Name: determination of protein-ligand interactions using differential scanning fluorimetry
Uploaded: 2014-09-13T15:00:00
Duration: 13 min 26 s
Description: Watch this Scientific Journal Video about Determination of Protein-ligand Interactions Using Differential Scanning Fluorimetry at JoVE.com

This work was funded by grant from the BBSRC (grant number BB/H019685/1 and BB/E527663/1) to the University of Exeter.

1. Determination of an Approximate Value for the Dissociation Constant (i.e., Within One Order of Magnitude)
<ol>
	<li>Prepare the mixture detailed in Table 1.</li>
	<li>Prepare stocks of the ligand of interest at the highest available concentration, and then at six ten fold dilutions of this. Where an approximate Kd is known from prior data, aim to have at least two concentrations above and below the Kd.</li>
	<li>Aliquot 18 &#181;l of the mixture into eight wells in a qPCR plate. Add 2 &#181;l of solvent to the first well. Add 2 &#181;l of each member of the ligand dilution series (step 1.2) to one well each of the remaining seven wells.</li>
	<li>Place a qPCR seal over the plate. To achieve a good seal of the plate, place a hand applicator (see tables of specific reagents) in the middle of the plate. Smooth down the seal to one side, and then repeat on the other half of the plate.</li>
	<li>Centrifuge the plate at 500 x g for two min to remove air bubbles.</li>
	<li>Place the plate in a StepOne qPCR instrument. Select the "Melt curve" option, the ROX filters, and choose the fast ramp speed (this provides a 2 min pause at 25 &#176;C, followed by a ramp to 99 &#176;C over 40 min, and then a 2 min pause). Run a thermal denaturation. 
		NOTE: Script files for performing a run are available online at http:// <a href="http://www.exeter.ac.uk/biosciences/capsular">www.exeter.ac.uk/biosciences/capsular.</a></li>
	<li>At the conclusion of the instrument run, click on the "Analyze" button on the screen. Save the result file.</li>
	<li>Open the Protein Thermal Shift software.
		<ol>
			<li>Create a new study; in the properties tab, give this a name, and in the Conditions tab, detail the ligands.</li>
			<li>Move to the Experiment Files tab, and import the saved results file (XXX.eds), and set the contents of each well (template files are available from the authors).</li>
			<li>Move to the Analysis tab, and press the "Analyze" button. 
				NOTE: This will analyze the results. It is possible to export the results for further investigation with Excel using the Export tab. The results are exported in a tab delineated format. It is best to open the exported file in Excel, and immediately save in Excel format.</li>
		</ol>
	</li>
	<li>Check that the protein in the presence of solvent alone gives a result similar to that shown in Figure 1A. Next, examine the melting temperatures observed in the results in the "replicate" pane. Ensure that this shows a clear increase in melting temperature with increasing ligand concentration. 
		NOTE: Ideally, this will provide a clear maximum melting temperature (assuming that the protein is fully ligand bound), and an approximate Kd where the melting temperature is half way between the ligand-free protein and the maximum.</li>
</ol>
2. Experimental Set-up for Determining the Dissociation Constant
<ol>
	<li>Prepare the mixture detailed in Table 2 as a master mix.</li>
	<li>Prepare stocks of the ligand at fifteen different concentrations, which will be diluted ten fold in the final experiment. Ideally, include concentrations at least two orders of magnitude above and below the estimated Kd, and center the concentrations on the estimated Kd. Focus on seven of the points within an order of magnitude of the estimated Kd, with another four points on either side of this; if there is a choice, include more points at values that are saturating. 
		NOTE: If necessary, it is feasible to alter the experimental conditions such that the ligand stocks are at double the experimental concentration, where ligand solubility is limiting.</li>
	<li>Add 120 &#181;l of the master mix to eight wells in a U-bottomed 96 well plate, to act as a reservoir for convenient dispensing of the master mix. Use an 8 channel pipette to dispense 18 &#181;l into one column of a PCR plate. Repeat for a further five columns, to give a total of 48 filled wells in a 6 x 8 pattern on the plate.</li>
	<li>Add 20 &#181;l of the ligand stocks, or the solvent, to separate wells in a U-bottomed 96 well plate. Using an 8 channel pipette, aspirate 2 &#181;l of eight different ligand stocks (or solvent). Add these to one column of the PCR plate that was filled with master mix in step 2.3. Repeat with the same eight ligand/solvent stocks for two further columns. Aspirate 2 &#181;l of the remaining eight ligand or solvent stocks, and add these to a fourth column in the plate. Repeat this for two further columns. This will give triplicate samples for all 16 ligand and solvent samples.</li>
	<li>Place a qPCR seal over the plate (see step 1.4).</li>
	<li>Centrifuge the plate at 500 x g for two min.</li>
	<li>Place the plate in the qPCR instrument. Run a thermal denaturation using the parameters specified in step 1.6.</li>
	<li>At the conclusion of the instrument run, click on the "Analyze" button on the screen. Save the result file.</li>
	<li>Open the Protein Thermal Shift software. Create a new study; in the properties tab, give this a name, and in the Conditions tab, detail the ligands.</li>
	<li>Move to the Experiment Files tab, and import the saved results file (XXX.eds), and set the contents of each well. 
		NOTE: template files are available online at http:// <a href="http://www.exeter.ac.uk/biosciences/capsular">www.exeter.ac.uk/biosciences/capsular.</a></li>
	<li>Move to the Analysis tab, and press the "Analyze" button.
		<ol>
			<li>Choose the "Replicates" tab from the menu on the left hand side of the screen to show the results as triplicates. Assess the reliability of the data based on how tight the triplicates are. Should the triplicates show poor reproducibility, examine the raw data closely.</li>
			<li>Analyze the data using both the Boltzmann or Derivative methods to assess the melting temperature. Select the "Replicate results" tab, and in the "Replicate results plot", toggle the "Plot by:" button between "Tm &#8211; Boltzmann" and "Tm &#8211; Derivative". Select the method that gives the greater reproducibility for the sample. Export the results for further investigation with Excel using the Export tab. 
				NOTE: For samples that show multiple transitions, it is almost always best to use the Derivative method in multiple melt mode. The results are exported in a tab delineated format. It is best to open the exported file in Excel, and immediately save in Excel format.</li>
			<li>Repeat the experiment at least twice, including a repeat on a separate day, to ensure reproducibility of the results. Should data analysis (see step 3 below) indicate that the value of Kd is significantly different to the original estimate, alter the ligand concentrations accordingly (see step 2.2) to ensure a good range of values around Kd.</li>
		</ol>
	</li>
</ol>
3. Data Analysis to Determine the Dissociation Constant under Thermal Denaturation
<ol>
	<li>Create a table in Excel of the ligand concentrations and the melting temperature.</li>
	<li>Open the GraphPad Prism software, and create an XY table. Enter the data, using the X column for the ligand concentrations and the Y column for melting temperature results. 
		NOTE: an example shown is Figure 1B. A script with equations preloaded, and alternative instructions for using the SPSS statistical package, are available online at http:// <a href="http://www.exeter.ac.uk/biosciences/capsular">www.exeter.ac.uk/biosciences/capsular.</a></li>
	<li>In the Analysis tab, select the option to change analysis parameters (Ctrl+T). To enter the correct model, select "New", and "Create new equation". Insert the equation detailed in Table 3 as "Single site ligand binding". 
		NOTE: An example of these steps is shown in Figure 1C. When using the script with equations preloaded, the relevant equation can be directed chosen from the list rather than entered. Derivation of this equation is provided in an Appendix.</li>
	<li>Select the "Rules for Initial Values" box, and enter rules for initial values as detailed in Table 3.</li>
	<li>Constrain the parameter P, as "Constant equal to" and enter the final concentration of protein (in the same units as the ligand is given in).</li>
	<li>Select OK to perform the analysis. 
		NOTE: An example of these steps is shown in Figure 1D. The graphing software produces a figure showing the data and the fit to the model. Examples of these analyses are shown in the representative data.</li>
</ol>
4. Fitting Data to Cooperative Models
To fit data to a cooperative model, choose between either a simple cooperative model, or a model where two separate dissociation constants are defined. The first approach is preferred in the case of negative cooperativity, or as an initial investigation. However, in principle it is better in cases of positive cooperativity to model two different dissociation constants 18. In this case, modelling can proceed assuming either sequential binding of ligands, or independent binding of ligands.
<ol>
	<li>Follow the same initial steps as in protocol 3. However, at step 3.3, insert one of the equations in Table 3 listed as "Simple cooperative model", "Sequential binding of two ligands", or "Independent binding of two ligands" 18.</li>
	<li>Select the relevant rules for initial values associated with each of these equations in Table 3.</li>
	<li>Examine the fit of the model to the data. Should the data fit poorly, consider another model. 
		NOTE: it is also important to carefully examine the fitting of the melting temperature to the data by the Protein Thermal Shift software (step 2.9): sometimes it is necessary to alter the parameters here to get the best results. Another consideration is whether the range of data points is ideal, and whether there are any anomalous points: either a limited set of data at either side of Kd, or a single anomalous point (especially at the highest ligand concentrations), can significantly affect the results.</li>
	<li>Repeat the experiment at least twice (see step 2.12) to ensure reproducibility.</li>
</ol>
5. Fitting Data to Curves Showing Binary Shifts in Melting Temperature
Occasionally, rather than a graded response to ligand, proteins have been observed to adopt a binary response, where bound sample is clearly separated from unbound sample. An example is provided in the representative results (Figure 4). In this case, fitting of the melting temperatures will not provide a good fit for Kd.
<ol>
	<li>Export the raw data output from the Protein Thermal Shift software. For each temperature point, calculate the mean fluorescence for the zero ligand, and highest ligand concentrations. Tabulate the results from each data point next to these. 
		NOTE: The error created here is less than the error in the fitted melting temperatures.</li>
	<li>Open the SPSS statistical package. Copy the temperatures, the two mean datasets, and data for each experiment to a data window in SPSS. In the variable tab, set the mean dataset for no ligand as "low", and the mean dataset for the highest ligand concentration as "high".</li>
	<li>Download the syntax file available online at at http:// <a href="http://www.exeter.ac.uk/biosciences/capsular">www.exeter.ac.uk/biosciences/capsular</a>. Select "Run &#8594; Run all".</li>
	<li>Copy the proportion bound results to a new Excel workbook, with the relevant ligand concentrations.</li>
	<li>Open the Graphpad software, and create an XY table. Enter the data, using the X column for the ligand concentrations and the Y column for melting temperature results. In the analysis tab, select "change analysis parameters". To enter the correct model, select "New", and "Create new equation". Enter the equation given in Table 3, listed as "Analysis of binary shifts in melting temperature".</li>
	<li>Select the "Rules for Initial Values" box, and enter rules for initial values detailed in Table 3. Constrain the parameter P, as "Constant equal to" and enter the final concentration of protein (in the same units as the ligand is given in). 
		NOTE: examples of completing these boxes for the protocol in section 3 are shown in Figure 1C, D.</li>
	<li>If there is a good fit, the results can be improved by extrapolating to the expected result at infinite ligand concentration. From the model of the proportion bound at each ligand concentration, examine the value for the highest value of ligand concentration. If this is 0.99 or greater, further analysis is unlikely to improve results.</li>
	<li>If the proportion is less than 0.99, an additional step is required to correct for the effects of the unbound protein in the highest ligand concentration sample. At step 5.2, write the proportion of ligand bound at the highest ligand concentration point (from step 5.7) into cell R2 (a different cell may be used, and R2 replaced appropriately in the equation in Table 3). Create an extra column after the mean of the highest ligand concentration results. In the first cell, copy the equation listed in Table 3 as "Extrapolation to infinite ligand concentration". Copy this formula to the remaining cells in this column. 
		NOTE: This calculation removes the effect of unbound protein in the highest ligand concentration. The difference between the ligand free protein and the highest ligand concentration is multiplied by the reciprocal of the proportion bound at the highest ligand concentration to provide the expected difference between fully bound and unbound protein states at each temperature point. This difference is added or subtracted from the unbound state to give the expected fluorescence for fully ligand bound protein.</li>
	<li>Replace the column for maximum ligand concentration in the SPSS datasheet with this new column, and repeat the data fitting. 
		NOTE: steps 5.7 - 5.9 may need to be repeated if the model suggests a further significant change in the proportion bound at the maximum ligand concentration (if this is the case, it would probably be ideal to repeat the experiment with a higher ligand concentration point included).</li>
	<li>In cases where the protein shows a binary shift and displays co-operative behavior, the equation suggested in step 5.5 should be substituted with those from step 4.1. The "Top" and "Bottom" parameters should be replaced by 1 and 0 respectively.</li>
	<li>Repeat the experiment at least twice (see step 2.12) to ensure reproducibility.</li>
</ol>

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The authors declare that they have nothing to disclose.

Differential scanning fluorimetry has demonstrated its power as a robust and versatile method for characterizing proteins, and identifying potential protein ligands. The well documented successes in expediting protein stabilization, drug discovery (especially in less well financed laboratories) and crystallization 10,23-25 have made it an attractive method for initial screening of compounds. Compounds added to proteins show a clear dose dependent increase in the apparent melting temperature 7,9. However, there have been few attempts to use the results from these experiments to determine apparent binding constants to aid in ranking compounds for their affinity. Here, we present a method for systematically determining an apparent dissociation constant for proteins in the presence of a ligand.
The results presented here demonstrate that DSF can rapidly and robustly provide estimates of the dissociation constant for a protein-ligand combination. The observed data can be manipulated with commercially available tools to provide a rapid determination of Kd, without the need to make assumptions regarding the likely value of parameters. The method has a significant advantage over some comparable methods of being parsimonious in both protein and time required. The experiment described here will consume 0.13 mg of protein per experiment (approximately 0.4 mg for experiments repeated in triplicate). This compares favorably with isothermal titration calorimetry (ITC), where a single experiment with an average 40 kDa protein will consume a similar amount. The full set of experiments required for this protocol would consume around 4 hr, including preparation, for a single set of experiments. Again, this is likely to be considerably quicker than methods such as ITC or surface plasmon resonance, which whilst powerful often require considerable optimization to achieve best data.
Our results demonstrate that there remains a requirement to carefully examine the raw data, the fit of these data to determine the melting temperature, and the fit of the melting temperature data to determine the dissociation constant. A first challenge is the shape of the raw data produced in the protein melting. In some cases, the shape may not approximate to that observed in Figure 1A. Common issues include low temperature shifts on ligand binding, high background fluorescence, and unusual multiple transitions in temperature. Low temperature shifts are seen on binding a number of ligands. For this method, the most critical parameter is the error in the Tm measurement, compared to the temperature shift. The data can usually be fitted reasonably well when the standard deviation of triplicate measurements do not exceed 10% of the melting temperature shift between unbound and fully bound protein. Our experience is that where such temperature shifts are only 2 &#176;C, this can be sufficient for fitting the data, if the individual data points are highly accurate. A second issue is unusually shaped curves. These often differ between free protein and ligand bound forms, as the ligand binding affects unfolding modes of the protein. In these cases, the user must consider whether the data can be used with appropriate consideration of the models to be used for determining the melting temperature and the dissociation constant. Another common issue is that addition of a cofactor to the protein (e.g., MgCl2 in our example with hexokinase) is required to obtain the most reliable data. Our experience has been that careful consideration of all likely factors in the experiment at the stage of taking initial readings is essential to obtaining the best results. Furthermore, alternative theoretical treatments can reveal features of these data 15,17. Finally, it is not uncommon for some proteins that contain natively exposed hydrophobic regions to show high background fluorescence. There are a number of solutions to these problems, which have been extensively reviewed elsewhere6,9.
In particular, the user must consider whether to use the Boltzmann or derivative models (e.g., Figure 4), and in the case of use of derivatives, whether multiple melts must be modelled. The two methods of modeling the thermal unfolding differ in that the Boltzmann method fits the experimental data to the Boltzmann equation, assuming a regular sigmoidal shape to the unfolding curve. In contrast, the derivative method takes the first derivative of the experimental data at each point (lower panel in Figure 1A), and considers the melting temperature to be the point of highest first derivative. The derivative method generally returns a higher melting temperature by around 2 - 3 &#176;C. Most proteins will return a more consistent result (i.e., the standard error of the melting temperature for triplicate experiments is lower) for one of the two methods. This is usually intimately related to the precise shape of the protein unfolding curve, and it is necessary to empirically determine the best method in each case. Where the derivative model is used, it is also important to consider multiple melting events. Some data clearly show evidence for multiple transitions, and in these cases the results are likely to be easier to interpret if these multiple melting events are modelled. In the context of this protocol, it is often the case that the addition of ligand can cause a protein to shift from having multiple melting transitions to a single transition (e.g., by stabilizing the most thermally fragile subdomain), or vice versa. We would therefore advocate that the raw data are examined together before considering which approach will be best to use.
Following the modelling of the individual melting temperatures, further issues can arise in fitting these to the models presented in the protocol section. It is imperative to carefully examine the fit to the dissociation constant equation using a logarithmic scale, as this analysis often highlights discrepancies between the observed data and the model (e.g., Figure 3). Whilst the results obtained are generally robust, care in interpretation offers the opportunity to extract better results, and the most meaning, from the data.
A particular issue raised by these data is the interpretation that should be placed on proteins that show cooperativity, or multiple binding events, in DSF. We have, to date, only observed this phenomenon in proteins that are expected to have multiple specific binding events (e.g., WcbM, a protein whose best homologue is a multimer 26, and which acts as a multimer on size exclusion chromatography [data not shown]). It is not at all clear that the negative cooperativity observed in DSF denaturation indicates that the enzyme will ultimately show negative cooperativity: rather, this may be an indication of complex binding that must be explored more thoroughly using a wider range of methods. This does suggest to us, however, that more extensive studies of such proteins are likely to identify interesting effects.
The values given for the dissociation constant using this method are generally of the same order as those provided by other methods, such as isothermal titration calorimetry and surface plasmon resonance. However, the absolute values observed are frequently higher than observed using these methods. This is at least partly a consequence of the fact that the dissociation constant is observed at the melting temperature of the protein with ligand. This Kd is generally higher than that at physiological temperatures. The dissociation constant is related to the temperature of the reaction by the equations:
<img alt="Equation 1" src="/files/ftp_upload/51809/51809eq1.jpg" />[1]
<img alt="Equation 2" src="/files/ftp_upload/51809/51809eq2.jpg" /> [2]
(where c&#952; is the standard reference concentration, &#916;rG is the Gibbs free energy change of the reaction, R is the molar gas constant, &#916;H is the enthalpy change in the reaction, and &#916;S is the entropy change in the reaction.)
Reactions with dissociation constants in the measurable range of this method will generally have a negative &#916;rG, and so the effect of an increase in temperature on equation [1] will be to increase the dissociation constant. Both the &#916;H and &#916;S terms that constitute the Gibbs free energy (equation [2]) are temperature dependent27, and the effect on the dissociation constant will depend on the magnitude and sign of these temperature dependencies, and will necessarily be interaction dependent. Consequently, it is not unexpected that the dissociation constants determined by this method are sometimes greater than those determined by methods that operate at RT. Temperature dependence is, of course, also a caveat of many other methods, which tend to provide the dissociation constant at temperatures lower than the physiological temperature.
Another caveat of the DSF method is that it is a labeled method, unlike ITC. The fluorescent label used (SYPRO Orange) is hydrophobic, and so in some cases can compete with the binding of hydrophobic ligands to proteins. Consequently, it is likely that in some cases, the dissociation constant obtained will be artificially raised due to competition with the label. However, for the comparison of diverse ligands, (the primary use of DSF), the differences are unlikely to be sufficiently significant to affect the ranking of compounds by affinity.
A potential drawback of this method is the limit of detection that can be achieved. In principle, it should not be possible to accurately measure a value for Kd that is lower than 50% of the protein concentration, and even values in this range are likely to be of dubious accuracy. Whilst the limit of detection at this end of the range may be extended a little by reducing the concentrations of protein and dye, the sensitivity of the instrument will prevent further reduction in protein concentration. Similarly, the upper end of the sensitivity will be determined by the solubility of the ligand. To obtain a mathematically robust estimate for Kd, it is most important to obtain data with 90% of the protein present in the ligand-bound form, which requires ligand concentrations to be approximately ten times Kd (assuming no cooperativity). The limit of detection will therefore necessarily be one tenth of the solubility of the ligand in the relevant buffer. This means that the limits of detection of the method will typically range between 1 &#181;M and between 1 and 100 mM, depending on the protein and ligand.
In conclusion, differential scanning fluorimetry is a versatile technique applicable to a wide range of proteins. Using the methods presented here, it is possible to rapidly and inexpensively determine the affinity of a protein for different ligands. This has great potential for application in protein purification and stabilization, elucidating the function or specificity of enzymes from metagenomes, and in drug discovery, especially in small laboratories.

All proteins will bind, with varying affinities, to a diverse range of other molecules from simple ions to other large macromolecules. In many cases, proteins bind to small molecule partners as part of their normal function (e.g., a kinase binding to ATP). Other interactions may be unrelated to function, but are experimentally useful as tools (e.g., small molecules that stabilize proteins to improve crystallization success, or assist in maintaining proteins in solution); whilst small molecules that bind to active sites and allosteric sites of proteins can act as inhibitors, and so modulate the activity of enzymes.
There are a wide range of techniques that can be used to determine the affinity of proteins for partner molecules. Isothermal titration calorimetry 1 is widely viewed as a &#8220;gold-standard&#8221;, as it provides rich information on reactions, is label free, and has limited opportunities for artifacts of the experiment. However, despite recent improvements in the sensitivity of the instrumentation and automation of experimental set-up, it is still relatively expensive in terms of protein requirements, has at best a low-to-medium throughput, and is best suited to interactions with moderate to high affinities (10 nM to 100 &#181;M Kd) 2. Other label free methods such as surface plasmon resonance or bilayer interferometry 3 offer higher throughputs, and have achieved the sensitivity to detect smaller molecules as low as 100 Da. However, high throughput instruments for these methods are comparatively expensive, are justified only where there will be a continual throughput of relevant projects, and so are likely to be inaccessible to many academic laboratories.
Differential scanning fluorimetry (DSF, or thermofluor) was first described in 2001 4 as a method for drug discovery. In this method, proteins are incubated with a fluorescent dye (initially naphthalene-sulfonic dyes were used), which alters its fluorescence upon binding to the hydrophobic regions of the proteins. The protein-dye sample is then heated, and the fluorescence monitored as the heat rises. The unfolding of the protein, and exposure of hydrophobic parts of the protein, gives rise to a characteristic pattern in the fluorescence as a function of temperature (Figure 1A). The experiment can be performed in small volumes in any commercial quantitative PCR instrument, and so in a single experiment, a large number of samples can be simultaneously tested (usually 48, 96 or 384 samples, depending on the instrument model). Experiments can usually be performed in around an hr, providing the possibility of high throughput analysis of the samples 5.
Further improvements to the methodology have seen the adoption of dyes with better spectral properties 6,7, generic tools for data analysis, and suggested protocols for initial screening 8,9. The range of applications of the method has been extended, with a particular focus on establishing optimal conditions for the preparation and storage of proteins 10, and on identifying potential binding partners to aid crystallization 11. The relatively high throughput of the method, relatively low cost in protein (~2 &#181;g per reaction), and the applicability to studying weak binding molecules has made DSF a valuable tool for fragment based drug design, especially in an academic context 12-14.
Despite the wide application of DSF to studying protein-ligand interactions, few studies have described determination of dissociation constants from these studies. However, these have tended to produce detailed equations describing the unfolding of the protein, with many parameters that must be fitted to sparse data or in some cases estimated 7,15-17. These methods are of particular relevance in challenging cases, such as tightly binding compounds, or proteins displaying unusual transitions. However, for many laboratories, these detailed analyses are too cumbersome for routine use. We therefore propose alternative treatments for different scenarios, and demonstrate how these can be used to fit data resulting from different protein-ligand interactions. Our method uses the StepOne qPCR instrument, for which bespoke data analysis software is available; whilst this speeds up the data analysis, results from other instruments can be processed using previously published methods 9, and the same downstream analysis can be performed.

<table border="0" cellpadding="0" cellspacing="0" width="573">
	<tbody>
		<tr height="61">
			<td height="61" style="height:61px;width:216px;">StepOne real time PCR instrument</td>
			<td style="width:71px;">Life Technologies</td>
			<td align="right" style="width:119px;">4376357</td>
			<td style="width:167px;">DSF can be performed with many other instruments. The StepOne instrument has very convenient software for data analysis.</td>
		</tr>
		<tr height="61">
			<td height="61" style="height:61px;width:216px;">Protein thermal shift software v1.0</td>
			<td style="width:71px;">Life Technologies</td>
			<td align="right" style="width:119px;">4466037</td>
			<td></td>
		</tr>
		<tr height="61">
			<td height="61" style="height:61px;">MicroAmp Fast optical 48-well plates&#160;</td>
			<td style="width:71px;">Life Technologies</td>
			<td align="right">4375816</td>
			<td></td>
		</tr>
		<tr height="61">
			<td height="61" style="height:61px;">Optical sealing tape&#160;</td>
			<td style="width:71px;">Life Technologies</td>
			<td align="right">4375323</td>
			<td>Bio-rad part no. 223-9444 is an alternative supplier</td>
		</tr>
		<tr height="21">
			<td height="21" style="height:21px;width:216px;">U-bottomed 96-well plates</td>
			<td style="width:71px;">Fisher</td>
			<td align="right">11521943</td>
			<td></td>
		</tr>
		<tr height="61">
			<td height="61" style="height:61px;width:216px;">SYPRO Orange</td>
			<td style="width:71px;">Life Technologies</td>
			<td style="width:119px;">S6650</td>
			<td>For a smaller volume supplier, use Sigma part no. S5692</td>
		</tr>
		<tr height="21">
			<td height="21" style="height:21px;width:216px;">SPSS statistics version 20</td>
			<td style="width:71px;">IBM</td>
			<td style="width:119px;">N/A</td>
			<td>Other statistics packages will provide similar functionality</td>
		</tr>
		<tr height="21">
			<td height="21" style="height:21px;width:216px;">GraphPad Prism 6.02</td>
			<td style="width:71px;">GraphPad</td>
			<td style="width:119px;">N/A</td>
			<td>Other statistics packages will provide similar functionality</td>
		</tr>
		<tr height="21">
			<td height="21" style="height:21px;width:216px;">Hand applicator (PA1)</td>
			<td style="width:71px;">3M</td>
			<td style="width:119px;">75-3454-4264-6</td>
			<td></td>
		</tr>
		<tr height="41">
			<td height="41" style="height:41px;width:216px;">Hexokinase from Saccharomyces cerevisiae</td>
			<td style="width:71px;">Sigma-Aldrich</td>
			<td style="width:119px;">H5000</td>
			<td></td>
		</tr>
		<tr height="41">
			<td height="41" style="height:41px;width:216px;">Glucose</td>
			<td style="width:71px;">Fisher scientific</td>
			<td align="right">10141520</td>
			<td></td>
		</tr>
	</tbody>
</table>

determination of protein-ligand interactions using differential scanning fluorimetry

A wide range of methods are currently available for determining the dissociation constant between a protein and interacting small molecules. However, most of these require access to specialist equipment, and often require a degree of expertise to effectively establish reliable experiments and analyze data. Differential scanning fluorimetry (DSF) is being increasingly used as a robust method for initial screening of proteins for interacting small molecules, either for identifying physiological partners or for hit discovery. This technique has the advantage that it requires only a PCR machine suitable for quantitative PCR, and so suitable instrumentation is available in most institutions; an excellent range of protocols are already available; and there are strong precedents in the literature for multiple uses of the method. Past work has proposed several means of calculating dissociation constants from DSF data, but these are mathematically demanding. Here, we demonstrate a method for estimating dissociation constants from a moderate amount of DSF experimental data. These data can typically be collected and analyzed within a single day. We demonstrate how different models can be used to fit data collected from simple binding events, and where cooperative binding or independent binding sites are present. Finally, we present an example of data analysis in a case where standard models do not apply. These methods are illustrated with data collected on commercially available control proteins, and two proteins from our research program. Overall, our method provides a straightforward way for researchers to rapidly gain further insight into protein-ligand interactions using DSF.

Watch this Scientific Journal Video about Determination of Protein-ligand Interactions Using Differential Scanning Fluorimetry at JoVE.com

Determination of Protein-ligand Interactions Using Differential Scanning Fluorimetry

Differential scanning fluorimetry is a widely used method for screening libraries of small molecules for interactions with proteins. Here, we present a straightforward method to extend these analyses to provide an estimate of the dissociation constant between a small molecule and its protein partner.

A wide range of methods are currently available for determining the dissociation constant between a protein and interacting small molecules. However, most ...

determination-protein-ligand-interactions-using-differential-scanning

Research

JoVE Journal

Biology

Microvolume Protein Concentration Determination using the NanoDrop 2000c Spectrophotometer

Traditional spectrophotometry requires placing samples into cuvettes or capillaries. This is often impractical due to the limited sample volumes often used for protein analysis. The Thermo Scientific NanoDrop 2000c Spectrophotometer solves this issue with an innovative sample retention system that holds microvolume samples between two measurement surfaces using the surface tension properties of liquids, enabling the quantification of samples in volumes as low as 0.5-2 &mu;L. The elimination of cuvettes or capillaries allows real time changes in path length, which reduces the measurement time while greatly increasing the dynamic range of protein concentrations that can be measured. The need for dilutions is also eliminated, and preparations for sample quantification are relatively easy as the measurement surfaces can be simply wiped with laboratory wipe. This video article presents modifications to traditional protein concentration determination methods for quantification of microvolume amounts of protein using A280 absorbance readings or the BCA colorimetric assay.

Microvolume samples are quantified by a spectrophotometer system that uses natural surface tension to retain samples without the use of cuvettes or capillaries. The dynamic range of protein concentrations and speed by which they can be measured are greatly increased with this method.

Traditional spectrophotometry requires placing samples into cuvettes or capillaries. This is often impractical due to the limited sample volumes often ...

Crystallizing Membrane Proteins for Structure Determination using Lipidic Mesophases

A detailed protocol for crystallizing membrane proteins by using lipidic mesophases is described. This method has variously been referred to as the lipidic cubic phase or in meso method. The method has been shown to be quite versatile in that it has been used to solve X-ray crystallographic structures of prokaryotic and eukaryotic proteins, proteins that are monomeric, homo- and hetero-multimeric, chromophore-containing and chromophore-free, and alpha-helical and beta-barrel proteins. Recent successes using in meso crystallization are the human engineered beta2-adrenergic and adenosine A2a G protein-coupled receptors. Protocols are presented for reconstituting the membrane protein into the monoolein-based mesophase, and for setting up crystallizations in the manual mode. Additional steps in the overall process, such as crystal harvesting, are to be addressed in future video articles. The time required to prepare the protein-loaded mesophase and to set up a crystallization plate manually is about one hour.

Herein is described the procedure implemented in the Caffrey Membrane Structural and Functional Biology Group to set up manually crystallization trials of membrane proteins in lipidic mesophases.

A detailed protocol for crystallizing membrane proteins by using lipidic mesophases is described. This method has variously been referred to as the ...

Tissue Determination Using the Animal Cap Transplant (ACT) Assay in Xenopus laevis

Many proteins play a dual role in embryonic development. Those that regulate cell fate determination in a specific tissue can also affect the development of a larger region of the embryo. This makes defining its role in a particular tissue difficult to analyze. For example, noggin overexpression in Xenopus laevis embryos causes the expansion of the entire anterior region, including the eye1,2. From this result, it is not known if Noggin plays a direct role in eye determination or that by causing an expansion of neural tissue, Noggin indirectly affects eye formation. Having this complex phenotype makes studying its eye-specific role in cell fate determination difficult to analyze. We have developed an assay that overcomes this problem. Taking advantage of the pluripotent nature of the Xenopus laevis animal cap 3, we have developed an assay to test the ability of gene product(s), like noggin or the eye field transcription factors (EFTFs), to transform caps into particular tissue or cell types by transplanting this tissue onto the side of the embryo 4. While we have found either Noggin protein treatment or a collection of transcription factors can determine retinal cell fate in animal caps, this procedure could be used to identify gene product(s) involved in specifying other tissues as well.

Tissue Determination Using the Animal Cap Transplant ACT Assay in Xenopus laevis

Animal caps overexpressing gene product(s) are transplanted to the flank of developing Xenopus laevis embryos in order to establish whether tissue is determined.

Many proteins play a dual role in embryonic development. Those that regulate cell fate determination in a specific tissue can also affect the development ...

Analysis of Schwann-astrocyte Interactions Using In Vitro Assays

Schwann cells are one of the commonly used cells in repair strategies following spinal cord injuries. Schwann cells are capable of supporting axonal regeneration and sprouting by secreting growth factors 1,2 and providing growth promoting adhesion molecules 3 and extracellular matrix molecules 4. In addition they myelinate the demyelinated axons at the site of injury 5.
However following transplantation, Schwann cells do not migrate from the site of implant and do not intermingle with the host astrocytes 6,7. This results in formation of a sharp boundary between the Schwann cells and astrocytes, creating an obstacle for growing axons trying to exit the graft back into the host tissue proximally and distally. Astrocytes in contact with Schwann cells also undergo hypertrophy and up-regulate the inhibitory molecules 8-13.
In vitro assays have been used to model Schwann cell-astrocyte interactions and have been important in understanding the mechanism underlying the cellular behaviour.
These in vitro assays include boundary assay, where a co-culture is made using two different cells with each cell type occupying different territories with only a small gap separating the two cell fronts. As the cells divide and migrate, the two cellular fronts get closer to each other and finally collide. This allows the behaviour of the two cellular populations to be analyzed at the boundary. Another variation of the same technique is to mix the two cellular populations in culture and over time the two cell types segregate with Schwann cells clumped together as islands in between astrocytes together creating multiple Schwann-astrocyte boundaries.
The second assay used in studying the interaction of two cell types is the migration assay where cellular movement can be tracked on the surface of the other cell type monolayer 14,15. This assay is commonly known as inverted coverslip assay. Schwann cells are cultured on small glass fragments and they are inverted face down onto the surface of astrocyte monolayers and migration is assessed from the edge of coverslip.
Both assays have been instrumental in studying the underlying mechanisms involved in the cellular exclusion and boundary formation. Some of the molecules identified using these techniques include N-Cadherins 15, Chondroitin Sulphate proteoglycans(CSPGs) 16,17, FGF/Heparin 18, Eph/Ephrins19.
This article intends to describe boundary assay and migration assay in stepwise fashion and elucidate the possible technical problems that might occur.

Analysis of Schwann-astrocyte Interactions Using In Vitro Assays

This article intends to describe in stepwise fashion the commonly used in vitro assays used in studying Schwann cell-asrtocyte interactions.

Schwann cells are one of the commonly used cells in repair strategies following spinal cord injuries. Schwann cells are capable of supporting axonal ...

The MultiBac Protein Complex Production Platform at the EMBL

Proteomics research revealed the impressive complexity of eukaryotic proteomes in unprecedented detail. It is now a commonly accepted notion that proteins in cells mostly exist not as isolated entities but exert their biological activity in association with many other proteins, in humans ten or more, forming assembly lines in the cell for most if not all vital functions.1,2 Knowledge of the function and architecture of these multiprotein assemblies requires their provision in superior quality and sufficient quantity for detailed analysis. The paucity of many protein complexes in cells, in particular in eukaryotes, prohibits their extraction from native sources, and necessitates recombinant production. The baculovirus expression vector system (BEVS) has proven to be particularly useful for producing eukaryotic proteins, the activity of which often relies on post-translational processing that other commonly used expression systems often cannot support.3 BEVS use a recombinant baculovirus into which the gene of interest was inserted to infect insect cell cultures which in turn produce the protein of choice. MultiBac is a BEVS that has been particularly tailored for the production of eukaryotic protein complexes that contain many subunits.4 A vital prerequisite for efficient production of proteins and their complexes are robust protocols for all steps involved in an expression experiment that ideally can be implemented as standard operating procedures (SOPs) and followed also by non-specialist users with comparative ease. The MultiBac platform at the European Molecular Biology Laboratory (EMBL) uses SOPs for all steps involved in a multiprotein complex expression experiment, starting from insertion of the genes into an engineered baculoviral genome optimized for heterologous protein production properties to small-scale analysis of the protein specimens produced.5-8 The platform is installed in an open-access mode at EMBL Grenoble and has supported many scientists from academia and industry to accelerate protein complex research projects.

Protein complexes catalyze key cellular functions. Detailed functional and structural characterization of many essential complexes requires recombinant production. MultiBac is a baculovirus/insect cell system particularly tailored for expressing eukaryotic proteins and their complexes. MultiBac was implemented as an open-access platform, and standard operating procedures developed to maximize its utility.

Proteomics  research revealed the impressive complexity of eukaryotic proteomes in  unprecedented detail. It is now a commonly accepted notion that ...

Using Coculture to Detect Chemically Mediated Interspecies Interactions

In nature, bacteria rarely exist in isolation; they are instead surrounded by a diverse array of other microorganisms that alter the local environment by secreting metabolites. These metabolites have the potential to modulate the physiology and differentiation of their microbial neighbors and are likely important factors in the establishment and maintenance of complex microbial communities. We have developed a fluorescence-based coculture screen to identify such chemically mediated microbial interactions. The screen involves combining a fluorescent transcriptional reporter strain with environmental microbes on solid media and allowing the colonies to grow in coculture. The fluorescent transcriptional reporter is designed so that the chosen bacterial strain fluoresces when it is expressing a particular phenotype of interest (i.e. biofilm formation, sporulation, virulence factor production, etc.) Screening is performed under growth conditions where this phenotype is not expressed (and therefore the reporter strain is typically nonfluorescent). When an environmental microbe secretes a metabolite that activates this phenotype, it diffuses through the agar and activates the fluorescent reporter construct. This allows the inducing-metabolite-producing microbe to be detected: they are the nonfluorescent colonies most proximal to the fluorescent colonies. Thus, this screen allows the identification of environmental microbes that produce diffusible metabolites that activate a particular physiological response in a reporter strain. This publication discusses how to: a) select appropriate coculture screening conditions, b) prepare the reporter and environmental microbes for screening, c) perform the coculture screen, d) isolate putative inducing organisms, and e) confirm their activity in a secondary screen. We developed this method to screen for soil organisms that activate biofilm matrix-production in Bacillus subtilis; however, we also discuss considerations for applying this approach to other genetically tractable bacteria.

Bacteria produce secreted compounds that have the potential to affect the physiology of their microbial neighbors. Here we describe a coculture screen that allows detection of such chemically mediated interspecies interactions by mixing soil microbes with fluorescent transcriptional reporter strains of Bacillus subtilis on solid media.

In nature, bacteria rarely exist in isolation; they are instead surrounded by a diverse array of other microorganisms that alter the local environment by ...

Real Time Measurements of Membrane Protein:Receptor Interactions Using Surface Plasmon Resonance (SPR)

Protein-protein interactions are pivotal to most, if not all, physiological processes, and understanding the nature of such interactions is a central step in biological research. Surface Plasmon Resonance (SPR) is a sensitive detection technique for label-free study of bio-molecular interactions in real time. In a typical SPR experiment, one component (usually a protein, termed 'ligand') is immobilized onto a sensor chip surface, while the other (the 'analyte') is free in solution and is injected over the surface. Association and dissociation of the analyte from the ligand are measured and plotted in real time on a graph called a sensogram, from which pre-equilibrium and equilibrium data is derived. Being label-free, consuming low amounts of material, and providing pre-equilibrium kinetic data, often makes SPR the method of choice when studying dynamics of protein interactions. However, one has to keep in mind that due to the method's high sensitivity, the data obtained needs to be carefully analyzed, and supported by other biochemical methods. 
SPR is particularly suitable for studying membrane proteins since it consumes small amounts of purified material, and is compatible with lipids and detergents. This protocol describes an SPR experiment characterizing the kinetic properties of the interaction between a membrane protein (an ABC transporter) and a soluble protein (the transporter's cognate substrate binding protein).

Real Time Measurements of Membrane Protein:Receptor Interactions Using Surface Plasmon Resonance SPR

Surface Plasmon Resonance (SPR) is a label-free method for detecting bio-molecular interactions in real time. Herein, a protocol for a membrane protein:receptor interaction experiment is described, while discussing the pros and cons of the technique.

Protein-protein interactions are pivotal to most, if not all, physiological processes, and  understanding the nature of such interactions is a central ...

Determination of the Relative Cell Surface and Total Expression of Recombinant Ion Channels Using Flow Cytometry

Inherited or de novo mutations in cation-selective channels may lead to sudden cardiac death. Alteration in the plasma membrane trafficking of these multi-spanning transmembrane proteins, with or without change in channel gating, is often postulated to contribute significantly in this process. It has thus become critical to develop a method to quantify the change of the relative cell surface expression of cardiac ion channels on a large scale. Herein, a detailed protocol is provided to determine the relative total and cell surface expression of cardiac L-type calcium channels CaV1.2 and membrane-associated subunits in tsA-201 cells using two-color fluorescent cytometry assays. Compared with other microscopy-based or immunoblotting-based qualitative methods, flow cytometry experiments are fast, reproducible, and large-volume assays that deliver quantifiable end-points on large samples of live cells (ranging from 104 to 106 cells) with similar cellular characteristics in a single flow. Constructs were designed to constitutively express mCherry at the intracellular C-terminus (thus allowing a rapid assessment of the total protein expression) and express an extracellular-facing hemagglutinin (HA) epitope to estimate the cell surface expression of membrane proteins using an anti-HA fluorescence conjugated antibody. To avoid false negative, experiments were also conducted in permeabilized cells to confirm the accessibility and proper expression of the HA epitope. The detailed procedure provides: (1) design of tagged DNA (deoxyribonucleic acid) constructs, (2) lipid-mediated transfection of constructs in tsA-201 cells, (3) culture, harvest, and staining of non-permeabilized and permeabilized cells, and (4) acquisition and analysis of fluorescent signals. Additionally, the basic principles of flow cytometry are explained and the experimental design, including the choice of fluorophores, titration of the HA antibody and control experiments, is thoroughly discussed. This specific approach offers objective relative quantification of the total and cell surface expression of ion channels that can be extended to study ion pumps and plasma membrane transporters.

Inherited cardiac arrhythmias are often caused by mutations that alter the surface delivery of one or more ion channels. Here, we adapt flow cytometry assays to provide a quantification of the relative total and cell surface protein expression of recombinant ion channels expressed in tsA-201 cells.

Inherited or de novo mutations in cation-selective channels may lead to sudden cardiac death. Alteration in the plasma membrane trafficking of these ...

Sperm Collection of Differential Quality Using Density Gradient Centrifugation

In sexual reproduction, a male gamete or sperm cell fuses with a female gamete to bring about fertilization. However, a large number of sperm cells with fertilizing ability are required to interact with a female gamete to ensure fertilization. As such, the fertilizing ability of individual sperm cells is critical for successful reproduction. Density gradient centrifugation has been utilized for several decades as a reproducible, fast, efficient, effective and extremely adaptable method to collect only high-quality sperm to be used in assisted reproductive technology. The protocols we described herein focus on the utilization of the discontinuous Percoll density gradient centrifugation (PDGC) technique to isolate three distinct populations of rooster sperm by their quality. We were able to collect low-, medium- and high-quality sperm. We also describe reproducible protocols that entail determining fertility potential of sperm by assessing their viability, mobility and penetrability. Collection of sperm by their quality using PDGC technique would be useful to accurately and thoroughly characterize sperm with differential fertility potential.

In this paper, we aim to describe the performance of the density gradient centrifugation technique and its application in sperm physiology research.

In sexual reproduction, a male gamete or sperm cell fuses with a female gamete to bring about fertilization. However, a large number of sperm cells with ...

Using Caenorhabditis elegans to Screen for Tissue-Specific Chaperone Interactions

Correct folding and assembly of proteins and protein complexes are essential for cellular function. Cells employ quality control pathways that correct, sequester or eliminate damaged proteins to maintain a healthy proteome, thus ensuring cellular proteostasis and preventing further protein damage. Because of redundant functions within the proteostasis network, screening for detectable phenotypes using knockdown or mutations in chaperone-encoding genes in the multicellular organism Caenorhabditis elegans results in the detection of minor or no phenotypes in most cases. We have developed a targeted screening strategy to identify chaperones required for a specific function and thus bridge the gap between phenotype and function. Specifically, we monitor novel chaperone interactions using RNAi synthetic interaction screens, knocking-down chaperone expression, one chaperone at a time, in animals carrying a mutation in a chaperone-encoding gene or over-expressing a chaperone of interest. By disrupting two chaperones that individually present no gross phenotype, we can identify chaperones that aggravate or expose a specific phenotype when both perturbed. We demonstrate that this approach can identify specific sets of chaperones that function together to modulate the folding of a protein or protein complexes associated with a given phenotype.

Using Caenorhabditis elegans to Screen for Tissue-Specific Chaperone Interactions

To study chaperone-chaperone and chaperone-substrate interactions, we perform synthetic interaction screens in Caenorhabditis elegans using RNA interference in combination with mild mutations or over-expression of chaperones and monitor tissue-specific protein dysfunction at the organismal level.

Correct folding and assembly of proteins and protein complexes are essential for cellular function. Cells employ quality control pathways that correct, ...