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.


Introduction
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 "gold-standard", 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 µM K d )

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.
1. 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 . 2. Select the relevant rules for initial values associated with each of these equations in Table 3. 3. 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 K d , or a single anomalous point (especially at the highest ligand concentrations), can significantly affect the results. 4. Repeat the experiment at least twice (see step 2.12) to ensure reproducibility.

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 K d .
1. 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. 2. 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". 3. Download the syntax file available online at at http:// www.exeter.ac.uk/biosciences/capsular. Select "Run → Run all". 4. Copy the proportion bound results to a new Excel workbook, with the relevant ligand concentrations. 5. 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". 6. 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.

Representative Results
An excellent test substrate for this method is hexokinase. This has the advantages of being readily commercially available, and having two substrates that are found in most laboratories, and which provide clear, reproducible results in the assay. An initial concentration screen (Protocol 1), using hexokinase and glucose (Figure 2A), suggests that the likely K d will be in the range from 0.2 to 1.7 mM. Therefore, a larger screen (Protocol 2) was performed, using the concentrations shown in Table 4. The results ( Figure 2B) show a good fit to the single site ligand binding equation (Protocol section 3.3) [9], and gave a K d of 1.2 ± 0.1 mM.
The putative heptose-guanyl transferase WcbM 19,20 shows a strong thermal shift on binding to GTP ( Figure 3A). An initial screen suggested that the K d would be in the range of around 100 µM. Therefore, a full screen was set up, using the concentrations shown in Table 5. Fitting of the results to equation 3.3 showed a reasonable fit (R 2 of 0.981; Figure 3B).However, there is an evident difference between the data and the model, suggesting that a different equation is needed. Searching of the Protein Databank 21 with the WcbM sequence showed that the closest homologues for which structures have been determined form dimers. The data were therefore analyzed using the three equations for cooperative, sequential, and independent binding of two ligands (Protocol 4). The fitting statistics for a cooperative model gave an R 2 value of 0.998 and standard deviation of residuals (Sy.x) of 0.215, whereas both sequential and independent binding models gave an R 2 value of 0.992 and a Sy.x of 0.480 and 0.461 respectively. This suggests that the model giving the best fit to the data was the cooperative model: here, a K ½ of 230 ± 10 µM was observed, with an n value of 0.52 ± 0.02 ( Figure 3C). This indicated a negative cooperativity to the binding. Note that a K ½ was used in this case rather than K d , as the units for K d would be the rather unsatisfactory µM 0.52 .
The putative GDP-6-deoxy-β-d-manno-heptopyranose 2-O-acetylase, WcbI 22 , shows a rather unusual result in differential scanning fluorimetry. In the absence of any ligands, it shows a clear and simple denaturation ( Figure 4A). Coenzyme A (CoA) was identified as a ligand of this protein using DSF, and the affinity of the protein for this partner was investigated as described in the protocol. In the presence of high concentrations of CoA, a strong shift to a higher temperature is observed, with a change in the melting temperature of 15 °C. However, at intermediate concentrations, rather than a shift to a monophasic melting at an intermediate melting temperature, WcbI showed a biphasic melting, with the protein appearing to melt at either the ligand-free temperature, or the fully bound melting temperature ( Figure 4A). The proportions of the two species altered in a dose dependent manner, with increasing substrate concentrations increasing the proportion that melted at the higher temperature ( Figure 4B). Direct analysis of these data was challenging: fitting to the Boltzmann equation gave very poor fits, whilst derivative methods highlighted that two melting events were occurring, but did not assist in demonstrating a change with increasing ligand concentration.
A less conventional approach to analyze these data was therefore adopted (Protocol 5). The fluorescence derivative results without ligand and at the highest ligand concentration were taken as representing essentially all protein in the lower melting temperature, or the higher melting temperature state. The remaining derivative data were fitted at each point as the sum of a proportion of each of these two states, with the proportion summed to unity ( Figure 4C). The data obtained were then fitted as before to obtain an apparent K d , using the same equations as before. This highlighted that the "high" ligand point is likely to be only 95% ligand bound. The data were then extrapolated to a prediction of the result for a 100% bound protein, and the data fitting repeated to give an apparent K d of 58 ± 2 µM. This provided an excellent fit of the experimental results to the binding model ( Figure 4D).  This describes the "master mix" of protein, detection reagent and buffer for an initial scouting experiment to provide an estimate of K d , as described in protocol section 1. This buffer mixture is appropriate for generic proteins. Where previous results suggest other buffers should be used, these should be substituted. If the protein stock is at a low concentration (i.e., less than 0.3 mg/ml), it may be necessary to reduce the amount of additional buffer added to compensate for buffer already present in the protein sample.  Step in experimental protocol Equation required Parameters required Description of variables and parameters

3.3
Single site ligand binding Y=Bottom + ((Top-Bottom)*(1-((P-K d -X+sqrt(((P+X+K d )^2)-(4*P*X)))/ (2*P))))   Hexokinase from the budding yeast Saccharomyces cerevisiae was added to the master mix as described in the protocol, supplemented with 10 mM MgCl 2 as magnesium is a known cofactor. The initial estimate of the K d was between 0.5 and 2 mM. Experiments were set up to provide the indicated final concentrations of glucose. WcbM from Burkholderia pseudomallei was added to the master mix as described in the protocol. The initial estimate of the K d was around 100 µM. Experiments were set up to provide the indicated final concentrations of GTP, aiming to cover at least two orders of magnitude above and below K d .

Discussion
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 K d , 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 T m 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 °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., MgCl 2 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 elsewhere 6,9 .
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 °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 K d is generally higher than that at physiological temperatures. The dissociation constant is related to the temperature of the reaction by the equations: (where c θ is the standard reference concentration, Δ r G is the Gibbs free energy change of the reaction, R is the molar gas constant, ΔH is the enthalpy change in the reaction, and ΔS is the entropy change in the reaction.) Reactions with dissociation constants in the measurable range of this method will generally have a negative Δ r G, and so the effect of an increase in temperature on equation [1] will be to increase the dissociation constant. Both the ΔH and ΔS terms that constitute the Gibbs free energy (equation [2]) are temperature dependent 27 , 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 K d 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 K d , it is most important to obtain data with 90% of the protein present in the ligandbound form, which requires ligand concentrations to be approximately ten times K d (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 µM 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.

Disclosures
The authors declare that they have nothing to disclose.