Name: determination of protein-ligand interactions using differential scanning fluorimetry
Uploaded: 2014-09-13T15:00:00.000+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><tbody><tr><td>StepOne real time PCR instrument</td><td>Life Technologies</td><td>4376357</td><td>DSF can be performed with many other instruments. The StepOne instrument has very convenient software for data analysis.</td></tr><tr><td>Protein thermal shift software v1.0</td><td>Life Technologies</td><td>4466037</td><td></td></tr><tr><td>MicroAmp Fast optical 48-well plates&amp;nbsp;</td><td>Life Technologies</td><td>4375816</td><td></td></tr><tr><td>Optical sealing tape&amp;nbsp;</td><td>Life Technologies</td><td>4375323</td><td>Bio-rad part no. 223-9444 is an alternative supplier</td></tr><tr><td>U-bottomed 96-well plates</td><td>Fisher</td><td>11521943</td><td></td></tr><tr><td>SYPRO Orange</td><td>Life Technologies</td><td>S6650</td><td>For a smaller volume supplier, use Sigma part no. S5692</td></tr><tr><td>SPSS statistics version 20</td><td>IBM</td><td></td><td>Other statistics packages will provide similar functionality</td></tr><tr><td>GraphPad Prism 6.02</td><td>GraphPad</td><td></td><td>Other statistics packages will provide similar functionality</td></tr><tr><td>Hand applicator (PA1)</td><td>3M</td><td>75-3454-4264-6</td><td></td></tr><tr><td>Hexokinase from Saccharomyces cerevisiae</td><td>Sigma-Aldrich</td><td>H5000</td><td></td></tr><tr><td>Glucose</td><td>Fisher scientific</td><td>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 ...

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Research

JoVE Journal

Biology

61.5K Views.  University of Exeter. 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.

Video: Determination of Protein-ligand Interactions Using Differential Scanning Fluorimetry

Fluorescence Anisotropy as a Tool to Study Protein-protein Interactions

Protein-protein interactions play an essential role in the function of a living organism. Once an interaction has been identified and validated it is necessary to characterize it at the structural and mechanistic level. Several biochemical and biophysical methods exist for such purpose. Among them, fluorescence anisotropy is a powerful technique particularly used when the fluorescence intensity of a fluorophore-labeled protein remains constant upon protein-protein interaction. In this technique, a fluorophore-labeled protein is excited with vertically polarized light of an appropriate wavelength that selectively excites a subset of the fluorophores according to their relative orientation with the incoming beam. The resulting emission also has a directionality whose relationship in the vertical and horizontal planes defines anisotropy (r) as follows: r=(IVV-IVH)/(IVV+2IVH), where IVV and IVH are the fluorescence intensities of the vertical and horizontal components, respectively. Fluorescence anisotropy is sensitive to the rotational diffusion of a fluorophore, namely the apparent molecular size of a fluorophore attached to a protein, which is altered upon protein-protein interaction. In the present text, the use of fluorescence anisotropy as a tool to study protein-protein interactions was exemplified to address the binding between the protein mutated in the Shwachman-Diamond Syndrome (SBDS) and the Elongation factor like-1 GTPase (EFL1). Conventionally, labeling of a protein with a fluorophore is carried out on the thiol groups (cysteine) or in the amino groups (the N-terminal amine or lysine) of the protein. However, SBDS possesses several cysteines and lysines that did not allow site directed labeling of it. As an alternative technique, the dye 4&#39;,5&#39;-bis(1,3,2 dithioarsolan-2-yl) fluorescein was used to specifically label a tetracysteine motif, Cys-Cys-Pro-Gly-Cys-Cys, genetically engineered in the C-terminus of the recombinant SBDS protein. Fitting of the experimental data provided quantitative and mechanistic information on the binding mode between these proteins.

Protein interactions are at the heart of a cell&#39;s function. Calorimetric and spectroscopic techniques are commonly used to characterize them. Here we describe fluorescence anisotropy as a tool to study the interaction between the protein mutated in the Shwachman-Diamond Syndrome (SBDS) and the Elongation factor-like 1 GTPase (EFL1).

Protein-protein interactions play an essential role in the function of a living organism. Once an interaction has been identified and validated it is ...

Biochemistry

Differential Scanning Calorimetry &#8212; A Method for Assessing the Thermal Stability and Conformation of Protein Antigen

Differential scanning calorimetry (DSC) is an analytical technique that measures the molar heat capacity of samples as a function of temperature. In the case of protein samples, DSC profiles provide information about thermal stability, and to some extent serves as a structural &#8220;fingerprint&#8221; that can be used to assess structural conformation. It is performed using a differential scanning calorimeter that measures the thermal transition temperature (melting temperature; Tm) and the energy required to disrupt the interactions stabilizing the tertiary structure (enthalpy; &#8710;H) of proteins. Comparisons are made between formulations as well as production lots, and differences in derived values indicate differences in thermal stability and structural conformation. Data illustrating the use of DSC in an industrial setting for stability studies as well as monitoring key manufacturing steps are provided as proof of the effectiveness of this protocol. In comparison to other methods for assessing the thermal stability of protein conformations, DSC is cost-effective, requires few sample preparation steps, and also provides a complete thermodynamic profile of the protein unfolding process.

Differential scanning calorimetry measures the thermal transition temperature(s) and total heat energy required to denature a protein. Results obtained are used to assess the thermal stability of protein antigens in vaccine formulations.

Differential scanning calorimetry (DSC) is an analytical technique that measures the molar heat capacity of samples as a function of temperature. In the ...

Measuring Biomolecular DSC Profiles with Thermolabile Ligands to Rapidly Characterize Folding and Binding Interactions

Differential scanning calorimetry (DSC) is a powerful technique for quantifying thermodynamic parameters governing biomolecular folding and binding interactions. This information is critical in the design of new pharmaceutical compounds. However, many pharmaceutically relevant ligands are chemically unstable at the high temperatures used in DSC analyses. Thus, measuring binding interactions is challenging because the concentrations of ligands and thermally-converted products are constantly changing within the calorimeter cell. Here, we present a protocol using thermolabile ligands and DSC for rapidly obtaining thermodynamic and kinetic information on the folding, binding, and ligand conversion processes. We have applied our method to the DNA aptamer MN4 that binds to the thermolabile ligand cocaine. Using a new global fitting analysis that accounts for thermolabile ligand conversion, the complete set of folding and binding parameters are obtained from a pair of DSC experiments. In addition, we show that the rate constant for thermolabile ligand conversion may be obtained with only one supplementary DSC dataset. The guidelines for identifying and analyzing data from several more complicated scenarios are presented, including irreversible aggregation of the biomolecule, slow folding, slow binding, and rapid depletion of the thermolabile ligand.

We present a protocol for rapid characterization of biomolecular folding and binding interactions with thermolabile ligands using differential scanning calorimetry.

Differential scanning calorimetry (DSC) is a powerful technique for quantifying thermodynamic parameters governing biomolecular folding and binding ...

Phase Contrast and Differential Interference Contrast (DIC) Microscopy

Phase-contrast microscopy is often used to produce contrast for transparent, non light-absorbing, biological specimens. The technique was discovered by Zernike, in 1942, who received the Nobel prize for his achievement. DIC microscopy, introduced in the late 1960s, has been popular in biomedical research because it highlights edges of specimen structural detail, provides high-resolution optical sections of thick specimens including tissue cells, eggs, and embryos and does not suffer from the  phase halos  typical of phase-contrast images. This protocol highlights the principles and practical applications of these microscopy techniques.

Phase Contrast and Differential Interference Contrast DIC Microscopy

This protocol highlights the principles and practical applications of Phase and Differential Interference Contrast (DIC) Microscopy

Phase-contrast microscopy is often used to produce contrast for transparent, non light-absorbing, biological specimens. The technique was discovered by ...

A Rapid and Quantitative Fluorimetric Method for Protein-Targeting Small Molecule Drug Screening

We demonstrate a new drug screening method for determining the binding affinity of small drug molecules to a target protein by forming fluorescent gold nanoclusters (Au NCs) within the drug-loaded protein, based on the differential fluorescence signal emitted by the Au NCs. Albumin proteins such as human serum albumin (HSA) and bovine serum albumin (BSA) are selected as the model proteins. Four small molecular drugs (e.g., ibuprofen, warfarin, phenytoin, and sulfanilamide) of different binding affinities to the albumin proteins are tested. It was found that the formation rate of fluorescent Au NCs inside the drug loaded albumin protein under denaturing conditions (i.e., 60 &#176;C or in the presence of urea) is slower than that formed in the pristine protein (without drugs). Moreover, the fluorescent intensity of the as-formed NCs is found to be inversely correlated to the binding affinities of these drugs to the albumin proteins. Particularly, the higher the drug-protein binding affinity, the slower the rate of Au NCs formation, and thus a lower fluorescence intensity of the resultant Au NCs is observed. The fluorescence intensity of the resultant Au NCs therefore provides a simple measure of the relative binding strength of different drugs tested. This method is also extendable to measure the specific drug-protein binding constant (KD) by simply varying the drug content preloaded in the protein at a fixed protein concentration. The measured results match well with the values obtained using other prestige but more complicated methods.

A protocol for small molecular drug screening based on in-situ synthesis of ultrasmall fluorescent gold nanoclusters (Au NCs) using drug-loaded protein as template is presented. This method is simple to determine the binding affinity of drugs to a target protein by a visible fluorescent signal emitted from the protein-templated Au NCs.

We demonstrate a new drug screening method for determining the binding affinity of small drug molecules to a target protein by forming fluorescent gold ...

Bioengineering

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 ...

Nano-Differential Scanning Fluorimetry for Screening in Fragment-based Lead Discovery

Thermal shift assays (TSAs) examine how the melting temperature (Tm) of a target protein changes in response to changes in its environment (e.g., buffer composition). The utility of TSA, and specifically of nano-Differential Scanning Fluorimetry (nano-DSF), has been established over the years, both for finding conditions that help stabilize a specific protein and for looking at ligand binding by monitoring changes in the apparent Tm. This paper presents an efficient screening of the Diamond-SGC-iNEXT Poised (DSi-Poised) fragment library (768 compounds) by the use of nano-DSF, monitoring Tm to identify potential fragment binding. The prerequisites regarding protein quality and concentration for performing nano-DSF experiments are briefly outlined followed by a step-by-step protocol that uses a nano-liter robotic dispenser commonly used in structural biology laboratories for preparing the required samples in 96-well plates. The protocol describes how the reagent mixtures are transferred to the capillaries needed for nano-DSF measurements. In addition, this paper provides protocols to measure thermal denaturation (monitoring intrinsic tryptophan fluorescence) and aggregation (monitoring light back-scattering) and the subsequent steps for data transfer and analysis. Finally, screening experiments with three different protein targets are discussed to illustrate the use of this procedure in the context of lead discovery campaigns. The overall principle of the method described can be easily transferred to other fragment libraries or adapted to other instruments.

Monitoring changes in the melting temperature of a target protein (i.e., thermal shift assay, TSA) is an efficient method for screening fragment libraries of a few hundred compounds. We present a TSA protocol implementing robotics-assisted nano-Differential Scanning Fluorimetry (nano-DSF) for monitoring intrinsic tryptophan fluorescence and light back-scattering for fragment screening.

Thermal shift assays (TSAs) examine how the melting temperature (Tm) of a target protein changes in response to changes in its environment (e.g., buffer ...

High-Sensitivity Detection of Compound-Protein Binding Using Fluorescence

Detection of CD40 Protein-Umbelliferone Interaction via Differential Scanning Fluorescence

The investigation of interactions between different molecules is a crucial aspect of understanding disease pathogenesis and screening for drug targets. Umbelliferone, an active ingredient in Tibetan medicine Vicatia thibetica, exhibits an immunomodulatory effect with an unknown mechanism. The CD40 protein is a key target in the immune response. Therefore, this study employs the principle of differential scanning fluorescence technology to analyze the interactions between CD40 protein and umbelliferone using fluorescent enzyme markers. Initially, the stability of the protein fluorescent orange dye was experimentally verified, and the optimal dilution ratio of 1:500 was determined. Subsequently, it was observed that the temperature melting (Tm) value of CD40 protein tended to decrease with an increase in concentration. Interestingly, the interaction between CD40 protein and umbelliferone was found to enhance the thermal stability of CD40 protein. This study represents the first attempt to detect the binding potential of small molecule compounds and proteins using fluorescence microplates and fluorescent dyes. The technique is characterized by high sensitivity and accuracy, promising advancements in the fields of protein stability, protein structure, and protein-ligand interactions, thus facilitating further research and exploration.

Author Spotlight: Streamlining PCR Methods for Enhanced Accessibility and Efficiency

This protocol describes a unique method for detecting the binding between compounds and protein molecules, offering the advantages of minimal protein sample loss and high data accuracy.

The investigation of interactions between different molecules is a crucial aspect of understanding disease pathogenesis and screening for drug targets. ...

Medicine

Differential Scanning Calorimetry to Study DNA Aptamer-Thermolabile Ligand Interaction

Source: Mittermaier, A. K., et al. <a href="https://app.jove.com/v/55959">Measuring Biomolecular DSC Profiles with Thermolabile Ligands to Rapidly Characterize Folding and Binding Interactions.</a> J. Vis. Exp. (2017)
This video demonstrates differential scanning calorimetry &#8212; a technique to study the interaction between a biomolecule and a ligand. The binding of a DNA aptamer and a thermolabile ligand is studied by applying repeated heating and cooling cycles to the sample and obtaining scans for thermodynamic analysis.

Różnicowa kalorymetria skaningowa do badania interakcji DNA aptamer-termolabilny ligand

Source: Mittermaier, A. K., et al. Measuring Biomolecular DSC Profiles with Thermolabile Ligands to Rapidly Characterize Folding and Binding Interactions. ...

JoVE Encyclopedia of Experiments

Biological Techniques

Biomolecular Interaction Detection Techniques

Protein-nucleic acid interactions

Fluorescence Anisotropy-Based Detection of Protein-Protein Interactions

Mix 3 nanomoles of the SBDS-FlAsH protein with 3 nanomoles of the Lumio Green dye in a 5-microliter volume of anisotropy buffer. Let the reaction proceed for 8 hours at 4 degrees Celsius. After 8 hours, dialyze the sample against the anisotropy buffer overnight to remove the free dye. Measure the absorbance at 280 nanometers and 508 nanometers in a spectrophotometer using a quartz cuvette. Then, use the Lambert-Beer law to quantify the percent of labeled protein as described in the text protocol.
Before measuring the anisotropy value, each titration step of the protein-ligand should be done carefully, ensuring that the entire sample is dispensed into the solution, and becomes homogeneous.
In a fluorescence cuvette, place 200 microliters of 30 nanomolar SBDS-FlAsH in anisotropy buffer, and titrate 2 microliters of 30 micromolar EFL1. Mix thoroughly, and let the reaction stand for 3 minutes before measuring the anisotropy and fluorescence value. Repeat this process until a total volume of 40 microliters of EFL1 has been added. As a final step, fit the data to a presumed binding model, as described in the text protocol.

Determination of Protein-ligand Interactions Using Differential Scanning Fluorimetry

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