Thermodynamics of Protein–Ligand Interactions: History, Presence, and Future Aspects

JOURNAL OF RECEPTORS AND SIGNAL TRANSDUCTION Vol. 24, Nos. 1 & 2, pp. 1–52, 2004

REVIEW

Remo Perozzo,* Gerd Folkers, and Leonardo Scapozza

Department of Chemistry and Applied BioSciences, Swiss Federal Institute of Technology (ETH), Zurich, Switzerland

ABSTRACT

The understanding of molecular recognition processes of small ligands and biological macromolecules requires a complete characterization of the binding energetics and correlation of thermodynamic data with interacting structures involved. A quantitative description of the forces that govern molecular associations requires determination of changes of all thermodynamic parameters, including free energy of binding (ÁG), enthalpy (ÁH ), and entropy (ÁS )of binding and the heat capacity change (ÁCp). A close insight into the binding process is of significant and practical interest, since it provides the fundamental know-how for development of structure-based molecular design strategies. The Downloaded By: [University of Missouri] At: 19:42 13 May 2009 only direct method to measure the heat change during complex formation at constant temperature is provided by isothermal titration calorimetry (ITC). With this method one binding partner is titrated into a solution containing the interaction partner, thereby generating or absorbing heat. This heat is the direct observable that can be quantified by the calorimeter. The use of ITC has been limited due to the lack of sensitivity, but recent developments in instrument design permit to measure heat effects generated by nanomol (typically 10–100) amounts of reactants. ITC has emerged as the primary tool for characterizing interactions

*Correspondence: Remo Perozzo, Department of Chemistry and Applied BioSciences, Swiss Federal Institute of Technology (ETH) Zurich, Winterthurerstr. 190, CH-8057 Zurich, Switzerland; Fax: þ41-1-6356884; E-mail: [email protected].

DOI: 10.1081/RRS-120037896 1079-9893 (Print); 1532-4281 (Online) Copyright & 2004 by Marcel Dekker, Inc. www.dekker.com ORDER REPRINTS

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in terms of thermodynamic parameters. Because heat changes occur in almost all chemical and biochemical processes, ITC can be used for numerous applications, e.g., binding studies of antibody–antigen, protein–peptide, protein–protein, enzyme–inhibitor or enzyme–substrate, carbohydrate–protein, DNA–protein (and many more) interactions as well as enzyme kinetics. Under appropriate conditions data analysis from a single experiment yields ÁH, KB, the stoichiometry (n), ÁG and ÁS of binding. Moreover, ITC experiments performed at different temperatures yield the heat capacity change (ÁCp). The informational content of thermodynamic data is large, and it has been shown that it plays an important role in the elucidation of binding mechanisms and, through the link to structural data, also in rational drug design. In this review we will present a comprehensive overview to ITC by giving some historical background to calorimetry, outline some critical experimental and data analysis aspects, discuss the latest developments, and give three recent examples of studies published with respect to macromolecule–ligand interactions that have utilized ITC technology.

Key Words: Isothermal titration calorimetry; Protein–ligand interaction; Thermodynamics.

INTRODUCTION

A fundamental principle of all biological processes is molecular organization and recognition. Biological macromolecules are able to interact with various small and large molecules, with a high degree of specificity and with high affinity, fascinating chemists and biologists from the very beginning of modern biochemistry. A prerequisite for a deeper understanding of the molecular basis of protein–ligand interactions is a thorough characterization and quantification of the energetics governing complex formation. Calorimetry is the only technique enabling us to study

Downloaded By: [University of Missouri] At: 19:42 13 May 2009 directly the basic physical forces between and within a macromolecule in sufficient detail by measuring heat quantities or heat effects.

Historical Background to Thermodynamics and Calorimetry

The background of the development of calorimetry and thermodynamics has been the subject of a variety of historical studies, and here we try to do a short summary of the most interesting aspects thereof (1–9). Calorimetry is a very old science. In principle, the historical development of calorimetry and thermodynamics began with the description and definition of temperature and heat. The first known documents from the early 17th century witness for very crude attempts to describe temperature, most of them derived by perception: ‘‘heat of a breeding hen, heat of boiling water, heat of glowing charcoal.’’ These estimations were too rough, and therefore it was necessary to develop objective standards. The invention of the first thermometer had its origin in the same time period. The concept of expansion of gases and liquids due to heat was already known from the antiquity ORDER REPRINTS

and was used by Galileo and Drebbel. They independently used a bulb with an open-ended stem inverted over water to observe the expansion of air. The results obtained with these so-called thermoscops were very inaccurate, confused with the effects of barometric pressure and lacking scaling. The next crucial step to make satisfactory thermometers was the use of (pure) liquids instead of air (water, ethanol, mercury) in a closed compartment. By the end of the 17th century, a reliable temperature scale was established by Fahrenheit and Celsius. It was around this time when the nature of heat and its quantitative aspects became of interest. People had speculated on the nature of heat since ancient times. It was a widespread belief that heat was a substance, some held the view that it was composed of atoms. During the 18th century the foundations of calorimetry were laid by Joseph Black. He preferred an alternative explanation of heat being a fluid that can be absorbed or squeezed out of bodies and can flow from one place to another. He recognized that heat applied to melting ice did not change the temperature of the mixture but was consumed for the solid–liquid phase transition, for the first time clearly discriminating between the ‘‘strength’’ and ‘‘amount’’ of heat. Black introduced the concept of latent heat and showed that quantities of heat could be estimated from the amount of melted ice. This view brought him to the first calorimetric experiments with a simple phase-transition calorimeter. A warm probe was placed in the cavity of an block of ice, covered with a plate of ice and brought to thermal equilibrium. Furthermore, he adopted the idea of mixing water of different temperatures (mixing calorimeter) from Brooke Tylor (1723) to determine a series of latent heats of different substances. At the same time, A. L. Lavoisier and P. S. Laplace became interested in the theory of heat. They considered the widespread mixing calorimeters as unsuitable because of several disadvantages: the need of delicate corrections for the heat capacity of vessel and thermometer, heat loss by cooling, chemically reacting substances, inmiscible liquids. Moreover, this method did not allow the

Downloaded By: [University of Missouri] At: 19:42 13 May 2009 measurement of the heat produced during combustion and other chemical reactions, and during respiration, topics in which they were mostly interested. They developed the first convenient phase transition calorimeter that led to reproducing results. It was a simple but ingenious ice calorimeter, a device for measuring heat release due to respiration and combustion (Fig. 1). The instrument consisted of a chamber surrounded by an ice-packed jacket, and the whole device was further insulated with another ice-packed jacket to improve accuracy. The amount of water collected from the melted ice of the inner jacket was used as a measure of the heat evolved in the chamber. The handling was difficult and experiments could only be performed on days when the outside temperature was a few degrees above freezing. With this device, Lavoisier and Laplace determined the specific heat of various substances and found fairly good results compared to modern standards. The most famous experiments were conducted around 1780, when Lavoisier and Laplace measured the heat generated by a guinea pig and determined the amount of carbon dioxide in its exhaled air during the experiment. They compared it to heat release and carbon dioxide formation when burning charcoal. The results were accurate enough to conclude that respiration was a form of combustion. ORDER REPRINTS

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Figure 1. The ice calorimeter of Lavoisier and Laplace (from Oeuvres de Lavoisier, Tome Premier, Paris, Imprimerie Impe´riale, 1862).

Despite this interesting experimental work, the resulting interpretations of the nature of heat remained unclear. Lavoisier still treated heat as a weightless substance and called it caloric, matter of fire. Laplace favored a mechanical explanation of heat as motion of particles of matter, a view that emerged toward the end of the

Downloaded By: [University of Missouri] At: 19:42 13 May 2009 18th century out of experimental evidence provided by Count Rumford, formerly known as Benjamin Thompson. Rumford noticed that drilling a hole into metal to build a cannon barrel generated a large quantity of heat, and he noticed that there was no limit to the amount of heat that could be produced by simple drilling. He concluded that heat was motion and not matter (or caloric), otherwise it had to stop when the cannon was running out of caloric. But there was a big controversy about this theory, and it was not until the middle of the 18th century when the caloric theory was finally overthrown. The kinetic gas theory was established and the concept of energy arose. With the Industrial Revolution beginning in the 19th century, the nature of matter became of more than academic interest. With the realization that heat from combustion could produce work, the science of thermodynamics was born. It is concerned with the rules governing the interconversion of energy and is able to predict the feasibility of chemical processes. Calorimetric measuring techniques remained more or less the same during this time period, although there were some modifications and improvements. In the last few decades, calorimetric techniques have started to become of interest to biochemists and biologists outside a few specialized laboratories. Since practically ORDER REPRINTS

every process, be it physical, chemical, or biological, is accompanied by heat changes, it is obvious that calorimetry could serve as powerful analytical tool for a variety of applications, particularly in biological sciences. With concurrent advances in molecular biology, expression and purification techniques, that made available significant amounts of homogeneous protein, there was an increasing need for more and reliable thermodynamic data. This gave the inputs to develop new and very sensitive calorimeters, requiring only small sample quantities and being able to detect accurately very small heat quantities. Since the middle of the 20th century several calorimetric principles of different practical design have emerged. But it is only since the last few years, with the development and improvement of sufficiently sensitive, stable, user friendly, and affordable commercial calorimeters, that made calorimetry to become an almost routine analytical procedure in biochemical and biophysical research. Since modern instruments are very sensitive, detecting heat changes in the range of microcalories, requiring only 10–100 nmol of sample in a volume of 0.2–1.4 mL, they are usually denominated as microcalorimeters.

CALORIMETRIC PRINCIPLES AND PROPERTIES

To avoid confusion in describing the principles of calorimetry and calorimeters it is useful to distinguish three important areas: the measuring principle, the operating mode, and the type of construction (5,10–12).

Principles of Measurement

Downloaded By: [University of Missouri] At: 19:42 13 May 2009 Calorimeters are instruments for quantification of heat effects. Several principles of measurement have come into use. Amongst solution calorimeters there are two main groups: adiabatic calorimeters and heat conduction calorimeters. With an ideal adiabatic calorimeter there is no heat exchange between the calorimeter and the surroundings, and the heat quantity Q evolved during the experiment is directly proportional to the observed temperature change ÁT, and to the heat capacity " of the reaction vessel and its contents: Q ¼ " ÁT ð1Þ Thus, in an experiment the heat quantity is determined by measuring the temperature change. In an ideal heat conduction calorimeter the heat evolved is quantitatively transferred from the reaction vessel to the heat sink, a body surrounding the calorimeter which is usually made of metal. With this type of calorimeter, some property proportional to the heat flow between vessel and heat sink is measured. Normally the heat flow is recorded by placing a thermopile wall between the vessel and the surrounding sink. The temperature difference over the thermopile gives rise to a potential or voltage signal S that is proportional to the heat flow. The time integral for the heat flow, multiplied by a calibration constant ", ORDER REPRINTS

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is proportional to the heat quantity released in the experiment: Z Q ¼ " Sdt ð2Þ

The heat quantity is thus proportional to the area under the signal time curve.

Operating Mode and Construction Design of the Instrument

The most common type of calorimeter in use is the isoperibol calorimeter, also called ‘‘constant temperature environment’’ calorimeter. The vessel is separated by thermal insulation from the surrounding thermostated bath, which forms the isothermal jacket. The insulation is usually filled with air or vacuum. Exothermic or endothermic processes will result in a temperature change that is recorded by a thermometer. In practice, there will always be a small heat loss from the vessel into the surrounding. Therefore, this calorimeters are not truly adiabatic, but quasi-adiabatic. The heat exchange cannot be neglected and must be corrected for. Isoperibol calorimeters are very simple and for fast processes also very precise instruments. They are used as reaction or solution calorimeters and as combustion calorimeters, but have not found widespread use in biochemical or biological studies. In an adiabatic shield calorimeters the reaction vessel is enclosed by an additional thin-walled metal envelope, the adiabatic shield. It is placed in the vacuum or air space between the reaction vessel and the thermostated bath. The temperature difference between the shield and the vessel is kept at zero during the experiment by automatically applying a suitable heat effect on the shield. Calorimeters can be in a single or a twin arrangement. Although the single arrangement is simpler, the twin calorimeter has some advantage which makes it very attractive for microcalorimetry. One of the calorimetric vessels, the reaction cell, contains the system of interest, whereas the other vessel, the reference cell,

Downloaded By: [University of Missouri] At: 19:42 13 May 2009 contains water or buffer. With such an arrangement the recorded signal is a differential signal, of which the effects of thermal disturbances from the surroundings are expected to cancel out.

ISOTHERMAL TITRATION CALORIMETRY

The main calorimetric techniques applied to investigate biological macromolecules are differential scanning calorimetry (DSC) and isothermal titration calorimetry (ITC). DSC measures the enthalpy and heat capacity of thermal denaturation, and researches have learned about stability of biological macromolecule (proteins and nucleic acids) and of macromolecular assemblies (13–16). In contrast, ITC measures the heat evolved during molecular association. The direct thermodynamic observable is the heat associated with a binding event, i.e., a ligand is titrated into a solution containing the macromolecule of interest and the heat evolved or absorbed is detected. It allows the simultaneous determination of the equilibrium binding constant (KB) and thus the standard Gibbs free energy change (ÁG), the enthalpy change (ÁH ), the entropy change (ÁS ), as well as the ORDER REPRINTS

stoichiometry (n) of the association event. Moreover, experiments performed at different temperatures yield the heat capacity change (ÁCp) of the binding reaction (17–19). As almost any interacting system is characterized by changes in enthalpy, there is a vast range of potential ITC applications.

ITC Instrumentation

A number of suppliers offer microcalorimetric instruments with sufficient sensitivity for the determination of binding reactions, but most studies published so far have used instruments from MicroCal (Northhampton, MA, USA). With the introduction of the commercially available Omega titration calorimeter in 1989 (19), titration calorimetry has had a broad impact throughout biotechnology, which is reflected by a large body of publications. The Omega unit has been subsequently modified and automated during the last decade, offering now the most sensitive instrument that is able to measure interaction heat effects of reactant concentrations as low as 1–10 nmol. This type of calorimeter is based on a cell feedback that measures the differential heat effects between a reference and sample cell. A constant power applied to the reference cell activates the power feedback circuit that in turn regulates the temperature in the sample cell, thus slowly increasing the temperature during a measurement (typically less than 0.1C/h). The resting power applied to the sample cell is the baseline signal. Exothermic reactions as a result of the addition of ligand will decrease the necessary feedback power, and endothermic reactions lead to an increase in feedback power. The enthalpy of reaction for each injection is obtained by integration of the deflections from the resting baseline. Both cells are accessible by long narrow access tubes through which samples are introduced or removed using long-needled syringes. Typically, the reference cell is filled with water and the sample cell with the system of interest. The ligand is applied by injection syringes with long needles having a

Downloaded By: [University of Missouri] At: 19:42 13 May 2009 stirring paddle attached to the extreme end. The syringe is continuously rotated during an experiment, leading to complete mixing in the cell within a few seconds after an injection. The mechanical heat of stirring is constant and becomes part of the resting baseline. With optimal performance (short equilibration time) a complete binding isotherm may be determined within 30 min, although in practice it takes usually 80–100 min to obtain reliable data. It is worth emphasizing that calorimetric binding experiments are very challenging since noncovalent binding heats are intrinsically small, typically in the range of 5–10 kcal mol1, and must be liberated stepwise during the binding experiment. Furthermore, ligand addition produces additional heat effects arising from dilution and mixing, for which corrections must be made, and which are frequently comparable to the binding heat of interest. Considering the case of a typical reaction of interest that exhibits the heat effect of 5kcalmol1 in a 1–2-ml solution containing 107 mol of protein (a few mg), the experiment would liberate about 0.5 mcal of heat after complete saturation of all binding sites. Assuming that this heat is released upon 10 injections, the mean individual contribution would be 50 mcal. Accurate detection, with an accuracy of 10% or better, of such small quantities would require instrumental sensitivity and noise levels as low as 5 mcal or less. This corresponds to ORDER REPRINTS

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temperature changes in the sample solution of just a few millionths of a degree and is comparable to the inevitable heat effects of dilution, mixing, and stirring. The absolute detection limit of the latest generation VP-ITC calorimeter, expressed as the minimum detectable heat quantity, is reported to be 0.1 mcal (20).

EXPERIMENTAL DESIGN

General Experimental Setup

The setup of an ITC experiment is largely dependent on the thermodynamic characteristics of the system of interest, i.e., the expected binding affinity and the heat effect of the interaction. The appropriate concentration range for the macromolecule placed in the cell depends on the binding constant of the reaction. The shape of the binding curve is dependent on the product of the binding constant 1 KB (in M ) and the molar concentration of macromolecule [MT] being titrated (19):

C ¼ KB½MT ð3Þ The sensitivity of the shape of the binding isotherm to the dimensionless parameter C is crucial for determination of the binding constant. At high values for the so-called C-value (C > 500), the shape of the curve approaches a step function and becomes increasingly insensitive to changes in KB (Fig. 2). Experience shows that conditions should be chosen to have a C-value in the range of 10–100 for an accurate determination of KB. Therefore, to measure at these C-values, very strong

1 0 -1 -2 Downloaded By: [University of Missouri] At: 19:42 13 May 2009 -3 -4 C=1 -5 -6 C = KB[MT] -7 -8 kcal/mol of injectant C=10 C=50 -9 C=1000 C=500 -10 -11 012 Molar Ratio Figure 2. Simulated calorimetric binding curves illustrating the dependence of the shape of the curve on the product of the association constant KB and the total macromolecule concentration MT (C ¼ KB [MT]). The curves are simulated for several C-values (as indicated in the plot) according to Eqs. (16) and (18) for ÁH ¼10 kcal mol1. For high C-values the binding isotherms approach a step function, becoming increasingly insensitive to changes in KB. At low C-values, the binding curve becomes a horizontal trace that yields very little information about KB, making it necessary to use high macromolecule concentrations to obtain suitable binding isotherms. ORDER REPRINTS

binding (107–108 M1) requires low concentrations of the macromolecule. With decreasing concentrations of the reactants, the signal arising from the interactions will also become smaller, leading to the detection limit of ITC. This gives the technical limit of the highest affinity constant that can be determined. At low C-values (C < 10), the binding curve forms a horizontal trace that again yields very little information about KB. Consequently, it is necessary to use high macromolecule concentrations to obtain informative binding isotherms if low binding affinity is expected. In principle, there is no lower limit to lower binding affinity events that can be determined, but in practice there are problems with solubility, stability, and often with availability of the macromolecule in order to measure in the C-value range of 10–100. From Fig. 2 it is evident that even at low ligand concentration, only a minor fraction of ligand is bound to the macromolecule, making it difficult to detect sufficient heat and to determine ÁH accurately. This often sets the limit of the lowest affinity constant measurable in the range of 104 M1. The correct choice of reactant concentrations depends not only on the magnitude of KB, but also on the objective of the experiment. If it is of interest to simultaneously determine KB, ÁH, and n for a binding event, a complete binding isotherm must be recorded. Considering the limiting sensitivity of 0.1 mcal, each injection should produce an average heat change of 1–2 mcal in the 1.4-ml cell. For a series of 10 injections, each of 10 mL, a total Q of 20 mcal in the sample volume are required to define a total binding curve:

Q ¼ ÁH½MT V0 ð4Þ

where ÁH is the enthalpy of binding and V0 is the reaction volume of the sample cell. Solving Eq. (4) for [MT] predicts a minimum concentration of about 1.4 mM for a protein with a ÁH of 10 kcal mol1 needed to generate a complete binding isotherm to yield n, KB, and ÁH. According to Eq. (3), an arbitrarily chosen KB of 106 M1 would result in a very low C-value of 1.4. In practice it will be necessary to increase the protein concentration to perform the experiment in the ideal range for Downloaded By: [University of Missouri] At: 19:42 13 May 2009 C-values of 10–100. As a desirable side effect the heat signals will become larger. If the same calculation were done using higher affinity (107 M1), the resulting C-value of 14 would be sufficient for a complete deconvolution of the binding isotherm. Taken together, the almost 10-fold increase in sensitivity achieved from first to third generation microcalorimeters allows now to measure higher affinity (up to 109 M1) faster, more accurately, and with less material. For some applications it is preferred to obtain ÁH not as a fitting parameter, but to directly determine ÁH very accurately. In this case it is common practice to measure ÁH at concentrations when the binding partners are fully associated and the saturation is still low, i.e., full association at partial saturation (21). At these conditions (C-value >100), the amount of heat released or absorbed is directly determined by the amount of ligand injected:

Q ¼ ÁH½LT Vinj ð5Þ

where [LT] is the concentration of the ligand solution in the syringe, and Vinj is the injection volume. In principle, this allows the determination of ÁH with a single injection. In practice ÁH will be determined as the mean of a number of injections from the same experiment. ORDER REPRINTS

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To obtain high quality data, an appropriate protocol has to be established by optimizing ligand and protein concentrations and the injection volume. Typically, the ligand concentration is much higher since several equivalents must be added to the sample cell. The titration experiment should be planned to approach or reach complete saturation of the binding sites at the end of the experiment. To generate a sufficient number of data points, which will improve data analysis, the ligand has to be added in small aliquots. However, the heat signal should not become too small to maintain high precision of each data point. If the interaction heat is small, it will be necessary to choose larger injection volumes. These prerequisites define the titration protocol, and it is up to the experimentator to find the ideal compromise. As a rule of thumb, 25 injections, each of 5 mL, of a ligand solution with a concentration 25 times higher than that of the protein solution will result in an adequate binding isotherm. If the ligand is poorly soluble, it is possible to place it in the sample cell and to inject the macromolecule. As long as the binding stoichiometry is 1:1, either interacting molecule can act as the titrant without adjusting the binding model. For more complicated cases where this assumption does not hold, the model must be modified accordingly (22). The time between successive injections is another important parameter. If association is rapid, the instrument baseline will be equilibrated in a short time, depending on the response time of the calorimeter. Under such conditions 3–4 min are sufficient to reach baseline again after injection. In contrast, heat signals of slow processes require much more time to reach thermal equilibrium. Several other issues related to experimental design should be mentioned. It is crucial that solutions of ligand and macromolecule are pure and exactly match with respect to pH, buffer capacity, and salt concentration. This means that macromolecule and ligand are preferably dissolved in the same buffer. To achieve this goal, it is good practice to dialyze the protein prior to the experiment and dissolve the ligand in the dialysis buffer. This procedure will prevent spurious heat effects resulting from mixing of different buffers. Both interacting components, often purified from biological

Downloaded By: [University of Missouri] At: 19:42 13 May 2009 source, must be free of contaminating enzymatic activity that could affect the association event under investigation. Furthermore, the formation of air bubbles has to be avoided. Thus it is very important to thoroughly degas all solutions prior to the experiment. Any air in the syringe can cause variation in the injected volume or lead to additional heat signals, and bubbles in the sample cell interfere with the thermal contact of solution and cell wall. Finally, in most experiments the heat effect of the first injection of a series of injections is obviously too small. This results from diffusion while equilibrating the system. Even if care is taken to avoid this leakage, the problem may persist. Therefore it is common practice to make a small first injection of 1 mL and then to remove the first data point before data analysis.

Control Experiments

Isothermal titration calorimetry not only measures the heat released or absorbed during binding reactions, but it detects the total heat effect in the calorimetric cell upon addition of ligand. Thus, the experimental heat effect contains contributions arising from nonspecific effects, such as dilution of ligand in the buffer, dilution of ORDER REPRINTS

the protein sample, heat of mixing, temperature differences between the cell and the syringe, and mixing of buffers of slightly different composition. These contributions need to be determined by performing control experiments in order to extract the heat of complex formation. This would need at least a further three titrations to measure these effects (ligand into buffer solution, buffer into protein solution, buffer into buffer solution). In practice however, the latter contributions are found to be small and frequently negligible, whereas the heat of ligand dilution may be significant and needs to be corrected for. Alternatively, if the titration experiment is designed to ensure complete saturation of the enzyme before the final injection, and if the blank experiments mentioned above show the heat of ligand dilution to be concentration- independent, then the nonspecific heat effects can be estimated very well by averaging the small heats at the end of the titration.

Evaluation of Protonation Effects

Whenever binding is coupled to changes in the protonation state of the system, the measured heat signal will contain the heat effect due to ionization of buffer. If the binding event changes the protonation state of free or bound ligand as well as of free or complexed macromolecule, proton transfer with the buffered medium occurs. As a consequence, the heat of protonation/deprotonation will contribute to the overall heat of binding and ÁHobs will depend on the ionization enthalpy of the buffer (ÁHion). Repeating the calorimetric experiment at the same pH in buffers of different ÁHion allows to determine the number of protons nHþ that are released (nHþ > 0) or taken up (nHþ < 0) by the buffer, and thus to calculate the intrinsic binding enthalpy, ÁHbind, corrected for protonation heats:

ÁHobs ¼ ÁHbind þ nHþÁHion ð6Þ

Downloaded By: [University of Missouri] At: 19:42 13 May 2009 In practice, it is recommended to perform ITC experiments in a series of buffers of different ionization heats under otherwise the same conditions. Values of ÁHion have been described (23,24) or can be determined by ITC (25). If the same ÁHobs is observed, there is no protonation event coupled to binding. Deviations of ÁHobs with different buffers point to a protonation event, and the intrinsic enthalpy of binding (ÁHbind) can be obtained from the intercept (ÁHion ¼ 0) of the regression line described by Eq. (6) (26). Although it would seem that such buffer effects would mean a nuisance, it is one of the most powerful means to investigate binding mechanisms, and this property can be exploited to increase the signal strength of an otherwise undetectable binding event by simply changing the buffer system to a different pH and to buffers with high ionization enthalpies (25).

DATA ANALYSIS

The signal monitored by ITC is the differential power applied to the sample cell. The total heat released or absorbed upon an injection of ligand into the cell ORDER REPRINTS

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Time (min) -10 0 10 20 30 40 50 60 70 80 90 100 0 A -2

-4 µ cal/sec -6

-8 0 B -2 -4 Ribonuclease A -6 Single site binding model − -8 KB(105M 1) 2.51 ± 0.029 -10 ∆H (kcal/mol) −12.98 ± 0.02 -12 n 0.953 ± 0.001

kcal/mole of-14 injectant 0.0 0.5 1.0 1.5 2.0 2.5 Molar Ratio [2´CMP]/[RNASE A]

Figure 3. Calorimetric data for the exothermic binding of cytidine 20-monophosphate (20CMP) to ribonuclease A (RNase A) at pH 5.5 (0.2 M K-acetate, 0.2 M KCl) and 28C (figure kindly provided by Dr. I. Jelesarov). A: raw data obtained for 25 automatic injections of 5 mL. Concentrations of RNase A and 20CMP are 0.145 mM and 3.72 mM respectively. The area of each peak represents the total heat evolved upon addition of a single aliquot of 20CMP. B: titration plot derived from the integrated heats of binding, corrected for heats of dilution. The solid line represents the nonlinear best fit to the data assuming a single-site binding model.

corresponds to the area under the signal vs. time curve (Fig. 3, panel A). The

Downloaded By: [University of Missouri] At: 19:42 13 May 2009 instrumental baseline and other unspecific heat effects must be carefully subtracted from the raw data. Experimental data can be presented as a sigmoid plot (differential mode) or as a hyperbolic saturation curve (integral mode). The differential mode treats each injection as an independent point and is plotted as heat evolved per injection vs. total ligand concentration or the ratio of the total ligand concentration to the concentration of macromolecule (Fig. 3, panel B). In the integral mode, the total cumulative heat is plotted against the total ligand concentration. Fitting a binding model to the calorimetric data plotted in either mode yields equivalent results. Generally, random errors tend to cancel out in the integral mode, whereas systematic errors tend to be amplified. Comparative statistical analysis of both modes can give information about the accumulation of systematic errors (27,28).

Ligand Binding in Titration Calorimetry

There are many techniques available to measure binding constants (KB). Equilibrium dialysis, radio-ligand binding assays or ultracentrifugation directly ORDER REPRINTS

yield concentrations for the macromolecule [M], ligand [L], or complex [ML] to calculate KB. Spectroscopic methods are more indirect by detecting an observable p, the change of which is proportional to the degree of saturation (18,28–30). ITC is the most direct method to measure the heat change on complex formation. In general, a simple reversible association between a macromolecule M and a ligand L, M þ L $ ML ð7Þ

is characterized by its binding constant KB: ½ML K ¼ ð8Þ B ½M½L The observable response of an ITC experiment is the heat change associated with each addition of ligand. For each injection, the heat released or absorbed is directly proportional to the total amount of formed complex. This can be expressed by

q ¼ V0 ÁH Á½MLð9Þ where q is the heat associated with the change in complex concentration, Á[ML], ÁH is the molar enthalpy of binding, and V0 is the reaction volume of the sample cell. In a calorimetric experiment, each addition of ligand gives rise to a heat change depending on the reaction volume, concentrations, molar enthalpy, binding constant, heat of dilution, stoichiometry, and the amount of previously added ligand. As the concentration of unoccupied binding sites begins to decrease, the heat changes decrease correspondingly as ligand is added. The total cumulative heat after the ith addition, Q, will be X Q ¼ V0ÁH Á½MLi ¼ V0ÁH½MLi ð10Þ

Downloaded By: [University of Missouri] At: 19:42 13 May 2009 where [ML]i is the total concentration of complex after the ith injection. Evaluation of microcalorimetric data requires the consideration of the observable response in terms of total ligand added or the total ligand concentration. Therefore, the binding equations must be expressed as a function of total ligand and macromolecule concentration:

½MT ¼½MLþ½Mð11Þ

½LT ¼½MLþ½Lð12Þ

where [MT]and[LT] are total macromolecule and ligand concentrations, respectively, and [M] and [L] are free concentrations of macromolecule and ligand, respectively. [ML] is the concentration of the formed complex.

Single Set of Independent Sites Model

In the simplest case of ligand binding, each macromolecule consists of only one type of binding sites with a finite number of identical noninteracting binding sites, ORDER REPRINTS

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all of which exhibiting the same intrinsic affinity for the ligand. For such a system, the binding constant KB is given by Â K ¼ ð13Þ B ð1 ÂÞ½L

where Â is the fractional saturation and [L] is the concentration of free ligand. It is related to the total ligand [LT] and macromolecule concentration [MT], by mass conservation:

½L¼½LT nÂ½MT ð14Þ

Combining Eqs. (13) and (14) gives the quadratic equation 1 ½L ½L Â2 Â 1 þ þ T þ T ¼ 0 ð15Þ nKB½MT n½MT n½MT

whose only meaningful root is 0 sffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi 1 1 1 ½L 1 ½L 2 4½L Â ¼ @1 þ þ T 1 þ þ T T A ð16Þ 2 nKB½MT n½MT nKB½MT n½MT n½MT

The integral heat of reaction Q after the ith injection is given by

Q ¼ n½MT V0ÁHÂi ð17Þ

where V0 is the cell volume and ÁH is the molar heat of ligand binding. The differential heat of the ith injection is

qi ¼ n½MT V0ÁHðÞÂi Âi1 ð18Þ Downloaded By: [University of Missouri] At: 19:42 13 May 2009 A nonlinear fit based on Eq. (17) to the hyperbolic saturation curve in the integral mode (Q vs. [LT]) yields the parameters KB, ÁH, and n from a single experiment. Based on Eq. (18), the titration data can be fitted to the sigmoid saturation curve in the differential heat mode (qi vs. [LT], or vs. [LT]/[MT]). The same parameters are obtained.

Complex Binding Models

Similar relationships as described above exist for other models, i.e., a model for multiple sets of independent binding sites, single set of interacting sites (cooperative sites), multiple sets of interacting binding sites. By use of statistical thermodynamic treatment it is possible to deconvolute a binding isotherm of such complex systems (31–33). Instructive examples from the literature demonstrate the strength of this approach (34–38). However, the success strongly depends on the quality and reliability of the experimental data. ORDER REPRINTS

BASIC THERMODYNAMIC RELATIONSHIPS

The binding enthalpy of protein–ligand interactions can be determined accurately by means of ITC. The association constant KB is related to the Gibbs free energy ÁG by the well-known relation

ÁG ¼RT lnKB ð19Þ where R is the universal gas constant and equals to 1.987 cal K1 mol1 and T is the temperature in degrees Kelvin. ÁG is again composed of an enthalpy term (ÁH) and an entropy term (ÁS), related by another fundamental equation: ÁG ¼ ÁH TÁS ð20Þ The Gibbs free energy is temperature dependent and is described by Z Z T T ÁGðTÞ¼ÁHðT0Þþ ÁCp dT TÁSðT0Þ ÁCp dlnT ð21Þ T0 T0

where ÁCp is the heat capacity change and T0 is an appropriate reference temperature. With ÁCp being independent of temperature in the range of interest, Eq. (21) simplifies to T ÁGðTÞ¼ÁHðT0ÞTÁSðT0ÞþÁCp T T0 T ln ð22Þ T0 Equation (22) shows that enthalpy and entropy changes are dependent on temperature through the heat capacity change ÁCp:

ÁHðTÞ¼ÁHðT0ÞþÁCpðT T0Þð23Þ

ÁSðTÞ¼ÁSðT0ÞþÁCp lnðT=T0Þð24Þ

Downloaded By: [University of Missouri] At: 19:42 13 May 2009 In a thermodynamic analysis the goal is to determine ÁG, ÁH, ÁS, and their temperature dependence by ÁCp, since these four parameters provide a full description of the energetics governing molecular interactions.

STRATEGIES FOR MEASURING TIGHT BINDING AFFINITY

High-affinity binding constants for protein–ligand interactions are inherently difficult to measure. With increasing affinity, it becomes necessary to work at low concentration of macromolecule, leading to difficulties in detecting the signal specific to the analytical method. In the case of ITC, the largest binding constant that can be measured reliably, approaches 109 M1 for a typical macromolecule–ligand interaction (18,19,39). The thermodynamic approach offers a powerful advantage for measuring tight binding affinities and thus Gibbs free energy changes (ÁG). Free energy changes are state functions, i.e., their values are defined by the initial and final thermodynamic states, regardless of the pathway connecting the two states. This being the case, it is ORDER REPRINTS

16 Perozzo, Folkers, and Scapozza

possible to determine the binding constant for a protein–ligand interaction under different conditions that allow measuring the affinity. The result can be corrected to other conditions of more physiological relevance, if it is known by what parameters the binding free energy is linked with the conditions being varied. In principal, any change of physical or chemical conditions that influence ligand association to a macromolecule is useful, but in most cases linkage of binding with pH and temperature are exploited.

Linked Protonation Effects in Ligand Binding

Often molecular interactions are very tight (>109 M1), and are not accurately measurable even with the most sensitive calorimeters available. Generally, molecular interactions occur to some degree in dependence of pH, reflecting the linkage between the association of a ligand and the binding of protons (proton linkage). The molecular basis of the linkage is the result of alterations of pKa values of ionizable amino acid groups concomitant with binding. If ligand binding is coupled with uptake of a single proton (Fig. 4), the observed ligand binding constant Kobs is given as

c pH 1 þ KP10 Kobs ¼ Kint ð25Þ f pH 1 þ KP10

c f where Kint is the intrinsic binding constant, KP and KP are the proton binding constants for the complex and free form of the protein and are equal to 10pKa,c and 10pKa, f, respectively, of the ionizing group (40,41). According to Eq. (25) proton linkage can be viewed as change in proton affinity, thus protons will either be

Downloaded By: [University of Missouri] At: 19:42 13 May 2009 released or absorbed due to ligand binding. If proton transfer occurs during binding, the ÁHobs is determined by the ionization enthalpy of the buffer and the enthalpy of binding corrected for buffer effects see according to Eq. (6), and both the number of protons (nHþ)and

Kint M M:L

f c Kp Kp

M+ M:L+

Figure 4. Scheme for proton binding linked to binding of a ligand L to a macromolecule M. Ligand binding reactions are shown in horizontal direction whereas proton binding occurs in vertical direction (see text for details). ORDER REPRINTS

the intrinsic binding enthalpy (ÁHbind) will vary as a function of pH. Thus nHþ is given by c f nHþ ¼ f f ð26Þ where f c and f fare the fractional saturation of protons at a given pH of the bound and free protein. In case of a single protonation event, f c and f f can be expressed as Kc 10pH c ¼ P ð Þ f c pH 27 1 þ KP10 and Kf 10pH f f ¼ P ð28Þ f pH 1 þ KP10 The change in the number of protons bound by the protein upon binding of the ligand is the difference between Eqs. (27) and (28): c pH f pH c f KP10 KP10 nHþ ¼ f f ¼ ð29Þ 1 þ Kc 10pH f pH P 1 þ KP10 Equation (29) clearly shows that at a minimum of two pH values pKa,c of the c pKa,c f pKa,f complexed (KP ¼ 10 ) and pKa,f of free protein (KP ¼ 10 ) can be calculated by simultaneously solving Eq. (29) for nHþ determined at the corresponding pH values, even when the ligand affinity is too tight to be measured (40). In practice, nHþ is determined in a series of buffers of different ionization enthalpies as a function of pH. Equation (29) is used to determine the pKa of the protein in the free and complexed state. With these values KB at the tight binding conditions can be calculated by Eq. (25) (40,41). In general this treatment can be applied to more complicated systems that involve two protons linked with binding (31,41). A similar approach based on free energy of binding linked to the number of protons transferred as a function of pH has been developed (39). Downloaded By: [University of Missouri] At: 19:42 13 May 2009 The power of the calorimetric approach in evaluating proton linkage lies in the fact that ÁH can be determined with high precision under conditions where KB is not measurable, and thus the contributions of the linkage.

Thermodynamic Linkage to Temperature

The fundamental Eqs. (19) and (20) demonstrate that the equilibrium constant for a process is related to the standard entropy and enthalpy changes, and to the absolute temperature. The temperature dependence of the changes in free energy (ÁG) of the Gibbs-Helmholtz equation for a thermodynamic system is described as ðÞÁG=T ÁH ¼ ð30Þ T T 2 where T is the absolute temperature and ÁH is the reaction enthalpy. Substitution of Eq. (30) with Eq. (19) yields the familiar van’t Hoff equation: lnK ÁH B ¼ ð31Þ ð1=TÞ R ORDER REPRINTS

18 Perozzo, Folkers, and Scapozza

where KB is the binding constant. The temperature dependence of KB is commonly analyzed by means of the van’t Hoff plot, whose basis is Eq. (31). Measuring KB over a temperature range and plotting lnKB vs. 1/T yields the van’t Hoff enthalpy (ÁHvH). It is calculated from the slope of the plot, according to Eq. (31). When ÁH is known, then integration of Eq. (31) gives the temperature dependence of the equilibrium constant: ÁH 1 1 KBðTÞ¼KBðT0Þ exp ð32Þ R T T0 Equation (32) explicitly assumes that ÁH is constant over the temperature range T–T0. It has been shown for many biological protein–ligand interactions that this assumption is not valid, with ÁH often being temperature dependent. The temperature dependence of the binding enthalpy, ÁCp, is described by Eq. (23), and combination with Eq. (32) leads to the extended form of the van’t Hoff equation that accounts for the temperature dependence of ÁH: ÁHTðÞ0 1 1 ÁCp T T0 KBðT Þ¼KBðT0Þ exp þ ln þ 1 ð33Þ R T T0 R T0 T

where KB (T) is the binding constant to be calculated at temperature T, KB (T0)is the binding constant experimentally determined at temperature T0, and ÁH(T0)is the experimental enthalpy change at temperature T0. In cases where the affinity is too tight to be measured directly by ITC under ambient conditions, the binding constant becomes accessible by changing to a temperature whereby binding is lowered. After determining ÁH and the temperature dependence thereof, the binding constant can easily be calculated for the temperature of interest (42).

Displacement Experiments Downloaded By: [University of Missouri] At: 19:42 13 May 2009 As an alternative approach for measuring tight binding constants, a displacement experiment can be carried out. The protein of interest is presaturated with a more weakly binding ligand whose binding parameter can be determined directly, and this ligand is displaced by injecting a ligand that binds more strongly. The first ligand will compete with the second and thus reduce the apparent binding constant (43–46). A first experiment yields the thermodynamic parameters for the first ligand A (ÁH1, K1), the second titration gives apparent values for the second ligand B obs obs obs (ÁH2 , K2 ). The observed association constant for binding of B ðK2 Þ in the presence of A is given by:

obs K2 K2 ¼ ð34Þ 1 þ K1½A obs The observed binding enthalpy (ÁH2 ) is given by:

obs K1½A ÁH2 ¼ ÁH2 ÁH1 ð35Þ 1 þ K1½A ORDER REPRINTS

obs obs Thus, from knowledge of ÁH2 and K2 the affinity K2 and binding heat ÁH2 for the tight binding ligand B can be deduced using Eqs. (34) and (35). Both equations assume that the concentration of B does not change during the measurment. This can be achieved when B is also present in the syringe solution. However, it is also possible to include dilution effects into the fitting procedure. An exact mathematical treatment for the analysis of competition ligand binding using displacement ITC has recently been published (47). A restriction to the displacement method occurs when both ligands bind with exothermic ÁH, as the tight binder will be reduced by the endothermic contribution of the dissociation of the first ligand. Therefore, it is necessary that the interaction enthalpies for both ligands differ significantly.

INFORMATIONAL CONTENT OF ITC DATA

The gain of knowledge through thermodynamic data of binding reactions is large and can have a dramatic impact on characterizing the molecular mechanism of binding. The thermodynamic profile of an interaction process reflects various types of forces that drive binding, including enthalpic contributions of bond formation, entropic effects such as restrictions of degrees of freedom, the release and uptake of water and ion molecules, the burial of water-accessible surface area and changes in vibrational content. As outlined before, calorimetry measures a global property of a system, and thus reflects the sum of all concomitant phenomena which must be carefully analyzed and quantified in order to yield parameters conforming to the binding event properly. Moreover, the quality of the deconvoluted parameters depends on the appropriate model used. Nevertheless, once reliable data are available, the informational content of thermodynamic data can have an intriguing impact on the characterization of molecular mechanisms of binding. Downloaded By: [University of Missouri] At: 19:42 13 May 2009

Binding Free Energy

The Gibbs free energy of binding is the most important thermodynamic description of binding, since it determines the stability of any given biological complex, and it has been (and still is) a useful analytical tool for the phenom- enological characterization of structure–function relationships. The typical analysis of calorimetric data involves fitting an appropriate model to the data, i.e., simple single-site or two-site binding model, which yields the binding constant. But often, the system under investigation exhibits a more complex behavior and more sophisticated models must be applied (multiple interacting-site models). The macromolecule may undergo ligand-induced changes, be they conformational adaptations in the binding site or self-association of the receptor, which will contribute to the total free energy of binding. These effects should be evaluated independently with companion methods, i.e., analytical ultracentrifugation, analytical gel filtration chromatography, and spectroscopic methods. ORDER REPRINTS

20 Perozzo, Folkers, and Scapozza

In several recent publications it has been proposed to dissect binding free energy into several contributing terms. The total binding free energy contains a contribution typically associated with the formation of secondary and tertiary structure (van der Waals interactions, hydrogen bonding, hydration, conformational entropy), electrostatic and ionization effects, contributions due to conformational transitions, loss of translational and rotational degrees of freedom, and others that must be accounted for on an individual basis (48–55). For example, an observed ÁG can be the same for, an interaction with positive ÁS and ÁH (binding dominated by hydrophobic effect) and an interaction with negative ÁS and ÁH (when specific interactions dominate). Moreover, interacting systems tend to compensate enthalpic and entropic contributions to ÁG, making binding free energy relative insensitive to changes in the molecular details of the interactions process (55–57). Thus, consideration of ÁH and ÁS are crucial for a detailed understanding of the free energy of binding.

Binding Enthalpy

The observed heat effect of a binding reaction is a global property of the whole system under investigation, reflecting the total heat change in the calorimetric cell upon addition of ligand. On the one hand, the measured heat contains contribution from unspecific heat effects, on the other hand, there might be protonation effects coupled to binding. Therefore it is of outmost importance to determine possible contributions to the intrinsic binding enthalpy and to correct for them. But even corrected heat changes are composed of different contributions, which is the reason why the enthalpy is an apparent or observed (ÁHobs) quantity (29). At first appearance, the physical meaning of ÁH seems to be simple: it represents the changes in noncovalent bond energy occurring during the interaction.

Downloaded By: [University of Missouri] At: 19:42 13 May 2009 This interpretation is too simple to describe observed ÁH values. The measured enthalpy must be the result of the formation and breaking of many individual bonds, since it is barely conceivable to form bonds without breaking any others, especially in aqueous medium. The enthalpy change of binding reflects the loss of protein– solvent hydrogen bonds and van der Waals interactions, formation of protein–ligand bonds, salt bridges and van der Waals contacts, and solvent reorganization near protein surfaces. These individual components may produce either favorable or unfavorable contributions, and the resultant is likely to be smaller than the specific interactions (30). From calorimetric studies carried out in water and D2O it was concluded that a large part of the observed enthalpy change is due to bulk hydration effect (58,59). Often, water molecules are placed in the complex interface, improving the complementarity of the complex surfaces and extending H-bond networks. This can make enthalpies more favorable, but is often counterbalanced by an entropic penalty (60–62). The role of interfacial water was directly examined by lowering water activity by means of glycerol or other osmolytes. Complexes with a low degree of surface complementarity and no change in hydration are tolerant to osmotic pressure (25,63–66). ORDER REPRINTS

Besides the unspecific hydration effects, all direct noncovalent bonds at the binding interface contribute to ÁH, actually reflecting the binding enthalpy in a strict sense. The dissection of each noncovalent interaction is very difficult since the net heat effect of a particular bond is the balance between the reaction enthalpy of the ligand to the macromolecule and to the solvent. Moreover, structural alterations at the binding site due to the binding event may contribute to the binding enthalpy. Several mutational approaches have been applied to investigate the energetics of individual bonds: alanine scanning mutagenesis (67), removal of particular H-bonds at the active site (68), construction of double mutant cycles (69). However, all these approaches suffer from the problem that a direct relation between the change in ÁH and the removal of the corresponding specific contact in the active site cannot be made a priori. On a theoretical basis it has been argued that decomposition of ÁH is not possible (70), but others favor a dissection into specific contributions (71,72).

Binding Entropy

The entropy of binding is directly calculated from ÁG and ÁH according to Eq. (20). In general, it represents all other positive and negative driving forces that contribute to the free energy. Recently, it has been proposed that the total entropy change associated with binding can be expressed as the sum of several contributing effects. The main factor contributing to ÁS of complex formation is due to hydration effects. Since the entropy of hydration of polar and apolar groups is large, the burial of water-accessible surface area on binding results in solvent release which contributes often large and positive to the total entropy of interaction. Another important, though unfavorable contribution reflects the reduction of rotational degrees of freedom around torsion angles of protein and ligand side-chains. An additional entropy term accounts for the reduction of the number of particles in solution and their degrees of freedom (73–75).

Downloaded By: [University of Missouri] At: 19:42 13 May 2009 A negative entropy change can entail different contributions, and it does not necessarily indicate increased or unchanged hydration interfaces, but a positive entropy change is a strong indication that water molecules have been released from the complex surface (76).

Heat Capacity Changes

If ÁH is determined at a range of temperatures (modern ITC instruments allow measurements between 2 and 80C), the change in the constant pressure heat capacity (ÁCp) for an interaction is given by the slope of the linear regression analysis of ÁHobs plotted vs. temperature. Often ÁCp does not depend on temperature within the small physiological temperature range, although several publications reported weak or strong dependencies (37,38). For the binding reaction, ÁCp is almost always negative when the complexed state of the macromolecule is taken as the reference state. Its origin lies in the fact that there is strong correlation between ÁCp and the surface area buried on forming a complex (77–81). It has been shown that the removal of protein surface area from ORDER REPRINTS

22 Perozzo, Folkers, and Scapozza

the contact with solvent results in a large negative ÁCp. The basis of this observation is the different behavior of solvent on the surface of a macromolecule and to that in the bulk, particularly with respect to water molecules interacting with hydrophobic surfaces. This means that for any process, in which water is released from the surface, ÁCp will be substantial and would be proportional to the amount of surface involved. Through this correlation of ÁCp and burial of surface area, the heat capacity provides a link between thermodynamic data and structural information of macromolecules. Therefore in the last years, there has been considerable progress in the parameterization of all thermodynamic parameters and the predictions thereof (82,83).

Enthalpy–Entropy Compensation

In most thermodynamic binding studies of biological systems, the phenomenon of enthalpy–entropy compensation has been described (56,57). It is characterized by the linear relationship between the change in enthalpy and the change in entropy, i.e., favorable changes in binding enthalpy are compensated by opposite changes in entropy and vice versa, resulting in small changes in binding affinity over a range of temperature. It is assumed that enthalpy–entropy compensation is connected to the properties of the solvent (water), and it appears also to be a general consequence of perturbing weak intermolecular interactions (84,85). This is in agreement with the fact that increased bonding in a binding process, resulting in more negative ÁH,will be at the expense of increased order, leading to more negative ÁS. As both ÁH and ÁS are connected to ÁCp, it is not surprising that both parameters are correlated. In terms of medicinal chemistry and rational drug design, this phenomenon is a difficult problem to overcome in order to increase binding affinity of a compound to the protein of interest. The ideal optimization strategy requires the implementation of enthalpic or entropic contributions that result in a minimal entropic or enthalpic

Downloaded By: [University of Missouri] At: 19:42 13 May 2009 penalty. This will induce the largest change in ÁG, thereby defeating the deleterious effects of enthalpy–entropy compensation at the thermodynamic level.

Binding Stoichiometry

The determination of binding stoichiometries (n) is of central importance for the characterization of binding mechanisms of biological macromolecules. ITC has emerged as an important tool, since it enables high-precision analysis with high reproducibility because of the computer-controlled injection of definite volumes. Assuming that the concentrations of both interacting species are known, the binding stoichiometry can be determined from the molar ratio of the interacting species at the equivalence point. During fitting procedures the parameter n can either be fixed as equal to the number binding sites per macromolecule, or it can be treated as an additional floating parameter that is determined by iterative fitting. There are a number of possible sources which lead to deviation from the expected values of n, i.e., high experimental uncertainty of the data set, error in concentration of either the ligand or the macromolecule, unspecific binding, degradation of ligand, and low ORDER REPRINTS

protein quality (unfolded, missfolded). If these systematic errors can be ruled out and the fitted values for n still deviate significantly from expected values known from additional independent information, they should be reexamined. It has been proposed to fit the data to the hyperbolic equation and then to reanalyze them by means of a double reciprocal plot (30). However, it is recommended to corroborate stoichiometric data obtained by microcalorimetry with additional independent information. Once the model is verified, ITC can be used as an excellent quality control tool for the analysis of the fractional binding activity of different lots of protein (e.g., antibodies), for stability testing (freeze-thaw) and many more (86).

PREDICTION OF BINDING ENERGETICS

A long-standing goal of biophysical chemistry is the prediction of binding energetics from the 3D-structure of protein–ligand complexes, and it is a key element in the field of structure based drug design. The rapidly increasing availability of high- resolution protein structures from X-ray crystallography and nuclear magnetic resonance (NMR) opened the field to combine structural information with thermodynamic data of the binding process. In the past few years, considerable progress has been made in characterizing molecular aspects of protein–ligand interactions by means of thermodynamic data. It has been shown that the major polar and apolar contributions to the enthalpy, entropy and heat capacity changes for protein folding and unfolding can be described in terms of changes in solvent-accessible polar and apolar surface area (77,80,81,87–91). The empirical parameterization based on calculation of changes in solvent accessible surface areas was first applied to the prediction of protein folding energetics (77,80,81,87,89,91). Since the atomic interactions involved in associations

Downloaded By: [University of Missouri] At: 19:42 13 May 2009 reactions are similar (92), it was suggested to apply this approach to protein–protein interactions, peptide binding to proteins, and to small ligand binding. The parameterization has now reached the state in which accurate prediction of protein folding energetics and binding energetics is possible (73,79,93,94).

Solvent-Accessible Surface Areas

According to Lee and Richards (95), the solvent-accessible surface area (ASA) is defined as the surface traced out by the center of a solvent probe (frequently taken as a sphere with a radius of 1.4 A˚) as it moves over the surface of the protein. There are a number of implementations described and used to determine ASA (95,96), and it is very important to recognize that each implementation yields slightly different results. The original description of the predicting parameters is based on the Lee and Richards algorithm as implemented in the program ACCESS, using a probe radius of 1.4 A˚and a slice width of 0.25 A˚, but there exist reparameterized values for other implementations (82). When performing calculations, it must be assured that the appropriate parameters are used, because they are dependent on the algorithm used. ORDER REPRINTS

24 Perozzo, Folkers, and Scapozza

Calculation of Thermodynamic Parameters

In general, changes in solvent-accessible surface area (ÁASA) are determined as the difference of ASA of the final state and of the initial state. For a molecular interaction process, this is the difference between the ASA of the complex and the sum of the ASA of the macromolecule and the ligand, resulting in negative values of ÁASA. ÁASA is further subdivided into nonpolar and polar contributions by simply defining which atoms take part in the surface. Oxygen, nitrogen and sulfur are treated as polar and all carbon atoms as apolar. If structured water molecules take part in the binding interface, they must be accounted for, since their presence will contribute to the amount and type of surface area buried (82,97). In the ideal case, structural information will be available for the complex and for both interacting species, i.e., the free macromolecule and the ligand. This will account for any structurally defined conformational differences that occur on binding. If there are no conformational changes linked with the association (rigid body binding), it is good practice to extract the free state of the protein by removing the coordinates of the ligand from the complex. However, the assumption of rigid body binding must be verified by other independent methods because any structural rearrangement will contribute to the energetics. For the case where binding is linked to order/disorder of particular regions which are not defined in the coordinate file of the macromolecule, it may be necessary to add a model of the region. This is especially found in protein peptide interactions, for which it is necessary to define a solution structure of the free peptide (73,79).

Calculation of DCp

The largest contribution to the ÁCp for a binding process arises from dehydration of protein and ligand surface with negative contributions due to Downloaded By: [University of Missouri] At: 19:42 13 May 2009 burial of apolar surfaces (ÁASAnp) and positive contributions due to burial of polar surfaces (ÁASAp). From studies of the dissolution of solid model compounds, the following relationship has been proposed (77,80):

ÁCp ¼ ÁcnpÁASAnp þ ÁcpÁASAp ð36Þ

The parameters Ácnp and Ácp that are suitable for calculations based on ASAs determined by different programs are given in Table 1. Equation (36) is used to predict the ÁCp for ligand binding.

Calculation of DH

The change in enthalpy of binding reflects the loss of protein–solvent hydrogen bonds, van der Waals interactions, and formation of protein–ligand bonds, salt bridges and van der Waals contacts, and solvent reorganization near protein surfaces. ÁH is calculated with reference to the temperature at which the apolar contribution is assumed to be zero ðTH Þ. The value of TH is the enthalpy convergence ORDER REPRINTS

Table 1. Empirical parameters for calculation of ÁCp and ÁH for three different implementations (units for Ác in cal K1 (molA˚2)1; for Áh in cal (molA˚2)1; probe radius 1.4 A˚; slice width 0.25 A˚).a

ACCESS ACCESS NACCESS Parameter (Presnell)b (Richards)c (Hubbard)d

Ácap 0.45 0.36 0.43 Ácp 0.26 0.25 0.26 Áhap 8.43 3.63 7.26 Áhp 31.29 23.75 29.14 e Áhap(25) 7.35 e Áhp(25) 31.06

aValues from Ref. (82) bRef. (141) cRef. (95) dRef. (96) eRef. (97).

temperature, which is obtained from protein unfolding studies and is determined to be 100C (373 K) (98). Thus the enthalpy change is calculated from: ÀÁ ÁH ¼ ÁH þ ÁCp T TH ð37Þ

where ÁH is the polar contribution to ÁH at TH The value for ÁH is directly proportional to the burial of polar surface area (77,80) and is described by ÁH ¼ 35ð6Þ ÁASAp ð38Þ

where ÁASAp is the change in polar accessible surface area and is negative for a

Downloaded By: [University of Missouri] At: 19:42 13 May 2009 binding reaction. Above dissection contains a linear extrapolation of protein unfolding enthalpies as a function of polar and apolar surface area (77,80,91). A regression analysis at the medium unfolding temperature of proteins (60C) minimizes the extrapolation ˚2 error and yields the elementary contributions per A of apolar (Áhap) and polar (Áhp) surface to the enthalpy function at the reference temperature of 60 C (ÁHbind(60 C)): ÁHbindð60 CÞ¼ÁhapÁASA þ ÁhpÁASAp ð39Þ

Values for Áhap and Áhp are given in Table 1. At any other temperature T, ÁH(T) is given by the standard equation ÁHbindðTÞ¼ÁHbindð60 CÞþÁCpðT 333:15Þð40Þ Above calculations were shown to hold reasonably well at predicting the binding enthalpy for protein–protein and protein–peptide interactions, but correlation of structure and enthalpy has yielded inconsistent results for interactions of small molecules (MW < 800) with biological macromolecules. A first attempt for empirical parameterization of ÁH for small ligands has been published recently (97). As a first ORDER REPRINTS

26 Perozzo, Folkers, and Scapozza

approximation the experimental binding heat (ÁHexp) can be considered as the combination of atleast three terms:

ÁHexp ¼ ÁHint þ ÁHconf þ ÁHprot ð41Þ

The intrinsic enthalpy (ÁHint) reflects the interaction of the ligand with the protein and corresponds to the situation when ligand and protein adopt the same conformation in the free and the bound state. These contributions to the enthalpy are assumed to scale with changes in accessible surface area that can be param- eterized. Contributions from conformational changes (ÁHconf) cannot be easily described in terms of ÁASA. ÁHprot contains binding enthalpy that is due to protonation effects that can and need to be dissected experimentally by performing experiments in buffers of different ionization enthalpies. Once corrected for ÁHprot, the calculated ÁHbind at 25 C, the temperature at which most binding studies are performed, will be the sum of the constant term ÁHint and a term that is specific for each ligand:

ÁHbindð25Þ¼ÁHconf ð25ÞþÁhapð25ÞÁASA þ Áhpð25ÞÁASAp ð42Þ

The intrinsic enthalpy is now described in terms of ÁASA with Áhap(25) and ˚2 Áhp(25) as the scaling coefficients that yield the elementary contributions per A of apolar and polar surface, respectively. The values for Áhap(25) and Áhp(25) are given in Table 1. In practice, a system under investigation will be analyzed using the scaling parameters, ÁASA and leaving ÁHconf as the only adjustable parameter that needs to be determined for each protein separately from a training set of known protein– ligand structures. This new approach is based on a HIV-1 protease database and has been validated by six different datasets for which structural data of complexed and free protein was available (97). Downloaded By: [University of Missouri] At: 19:42 13 May 2009

Calculation of DS

The total entropic contributions associated with binding reactions can be expressed as the sum of three terms (75):

ÁStot ¼ ÁSsolv þ ÁSconf þ ÁSr=t ð43Þ

where ÁSsolv describes the change in entropy resulting from solvent release upon binding, ÁSconf is a configurational term reflecting the reduction of rotational degrees of freedom around torsion angles of protein and ligand. ÁSr/t entails the loss of translational and rotational degrees of freedom when a complex is formed from two molecules free in solution. The most important contribution to the entropy change arises from the solvation term (ÁSsolv), primarily due to burial of apolar surface area and is approximated for any temperature (T ) by following equation: ÀÁ ÁSsolv ¼ ÁCp ln T=TS ð44Þ ORDER REPRINTS

where TS is the temperature at which there is no solvent contribution to the hydrophobic entropy change and is equal to 112C (385 K) (74,99,100). The translational/rotational entropy term (ÁSr/t) accounts for the reduction of the number of particles in solution and their degrees of freedom. Very different estimates of the magnitude of ÁSr/t have been discussed (73,101). Empirical and theoretical considerations have suggested that this term contributes 4to10 cal K1 mol1 to the overall entropy of a bimolecular binding event (74,102,103). This value is numerically close to the cratic entropy of 8 cal K1 mol1(104) even if there is no sound physical reason to equate translational/rotational entropy to the statistical part of the mixing entropy (105). Finally, the configurational entropy ÁSconf reflects contributions from changes in side-chain conformational entropy as well as all other structural rearrangement of protein and ligand induced by complex formation. Since ÁStot is experimentally accessible, and ÁSsolv and ÁSr/t can be estimated, ÁSconf is calculated from:

ÁSconf ¼ ÁStot ÁSsolv ÁSr=t ð45Þ The contribution to ÁS from side chains involved in binding can be estimated by 1 1 considering an average contribution of 4.3 cal K mol per residue to ÁSconf (73,100,107). On the assumption that only minor configurational entropic contributions of ligand occur, a rough estimate of the number of amino acids (Xres) participating in the interaction is available by

ÁSconf Xres ¼ ð46Þ 4:3 cal K1mol1 If the amino acid side chains directly participating in the interaction process are known from theoretical or experimental studies, it is possible to calculate each contribution to ÁSconf as the sum over the amino acids involved in binding (108–111). A binding process involves two contributions to ÁSconf: (i) restrictions around side-chain torsion angles and (ii) immobilization of the peptide backbone. If Downloaded By: [University of Missouri] At: 19:42 13 May 2009 binding is not involved in order/disorder transitions, only the side-chain component applies. The side-chain contribution is then calculated by assuming that the entropy is zero when fully buried and scales linearly with ASA, resulting in a maximum value when fully exposed. By applying this model, a term ÁSbu!ex, i must be introduced to account for the buried-to-exposed entropy gain which differs for each side chain. The contribution is then calculated as X ÁASAsc;i ÁSconf ¼ ÁSbu!ex;i ð47Þ i ÁASAAXA;i

where ÁASAsc,i is the change in ASA of side chain i on binding, and ÁASAAXA,i is the ASA of the side chain in an extended Ala-X-Ala tripeptide (82). The summation is carried out over all side chains participating in the interface. Values for ÁASAsc,i are determined from structural data, estimates for ÁASAAXA,i and ÁSbu!ex,i (111) are available, and they have been adapted for different implementations (82). These data are presented in Table 2. For interaction processes involving transitions, two additional terms associated with the backbone entropy (ÁSbb) and with the entropy of the transition from ORDER REPRINTS

28 Perozzo, Folkers, and Scapozza

Table 2. Total side-chain ASA (ÁASAsc) of Ala-X-Ala tripeptides used in parameterization for three ASA implementations (units in A˚2), and side-chain and backbone conformational 1 1 a entropy values (ÁSbu!ex, ÁSbb, ÁSex!u; units in cal K mol ).

ACCESS ACCESS NACCESS (Presnell)b (Richards)c (Hubbard)d ÁASAsc ÁASAsc ÁASAsc ÁSbu!ex ÁSbb ÁSex!u

Ala 52.1 60.4 54.6 0.00 4.11 0.00 Arg 187.9 210.2 199.6 7.10 3.39 0.84 Asn 113.8 123.0 112.9 3.30 3.39 2.25 Asp 102.4 106.5 99.4 2.01 3.39 2.15 Cyse 70.8 69.1 70.3 3.56 3.39 0.62 Cysf 91.9 94.5 92.0 3.56 3.39 0.62 Gln 128.7 142.8 122.3 5.02 3.39 2.13 Glu 117.5 135.8 124.5 3.54 3.39 2.27 Gly 0.0 0.0 0.0 0.00 6.50 0.00 His 144.3 152.2 144.9 3.44 3.39 0.79 Ile 123.8 138.5 135.9 1.74 2.17 0.67 Leu 134.5 150.6 143.8 1.62 3.39 0.24 Lys 156.9 177.7 155.6 5.85 3.39 1.03 Met 158.5 160.8 158.0 4.54 3.39 0.57 Phe 176.7 179.4 172.0 1.41 3.39 2.89 Pro 90.7 104.8 90.7 ——— Ser 68.6 76.6 71.7 3.68 3.39 0.55 Thr 105.3 112.1 105.4 3.30 3.39 0.48 Trp 222.7 218.7 222.4 2.75 3.39 1.15 Tyr 188.5 196.4 190.2 2.77 3.39 3.13 Val 105.6 118.0 105.6 0.12 2.17 1.29

aValues from Ref. (82) bRef. (141) c Downloaded By: [University of Missouri] At: 19:42 13 May 2009 Ref. (95) dRef. (96) eValues for disulfide-bonded cystine; fValues for free cysteine.

the exposed/folded state to the exposed/unfolded state (ÁSex!u) must be included (82). Estimates of these contributions are also available (110), and are summarized in Table 2. For nonpeptide ligands a special empirical parameterization has been proposed to account for changes in conformational degrees of freedom between free and complexed forms of ligand (112). As a first approximation, it is assumed that the conformational entropy will be proportional to the number of rotatable bonds. Since effects from excluded volume increase with the number of atoms for a given number of rotatable bonds, the conformational entropy change for a nonpeptide ligand (ÁSconf, np) is considered to be a linear function of the number of rotatable bonds (Nrb) and the total number of atoms (Nat):

ÁSconf;np ¼ k1Nrb þ k2Nat ð48Þ ORDER REPRINTS

To date, Eq. (48) has not yet been widely applied, and in fact it has only been tested for the analysis of HIV-1 protease inhibitors. However, for these interactions 1 1 1 1 k1 was found to be 1.76 cal K mol , whereas k2 equals 0.414 cal K mol (83,112).

Linkage Effects

Above calculations apply for systems that do not involve linked equilibria. If binding of a second ligand is coupled to binding of a first one, the contributions of the second equilibrium must be considered. Protonation linkage is a common phenomenon in interaction processes. Its contributions to the binding energetics can be determined experimentally, using a global analysis of experimental data as a function of pH, temperature, and buffer ionization enthalpy (40,94).

THERMODYNAMICS AND RATIONAL DRUG DESIGN

The rapidly increasing availability of high-resolution protein structures has opened the possibility to use structural information in the design of new drugs. The central problem of structure-based design studies is the understanding of the features dictating the energetics of the interaction of a ligand with a macromolecule, i.e., the accurate prediction of the Gibbs free energy that determines the binding affinity.

The Thermodynamic Approach

The prediction of binding energetics is greatly complicated by the effects of enthalpy–entropy compensation (84,85) which means that an increase in ÁH does

Downloaded By: [University of Missouri] At: 19:42 13 May 2009 not contribute to the binding affinity, as the improvement is only achieved by a compensation cost in the TÁS term. The sheer number of entropic and enthalpic effects contributing to the observed ÁG makes it difficult to rationalize and predict binding affinities. Additional energetic effects will arise from any differences in ligand conformations in the free state in solution and bound to the macromolecule. Moreover, water molecules play an important role in adapting the binding pocket to different ligands. The strength of the thermodynamic approach is that it has become possible to dissect the contributing forces (ÁH, ÁS, ÁCp) which make up the free binding energy of the interaction by direct calorimetric measurements, yielding effective energetic contributions, including all interactions that are found in the system (protein, ligand, and solvent interactions).

Current Status of the Thermodynamic Approach

Until recently, thermodynamic data have not played an important role in molecular design, since no theoretical framework relating structural and ORDER REPRINTS

30 Perozzo, Folkers, and Scapozza

thermodynamic data has been established. The situation has changed with the realization that changes in solvent-accessible surface area (ÁASA) are related to the thermodynamic parameters. The semi-empirically derived set of structural energetic parameters (summarized in Table 1), based on the parameterization of ÁG, ÁH, ÁS,andÁCp in terms of ÁASA, is a promising thermodynamic approach to drug design. It is possible now to predict the energetics of folding of a globular protein (77,80,81,87,89,91) with an accuracy within 9–12% (82). The parameterization has reached the state in which accurate predictions of binding energetics is possible as well, as shown for peptide–protein and protein–protein association (73,79,93,94), and also for nonpeptide ligand–protein interactions (112). However, the thermodynamic approach is still in its infancy, as there are no comprehensive reports documenting the design of a high-affinity drug using a ‘‘thermodynamic-directed rational drug-design’’ approach. Furthermore, current parameterization is still based on protein unfolding data, and there is need for more thermodynamic and structural characterization of protein–ligand interactions, including not only ÁH, ÁS,andÁCp, but also thorough investigation of phenomena linked to binding, i.e., protonation, ion binding, and conformational changes. As the research in this field makes progress, the structural thermodynamic database is expanded and the set of energetic parameters can be refined to the point that will allow accurate prediction of effects of small molecular changes. Thus, this will allow a much easier assessment of better binding drugs.

CASE STUDIES FOR PROTEIN–LIGAND INTERACTIONS STUDIED BY ITC Downloaded By: [University of Missouri] At: 19:42 13 May 2009 The term ‘‘protein–ligand interactions’’ stands for the association of a biological macromolecule (a protein) with any molecule (the ligand). This is an arbitrary restriction to biological system and does not mean that other binding partners do not exist or cannot be measured. As mentioned before, any binding event that will produce sufficient binding heat will be amenable to the calorimetric approach. This has been shown for a variety of interacting systems (for a list see http://www.calorimetry.com) that are not subject of the present discussion. Whereas the biological systems under investigation deal with proteins as one binding partner, e.g., antibodies, enzymes, or receptors (soluble and membrane bound), the ligand will include all possible interaction partners for the protein. These can be proteins, peptides, DNA, carbohydrates, lipids, metal ions, inhibitors, substrates, or cofactors. At this point we describe three representative examples for biological systems, i.e., enzymes, soluble receptor moieties, and membrane-bound receptors that have been investigated using the direct thermodynamic approach. This will give a deeper understanding to the reader with respect to the informational content, possibilities, and potential drawbacks for the calorimetric experiments. ORDER REPRINTS

Substrate Binding to HSV1 Thymidine Kinase

Introduction

An example of high medicinal interest where thermodynamic information is essential is thymidine kinase from Herpes simplex virus type-1 (HSV1 TK). The structure of this enzyme is known at high resolution in complex with a series of ligands, including various substrates (natural and non-natural) and inhibitors (113–117). Thymidine kinases (EC 2.7.1.21) catalyze the phosphorylation of dT to thymidine monophosphate (dTMP) in presence of magnesium ions by transferring the -phosphate group of adenosine triphosphate (ATP) to the 50-OH group of dT. Herpes viruses encode their own thymidine kinases, which differ considerably from the enzyme of the human cellular host (hTK1). While the human enzyme is highly specific, HSV1 TK is a multifunctional enzyme of broad substrate specificity and requires low stereo-chemical specificity. Therapeutic applications involving HSV1 TK make use of the broad substrate diversity of the viral enzyme in the background of strict substrate selectivity of the host cell enzyme. Therefore, a detailed thermodynamic analysis of substrate binding to the viral kinase is a prerequisite for the successful design of new therapeutically useful compounds. ITC has been used to investigate the thermodynamic parameters of the binding of thymidine (dT) and adenosine triphosphate (ATP) to wild type and mutated HSV1 TK (118,119).

Thermodynamic Parameters for dT and ATP Binding to HSV1 TK

In the ternary complex TK:dT:ATP, the substrate dT and the cofactor ATP are located in separate and well defined binding pockets of the enzyme. Formation of the Downloaded By: [University of Missouri] At: 19:42 13 May 2009 ternary enzyme–substrate complex may either proceed through an obligatory sequential pathway or by a random mechanism. Two sequential pathways are possible: TK!TK:dT!TK:dT:ATP (reactions (i) and (ii) of Fig. 5), or TK! TK: ATP!TK:dT:ATP (reactions (iii) and (iv) of Fig. 5). In a random mechanism,

TK TK:dT (i)

(iii) (ii)

(iv) TK:ATP TK:dT:ATP

Figure 5. Formation of the ternary enzyme–substrate complex TK:dT:ATP. The two ordered sequential pathways are (i), plus (ii) and (iii), plus (iv), respectively. In a random binding mechanism, all four reactions will take place. ORDER REPRINTS

32 Perozzo, Folkers, and Scapozza

binding of one substrate is not a prerequisite for binding of the other, and all four reactions of Fig. 5 can take place. To distinguish between ordered and random binding, HSV1 TK was titrated with dT and with ATP, respectively. The titration with dT was characterized by a significant exothermic heat effect. The nonlinear least 5 1 squares fit for a single-site binding model gave a KB of 1.9 10 M and ÁHbind of 19.1kcal mol1 (Table 3). Under identical conditions ATP did not bind to empty HSV1 TK. An ordered binding mechanism was confirmed by titrating the preformed TK:dT binary complex with ATP (reaction ii of Fig. 5). The reaction was again 6 1 1 exothermic and yielded a KB of 3.9 10 M and ÁHbind ¼13.8 kcal mol . Moreover, titration of TK with a 1:1 mixture of dT and ATP yielded within error a heat change corresponding to the sum of the heat changes for reactions (i) and (ii) of

Fig. 5. The ITC experiment provides the binding constant KB for a single-site reaction, and ÁG of reactions (i) and (ii) were calculated from Eq. (19). ÁS was obtained from Eq. (20). Reactions (i) and (ii) were driven by favorable negative changes in binding enthalpy and strongly opposed by unfavorable entropic contributions. Although the reaction with the 1:1 mixture of dT and ATP was more complex, it could still be treated as a single-site reaction if one considered dT þ ATP as one ligand. ITC measurements performed in the temperature range of 10–25C showed strong dependence of ÁH and TÁS on temperature while ÁG was almost insensitive due to enthalpy–entropy compensation. Values of ÁCp were

calculated from the slopes of the regression lines of ÁHbind vs. temperature. Binding of dT to the free enzyme was characterized by ÁCp of 360 cal K1 mol1. For ATP binding to the TK:dT complex, ÁCp of 140 cal K1 mol1 was determined, and for the titration of the enzyme with a 1:1 mixture of dT and ATP, a ÁCp

Table 3. Thermodynamic parameters for the binding of thymidine and ATP to HSV1 TK ab

Downloaded By: [University of Missouri] At: 19:42 13 May 2009 at pH 7.5 and 25 C. Values of ÁH are corrected for protonation effects by Eq. (6).

TK þ dT (reaction (i)) TK:dT þ ATP (reaction (ii)) TK þ dT/ATPc Temp. (C) ÁH ÁGTÁS ÁH ÁGTÁS ÁH ÁG d TÁS

10 13.6 7.1 6.5 11.7 8.9 2.8 25.4 18.9 6.5 15 15.8 7.0 8.8 12.1 8.9 3.2 27.9 18.9 9.0 20 17.4 7.1 10.2 13.0 9.1 3.9 30.5 17.2 13.3 25 19.1 7.2 11.9 13.8 9.0 4.8 33.1 16.8 16.3 ÁCp 0.36 0.14 0.51

aValues of ÁG, ÁH, and TÁS in kcal mol1, ÁCp in kcal K1 mol1. b Values are the mean of triplicates. ÁG was calculated from ÁG ¼RT lnKB, where KB is the binding constant determined by ITC. Uncertainty of ÁG is within 0.35 kcal mol1 of the mean. Errors of ÁH are about 5% and mainly reflect the error in ligand concentration. Maximal possible errors of TÁS are 1.5 kcal mol1. Errors of ÁCp were estimated by reduction of the data set by one data point at a time and were, on average, 0.02 kcal K1 mol1, i.e., within 5–15% of the reported mean. c1:1 mixture of dT and ATP. d 2 Calculated from ÁG ¼RT ln(KB) . ORDER REPRINTS

of 510 kcal K1 mol1 was measured. The latter value was very close to the sum of ÁCp’s of reactions (i) and (ii). The stoichiometry of binding obtained from all experiments was in the range 0.7–0.8 mol ligand per mol of HSV1 TK monomer. Deviation from a value of 1 was due to the presence of inactive protein (118).

Protonation Effects

Titration experiments were repeated in various buffers of different ÁHion. The intrinsic enthalpy of binding, ÁHbind, was obtained from the intercept (ÁHion ¼ 0) of a plot according to Eq. (6). Protonation/deprotonation was negligible in the case of dT binding to the free enzyme (ÁHobs ¼ ÁHbind). An uptake of 0.31 protons was observed with ATP binding to the TK:dT complex. Titration with the 1:1 mixture of dT and ATP lead to the uptake of 0.35 protons. It follows that proton uptake occurred with ATP binding but not with dT binding. Heat changes from ITC experiments were corrected accordingly (118).

Decomposition of Entropy Changes

The results for the decomposition of ÁStot for substrate binding to HSV1 TK are summarized in Table 4. It is evident that the favorable solvent contribution ÁSsolv is overcompensated by the large unfavorable conformational entropy change ÁSconf. The contribution by ÁSr/t is unfavorable but small. In accordance with a thermodynamic cycle, the decomposed entropy changes of reactions (i) and (ii) add up to the changes calculated for the titration of the enzyme with the 1:1 mixture of dT and ATP. Solvent reorganization provided a substantial gain in binding entropy due to Downloaded By: [University of Missouri] At: 19:42 13 May 2009 water release, about 90 cal K1mol1 for dT binding and about 35 cal K1mol1 for ATP binding. This considerable ÁSsolv is in agreement with fact that phosphorylation of an acceptor OH-group must occur in the absence of competing water molecules. After subtracting from ÁStot the favorable ÁSsolv

Table 4. Decomposition of entropy changes for substrate binding to HSV1 TK at 25C.a

b c d e Reaction ÁStot ÁSsolv ÁSconf ÁSr/t

TK þ dT, reaction (i) 39.9 92 124 8 TK:dT þ ATP, reaction (ii) 16.1 36 44 8 TK þ dT/ATP 54.7 133 172 16

aIn cal K1mol1. bCalculated from values of TÁS in Table 3. cCalculated from Eq. (44) using ÁCp from Table 3. dCalculated from Eq. (43). eValue for bimolecular and trimolecular reaction, respectively (104). ORDER REPRINTS

34 Perozzo, Folkers, and Scapozza

term and the small unfavorable cratic ÁSr/t, a large unfavorable entropic contribution ÁSconf remained. ÁSconf may have two main origins, the first being ‘‘freezing’’ of bond rotations, i.e., amino acid side chains which are in direct contact with the bound substrate or involved in building a closed or more compact conformation, become less mobile. The other contribution to ÁSconf is partial folding or tightening of domains, particularly in the area of substrate binding. Interdomain movements within the enzyme dimer also can contribute to ÁSconf if the HSV1 TK dimer becomes more closed in the substrate-bound form. An estimation of these contributions according to Eq. (47) showed that approximately 95% of ÁSconf may be due to conformational domain movements and partial folding, or refolding, of domains, in line with the general concept that nucleoside–nucleotide kinases undergo conformational changes during their catalytic cycle (120,121).

Correlation Between ÁCp and Surface Area Buried on Substrate Binding

The experimentally determined parameters ÁCp and ÁHbind (Table 3) were used to simultaneously solve Eqs. (36) and (39), yielding the total area apparently buried (ÁASAtot) during the binding reactions (Table 5). ÁASAtot for the reaction with a 1:1 mixture of dT and ATP was 5100 A˚2. This change in area corresponded very well to the sum of the surface changes for the individual reactions (i) and (ii) (Fig. 5). These figures seem to be very large for the binding of the two small substrate molecules and are comparable to the surface buried on folding of a small globular protein of 50–60 residues. As no structural information is available for HSV1 TK in the ligand-free state, ÁASAap

Table 5. Changes of solvent accessible surface area (ÁASA) caused by substrate binding to Downloaded By: [University of Missouri] At: 19:42 13 May 2009 HSV1 TK.a

d e Calculation Reaction ÁASAap ÁASAp ÁASAtot ÁCpcalc ÁCpexp Rigid bodyb TK þ dT 400 200 600 130 360 TD parametersc Reaction (i) 1750 1500 3250 Rigid bodyb TK:dT þ ATP 300 450 750 20 140 TD parametersc Reaction (ii) 850 850 1700 Rigid bodyb TK þ dT/ATPf 700 650 1350 150 510 TD parametersc 2650 2450 5100

aÁASA in A˚2, ÁCp in cal K1 mol1. bÁASA calculated by removing dT, ATP, or both, from the crystal structure of the TK:dT:ATP complex (113). cÁASA calculated by simultaneously solving Eqs. (36) and (39) using experimental values of ÁCp and ÁHbind from Table 3. dCalculated from Eq. (36) with ÁASA calculated from the crystal structure of TK:dT:ATP (rigid body assumption). eFrom Table 3. fReaction with 1:1 mixture of dT and ATP. ORDER REPRINTS

and ÁASAp could not be tested against actual structural data. As an approximation, ÁASA values were obtained for the ternary crystal complex after removal of dT and ATP. The buried surface area calculated in this way corresponds to a rigid body binding model in which no conformational changes take place when dT and ATP bind. As seen in Table 5, ÁASAtot was very much smaller, resulting in calculated ÁCp values, using Eq. (36), that were correspondingly smaller than experimental ÁCp. Even so the calculated surface area changes may be rough estimations, the significant discrepancy to a rigid binding model is an indisputable sign for significant conformational rearrangements accompanying substrate binding to HSV1 TK. Moreover it is in very good agreement with the finding that the major unfavorable entropic contribution is due to loss of conformational entropy.

Mutational Studies

The mechanism of the broad substrate diversity observed with HSV TK1 was investigated by a thorough mutational study, kinetic measurements and ITC. The residue triad H58/M128/Y172 was identified to confer distinctive binding of an exceptionally large variety of substrates to HSV TK1 and to guide catalytic properties. Mutations in this triad (H58L, M128F, Y172F) have been prepared and were analyzed by ITC (Table 6). The mutation M128F with a phenylalanine in this position resulted in a completely inactive enzyme when examined by kinetic measurements and an HPLC assay. It was shown that the lack of activity for the M128F mutant was not due to hydrophobic collapse initiated by the mutation, but was the result of disturbed dT binding (200-fold reduced affinity compared to the wild-type enzyme) that yielded an unproductive orientation of the substrate. This view is corroborated by the strongly Downloaded By: [University of Missouri] At: 19:42 13 May 2009 reduced binding enthalpy, in combination with the more than seven-fold reduction of unfavorable entropy. It is evident that the major structural changes needed for catalytic competence, which are induced by dT in wild-type HSV1 TK, could not take place. Similar results were found with the double mutant M128F/Y172F. In this mutant the dT binding site is formed by a ‘‘double-F sandwich,’’ a particular

Table 6. Thermodynamic data for dT binding in presence of ATP of wild type HSV1 TK and triad H58/M128/Y172 mutants.

1 5 1 1 1 HSV1 TK ÁH (kcal mol ) KB (10 M ) ÁG (kcal mol ) TÁS (kcal mol )

Wild type 26.35 0.47 229 146 9.95 0.40 16.4 0.85 M128F 8.14 0.20 1.04 0.10 6.84 0.08 1.30 0.20 M128F/Y172F 3.71 0.08 0.34 0.19 6.14 0.34 2.43 0.42 H58L/M128F/Y172F 19.82 0.13 2.61 0.06 7.38 0.01 12.44 0.14 ORDER REPRINTS

36 Perozzo, Folkers, and Scapozza

combination that also occurs in various thymidine kinases. In this case even the sign for the entropy changed, giving evidence for a favorable preformed binding pocket with a subsequent reduced induced fit movement. However, the hydrogen bonding network seems to be impaired as deduced from the almost 700-fold reduced dT affinity as compared to wild-type HSV1 TK. Sequence alignments show that this double-F combination never occurs with a histidine at position 58 (HSV1 TK numbering). Subsequently H58 was replaced by leucine, giving the triple mutant H58L/M128F/Y172F. With this third mutation enzymatic activity was regained, but the broad substrate diversity was lost since activity and kinetic studies as well as HPLC assays did not show any activity of the triple mutant against non-natural substrates. Although the binding affinity is almost 90-fold reduced, binding enthalpy and entropy adopt similar values as for wild-type HSV1 TK, indicating restored flexibility of the enzyme.

Conclusion

This is the first report providing a comprehensive thermodynamic description of substrate and cofactor binding to HSV TK1, a representative of the large family of nucleotide and nucleoside kinases. The results obtained by titration microcalorimetry reveal an extreme case of positive heterotropic interaction. Formation of a binary complex of thymidine with HSV1 TK is a stringent prerequisite for ATP binding. Since the ATP binding site is in fact generated by thymidine binding, one expects the enzyme to undergo considerable conformational rearrangements. This has been supported by a semi-empirical analysis of the observed heat capacity and entropy changes, which were large and negative and indicated burial of molecular surface to an extent much larger than expected if the substrate binding Downloaded By: [University of Missouri] At: 19:42 13 May 2009 sites would pre-exist on the apo-enzyme and no rearrangement would occur (rigid body binding model). The favorable gain in entropy from water release from buried surface was overcompensated by a large decrease in conformational entropy. The findings support the view that substrate binding to HSV1 TK leads to a conformational closing of the substrate binding sites to bring thymidine and ATP into an orientation appropriate for catalysis. Further mutational studies in the residue triad H58/M128/Y172 supported this view and could shed light on possible mechanisms important for substrate diversity. Nevertheless, the details of the predicted rearrangements have to await a firm structural foundation. The direct thermodynamic approach using ITC proved to be an invaluable tool for this study. It made not only possible to characterize HSV1 TK interactions with its substrate and cofactor thermodynamically, but it was also an invaluable tool to investigate mutants that were not amenable through standard kinetic measurements. The lack of observable activity does not necessarily mean that binding is prevented, but the association can be altered either subtly or dramatically, thus leading to catalytic incompetence. ITC clearly is the method of choice for studies of that kind. ORDER REPRINTS

Specificity of Citrate Binding to the Histidine Autokinase CitA Receptor

Introduction

The periplasmic receptor domain of the sensor kinase CitA of Klebsiella pneumoniae represents another very interesting biological system that has been investigated by the direct thermodynamic approach (122). In bacteria, regulation of gene expression in response to changing environmental conditions is often accomplished by two-component regulatory systems. In their simplest form they consist of two modular proteins, the sensor kinase and the response regulator. The sensor kinase responds to a certain stimulus by autophosphorylation of a conserved histidine residue. Subsequently the phosphate group is transferred to a conserved aspartate residue of the response regulator that mediates adaptations in gene expression or cell behavior. In most cases sensor kinases are transmembrane proteins with an extracellular N-terminal sensor domain flanked by two transmembrane helices and a cytoplasmic C-terminal autokinase domain that is connected to the second transmembrane helix via a linker region of variable length (123). With this architecture bacteria are capable to sense external stimuli and transduce information to the cytoplasm. Although many two-component systems are known, only in a few cases have the primary stimulus of the sensor kinase been identified. In part this lack of knowledge is due to the difficulties in isolating and purifying the membrane- bound kinases. The strategy to overcome this problem is the expression of the extracellular N-terminal sensor domain as a separate and soluble protein that allows studying its binding properties. The sensor kinase CitA of Klebsiella pneumoniae and its cognate response regulator CitB form the paradigm of a subfamily of bacterial two-component systems (124). It is involved in the regulation and expression of genes needed for citrate fermentation. In a previous study it was proposed that citrate was the favorite signal recognized by CitA (125). Attempts to demonstrate this function with an E.

Downloaded By: [University of Missouri] At: 19:42 13 May 2009 coli in vivo system were not successful. Therefore, the periplasmic domain of CitA was cloned and expressed as a separate, soluble receptor domain supplemented with a C-terminal His6-tag (CitAPHis). Ligand specificity and thermodynamic binding properties were investigated using ITC (122).

Citrate Binding Studies Using ITC

Isothermal titration calorimetry was applied to describe the thermodynamic properties of citrate and citrate-analog binding to CitAPHis. Citrate binding to CitAPHis was shown to be an exothermic process that could be fit according to a single-site binding model, yielding a KD value (KD ¼ 1/KB)of5.5mM, an observed 1 enthalpy change (ÁHobs)of18.25 kcal mol . In contrast to citrate, neither isocitrate nor tricarballylate exhibited demonstrable binding to CitAPHis under the same conditions. Additional displacement experiments were necessary in order to exclude complete entropically driven binding which would not release significant binding heat. The results of these experiments showed that the presence of isocitrate and tricarballylate did not alter the binding behavior of citrate towards CitAPHis. ORDER REPRINTS

38 Perozzo, Folkers, and Scapozza

Therefore it was concluded that both isocitrate and tricarballylate do not bind to the citrate binding site.

Investigation of the Probable Citrate Species Binding

Depending on the pH, citrate exists in four different species, i.e., H3-citrate, 2 3 H2-citrate , H-citrate , and citrate with pK1 ¼ 3.13, pK2 ¼ 4.76, and pK3 ¼ 6.40 (126). In order to determine the preferred ligand species, the pH-dependency of

the citrate binding constant to CitAPHis was determined in the range of pH 4.0–9.0 (Table 7). The curve obtained from a plot of log KB vs. pH showed a maximum at about pH 5.7 and decreased above and below this value, indicating that citrate binding is linked to ionization events (41). The ÁHobs and ÁS values were negative over the pH range studied, showing that binding of citrate to CitAPHis was driven by the enthalpy change, whereas the entropy change was opposite. It is very likely that this result can be explained by the assumption that H-citrate2 is the citrate species responsible for the interaction, as the affinity of citrate to CitAPHis at different pH values closely reflects the appearance of the H-citrate2 form at the corresponding pH. As deduced from Table 7, the maximal affinity was found at pH 5.7 and it decreased above and below this value, similar to the occurrence of the divalent citrate form. An obvious explanation of this result 2 is that CitAPHis binds the H-citrate species specifically and therefore proton release has to occur at pH values below pH 5.7 (where the H2-citrate species becomes dominant) and proton uptake must take place at pH values above pH 5.7 (where the citrate3 form becomes prevalent). Assuming that the H-citrate2 species is recognized by CitAPHis, four functional groups are available for binding interactions, i.e., one uncharged and two charged carboxyl groups and the hydroxyl

Downloaded By: [University of Missouri] At: 19:42 13 May 2009 group. Current crystallographic data of macromolecules involved in binding of uncomplexed citrate clearly indicate that citrate binding is preferred in the extended conformation with important interactions of all four functional groups (127–130).

Table 7. Thermodynamic parameters of citrate binding to CitAPHis at 25 Cin50mM sodium phosphate buffer at different pH values as determined by ITC.a

KB KD ÁHobs ÁG TÁS pH n (104 M1) (mM) (kcal mol1) (kcal mol1) (kcal mol1)

4.0 0.98 0.01 6.26 1.33 16.7 3.6 24.71 0.04 6.53 0.13 18.18 0.17 5.0 0.85 0.01 38.55 1.25 2.6 0.1 23.37 0.40 7.62 0.02 15.75 0.41 6.0 0.80 0.01 51.65 2.95 1.9 0.1 20.60 0.19 7.79 0.03 12.81 0.15 7.0 0.89 0.01 18.25 0.75 5.5 0.2 18.26 0.05 7.17 0.02 11.08 0.02 8.0 0.85 0.01 9.05 0.09 11.1 0.1 17.52 0.18 6.76 0.01 10.76 0.17 9.0 0.83 0.01 6.47 0.02 15.5 0.1 17.89 0.04 6.56 0.01 11.33 0.04

aThe data represent the mean of two independent experiments. Errors are quoted as standard deviations for the two repeats of each titration. ORDER REPRINTS

Table 8. Thermodynamic parameters of citrate binding to CitAPHis at 25 Cin50mM sodium phosphate buffer pH 7.0 including different Mg2þ concentrations as determined by ITC.a

MgCl2 KB KD ÁHobs ÁG TÁS (mM) n (104 M1) (mM) (kcal mol1) (kcal mol1) (kcal mol1)

0 0.89 0.01 18.25 0.75 5.5 18.26 0.05 7.17 11.08 2 0.83 0.01 7.78 0.17 12.9 18.88 0.02 6.67 12.21 10 0.85 0.05 2.47 0.20 40.5 21.30 0.20 5.99 15.31 20 0.89 0.03 1.74 0.11 57.5 21.40 0.11 5.78 15.62

aThe data were obtained from a single experiment for each Mg2þ concentration and the errors quoted for n, KB, and ÁH are those for the least-squares fit to the binding isotherms in the individual titrations.

The fact that neither isocitrate nor tricarballylate did bind to CitAPHis showed the essential role of the hydroxyl group at position C3 in the citrate molecule.

Influence of Mg2þon Citrate Binding

The inhibitory influence of Mg2þ ions on citrate binding observed before could be confirmed by ITC. As shown by the titration experiments presented in Table 8, increasing Mg2þ concentrations resulted in an exponential decrease of the binding 2þ affinity. At 20 mM Mg , 10-fold reduction of KB was measured. In the context with the above discussion of the four citrate species at different pH, it became clear that the reduced binding affinity of citrate in the presence of Mg2þ ions was due to Mg2þ complexation by citrate3. Using a stability constant of 1585 M1(126) for the Mg–citrate complex, the preferred H–citrate2 species is reduced in the presence of 2þ

Downloaded By: [University of Missouri] At: 19:42 13 May 2009 20 mM Mg at pH 7.0 to less than 0.01%, and nearly all of the citrate (96.2%) is complexed as Mg–citrate. Crystallographic data of metal citrate complexes generally show the hydroxyl group and either one or two of the carboxyl groups being involved in metal binding (131). This means that the Mg–citrate complex has to dissociate before the functional groups can interact with CitAPHis. This behavior is reflected by the occurrence of additional binding enthalpy in the presence of Mg2þ. In control experiments the additional amount of binding enthalpy (ÁÁHobs) and entropy (ÁÁS) corresponded very well to the dissociation heat and entropy of the Mg–citrate complex. In summary, the pH and Mg2þ dependency of citrate binding 2 to CitAPHis indicated that H–citrate was the preferred binding species in a nonrestricted, extended conformation and that the inhibitory effect of Mg2þ was not due to an interaction with CitAPHis, but only due to complex formation with citrate.

Binding Stoichiometry

All data fitted excellently to a single-site binding model. Analysis of all ITC experiments for the binding stoichiometry revealed values that varied between ORDER REPRINTS

40 Perozzo, Folkers, and Scapozza

0.85 and 0.95 (mean value 0.87 0.06), which was in agreement with independent binding assays using [14C]-citrate that typically gave values between 0.6 and 0.8 (122). These data clearly show that one molecule of citrate binds per CitAPHis molecule. Thus deviations from the integer value are most likely due to inaccurate protein determination and/or to missfolded protein.

Conclusion

In this study the direct thermodynamic approach turned out to be very powerful. For the first time it was possible to describe the sensory characteristics of the periplasmic domain of the sensor kinase CitA, and it could be shown that it functions as a highly specific citrate receptor that binds citrate in a nonrestricted, extended conformation with a 1:1 stoichiometry. Measurements at different pH values shed light on the responsible and preferred ligand species for the receptor. The displacement studies using the citrate analogs isocitrate and tricarballylate gave important hints to the essential role of the hydroxyl group at C3. In addition the inhibitory effect of Mg2þ could be very well observed in the ITC experiments, and the interpretation of the results clearly indicated that CitAPHis is not inhibited due to a separate receptor-Mg2þ interaction, but only due to complex formation with citrate. ITC was also used to localize important amino acids involved in citrate binding by studying the influence of point mutations in CitAPHis(132). Just recently, the crystal structure of CitAPHis in complex with citrate has been solved (133), and it shows that the findings learned from the thermodynamic data fit very well to the structural data.

Protein–Ligand Interactions of Membrane Bound Receptors

Downloaded By: [University of Missouri] At: 19:42 13 May 2009 In this part of the review we would like to focus on biological systems that are difficult to address: membrane bound receptors. In general, the direct thermodynamic approach is also applicable to such systems as long as ligand binding is accompanied by a significant heat change. The most severe problems to address are the quantities of receptor that are needed, and the stability and preparation of concentrated receptor solutions. Membrane associated proteins are intrinsically difficult to treat and usually need special purification protocols that include detergents, vesicles and/or liposome containing preparations. At this point it must be emphasized that ITC instrumentation does not at all exclude the use of such complicated or even turbid systems. As the preparation of membrane bound receptor can be solved in a lot of cases, it remains to provide sufficient amounts of protein. With the latest advances in recombinant DNA technology and procaryotic as well as eucaryotic expression systems, it has become possible to prepare significant (i.e., mg) quantities of pure receptors. This means that in the near future more membrane receptor systems will become amenable to the direct thermodynamic approach. To our knowledge the first system to which ITC was applied for investigating membrane bound protein–ligand interaction, was the serine receptor of Escherichia ORDER REPRINTS

coli chemotaxis (134). Two years later followed the thermodynamic work on colicin- N binding to the trimeric membrane bound porins OmpF, OmpC, and Phoe (135). Later in 2003 ITC was used to shed light onto human apo- and holo-transferrin binding to the Neisseria meningitidis transferrin receptor (136). The next section is dedicated to the most recent example published, in which the interaction characteristics for the sensory rhodopsin II of Natronobacterium pharaonis with its cognate transducer are examined using ITC (137). It is a particularly interesting example for the case of lateral membrane bound receptor interaction. Some methodological aspects will be discussed since they may serve as starting point for the development of new projects in this field.

Interaction of Transducer Fragments with Natronobacterium pharaonis Sensory Rhodopsin II

Introduction

Phototaxis of Natronobacterium pharaonis is mediated by the two sensory rhodopsins SRI and SRII. They consist of seven transmembrane helices and a retinal chromophore attached to the protein, and they share structural homologies as well as functional properties. The first photoreceptor SRI enables the bacteria to seek light conditions optimal for the function of the light-driven ion pumps and to avoid ultraviolet light. The second photoreceptor SRII conveys negative phototaxis, which might enable the bacteria to evade harmful conditions of high oxygen concentrations in the presence of light (138). Both receptors are bound to membrane proteins (halobacterial transducers of rhodopsins; HtrI, HtrII) that consist of a transmembrane two-helical domain and a coiled/coil cytoplasmic domain. SRII binds strongly to its cognate transducer NpHtrII. Light excitation of the SRII:NpHtrII complex leads to conformational changes in both proteins. The cytoplasmic domain of the

Downloaded By: [University of Missouri] At: 19:42 13 May 2009 transducer becomes activated and in turn triggers the two-component signalling cascade. SRII and its cognate transducer NpHtrII form a tight 2:2 complex on membranes, whereas in micelles it dissociates to a 1:1 homodimer (139). In order to elucidate the dimerization and to determine the size of the receptor-binding domain of the transducer, the dissociation constant (KD) of a series of shortened transducers to the receptor has been analyzed by ITC (137).

Methodological Aspects

A major problem to be solved for calorimetric studies remains the availability of sufficient quantities of the membrane bound receptor SRII. The authors of this article used standard DNA techniques for the cloning. The gene of interest was cloned into a pET plasmid and the receptor was expressed in BL21(DE3) as a C-terminal His6-tagged protein. Expression took place at 37 C in 2YT medium in a 30-l fermenter. The receptor was localized in the E. coli membrane. Subsequently crude membranes were sedimented by centrifugation and solubilized in buffer ORDER REPRINTS

42 Perozzo, Folkers, and Scapozza

containing 1.5% n-dodecyl--d-maltopyranoside (DDM). Isolation and purification was achieved by means of Ni-NTA agarose, but always in presence of the detergent (0.5%). The receptor was purified with a yield of up to 1 mg/L of cell culture with a purity of >90%. The various transducer variants are membrane-bound proteins as well, and they were produced in a similar way affording 1–2 mg/L of culture. For the ITC measurements the receptor was concentrated to 700–1000 mM (18–26 mg/mL). The transducer solutions were used at 70–100 mM (1.3–1.8 mg/mL). Both solutions were dialyzed at 4C overnight against degassed buffer containing 0.05% (w/v) DDM. The receptor solution (250 mL) was titrated into the solution of the transducer placed in the cell (1.4 mL). Heat effects due to the detergent were evaluated by control experiments using detergent-containing buffers (1% DDM in the syringe; 0.1% DDM in the cell chamber). Titrations were carried out at 22 and 45C in order to improve signal-to-noise ratios.

Results and Discussion

Binding affinities of the four shortened transducer fragments (NpHtrII-82, NpHtrII-101, NpHtrII-114, NpHtrII-157) to the receptor SRII were determined using ITC. It was necessary to measure the two longer fragments (NpHtrII-114, NpHtrII-157) at elevated temperature (45C) in order to detect sufficient binding heat. Shorter fragments aggregated at higher temperature and were therefore measured at 22C. Both of the longer transducer fragments displayed no detectable heat effect at lower temperature. The thermodynamic parameters of fragment association to the receptor are shown in Table 9. The dissociation constants for NpHtrII-157 and NpHtrII-114 were almost identical in the range of 200 nM, and the 1 binding heat was almost identical with 4.3 kcal mol . However, the KD of NpHtrII-101 was increased by almost two orders of magnitude, indicating that the sequence between 101 and 114 is important for the receptor–transducer interaction. Downloaded By: [University of Missouri] At: 19:42 13 May 2009 The shortest fragment (NpHtrII-82) was inactive in the binding assay. All three NpHtrII fragments exhibited a stoichiometry of 1:1 within experimental error. It must be pointed out that the data do not necessarily mirror the situation in native membranes and the association constants and ÁH measured might represent minimal values. For example from the well-documented glycophorin A dimerization

Table 9. Thermodynamic parameters of the association of NpHtrII fragments to NpSRII.a

KB KD ÁHobs ÁG ÁS NpHtrII T (K) (106 M1) (nM) (kcal mol1) (kcal mol1) (cal K1 mol1)

157 318 6.2 160 4.28 9.88 17.62 114 318 4.32 240 4.20 9.65 17.0 101 295 0.1 104 1.39 6.74 18.20 82 295 <0.01 >105 —— —

aThe measurements were performed at indicated temperatures in degassed buffer containing 150 mM NaCl, 10 mM Tris/HCl (pH 8), and 0.05% DDM. ORDER REPRINTS

in membranes it could be shown that ÁG of monomer association was influenced by the type and concentration of the detergent used, strongly influencing the thermodynamics of membrane–protein interactions. Despite these restrictions the data provide important results on membrane–protein interactions and shed light on the structure and size of the receptor-binding domain.

Conclusion

Complex formation of NpSRII and its transducer NpHtrII is a special case in the sense that the association proceeds laterally along the membrane to form a functional complex. Such lateral complex formation has only been elucidated in a few examples. To date there is only rare data available that were primarily gained by using sedimentation equilibrium and/or analytical ultracentrifugation (140). Of general interest are those experiments that provide information about the thermodynamics and kinetics of the transducer binding. This can be accomplished by using a direct thermodynamic approach. As these data are difficult to determine for membrane proteins, the present results provide a new approach to study these interactions using a direct thermodynamic approach by means of ITC. This method is rarely used so far for the study of membrane protein assembly due to the difficult nature of the membrane protein family. To our knowledge this work presents the first calorimetric approach to gain information about the strength and energetics of lateral binding processes. This is of general interest because it could be shown that ITC is a suitable method to study directly membrane protein/protein interactions (137). It will contribute to our understanding about this class of proteins, although the experiments need to be performed in a micelle environment.

Downloaded By: [University of Missouri] At: 19:42 13 May 2009 FUTURE DEVELOPMENTS

As outlined in the text and also shown with the examples, the ITC is a powerful tool with high informational content that is applicable to various biological and non- biological systems. A major drawback of first generation instrumentation has been overcome by markedly increasing the sensitivity in a way that it has now reached a physical limit. Significant further improvements with respect to sensitivity do not seem possible in the near future (MicroCal, personal communication). Another potential approach to decrease sample size may be miniaturization. Integrated circuit microcalorimeters are being built and may allow the application of ITC to micro-titre plate formats in the future. Modern screening processes for drug candidates would profit from development of high-throughput instruments. This would be a major breakthrough in the detection of compounds in pharmaceutical chemistry. The current throughput only allows measuring a limited number of promising candidates from a series which could lead to missing candidates with unusual and unexpected binding characteristics worth being further investigated. New calorimetric methods using such technology are now being developed, and with this new methodology the ORDER REPRINTS

44 Perozzo, Folkers, and Scapozza

calorimetric approach will undoubtedly play a dominant role in high-throughput screening processes in the future.

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