) on the Sam68 N-terminal, and the adenosine/uracil (A/U) rich motif (\\u003csup\\u003e22\\u003c/sup\\u003eAUUAAAA\\u003csup\\u003e\\u003cspan citationid=\\\"CR28\\\" class=\\\"CitationRef\\\"\\u003e28\\u003c/span\\u003e\\u003c/sup\\u003e) in G8.5 RNA\\u003csup\\u003e\\u003cspan citationid=\\\"CR58\\\" class=\\\"CitationRef\\\"\\u003e58\\u003c/span\\u003e\\u003c/sup\\u003e (full sequences are presented in Figs. S15a and S15b). We trapped the N-terminal of Sam68 for ~\\u0026thinsp;20 minutes before introducing 1 \\u0026micro;M G8.5 RNA to monitor binding dynamics (Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig4\\\" class=\\\"InternalRef\\\"\\u003e4\\u003c/span\\u003ea). Upon RNA arrival at the trapping site (~\\u0026thinsp;30 min after initial trapping), a sharp increase in transmission occurs, indicative of a higher polarisability for the RNA-bound complex compared to the unbound protein. This increase is accompanied by a significant reduction in signal fluctuations (Figs.\\u0026nbsp;\\u003cspan refid=\\\"Fig4\\\" class=\\\"InternalRef\\\"\\u003e4\\u003c/span\\u003ea and \\u003cspan refid=\\\"Fig4\\\" class=\\\"InternalRef\\\"\\u003e4\\u003c/span\\u003eb), consistent with a transition to a more stable and ordered structure, representing the first direct observation of a disorder-to-order transition in the Sam68 N-terminal IDR.\\u003c/p\\u003e\\u003cp\\u003eFigure \\u003cspan refid=\\\"Fig4\\\" class=\\\"InternalRef\\\"\\u003e4\\u003c/span\\u003ec compares PSDs of the RNA-bound and unbound states of N-terminal region. The RNA-bound state exhibits reduced signal fluctuations compared to the unbound state, particularly at frequencies below 1 kHz (\\u0026gt;\\u0026thinsp;1 ms), suggesting restricted dynamics and enhanced structural stability induced by RNA binding. This binding is reversible, with the signal returning to pre-binding levels upon RNA dissociation. The transmission trajectory during binding/unbinding shows three discrete transmission levels (Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig4\\\" class=\\\"InternalRef\\\"\\u003e4\\u003c/span\\u003ed), with PDF peaks corresponding to: (i) the unbound Sam68 N-terminal, (ii) the RNA-bound state, and (iii) an intermediate level likely reflecting co-localisation of Sam68 and G8.5 RNA within the trap without complex formation. To quantify these binding kinetics, we applied a three-step fitting model, assigning levels to the bound and unbound states (Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig4\\\" class=\\\"InternalRef\\\"\\u003e4\\u003c/span\\u003ee). Exponential decay fits applied to the histogram of association time (\\u003cspan class=\\\"InlineEquation\\\"\\u003e\\u003cspan class=\\\"mathinline\\\"\\u003e\\\\(\\\\:{\\\\tau\\\\:}_{\\\\text{o}\\\\text{n}}\\\\)\\u003c/span\\u003e\\u003c/span\\u003e) and dissociation time (\\u003cspan class=\\\"InlineEquation\\\"\\u003e\\u003cspan class=\\\"mathinline\\\"\\u003e\\\\(\\\\:{\\\\tau\\\\:}_{\\\\text{o}\\\\text{f}\\\\text{f}}\\\\)\\u003c/span\\u003e\\u003c/span\\u003e) yielded average \\u003cspan class=\\\"InlineEquation\\\"\\u003e\\u003cspan class=\\\"mathinline\\\"\\u003e\\\\(\\\\:{\\\\tau\\\\:}_{\\\\text{o}\\\\text{n}}\\\\)\\u003c/span\\u003e\\u003c/span\\u003e and \\u003cspan class=\\\"InlineEquation\\\"\\u003e\\u003cspan class=\\\"mathinline\\\"\\u003e\\\\(\\\\:{\\\\tau\\\\:}_{\\\\text{o}\\\\text{f}\\\\text{f}}\\\\)\\u003c/span\\u003e\\u003c/span\\u003e values of 1416 ms and 177 ms, respectively (Figs.\\u0026nbsp;\\u003cspan refid=\\\"Fig4\\\" class=\\\"InternalRef\\\"\\u003e4\\u003c/span\\u003ef and \\u003cspan refid=\\\"Fig4\\\" class=\\\"InternalRef\\\"\\u003e4\\u003c/span\\u003eg). From these values, we estimated a dissociation constant (\\u003cem\\u003eK\\u003c/em\\u003e\\u003csub\\u003ed\\u003c/sub\\u003e) of ~\\u0026thinsp;8 \\u0026micro;M, consistent with previous NMR results (1\\u0026ndash;10 \\u0026micro;M)\\u003csup\\u003e57\\u003c/sup\\u003e. Details of the \\u003cem\\u003eK\\u003c/em\\u003e\\u003csub\\u003ed\\u003c/sub\\u003e calculation are available in Supplementary SI-14. Notably, in some instances, RNA binding induced a transition to a persistent ordered conformation (Fig. S17), likely due to the relatively high affinity. While such traces cannot quantify the dissociation constant, they consistently showed attenuated signal fluctuations in the RNA-bound state, with dynamics reduced on timescales of hundreds of microseconds to seconds. Additional context for this observation is provided in Supplementary SI-15.\\u003c/p\\u003e\\u003cp\\u003e\\u003c/p\\u003e\\u003c/div\\u003e\"},{\"header\":\"Conclusions\",\"content\":\"\\u003cp\\u003eThis work demonstrates a key strategy for directly probing the conformational dynamics of single, label-free intrinsically disordered proteins (IDPs) and intrinsically disordered regions in structured proteins (IDRs) in solution. The trapping signals from nanoaperture optical tweezers (NOTs) reveal how IDP/IDR structural disorder manifests through distinct intensity fluctuations, energy states, and dynamic timescales, compared to globular proteins. At single-molecule resolution, we provide experimental evidence that phosphorylation of native tau-441 by GSK3β reduces conformational heterogeneity, with suppressed dynamics observed on the millisecond\\u0026ndash;second timescale. We further demonstrate the real-time observation of a disorder-to-order transition in the Sam68 N-terminal region upon RNA binding, with dissociation kinetics consistent with ensemble measurements. These findings provide unique insights into the conformational dynamics of disordered proteins that are inaccessible to conventional single-molecule techniques, expanding the current experimental toolkit for studying protein disorder and its role in biological function and disease.\\u003c/p\\u003e\"},{\"header\":\"Materials and Methods\",\"content\":\"\\u003cp\\u003e\\u003cb\\u003eFabrication of double nanohole structures in gold film.\\u003c/b\\u003e The double nanohole (DNH) structures used in this work were fabricated as previously described\\u003csup\\u003e\\u003cspan citationid=\\\"CR31\\\" class=\\\"CitationRef\\\"\\u003e31\\u003c/span\\u003e,\\u003cspan citationid=\\\"CR32\\\" class=\\\"CitationRef\\\"\\u003e32\\u003c/span\\u003e,\\u003cspan citationid=\\\"CR37\\\" class=\\\"CitationRef\\\"\\u003e37\\u003c/span\\u003e\\u003c/sup\\u003e. A 550 \\u0026micro;m thick fused silica wafer was coated with a 30 nm silicon nitride layer using low-pressure chemical vapor deposition (LPCVD). Subsequently, a 5 nm titanium layer and a 100 nm gold layer were deposited using electron-beam evaporation at 190\\u0026deg;C. The wafers were diced to 10 mm \\u0026times; 10 mm chips for further use. Two nanoholes, each with a depth of 90 nm, diameter of 160 nm and a centre-to-centre distance of 200 nm were etched into the gold layer using a focused ion beam (FIB, Zeiss Crossbeam) with a gallium ion source. A rectangle of 3 nm in height was then etched to connect the edges of the two holes to form the DNH gap. The FIB was operated at 30 kV with a beam current of 1 pA.\\u003c/p\\u003e\\u003cp\\u003e\\u003cb\\u003eDouble nanohole surface passivation.\\u003c/b\\u003e Double nanohole structures were passivated using polyethylene glycol methyl ether thiol (PEG-thiol, average MW 800 Da, 729108, Sigma Aldrich) as previously described\\u003csup\\u003e\\u003cspan citationid=\\\"CR31\\\" class=\\\"CitationRef\\\"\\u003e31\\u003c/span\\u003e,\\u003cspan citationid=\\\"CR37\\\" class=\\\"CitationRef\\\"\\u003e37\\u003c/span\\u003e\\u003c/sup\\u003e. The double nanohole samples were immersed in a solution comprised of 2 mM PEG-thiol in ethanol and left overnight (~\\u0026thinsp;18 hours) before being rinsed thoroughly with ethanol and dried using an air gun. Solutions were freshly prepared before each use.\\u003c/p\\u003e\\u003cp\\u003e\\u003cb\\u003eNanoaperture optical tweezers setup.\\u003c/b\\u003e Optical components were purchased from Thorlabs as previously described\\u003csup\\u003e\\u003cspan citationid=\\\"CR31\\\" class=\\\"CitationRef\\\"\\u003e31\\u003c/span\\u003e,\\u003cspan citationid=\\\"CR32\\\" class=\\\"CitationRef\\\"\\u003e32\\u003c/span\\u003e,\\u003cspan citationid=\\\"CR37\\\" class=\\\"CitationRef\\\"\\u003e37\\u003c/span\\u003e\\u003c/sup\\u003e. A half-wave plate adjusts the polarisation of the 852 nm laser (Thorlabs, FPL852) to be across the pointed edges of the gap between the two nanoholes (\\u003cem\\u003ey-\\u003c/em\\u003eaxis, Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig1\\\" class=\\\"InternalRef\\\"\\u003e1\\u003c/span\\u003ea)\\u003csup\\u003e\\u003cspan citationid=\\\"CR31\\\" class=\\\"CitationRef\\\"\\u003e31\\u003c/span\\u003e,\\u003cspan citationid=\\\"CR59\\\" class=\\\"CitationRef\\\"\\u003e59\\u003c/span\\u003e\\u003c/sup\\u003e. The laser was collimated and expanded to 5 mm in diameter and then focused onto the DNH using a 100\\u0026times; objective (1.25 NA PLN100XO, Olympus). The laser power on the DNH was around 20 mW correlating to around 37\\u003csup\\u003eo\\u003c/sup\\u003eC at the focusing point due to laser heating (Fig. S2). Light passing through the sample was collected with a 4\\u0026times; objective (0.1 NA PLN4XP, Olympus), then was focused onto an avalanche photodiode (APD120A/M, Thorlabs), which converted the light intensity to a voltage signal.\\u003c/p\\u003e\\u003cp\\u003e\\u003cb\\u003eData acquisition.\\u003c/b\\u003e The avalanche photodiode (APD120A/M, Thorlabs) has a bandwidth of 50 MHz. However, considering the signal bandwidth of the system (~\\u0026thinsp;10 kHz) and to optimise the file size, the voltage signal was recorded at a sampling rate of 1 MHz using a data acquisition card (USB-6361, NI) controlled by a custom LabVIEW program. Based on the Nyquist frequency, this system provides a theoretical time resolution of 2 \\u0026micro;s.\\u003c/p\\u003e\\u003cp\\u003e\\u003cb\\u003eMicrofluidics system.\\u003c/b\\u003e Flow cells were printed using a FormLab 2 printer with Clear V4 resin at a resolution of 50 \\u0026micro;m (FormLabs Inc, USA), as previously described\\u003csup\\u003e\\u003cspan citationid=\\\"CR31\\\" class=\\\"CitationRef\\\"\\u003e31\\u003c/span\\u003e,\\u003cspan citationid=\\\"CR32\\\" class=\\\"CitationRef\\\"\\u003e32\\u003c/span\\u003e,\\u003cspan citationid=\\\"CR37\\\" class=\\\"CitationRef\\\"\\u003e37\\u003c/span\\u003e\\u003c/sup\\u003e. Two component silicone glue (Twinsil, Picodent, Germany) was used to seal the DNH structure within the flow cell with a 0.17 mm thick glass coverslip. A 50 \\u0026micro;m thick piece of double-sided tape (Arcare 92712, Adhesive Research, Inc) was used to separate the DNH and glass coverslip, creating a chamber with a volume of 3.5 \\u0026micro;L. Flow rate and flow direction were controlled using a syringe pump (Harvard Apparatus, US) through a 12-valve distributor (MUX Distributor, Elveflow, France). For Sam68 N-terminal experiments, the RNA solution is infused to the chamber at a flow rate of ~\\u0026thinsp;1\\u0026ndash;2 \\u0026micro;L/min while Sam68 N-terminal was trapped. Given the ~\\u0026thinsp;10 \\u0026micro;L dead volume of the intake tubing, this flow rate required\\u0026thinsp;~\\u0026thinsp;5\\u0026ndash;10 minutes for RNA solution to arrive at the trapping site.\\u003c/p\\u003e\\u003cp\\u003e\\u003cb\\u003eProtein and RNA preparation.\\u003c/b\\u003e Human haemoglobin (H7379, Sigma Aldrich), human native tau-441 (T0576, Sigma Aldrich), human GSK3β-tau-441 (SRP0689, Sigma Aldrich), bovine ribonuclease A (R5500, Sigma Aldrich), and bovine serum albumin (A8531, Sigma Aldrich) were prepared in a filtered buffer solution of 0.1 M bis-tris propane, 150 mM NaCl and 20% glycerol at pH 7.2. Bovine actin (A3653, Sigma Aldrich) was prepared in 0.1 M bis-tris propane, 0.2 mM CaCl\\u003csub\\u003e2\\u003c/sub\\u003e, 0.2 mM ATP and 20% glycerol at pH 7.2.\\u003c/p\\u003e\\u003cp\\u003eHuman Sam68 N-terminal (amino acids 1\\u0026ndash;96) and C-terminal (amino acids 267\\u0026ndash;368) sequences were cloned and produced at the University of Leicester and comprised as previously described\\u003csup\\u003e\\u003cspan citationid=\\\"CR57\\\" class=\\\"CitationRef\\\"\\u003e57\\u003c/span\\u003e\\u003c/sup\\u003e (sequence also available in Fig. S15a). G8.5 RNA sequence was bought from Dharmacon, Horizon Discovery and is the same as previously described\\u003csup\\u003e\\u003cspan citationid=\\\"CR55\\\" class=\\\"CitationRef\\\"\\u003e55\\u003c/span\\u003e\\u003c/sup\\u003e (sequence also available in Fig. S15b). Both protein solutions were prepared in a filtered solution containing 50 mM sodium phosphate and 150 mM NaCl at pH 6.8.\\u003c/p\\u003e\\u003cp\\u003eProteins were aliquoted into 1 \\u0026micro;M aliquots of 100 \\u0026micro;L volume and immediately flash frozen in liquid nitrogen before being stored at -20\\u0026deg;C, except for GSK3β-tau which was stored at -80\\u003csup\\u003eo\\u003c/sup\\u003eC. The proteins were slow thawed on wet ice on the day of use for an experiment.\\u003c/p\\u003e\\u003cp\\u003e\\u003cb\\u003eData analysis.\\u003c/b\\u003e Custom MATLAB scripts were used to analyse all the data in this work.\\u003c/p\\u003e\\u003cp\\u003e\\u003cem\\u003eData filtering\\u003c/em\\u003e. Raw data were filtered using a zero-phase Gaussian low-pass filter to the desired cut-off frequency by using the \\u003cb\\u003efiltfilt.m\\u003c/b\\u003e function.\\u003c/p\\u003e\\u003cp\\u003e\\u003cem\\u003eNormalisation of optical transmission traces\\u003c/em\\u003e. We used normalised transmission intensity, Δ\\u003cem\\u003eI/I\\u003c/em\\u003e\\u003csub\\u003e\\u003cem\\u003e0\\u003c/em\\u003e\\u003c/sub\\u003e, to quantify the relative transmission change upon trapping a single protein. Since the optical signal was recorded by the avalanche photodiode as voltage (V), with Δ\\u003cem\\u003eI/I\\u003c/em\\u003e\\u003csub\\u003e\\u003cem\\u003e0\\u003c/em\\u003e\\u003c/sub\\u003e calculated as Δ\\u003cem\\u003eI/I\\u003c/em\\u003e\\u003csub\\u003e\\u003cem\\u003e0\\u003c/em\\u003e\\u003c/sub\\u003e = (\\u003cem\\u003eV \\u0026ndash; V\\u003c/em\\u003e\\u003csub\\u003e\\u003cem\\u003e0\\u003c/em\\u003e\\u003c/sub\\u003e)/ \\u003cem\\u003eV\\u003c/em\\u003e\\u003csub\\u003e\\u003cem\\u003e0\\u003c/em\\u003e\\u003c/sub\\u003e. For trapping traces shown in Figs.\\u0026nbsp;2a-c, 4a, S4a-b, S9a-c, and S17a, \\u003cem\\u003eV\\u003c/em\\u003e\\u003csub\\u003e\\u003cem\\u003e0\\u003c/em\\u003e\\u003c/sub\\u003e is the mean value of the baseline, whilst for the trapped transmission traces (Figs.\\u0026nbsp;\\u003cspan refid=\\\"Fig2\\\" class=\\\"InternalRef\\\"\\u003e2\\u003c/span\\u003ef-h, S8a-c, and S17b-c), \\u003cem\\u003eV\\u003c/em\\u003e\\u003csub\\u003e\\u003cem\\u003e0\\u003c/em\\u003e\\u003c/sub\\u003e corresponds to the mean value of the trace.\\u003c/p\\u003e\\u003cp\\u003e\\u003cem\\u003eProbability density function (PDF)\\u003c/em\\u003e. We filtered the 20-s trace with a cutoff frequency of 10 kHz and calculated the PDF by estimating the kernel density using the \\u003cb\\u003eksdensity.m\\u003c/b\\u003e function with 300 points. This cutoff frequency was chosen based on the PSD analysis (Figs.\\u0026nbsp;\\u003cspan refid=\\\"Fig4\\\" class=\\\"InternalRef\\\"\\u003e4\\u003c/span\\u003ec, S8d, and S17d), which shows that protein-induced signal variations remained distinguishable from empty DNH noise up to 10 kHz.\\u003c/p\\u003e\\u003cp\\u003e\\u003cem\\u003eTrace detrending\\u003c/em\\u003e. To allow well-assigned levels for step fitting, we removed the linear drift of the whole trace using the \\u003cb\\u003edetrend.m\\u003c/b\\u003e function from MATLAB 2022b, as shown in Fig. S16.\\u003c/p\\u003e\\u003cp\\u003e\\u003cem\\u003eDeconvolution of PDF and energy landscapes\\u003c/em\\u003e. See details in Supplementary SI-11.\\u003c/p\\u003e\\u003cp\\u003e\\u003cem\\u003ePower spectral density (PSD)\\u003c/em\\u003e. To estimate the PSD of the time-domain signal across the trapping trace, we used the sampling frequency (\\u003cem\\u003ef\\u003c/em\\u003e) and the signal vector (\\u003cem\\u003eXXX\\u003c/em\\u003e). The frequency vector was taken over half of the frequency spectrum, from 0 to \\u003cem\\u003ef\\u003c/em\\u003e/2 for the Nyquist frequency using a linearly spaced grid with the number of data points (\\u003cem\\u003eN\\u003c/em\\u003e)/2 size. The power spectrum was then computed using the Fast Fourier Transform (FFT) and the squared magnitude was obtained using |FFT(\\u003cem\\u003eXXX\\u003c/em\\u003e)|\\u003csup\\u003e2\\u003c/sup\\u003e before normalisation by \\u003cem\\u003eN\\u003c/em\\u003e multiplied by \\u003cem\\u003ef\\u003c/em\\u003e as shown in Eq.\\u0026nbsp;\\u003cspan refid=\\\"Equ1\\\" class=\\\"InternalRef\\\"\\u003e1\\u003c/span\\u003e:\\u003cdiv id=\\\"Equ1\\\" class=\\\"Equation\\\"\\u003e\\u003cdiv format=\\\"TEX\\\" class=\\\"mathdisplay\\\" id=\\\"FileID_Equ1\\\" name=\\\"EquationSource\\\"\\u003e\\n$$\\\\:{Pxx}_{\\\\text{t}\\\\text{e}\\\\text{m}\\\\text{p}}=\\\\:\\\\frac{FFT\\\\left(XXX\\\\right)\\\\times\\\\:conj\\\\left(FFT\\\\left(XXX\\\\right)\\\\right)}{N\\\\times\\\\:f}$$\\u003c/div\\u003e\\u003cdiv class=\\\"EquationNumber\\\"\\u003e1\\u003c/div\\u003e\\u003c/div\\u003e\\u003c/p\\u003e\\u003cp\\u003eTo remove negative frequency components and conserve the total power in the spectrum, the power spectrum was truncated to \\u003cem\\u003eN\\u003c/em\\u003e/2 points and multiplied by 2 as shown in Eq.\\u0026nbsp;2:\\u003c/p\\u003e\\u003cp\\u003e\\u003cspan class=\\\"InlineEquation\\\"\\u003e\\u003cspan class=\\\"mathinline\\\"\\u003e\\\\(\\\\:Pxx=2\\\\times\\\\:{Pxx}_{\\\\text{t}\\\\text{e}\\\\text{m}\\\\text{p}}(1:[\\\\frac{N}{2}]\\\\)\\u003c/span\\u003e\\u003c/span\\u003e) (2)\\u003c/p\\u003e\\u003cp\\u003e\\u003cb\\u003eFinite-difference time-domain simulations.\\u003c/b\\u003e The transmission of DNH structures were modelled based on finite-difference time-domain (FDTD) using a commercial software (Lumerical, Ansys). See Tables S1 and S2 in Supplementary SI-3 for parameters.\\u003c/p\\u003e\\u003cp\\u003e\\u003cem\\u003eVolume versus transmission changes\\u003c/em\\u003e. The relationship between particle volume and the change in transmission (Fig. S3d) were simulated by placing a spherical particle (refractive index \\u003cem\\u003en\\u003c/em\\u003e\\u0026thinsp;=\\u0026thinsp;2.0) with radii ranging from 2 to 6 nm in the centre of the DNH (\\u003cem\\u003ex\\u003c/em\\u003e\\u0026thinsp;=\\u0026thinsp;0, \\u003cem\\u003ey\\u003c/em\\u003e\\u0026thinsp;=\\u0026thinsp;0, \\u003cem\\u003ez\\u003c/em\\u003e\\u0026thinsp;=\\u0026thinsp;8) (Table \\u003cspan refid=\\\"MOESM1\\\" class=\\\"InternalRef\\\"\\u003eS1\\u003c/span\\u003e).\\u003c/p\\u003e\\u003cp\\u003e\\u003cb\\u003eLaser heating Simulation.\\u003c/b\\u003e The laser heating simulation is similar to that described previously\\u003csup\\u003e\\u003cspan citationid=\\\"CR32\\\" class=\\\"CitationRef\\\"\\u003e32\\u003c/span\\u003e\\u003c/sup\\u003e, with details listed in SI-2.\\u003c/p\\u003e\"},{\"header\":\"Declarations\",\"content\":\"\\u003cp\\u003e\\u003ch2\\u003eDisclosures\\u003c/h2\\u003e\\u003cp\\u003eThe authors declare that they have no competing interests.\\u003c/p\\u003e\\u003c/p\\u003e\\u003cp\\u003e\\u003ch2\\u003eCode and Data Availability\\u003c/h2\\u003e\\u003cp\\u003eThe code and data supporting the presented findings are available from the corresponding author upon reasonable request.\\u003c/p\\u003e\\u003c/p\\u003e\\u003ch2\\u003eFunding\\u003c/h2\\u003e\\u003cp\\u003eThis research work was supported by the UK-India Education Research Initiative (UKIERI), Scheme for Promotion of Academic and Research Collaboration (SPARC), and the Academy of Medical Sciences Springboard Award (SBF0010\\\\1008). S.Z. acknowledges support from the Biotechnology and Biological Sciences Research Council Doctoral Training Partnerships (BBSRC DTP) (BB/T0083690/1). M.R. appreciates the support from the Royal Society, the Royal Society Yusuf Hamied Visiting Fellowship, and the Wolfson Foundation. C.D and A.H. acknowledge support from the BBSRC sLoLa grant (BB/T000627/1).\\u003c/p\\u003e\\u003ch2\\u003eAuthor Contribution\\u003c/h2\\u003e\\u003cp\\u003eC.Y. conceived the project. S.Z. and Y.W. performed trapping experiments for all proteins and prepared all non-Sam68 protein buffers. S.M. prepared Sam68 constructs. S.Z. and A.Y. performed nanofabrication of DNHs. A.Y. provided support for trapping experiments and nanofabrication. C.D and A.H. provided support for Sam68 content. C.Y. and M.P. conducted energy landscape calculations. S.Z. and C.Y. performed the data analysis. S.C. provided guidance on native tau-441 and GSK3β-tau. R.G. provided guidance on plasmonic nanotweezers and data interpretation. C.J.M., L.X., M.R., and C.Y. provided S.Z. supervisory guidance. The manuscript was written by S.Z., with assistance from C.D., M.P., A.Y., S.C., A.H., R.G., C.J.M., L.X., M.R., and C.Y.\\u003c/p\\u003e\\u003ch2\\u003eData Availability\\u003c/h2\\u003e\\u003cp\\u003eThe code and data supporting the presented findings are available from the corresponding author upon reasonable request.\\u003c/p\\u003e\"},{\"header\":\"References\",\"content\":\"\\u003col\\u003e\\u003cli\\u003e\\u003cspan\\u003eHolehouse, A. S. \\u0026amp; Kragelund, B. B. The molecular basis for cellular function of intrinsically disordered protein regions. \\u003cem\\u003eNat Rev Mol Cell Biol\\u003c/em\\u003e 25, 187\\u0026ndash;211 (2024).\\u003c/span\\u003e\\u003c/li\\u003e\\u003cli\\u003e\\u003cspan\\u003eSnead, D. \\u0026amp; Eliezer, D. 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Improvement of Sensing and Trapping Efficiency of Double Nanohole Apertures via Enhancing the Wedge Plasmon Polariton Modes with Tapered Cusps. \\u003cem\\u003eACS Photonics\\u003c/em\\u003e 4, 1108\\u0026ndash;1113 (2017).\\u003c/span\\u003e\\u003c/li\\u003e\\u003c/ol\\u003e\"}],\"fulltextSource\":\"\",\"fullText\":\"\",\"funders\":[],\"hasAdminPriorityOnWorkflow\":false,\"hasManuscriptDocX\":true,\"hasOptedInToPreprint\":true,\"hasPassedJournalQc\":\"\",\"hasAnyPriority\":false,\"hideJournal\":false,\"highlight\":\"\",\"institution\":\"\",\"isAcceptedByJournal\":true,\"isAuthorSuppliedPdf\":false,\"isDeskRejected\":\"\",\"isHiddenFromSearch\":false,\"isInQc\":false,\"isInWorkflow\":false,\"isPdf\":false,\"isPdfUpToDate\":true,\"isWithdrawnOrRetracted\":false,\"journal\":{\"display\":true,\"email\":\"info@researchsquare.com\",\"identity\":\"npj-biosensing\",\"isNatureJournal\":false,\"hasQc\":true,\"allowDirectSubmit\":false,\"externalIdentity\":\"\",\"sideBox\":\"Learn more about [npj Biosensing](https://www.nature.com/npjbiosensing)\",\"snPcode\":\"44328\",\"submissionUrl\":\"https://submission.springernature.com/new-submission/44328/3\",\"title\":\"npj Biosensing\",\"twitterHandle\":\"\",\"acdcEnabled\":true,\"dfaEnabled\":true,\"editorialSystem\":\"stoa\",\"reportingPortfolio\":\"NPJ\",\"inReviewEnabled\":true,\"inReviewRevisionsEnabled\":true},\"keywords\":\"\",\"lastPublishedDoi\":\"10.21203/rs.3.rs-8222117/v1\",\"lastPublishedDoiUrl\":\"https://doi.org/10.21203/rs.3.rs-8222117/v1\",\"license\":{\"name\":\"CC BY 4.0\",\"url\":\"https://creativecommons.org/licenses/by/4.0/\"},\"manuscriptAbstract\":\"\\u003cp\\u003eIntrinsically disordered proteins (IDPs) and structured proteins with intrinsically disordered regions (IDRs) lack a definitive tertiary structure and contribute to the onset of diseases such as Alzheimer\\u0026rsquo;s and cancer. To date, experimental observation of single, label-free IDPs/IDRs poses a significant challenge due to their structural heterogeneity limiting ensemble techniques from fully capturing their properties, whilst single-molecule measurements require site-specific modifications or non-physiological conditions, perturbing their native biophysics. Here, we demonstrate the first experimental observation of unmodified IDP/IDR conformational dynamics at the single-molecule level, achieved by optical trapping and investigation of individual IDPs/IDRs using nanoaperture optical tweezers. Our results reveal that IDPs/IDRs exhibit significantly larger conformational variations compared to globular proteins of similar size. We demonstrate that phosphorylation of native tau-441 by glycogen synthase kinase 3-beta (GSK3β-tau) induces compaction and reduced conformational dynamics. We further observed a disorder-to-order transition during binding of the N-terminal region of Src-associated protein in mitosis of 68 kDa (Sam68) to G8.5 RNA. These findings present nanoaperture optical tweezers as a powerful approach to advance our understanding of IDPs/IDRs and further decode their roles in associated diseases.\\u003c/p\\u003e\",\"manuscriptTitle\":\"Label-free optical observation of disordered-to-ordered transitions in single intrinsically disordered proteins\",\"msid\":\"\",\"msnumber\":\"\",\"nonDraftVersions\":[{\"code\":1,\"date\":\"2025-12-17 18:53:47\",\"doi\":\"10.21203/rs.3.rs-8222117/v1\",\"editorialEvents\":[{\"type\":\"communityComments\",\"content\":0},{\"type\":\"decision\",\"content\":\"Revision requested\",\"date\":\"2026-01-15T08:26:08+00:00\",\"index\":\"\",\"fulltext\":\"\"},{\"type\":\"editorInvitedReview\",\"content\":\"\",\"date\":\"2026-01-15T08:01:28+00:00\",\"index\":\"hide\",\"fulltext\":\"\"},{\"type\":\"editorInvitedReview\",\"content\":\"\",\"date\":\"2026-01-14T20:15:28+00:00\",\"index\":\"hide\",\"fulltext\":\"\"},{\"type\":\"editorInvitedReview\",\"content\":\"\",\"date\":\"2026-01-04T12:32:46+00:00\",\"index\":\"hide\",\"fulltext\":\"\"},{\"type\":\"reviewerAgreed\",\"content\":\"267610676748015353152291057517245229000\",\"date\":\"2025-12-19T16:31:01+00:00\",\"index\":\"hide\",\"fulltext\":\"\"},{\"type\":\"reviewerAgreed\",\"content\":\"17168222742370030126906142128284044193\",\"date\":\"2025-12-17T13:22:28+00:00\",\"index\":\"hide\",\"fulltext\":\"\"},{\"type\":\"reviewerAgreed\",\"content\":\"159799766964603825843423172971002402484\",\"date\":\"2025-12-13T04:58:55+00:00\",\"index\":\"hide\",\"fulltext\":\"\"},{\"type\":\"reviewersInvited\",\"content\":\"\",\"date\":\"2025-12-12T12:49:37+00:00\",\"index\":\"\",\"fulltext\":\"\"},{\"type\":\"editorAssigned\",\"content\":\"\",\"date\":\"2025-12-12T12:46:59+00:00\",\"index\":\"\",\"fulltext\":\"\"},{\"type\":\"checksComplete\",\"content\":\"\",\"date\":\"2025-12-12T10:56:20+00:00\",\"index\":\"\",\"fulltext\":\"\"},{\"type\":\"submitted\",\"content\":\"npj Biosensing\",\"date\":\"2025-11-27T12:33:03+00:00\",\"index\":\"\",\"fulltext\":\"\"}],\"status\":\"published\",\"journal\":{\"display\":true,\"email\":\"info@researchsquare.com\",\"identity\":\"npj-biosensing\",\"isNatureJournal\":false,\"hasQc\":true,\"allowDirectSubmit\":false,\"externalIdentity\":\"\",\"sideBox\":\"Learn more about [npj Biosensing](https://www.nature.com/npjbiosensing)\",\"snPcode\":\"44328\",\"submissionUrl\":\"https://submission.springernature.com/new-submission/44328/3\",\"title\":\"npj Biosensing\",\"twitterHandle\":\"\",\"acdcEnabled\":true,\"dfaEnabled\":true,\"editorialSystem\":\"stoa\",\"reportingPortfolio\":\"NPJ\",\"inReviewEnabled\":true,\"inReviewRevisionsEnabled\":true}}],\"origin\":\"\",\"ownerIdentity\":\"5e146e66-1dad-4515-9a7c-65bb7d3f9b58\",\"owner\":[],\"postedDate\":\"December 17th, 2025\",\"published\":true,\"recentEditorialEvents\":[],\"rejectedJournal\":[],\"revision\":\"\",\"amendment\":\"\",\"status\":\"under-review\",\"subjectAreas\":[{\"id\":59724381,\"name\":\"Biological sciences/Biochemistry\"},{\"id\":59724382,\"name\":\"Biological sciences/Biophysics\"},{\"id\":59724383,\"name\":\"Biological sciences/Structural biology\"}],\"tags\":[],\"updatedAt\":\"2026-04-17T20:23:38+00:00\",\"versionOfRecord\":[],\"versionCreatedAt\":\"2025-12-17 18:53:47\",\"video\":\"\",\"vorDoi\":\"\",\"vorDoiUrl\":\"\",\"workflowStages\":[]},\"version\":\"v1\",\"identity\":\"rs-8222117\",\"journalConfig\":\"researchsquare\"},\"__N_SSP\":true},\"page\":\"/article/[identity]/[[...version]]\",\"query\":{\"redirect\":\"/article/rs-8222117\",\"identity\":\"rs-8222117\",\"version\":[\"v1\"]},\"buildId\":\"8U1c8b4HqxoKbykW_rLl7\",\"isFallback\":false,\"isExperimentalCompile\":false,\"dynamicIds\":[84888],\"gssp\":true,\"scriptLoader\":[]}","source_license":"CC-BY-4.0","license_restricted":false}