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Quantification of Millisecond Protein-Folding Dynamics in MembraneMimetic Environments by Single-Molecule FRET Spectroscopy Andreas Hartmann, Georg Krainer, Sandro Keller, and Michael Schlierf Anal. Chem., Just Accepted Manuscript • DOI: 10.1021/acs.analchem.5b03207 • Publication Date (Web): 12 Oct 2015 Downloaded from http://pubs.acs.org on October 17, 2015
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Analytical Chemistry
Quantification of Millisecond Protein-Folding Dynamics in Membrane-Mimetic Environments by Single-Molecule FRET Spectroscopy Andreas Hartmann,1,‡ Georg Krainer,1,2,‡ Sandro Keller,2 and Michael Schlierf1,* 1 2
B CUBE – Center for Molecular Bioengineering, Technische Universität Dresden, Arnoldstr. 18, 01307 Dresden, Germany Molecular Biophysics, University of Kaiserslautern, Erwin-Schrödinger-Str. 13, 67663 Kaiserslautern, Germany
Keywords: smFRET, confocal spectroscopy, millisecond kinetics, membrane-protein folding, detergent micelles, Mistic
ABSTRACT: An increasing number of membrane proteins in different membrane-mimetic systems have become accessible to reversible unfolding experiments monitored by well-established ensemble techniques. However, only little information is available about kinetic processes during membrane-protein folding, mainly because of experimental challenges and a lack of methods suitable for observing highly dynamic membrane proteins. Here, we present single-molecule Förster resonance energy transfer (smFRET) confocal spectroscopy as a powerful tool in kinetic studies of membrane-protein folding in membrane-mimetic environments. We have developed a rigorous workflow demonstrating how to identify and quantify such dynamic processes using a set of qualitative, semi-quantitative, and quantitative analytical tools. Using this workflow, we analyzed urea-induced folding and unfolding experiments on the α-helical membrane protein Mistic in the presence of the zwitterionic detergent n-dodecylphosphocholine (DPC). We identified two-state interconversion dynamics on the millisecond timescale of a protein folding into and out of detergent micelles. Our results demonstrate that smFRET is a promising tool for probing the chemical physics of membrane-protein structure and dynamics in the complex and anisotropic environment of a hydrophilic/hydrophobic interface, providing insights into protein interconversion dynamics without the need and challenges of synchronization.
Understanding the structural dynamics of the protein-folding process in the complex, anisotropic, and chemically heterogeneous milieu of a lipid bilayer or a membrane-mimetic environment is a major challenge in membrane-protein folding. In recent years, an increasing number of membrane proteins have become accessible to reversible unfolding/refolding studies in membrane-mimetic systems; and several established ensemble techniques adapted from water-soluble proteins have shed light on the molecular forces that control the folding of membrane proteins.1–3 Despite their importance, our current knowledge of kinetic processes during membrane-protein folding is still limited for two principal reasons. On the one hand, perturbation approaches such as stopped-flow experiments face considerable challenges in the study of complex membrane-protein systems. On the other hand, there is a general lack of methods able to quantify highly dynamic membraneprotein systems in the millisecond-to-microsecond time regime. Single-molecule Förster resonance energy transfer (smFRET) measurements have provided key insights into the thermodynamics and kinetics of soluble-protein folding.4,5 Serving as a molecular ruler for monitoring distance changes of 2−8 nm with subnanometer resolution, smFRET allows for the investigation of heterogeneous and unsynchronized molecular processes even from equilibrium measurements, providing access to protein subpopulations and their interconversion dynamics.6 A large number of analytical procedures have been developed over the past years to expand the scope and timescale of smFRET experiments, yielding insights into structural dynamics ranging from nanoseconds to minutes.7 Pioneering
smFRET confocal spectroscopy protein-folding studies have focused on soluble proteins under dilute isotropic solution conditions,8,9 however, methodological advances have recently enabled the investigation of increasingly complex systems with intricate folding scenarios.10–13 These studies provide detailed mechanistic insights into the structural dynamics of proteins, highlighting the ability of smFRET to analyze systems under complex folding conditions. Nevertheless, such a complex scenario is also encountered in the study of the folding of membrane proteins into lipid bilayer membranes or membrane-mimetic systems such as detergent micelles. In this respect, smFRET confocal spectroscopy should lend itself as an excellent tool to study the folding and conformational dynamics of membrane proteins in the highly complex, anisotropic, and heterogeneous milieu of a lipidic environment, thereby expanding the scope and timescales accessible to membrane-protein folding. However, this potential in resolving conformational changes of subpopulations and quantifying structural dynamics has to date not been exploited in membrane-protein folding studies. Here, we describe a comprehensive workflow combining and comparing a set of independent and well-established analytical tools, which allows systematic and rigorous analysis of membrane-protein folding dynamics on the millisecond-tosubmillisecond timescale. We performed urea-induced folding and unfolding experiments on the self-inserting α-helical membrane protein Mistic from Bacillus subtilis.14 Mistic is one of a few membrane proteins that can be reversibly unfolded from detergent micelles, with the unfolded protein being dissociated from micelles and mostly devoid of secondary
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structure.15,16 smFRET confocal spectroscopy experiments performed in the presence of n-dodecylphosphocholine (DPC) micelles on freely diffusing, fluorescently labeled Mistic revealed frequent transitions between the folded and unfolded states during diffusion through the confocal detection volume. By unveiling, for the first time, millisecond kinetics of a membrane protein folding into and out of detergent micelles, we illustrate in our workflow how to identify such dynamic processes by employing qualitative methods to detect millisecond dynamics using the correlation of relative donor lifetime (() /() ) vs. FRET efficiency (E),17–19 burst-variance analysis (BVA),20 and a FRET-two channel kernel-based density distribution estimator (FRET–2CDE).21 Subsequent bintime analysis (BTA)22,23 allows coarse estimation of folding kinetics from simulated histograms. Finally, we apply three different methods to quantify transition rates: two histogrambased methods involving a three-Gaussian (3G) approximation24 and dynamic probability distribution analysis (PDA)17,25 and a maximum likelihood estimator (MLE) analysis22,26 from the observed photon trajectories. Transition rates are compared to each other and tested against a simulation approach employing recoloring of the photon stream.23,26
EXPERIMENTAL SECTION Protein design, production, purification, and labeling A protein variant for site-specific double-labeling was constructed with an unnatural amino acid (N-propargyl-L-lysine, PrK, SiChem, Bremen, Germany) and a unique Cys incorporated at residues 30 and 110, respectively. The protein was produced and purified following published procedures15,16 using the pEvol vector system (pEvol PylRS)27,28 from Methanosarcina mazei for incorporation of PrK.29 Bio-orthogonal labeling with acceptor (ATTO647N-Azide, Atto-Tec, Siegen, Germany) and donor (ATTO532-Maleimide, Atto-Tec) was performed using click chemistry and thiol–maleimide coupling as described.29,30 Details are given in the Supporting Information (SI). Single-molecule FRET measurements Experiments were carried out using a custom-built dual-color and dual-polarization single-molecule confocal fluorescence microscope as previously described.31,32 Measurements were performed in 50 mM Tris buffer (pH 7.4) containing 12 mM n-dodecylphosphocholine (DPC, Anatrace, Maumee, USA), 6 M urea, and 50 mM NaCl at 24°C. Details of the optical setup and single-molecule experiments can be found in the SI. Data Analysis Data analysis was performed with custom-written Matlab scripts (Mathworks, Natick, USA). The analysis software is available upon request from the authors. Details about analysis procedures, including burst selection, data reduction, calculation of FRET efficiency, stoichiometry, and fluorescence lifetime are provided in the SI. Background information on theory for identifying and quantifying dynamic processes is also given in the SI, including (() /() ) vs. E, BVA, FRET– 2CDE, BTA, 3G approximation, PDA, MLE, and recoloring.
RESULTS AND DISCUSSION Observing membrane-protein folding with smFRET spectroscopy. To report on structural changes of Mistic while folding/unfolding into/out of detergent micelles, the protein was
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site-specifically labeled at residues 30 and 110 with acceptor (ATTO647N) and donor dye (ATTO532), respectively (Fig. 1a). The labeled protein was subjected to equilibrium folding/refolding at 6 M urea in the presence of micelles composed of the zwitterionic detergent DPC (12 mM) at very low protein concentrations (~5 pM) to ensure single-molecule detection. Notably, such low concentrations can be advantageous in studying membrane proteins because they reduce the risk of aggregation, a common experimental challenge in membraneprotein folding studies. Single-molecule fluorescence bursts (Fig. S1) originating from freely diffusing protein molecules were detected with a confocal microscope using pulsed interleaved excitation (PIE33) in combination with time-correlated single-photon counting (TCSPC) in a four-channel configuration (Fig. S2). Bursts acquired by such a multiparameter fluorescence detection (MFD) scheme34,35 provide the FRET efficiency () (Eqs. S1 and 3), the fluorescence lifetime (τ), as well as the stoichiometry (S) (Eq. S2), which allows for fluorescence-aided sorting of doubly labeled molecules from those molecules with only one fluorophore active (Fig S3). A FRET efficiency histogram created from a large number of singlemolecule events after removal of donor-only and acceptoronly events is shown in Fig. 1b (blue histogram), where two main peaks are resolved: The peak at high FRET efficiency (EF ≈ 0.8) corresponds to folded (F) molecules, whereas the peak at low FRET efficiency (EU ≈ 0.2) corresponds to unfolded (U) molecules, in accordance with the expected distances between donor and acceptor. The observation of only two subpopulations in thermal equilibrium corroborates previous ensemble-based spectroscopic findings that Mistic is a reversible two-state folder in DPC.16 Moreover, at 6 M urea, the percentage of folded protein is ~50%, which is in good accordance with ensemble experiments on the wild-type protein.16 For a two-state folder switching between the folded and unfolded state on a timescale much slower than the average observation time in the confocal volume (i.e., 〈 〉 ≈ 2.4 ms), two static populations that are shot-noise-limited would be expected (Fig. 1b, red cityscape). The experimental histogram, however, clearly features folded and unfolded states that are broadened with a strong bridge-like population between the two states, despite the generally high signal-to-noise ratio. This indicates either additional, partially folded static states or rapid interconversion between folded and unfolded states during diffusion through the confocal volume. Qualitative methods to detect millisecond kinetics In the following, we utilize qualitative methods to detect millisecond dynamics and thereby enable a clear discrimination between static and dynamic scenarios. We show that the observed broadening and the occurrence of a bridge-like population in the histogram are indeed rooted in interconversion dynamics, revealing the first protein to fold into and out of detergent micelles on the millisecond timescale. Correlation of relative donor lifetime ( () / () ) with FRET efficiency (). Correlation of (() /() ) with 17–19 is a very powerful and simple tool to distinguish static from dynamic subpopulations on the millisecond to (sub)microsecond timescale. Here, () and () denote the fluorescence lifetimes of the donor in the presence and absence of acceptor, respectively. In a 2D plot of (() /() ) vs. , all FRET populations originating from molecules with a
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fixed interdye distance during diffusion through the confocal volume are located along a “static FRET line” (Eq. S4), which represents the theoretical relationship between FRET efficiency and donor lifetime. FRET efficiencies originating from dynamics on the millisecond to (sub)microsecond timescale deviate from the static FRET line. In particular, molecules interconverting between their respective states on the millisecond timescale are confined to a curved “dynamic FRET line” (Eq. S5) connecting the populations. Visual inspection of the 2D scatter plot in Fig. 2a for Mistic reveals two distinct FRET populations, which can be assigned to a folded-state population (EF ≈ 0.8) which is slightly displaced from the static FRET line and the unfolded population located on the static FRET line (EU ≈ 0.2). The additional, smeared, and broad population bridging the two FRET states, which is evident as a systematic deviation from the static FRET line, is well described by the theoretical dynamic FRET line, indicating a two-state dynamic interconversion with millisecond kinetics. Moreover, the displacement of the folded state indicates dynamics on the (sub)microsecond timescale and suggests a malleable ensemble of folded-state conformations with fast dynamics,19,36 which could originate from a rough energy well. Burst-variance analysis (BVA). This first identification of interconversion dynamics on the millisecond timescale was further substantiated by burst-variance analysis (BVA).20 The strength of BVA is its ability to discriminate multiple static states from interconverting dynamic states on the basis of FRET efficiency fluctuations. BVA detects dynamics by comparing the burstwide standard deviation ( ) of the FRET efficiency from segments of consecutive photons (Eq. S6) to that expected from theory for a photon distribution ( ) based exclusively on shot noise. For a static molecule, FRET fluctuations are determined by shot noise, and the observed standard deviation of FRET efficiency matches the prediction from shot noise ( ≈ ), whereas molecules with dynamic fluctuations in the FRET efficiency are characterized by an increased standard deviation ( > ). As depicted in Fig. 2b, BVA revealed a significant increase in at intermediate apparent FRET efficiencies above the 99.9% confidence interval, providing clear evidence for dynamics. In these bursts, molecules switch between their folded and unfolded conformations during the transit through the confocal volume. By contrast, Mistic molecules residing in either the folded or the unfolded state during the time spent in the confocal volume appeared static by BVA. The upper margin of the 99.9% confidence interval (Fig. 2b, red dashed line) can be employed to discriminate static from dynamic molecules. A histogram plotted from bursts that fall within the area below the 99.9% confidence interval reveals the two static populations (Fig. 2b, black cityscape). The remaining bursts above the confidence boundary mainly populate the region between the two static bursts containing molecules with changing conformations (Fig. 2b, red cityscape). FRET–two-channel kernel-based density distribution estimator (FRET–2CDE). As a third approach to validate Mistic’s millisecond dynamics, we used the FRET–2CDE algorithm.21 To this end, a score is calculated for each burst on the basis of a kernel density estimator that estimates local photon densities in the donor and acceptor channel (Eqs. S7–10). This score reflects changes in the underlying FRET efficiency distributions along the burst. FRET–2CDE scores around 10 reflect static FRET bursts, whereas a significantly higher score indicates FRET efficiency fluctuations larger than expected
from a static molecule and, thus, dynamic rearrangements. We calculated the FRET–2CDE value for each burst and plotted the results as a FRET–2CDE-E scatter plot (Fig. 2c). Static burst molecules that do not exhibit large changes in FRET efficiency (FRET–2CDE < 20) can be easily separated from dynamic bursts with FRET efficiency changes during the transit time (FRET–2CDE > 20). This arc-shaped distribution is typical of dynamically interconverting species with an interconversion rate on the order of the diffusion time.21 The calculated FRET–2CDE score was further used as a filter to separate static and dynamic Mistic bursts. The resulting FRET efficiency histogram of the filtered FRET–2CDE < 20 data contains two well-separated subpopulations revealing the underlying static folded and unfolded states (Fig. 2c, black cityscape). This further supports the conclusion that Mistic is a two-state folder.16 The FRET–2CDE > 20 filter yielded a very broad population between the two static peaks (Fig. 2c, red cityscape). Quantification of millisecond folding kinetics After having identified two-state millisecond folding kinetics of Mistic in 12 mM DPC and 6 M urea, we apply a set of quantitative tools to assess folding ( ) and unfolding ( ) transition rates assuming an underlying two-state equilibrium: "
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(1)
Estimation of the conformational relaxation rate using bintime analysis (BTA). As a first step, we used BTA22,23 as a rapid approach to narrow down the timescale of millisecond conformational changes. It is based on dividing photon bursts into bin times (Tb) of equal length and creating a series of histograms with various bin times. For a dynamic system interconverting between two states with a conformational relaxation rate = + in the millisecond time regime, the shape of the FRET efficiency histogram is sensitive to Tb. Hence, BTA not only serves as a diagnostic tool to distinguish between static and dynamic systems, but can be also used as an approach to obtain a rough estimate of rate constants. To estimate Mistic’s conformational relaxation rate, we first created a set of simulated FRET efficiency histograms with k ranging from 1 to 4 ms-1 and Tb ranging from 0.5 ms to 4 ms on the basis of experimentally derived, FRET–2CDE 1; e.g., k = 4 ms-1, Tb = 4 ms), molecules undergo many conformational changes during the observation time, and the histogram, hence, will evolve into a single peak at an average FRET efficiency representing a dynamically averaged FRET population.
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A set of BTA histograms constructed from the experimental data on Mistic with Tb ranging from 0.5 ms to 4 ms (Fig. 3b, data) revealed two well-defined peaks with the buildup of FRET efficiencies at values intermediate between those of the folded and unfolded subpopulations as Tb increases. Visual comparison with simulated FRET efficiency histograms indicates a dynamic process occurring with a relaxation rate k in the range of 1 to 2 ms-1. Extracting folding kinetics using the three-Gaussian (3G) approximation. Folding and unfolding rates of Mistic can further be quantified using a recently developed model that approximates the FRET efficiency histogram by fitting a sum of three coupled Gaussian distributions (Eqs. S11–12).24 Two static Gaussians describe the contribution of the bin times in which the molecule resides in either the folded or the unfolded conformation, whereas a third Gaussian accounts for bins in which the molecule switches between the two states. The amplitude difference of the two Gaussians reflects the ratio of the unfolding and folding rates /, whereas the size of the third Gaussian depends on the timescale of the relaxation rate k. Fig. 4a (red cityscape) shows a 3G fit of the apparent FRET efficiency histogram using a bin time of Tb = 1.5 ms. The bestfit values are ∗ = 0.176±0.008, ∗ = 0.766±0.012, kF = (0.49±0.06) ms-1, and kU = (0.61±0.08) ms-1 (Table 1). A low residual (Fig. 4a, top) reflects good agreement of the 3G fit with experimental data and the underlying two-state model. Furthermore, to minimize the influence of the bin time, we applied a global 3G fit to a set of four histograms with various bin times (Tb = 0.5, 1, 2, and 4 ms), yielding ∗ = 0.176±0.006, ∗ = 0.768±0.008, kF = (0.57±0.05) ms-1, and -1 kU = (0.69±0.07) ms (Table 1). These values are in good agreement with those determined from the single fit, indicating bin-time independency of rates. Notably, overall relaxation rates extracted from the 3G fitting (k = 1.26 ms-1) fall within the range of the overall relaxation rate estimated by BTA (k ≈ 1–2 ms-1), giving a consistent picture of the extracted rates. Kinetic parameters from dynamic probability-distribution analysis (PDA). PDA, originally developed to identify molecular heterogeneity and to highlight dynamic behavior of single molecules,37,38 has recently been extended to quantify millisecond interconversion dynamics.17,25 Dynamic PDA predicts the shape of smFRET histograms based on a rigorous framework of photon statistics and experimental intensity distributions (Eqs. S13–14). By fitting experimental data with an expected distribution incorporating molecular dynamics and underlying stochastic processes, PDA quantitatively and precisely remodels the FRET distribution and retrieves the FRET states and their interconversion rates. Application of PDA yields an excellent reconstruction of the experimentally determined FRET efficiency histogram of Mistic (Fig. 4b, red cityscape), with ∗ = 0.157±0.003, ∗ = 0.780±0.005, kF = (0.66±0.09) ms-1, and kU = (0.75±0.04) ms-1 (Table 1). Again, a low residual for the single bin time Tb = 1.5 ms attests to the good agreement of the kinetic model with the measured FRET efficiency (residual, top). Additionally, we applied a global PDA fit using a set of four bin times (Tb = 0.5, 1, 2, and 4 ms) to test bin-time independency of the fit results, yielding almost identical values within error (∗ = 0.159±0.003, ∗ = 0.774±0.003, kF = (0.71±0.08) ms-1, and kU = (0.81±0.03) ms-1) (Table 1). The rates extracted from the dynamic PDA fit are in good accord with those from the
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3G fit, and the overall relaxation rate (k ≈ 1.52 ms-1) agrees with the expected range estimated by BTA (k ≈ 1–2 ms-1). Folding and unfolding rates estimated using a maximumlikelihood estimator (MLE). A histogram-independent method for the extraction of folding and unfolding rates is the MLE developed by Gopich and Szabo.22,26 This method returns kinetic parameters inherent to a particular model (e.g., a twostate model) that can reproduce the observed photon trajectories (Eqs. S15–16). Essentially, all information about conformations and their transitions is encoded in the photon-color pattern of a TCSPC measurement. By maximizing the likelihood function obtained from the probability of the observed photon colors and arrival times within a fluorescence burst, kinetic rates and, thereby, also the state trajectory of folded and unfolded segments can be obtained. Fig. 4c (top) shows a representative photon trajectory of a single Mistic molecule diffusing through the confocal volume that contains several transitions between folded and unfolded states. The state trajectory (black line) illustrates the most probable conformational transitions within the burst analyzed with the MLE assuming a two-state model. The log-likelihood functions for individual bursts were calculated, and the total negative loglikelihood function of all trajectories was obtained by summation (i.e., − ∑. log (-. )) over all trajectories (Fig. 4c, bottom), yielding a global minimum from which folding and unfolding rates were extracted as kF = (1.04±0.66) ms-1 and kU = (1.44±0.71) ms-1, respectively (Table 1). Interestingly, the absolute values of the rates extracted from the MLE are significantly larger than those derived from the 3G and PDA fits. In the following, we employ a simulation approach to evaluate the obtained rates and discuss these discrepancies. Validation of kinetic parameters by reconstructing FRET efficiency histograms using recoloring Recoloring photon trajectories and reconstructing the FRET efficiency histogram is a powerful way of testing whether the rates extracted on the basis of an assumed model describe the experimentally derived FRET histogram.23,26 For recoloring (see SI section 2.4), all color information was removed from the obtained photon trajectories without changing the interphoton time and the experimentally determined burst-duration distribution. In a second step, the trajectories were recolored on a photon-by-photon basis by Monte Carlo simulation using the FRET efficiencies and transition rates previously extracted. Finally, from this set of recolored photon trajectories, a FRET efficiency histogram was created. The recolored FRET distributions from the 3G approximation, PDA, and the MLE are shown in Fig. 5 (black bars). Recoloring with the extracted rates from 3G and PDA fit reproduced the experimental FRET efficiency histogram (blue), as sup3 ported by a low reduced chi-square value of recoloring /χ10 2 (Table 1), thus corroborating that the extracted folding rates based on a two-state model describe the data sufficiently well. By contrast, recoloring with the MLE-extracted parameters revealed a strikingly large discrepancy between the recolored FRET histogram and the experimentally obtained histogram, 3 as judged by a large /χ10 2 (Table 1). Reflecting the overall larger folding and unfolding rates determined by MLE, the shape of the recolored histogram shows a situation characteristic of a two-state folder with faster interconversion dynamics than actually supported by the shape of the experimental histogram.
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This raises questions about the origin of this inconsistency, in particular, why recoloring with rates extracted from the 3G and PDA fits adequately describe the histogram, whereas rates derived from MLE are apparently overestimated and fail to reproduce the experimental histogram. Methodologically, 3G and PDA are histogram-based approaches that analyze the intensity-calculated FRET histogram by considering photon statistics. MLE, by contrast, analyzes transitions at the level of individual photon trajectories on the basis of photon colors and arrival times. Therefore, the MLE is sensitive not only to changes in FRET on the millisecond timescale but also to photo-physical phenomena or fast FRET fluctuations on the timescale of (sub)microseconds,19,36 for example, acceptor blinking or fast intrinsic protein dynamics. These fast dynamics are largely averaged out in the calculation of burstwise FRET efficiencies using 3G and PDA, making them insensitive to any additional fast processes besides millisecond kinetics. Anti-correlated FRET fluctuations induced by acceptor blinking can be largely ruled out as a source of higher rates from MLE, as only 0.25% of the time is spent in the dark state. Hence, in light of fast dynamics in the folded state detected from the correlation of (() /() ) vs. E, this suggests that the two-state millisecond folding kinetics extracted from MLE are influenced by the presence of fast protein kinetics in the folded state. Therefore, MLE overestimates the apparent transition rates, with large errors, resulting in a biased recolored histogram pretending a more pronounced two-state dynamic behavior than actually present. To account for fast kinetics in the folded state, an extended kinetic scheme could be envisioned provided that the exact nature of the folded-state dynamics were known. While MLE can be extended to account for three- and four-state systems,19,36 it is not yet clear whether such kinetic schemes are warranted for describing Mistic’s folded-state dynamics or whether, instead, an ensemble of flexible folded-state structures reflecting a broadened energy landscape would better represent this state. We anticipate that future work will shed more light on these intrinsic protein motions to quantify time scales of the underlying folded-state fluctuations that are superimposed on the underlying two-state millisecond interconversion dynamics.
CONCLUSIONS We have presented a step-by-step workflow integrating a variety of state-of-the-art smFRET confocal-spectroscopy-based analysis procedures to detect and quantify millisecond kinetics, thereby expanding the scope and timescales accessible to membrane-protein folding. By dissecting global two-state millisecond folding kinetics of Mistic, we show that BVA and FRET–2CDE are complementary tools suitable for extracting FRET fluctuations from photon intensities on the millisecond timescale. The additional dimension of fluorescence lifetime afforded by correlating (() /() ) with E further enables identification of (sub)microsecond dynamics in the folded state, suggesting a conformationally flexible and heterogeneous ensemble of folded structures. Quantification of millisecond folding kinetics employing 3G, dynamic PDA, and MLE yielded overall relaxation rates on the order of 1–2 ms-1 as predicted by a coarse identification based on BTA. A very good agreement of transition rates was found between 3G and PDA, both of which describe two-state folding kinetics on the basis of intensity-calculated FRET histograms. By contrast, rates extracted
from MLE were overestimated because of its sensitivity to fast protein dynamics present in the folded state. Hence, histogram-based analysis procedures such as 3G or PDA reproduce kinetics encoded in the histogram, whereas rates detected by MLE are also influenced by intrastate dynamics ranging from nanoseconds to submilliseconds. Mistic is the first micelle-embedded protein shown to undergo dynamic interconversion on the millisecond timescale between its folded and unfolded states. Because of its unusual physicochemical properties, this protein represents a unique model system for studying protein folding at a complex hydrophilic/hydrophobic interface.16 However, our approach is fully compatible with and readily applicable to other proteins in micellar or bilayer folding environments once more members of the biologically and pharmacologically important class of membrane proteins become amenable to reversible unfolding trials. A key advantage of smFRET in studying membrane-protein folding is the possibility to extract kinetic data on folding and unfolding transitions from equilibrium measurements, without the need for synchronization and without complications arising from superposition of micelle reorganization kinetics. The latter point is particularly advantageous, since this problem is encountered in classical perturbation/relaxation assays employing, for example, stopped-flow systems. There, each mixing step changes the critical micellar concentration, which depends on the denaturant concentration39 and, therefore, triggers micelle formation or disintegration, thus superimposing a second kinetic process that would be hard if not impossible to control or separate. Moreover, the toolbox of quantitative methods presented here also expands the timescale of extractable folding rates in membrane-protein folding, enabling the observation of folding dynamics ranging from milliseconds down to the microsecond time regime. Classical stoppedflow perturbation methods are limited by the mixing dead-time of the instrument, which is typically on the order of 1 ms. The extraction of folding rates from smFRET experiments opens the possibility to derive novel insights into the chemical physics of membrane-protein structural dynamics and the insertion process into lipidic environments. By systematically varying denaturant concentration and plotting the obtained rates against denaturant concentration, folding and unfolding rates in the absence of denaturant can be extrapolated. Such a kinetic analysis paves the way for a detailed mechanistic characterization and comparison of folding in different lipidic environments, shedding light on forces that govern membrane insertion. Taken together, our results highlight smFRET confocal spectroscopy as an auspicious approach for probing membraneprotein structural dynamics in the complex and anisotropic environment of a hydrophilic/hydrophobic interface, providing insights into protein subpopulations and their unsynchronized interconversion dynamics, including the structural dynamics of the folded and unfolded states.
ASSOCIATED CONTENT Supporting Information Details on protein design, production, purification, and labeling; instrumental setup and smFRET measurements; data-analysis theory and procedures. 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] Author Contributions ‡ A.H. and G.K. contributed equally to this work. G.K. prepared protein and performed experiments. A.H. developed software and analytical tools for kinetic analysis. A.H. and G.K. analyzed data. All authors designed research, discussed results, wrote the manuscript, and have given approval to the final version of the manuscript.
Notes The authors declare no competing financial interest.
ACKNOWLEDGMENT The pEvol PylRS plasmid was kindly provided by Dr. Edward A. Lemke (EMBL, Heidelberg, Germany). The authors thank all members of the Schlierf and Keller groups for discussions, in particular Pablo Gracia for help with protein preparation and Anastasiia Vlasiuk for support with protein-structure visualization. We gratefully acknowledge Stefanie Kliemt for massspectrometric analysis and Nicole Poulsen for comments on the manuscript. This work was supported by the German Federal Ministry of Education and Research BMBF 03Z2EN11 and 03Z2ES1 (to M.S.), the Deutsche Forschungsgemeinschaft (DFG) with grant KE 1478/1-2 (to. S.K.) and a scholarship by the Stipendienstiftung Rheinland-Pfalz (to. G.K.).
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Figure 1. Folding and unfolding of Mistic in 12 mM DPC and 6 M urea as probed by smFRET confocal spectroscopy. (a) (top) Schematic illustration of dye-labeled Mistic (1YGM) in a protein/detergent complex. The protein was labeled at residues 30 and 110 with acceptor (ATTO647N) and donor (ATTO532), respectively. (Bottom) Chemical structure of DPC. (b) Single-molecule FRET efficiency histogram obtained from stoichiometry-filtered FRET populations (see Fig. S3). Peaks at high FRET (E ≈ 0.8) and low FRET (E ≈ 0.2) correspond to the folded and unfolded states of Mistic, respectively. Simulated static, shot-noise-limited populations are shown as red cityscapes.
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Figure 2. Qualitative methods to detect millisecond folding kinetics of Mistic in 12 mM DPC and 6 M urea. (a) 2D scatter plot of relative donor fluorescence lifetimes (() /() ) vs. FRET efficiency (E). The static FRET line (black solid, with () = 3.517 ns) and the dynamic FRET line (red dashed, with EU = 0.135, EF = 0.873) describe molecules with fixed distances and changing conformations between folded and unfolded states within a burst, respectively. (top) Simulated static, shot-noise-limited populations (black cityscapes). (b) 2D scatter plot of the burstwide standard deviation of FRET (sE) plotted vs. apparent FRET efficiency ( ∗ ). The black line represents the expected standard deviation ( ) from a shot-noise-limited distribution of static molecules. Millisecond dynamics within bursts are above the 99.9% confidence interval (red dashed). (top) FRET efficiency histograms from molecules below and above the 99.9% confidence interval line are shown as black and red cityscapes, respectively. (c) 2D scatter plot of FRET–2CDE versus E. Static FRET populations (FRET–2CDE = 10, black line) and molecules with millisecond dynamics FRET–2CDE > 20, red dashed) are separated by the FRET–2CDE score. (top) A FRET–2CDE filter separates in the FRET efficiency distribution static (black cityscape, FRET–2CDE < 20) from molecules with millisecond dynamics (red cityscape, FRET–2CDE > 20).
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Figure 3. Estimating millisecond folding kinetics of Mistic using bin-time analysis (BTA). (a) Simulated FRET efficiency histograms of a two-state system in free diffusion with different conformational relaxation rates (k) (columns) and varying bin time (Tb) (rows). Histograms were obtained from simulations using the recoloring approach of experimental data with FRET–2CDE
