The Effect of Intrachain Electrostatic Repulsion on Conformational

Mar 27, 2014 - Yi-Hsuan Lin , Hue Sun Chan. Biophysical Journal 2017 112 ... Peter Tompa , Eva Schad , Agnes Tantos , Lajos Kalmar. Current Opinion in...
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The Effect of Intrachain Electrostatic Repulsion on Conformational Disorder and Dynamics of the Sic1 Protein Baoxu Liu,† Darius Chia,† Veronika Csizmok,§,∥ Patrick Farber,§,∥ Julie D. Forman-Kay,§,∥ and Claudiu C. Gradinaru*,†,‡ †

Department of Chemical and Physical Sciences, University of Toronto Mississauga, Mississauga, Ontario L5L 1C6, Canada Department of Physics, University of Toronto, Toronto, Ontario M5S 1A7, Canada § Molecular Structure and Function Program, Hospital for Sick Children, Toronto, Ontario M5G 1X8, Canada ∥ Department of Biochemistry, University of Toronto, Toronto, Ontario M5S 1A8, Canada ‡

S Supporting Information *

ABSTRACT: The yeast cyclin-dependent kinase inhibitor Sic1 is a disordered protein that, upon multisite phosphorylation, forms a dynamic complex with the Cdc4 subunit of an SCF ubiquitin ligase. To understand the multisite phosphorylation dependence of the Sic1:Cdc4 interaction, which ultimately leads to a sharp cell cycle transition, the conformational properties of the disordered Sic1 N-terminal targeting region were studied using single-molecule fluorescence spectroscopy. Multiple conformational populations with different sensitivities to charge screening were identified by performing experiments in nondenaturing salts and ionic denaturants. Both the end-to-end distance and the hydrodynamic radius decrease monotonically with increasing the salt concentration, and a rollover of the chain dimensions in high denaturant conditions is observed. The data were fit to the polyelectrolyte binding-screening model, yielding parameters such as the excluded volume of the uncharged chain and the binding constant to denaturant. An overall scaling factor of ∼1.2 was needed for fitting the data, which implies that Sic1 cannot be approximated by a random Gaussian chain. Fluorescence correlation spectroscopy reveals Sic1 structure fluctuations occurring on both fast (10−100 ns) and slow (∼10 ms) time scales, with the fast phase absent in low salt solutions. The results of this study provide direct evidence that longrange intrachain electrostatic repulsions are a significant factor for the conformational landscape of Sic1, and support the role of electrostatics in determining the overall shape and hydrodynamic properties of intrinsically disordered proteins.

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phosphodegron (CPD) motifs in Sic1.3,5 The N-terminal 90residue “targeting region” contains seven of these sites and has been used as a model for understanding binding. Multisite phosphorylation leads to a switch-like Sic1 degradation response to the concentration of the kinase that phosphorylates Sic1, enabling fine-tuned regulation and safe-guarding the stability of the yeast genome. NMR data support the presence of a dynamic complex of Sic1:Cdc4, with each suboptimal CPD site dynamically exchanging on and off of the single arginine-rich binding site on Cdc4 and Sic1 remaining predominantly disordered upon phosphorylation and binding.6 A polyelectrostatic model has been proposed, which relies on long-range electrostatic interactions dominating the binding in the case of suboptimal CPDs.7 In order to provide further information on the structural and electrostatic properties of Sic1 critical for its binding, NMR data including chemical shifts, amide proton/ nitrogen residual dipolar coupling (RDC) values, paramagnetic

INTRODUCTION Much of our understanding of the physical basis underlying biology has come from detailed but static structures of various stably folded proteins and their complexes. However, it has become increasingly clear that intrinsically disordered proteins (IDPs) and regions, which lack stable tertiary structure under nondenaturing conditions, are not only common but play critical roles in many cellular processes.1 The enrichment of disorder in proteins involved in regulatory, signaling and disease proteins requires understanding of the structural and dynamic characteristics of these states, for which disorder and conformational flexibility are essential, functional properties. Disordered regions are primary sites of regulatory posttranslational modification of proteins, such as phosphorylation, due to chain accessibility.2 One example is the 283-residue disordered cyclin-dependent kinase inhibitor, Sic1, which regulates cell cycle of the budding yeast Saccharomyces cerevisiae.3,4 The phosphorylation-dependent binding of Sic1 to the Cdc4 component of a ubiquitin ligase targets it for degradation in late G1 phase. In vitro Cdc4 binding and in vivo ubiquitination both require phosphorylation of Sic1 on (approximately) any six or more of the nine suboptimal Cdc4 © 2014 American Chemical Society

Received: January 22, 2014 Revised: March 24, 2014 Published: March 27, 2014 4088

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GST fusion protein at 16 °C in Escherichia coli BL21(DE3) codon plus cells and purified by purified by affinity, ion exchange, and gel filtration chromatography, as confirmed by mass spectrometry. The protein was randomly labeled with a mixture of maleimide derivatives of tetramethylrhodamine (TMR, T6027, Life Technologies, Canada), Alexa488 (A488, A10254, Life Technologies, Canada), AttoRh101 and Atto647N (73522 and 05316 respectively, Sigma-Aldrich, Canada) using the manufacturers’ protocols. Upon labeling, the free dye was removed using gel chromatography and the purified protein was analyzed using mass spectrometry (Figure S2). SMF Experiments. Burst smFRET and FCS experiments were performed on a custom-built multiparameter confocal fluorescence microscope.22,23 For smFRET, the samples were prepared at a protein concentration of 50−100 pM in buffers or in MilliQ water in the presence of various amounts of KCl and GdmCl and were excited at intensities of ∼100 kW/cm2 at 532 nm. Two avalanche photodetectors recorded the stream of photons from the sample: a blue-sensitive module in the donor channel (PDM-5CTC, MPD, Milano, Italy), and a red-sensitive module in the acceptor channel (COUNT-100C, Laser Components, Hudson, NH, USA). Photon arrival times were recorded with 4 ps resolution using a multichannel counter (PicoHarp300, PicoQuant, Germany). The acquisition time for a single FRET histogram was 30−60 min, during which about 20 000 single-molecule bursts were typically detected. The fluorescence bursts were distinguished from the background signal using a program based on a “sliding MLT scheme” 24 and a FRET efficiency value was estimated for each burst by applying typical corrections (details in the Supporting Information). Different protein conformational clusters were identified in the smFRET histograms by nonlinear least-squared fitting to a sum of Gaussian functions. FCS measurements were done overnight (10−12 h) on samples containing 5−10 nM of protein and using lower excitation intensity (∼20 kW/cm2), which resulted in a total of about 109 photons per data set. For these experiments, a nonpolarizing cube divided the donor signal in two equal parts and enabled the recording of the donor pseudoautocorrelation curve (GDD). By correlating the total donor signal and the acceptor signal the donor−acceptor cross-correlation curve (GDA) was simultaneously obtained. These correlation curves were acquired using a four-channel photon counting digital correlator with 1.6 ns resolution (Flex02−01D, Correlator.com, Bridgewater, NJ, USA). Upon dividing GDD over GDA, the fluctuations due to translational diffusion were canceled and the anticorrelated fluctuations of the donor and acceptor fluorescence become visible.25,26 The decays observed in the GDD/GDA curves were fitted to simple exponential decay functions and used as a marker for intramolecular conformational dynamics.25 Additional information about the methodology is available in the Supporting Information online.

relaxation enhancements (PRE), and R2 relaxation values, as well as small-angle X-ray scattering (SAXS) data, were recorded.8 Structural ensembles were calculated using the ENSEMBLE approach,9 which defines sets of conformations that together best fit the experimental data for disordered proteins. Recently, single-molecule fluorescence (SMF) spectroscopy techniques have contributed significantly toward understanding the conformational landscape of proteins.10−12 The ability to investigate population clusters of an ensemble is especially important for IDPs, since they are known to display a broad conformational heterogeneity and complex binding-induced conformational changes.13 SMF can probe the entire dynamic progressions of a single IDP molecule,14 and thus quantify rapid conformational fluctuations, rare intermediate states, and exchange pathways, which are hard to measure in ensembleaveraged experiments.15 Among the SMF techniques, single-molecule Fö rster Resonance Energy Transfer (smFRET) is particularly useful to investigate protein folding and conformational disorder.16−18 For instance, smFRET measurements of the NM domain of the yeast prion protein Sup 35 19 revealed that it simultaneously populates a few distinct subsets of conformations, instead of a single stable structure like most globular proteins. smFRET on a number of unfolded and disordered proteins provided insight into the dependence of the polymer chain dimensions on the amino acid composition and the ionic strength of the solution.20 The authors used a polyampholyte model to describe the variation of the protein size induced by denaturing salts and suggested a critical role for electrostatics in fine-tuning the structural properties of IDPs. Fluorescence correlation spectroscopy (FCS) can detect the conformational dynamics of IDPs.15,21 The inherent diversity of conformational states and their fast interconversion makes the correlation-based techniques a natural choice for investigating the dynamic processes in IDPs. FCS experiments on the NM domain of Sup 35 revealed conformational fluctuations on the nanosecond time scale.19 FCS and FRET were combined to quantify structural dynamics of different α-synuclein SDS binding modes,17 and processes with lifetimes spanning 3 orders of magnitude were observed for different conformational states. In this work, we used smFRET and FCS experiments to measure the conformations and the dynamics of the disordered Sic1 N-terminal targeting region (1−90) to provide insights into the role of intrachain electrostatics and to compare with the structural ensembles calculated from NMR and SAXS data. Under nondenaturing conditions, smFRET histograms show that Sic1 adopts a broad size distribution, in agreement with the outcome of ENSEMBLE calculations. Upon varying the amount of salt and denaturant in solution, several populations (clusters) of Sic1 structures were resolved based on different sensitivities to charge screening. The techniques and the data analysis developed, as well as our findings about the role of charge screening and charge neutralization for the conformational properties of Sic1, could be applied to other IDPs and help shed light on the mechanisms utilized by disordered proteins to mediate various functions in biology.

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RESULTS Effect of Salt on Sic1 Conformations. It has been shown that the conformational properties of IDPs are dependent on the amount and distribution of charged residues in the sequence.27,28 The N-terminal 90-residue targeting region of Sic1 (hereafter called “Sic1”) contains 11 positively charged amino acids at neutral pH. Sic1 can be considered a “wellmixed”, relatively weak poly ampholyte, with κ = 0.16 and fraction of charged residues (FCR) = 0.12,27 indicating a

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MATERIALS AND METHODS Protein Labeling. Two cysteines were added at each terminus of the Sic1 targeting region in order to label the protein for FRET experiments. Sic1-1C90C was expressed as a 4089

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Cluster 1 has the lowest FRET and does not appear to shift significantly even at the highest salt concentration. This peak is centered near E = 0%, and it accounts for about 2/3 of the single-molecule population measured at zero salt. Typically, in smFRET studies, the zero-FRET peak is discarded as an artifact due to proteins labeled only with the donor dye. Mass spectrometry analysis of dye-labeled Sic1 (Figure S2 in the Supporting Information) indicates that the fraction of donoronly protein is negligible and cannot account for such a significant zero-FRET population (see also below). It is useful to note that the resolution of FRET measurements is poor for E < 10% (REE > 8.8 nm for this FRET pair) and E > 90%, due to the nonlinear dependence of the energy transfer efficiency on the donor−acceptor distance. Therefore, in this set of experiments, Sic1 conformations with end-to-end distances longer than 8.8 nm will all have nearly zero FRET and will not be resolved. To test whether cluster 1 consists of unresolved subsets of extended conformations, Sic1 was labeled with a dye pair (AttoRh101−Atto647N) with a longer Förster radius, 7.0 nm vs 6.0 nm for TMR-Atto647. In this case, the cutoff distance for FRET sensitivity is about 10.1 nm, so that conformations with end-to-end distances between 8.8 and 10.1 nm could be resolved. The smFRET histograms recorded at zero and 1 M KCl are shown in Figure 2.

relatively uniform distribution of charges (Supporting Information, Figure S1). Sic1 has been shown to be relatively compact compared to random coil, with significant secondary structure propensity.6,8 This appears to agree with the findings of Das and Pappu,27 who have found that weak polyampholytes show compaction and charged residues located on the surface of the proteins. The electrostatic repulsion between the charges is a factor preventing the formation of stable structure in Sic1. Adding salt to the solution reduces the effective charge of the polypeptide chain according to the Debye−Hückel screening theory,5,29 and consequently the strength of the electrostatic repulsion between different segments diminishes. If Sic1 has a similar charge exposure in all of its conformations, or those conformations undergo very fast exchange, then the chain compaction with increasing salt concentration will be uniform and monotonous. This salt-induced collapse of Sic1 will show up in the smFRET experiments as a continuous shift of the entire energy transfer efficiency (E) distribution from low to high values. Alternatively, if different Sic1 conformations have different degrees of charge exposure and/or they interchange slowly relative to the time scale of FRET experiments, then a complex, heterogeneous response to salt will be observed instead. Figure 1 shows the smFRET data of end-labeled Sic1 (TMRSic1-Atto647N), recorded in the presence of KCl at selected

Figure 2. smFRET data in KCl using a dye pair with a longer Förster radius (AttoRh101− Atto647N, R0 = 7.0 nm), fitted to a sum of Gaussians.

The broadening in the low-FRET region (0−30%) is more pronounced and cluster 1 is now clearly resolved into two distinct subpopulations: one still centered at zero FRET, and the other one (1′) centered at E = 15%. The latter confirms the existence of elongated Sic1 conformations (average REE ∼ 9.5 nm), which were not resolved by the other FRET pair. The zero-FRET peak, with a fraction population of ∼0.15, could arise from even more extended Sic1 conformations (REE > 10.1 nm), or from donor-only molecules. We therefore consider 0.15 to be an upper limit for the fraction of donor-only molecules in our measurements. Turning back to Figure 1 data, cluster 2, unlike cluster 1, shifts gradually from E ≈ 10% in pure water to E ≈ 100% in high salt. This suggests a significant compaction of the chain as a function of salt concentration, as expected for a charged polymer. The population fraction of cluster 2 varies between 0.36 in pure water and 0.57 in 0.15 M KCl. Uncertainties due to the contributions of proteins with absent/inactive acceptors in different measurements are, at least partly, responsible for the observed population variance. A third population, with intermediate FRET values and a fraction of ∼0.2, was also resolved in the data. This cluster is broader and could be the result of conformational exchange between the other two Sic1 clusters. The presence of salt-

Figure 1. smFRET at different salt concentrations, fitted to a sum (red) of three Gaussians (blue), as indicated for 1 M KCl. See Table S1 in the Supporting Information for fitting results.

concentrations, from zero up to a maximum of 4 M. The average FRET efficiency increases with adding salt, consistent with the overall chain compaction (seen as smaller end-to-end distances) produced by the screening of electrostatic repulsion. However, the measured FRET distribution is not homogeneous (single-peak) and it does not change uniformly. Seventeen FRET histograms acquired at different [KCl] were each fitted to a sum of three Gaussians, except for the data in pure water, which was fitted by two Gaussians. Each Gaussian is assigned to a cluster of Sic1 conformations having a similar size (end-toend distance, REE). 4090

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FRET efficiency. GdmCl is a salt and the chloride ions released in the aqueous buffer screen the repulsion between the positive Sic1 charges, similar to KCl. Above ∼ 0.6 M, the guanidinium “denatures” Sic1 by interacting favorably with hydrophobic residues and with carbonyl groups, inhibiting H-bonds and sterically blocking transient contacts. This leads to a gradual chain expansion accompanied by the broadening of the end-toend distance distribution, as typically seen in smFRET denaturation studies of globular proteins.11,30,31 The histograms recorded for Sic1 in GdmCl were each fitted to a sum of two Gaussians, with the fitting results given in Table S2. Cluster 1 is centered near zero-FRET and does not appear to shift to higher values in high denaturant, although its population fraction varies somewhat. This corresponds to extended Sic1 conformations having REE > 9 nm, although there may be some contribution from donor-only proteins. Conversely, cluster 2 is sensitive to increasing denaturant concentration. It gradually shifts to higher FRET values and it gains in amplitude up to [GdmCl] ≈ 0.6 M, then it reverses this trend by shifting toward lower FRET accompanied by significant broadening when [GdmCl] is increased to 8 M. To shed more light on how charge-mediated interactions modulate the Sic1 conformations, we compared the maximum screening effects observed in KCl and GdmCl to the effects induced by sodium dodecyl sulfate (SDS) (Figure 4). Upon binding to proteins, the repulsion between the DS− ions typically expands the polypeptide chain and leads to denaturation.32 However, for the positively charged Sic1, a relatively small amount of SDS (0.5 mM) induces significant chain compaction (Figure 4C). The effect is very similar to results reported on α-synuclein 17 and is much more significant than the effect of salt, recorded at nearly 10 000 times higher concentrations (Figure 4A), thus suggesting that charge screening by clouding salt anions is much weaker than charge neutralization by bound DS− ions. The screening effect induced by GdmCl on Sic1 (Figure 4B) is also limited compared to SDS, and smFRET competition experiments using both compounds show at least 1000-fold higher binding affinity for SDS than for GdmCl (data not shown). The data in Figure 4 suggest that both KCl and GdmCl screen charged amino acids and reduce electrostatic repulsion between them much less compared to SDS, which binds tightly to proteins. In addition, the SDS data shows a minor fraction population (∼ 0.15) in the zero-FRET region, which supports the limiting contribution of donor-only Sic1 proteins in our experiments. Timescales of Conformational Exchange. Previous NMR experiments suggested that Sic1 undergoes rapid conformational exchange.6,8 The lack of line broadening in the Sic1 NMR spectra was interpreted as a signature of very fast conformational fluctuations, on a submicrosecond time scale. On the other hand, in the same study, elevated R2 relaxation of

dependent exchange rates between different conformations could explain the variance of the fractional population of the two major FRET clusters. At the same time, we cannot exclude that these intermediate FRET values are due to tailing effects caused by the photobleaching of the dyes (Figure S9 in the Supporting Information). Sic1 Conformations under Denaturing Conditions. Guanidinium chloride (GdmCl) is widely used as a protein denaturant, because of its ability to break intrachain hydrogen bonds and promote hydrophobic interactions between itself and nonpolar residues. Upon titration with GdmCl, globular proteins are usually driven from folded to unfolded states, and this transition can give insight into the protein folding freeenergy landscape.12 At high concentrations, typically above 4 M, the guanidinium eliminates the secondary and tertiary structure of most proteins and transforms them to quasirandom coils. GdmCl is also a chloride salt of guanidine, which is a strong base in water. Its ionic nature often makes it a more effective denaturant than neutral denaturants such as urea, because it also screens structure-stabilizing charge−charge interactions in proteins. Sic 1 smFRET histograms were measured at 17 different concentrations of denaturant between 0 and 8 M, six of which are shown in Figure 3. Interestingly, in the low concentration

Figure 3. smFRET for TMR-Sic1-Atto647N at different GdmCl concentrations fitted to a sum (red) of two Gaussian components (blue), as indicated for 4 M GdmCl. See Table S2 in the Supporting Information for fitting results.

range, i.e., [GdmCl] = 0−0.6 M, the chain adopts more compact conformations, as suggested by the increase in average

Figure 4. Sic1 smFRET data obtained in the presence of (A) 4 M KCl, (B) 0.6 M GdmCl, and (C) 0.5 mM SDS. 4091

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rotation of the polypeptide chain. In contrast, in pure water, the Sic1 chain stiffens up due to electrostatic repulsion between its unscreened positively charged residues, and this ultrafast structural flexibility disappears. The slow Sic1 dynamics revealed by the FRET-FCS is consistent with the heterogeneous distribution of Sic1 conformations detected in the smFRET experiments. We ran a series of Monte Carlo simulations in which two FRET states were allowed to exchange at a variable rate compared to the average molecular diffusion rate. The simulated smFRET histograms for the slow exchange regime resemble the experimental FRET data obtained for Sic1 (Supporting Information, Figure S4). Similar slow fluctuations were recently measured in a disordered state of the IκBα protein by crosscorrelating the donor and acceptor intensity traces, and were consistent with a heterogeneous distribution of conformations observed in smFRET histograms.14 The tumor suppressor p53, a protein having both folded and intrinsically disordered domains, was also reported to have multiple conformations which interconvert in milliseconds.34 As a complementary approach, each detected burst in smFRET experiments was divided into two consecutive halfbursts with the same number of photons. Initial and final values of the FRET efficiency, Ei and Ef, were calculated for each burst, and a 2-D histogram was built using these values. A statistical routine was applied to assign each burst to a specific cluster in the FRET histogram. For instance, for the Sic1 data acquired in 1.5 M KCl (Figure 6A), a burst with E = 25% is likely to belong

NMR signals was observed at some residues compared to the rest of the protein, and could be pointing to long microsecondshort millisecond time scale motions within the Sic1 chain.6 Similarly, the observation of distinct populations of Sic1 in burst smFRET experiments clearly indicates the existence of structural dynamics on a slow time scale.33,34 To gain insight into the kinetics of conformational exchange in Sic1, we coupled FRET with FCS, according to a method described previously.17,25 Donor autocorrelation (GDD) and the donor−acceptor cross-correlation (GDA) curves were measured overnight in Tris buffer (50 mM Tris, 0.15 M NaCl and pH 7.4) (Figure 5A) and in pure water (Figure 5B). The ratio of the two curves, GDD/GDA, contains information about the kinetics of end−end distance fluctuations in Sic1.25

Figure 5. FRET-FCS measurements on A488-Sic1-TMR in (A) Tris buffer (pH 7.4), and (B) in MilliQ water. GDD/GDA curves (circles) were fitted to exponential decays (green in the ns range, and purple in the ms range). The inset in panel B is a zoom of the measured correlation curves between 1 ms and 1 s.

A fast decay of the GDD/GDA curve is clearly resolved in Figure 5 on the nanosecond time scale and followed by decays on the millisecond time scale with lifetimes of 17.3 ± 0.7 ms (67% amplitude) and 83.7 ± 6.3 ms (33% amplitude). In the absence of charge screening, no fast-decaying component is detected on the sub-microsecond time scale (Figure 5B). The curve decays with lifetimes of 13.1 ± 0.9 ms (63%) and 58.6 ± 6.1 ms (37%), which are very similar to those obtained in the salt buffer. Control experiments using scattered light and a DNA sample were performed to ensure that the millisecond kinetics is not an experimental artifact (Supporting Information, Figure S10). The fast nanosecond process could be attributed to a restricted pool of conformations interchanging around a local free-energy minimum (a metastable state). This subset of Sic1 conformations is highly flexible, as observed for other IDPs, such as the yeast prion protein Sup35 19 and ProTα.20 Alternatively, the fast kinetics may be due to local chain dynamics around the N- and C-termini, which typically consist of flexible loop domains even in structured proteins. A similar fast phase of the correlation decay was observed in the DrkN SH3 domain,23 where it was attributed to local segment

Figure 6. Intraburst smFRET correlation analysis. (A) Sic1 smFRET data measured in 1.5 M KCl, (B−D) intraburst correlation maps (final FRET vs initial FRET) for clusters 1, 2, and 3, respectively.

to cluster 3, while a burst with E = 75% is likely to belong to cluster 2. Ef versus Ei correlation maps can thus be generated for each FRET cluster (Figure 6B−D). Based on the comparison with the simulations of a two-state system (Supporting Information, Figures S4 and S5), cluster 2 most likely consists of unresolved subpopulations of protein conformations that exhibit similar E values and do not interconvert on a very fast time scale. The spot elongation along the diagonal direction (Figure 6C) is about twice as broad as the counting-noise limit, and it is a characteristic signature of static heterogeneity.35 The same is probably true 4092

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fluorescence burst, and the simulated distribution exhibits distinct populations spanning the entire energy transfer efficiency scale, with dominant peaks near zero and 100% FRET efficiency and a broad peak near 50% efficiency. In the fast exchange regime, with lifetimes of 1 μs or faster, nearly all possible conformations are sampled within a single burst, and the simulated FRET distribution consists of only a narrow, shot-noise limited peak at ⟨E⟩ ≈ 50%. To match the conditions used for the ENSEMBLE data, smFRET burst experiments were performed in phosphate buffered saline (PBS) buffer at pH 7.0 (Figure 7D). The data, which resembles the data obtained at 150 mM salt (Figure 1), appears to be a combination between the two extreme cases described above. In fact, the peak near E = 50% is reproduced well by the simulation with an intermediate exchange lifetime, 50 μs. The peak near E = 0% appears in the simulations only in the slow exchange regime, while in the experiments it overlaps with the contribution of donor-only species. Evidence of extended conformations that are in slow exchange with the rest of Sic1 conformations comes from the inhomogeneity of the zero-FRET peak measured with another FRET pair (Figure 2) and possibly from the FRET-FCS data (Figure 5). Note that in these simple simulations the same exchange rate is assumed between all pairs of Sic1 conformations, which is equivalent to an idealized, flat freeenergy landscape. FRET-time trajectories measured on surface immobilized proteins will provide essential information about the rates and the pathways of slow conformational dynamics in Sic1. Based on the results of comparison with current ENSEMBLE conformations, it will be valuable to incorporate the smFRET-derived end-to-end distance distribution data as constraints within future ENSEMBLE calculations and other conformational ensemble IDP approaches, as these data provide additional, complementary information relative to the SAXS and NMR data. The conformational heterogeneity of Sic1 and the behavior observed in high salt are not typically encountered in other IDPs studied at the single-molecule level.15,18,20,37 A somewhat similar smFRET distribution, with coexisting multiple peaks and a broad distribution, was reported previously for p53.34 Sic1 is an IDP with some transient secondary and tertiary contacts leading to a relatively compact state under moderate salt conditions. It also does not undergo an extensive disorderto-order transition upon binding its target Cdc4, leading to a highly dynamic or “fuzzy” complex.8 This type of structural heterogeneity complicates the description of charge screening effects and the action of denaturing agents on proteins using analytical models, such as the polyampholyte model,38 the Debye−Hückel screening theory,39 or a simple binding model.20,40 By default, these models assume a homogeneous distribution of protein conformations and they all predict strong charge screening effects on proteins rich in charged amino acids. The smFRET data (Figure 1, 3) show that the low-FRET cluster 1 is nearly invariant with increasing [KCl] and [GdmCl], with cluster 3 (in KCl) shifting toward moderately compact conformations while undergoing significant broadening. Cluster 2 is by far the most sensitive to increasing [KCl] and [GdmCl], and its variation with the ionic strength of the solution resembles other disordered proteins, such as the human prothymosin α (ProTα) and the N-terminal domain of HIV-1 integrase (IN).20

for cluster 1, although limitations inherent to the low FRET regime prevent a significant elongation (Figure 6B). The Ef versus Ei map of cluster 3 (Figure 6D) shows a pattern similar to that expected from an intermediate regime in the simulations, which suggests that cluster 3 could arise from exchange between clusters 1 and 2 on the time scale of molecular diffusion, ∼ 0.2 ms. However, a significant asymmetry toward the E f = 0% region suggests that photobleaching of the dyes may affect the appearance of this cluster. Slow Sic1 structural dynamics can be measured directly in single-molecule immobilization experiments, which will provide a more detailed map of the pathways of conformational interconversions on the free-energy landscape of the protein.

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DISCUSSION smFRET data show that the Sic1 structure (end-to-end distance) is very heterogeneous, consisting of both extended and compacted conformations under nondenaturing conditions. Similar evidence was found previously in SAXS experiments, which were interpreted as Sic1 having a radius of gyration (Rg) distributed between 10 Å and 60 Å.8 In order to define an ensemble of protein conformations that are collectively consistent with NMR and SAXS data for a disordered protein, Forman-Kay and co-workers have developed the computational approach ENSEMBLE.9 Individual structures taken from the 44 conformations that collectively fit the Sic1 data are shown in Figure 7A. The ENSEMBLE REE

Figure 7. smFRET: ENSEMBLE calculations versus experimental results. (A) Representative Sic1 structures calculated with ENSEMBLE;8 (B) REE distribution obtained from each ENSEMBLE conformation (in %); (C) Simulated FRET efficiency distribution calculated using the ENSEMBLE REE distribution shown in panel B and different exchange rates; (D) smFRET distribution measured in a PBS buffer (10 mM Na2HPO4, 140 mM NaCl, 1 mM EDTA, pH 7.0).

distribution (Figure 7B) is spread between 1 and 13 nm, indicating the coexistence of compact, intermediate and extended conformations. Sic1 appears to be significantly different than a noninteracting random chain, for which the REE distribution is Gaussian.36 smFRET distributions derived from the ENSEMBLE Sic1 conformations were simulated using Brownian motion, photon counting statistics, and different time scales of conformational exchange (Figure 7C; see Supporting Information, section 5, for details). For slow exchange, with lifetimes of 1 ms or longer, there is little conformational averaging during each detected 4093

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Figure 8. Sic1 FRET cluster 2 end-to-end distance (REE), hydrodynamic radius (Rh) and radius of gyration (Rg) versus salt (A,C) and denaturant (B,D). The dependence of Rg on [KCl] and [GdmCl] was fitted to a polyelectrolyte binding-screening model with or without a scaling factor, according to eq 1 and eq 3, respectively. See Table S3 for the complete fitting results.

proteins under denaturing conditions.31 Unlike the measurements in KCl, the hydrodynamic radius remains nearly constant at [GdmCl] < 1 M and it increases steeply in the molar range. A similar rollover of the protein dimensions upon increasing [GdmCl] was observed in other IDPs, such as IN and ProTα.20 In that study, the polyampholyte theory was used to describe the interactions between charged polypeptides and ionic denaturants and to extract Flory-like scaling factors for disordered and unfolded proteins. Assuming a Gaussian REE distribution for the polypeptide chain, the mean FRET efficiency was used to estimate the average radius of gyration, Rg.20 The dependence of Rg on [GdmCl] was fit to a general binding model:

In Figure 8 the average REE of cluster 2 is plotted against the concentration of salt and denaturant, respectively. A biphasic, monotonic decrease of REE with increasing salt concentration was observed (Figure 8A). At low salt, the protein contracts quasi-linearly (on the log scale) from REE = 7.3 ± 0.3 nm at zero salt to REE = 5.5 ± 0.1 nm at 1 M KCl. We attribute this to the screening of electrostatic repulsion between positively charged amino acid residues in presence of Cl− counterions in solution. In the molar range, REE decreases steeply to a value of 3.0 ± 0.6 nm at 4 M KCl. The sharp reduction of the chain dimensions is probably caused by the Hofmeister effect at high salt, which results from specific interactions between ions, proteins, and water molecules,41 leading to changes in solvent surface tension and solubility of proteins. As hydrophobic interactions are likely favored by increased surface tension,41 and the Sic1 sequence contains a significant number of nonpolar residues (40 out of 92), more compact protein conformations will be stabilized in high salt. FCS analysis was performed on the SMF burst data in order to estimate the hydrodynamic radius (Rh) of different FRET clusters in different salt conditions. For instance, the Rh for cluster 2 was determined by constructing the correlation curve using only photons from bursts with corresponding EFT values and fitting it to a simple diffusion model 42 (Supporting Information, section 3). Thus, the dependence of Rh on [KCl] can be studied concomitantly with REE (Figure 8A). Rh decreases monotonically when increasing salt concentration, which reinforces the idea of Sic1 (cluster 2) compaction. At high salt concentrations, the Hofmeister effect is known to promote direct ion-macromolecule interactions in the first hydration shell of the macromolecule, which can lead to an increase of the hydrodynamic radius and can partially compensate for the compaction of the protein.43 Similarly, the dependence of the cluster 2 chain dimensions (REE and Rh) on denaturant is shown in Figure 8B. REE clearly exhibits a nonmonotonic variation, which is most likely the result of the competition between charge screening and protein denaturation. At low GdmCl concentrations, the chain collapses because GdmCl is a salt, and the interaction between the positive residues and the Cl− ions is dominant. At high concentrations, hydrophobic interactions and hydrogen binding cause the cluster 2 conformations to expand, similar to globular

Rg =

⎛ Ka ⎞ ⎟ N /6 αb⎜1 + ρ ⎝ 1 + Ka ⎠

(1)

where N is number of amino acids, b is the segment length (0.38 nm), ρ is the relative change in Rg at high denaturant activities, K is an effective binding constant in the general binding model, a is the denaturant activity, and α is an expansion factor due to charge interaction and salt screening effects:20,38 ⎛ 4πlB(f − g )2 ⎞ ⎟ α 5 − α 3 = 0.44 N ⎜ν + b3κ 2 ⎝ ⎠

(2)

where lB is the Bjerrum length (0.7 nm in water), κ is the inverse Debye length, νb3 is the excluded volume of the uncharged chain, and f and g are the fractions of positive and negative charges, respectively. Most of the parameters in eqs 1 and 2) are known or can be calculated, two parameters (a and κ) depend explicitly on [GdmCl], and three parameters, K, ρ and ν, are obtained by fitting (see Supporting Information for details). In contrast to ProTα, Sic1 shows multiple FRET peaks with different sensitivities to charge screening. However, we will apply a similar approach to model the charge-mediated collapse of the most sensitive Sic1 subpopulations (cluster 2), in order to have at least a partial comparison between Sic1 and other IDPs. The Sic1 sequence in our study exhibits only positive side chain charges (+11) with no terminal backbone charges modeled, and thus it can be regarded as a polyelectrolyte 4094

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create an additional layer, which increases the protein’s effective hydrodynamic radius. The decrease of Rh due to conformational collapse is balanced by the increase of Rh due to specific GdmCl−Sic1 binding.

chain for modeling of the size dependence on salt and denaturant concentration. As shown in Figure 8D, the polyelectrolyte binding model fits poorly the Sic1’s (cluster 2) Rg dependence on [GdmCl]. However, by introducing a general scaling factor C in the right term of eq 1, we obtained a much better fit with C = 1.18 ± 0.03 (see Table S3 in the Supporting Information for fitting results): ⎛ Ka ⎞ ⎟ R g = C· N /6 αb⎜1 + ρ ⎝ 1 + Ka ⎠

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CONCLUSIONS Under nondenaturing conditions, smFRET data show that Sic1 adopts a broad size distribution, in accordance with the outcome of a comprehensive computational approach, ENSEMBLE, based on previous SAXS and NMR data. Several recent studies highlighted the important role that electrostatic repulsion may play in preventing the formation of stable secondary and tertiary contacts in disordered proteins.46 Since about 1/8 of the Sic1 sequence is positively charged at normal pH, we focused on understanding how the microscopic distribution of Sic1 conformations is affected by increasing the ionic strength of the solution. At least three distinct FRET subpopulations were identified when increasing the salt/ denaturant concentration. This is quite surprising, as Sic1 was thought to be a collection of conformations that exchange very rapidly and each molecule would sample most of them during a single fluorescence burst (∼200 μs). Moreover, unlike other IDPs,20,47 Sic1 clusters show different sensitivities to electrostatic screening by the ions in solution. Possible causes for this behavior could be the partial burial of charged residues inside the disordered chain, or the influence of hydrophobic interactions, since the Sic1 sequence also contains a considerable number of nonpolar residues. For the Sic1 population with the largest response to charge screening (FRET cluster 2), we used the polyelectrolyte model to describe its size dependence to salt and denaturant concentration. The model produces parameters such as the excluded volume of the uncharged chain, the binding constant to denaturant and the scaling factor of the radius of gyration at high salt/denaturant. Our data was fitted by the classic bindingscreening model only if an overall scaling factor of ∼1.2 was used, suggesting that the FRET-calculated Rg values based on the Gaussian chain approximation are underestimated. Unlike denatured proteins or other IDPs, the analysis of SMF burst data show that Sic1 cannot be modeled as a simple random coil in the absence or presence of salt and denaturant, pointing to the existence of more compact subpopulations, more extended ones, or a mixture of both. smFRET data supports the idea that ions in solution can only partially screen the intrachain charge repulsions and play a limited role in determining the Sic1 conformations. Sic1 is probably not very sensitive to variations of salt concentration, at least at the levels occurring in cells. However, Sic1 can adopt very compact conformations upon a full neutralization of its net charges, as observed, for instance, in the presence of SDS. This mechanism, although it is not site specific, induces changes in the conformational distribution of Sic1 that may resemble those occurring upon the functional phosphorylation of Sic1. On the other hand, Sic1 at low phosphorylation levels (1−3) will possess some net charge, resembling the partial charge screening observed in salt solutions. This single-molecule study resolved the conformational heterogeneity and dynamics of the Sic1 protein, and it is an important first step toward the validation of the poly electrostatic model proposed for describing the dynamic Sic1Cdc4 interactions.7 The techniques and the data analysis developed in this study, as well as our findings about the role of charge screening and charge neutralization for the conforma-

(3)

The excluded volume parameter obtained from the fit, 0.11 nm3, is similar to that of a charge-balanced protein, CspTm, (0.05 nm3), but about 1 order of magnitude smaller than those estimated for N- (0.86 nm3) and the C-terminal (0.72 nm3) regions of ProTα.20 This suggests that the impact of high net charge density on Sic1 is better described by a larger apparent radius of gyration rather than a larger excluded volume. For instance, this could happen in a polymer chain that is more compact than a random coil, or, alternatively, if the chain conformations average to some overall asymmetric shape. The latter case probably applies to the other major Sic1 population (cluster 1), which is more extended and is therefore expected to deviate more significantly from the random coil behavior. A similar result was obtained by fitting the salt dependence of the average Rg of the Sic1 FRET cluster 2. Unlike the denaturant, KCl does not bind to the polypeptide chain, and thus only the screening term must be kept in the model Rg = (N/6)1/2αb. As shown in Figure 8C, this simple polyelectrolyte model produced a poor fit to the experimental Rg dependence on [KCl]. The modified model considerably improved the fitting, and produced an excluded volume of 0.04 nm3 with a scaling factor C = 1.17 ± 0.03, consistent with the values obtained for GdmCl. Note that the fitting was only applied for data up to 0.4 M KCl. At higher salt concentrations the contribution from Hofmeister effect becomes significant, and the polyelectrolyte model is no longer appropriate, as shown by the extrapolation of the fit in Figure 8C. One can argue whether the polyampholyte/polyelectrolyte binding model can be applied to Sic1, which exhibits a high degree of structural heterogeneity. The random Gaussian chain seems to be a poor approximation for describing the Sic1 structural ensemble. From the SMF burst data analysis, we can simultaneously estimate the average REE and average Rh for each cluster at each salt or denaturant concentration. Interestingly, for Sic1 cluster 2, the ratio Rh/REE ∼ 0.35 for concentrations of up to 4 M GdmCl and 2 M KCl. This value is significantly larger than the theoretical ratio of the Gaussian chain (∼0.27),36 indicating that (1) the polymeric nature of this Sic1 subpopulation does not change, (2) this subpopulation is a hydrated random coil, or (3) it is not a random coil at all. The concomitant decrease of REE and Rh in salt indicates that the electrostatic repulsion between charged residues is a major factor affecting the size of the protein (Figure 8A). Although a similar trend is observed for GdmCl at low concentrations, the mechanism of interaction with Sic1 is quite different. Nondenaturing salts, such as NaCl and KCl, were found to be more efficient for screening long-range interactions than short-range ones.44,45 This is probably because ions do not come in direct contact with the proteins. On the contrary, charged denaturant molecules such as GdmCl effectively screen short-range electrostatic interactions by binding to the protein surface.45 They accumulate on the surface of the protein and 4095

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(7) Borg, M.; Mittag, T.; Pawson, T.; Tyers, M.; Forman-Kay, J. D.; Chan, H. S. Polyelectrostatic Interactions of Disordered Ligands Suggest a Physical Basis for Ultrasensitivity. Proc. Natl. Acad. Sci. U. S. A. 2007, 104 (23), 9650−5. (8) Mittag, T.; Marsh, J.; Grishaev, A.; Orlicky, S.; Lin, H.; Sicheri, F.; Tyers, M.; Forman-Kay, J. D. Structure/Function Implications in a Dynamic Complex of the Intrinsically Disordered Sic1 with the Cdc4 Subunit of an SCF Ubiquitin Ligase. Structure 2010, 18 (4), 494−506. (9) Krzeminski, M.; Marsh, J. A.; Neale, C.; Choy, W. Y.; FormanKay, J. D. Characterization of Disordered Proteins with ENSEMBLE. Bioinformatics 2013, 29 (3), 398−9. (10) Ritort, F. Single-Molecule Experiments in Biological Physics: Methods and Applications. J. Phys.: Condens. Matter 2006, 18 (32), R531−83. (11) Borgia, A.; Williams, P. M.; Clarke, J. Single-Molecule Studies of Protein Folding. Annu. Rev. Biochem. 2008, 77, 101−25. (12) Schuler, B.; Eaton, W. A. Protein Folding Studied by SingleMolecule FRET. Curr. Opin. Struct. Biol. 2008, 18 (1), 16−26. (13) Mittag, T.; Kay, L. E.; Forman-Kay, J. D. Protein Dynamics and Conformational Disorder in Molecular Recognition. J. Mol. Recognit. 2010, 23 (2), 105−16. (14) Lamboy, J. A.; Kim, H.; Lee, K. S.; Ha, T.; Komives, E. A. Visualization of the Nanospring Dynamics of the Iκbα Ankyrin Repeat Domain in Real Time. Proc. Natl. Acad. Sci. U. S. A. 2011, 108 (25), 10178−83. (15) Ferreon, A. C.; Moran, C. R.; Gambin, Y.; Deniz, A. A. SingleMolecule Fluorescence Studies of Intrinsically Disordered Proteins. Methods Enzymol. 2010, 472, 179−204. (16) Schuler, B.; Lipman, E. A.; Eaton, W. A. Probing the FreeEnergy Surface for Protein Folding with Single-Molecule Fluorescence Spectroscopy. Nature 2002, 419 (6908), 743−7. Huang, F.; Ying, L.; Fersht, A. R. Direct Observation of Barrier-Limited Folding of BBL by Single-Molecule Fluorescence Resonance Energy Transfer. Proc. Natl. Acad. Sci. U. S. A. 2009, 106 (38), 16239−44. Trexler, A. J.; Rhoades, E. Single Molecule Characterization of Alpha-Synuclein in Aggregation-Prone States. Biophys. J. 2010, 99 (9), 3048−55. (17) Ferreon, A. C.; Gambin, Y.; Lemke, E. A.; Deniz, A. A. Interplay of Alpha-Synuclein Binding and Conformational Switching Probed by Single-Molecule Fluorescence. Proc. Natl. Acad. Sci. U. S. A. 2009, 106 (14), 5645−50. (18) Milles, S.; Lemke, E. A. Single Molecule Study of the Intrinsically Disordered FG-Repeat Nucleoporin 153. Biophys. J. 2011, 101 (7), 1710−9. (19) Mukhopadhyay, S.; Krishnan, R.; Lemke, E. A.; Lindquist, S.; Deniz, A. A. A Natively Unfolded Yeast Prion Monomer Adopts an Ensemble of Collapsed and Rapidly Fluctuating Structures. Proc. Natl. Acad. Sci. U. S. A. 2007, 104 (8), 2649−54. (20) Muller-Spath, S.; Soranno, A.; Hirschfeld, V.; Hofmann, H.; Ruegger, S.; Reymond, L.; Nettels, D.; Schuler, B. Charge Interactions Can Dominate the Dimensions of Intrinsically Disordered Proteins. Proc. Natl. Acad. Sci. U. S. A. 2010, 107 (33), 14609−14. (21) Price, E. S.; Aleksiejew, M.; Johnson, C. K. FRET-FCS Detection of Intralobe Dynamics in Calmodulin. J. Phys. Chem. B 2011, 115 (29), 9320−6. Mao, A. H.; Crick, S. L.; Vitalis, A.; Chicoine, C. L.; Pappu, R. V. Net Charge per Residue Modulates Conformational Ensembles of Intrinsically Disordered Proteins. Proc. Natl. Acad. Sci. U. S. A. 2010, 107 (18), 8183−8. (22) Liu, B.; Mazouchi, A.; Gradinaru, C. C. Trapping Single Molecules in Liposomes: Surface Interactions and Freeze-Thaw Effects. J Phys Chem B 2010, 114 (46), 15191−8. (23) Mazouchi, A.; Bahram, A.; Gradinaru, C. C. Sub-Diffusion Decays in Fluorescence Correlation Spectroscopy: Dye Photophysics or Protein Dynamics? J. Phys. Chem. B 2013, 117 (38), 11100−11. (24) Nir, E.; Michalet, X.; Hamadani, K. M.; Laurence, T. A.; Neuhauser, D.; Kovchegov, Y.; Weiss, S. Shot-Noise Limited SingleMolecule FRET Histograms: Comparison between Theory and Experiments. J. Phys. Chem. B 2006, 110 (44), 22103−24.

tional properties of proteins, could be applied to other IDPs to shed light on the mechanisms responsible for structural disorder and their roles in biology.

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ASSOCIATED CONTENT

S Supporting Information *

Protein expression and purification, mass spectrometry, singlemolecule methods, FRET histograms fitting results, the polyelectrolyte binding-screening model, simulations of FRET histograms, control experiments at different pH, different salt and different laser excitation powers, bulk FRET data, and control FRET-FCS experiments are provided (18 pages). This material is available free of charge via the Internet at http:// pubs.acs.org.

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AUTHOR INFORMATION

Corresponding Author

*E-mail: [email protected]. Notes

The authors declare no competing financial interest.

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ACKNOWLEDGMENTS Financial support was provided through NSERC Discovery Grants (C.G. and J.D.F.-K.) and the Canadian Cancer Society (J.D.F.-K.). B.L. was supported by the CIHR Training Program in Protein Folding and Interaction Dynamics, and D.C. was supported by an NSERC-USRA award.

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