A protocol for high-precision FRET experiments at the single molecule level is presented here. Additionally, this methodology can be used to identify three conformational states in the ligand-binding domain of the N-methyl-D-aspartate (NMDA) receptor. Determining precise distances is the first step towards building structural models based on FRET experiments.
A protocol on how to perform high-precision interdye distance measurements using Förster resonance energy transfer (FRET) at the single-molecule level in multiparameter fluorescence detection (MFD) mode is presented here. MFD maximizes the usage of all "dimensions" of fluorescence to reduce photophysical and experimental artifacts and allows for the measurement of interdye distance with an accuracy up to ~1 Å in rigid biomolecules. This method was used to identify three conformational states of the ligand-binding domain of the N-methyl-D-aspartate (NMDA) receptor to explain the activation of the receptor upon ligand binding. When comparing the known crystallographic structures with experimental measurements, they agreed within less than 3 Å for more dynamic biomolecules. Gathering a set of distance restraints that covers the entire dimensionality of the biomolecules would make it possible to provide a structural model of dynamic biomolecules.
A fundamental goal of structural biology studies is to unravel the relationship between the structure and function of biomolecular machines. The first visual impression of biomolecules (e.g., proteins and nucleic acids) occurred in the 1950s through the development X-ray crystallography1,2. X-ray crystallography provides high-resolution, static structural information constrained by the crystal packing. Therefore, the inherent immobility of X-ray structural models shuns the dynamic nature of biomolecules, a factor that impacts most biological functions3,4,5. Nuclear magnetic resonance (NMR)6,7,8 provided an alternative solution to the problem by resolving structural models in aqueous solutions. A great advantage of NMR is its ability to recover the intrinsic dynamic nature of biomolecules and conformational ensembles, which helps to clarify the intrinsic relationships between structure, dynamics, and function3,4,5. Nevertheless, NMR, limited by sample size and large amounts of sample, requires complex labeling strategies for larger systems. Therefore, there is a pressing need to develop alternative methods in structural biology.
Historically, Förster resonance energy transfer (FRET)9 has not taken an important role in structural biology because of the misconception that FRET provides low-accuracy distance measurements. It is the purpose of this protocol to revisit the ability of FRET to determine distances on the nanometer scale, such that these distances can be used for building structural models of biomolecules. The first experimental verification of the R–6 dependence on the FRET efficiency was done by Stryer in 196710 by measuring polyprolines of various lengths as a "spectroscopic ruler." A similar experiment was accomplished at the single-molecule level in 200511. Polyproline molecules turned out to be non-ideal, and thus, double-stranded DNA molecules were later used12. This opened the window for precise distance measurements and the idea of using FRET to identify structural properties of biomolecules.
FRET is optimal when the interdye distance range is from ~0.6-1.3 R0, where R0 is the Förster distance. For typical fluorophores used in single-molecule FRET experiments, R0 is ~50 Å. Typically, FRET offers many advantages over other methods in its ability to resolve and differentiate the structures and dynamics in heterogeneous systems: (i) Due to the ultimate sensitivity of fluorescence, single-molecule FRET experiments13,14,15,16 can resolve heterogeneous ensembles by directly counting and simultaneously characterizing the structures of its individual members. (ii) Complex reaction pathways can be directly deciphered in single-molecule FRET studies because no synchronization of an ensemble is needed. (iii) FRET can access a wide range of temporal domains that span over 10 decades in time, covering a wide variety of biologically relevant dynamics. (iv) FRET experiments can be performed in any solution conditions, in vitro as well as in vivo. The combination of FRET with fluorescence microscopy allows for the study of molecular structures and interactions directly in living cells15,16,17,18,19, even with high precision20. (v) FRET can be applied to systems of nearly any size (e.g., polyproline oligomers21,22,23,24, Hsp9025, HIV reverse transcriptase26, and ribosomes27). (vi) Finally, a network of distances that contains all the dimensionality of biomolecules could be used to derive structural models of static or dynamic molecules18,28,29,30,31,32,33,34,35,36,37.
Therefore, single-molecule FRET spectroscopy can be used to derive distances that are precise enough to be used for distance-restrained structural modeling26. This is possible by taking advantage of multiparameter fluorescence detection (MFD)28,38,39,40,41,42, which utilizes eight dimensions of fluorescence information (i.e., excitation spectrum, fluorescence spectrum, anisotropy, fluorescence lifetime, fluorescence quantum yield, macroscopic time, the fluorescence intensities, and the distance between fluorophores) to accurately and precisely provide distance restraints. Additionally, pulsed interleaved excitation (PIE) is combined with MFD (PIE-MFD)42 to monitor direct excitation acceptor fluorescence and to select single-molecule events arising from samples containing a 1:1 donor-to-acceptor stoichiometry. A typical PIE-MFD setup uses two-pulsed interleaved excitation lasers connected to a confocal microscope body, where photon detection is split into four different channels in different spectral windows and polarization characteristics. More details can be found in Figure 1.
It is important to note that FRET must be combined with computational methods to achieve atomistic-like structural models that are consistent with FRET results26,30. It is not the goal of the present protocol to go over the associated methodology to build structural models with FRET-derived distances. However, these approaches have been applied in combination with other techniques (e.g., small-angle X-ray scattering or electron paramagnetic resonance), giving birth to the field of integrative structural biology43,44,45,46. The current goal is to pave the way for FRET as a quantitative tool in structural biology. As an example, this methodology was used to identify three conformational states in the ligand-binding domain (LBD) of the N-methyl-D-aspartate (NMDA) receptor. The ultimate aim is to overcome the aforementioned limitations and to bring FRET amongst the integrative methods used for the structural determination of biomolecules by providing measured distances with high precision.
1. PBS Buffer Preparation and Chamber Treatment
NOTE: Wear a laboratory coat and disposable gloves when performing wet chemical experiments. Use eye protection when aligning the laser.
2. DNA Sample Preparation
NOTE: Use designed labeled DNA strands (see the Materials List) for the creation of double-stranded DNA (dsDNA) standard samples. Designed oligos must not have dyes at the end of a polymer in order to avoid artifacts that can compromise the determined distance. The DNA sequence should be chosen to behave as a rigid body.
3. Protein Sample Preparation
Note: Starting with recombinant DNA for the expression of the protein of interest in bacterial systems, it is possible to mutate the residues from which the distances are to be measured into cysteines. To do so, use standard site-directed mutagenesis techniques47. To facilitate protein purification, clone the recombinant and mutated DNA into a vector containing a purification tag (e.g., a His-tag). The glutamate subunit 1 ligand-binding domain (LBD) from the NMDA glutamate ionotropic receptor (GluN1) LBD (i.e., NMDA GluN1 LBD cloned into the pET-22b (+) vector) was used.
4. Measurements Needed in Ensemble Conditions (in Cuvette)
5. Experiment Alignment for PIE-MFD Single-molecule Detection (SMD)
NOTE: It is better to turn off the lights when taking measurements.
6. dsDNA Standards and Sample Measurements
In typical smFRET experiments using an MFD setup (laser lines: 485 nm at 60 µW and 640 nm at 23 µW, section 5.1), the fluorescence sample is diluted to a low-picomolar concentration (10-12 M = 1 pM) and placed in a confocal microscope, where a sub-nanosecond laser pulse excites labeled molecules freely diffusing through an excitation volume. A typical confocal volume is <4 femtoliters (fL). At such low concentrations, only single molecules are detected one at a time. The emitted fluorescence from the labeled molecules is collected through the objective and is spatially filtered using a pinhole. This step defines an effective confocal detection volume. Then, the signal is split into parallel and perpendicular components at two (or more) different spectral windows (e.g., "green" and "red"). Each photon detector channel is then coupled to time-correlated single-photon counting (TCSPC) electronics for data registration (Figure 1).
After following the calibration of the MFD setup, a procedure summarized in Table 1 (steps 5-6), measurement of the dsDNA standards, can be started. Then, PIE-MFD is used to analyze multiple parameters, such as mean macrotime, fluorescence lifetime, burst-integrated anisotropy, ratio of the signal in green over the signal in red, burst duration in the prompt channel (T(G+R)|D), burst duration in the delayed channel (TR|A), and others65,66 (Figure 2). Important in this analysis is the stoichiometry parameter (SPIE), defined as:
where FG|D = FD, FR|D = FA, and FR|A are background-corrected fluorescence intensities63. For example, FG|D = IG|D – 〈BG〉, where IG|D is the detected intensity in the green channel from the donor and 〈BG〉 is the mean background count rate on the green channel. Similar corrections are done for the fluorescence of the acceptor from direct excitation of the acceptor (FR|A) and for the sensitized emission of the acceptor (FR|D). In Equation 1, α is the correction factor for donor-fluorescence crosstalk into the acceptor channel; β is the correction factor for acceptor excitation by the donor excitation source; and γ, where
is a function of the donor and acceptor quantum yields, ΦF,D and ΦF,A, respectively, and of the detection efficiencies on the green and red detectors, gG and gR. Using SPIE, it is possible to calibrate the proper instrumental factors, such as α, β, and γ, to satisfy SPIE = 1 for the donor-only labeled sample, SPIE = 0 for the acceptor-only sample, and SPIE = 0.5 for the FRET sample. Alternatively, it is possible to use:
to derive the quantum yield of a second sample, given that the quantum yield of one sample () is known and that the and are determined from the PIE-MFD experiment. In this case, it is assumed that the quantum yield of the high-FRET dsDNA is 0.32 and the quantum yield of the low-FRET dsDNA is determined. The reason for doing this procedure is because it has been noted that the SPIE is different for both low-FRET and high-FRET samples, even though both have one donor on the same location and only one acceptor, but at different locations. After determining the proper quantum yield of the standard samples, as described in Equation (3), the FRET efficiency (E) versus 〈τD(A)〉f and FD/FA versus 〈τD(A)〉f representations are used for further evaluation. The parametric relationship between the FRET efficiency (Estatic), FD/FA, and 〈τD(A)〉f parameters is described by the following set of equations (Equation 4):
Here, FD|D is the donor fluorescence detected in the donor or green channel; FA|D is the acceptor-sensitized emission; is the donor fluorescence lifetime in the absence of the acceptor; and 〈τD(A)〉x is the species average lifetime, which is related to the fluorescence average lifetime by an empirical polynomial 56,57. These equations are known as the static FRET lines57,67 because the lines should cross both populations equally well, in the absence of dynamics (Figure 3).
Last comes the analysis of FRET efficiency histograms (Figure 4) using probability distribution analysis (PDA) for the two dsDNA samples68,69. PDA has been used to model the smFRET histograms with high accuracy57. The information of single or multi-static species can be obtained from a single histogram. After fitting the shape of the expected distribution to the experimental data obtained, the distance between the donor and acceptor can be revealed. In short, the FRET efficiency, or FD/FA distributions, are calculated by first obtaining the probability [Equation] of observing a certain combination of photons collected in the "green" (G) and "red" (R) detection channels given a certain time-window; use Equation 5:
Here, the fluorescence intensity distribution, P(F), is obtained from the total signal intensity distribution P(S), assuming that the background signals BG and BR are distributed according to Poisson distributions, P(BG) and P(BR), with known mean background count-rate intensities, 〈BG〉 and 〈BR〉. The conditional probability P(FG, FR|F) is the probability of observing a particular combination of green and red fluorescence photons, FG and FR, for a given FRET state.
PDA analysis shows that the interdye distance for the high-FRET dsDNA is 〈RDA〉E(HFRET) = 45.7 Å, while for the low-FRET dsDNA, the distance 〈RDA〉E(LFRET) = 59.7 Å. When compared to the expected distances using the FRET positioning and screening system (FPS)26, an expected interdye distance 〈RDA〉E,AV(HFRET) = 44.7 Å was found as derived using FPS for the high-FRET dsDNA and 〈RDA〉E,AV(LFRET) = 59.1 Å for the low-FRET dsDNA. AV stands for the accessible volume calculation embedded in the FPS toolkit. AV is a coarse-grained Monte Carlo simulation, where fluorophores represent three radii hard sphere models connected to an attachment point in the biomolecule with a flexible connecting linker26,57. A correction for the quantum yield for the low-FRET dsDNA is required, based on the measured SPIE. With these conditions, it is possible to obtain an agreement of ~1 Å between the experimental value and expected value from the AV simulations.
Next, the NMDA GluN1 LBD is measured. The NMDA receptor (NMDAR) is a heteromeric, non-selective cation channel that requires the binding of glycine and glutamate for gating70. The LBD, which has a clamshell-like structure, is known to adopt an open clamshell and a closed clamshell-like configuration upon ligand binding based on crystallographic information71,72. For MFD experiments, the NMDA GluN1 LBD was mutated at Ser507 and Thr701 (full-length sequence) on opposite sides of the cleft, as has previously been described. It was then labeled using the FRET pair of a cyan-green fluorophore and a far-red fluorophore (see the Materials List), with an R0 of 52 Å. This construct was used to study the motion of the ligand-binding domain, without the complexity associated with working with a solubilized receptor. Using this construct, at least three configurations of the LBD were found. It was suggested that a conformational selection mechanism selectively populated one of the identified populations upon ligand binding73. In the inactivated form, or in the presence of the antagonist 5,7-dichlorokynurenic acid (DCKA), mostly medium- to low-FRET states are explored, with a longer donor fluorescence lifetime and a larger donor-to-acceptor fluorescence ratio peaking at FD/FA = 3.3 (Figure 5A). This is consistent with the stabilization of an open-cleft conformation. PDA and time window analysis were used to identify three configurations that the LBD can adopt (the high-FRET (HF) (〈RDA〉E = 33.9 Å), medium-FRET (MF) (〈RDA〉E = 45.8 Å), and low-FRET states (LF) (〈RDA〉E = 55.8 Å)). However, mostly the medium-FRET and the low-FRET were populated. This suggests that the high-FRET is the state that leads to the activation of the NMDAR. It is worth noting that experimentally derived distances and those derived by the FPS using in silico labeling and using the crystallographic information (Protein Data Bank Identification (PDBID): 1PB7 and 1PBQ) were compared. It was found that the interdye distance for the medium-FRET and low-FRET populations were 〈RDA〉E,AV = 48.7 Å and 54.2 Å for both structures, respectively (Figure 5B). The largest deviation of 2.9 Å was found in the medium-FRET state. When considering the uncertainty of the distribution, from the assumption of κ2 = 2/3, there is a maximum error of 2.5% in the measured distance. In short, one can conclude that it is possible to reach Angstrom accuracy on experimentally determined distances.
Figure 1: Experimental setup and data registration for PIE-MFD. (A) A typical multiparameter fluorescence detection setup is shown and consists of four detectors covering two different spectral windows. Detectors are connected to the time-correlated single-photon counting (TCSPC) electronics. (B) In TCSPC, each photon is identified by three parameters: (i) micro-time, or time after the excitation pulse; (ii) macro-time, or the number of excitation pulses from the start of the experiment; and (iii) channel number. These three parameters are required for off-line analysis. (C) Single molecules diffuse freely through the confocal volume, and photons are emitted, leaving a burst of photons as a function of time. (D) Each selected burst is fitted accordingly and used for displaying multi-dimensional histograms. Please click here to view a larger version of this figure.
Figure 2: Burst analysis using various fluorescence parameters. (A) FRET efficiency versus macrotime, (B) FRET efficiency versus T(G+R)|D – TR|A, and (C) FRET efficiency versus SPIE for the low-FRET or 15 bp dsDNA. T(G+R)|D is the burst duration in the prompt channel, and TR|A is the burst duration in the delayed channel (TR|A) Please click here to view a larger version of this figure.
Figure 3: FD/FA and lifetime of the donor versus FRET efficiency. Two-dimensional histograms to represent FRET efficiency (A); the ratio of donor over acceptor fluorescence, FD/FA, (B); and donor anisotropy rD (C) versus the average fluorescence lifetime of the donor in the presence of acceptor 〈τD(A)〉f. The determined correction factors are: 〈BG〉 = 0.64, 〈BR〉 = 0.37, β = 0.08 (the fraction of the direct excitation of the acceptor with the donor excitation laser), α = 0.017, and gG/gR = 3.7 Please click here to view a larger version of this figure.
Figure 4: PDA comparisons of high-FRET and low-FRET dsDNA. Time window PDA analysis at 2 ms, with a half-width of 6% of the mean FRET efficiency distance. Each distance is Gaussian distributed with 6% of the 〈RDA〉E as the width (hwDA). (A) For the sample HFRET, the interdye distance is 〈RDA〉E(HFRET) = 45.7 Å. (B) For the sample LFRET, the distance is 〈RDA〉E(LFRET) = 59.7 Å. Please click here to view a larger version of this figure.
Figure 5: PIE-MFD of the ligand-binding domain of the NMDA receptor in the presence of the antagonist, DCKA. (A) Two-dimensional histogram of FD/FA versus the lifetime of the donor in the presence of acceptor 〈τD(A)〉f and the anisotropy of the donor versus 〈τD(A)〉f for the LBD with DCKA. One-dimensional projections for FD/FA and are also shown. The static FRET line is shown in red. Pure donor and acceptor fluorescence (FD and FA) are corrected for background (〈BG〉 = 0. 940 kHz and 〈BR〉 = 0.522 kHz), spectral cross-talk (α = 1.7%), and detection efficiency ratio (gG/gR = 3.7). On the anisotropy versus 〈τD(A)〉f histograms, the Perrin's equation has a rotational correlation of ρ = 2.5 ns. (B) PDA at a 10-ms time window Δt. A single state is needed. The model fits all time windows nicely. Please click here to view a larger version of this figure.
Action | Goal |
Center laser beam. | |
Align pinhole. | FCS experiment (section 5). |
Align detectors. | Maximize CPM. |
Adjust objective correction ring. | Minimize tdiff and maximize CPM. |
Determine instrument response function (IRF). | TCSPC @ SMD in TTTR mode. Measure scatter decay pattern. |
Determine G-factor for each spectral window. | TCSPC @ SMD in TTTR mode. Compare intensities form decay tails fitting for polarizations. |
Determine detection efficiency ratio in spectral windows (gR⁄gG). | (i) Intensity measurements of a dye with broad emission spectrum (nM concentration). (ii) Measure reference FRET rulers (pM concentration). In MFD the subpopulation should fall on the static FRET line. |
Perform final check (lifetime and anisotropy). | Control fitted lifetime and anisotropy from single-molecule measurement of freely diffusing dye with a single exponential decay (e.g. Rhodamine 110). |
Determine ratio of acceptor over donor quantum yield. | (i) Stoichiometry plot (SPIE) Eq. 1. and 4.2. |
Determine background count rate. | Intensity measurements of the selected “buffer”. |
Determine cross-talk (α). | Intensity measurements of donor dye into account the fluorescence emission spectrum and detection efficiencies. |
Table 1: Calibration steps for FRET experiments in single-molecule experiments.
In this work, the protocol to align, calibrate, and measure interdye distances with high precision using PIE-MFD single-molecule FRET experiments is presented. By carefully calibrating all instrumental parameters, one can increase the accuracy of the measured distances and reach Angstrom accuracy. To do so, various multidimensional histograms are used to analyze and identify populations for further characterization. Using the mean macro time to verify the stability of the measured samples, it is possible to correct for donor and acceptor photobleaching and to select FRET populations based on the stoichiometry parameter. However, the photophysical properties of the acceptor can change depending on the location of the label. Thus, one can use a SPIE distribution to properly correct for the acceptor quantum yield. Proper photophysical characterization is necessary to determine the gamma factor (ƴ), which, together with other correction factors (e.g., α for crosstalk and β for acceptor excitation with the donor laser), can be used to increase the accuracy of the measured interdye distance. This approach was corroborated using two designed dsDNA standard samples, and an accuracy of ~1 Å when compared to expected values was determined.
Different dye selections require adapting the microscope optical elements, such as the dichroic and bandpass filters, to accommodate the proper spectral window of the selected dyes. Accordingly, pulsed lasers need to be selected. More importantly, the selection of dyes is crucial because of several possible photophysical artifacts, such as acceptor and donor bleaching, triplet or dye blinking, or dye sticking to protein surfaces. These artifacts could compromise the interpretation of experimental data. MFD is ideal in this scenario because, by inspecting multiple parameters, it is possible to identify the sources of these artifacts, correct for them, or at least be aware of their existence. The dipole orientation parameter, most of the time assumed as κ2 = 2/3, can cause larger deviations of the determined distance if the dye sticks preferentially to the surface of the biomolecules. Anisotropy of the donor sample, acceptor sample, and donor acceptor can help to resolve whether this assumption is valid or not. In this experiment, it has been found that there is a maximum error of ~2.5% on the measured distance, compared to not making the proper correction and obtaining a 10-20% error. The quantum yield of the acceptor can create a larger source of error. Thus, SPIE is important for addressing this important issue.
It is possible to apply a similar strategy to understand the conformational landscape of the ligand-binding domain of the NMDA receptor to understand the mechanism of agonism on the NMDAR. It was found that the LBD in the presence of an antagonist shuns the accessibility of a high-FRET state, postulated to be responsible for opening the channel73. When comparing experimentally derived distances and the expected values based on crystallographic information, an agreement within 3 Å was achieved. More importantly, the new low-populated states can be identified with similar precision.
In summary, single-molecule FRET experiments in MFD mode42 allow one to properly account for experimental artifacts and to derive interdye distance in the range of ~30-70 Å. If, instead of a single measured distance, a network of distances is derived, it is possible to use these as restraints in structural modeling, particularly for states that are difficult to characterize with more standard methods of structural biology.
The authors have nothing to disclose.
VJ and HS acknowledge support from NIH R01 GM094246 to VJ. HS acknowledges start-up funds from the Clemson University Creative Inquiry Program and the Center for Optical Materials Science and Engineering Technologies at Clemson University. This project was also supported by a training fellowship from the Keck Center for Interdisciplinary Bioscience Training of the Gulf Coast Consortia (NIGMS Grant No. 1 T32GM089657-05) and the Schissler Foundation Fellowship for Translational Studies of Common Human Diseases to DD. The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health.
charcoal | Merck KGaA | K42964486 320 | |
syringe filter | Fisherbrand | 09-719C | size: 0.20um |
chambered coverglass | Fisher Scientific | 155409 | 1.5 borosilicate glass, 8 wells |
microscope cover glass | Fisher Scientific | 063014-9 | size: 24X60-1.5 |
Nuclease free water | Fisher Scientific | 148859 | nuclease free |
tween-20 | Thermo Scientific | 28320 | 10% solution of Polysorbate 20 |
acceptor DNA strand (High FRET) | Integrated DNA Technologies | 178124895 | 5´-d(CGG CCT ATT TCG GAG TTG TAA ACA GAG AT(Cy5)C GCC TTA AAC GTT CGC CTA GAC TAG TCC AAG TAT TGC) |
acceptor DNA strand (Low FRET) | Integrated DNA Technologies | 177956424 | 5´-d(CGG CCT ATT TCG GAG TTG TAA ACA GAG ATC GCC TT(Cy5)A AAC GTT CGC CTA GAC TAG TCC AAG TAT TGC) |
donor DNA strand | Integrated DNA Technologies | 177951437 | 5´ -d(GCA ATA CTT GGA CTA GTC TAG GCG AAC GTT TAA GGC GAT CTC TGT TT(Alexa488)A CAA CTC CGA AAT AGG CCG) |
DNA strand (No FRET) | Integrated DNA Technologies | 5´ -d(CGG CCT ATT TCG GAG TTG TAA ACA GAG ATC GCC TTA AAC GTT CGC CTA GAC TAG TCC AAG TAT TGC) | |
thermal cycler | Eppendorf | E6331000025 | nexus gradient |
Alexa Fluor 488 C5 Maleimide | Thermo Scientific | A10254 | termed cyan-green fluorophore in the manuscript |
Alexa Fluor 647 C2 Maleimide | Thermo Scientific | A20347 | termed far-red fluorophore in the manuscript |
Rhodamine 110 | Sigma-Aldrich | 83695-250MG | |
Rhodamine 101 | Sigma-Aldrich | 83694-500MG | |
LB Broth, Miller | Fisher Scientific | BP1426 | For culture of E. coli |
Ampicillin | Sigma-Aldrich | A0166 | Used at 100 ug/ml final concentration in selective LB medium to maintain plasmid selection |
Tetracyline | Calbiochem | 58346 | Used at 12.5 ug/ml final concentration in selective LB medium to maintain gor (flutathione reductase) mutation in Origami B(DE3) strains to facilitate disulfide bond oxidation |
Kanamycin | Fisher Scientific | BP906-5 | Used at 15 ug/ml final concentration in selective LB medium to maintain trxB (rhioredoxin reductase) mutation in B(DE3) stains to facilitate disulfide bond oxidation |
Origami B(DE3) Competent Cells | Millipore | 70837-3 | Competent E. coli cells for expression of protein with disulfide bridges |
Isopropyl-β-D-thiogalactopyranoside (IPTG) | Fisher Scientific | BP1755 | For induction of E. coli protein expression |
HiTrap Chelating HP | GE Life Sciences | 17-0409-01 | For Large-scale FPLC Purification of His-tagged protein |
Imidazole | Sigma-Aldrich | 56749 | |
Ni-NTA Agarose | Qiagen | 30210 | |
PD-10 Desalting Column | GE Life Sciences | 17-0851-01 | |
AktaPurifier | GE Life Sciences | 28406264 | FPLC Instrument |
Dialysis tubing | Spectrum labs | 132562 | 15 kD MWCO 24 mm Flath width, 10 meters/roll |
Dichroics | Semrock | FF500/646-Di01-25×36 | 500/646 BrightLight |
50/50 Beam splitter polarizer | Qioptiq Linos | G33 5743 000 | 10×10 film polarizer |
Green pass filer | Chroma | ET525/50m | ET525/50m 25 mm diameter mount |
Red pass filter | Chroma | ET720/150m | ET720/150m 25 mm diameter mount |
Power Meter | ThorLabd | PM200 | |
UV-Vis spectrophotometer | Varian | Cary300Bio | |
Fluorolog 3 fluorometer | Horiba | FL3-22-R3 | |
Fluorohub TCSPC controller | Horiba | Fluorohub-B | TCSPC electronics for ensemble measurements |
NanoLed 485L | Horiba | 485L | Blue diode laser |
NanoLed 635L | Horiba | 635L | Red diode laser |
Olympus IX73 Microscope | Olympus | IX73P2F | Microscope frame |
PMA 40 Hybrid Detector | PicoQuant GmbH | 932200, PMA 40 | Optimized for green detection |
PMA 50 Hybrid Detector | PicoQuant GmbH | 932201, PMA 50 | Optimized for ed shifter sensitivity |
485nm laser | PicoQuant GmbH | LDH-D-C-485 | |
640nm laser | PicoQuant GmbH | LDH-D-C-640 | |
Hydraharp 400 and TTTR acqusition software | PicoQuant | 930021 | Picosecond event timer and Time Correlated Single Photon Coutning Unit, includes TTTR acqusition software |
SEPIA II SLM 828 and SEPIA software | PicoQuant | 910028 | Laser driver for picosecond pulses , includes SEPIA software controller. |
computer | Dell | optiplex 7010 | cpu: i7-3770 ram:16GB |
FRET Positioning and Screening (FPS) software | Heinrich Heine Unviersity | It include the Accesibel Volume clacualtor available at http://www.mpc.hhu.de/software/fps.html | |
MFD suite | Heinrich Heine Unviersity | It includes the BIFL software package Paris; Margarita for visualization of the multiparameter hisotrams, and Probability Distribution Analysis software availabel at http://www.mpc.hhu.de/software/software-package.html |