Here, we present a protocol to obtain three-color smFRET data and its analysis with a 3D ensemble Hidden Markov Model. With this approach, scientists can extract kinetic information from complex protein systems, including cooperativity or correlated interactions.
Single-molecule Förster resonance energy transfer (smFRET) has become a widely used biophysical technique to study the dynamics of biomolecules. For many molecular machines in a cell proteins have to act together with interaction partners in a functional cycle to fulfill their task. The extension of two-color to multi-color smFRET makes it possible to simultaneously probe more than one interaction or conformational change. This not only adds a new dimension to smFRET experiments but it also offers the unique possibility to directly study the sequence of events and to detect correlated interactions when using an immobilized sample and a total internal reflection fluorescence microscope (TIRFM). Therefore, multi-color smFRET is a versatile tool for studying biomolecular complexes in a quantitative manner and in a previously unachievable detail.
Here, we demonstrate how to overcome the special challenges of multi-color smFRET experiments on proteins. We present detailed protocols for obtaining the data and for extracting kinetic information. This includes trace selection criteria, state separation, and the recovery of state trajectories from the noisy data using a 3D ensemble Hidden Markov Model (HMM). Compared to other methods, the kinetic information is not recovered from dwell time histograms but directly from the HMM. The maximum likelihood framework allows us to critically evaluate the kinetic model and to provide meaningful uncertainties for the rates.
By applying our method to the heat shock protein 90 (Hsp90), we are able to disentangle the nucleotide binding and the global conformational changes of the protein. This allows us to directly observe the cooperativity between the two nucleotide binding pockets of the Hsp90 dimer.
Many proteins fulfill their function in dynamic complexes with other molecules, mediated by conformational changes and transient associations on a broad range of timescales1,2,3. Coupled to an external energy source (e.g., ATP) these dynamic interactions can lead to directionality in a functional cycle and ultimately maintain the non-equilibrium steady-state in a cell, the prerequisite for life.
In order to fully understand these molecular machines, a static description guided by structural studies is not sufficient. In addition, it is essential to have knowledge of the underlying kinetic model and to determine the kinetic rate constants. Several existing methods allow researchers to study the dynamics of binary interactions between two molecules of interest, e.g., surface plasmon resonance, relaxation methods with a spectroscopic readout (e.g., jump or stopped-flow techniques), and nuclear magnetic resonance. However, their applicability is in most cases limited to simple two-state systems (e.g., one bound and one unbound state) due to the averaging inherent to bulk experiments. In cases where more states or intermediates are involved, they yield only a complex mixture of the rate constants. Single-molecule methods such as optical or magnetic tweezers or two-color smFRET, i.e., one donor and one acceptor fluorophore, with a surface-immobilized sample can recover the rate constants for all observed conformational changes. However, when it comes to interactions affecting more than one binding site, these methods remain limited and the information on the possible correlation of the two (or more) interactions will only be accessible via indirect conclusions from a set of experiments.
Multi-color smFRET4,5,6,7,8,9 offers the opportunity to study the interaction between these components directly, at real time and under near-physiological conditions10. This permits one to investigate for example, the conformation-dependent binding of a ligand or another protein8,9,11. The overall approach presented here is to label the protein(s) of interest at specific positions, to attach one protein to the surface of the measurement chamber, and to track the fluorescence intensity over time on a prism-type TIRFM (for details see 9,12). The spatial proximity of the different dyes can then be determined from the energy transfer between them. Labeling strategies may vary from protein to protein (reviewed in 13) and guidelines to avoid artifacts in smFRET measurements exist14.
Since a donor dye may transfer energy to different acceptor dyes in a multi-color smFRET experiment, the relative position of all dyes is not accessible from excitation of one dye alone15,16. But in combination with alternating laser excitation (ALEX17, and reviewed in 18) this method provides all spatio-temporal information at sub-second and sub-nanometer resolution.
In principal, high resolution structural information can be achieved by using the inter-dye distances calculated from the combination of all fluorescence intensities in a multi-color smFRET experiment with ALEX. However, here we focus on state identification and separation as well as the extraction of kinetic models, where multi-color smFRET is indispensable. When "only" structure determination by triangulation is desired, a set of simpler two-color smFRET experiments with high signal-to-noise ratio can be performed12,19.
We use the partial fluorescence () as a proxy for the energy transfer between two fluorophores7. The PF is calculated from the fluorescence intensity analogous to the FRET efficiency of a two-color experiment:
Where, is the intensity in emission channel em after excitation with color ex, and c is the acceptor with the longest wavelength. Detection channels represent the same position in the sample chamber but record different spectral ranges of the fluorescence light. The same identifier for excitation and emission are used in this protocol (i.e., "blue," "green," and "red").
Because of experimental shortcomings the measured fluorescence intensities depend not only on the energy transfer but also on fluorophore and setup properties. In order to obtain the true energy transfer efficiency between two fluorophores, the measured intensities have to be corrected. The following procedure is based on reference9. Correction factors for apparent leakage (lk, i.e., the detection of photons from a fluorophore in a channel designated for another dye) and apparent gamma (ag, i.e., the fluorescence quantum yield of the dye and the detection efficiency of the channel) are obtained from single-molecule traces that show an acceptor bleaching event.
The leakage of the donor dye into every possible acceptor channel is calculated from all data points in the recorded fluorescence traces where the acceptor dye bleached but the donor is still fluorescent ():
The median of the leakage histogram is used as the apparent leakage factor. After correction for leakage, the apparent gamma factor is determined from the same set of traces. It is calculated by dividing the change of fluorescence in the acceptor channel by the change of fluorescence in the donor channel upon bleaching of the acceptor dye:
Where c again is the detection channel for the acceptor with the longest wavelength. The median of the resulting distribution is used as the apparent correction factor.
The corrected intensities in each channel are obtained by:
The PF is then calculated according to:
Different populations can be separated in the multi-dimensional space spanned by the PFs. The position and width of each state is determined by fitting the data with multi-dimensional Gaussian functions. Subsequent optimization of one global HMM based on all PF traces provides a quantitative description of the observed kinetics. Even small changes of the rates are detectable.
HMMs provide a way of inferring a state model from a collection of noisy time traces. The system is considered to be in one of a set of discrete, hidden states at any given time and the actual observation (i.e., the emission) is a probabilistic function of this hidden state20. In the case of TIRFM smFRET data, the emission probabilities bi per state i can be modeled by continuous Gaussian probability density functions. At regularly spaced discrete time points, transitions from one to another state can occur according to the transition probability that is time-invariant and only depends on the current state. The transition matrix A contains these transition probabilities aij between all hidden states. The initial state distribution gives the state-specific probabilities
for the first time point of a time trace. Using a maximum-likelihood approach, these parameters can be optimized to best describe the data with the Forward-Backward and Baum-Welch algorithms20,21. This yields the maximum likelihood estimators (MLE). Finally, the state sequence that most likely produced the trajectory of observations can be inferred with the Viterbi algorithm. In contrast to other HMM analyses of smFRET data24,25,26 we do not use the HMM as a mere "smoothing" of the data but extract the kinetic state model from the data set without the need for fitting dwell time histograms27. HMM analysis is done with in-house scripts using Igor Pro. Implementation of the code is based on reference21. We provide a software kit and exemplary data on our webpage in order to follow sections 5 and 6 of this protocol (https://www.singlemolecule.uni-freiburg.de/software/3d-fret). Full software is available upon request.
Time points in the data with PF <-1 or PF >2 in any detection channel are assigned the minimal emission probability for all states (10-200). This prevents artificial transitions at these data points.
The parameters for the emission probabilities are obtained from the fit of the 3D PF histogram with Gaussian functions as described in step 5.7. These parameters are kept fixed during the optimization of the HMM.
In the presented approach, the initial state distribution vector and the transition matrix are used globally to describe the entire ensemble of traces. They are updated based on all N molecules from the data set according to reference27.
Start parameters for the initial state distribution are determined from 2D projections of the PF histogram (step 5.3) and the transition probabilities are set to 0.05 with the exception of the probabilities to stay in the same state, which are chosen such that the probability to leave a certain state is normalized to unity.
A likelihood profiling method is used to give confidence intervals (CIs) for all transition rates21,22, which serve as meaningful estimates for their uncertainty. To calculate the bounds of the CI for a specific rate, the transition probability of interest is fixed to a value other than the MLE. This yields the test model λ'. A likelihood ratio (LR) test of the likelihood given the data set 0 is performed according to:
The 95% confidence bound for the parameter is reached when LR exceeds 3.841, the 95% quantile of a x2-distribution with one degree of freedom22,23.
The power of the method is demonstrated using the Hsp90. This abundant protein is found in bacteria and eukaryotes and is part of the cellular stress response28. It is a promising drug target in cancer treatment29. Hsp90 is a homodimer with one nucleotide binding pocket in the N-terminal domain of each subunit30. It can undergo transitions between at least two globally distinct conformations, one closed and one N-terminal open, V-shaped conformation19,31,32. The dimeric nature directly raises the question of the interplay between the two nucleotide binding sites in Hsp90.
In the following, we provide a step-by-step protocol for the data acquisition and analysis of a three-color smFRET experiment on yeast Hsp90 and nucleotide. The conformation-dependent binding of fluorescently labeled AMP-PNP (AMP-PNP*, a non-hydrolyzable analog of ATP) is analyzed. The application of the described procedure permits the study of the nucleotide binding and at the same time the conformational changes of Hsp90 and thereby reveals the cooperativity between the two nucleotide binding pockets of Hsp90.
We present the experimental procedure to obtain three-color smFRET data for a complex protein system and a step-by-step description of the analysis of these measurements. This approach offers the unique possibility to directly assess the correlation between multiple interaction sites or conformational changes.
In order to obtain suitable multi-color single-molecule data on proteins it is important to perform reproducible measurements at a low noise level. This can be achieved by using an effic…
The authors have nothing to disclose.
This work is funded by the German Research Foundation (INST 39/969-1) and the European Research Council through the ERC Grant Agreement n. 681891.
Setup | |||
vibration-damped optical table | Newport, Irvine, CA, USA | RS2000 | |
OBIS 473nm LX 75mW LASER | Coherent Inc, Santa Clara, CA, USA | 1185052 | |
OBIS 532nm LS 50mW LASER | Coherent Inc, Santa Clara, CA, USA | 1261779 | |
OBIS 594nm LS 60mW LASER | Coherent Inc, Santa Clara, CA, USA | 1233470 | |
OBIS 637nm LX 140mW LASER | Coherent Inc, Santa Clara, CA, USA | 1196625 | |
laser control unit | Coherent Inc, Santa Clara, CA, USA | 1234465 | Scientific Remote |
aspheric telescope lenses | Thorlabs Inc, Newton, New Jersey, USA | d=25.4mm, f=50mm and f=100mm | |
CF ex1 | AHF analysentechnik AG, Tübingen, Germany | ZET 473/10 | cleanup filter excitation |
CF ex2 | AHF analysentechnik AG, Tübingen, Germany | ZET 532/10 | cleanup filter excitation |
CF ex3 | AHF analysentechnik AG, Tübingen, Germany | ZET 594/10 | cleanup filter excitation |
CF ex4 | Thorlabs Inc, Newton, New Jersey, USA | FL635-10 | cleanup filter excitation |
DM ex1 | AHF analysentechnik AG, Tübingen, Germany | ZQ594RDC | dichroic mirror excitation |
DM ex2 | AHF analysentechnik AG, Tübingen, Germany | 570DCXR | dichroic mirror excitation |
DM ex3 | AHF analysentechnik AG, Tübingen, Germany | ZQ491RDC | dichroic mirror excitation |
AOTFnC-Vis | AA Opto-Electronic, Orsay, France | ||
λ/4 plate | Thorlabs Inc, Newton, New Jersey, USA | AQWP05M-600 | |
CFI Apo TIRF 100x | Nikon Instruments Inc, Melville, NY, USA | high-NA objective | |
piezo focus positioner MIPOS 250 CAP | piezosystem jena GmbH, Jena, Germany | Piezo Controller NV 40/1 CLE | |
piezo stepper | Newport, Irvine, CA, USA | PZA12 | PZC200-KT NanoPZ Actuator Kit |
achromatic aspheric lenses | Qioptiq Photonics GmbH & Co. KG, Göttingen, Germany | G322-304-000 | d=50mm, f=200mm |
adjustable optical slit | Owis GmbH, Staufen i. Br., Germany | 27.160.1212 | max. aperture 12 x 12 mm |
DM det1 | AHF analysentechnik AG, Tübingen, Germany | T 600 LPXR | dichroic mirror detection |
DM det2 | AHF analysentechnik AG, Tübingen, Germany | H 560 LPXR superflat | dichroic mirror detection |
DM det3 | AHF analysentechnik AG, Tübingen, Germany | HC BS R635 | dichroic mirror detection |
BP det1 | AHF analysentechnik AG, Tübingen, Germany | 525/40 BrightLine HC | bandpass filter detection |
BP det2 | AHF analysentechnik AG, Tübingen, Germany | 586/20 BrightLine HC | bandpass filter detection |
BP det3 | AHF analysentechnik AG, Tübingen, Germany | 631/36 BrightLine HC | bandpass filter detection |
BP det4 | AHF analysentechnik AG, Tübingen, Germany | 700/75 ET Bandpass | bandpass filter detection |
optical shutters detection | Vincent Associates, Rochester, NY, USA | Uniblitz VS25S2T0 | |
EMCCD iXon Ultra 897 | Andor Technology Ltd, Belfast, Northern Ireland | ||
digital I/O card, PCIe-6535 | National Instruments, Austin, Texas, USA | ||
syringe pump | Harvard Apparatus, Holliston, MA, USA | PHD22/2000 | |
Name | Company | Catalog Number | Comments |
Flow chamber | |||
quartz slides | G. Finkenbeiner Inc, Waltham, MA, USA | Spectrosil2000, h=3mm | |
TEGADERM film | 3M Deutschland GmbH, Neuss, Germany | 1626W | 10 x 12cm |
spray adhesive | 3M Deutschland GmbH, Neuss, Germany | Photo Mount 050777 | |
glycerol | Carl Zeiss AG, Oberkochen, Germany | Immersol G | |
immersion oil | OLYMPUS EUROPA SE & CO. KG, Hamburg, Germany | IMMOIL-F30CC | |
prism | Vogelsberger Quarzglastechnik GmbH, Hauzenberg, Germany | Suprasil1 | |
aluminium prism holder | custom built | ||
hollow setscrews | Thorlabs Inc, Newton, New Jersey, USA | with custom drilling | |
Tygon S3 E-3603 tubing | neoLab Migge GmbH, Heidelberg, Germany | 2-4450 | ACF00001 |
PTFE tubing | Bohlender GmbH, Grünsfeld, Germany | S1810-08 | |
Name | Company | Catalog Number | Comments |
Sample | |||
yeast Hsp90 D61C, Q385C_biotin | UniProt ID P02829 | ||
Maleimide derivatives of Atto488, Atto550 | ATTO-TEC GmbH, Siegen, Germany | ||
AMP-PNP* | Jena Bioscience, Jena, Germany | γ-[(6-Aminohexyl)-imido]-AMP-PNP-Atto647N | |
Fluospheres | Thermo Fisher Scientific, Waltham, MA, USA | F8764 | amine-modified, 0.2 μm, yellow-green fluorescent |
Name | Company | Catalog Number | Comments |
Software | |||
Andor Solis | Andor Technology Ltd, Belfast, Northern Ireland | version 4.30 | |
LabVIEW | National Instruments, Austin, Texas, USA | version 2012, 32bit; misc. hardware control | |
MDS control software | AA Opto-Electronic, Orsay, France | version 2.03a | |
Coherent Connection | Coherent Inc, Santa Clara, CA, USA | version 3 | |
Igor Pro | WaveMetrics Inc, Portland, OR, USA | version 6.37 |