The authors gratefully acknowledge the following funding sources: BBSRC (BB/T008032/1); EPSRC (Studentship to B.A.) and MRC (Studentship to A. R.-T.).

1. Power-on components
<ol>
	<li>Power on the six plugs (no particular order): Piezoconcept (z-focus), APD0, APD1, green laser, red laser and photodetector.</li>
</ol>
2. Software 1-Experimental setup
<ol>
	<li>Launch the laser control center.
	<ol>
		<li>Ensure Continuous Wave - Alternating Constant Current (CW-ACC) mode is selected.</li>
		<li>Power on both the lasers.</li>
		<li>Check/set the laser powers. 
		NOTE: The laser power will need to be adjusted as measured just before the excitation dichroic, as ND filters and beam splitters in the excitation path will reduce the laser power from the number given on the laser control panel. The numbers given here are power at the excitation dichroic, but the power on the laser control that corresponds to this will need to be worked out.
		<ol>
			<li>220 &#181;W green laser (515 nm, 40 mW)</li>
			<li>&#181;70 W for red laser (638 nm, 10 mW)</li>
		</ol>
		</li>
	</ol>
	</li>
	<li>smOTTER acquisition software
	<ol>
		<li>Connect the lasers, detectors, z-stage, and camera. Configure them correctly (might vary depending on settings of particular NI card setup).</li>
	</ol>
	</li>
</ol>
3. Alignment of emission path (not routinely required)
<ol>
	<li>Setting up alignment
	<ol>
		<li>Pipette 10 &#181;L of free Cy3B dye (~100 nM) onto the microscope and focus as described in steps 4.3-4.5 below. 
		NOTE: Another donor dye with some leakage into the acceptor channel would also work.</li>
		<li>Open the Alignment tab in smOTTER, lower the laser power, and alter the y-axis scale until readout is seen from the detectors. 
		NOTE: The goal here is to increase the signal, so the scale may need to be changed again after the signal increases.</li>
		<li>Unscrew the four screws at the front of the smfBox and remove the front panel.</li>
	</ol>
	</li>
	<li>Pinhole alignment
	<ol>
		<li>Turn the x knob on the pinhole positioner while watching the signal on the Alignment tab, trying to increase the signal in green and red.</li>
		<li>Now turn the y knob to align the pinhole in the other direction.</li>
		<li>Return to the x knob to check for any further increase in signal.</li>
	</ol>
	</li>
	<li>Pinhole-lens alignment
	<ol>
		<li>Turn the lens x knob in one direction, this will decrease the signal.</li>
		<li>Turn the pinhole x knob in the same direction to increase the signal again.</li>
		<li>If the new max signal is higher than before, continue to iteratively move both the pinhole and lens in that direction. If it is lower than before, iteratively move in the opposite direction.</li>
		<li>Repeat above for y.</li>
	</ol>
	</li>
	<li>APD lens alignment
	<ol>
		<li>Starting with the green APD, move the x knob until the green signal is at a maximum.</li>
		<li>Repeat for the y knob.</li>
		<li>Return to the x knob, move back and forth to find the threshold points where the signal begins to fall, and leave it at a position halfway between these two points.</li>
		<li>Repeat for the x knob.</li>
		<li>Repeat the above steps for the red APD lens, watching the red signal.</li>
	</ol>
	</li>
	<li>Place the front panel back on the smfBox and replace the screws. 
	NOTE: Perform the above alignment monthyl, and as a precautionary measure if it is suspected that the microscope has developed an alignment fault between measurements.</li>
</ol>
4. Measurement-data acquisition
<ol>
	<li>For the first sample, clean the stage with lens cleaning tissue soaked in methanol; wipe gently across the objective from one end to the other.</li>
	<li>Apply 3-4 drops of immersion oil for microscopy onto the center of the objective; replenish as needed between samples.</li>
	<li>Sample preparation
	<ol>
		<li>Pipette 10 &#181;L of sample (first use Type I ultrapure water) onto the center of a clean cover glass.</li>
		<li>Carefully place the cover glass onto the objective lens lowering it at an angle to the oil to prevent trapping air bubbles between the cover glass and the objective. 
		NOTE: If necessary, slide cover glass around to push air bubbles in the oil away from the focal point.</li>
	</ol>
	</li>
	<li>To perform alternating-laser excitation (ALEX) experiments configure the laser in the laser duty cycles panel as follows.
	<ol>
		<li>Donor (green laser) 0 off, 45 on, 55 off</li>
		<li>Acceptor (red laser) 50 off, 45 on, 5 off</li>
		<li>ALEX period (&#181;s): 100</li>
	</ol>
	</li>
	<li>Click on the Z focus tab in the ribbon; in the acquisition panel, switch the lasers to Live and start the camera; adjust the exposure so that a bright spot appears centrally surrounded by black. 
	&#8203;NOTE: The camera captures light reflected by the glass/water interface between the coverslip and the sample and the visible circle of light is at its smallest diameter when the focal point is at this interface. It is therefore necessary to start below this interface, increase the z-height to the interface, and then slightly further to focus above the coverslip and in the sample being measured.
	<ol>
		<li>Starting from a low Z position, increase the height until the bright spot reaches its minimal size, then raise the height further by up to 20 &#181;m to focus the laser above the oil and cover glass and in the sample.</li>
		<li>Stop the camera once in focus.</li>
	</ol>
	</li>
	<li>To confirm that the setup has been performed appropriately, monitor the trace of Type I ultrapure water to see no fluorescence signals; repeat confirmation of purity for the buffers in which samples are prepared.</li>
	<li>Carefully remove the cover glass with rubber-ended tweezers to avoid damaging objective, then place the sample of interest prepared as above onto the objective. Replenish the immersion oil as necessary to maintain a uniform area of contact between cover glass and objective (special attention paid to sample&#39;s contact area on cover glass and focal area of objective)</li>
	<li>Concentration bingo
	<ol>
		<li>To ensure single-molecule data is obtained, samples need to be at a concentration at which 1-5 bursts per second are observed in the live trace panel of smOTTER (this minimizes the chance of more than one molecule being present in the detection volume at once). Take the measurements when the appropriate concentration has been determined.</li>
		<li>To prevent evaporation during long experiments, prepare an airtight sample chamber. Press the silicone isolators (8-9 mm diameter hole) down onto the center of a cover glass (colored-in with a pipette tip). Then, carefully pipette a sample into the center, avoiding any contact with the silicone. Then, place a second cover glass on top and press to form a seal.</li>
		<li>Check the live stoichiometry vs FRET efficiency (ES) histogram to see if the sample is behaving as expected, i.e., expected FRET efficiency and reasonable stoichiometry (~0.5).</li>
	</ol>
	</li>
	<li>When the sample is ready, enter the details for the experiment into the save settings panel. Choose an appropriate directory and filename by clicking on the ellipsis icon (files saved in hdf5/h5 format) in the save settings panel.
	<ol>
		<li>Enter information for sample name, sample details, donor and acceptor labels, buffer, donor and acceptor excitation wavelengths, detection wavelengths and laser power, and user and user affiliation.</li>
	</ol>
	</li>
	<li>Return to the live trace tab in the ribbon, and in the acquisition panel enter experiment length (in minutes), select an appropriate save interval to mitigate potential data-loss in case of an error. Select Save Laser Powers, if required.</li>
	<li>Press Start to take the data. 
	&#8203;NOTE: To enable accurate FRET determination, at least two samples will need to be measured containing the same donor and acceptor dyes, but with sufficiently different FRET efficiencies.</li>
</ol>
5. Analysis/software 2
<ol>
	<li>Launch Jupyter notebook with the FRETBursts python package (setup instructions can be found here: https://craggslab.github.io/smfBox/anasoftware.html)</li>
	<li>If correction for absolute FRET efficiencies is not needed (i.e., only relative changes in FRET are sufficient for the measurement) launch Jupyter notebook FRET Analysis 1.4 Uncorrected.ipynb. Use this to export figures or data as csv files for further analysis or plotting in other software. 
	NOTE: Each notebook contains detailed instructions to guide the user through the analysis procedures. For a more detailed discussion of analysis procedures, including burst search algorithms, background correction, and all correction parameters see23,25.</li>
	<li>If correction for absolute FRET efficiencies is needed, begin by calculating the crosstalk parameters alpha (leakage of donor emission into the acceptor channel) and delta (proportion of direct excitation of the acceptor under donor excitation).
	<ol>
		<li>First launch Jupyter notebook Correction Factor Finder Alpha-Delta.ipynb and determine the alpha and delta parameters. 
		NOTE: If these are inconsistent between samples, the microscope may have developed an alignment problem between measurements or the spectra of the dyes differs between samples.</li>
		<li>If the alpha and delta parameters are consistent, launch Jupyter notebook Correction Factor Finder Gamma-Beta.ipynb to determine the gamma and beta parameters (which account for differences in excitation and detection efficiencies between the dyes). 
		&#8203;NOTE: If the gamma and beta plot does not fit well, the microscope may have developed an alignment problem between measurements, or the quantum yields, or the extinction coefficients of the dyes differs between samples.</li>
		<li>With the four crosstalk parameters determined, these factors may be used in the FRET Analysis 1.4 Corrected.ipynb Jupyter notebook to determine absolute FRET efficiencies.</li>
	</ol>
	</li>
</ol>
6. Troubleshooting
<ol>
	<li>If all signals are low or counts per burst are lower than expected (this is dye dependant-but for ATTO-550 and ATTO-647N on the smfBox typical values are between 50 and 100 counts per ms during a single-molecule burst), realign the smfBox.</li>
	<li>Bridge between doubly and singly labeled populations on ES histogram. 
	NOTE: This can be caused either by working at too high a concentration (remedy this by diluting the sample), or by photobleaching, which can be caused by using too high a laser power (remedy this by reducing laser power).</li>
	<li>If the burst rate falls throughout the experiment (fluorescently labeled molecules are likely adhering to the glass coverslip), use an increased concentration of BSA to rectify.</li>
	<li>If the burst rate increases throughout the experiment (sample is likely evaporating), prepare an air-tight sample chamber as described above.</li>
	<li>If the signal crashes to zero during alignment (the software is likely being overwhelmed by signal from the detectors), lower the laser power or use a more dilute alignment sample.</li>
	<li>If Z-focus shows concentric rings (can happen if multiple coverslips have been placed on the objective), check for multiple coverslips on the objective.</li>
	<li>If the sample contains bright, long bursts of intermediate stoichiometries (caused by aggregated molecules), use detergents or modify sample purification protocols. 
	NOTE: Getting a clean buffer can be an issue, as some buffer components will often contain very small amounts of moderately fluorescent contaminants which are enough to present as single color bursts on the time trace. If there is too much of this in the buffer, then it can coincide with sample bursts and change the FRET efficiency or stoichiometry being measured. BSA in particular can often be problematic in this regard, so it is helpful to expose a stock BSA solution to a strong light source to photobleach the contaminants.</li>
</ol>

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A., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Regional+conformational+flexibility+couples+substrate+specificity+and+scissile+phosphate+diester+selectivity+in+human+flap+endonuclease+1.">Regional conformational flexibility couples substrate specificity and scissile phosphate diester selectivity in human flap endonuclease 1.</a> Nucleic Acids Research. 46 (11), 5618-5633 (2018).</li></ol>

The authors declare no competing interests.

The most critical steps in the protocol are the alignment of the microscope and adjusting the sample concentration to the correct dilution. If alignment is off, then there might be insufficient signal to identify bursts and plot histograms, and if misalignment occurs between samples then accurate FRET correction may fail due to changes in leakage and detection / excitation efficiencies. The use of an appropriate concentration is also important, too high a concentration will give coincident bursts, containing multiple molecules with potentially different FRET efficiencies or labeling stoichiometries. Too low a concentration will give too few bursts for robust data analysis.
The protocol described here is for measuring distances in static single FRET species. If the sample has more than one peak in the FRET efficiency histogram, or peaks appear broad (which can happen with dynamic species), then more bursts may be needed to fit histograms to the same degree of precision. For two well-separated peaks then approximately twice as much data will be needed, but if the populations overlap slightly then even more data is required.
If the two populations interconvert on the time scale of the experiment, the dynamics and kinetics of the system can potentially be determined. Tests such as BVA27 and 2CDE28 can confirm that the intermediate bursts are dynamic in nature, whereas analyses including dPDA29,30 or H2MM31 can determine the rates of interconversion. Jupyter notebooks for BVA and 2CDE are available on the FRETBursts26 website, and the MATLab based software PAM32 can run BVA, 2CDE, and PDA analyses.
Confocal single-molecule FRET can easily observe states much more shortly lived (~1 ms) than TIRF; however, the short observation times, limited by diffusion, give no molecular history, and so cannot determine longer dwell times, or complex transition networks in the way that surface immobilized experiments can.
As the protocol measures freely diffusing molecules at a very low concentration, it works best when measuring intramolecular distances on the same molecule. Intermolecular distances between transiently bound molecules can be measured provided that the Kd of the two molecules is low enough that the complex exists at a significant quantity at the low working concentration required by the experiment (~100 pM). If the Kd is much higher than this, then only singly labeled molecules will be seen. This problem can be overcome by using microfluidics to mix the two labeled components together at a high concentration and then rapidly diluting and flowing over the objective before the complex dissociates33,34.
Measuring FRET efficiencies at the single-molecule level has a significant advantage over ensemble techniques, as it informs on heterogeneous subpopulations, which in an ensemble experiment would be averaged. Furthermore, single-molecule FRET with ALEX gives access to accurate FRET efficiencies, which can be converted to accurate distances. This enables determination of more detailed structural information rather than simply probing relative distance changes. The smfBox carries all these benefits and capabilities but can be constructed on a much lower budget than comparable commercially available microscopes capable of confocal smFRET23.
The smfBox represents a much lower barrier to entry for smFRET techniques, allowing researchers to measure conformational changes, and accurate distances within and between proteins and nucleic acids7,8,9,10,11,35.

Single-molecule F&#246;rster&#160;resonance energy transfer (smFRET) is a technique that measures the FRET efficiency between two dyes-a donor and an acceptor-at the level of individual molecules. FRET is a photophysical process arising from the overlapping energy spectra of two dyes: the donor is excited by light of a specific wavelength and transfers energy non-radiatively to the acceptor, resulting in emission from the acceptor. The efficiency of this transfer is inversely proportional to the sixth power of the distance between the two dyes, so the transfer efficiency varies with distance1. Thus, this FRET efficiency can be used to determine spatial information about the molecule(s)2 to which the dyes are attached, within a range of 3-10 nm. This scale, and the fact that changes in FRET efficiency are sensitive to Angstrom molecular movements3, makes the technique well suited to investigating structural information about biomolecules-such as nucleic acids and proteins-without the complications of ensemble averaging4,5,6. While changes in relative FRET efficiencies can be used to monitor biomolecular interactions and conformational dynamics, shedding light on key cellular processes such as protein (un)folding, transcription, and DNA replication and repair, absolute FRET efficiencies have been used to determine precise distances for biomolecular structure determination7,8,9,10,11, overcoming the need for crystallization or freezing as is required for some other structural methods4,12.
smFRET experiments most commonly take two forms, confocal or total internal reflection fluorescence (TIRF) microscopy. Between both approaches the molecular dynamics of biomolecules can typically be investigated on timescales from pico- to millisecond (confocal, freely diffusing molecules) up to millisecond to hours (TIRF, surface immobilized molecules). This is due to the different setups involved in each technique. In TIRF microscopy, molecules are immobilized on the surface of a slide and excited by an evanescent wave (Figure 1A). Here, however, the focus is on confocal microscopy as this is the format of the smfBox. In confocal microscopy, molecules are not immobilized and instead freely diffuse via Brownian motion through the confocal volume (~1 fL), formed by focusing a laser beam through a high numerical aperture lens into a spot at some designated depth within the solution (Figure 1B). The resulting emission is focused back through the same aperture and filtered through a dichroic mirror (Figure 1C for full schematic). It is then focused through a pinhole in order to remove any out-of-focus light and onto an avalanche photodiode (APD). When the APD detects a photon, it outputs a TTL pulse, the timing of which can be recorded with up to picosecond resolution. The observation time of these freely diffusing molecules within the vicinity of the confocal volume is commonly within the order of milliseconds.
<img alt="Figure 1" class="xfigimg" src="/files/ftp_upload/62378/62378fig01.jpg" /> 
Figure 1: Schematics showing principles of microscopy and the smfBox setup. (A) Total Internal Reflection Fluorescence (TIRF) Microscopy principle: excitation light is directed into the edge of the objective (Obj.) and undergoes total internal reflection at the coverslip-buffer interface generating an exponentially decaying evanescence field to excite surface attached molecules. (B) Confocal Microscopy: Freely diffusing molecules are excited by a near diffraction-limited spot focused into the sample. (C) The smfBox setup used in this protocol, showing all key components: avalanche photodiodes (APD), beam splitter (BS), dichroic mirrors (DM), filters (F), mirrors (M), objective (O) and pinhole (P). <a href="https://jove.unicartagenaproxy.elogim.com/files/ftp_upload/62378/62378fig01large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
More recently, smFRET techniques incorporated two color excitation, where lasers matching the donor and acceptor excitation wavelengths are alternated5. This can be done in one of two ways, the first by modulating continuous wave lasers on the KHz timescale, which is known as alternating laser excitation (ALEX)13,14. The second method interleaves fast pulses on the MHz timescale; this is nanosecond-ALEX15 or pulsed interleaved excitation (PIE)16. In all these approaches, information from the acceptor laser leads to calculation of the so-called stoichiometry, which can discriminate between molecules with a low FRET efficiency and those lacking an acceptor (either through incomplete labeling or photobleaching). Using PIE/ns-ALEX additionally gives access to fluorescent lifetimes on the single-molecule level, and anisotropies can be measured when coupled with polarizing optics. This combination of measurements is known as multiparameter fluorescence detection (MFD)9.
Despite the many advantages of smFRET, it is not widely used outside of specialist labs due to the high costs of commercial instruments and a lack of simple, self-build alternatives. A growing trend towards development of low-cost opensource microscopy is taking place and other platforms have recently emerged, including Planktonscope17, OpenFlexure Microscope18, Flexiscope19, miCube20, liteTIRF21, and Squid22. Herein the study describes the protocol for using the smfBox, a recently developed cost-effective confocal set-up capable of measuring the FRET efficiency between two dyes on freely diffusing single molecules. Detailed build instructions and all the necessary operational software are freely available at: https://craggslab.github.io/smfBox/23. The optical arrangement of the smfBox is assembled from readily available components purchased from affordable and widely-accessible manufacturers, while the microscope body (responsible for the majority of the expense in a standard confocal set-up) has been replaced by a custom light-tight anodized-aluminium box (allowing measurements to be made under ambient light conditions). This box houses key optical components, including the excitation dichroic, objective, and pinhole, and a mechanical laser interlock, enabling its safe operation as a Class I laser product (see&#160;Figure 1C for a full schematic). The smfBox uses ALEX to validate the dye stoichiometry and to determine accurate FRET correction factors. It is operated using custom-written, open-source software (smOTTER), which controls all aspects of the data acquisition and outputs the data in the open-source photon-HDF5 format24, compatible with many third-party analysis tools. The smfBox and the acquisition and data analysis protocols were recently tested against &#62;20 other instruments (both confocal and TIRF) in a multi-lab blind study25. The FRET efficiencies obtained were in excellent agreement with all the other instruments, despite the smfBox costing only a fraction of the price of commercially available setups.
Here, a step-by-step protocol is outlined for acquiring and analyzing accurate, absolute FRET efficiencies on freely diffusing DNA duplexes using the smfBox, all the way from switch on, through alignment and focusing, to data collection and analysis. The samples used here are three duplex DNAs (exhibiting high-, mid- and low-FRET efficiencies, see Table 1) that were assessed in the world-wide blind study25; however, the method is adaptable to many molecular systems, including proteins and other nucleic acids. The hope is that such a detailed protocol, along with the already existing build instructions for the smfBox23, will help to make this powerful technique even more accessible to a wide range of labs.

<table border="1" fo:keep-together.within-page="1" fo:keep-with-next.within-page="always">
	<tbody>
		<tr>
			<td>Amino modified oligonucleotide</td>
			<td>Eurogentec</td>
			<td>N/A</td>
			<td>May be ordered from various suppliers or synthesised; amino modification enables labeling with NHS-ester modified dyes</td>
		</tr>
		<tr>
			<td>Avalanche photodiode (APD)</td>
			<td>Excelitas</td>
			<td>SPCM-AQRH-14</td>
			<td>Two APDs are required for the smfBox setup</td>
		</tr>
		<tr>
			<td>Bovine Serum Albumin (BSA)</td>
			<td>Merck</td>
			<td>A2153</td>
			<td>System dependant; imaging buffer component (0.1 mg/mL in buffer)</td>
		</tr>
		<tr>
			<td>Compact Laser Combiner</td>
			<td>OMICRON</td>
			<td>LightHUB-2</td>
			<td>515 nm (80 mW) and 638 nm (100 mW) lasers</td>
		</tr>
		<tr>
			<td>Coverglass</td>
			<td>VWR</td>
			<td>630-2742</td>
			<td>Thickness: 0.17 &#177; 0.01 mm, LxW: 22x22 mm</td>
		</tr>
		<tr>
			<td>Cy3B</td>
			<td>Cytiva</td>
			<td>PA63101</td>
			<td>1 mg, PA63100 (5 mg), PA96106 (25 mg)</td>
		</tr>
		<tr>
			<td>FRETBursts Python Package</td>
			<td>N/A</td>
			<td>N/A</td>
			<td>Open-source python package for burst analysis of freely-diffusing single-molecule FRET data: https://fretbursts.readthedocs.io</td>
		</tr>
		<tr>
			<td>Imaging Buffer</td>
			<td>N/A</td>
			<td></td>
			<td>System dependant; 5 mM NaCl, 20 mM MgCl2, 5 mM Tris pH 7.5 and 0.1 mg/mL BSA</td>
		</tr>
		<tr>
			<td>Immersion Oil</td>
			<td>Olympus</td>
			<td>IMMOIL-F30CC</td>
			<td></td>
		</tr>
		<tr>
			<td>Jupyter notebooks</td>
			<td>Project Jupyter</td>
			<td>N/A</td>
			<td>Open-source web application to create and share documents that contain live code, equations, visualizations and text; data analysis notebooks for smfBox can be found in the SI</td>
		</tr>
		<tr>
			<td>Lens Tissue</td>
			<td>ThorLabs</td>
			<td>MC-5</td>
			<td>MC-50E is same item in bulk</td>
		</tr>
		<tr>
			<td>Magnesium Chloride</td>
			<td>Merck</td>
			<td>M2670</td>
			<td>System dependant; imaging buffer component (20 mM in buffer)</td>
		</tr>
		<tr>
			<td>MilliQ/Ultrapure water</td>
			<td>N/A</td>
			<td></td>
			<td></td>
		</tr>
		<tr>
			<td>Nanopoistioner</td>
			<td>Piezoconcept</td>
			<td>FOC300</td>
			<td>Nanopositioner for accurate positioning of microscope objective</td>
		</tr>
		<tr>
			<td>NHS-ester modified ATTO-550</td>
			<td>ATTO-TEC</td>
			<td>AD 550-31</td>
			<td>1 mg, AD 550-35 (5 mg)</td>
		</tr>
		<tr>
			<td>NHS-ester modified ATTO-647N</td>
			<td>ATTO-TEC</td>
			<td>AD 647N-31</td>
			<td>1 mg, AD 647N-35 (5 mg)</td>
		</tr>
		<tr>
			<td>Objective lens</td>
			<td>Olympus</td>
			<td>N1480700</td>
			<td>Olympus objective series from orignal smfBox discontinued; replaced by N5702300</td>
		</tr>
		<tr>
			<td>OMICRON Control Center (OCC)- laser control center</td>
			<td>OMICRON</td>
			<td>N/A</td>
			<td>v3.5.34 - OMICRON laser driver software</td>
		</tr>
		<tr>
			<td>Press-To-Seal silicone isolator</td>
			<td>Grace Bio-Labs</td>
			<td>664201</td>
			<td>&#160;8-9 mm Diameter x 1.7 mm Depth</td>
		</tr>
		<tr>
			<td>smOTTER</td>
			<td>N/A</td>
			<td>N/A</td>
			<td>Open-source acquisition software for the Craggs Lab smfBox: https://github.com/craggslab/smOTTER</td>
		</tr>
		<tr>
			<td>Sodium Chloride</td>
			<td>Merck</td>
			<td>S7653</td>
			<td>System dependant; imaging buffer component (5 mM in buffer)</td>
		</tr>
		<tr>
			<td>Tris base</td>
			<td>Merck</td>
			<td>93362</td>
			<td>System dependant; imaging buffer component (5 mM, pH 7.5 in buffer)</td>
		</tr>
		<tr>
			<td>Type I ultrapure water</td>
			<td>Merck</td>
			<td>ZIQ7000T0</td>
			<td>Milli-Q&#174; IQ 7000 Ultrapure Water System</td>
		</tr>
	</tbody>
</table>

making precise and accurate single-molecule fret measurements using the open-source smfbox

The smfBox is a recently developed cost-effective, open-source instrument for single-molecule F&#246;rster Resonance Energy Transfer (smFRET), which makes measurements on freely diffusing biomolecules more accessible. This overview includes a step-by-step protocol for using this instrument to make measurements of precise FRET efficiencies in duplex DNA samples, including details of the sample preparation, instrument setup and alignment, data acquisition, and complete analysis routines. The presented approach, which includes how to determine all the correction factors required for accurate FRET-derived distance measurements, builds on a large body of recent collaborative work across the FRET Community, which aims to establish standard protocols and analysis approaches. This protocol, which is easily adaptable to a range of biomolecular systems, adds to the growing efforts in democratising smFRET for the wider scientific community.

The protocol necessitates critical assessment of experimental conditions during setup (see protocol step 4.8). The first results acquired which determine success or failure of the experiment are achieved at this stage. A positive result would be to have between five and one bursts per second (see Figure 2B,C). A negative result would be having too many (Figure 2A) or too few bursts (Figure 2D) within that time frame. It remains possible at this stage to rectify these errors: a sample with too high a concentration needs simply to be diluted; if the concentration is too low, however, a new sample may need to be prepared (the determinant being whether it remains possible at this low concentration to collect data in a reasonable time frame).
<img alt="Figure 2" class="xfigimg" src="/files/ftp_upload/62378/62378fig02.jpg" /> 
Figure 2: Screen shots from live trace during experimental setup showing different concentrations of doubly labeled duplex DNA samples. (A) too high, (B) upper acceptable limit, (C) target concentration, (D) too low. Photon counts (1 ms bins) are shown in the three detection channels; donor emission after donor excitation (DD), acceptor emission after donor excitation (DA), and acceptor emission after acceptor excitation (AA). <a href="https://jove.unicartagenaproxy.elogim.com/files/ftp_upload/62378/62378fig02large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
A static single-species system would typically require 30 to 60 min of measurement to obtain the necessary ~1,000 bursts needed for robust data analysis. The length of time and number of bursts required will increase with multiple species or dynamic systems. Following data collection and analysis using the protocol figures are exported from the Jupyter notebooks. The alternation plot (Figure 3A) should match the ALEX period of the experimental setup. The time trace (Figure 3B) is used to qualitatively assess that the sample concentration is reasonable. The background plot (Figure 3C) shows the distribution of inter-photon delay periods with a linear fit to the longer times to estimate the background rate26. The background trace (Figure 3D) can identify if there were changes in the sample over the duration of the experiment; primarily this would be due to evaporation during longer acquisition times. ES histograms are generated for all photons (Figure 3E) and doubly labeled species (Figure 3F). Finally, a 1D E histogram (Figure 3G) is generated with gaussian fitting of the burst data.
<img alt="Figure 3" class="xfigimg" src="/files/ftp_upload/62378/62378fig03.jpg" /> 
Figure 3: Example figure output of analyzed data generated by the Jupyter Notebooks. (A) Alternation plot, (B) Time trace, (C) Background determination, (D) Background rates, (E) All photon ES histogram, (F) Dual channel ES histogram, and (G) 1D E histogram. <a href="https://jove.unicartagenaproxy.elogim.com/files/ftp_upload/62378/62378fig03large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
<table border="1" fo:keep-together.within-page="1" fo:keep-with-next.within-page="always">
	<tbody>
		<tr>
			<td>Name</td>
			<td>Sequence</td>
		</tr>
		<tr>
			<td rowspan="2">1a</td>
			<td>5&#8217;- GAG CTG AAA GTG TCG AGT TTG TTT GAG TGT TTG TCT GG - 3&#8217;</td>
		</tr>
		<tr>
			<td>3&#8217;- CTC GAC TTT CAC AGC TCA AAC AAA CTC ACA AAC AGA CC - 5&#8217;</td>
		</tr>
		<tr>
			<td rowspan="2">1b</td>
			<td>5&#8217;- GAG CTG AAA GTG TCG AGT TTG TTT GAG TGT TTG TCT GG - 3&#8217;</td>
		</tr>
		<tr>
			<td>3&#8217;- CTC GAC TTT CAC AGC TCA AAC AAA CTC ACA AAC AGA CC - 5&#8217;</td>
		</tr>
		<tr>
			<td rowspan="2">1c</td>
			<td>5&#8217;- GAG CTG AAA GTG TCG AGT TTG TTT GAG TGT TTG TCT GG - 3&#8217;</td>
		</tr>
		<tr>
			<td>3&#8217;- CTC GAC TTT CAC AGC TCA AAC AAA CTC ACA AAC AGA CC - 5&#8217;</td>
		</tr>
	</tbody>
</table>
Table 1: DNA sequences used in the protocol. Nucleotides are highlighted in blue and red representing C2 amino modified thymine residues labeled with Atto-550 and Atto-647N, respectively.
Correction Factor Finder Alpha-Delta: <a href="https://jove.unicartagenaproxy.elogim.com/files/ftp_upload/62378/Correction Factor Finder Alpha-Delta.ipynb" target="_blank">Please click here to download this File.</a>
Correction Factor Finder Gamma-Beta:&#160;<a href="https://jove.unicartagenaproxy.elogim.com/files/ftp_upload/62378/Correction Factor Finder Gamma-Beta.ipynb" target="_blank">Please click here to download this File.</a>
FRET Analysis 1.4 Corrected:&#160;<a href="https://jove.unicartagenaproxy.elogim.com/files/ftp_upload/62378/FRET Analysis 1.4 Corrected.ipynb" target="_blank">Please click here to download this File.</a>
FRET Analysis 1.4 Uncorrected:&#160;<a href="https://jove.unicartagenaproxy.elogim.com/files/ftp_upload/62378/FRET Analysis 1.4 Uncorrected.ipynb" target="_blank">Please click here to download this File.</a>

Watch this Scientific Journal Video about Making Precise and Accurate Single-Molecule FRET Measurements using the Open-Source smfBox at JoVE.com

Making Precise and Accurate Single-Molecule FRET Measurements using the Open-Source smfBox

This article provides step-by-step instructions for making fully-corrected accurate FRET measurements on individual, freely diffusing biomolecules using the open-source, inexpensive smfBox, from switch on, through alignment and focusing, to data collection and analysis.

The smfBox is a recently developed cost-effective, open-source instrument for single-molecule F&#246;rster Resonance Energy Transfer (smFRET), which makes ...

making-precise-accurate-single-molecule-fret-measurements-using-open

Universidad de Cartagena UDEC

Research

JoVE Journal

Biochemistry

3.3K Views.  University of Sheffield. This article provides step-by-step instructions for making fully-corrected accurate FRET measurements on individual, freely diffusing biomolecules using the open-source, inexpensive smfBox, from switch on, through alignment and focusing, to data collection and analysis.

Video: Making Precise and Accurate Single-Molecule FRET Measurements using the Open-Source smfBox

Structural Information from Single-molecule FRET Experiments Using the Fast Nano-positioning System

Single-molecule F&#246;rster Resonance Energy Transfer (smFRET) can be used to obtain structural information on biomolecular complexes in real-time. Thereby, multiple smFRET measurements are used to localize an unknown dye position inside a protein complex by means of trilateration. In order to obtain quantitative information, the Nano-Positioning System (NPS) uses probabilistic data analysis to combine structural information from X-ray crystallography with single-molecule fluorescence data to calculate not only the most probable position but the complete three-dimensional probability distribution, termed posterior, which indicates the experimental uncertainty. The concept was generalized for the analysis of smFRET networks containing numerous dye molecules. The latest version of NPS, Fast-NPS, features a new algorithm using Bayesian parameter estimation based on Markov Chain Monte Carlo sampling and parallel tempering that allows for the analysis of large smFRET networks in a comparably short time. Moreover, Fast-NPS allows the calculation of the posterior by choosing one of five different models for each dye, that account for the different spatial and orientational behavior exhibited by the dye molecules due to their local environment.
Here we present a detailed protocol for obtaining smFRET data and applying the Fast-NPS. We provide detailed instructions for the acquisition of the three input parameters of Fast-NPS: the smFRET values, as well as the quantum yield and anisotropy of the dye molecules. Recently, the NPS has been used to elucidate the architecture of an archaeal open promotor complex. This data is used to demonstrate the influence of the five different dye models on the posterior distribution.

We present the setup and experimental procedure to obtain smFRET data from large donor-acceptor networks with a TIRF microscope. The step-by-step analysis of these measurements with the Bayesian inference software Fast-NPS yields high-resolved structural information via the application of adapted dye models.

Single-molecule F&#246;rster Resonance Energy Transfer (smFRET) can be used to obtain structural information on biomolecular complexes in real-time. ...

High Precision FRET at Single-molecule Level for Biomolecule Structure Determination

A protocol on how to perform high-precision interdye distance measurements using F&#246;rster resonance energy transfer (FRET) at the single-molecule level in multiparameter fluorescence detection (MFD) mode is presented here. MFD maximizes the usage of all &#34;dimensions&#34; of fluorescence to reduce photophysical and experimental artifacts and allows for the measurement of interdye distance with an accuracy up to ~1 &#197; 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 &#197; 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 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 F&#246;rster resonance energy transfer (FRET) at the single-molecule ...

Using Three-color Single-molecule FRET to Study the Correlation of Protein Interactions

Single-molecule F&#246;rster 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&#160;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.

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 F&#246;rster resonance energy transfer (smFRET) has become a widely used biophysical technique to study the dynamics of biomolecules. For ...

Calibration-free In Vitro Quantification of Protein Homo-oligomerization Using Commercial Instrumentation and Free, Open Source Brightness Analysis Software

Number and brightness is a calibration-free fluorescence fluctuation spectroscopy (FFS) technique for detecting protein homo-oligomerization. It can be employed using a conventional confocal microscope equipped with digital detectors. A protocol for the use of the technique in vitro is shown by means of a use case where number and brightness can be seen to accurately quantify the oligomeric state of mVenus-labelled FKBP12F36V before and after the addition of the dimerizing drug AP20187. The importance of using the correct microscope acquisition parameters and the correct data preprocessing and analysis methods are discussed. In particular, the importance of the choice of photobleaching correction is stressed. This inexpensive method can be employed to study protein-protein interactions in many biological contexts.

Calibration-free In Vitro Quantification of Protein Homo-oligomerization Using Commercial Instrumentation and Free, Open Source Brightness Analysis Software

This protocol describes a calibration-free approach for quantifying protein homo-oligomerization in vitro based on fluorescence fluctuation spectroscopy using commercial light scanning microscopy. The correct acquisition settings and analysis methods are shown.

Number and brightness is a calibration-free fluorescence fluctuation spectroscopy (FFS) technique for detecting protein homo-oligomerization. It can be ...

Covalent Immobilization of Proteins for the Single Molecule Force Spectroscopy

In recent years, atomic force microscopy (AFM) based single molecule force spectroscopy (SMFS) extended our understanding of molecular properties and functions. It gave us the opportunity to explore a multiplicity of biophysical mechanisms, e.g., how bacterial adhesins bind to host surface receptors in more detail. Among other factors, the success of SMFS experiments depends on the functional and native immobilization of the biomolecules of interest on solid surfaces and AFM tips. Here, we describe a straightforward protocol for the covalent coupling of proteins to silicon surfaces using silane-PEG-carboxyls and the well-established N-hydroxysuccinimid/1-ethyl-3-(3-dimethyl-aminopropyl)carbodiimid (EDC/NHS) chemistry in order to explore the interaction of pilus-1 adhesin RrgA from the Gram-positive bacterium Streptococcus pneumoniae (S. pneumoniae) with the extracellular matrix protein fibronectin (Fn). Our results show that the surface functionalization leads to a homogenous distribution of Fn on the glass surface and to an appropriate concentration of RrgA on the AFM cantilever tip, apparent by the target value of up to 20% of interaction events during SMFS measurements and revealed that RrgA binds to Fn with a mean force of 52 pN. The protocol can be adjusted to couple via site specific free thiol groups. This results in a predefined protein or molecule orientation and is suitable for other biophysical applications besides the SMFS.

This protocol describes the covalent immobilization of proteins with a heterobifunctional silane coupling agent to silicon-oxide surfaces designed for the atomic force microscopy based single molecule force spectroscopy which is exemplified by the interaction of RrgA (pilus-1 tip adhesin of S. pneumoniae) with fibronectin.

In recent years, atomic force microscopy (AFM) based single molecule force spectroscopy (SMFS) extended our understanding of molecular properties and ...

Proteome-wide Quantification of Labeling Homogeneity at the Single Molecule Level

Cell proteomes are often characterized using electrophoresis assays, where all species of proteins in the cells are non-specifically labeled with a fluorescent dye and are spotted by a photodetector following their separation. Single molecule fluorescence imaging can provide ultrasensitive protein detection with its ability for visualizing individual fluorescent molecules. However, the application of this powerful imaging method to electrophoresis assays is hampered by the lack of ways to characterize the homogeneity of fluorescent labeling of each protein species across the proteome. Here, we developed a method to evaluate the labeling homogeneity across the proteome based on a single molecule fluorescence imaging assay. In our measurement using a HeLa cell sample, the proportion of proteins labeled with at least one dye, which we termed &#8216;labeling occupancy&#8217; (LO), was determined to range from 50% to 90%, supporting the high potential of the application of single molecule imaging to sensitive and precise proteome analysis.

Here, we present a protocol to assess the labeling homogeneity for each protein species in a complex protein sample at the single molecule level.

Cell proteomes are often characterized using electrophoresis assays, where all species of proteins in the cells are non-specifically labeled with a ...

Using the Open-Source MALDI TOF-MS IDBac Pipeline for Analysis of Microbial Protein and Specialized Metabolite Data

In order to visualize the relationship between bacterial phylogeny and specialized metabolite production of bacterial colonies growing on nutrient agar, we developed IDBac&#8212;a low-cost and high-throughput matrix-assisted laser desorption/ionization time-of-flight mass spectrometry (MALDI-TOF MS) bioinformatics pipeline. IDBac software is designed for non-experts, is freely available, and capable of analyzing a few to thousands of bacterial colonies. Here, we present procedures for the preparation of bacterial colonies for MALDI-TOF MS analysis, MS instrument operation, and data processing and visualization in IDBac. In particular, we instruct users how to cluster bacteria into dendrograms based on protein MS fingerprints and interactively create Metabolite Association Networks (MANs) from specialized metabolite data.

IDBac is an open-source mass spectrometry-based bioinformatics pipeline that integrates data from both intact protein and specialized metabolite spectra, collected on cell material scraped from bacterial colonies. The pipeline allows researchers to rapidly organize hundreds to thousands of bacterial colonies into putative taxonomic groups, and further differentiate them based on specialized metabolite production.

In order to visualize the relationship between bacterial phylogeny and specialized metabolite production of bacterial colonies growing on nutrient agar, ...

Shotgun Proteomics Sample Processing Automated by an Open-Source Lab Robot

Mass spectrometry-based shotgun proteomics experiments require multiple sample preparation steps, including enzymatic protein digestion and clean-up, which can take up significant person-hours of bench labor and present a source of batch-to-batch variability. Lab automation with pipetting robots can reduce manual work, maximize throughput, and increase research reproducibility. Still, the steep starting prices of standard automation stations make them unaffordable for many academic laboratories. This article describes a proteomics sample preparation workflow using an affordable, open-source automation system (The Opentrons OT-2), including instructions for setting up semi-automated protein reduction, alkylation, digestion, and clean-up steps; as well as accompanying open-source Python scripts to program the OT-2 system through its application programming interface.

Detailed protocol and three Python scripts are provided for operating an open-source robotic liquid handling system to perform semi-automated protein sample preparation for mass spectrometry experiments, covering detergent removal, protein digestion, and peptide desalting steps.

Mass spectrometry-based shotgun proteomics experiments require multiple sample preparation steps, including enzymatic protein digestion and clean-up, ...

Visualizing the Conformational Dynamics of Membrane Receptors Using Single-Molecule FRET

The ability of cells to respond to external signals is essential for cellular development, growth, and survival. To respond to a signal from the environment, a cell must be able to recognize and process it. This task mainly relies on the function of membrane receptors, whose role is to convert signals into the biochemical language of the cell. G protein-coupled receptors (GPCRs) constitute the largest family of membrane receptor proteins in humans. Among GPCRs, metabotropic glutamate receptors (mGluRs) are a unique subclass that function as obligate dimers and possess a large extracellular domain that contains the ligand-binding site. Recent advances in structural studies of mGluRs have improved the understanding of their activation process. However, the propagation of large-scale conformational changes through mGluRs during activation and modulation is poorly understood. Single-molecule fluorescence resonance energy transfer (smFRET) is a powerful technique to visualize and quantify the structural dynamics of biomolecules at the single-protein level. To visualize the dynamic process of mGluR2 activation, fluorescent conformational sensors based on unnatural amino acid (UAA) incorporation were developed that allowed site-specific protein labeling without perturbation of the native structure of receptors. The protocol described here explains how to perform these experiments, including the novel UAA labeling approach, sample preparation, and smFRET data acquisition and analysis. These strategies are generalizable and can be extended to investigate the conformational dynamics of a variety of membrane proteins.

This study presents a detailed procedure to perform single-molecule fluorescence resonance energy transfer (smFRET) experiments on G protein-coupled receptors (GPCRs) using site-specific labeling via unnatural amino acid (UAA) incorporation. The protocol provides a step-by-step guide for smFRET sample preparation, experiments, and data analysis.

The ability of cells to respond to external signals is essential for cellular development, growth, and survival. To respond to a signal from the ...

A Python-Based Tool for Accurate Mass Calculation of Biotherapeutic Entities

An Open-Source Framework for Mass Calculation of Antibody-Based Therapeutic Molecules

Biotherapeutic masses are a means of verifying identity and structural integrity. Mass spectrometry (MS) of intact proteins or protein subunits provides an easy analytical tool for different stages of biopharmaceutical development. The protein's identity is confirmed when the experimental mass from MS is within a pre-defined mass error range of the theoretical mass. While several computational tools exist for the calculation of protein and peptide molecular weights, they either were not designed for direct application to biotherapeutic entities, have access limitations due to paid licenses, or require uploading protein sequences to host servers. 
We have developed a modular mass calculation routine that enables the easy determination of the average or monoisotopic masses and elemental compositions of therapeutic glycoproteins, including monoclonal antibodies (mAb), bispecific antibodies (bsAb), and antibody-drug conjugates (ADC). The modular nature of this Python-based calculation framework will allow the extension of this platform to other modalities such as vaccines, fusion proteins, and oligonucleotides in the future, and this framework could also be useful for the interrogation of top-down mass spectrometry data. By creating an open-source standalone desktop application with a graphical user interface (GUI), we hope to overcome the restrictions around use in environments where proprietary information cannot be uploaded to web-based tools. This article describes the algorithms and application of this tool, mAbScale, to different antibody-based therapeutic modalities.

Author Spotlight: Advancing Biotherapeutic Mass Calculation by Introducing mAbScale, a Python-Based Desktop Application

This article describes the use of a software application, mAbScale, for the calculation of masses for monoclonal antibody-based protein therapeutics.

Biotherapeutic masses are a means of verifying identity and structural integrity. Mass spectrometry (MS) of intact proteins or protein subunits provides ...

Making Precise and Accurate Single-Molecule FRET Measurements using the Open-Source smfBox

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Structural Information from Single-molecule FRET Experiments Using the Fast Nano-positioning System

High Precision FRET at Single-molecule Level for Biomolecule Structure Determination

Using Three-color Single-molecule FRET to Study the Correlation of Protein Interactions

Calibration-free In Vitro Quantification of Protein Homo-oligomerization Using Commercial Instrumentation and Free, Open Source Brightness Analysis Software

Covalent Immobilization of Proteins for the Single Molecule Force Spectroscopy

Proteome-wide Quantification of Labeling Homogeneity at the Single Molecule Level

Using the Open-Source MALDI TOF-MS IDBac Pipeline for Analysis of Microbial Protein and Specialized Metabolite Data

Shotgun Proteomics Sample Processing Automated by an Open-Source Lab Robot

Visualizing the Conformational Dynamics of Membrane Receptors Using Single-Molecule FRET

An Open-Source Framework for Mass Calculation of Antibody-Based Therapeutic Molecules