Kinetic and Thermodynamic Analysis of Ligand–Receptor ...

CHAPTER 5

Kinetic and ThermodynamicAnalysis of Ligand–ReceptorInteractions: SPR Applicationsin Drug Development

NICO J. DE MOL AND MARCEL J.E. FISCHER

Department of Medicinal Chemistry and Chemical Biology, Utrecht Institutefor Pharmaceutical Sciences, Faculty of Science, Utrecht University,P.O. Box 80082, 3508 TB Utrecht, The Netherlands

5.1 Introduction

Increasing evidence can be found that describing receptor ligand interactions interms of a ‘‘lock-and-key’’ model is no longer adequate. Receptors can beregarded as part of a ‘‘molecular machinery’’, in which ligand binding forms atrigger to activate or deactivate the machinery. According to this view, it is nolonger sufficient to know how the key fits into the lock, but we should also findout the mechanism with which the key opens and closes the lock. In otherwords, in drug design we would be interested not only in the affinity of theligand for the receptor, but also in the changes of a biological receptor moleculewhen it forms a complex with a ligand. Such changes may involve conforma-tional adaptation, changes in solvation (i.e. ordering of water molecules) andchanges in molecular flexibility.Kinetics is a rather underdeveloped aspect of ligand–receptor interactions. It

is readily conceivable that in some cases, such as in dynamic regulation of signaltransduction processes, kinetic control prevails rather than affinity control.Rapid onset of formation and an optimum lifetime of the complex can be finetuned by appropriate association and dissociation kinetics. Explicit referencesto the biological significance of binding kinetics are rather scarce; someexamples are given by Schreiber [1]. Other examples include the serial triggering

123

of T-cell receptors [2] and the activation of the epidermal growth factorreceptor ErbB-1 [3].Elucidation of the molecular architecture, using especially X-ray and NMR

techniques has been of crucial importance for understanding how a ligand–protein or protein–protein interaction functions in the molecular machinery.However, for a more complete understanding of the dynamic processesunderlying receptor activation, kinetic and thermodynamic studies of ligand–receptor interactions are needed. It is increasingly acknowledged that, to fullyappreciate relevant molecular properties of potential drug candidates in a drugdesign process, there is a need for thermodynamic and kinetic studies [4–8].Traditionally, van’t Hoff analysis has been used for thermodynamic studies.

More recently, the use of sensitive calorimetric techniques in drug research isemerging [9,10]. Stopped-flow has been the method of choice to study kineticsof molecular interactions. With SPR one now can derive kinetic and thermo-dynamic parameters from a single set of experiments. SPR allows to follow themass change on the sensor surface in real time, yielding affinity and kineticdata. Thermodynamic and kinetic parameters can be derived from a series ofexperiments in a temperature range. The combination of kinetic and thermo-dynamic information from well-designed SPR experiments is unique and offersan added value compared with separate techniques for kinetic and thermo-dynamic information: it allows even a full transition state analysis of thebinding process from a single data set.In this chapter, we describe examples of thermodynamic and kinetic analysis

of biomolecular interactions using SPR-based approaches that we have devel-oped in recent years. These examples apply mainly to peptides, interactingmonovalently or bivalently with important signal transduction proteins, contain-ing Src Homology 2 (SH2) and SH3 domains. These signal transduction proteinsare attractive targets for drug design. The underlying investigations are aimed atvalidation of the SPR-based approach, at gaining insight into the mechanism ofthe binding process and finally at using this insight in ligand design.To be able to exploit SPR fully, a few initial problems had to be solved. Part

of these problems originated from the fact that in our investigations cuvette-based SPR instruments were used. As discussed in Chapters 3 and 4, in flow-based SPR instruments (e.g. Biacore), a continuous flow of the sampleenhances diffusion of analyte towards the sensor surface. In cuvette-basedinstruments, the hydrodynamic properties are controlled by constant agitationof the bulk solution in contact with the sensor surface. The cuvette-baseddesign offers the following advantages: (1) open architecture allowing manualinterventions during a run and (2) long association times without extensiveconsumption of often precious biological material. A disadvantage might bethat during the binding process the concentration of unbound analyte in thecuvette is not constant. In this chapter, a correction for this effect is described.Another complication associated with the cuvette design is that in the dissocia-tion phase the analyte released is not removed from the bulk solution. Thisproblem has been solved by adding competing ligand to prevent rebinding ofreleased analyte during the dissociation phase.

124 Chapter 5

When the association rates are high compared with the diffusion in the bulksolution, mass transport limitation (MTL) occurs.1 Association and dissociationare affected byMTL to the same extent. We describe a simple method to estimatethe extent of MTL. As MTL can be easily included in a simple kinetic model, theexperimental association curves can be analyzed. Another problem, not relatedto instrumental design, is that in principle the affinity of the analyte for theimmobilized ligand at the sensor surface, as obtained in a direct SPR assay, is notnecessarily identical with that in solution. A method is described to obtainthermodynamic binding constants for the interaction in solution, using competi-tion experiments with a concentration range of the ligand of interest. Using thisapproach, several ligands can be studied using the same sensor surface.We should emphasize that in this chapter the focus is not so much on theory,

but rather on application. We would like to give the reader practical tools toobtain reliable kinetic and thermodynamic parameters on the binding pro-cesses. In order to achieve this, we need to provide the correspondingconceptual and theoretical background. Following the outlined approach,reliable kinetic and thermodynamic parameters can be obtained, which cangreatly increase our knowledge of binding processes. Later in this chapter weshow examples of how kinetic and thermodynamic analysis of interactionsusing SPR can support chemical biology studies in general and rationalstructure-based drug design in particular.

5.2 Affinity and Kinetics of a Transport-limited

Bimolecular2 Interaction at the Sensor Surface

In a standard SPR assay, one of the interacting partners (the ligand) isimmobilized on the sensor surface. The other component (the analyte) is addedin the solution, in our case in a cuvette. In our experiments, the ligand is usuallya peptide provided with a linker, to increase the distance between the bindingepitope and the matrix on the sensor surface, avoiding steric hindrance betweenthe bound analyte and the sensor matrix (see Figure 5.1).The linker is also provided with a free NH2 terminus, for covalent coupling

to the sensor surface using EDC/NHS chemistry.3 This system guarantees ahomogeneous surface by uniform coupling of the ligand through the NH2

group. The analyte is a protein with generally a much higher molecular weightthan the ligand. This increases the sensitivity of the assay, as the change inSPR angle is proportional to the amount of bound mass. Hydrogel SPRsensor chips are used, containing carboxymethylated dextran chains on a50 nm gold surface (Figure 5.1), either from Biacore (Uppsala, Sweden) orXantec (Munster, Germany). Negatively charged ligands, e.g. peptides

1For a more detailed treatment of mass transport limitation and diffusion, see Chapter 4, Section4.2.1.2A bimolecular interaction is a biomolecular interaction of only one analyte (A) which binds withone ligand (B) to form complex (AB).3For further details, see Chapter 7.

125Kinetic and Thermodynamic Analysis of Ligand–Receptor Interactions

containing phosphotyrosines (pY), are more difficult to immobilize, due to lackof preconcentration at the sensor matrix, caused by electrostatic repulsionbetween the negatively charged peptide and the negatively charged carboxylgroups on the dextran chains. In such cases 1mol l�1 NaCl is added to thecoupling buffer to diminish electrostatic repulsion [11]. Our SPR instruments(IBIS and Autolab ESPRIT) have two cuvette cells: a sample cell and areference cell. The two cells are treated in an identical way, the only differencebeing that only the sample cell contains immobilized ligands. The net SPRsignal (the signal in the reference cell subtracted from the signal in the samplecell) is used for further analysis. Subtraction of the reference signal allowscorrection for bulk effects due to addition of the analyte, for transienttemperature effects and for non-specific binding which occurs incidentally.In a series of experiments at different analyte concentrations, the affinity of

the analyte for the immobilized ligand can be assayed in several ways. Themethod preferred by us is non-linear fitting of the SPR signal at equilibriumwith a Langmuir binding isotherm. Alternative methods are based on thekinetics of the interaction. These methods for determining the affinity of theanalyte will be described in the following sections.

5.2.1 Affinity Constants Derived from Equilibrium SPR Signals

For a simple bimolecular interaction with molecules A and B forming thecomplex AB, the equilibrium association constant KA and dissociation constantKD are given by eqs. (5.1a) and (5.1b):

KA ¼ ½AB�½A�½B� ; with KA in l mol�1 ð5:1aÞ

Figure 5.1 Schematic view showing (a) the SPR sensor matrix existing of dextranchains with carboxymethyl groups on a gold surface (50 nm) beforecoupling, (b) immobilization of the ligand after coupling and (c) bindingof analyte to the surface.

126 Chapter 5

KD ¼ ½A�½B�½AB� ; with KD ¼ 1

KAin mol l�1 ð5:1bÞ

where the brackets [A], etc., indicate concentration of the molecules. In a well-designed affinity experiment, several analyte concentrations are used, whichshould be in a range around the KD value. In SPR experiments, [AB] and [B] arenot approached as concentrations in solution, but as amounts at the surfaceexpressed as SPR signal. The amount of complex AB is proportional to theshift in SPR angle [expressed in millidegrees (m1) or so-called ‘‘response units’’(RU)]. A conversion factor can be calculated for SPR response to concentra-tions in the volume of the 100 nm dextran layer at the sensor surface (see, e.g.,Box 5.1). The shift in SPR angle is recorded as function of time in asensorgram. In Figure 5.2, an example of sensorgrams, based on the net SPRsignal (Rsample cell –Rreference cell), at different analyte concentrations is shown.Using the kinetic evaluation software supplied with SPR instruments, the

shift in SPR angle at equilibrium (Req) is readily determined (see Section5.2.2.1). Frequently, the data are represented in a Scatchard plot (Req/[analyte]vs. Req) as a straight line. However, large errors can occur in Scatchard plots,especially at low concentrations, with small amounts of binding [13], thereforewe prefer non-linear regression using the Langmuir binding isotherm [eq. (5.2)],in which [A] is the free analyte concentration and Bmax is the maximum bindingcapacity in m1, when all binding sites on the sensor surface are occupied.

Req ¼ ½A�½A� þ KD

� �Bmax ð5:2Þ

Examples of plots with fits according to the Langmuir binding isotherm areshown in Figure 5.3.

Figure 5.2 Sensorgrams (net signal) of binding of v-Src SH2 protein to immobilizedEPQpYEEIPIYL-peptide. Start of dissociation is indicated by the arrow.v-Src SH2 concentrations form top to bottom: 500, 333, 208, 125, 83.3 and62.5 nmol l�1. For further details, see ref. [12].


5.2.1.1 Correction for Depletion of Free Analyte Concentration inthe Cuvette

In a cuvette, the free analyte concentration decreases due to binding to thesensor. This section describes how depletion of analyte can be quantified andcorrected for. The change in SPR angle (in m1) due to a binding process isdirectly related to the amount4 of bound material per mm2. Under equilibriumconditions the amount of bound analyte is proportional to Req (in m1) and thesurface of the sensor S (in mm2) in contact with the bulk solution. To relatethe amount of bound analyte to a decrease in the free analyte concentration, themolecular weight (MW) of the analyte and the volume of the bulk solution(Vbulk, in liters) must be known. The depletion correction can be calculatedusing eq. (5.3).

½A�free ¼ ½A�0 �ReqS � 10�9

122�MW� Vbulkð5:3Þ

Here [A]0 is the initially added analyte concentration in the bulk and [A]free isthe corrected analyte concentration, both in nmol l�1.Two examples of depletion correction are presented in Figure 5.3 in (A) for

v-Src SH2 with molecular weight 12 300Da and in (B) Syk kinase tandem SH2

Figure 5.3 SPR signal at equilibrium as function of analyte concentration. The linesare the fits with the Langmuir binding isotherm [eq. (5.2)]. Data withoutdepletion correction, open circles; with depletion correction (see Section5.2.1.1), closed circles. (A) Binding of v-Src SH2 domain (conditions as inFigure 5.2). (B) Binding of Syk kinase tandem SH2 domain. For furtherdetails on this interaction, see ref. [14].

4For the IBIS and Autolab ESPRIT instruments used by us, an SPR signal of 122m1 correspondsto 1 ngmm�2 at 25 1C.

128 Chapter 5

(Syk tSH2) with MW is 29 800Da. With eq. (5.3), using values for S (6mm2)and Vbulk (35 ml, the applied volume), the depletion correction is calculated as0.114 nmol l�1/m1 for v-Src SH2 protein and 0.0472 nmol l�1/m1 for Syk tSH2protein. It is obvious from Figure 5.3 that for Syk tSH2 the depletioncorrection has a larger effect: without correction KD is 8.7 nmol l�1 and withcorrection KD is 5.9 nmol l�1. Although the correction factor for v-Src proteinis larger due to the lower molecular weight, the depletion correction has only alimited effect in this case: KD goes from 294 nmol l�1 without correction to252 nmol l�1 with correction. For Syk tSH2 the effect of correction on KD ismuch larger. Owing to the high affinity, the Syk tSH2 concentration used in theassay is very low (Figure 5.3). Depletion caused by binding to the sensor surfacehas a large effect at low concentrations. Another factor with direct effect ondepletion correction is the binding capacity Bmax of the sensor surface, as it isdirectly proportional to Req [see eq. (5.2)]. To minimize depletion and the needfor correction, a low binding capacity is advised. In general, a value of Bmax of100m1 (or 500RU in Biacore systems) is more than sufficient for reliableassays.5

In equilibrium affinity assays using, e.g., the Langmuir binding isotherm, thedepletion correction can be readily calculated by entering the proper numbersin eq. (5.3) and by using a spreadsheet, the correction can be automaticallycalculated for every data point. Problems may arise when the sensorgrams areused for kinetic analysis. If the depletion correction is large, the free analyteconcentration will substantially diminish during the association phase andsecond order kinetics might apply [15]. In our experience, as long as thedepletion correction is below 10% of the total analyte concentration, first-order kinetics can be safely used [11].From eq. (5.3) it can be concluded that if Bmax is below 100m1, for medium

strong interactions (KDE 100 nmol l�1) and analyte molecular weights higherthan 10 kDa, depletion corrections will be smaller than 10%. In kinetic analysisof high-affinity interactions as in the case of Syk tSH2 (see Section 5.4.1), oneshould be aware of the occurrence of second-order kinetics. In these systems,reliable kinetic analysis is possible based on first-order association kinetics, onsurfaces with low Bmax (B50m1) and only at higher concentrations, such thatdepletion correction remains below B10%.

5.2.2 Affinity Constants and Rate Constants Derived from

Kinetic Analysis

In the previous section we focused on equilibrium affinity assays based on Req.Alternatives are offered by kinetic analysis based on the shape of the sensor-grams, which can be useful when the association rate is slow. Especially in flow-based SPR instruments lengthy association times to reach equilibrium maycause problems due to large analyte consumption.

5Or even lower, depending on the sensitivity of the instrument.


5.2.2.1 kobs Kinetic Analysis

Assuming a simple bimolecular interaction with analyte A interacting withimmobilized ligand B, forming the complex AB at the sensor surface, ideally theSPR signal vs. time (Rt) is given by eq. (5.4) [16].

Rt ¼kon½A�Bmax

kon½A� þ koff� 1� e�ðkon½A�þkoff Þt� �

ð5:4Þ

Here kon and koff are the association and dissociation rate constants forformation and dissociation of the complex AB, respectively. Note that Rt isproportional to the amount of complex AB. A new parameter kobs

6 is defined askobs¼ kon[A]+ koff. Using software that is generally supplied with SPR instru-ments, a fit of the curve of Rt vs. time yields kobs. When kobs is plotted vs. [A],kon can be obtained from the slope of the curve and koff from the intercept. Theaffinity can be calculated as KA¼ kon/koff or KD¼ koff/kon. In Figure 5.4,examples of kobs analysis are shown, using the data sets of Figure 5.3. Theparameters of the kobs analysis for v-Src SH2 are included in Table 5.1.Although the plots are linear, as expected from theory, the results deviate

from the equilibrium analysis. Now for v-Src SH2 a KD value of 11.9 nmol l�1 isfound, which is almost two orders higher than obtained from the Langmuirbinding isotherm. For Syk tSH2 no affinity could be determined using thisanalysis because the intercept is slightly below zero. The reason for thesedeviations is that these interactions are severely affected by mass transportlimitation (MTL), as appears in Sections 5.2.2.2 and 5.3. Under such condi-tions, eq. (5.4), which forms the base for this analysis, no longer holds. Schuckand Minton [17] showed with theoretical data how MTL influences the kobs vs.[A] plot. Further examples of howMTL affects the outcome of kobs analysis canbe found in the literature [17,18].As shown above, a straight kobs plot by no means indicates that reliable

kinetic data can be derived. Unfortunately, a number of examples of erroneousinterpretations of kinetic data can be found in the literature, especially usingkobs-analysis or closely related methods. To avoid this pitfall, one shouldbe absolutely sure that MTL is not involved. A number of simple self-consistency tests, e.g. comparing data from equilibrium and kinetic analyses,should be performed before interpreting such kinetic analysis [20]. A simpleexperiment to test whether MTL is involved is to measure the effect of additionof binding ligand during analyte dissociation to prevent rebinding (see Section5.3.2.2). The kobs analysis presented in the previous section has severe short-comings as the outcome is very sensitive to MTL. In the following, analternative model is offered which also includes MTL. This approach is basedon analysis of sensorgrams and curve fitting according to predefined bindingmodels.

6 In earlier publications regarding kinetic evaluations [16], this parameter is denoted ks.

130 Chapter 5

5.2.2.2 Global Kinetic Analysis with a Simple BimolecularBinding Model

The real-time information on the mass changes resulting from the interactioncan be used to study various binding models, also including MTL. In this

Table 5.1 Kinetic and affinity parameters for v-Src SH2 protein binding toimmobilized EPQpYEEIPIYL-peptide (experimental data shownin Figure 5.2), as derived with different approaches (see text).

Method parameter Binding isotherm kobs analysis

CLAMP global analysis

Model 1 Model 2

KD (nmol l�1) 252� 13 11.9a 308a 250a

Bmax (m1) 323� 8 – 363� 3 323kon (lmol�1 s�1) – 9.2 (�0.3)� 104 3.35� 104 7.99� 106

koff (s�1) – 1.1 (�0.8)� 10�3 0.01 2b

Lm (m s�1)c – – – 6.3� 10�6

Res Ssqd – – 4.19 2.00

a Calculated from koff/kon.b Experimental value from dissociation in the presence of peptide to prevent rebinding.c Calculated from ktr with conversion factor (see Box 5.1).d Residual sum of squares [19], indicates quality of the fit.

Figure 5.4 kobs plots for binding of v-Src SH2 (closed circles) and Syk tSH2(open circles) to immobilized ligands. Analysis of datasets presented inFigure 5.3.


section, the emphasis is on the rate constants of a simple, transport-limitedbimolecular reaction, more complicated binding models are presented inSection 5.4. In this chapter, the kinetic analysis is treated using basic chemicalkinetics concepts applied to experimental SPR data. In Chapter 4, kinetics istreated with concepts based on physical solute absorption to surfaces.7

In general, differential rate equations for species binding to the sensor surfacecan be derived from a binding model. Experimental sensorgrams can be fitted toa model and one can analyze how far the experimental data agree with the model.Furthermore, parameters such as rate constants and maximum binding capacitycan be derived from the fits. In a global analysis such fit procedures are applied toseveral curves obtained at different analyte concentrations simultaneously, usingthe same fit parameters. To explain the procedure we use a simple bimolecularbinding model with and without mass transport step (see Scheme 5.1).For model 1, the following differential rate equations can be derived:

Association :d½AB�dt

¼ kon½A�½B� � koff ½AB� ð5:5aÞ

Dissociation :�d½AB�

dt¼ koff ½AB� ð5:5bÞ

The analytical solution for the association phase is eq. (5.4) and for dissociationit is eq. (5.6), where [AB] is directly proportional to Rt, the net SPR signal attime t, and Req the net SPR signal at dissociation time zero. Equation (5.6)describes first-order exponential decay from which koff can be obtained.

Dissociation : Rt ¼ Reqe�koff t ð5:6Þ

Model 2 is somewhat more complicated: again in the association phase the timedependence of [AB] is given by the differential rate equation [eq. (5.5a)], butnow also the time dependence of diffusion of analyte from the bulk ([A]0) to thesensor surface ([A]) has to be taken into account. The accompanying rateequations are given in eqs. (5.7a) and (5.7b).

d½A�0dt

¼ �ktr½A�0 þ ktr½A� ð5:7aÞ

1: Bimolecular model 2: Bimolecular model + transport step

ABBAon

off

k

k+

ABBA

AA

on

off

tr

tr

k

k

k

k

+

−

0

Scheme 5.1 Binding models for a simple bimolecular reaction (1) and a bimolecularreaction including a transport step of analyte from the bulk to the sensorsurface (2).

7Remark: this is also the reason why terminology, symbols, etc., differ in Chapters 4 and 5.

132 Chapter 5

d½A�dt

¼ ktr½A�0 � ktr½A� � kon½A�½B� þ koff ½AB� ð5:7bÞ

Several programs can be used for solving such differential equations bynumerical integration. We used the program CLAMP developed for fittingexperimental sensorgrams [19].8 Consistency of the fits is greatly improved byfitting several curves for different analyte concentrations simultaneously with thesame kinetic parameters in a so-called global analysis. Examples of global kineticanalysis with CLAMP are shown in Figure 5.5, with the data set of Figure 5.2.To emphasize the kinetic phase, only a relatively short association time intervalwas allowed. The quality of the fits compared with the experimental data isindicated by the residual sum of squares (res Ssq) parameter [19]. For models 1and 2 this is rather similar (Table 5.1). However, the initial linear phase observedfor the higher concentrations is not very well fitted with model 1, and the fitreturns a koff value of 0.01 s�1. This linear phase is indicative for MTL [21].From experiments in the presence of competing peptide to prevent rebinding ofprotein during the dissociation phase (see Section 5.3.2.2), a much higherexperimental value of 2 s�1 for koff is obtained. Therefore, we conclude thatthis model does not yield a satisfactory description of the kinetic parameters.The experimental values of KD and Bmax are known from the binding

isotherm (Figure 5.3), koff is known from dissociation experiments and koncan be calculated from koff/KD. Therefore, all parameters in model 1 are known,allowing simulation of the sensorgrams based on model 1 (Figure 5.5B). Thissimulation demonstrates that in practice the association phase proceeds muchslower than expected for the high on-rate of 8� 106 lmol�1 s�1. This

Figure 5.5 Global analysis using the program CLAMP of the association phase ofbinding of v-Src SH2 protein to a pY-containing peptide (data set as inFigure 5.2). Black lines, experimental curves, red lines, fitted curves. (A)Fit with bimolecular model 1, all fit parameters are left free. (B) Simula-tion of model 1 with fixed parameters for Bmax (320m1), kon(8� 106 lmol�1 s�1) and koff (2 s�1). (C) Fit with transport model 2, withkoff fixed on experimental value. See text for further details.

8Currently, the features of CLAMP are included in a more extended biosensor data analysis toolnamed Scrubber2 from the results of David Myszka (see http://www.cores.utah.edu/interaction/software.html).


underscores MTL: due to the high on-rate, diffusion of analyte to the sensorsurface is much slower and becomes rate limiting. In model 2, a step fortransport of analyte from the bulk to the sensor surface is included. This model,using the fixed experimental koff value of 2 s�1, gives an excellent fit to theexperimental data. The diffusion of analyte to and from the sensor surface isassumed to be equal and is characterized by the rate constant ktr.From eq. (5.7b) it follows that the units of ktr obtained from the CLAMP fit

are m1 s�1 lmol�1, as [B] and [AB] are in m1 and the fitted curves are SPR signal(m1) vs. time (s). The value of ktr in m1 s�1 lmol�1 can be converted to the masstransport coefficient [21] (Lm) in m s�1 (see Box 5.1). For v-Src SH2 protein(MW¼ 12.3 kDa) this conversion factor is 6.66� 10�13; applying this conversionyields Lm in m s�1. In Table 5.1, the affinity and kinetic parameters derived fromglobal analysis of the dataset of Figure 5.2, using models 1 and 2, are included.The results illustrate once more that in this severely transport-limited system

the outcome of kobs analysis is not reliable. The affinity from MTL model 2agrees perfectly with that from equilibrium analysis using the binding isotherm(Table 5.1). This is not surprising, as in a considerable part of the fitted curvesthe signal is at equilibrium (see Figure 5.5C). In model 2, the use of anexperimental value for koff is crucial for the outcome. In general, it helps touse in the fits fixed experimental values for, e.g., koff and Bmax.

Box 5.1 Conversion of ktr (in m1 s�1

l mol�1) into the mass

transfer coefficient Lm (in m s�1)

To calculate Lm two conversions have to be applied: (1) from m1 to molm�2 and (2)

from lmol�1 to m3mol�1.

1. The SPR signal in m1 corresponds to a fixed amount of material/surface unit.

For the IBIS and Autolab ESPRIT instruments used in these studies, 122m1

corresponds to 1 ng mm�2 or 10�3 gm�2. Taking into account the molecular

weight (MW) of the analyte, 1m1 corresponds to 10�3

122MWmolm�2.

2. 1 lmol�1 is 1 dm3mol�1; this corresponds to 10�3m3mol�1. Combining 1 and

2, the conversion factor from m1 s�1 lmol�1 to m s�1 is 10�6

122MW. For v-Src SH2

protein with an MW of 12.3 kDa, the conversion factor is 6.66� 10�13. From

the fit with model 2, ktr is found to be 9.49� 106m1 s�1 lmol�1. Applying the

conversion factor, this corresponds to Lm¼ 6.3� 10�6m s�1.

In Biacore instruments, the SPR signal is expressed in response units (RU); 1RU

corresponds to 1 pgmm�2. As 122 m1 corresponds to 1 ngmm�2 (see above),

1000RU corresponds to 122 m1, and 1m1 is 8.2RU.

In this chapter, calculations are based on m1. By using the conversion factor of 8.2,

these calculations can be adapted for RU-values.

It is surprising that the experimental data can be described by such relativelysimple models. For example, usually not all binding sites are equal: within the

134 Chapter 5

dextran layer of the sensor the binding sites more close to the gold surface have ahigher intrinsic contribution to the SPR signal, due to the exponentially decay-ing evanescent field (Chapter 2). Furthermore, especially on high bindingcapacity surfaces approaching saturation of binding, heterogeneity of bindingsites is expected. The global kinetic analysis presented here is attractive becauseit yields thermodynamic and kinetic parameters. However, one should be carefulin the interpretation of kinetic parameters as in the fit procedure these can bemutually correlated [22] and in more complicated binding models the separatesteps may not be completely kinetically resolved, as described in Section 5.4.

5.3 Detecting Mass Transport Limitation: A Practical

Approach

Kinetic and affinity analysis with simple models can lead to large errors whenMTL is unaccounted for, as shown in Section 5.2. Therefore, it is necessary todetect MTL. In this section, practical methods are described to find transportlimitation.9 Furthermore, we describe here two approaches to obtain trueoff-rates10 from severely MTL-affected dissociation phases.

5.3.1 Effect of Viscosity Change on the Association Phase

The essence of MTL is that the on-rate is high and diffusion of analyte from thebulk phase to the biosensor (and partly in the biosensor dextran layer [23])becomes rate limiting. Viscosity changes of the bulk solution will affectdiffusion of the analyte and this should be visible in the sensorgrams of anMTL-controlled interaction. We performed experiments with increasingamounts of glycerol to increase the viscosity. In Figure 5.6, the effect ofglycerol on the association of the GST fusion protein of the Lck SH2 domainto immobilized pY-peptide is shown.As expected, increasing the viscosity slows down the association. No effect of

glycerol in the applied amounts on the affinity was observed (equilibrium signalnot affected). Kinetic analysis of data sets obtained with a range of glycerolconcentrations, using model 2 (Scheme 5.1), yields a series of ktr values and Lm

transport coefficients. For flow cells, the flux to the sensor surface due to masstransfer (i.e. Lm) was derived to be proportional to D2/3 [24]. The diffusioncoefficient D is reciprocally related to the viscosity Z, according to the Stokes–Einstein equation, and therefore Lm should be proportional to Z�2/3. FromFigure 5.7, it appears that a plot of Lm vs. Z�2/3 yields a linear relation aspredicted for flow systems. For a cuvette instrument, the hydrodynamics mightbe different, as the bulk solution is subject to continuous agitation. Actually,with this data range it is not possible to discern the flow model from othermodels, as a plot of Lm vs. Z also has an excellent linear correlation.

9For a more basic treatment of mass transport phenomena, see Chapter 4.10From theoretical considerations by Schuck and Minton [17], it follows that MTL affectsassociation and dissociation to the same extent.


Figure 5.6 Effect of glycerol on the association phase of 30 nmol l�1 Lck SH2 GSTfusion protein to immobilized Ahx-EPQpYEEIPIYL-peptide. Solid line,no glycerol; dashed line, 5% glycerol; dot-dashed line, 7.5% glycerol.Reprinted from ref. [11], Copyright (2000), with permission from Elsevier.

Figure 5.7 Relation between mass transport coefficient (Lm) from bulk solution to thesensor surface and viscosity (Z) as predicted for the hydrodynamics in aflow cell. Determined in a cuvette based instrument for binding of LckSH2 GST fusion protein to immobilized EPQpYEEIPIYL-peptide in thepresence of 0 to 10% glycerol.

136 Chapter 5

Attempts to correlate Lm values with molecular weight have not been successful.This is probably caused by differences among individual sensors surfaces and thefact that Lm depends not only on diffusion in the bulk solution, but also ondiffusion within the sensor surface dextran layer as proposed by Schuck [23].

5.3.2 Transport Limitation in the Dissociation Phase

The high on-rate compared with diffusion also affects the apparent dissociationkinetics. If diffusion is slow and the on-rate is high, a considerable amount ofdissociated analyte will rebind before there is an opportunity to diffuse awayfrom the sensor surface into the bulk. This implies that if the association phaseis transport limited, the dissociation is also transport limited. In cuvetteinstruments used in a static mode (i.e. released analyte is not removed fromthe cell), rebinding will always occur due to the equilibrium between freereleased analyte in the bulk and bound analyte on the sensor surface, evenunder conditions that transport limitation does not apply!We present two independent methods to assay true dissociation rates, which

gave comparable koff values. The first method takes rebinding into account andthe second uses added competing ligands during dissociation to prevent rebinding.

5.3.2.1 Rebinding Model for Transport-limited Dissociation

If transport limitation applies, the dissociation phase for a simple bimolecularinteraction on a homogenous surface will no longer be described by first-orderdecay kinetics according to eq. (5.6). A high on-rate compared with diffusionaway from the biosensor compartment and the availability of free binding siteson the surface will increase rebinding of released analyte. Schuck and Minton[17] have developed a two-compartment model as an approximate descriptionfor the dissociation phase under flow conditions. This model is characterized bythe differential eq. (5.8).

dRt

dt¼ �koffRt

1þ konktr

ðBmax � RtÞð5:8Þ

In this model ktr (in m1 s�1 lmol�1) has the same meaning as in model 2

(Scheme 5.1) and represents transport between the bulk and the sensor surface.If ktrc kon no transport limitation will occur and eq. (5.8) then changes toeq. (5.5b). (Bmax –Rt) represents the amount of free binding sites and will beproportional to the amount of rebinding.An example of application of this model using the program REBIND11 is

shown in Figure 5.8. Continuous wash steps were performed to remove releasedanalyte. Initially, dissociation proceeds fast (Figure 5.8A), as at the start of thedissociation practically all binding sites are occupied and rebinding is negli-gible. Soon more binding sites become available, slowing dissociation due to

11Kindly provided by Dr. Peter Schuck.


rebinding. Continuous renewal of the bulk solution increases the apparentdissociation rate. The dissociation phase with the wash steps is excellentlymatched by the model in eq. (5.8) (Figure 5.8B) with Bmax fixed at theexperimental value derived from the binding isotherm [eq. (5.2)].Using the sum of squared residuals (SSR) analysis of REBIND, a large

interval of 0.06o koffo 0.95 s�1 falls within 5% of the best SSR value (seeFigure 5.9, lower curve). In this interval, koff appears to be strongly correlatedwith kon/ktr. This especially occurs if (kon/koff)(Bmax�Rt) c 1 [see eq. (5.8)],which is the case under conditions of considerable transport limitation (kon andBmax –Rt are large).If a surface is used with a lower Bmax, effects of transport limitation can be

somewhat diminished; however, in a severe transport-limited system, Bmax

should be unrealistically low to prevent transport limitation completely (seeSection 5.3.3). Introduction of experimental values for kon and ktr, as derivedfrom global analysis of the association phase with, e.g., model 2 greatlyimproves the results. As can be seen in Figure 5.9, the SSR analysis indicatesa discrete value for koff if kon/ktr is kept at the value from the association phase.The obtained koff-value of 0.38 s�1 is close to the value found for the sameinteraction (0.6 s�1), in the presence of large amounts of competing peptide toavoid rebinding (see Section 5.3.2.2.). The found value is also in accordancewith the rapid dissociation found for other SH2 domains [25].For several reasons, this result is impressive. First, the slow decay in the

dissociation phase in Figure 5.8 in no way suggests such a high koff (see alsoFigure 5.10). The results confirm that ignoring transport limitation can yield

Figure 5.8 Sensorgrams of binding of Lck SH2 GST fusion protein to immobilizedEPQpYEEIPIYL peptide. (A) Solid line, dissociation without renewal ofbulk solution; dashed line, dissociation with continuous wash steps toremove released protein from the cuvette. (B) Upper lines: dotted line,experimental data for dissociation with continuous wash steps; continuousline, fit of the data with the program Rebind based on differential eq. (5.8).Below: residuals of the fit. Reprinted from ref. [11], Copyright (2000), withpermission from Elsevier.

138 Chapter 5

several orders too low koff values, as can be seen in Table 5.1. Second, the factthat the transport parameter, ktr, derived independently from the associationphase, gives consistent results in the dissociation phase is a solid experimentalconfirmation that both the association phase and the dissociation phase areaffected to the same extent by transport limitation, as concluded fromtheoretical considerations [17].In practice, it appears that dissociation curves that can be fitted well with eq.

(5.8) can also be well fitted by a double exponential dissociation equation fortwo independent binding sites with each their own dissociation rate [17]. Inmany cases such a fit will result in an artifact and the obtained rates are notmeaningful. Before concluding from a double exponential fit that two differentbinding sites are involved, a simple consistency check should be performed, e.g.by adding competing peptide to diminish/prevent rebinding (see Section 5.3.2.2).

5.3.2.2 Competing Ligand to Prevent Rebinding DuringDissociation

As indicated in the previous section, under transport-limited conditions thedissociation phase is considerably influenced by rebinding of released analyte.In principle, this rebinding can be prevented by adding an excess of competingligand with high affinity for the analyte. In Figure 5.10, examples are shown ofdissociation in the presence of increasing amounts of competing ligand for amonovalent Lck protein and bivalent binding Syk protein.

Figure 5.9 Sum of squared residuals (SSR) as a function of koff. Broken line, analysiswith kon/ktr derived from global kinetic analysis of the association phasebased on model 2. Reprinted from ref. [11], Copyright (2000), withpermission from Elsevier.


The effect of the ligands is dramatic and illustrates that for a transport-limitedinteraction, the off-rate can be several orders larger than expected from thedissociation phase without competing ligand. Although the affinity of theseproteins for the ligands is rather high, relatively high concentrations are neededto prevent rebinding completely. In control experiments with high concentrations(4200mmol l�1) of non-binding peptides, the dissociation rate is at maximum,only a bulk effect, a higher baseline is seen, as also occurs in Figure 5.10B, for4.4� 10�4mol l�1 ITAM peptide. At high concentration of binding peptide thedissociation phase approaches a monophasic exponential decay (see Figure 5.11)and the curve can be fitted with eq. (5.6) to derive the off-rate.In both cases the dissociation rate is very high. For the Lck protein,

rebinding (Figure 5.11A) still seems not to be completely suppressed in thepresence of even 10�4mol l�1 peptide. Especially at low R values with more freesites on the surface available for rebinding [see model eq. (5.8)], deviation fromfirst-order decay kinetics is observed; however, the first ca. 80% of the decaycan be approximated by the exponential function. The half-lifetime is about 1 sand koff is 0.6� 0.1 s�1, close to the value obtained from the rebinding modelwith fixed experimental value for kon/ktr (0.38 s

�1; see Section 5.3.2.1).The dissociation of the Syk protein (Figure 5.11B) is also speeded up in the

presence of competing peptide and shows monophasic exponential decay, withkoff¼ 0.13� 0.02 s�1, close to that of comparable mono- and bivalent interac-tions involving SH2 domains [25,26]. The high dissociation rate in combinationwith the high affinity (KD¼ 5 nmol l�1) is intriguing. As a rule, high-affinitymonovalent interactions have slow dissociation rates, hence the complex haslong half-lifetimes, e.g. for the avidin–biotin complex it is over 1 week [27]. Thebinding of a double phosphorylated ITAM-peptide with the tandem SH2

Figure 5.10 Effect of different concentrations of competing peptide ligand on thedissociation rate. (A) 200 nmol l�1 Lck SH2 GST fusion protein withEPQpYEEIPIYL peptide and (B) 5 nmol l�1 Syk tandem SH2 domainwith g-ITAM peptide. Reprinted from ref. [11], Copyright (2000), withpermission from Elsevier (A) and from ref. [14], with permission fromWiley-VCH (B).

140 Chapter 5

domain is bivalent, existing of two weak monovalent interactions (see Section5.4.1 for more structural details). For such multivalent interaction, the dis-sociation rate approaches that of a single (much weaker) monovalent interac-tion contributing to the bivalent binding, when competing (monovalent) ligandis present [28]. Therefore, in the presence of proper ligands, multivalentinteractions offer the opportunity to combine high affinity with high off-ratesand short lifetime of the complex. In signal transduction processes, thecombination of high affinity and short half lifetimes might be decisive forspecificity and transiency of protein–protein interactions. It is remarkable thatmultivalent interactions, e.g. involving tandem-SH2 and tandem-SH3 domains,are abundant in signal transduction processes.

5.3.2.3 Experimental Procedure to Assay High Off-rates

Off-rates approaching 1 s�1, as obtained in the previous section, are at the limitof what can be accurately measured by SPR. For really fast decay kinetics wethink that the open cuvette structure is an advance as it allows directaccessibility for manual handling. Our protocol developed for assaying rapiddissociation kinetics in cuvette-based instruments starts with setting the instru-mental sampling rate high (5 data points s�1). The instrument is operated inone-channel mode. A 25 ml volume of the analyte protein, preferably with aconcentration that saturates 490% of the binding sites,12 is pipetted manuallyinto the sample cell. After reaching equilibrium of binding, the measurement is

Figure 5.11 Dissociation of bound proteins in the presence of competing ligands toprevent rebinding. (A) Lck SH2 GST fusion protein in the presence of100mmol l�1 peptide. (B) Syk tandem SH2 protein with 220 mmol l�1 ofbivalent binding ITAM-peptide. The monophasic-exponential or first-order decay fits [eq. (5.6)] are indicated by the dotted lines. Reprintedfrom ref. [11], Copyright (2000), with permission from Elsevier (A).

12This analyte concentration is approximately 10�KD.


started and very quickly 10 ml of a high-concentration competing peptide ispipetted manually. The required concentration of peptide to prevent rebindinghas to be determined experimentally (Figure 5.10). The data points of theresulting sensorgram can be exported, processed in a spreadsheet and fitted toan exponential function. The data points within 1 s after peptide addition arediscarded, as these are often affected by distortions. The experiment is repeatedat least in triplicate with ample manual washing steps in between.

5.3.3 Quantitative Considerations on Mass Transport Limitation

As explained previously, MTL occurs if the reaction (binding) flux is muchhigher than the transport flux of analyte from the bulk solution to the sensor.These fluxes are described by the transport coefficient Lm and the Onsagercoefficient Lr for the reaction flux [21]. A quantitative measure for MTL isexpressed in eq. (5.9).

MTL ¼ Lr

Lm þ Lrð5:9Þ

If the analyte transport is totally rate limiting in the binding kinetics (LrcLm),MTL will approach 1. Lm is directly related to ktr as defined in model 2

(Scheme 5.1) for the association phase and eq. (5.8) for the dissociation phase.Lm in m s�1 is obtained by conversion of ktr as indicated in Box 5.1 in Section5.2.2.2. The Onsager coefficient of reaction flux (Lr) in m s�1 is obtained fromeq. (5.10) [21].

Lr ¼ kon½B� ð5:10Þ

Here kon is converted to m3mol�1 s�1units.13 At the start of the interaction [B]equals Bmax, the maximum binding capacity of the sensor surface, i.e. theconcentration of free analyte binding sites on the sensor surface. Bmax can beobtained from the Langmuir binding isotherm [eq. (5.2)] or from global kineticanalysis in m1. Using the same approach as explained in Box 5.1 Bmax isconverted to [B] in molm�2 with eq. (5.11).

½B� ¼ Bmax

122� 10�3

MWðmolm�2Þ ð5:11Þ

The extent of MTL allows one to consider whether MTL can be avoided byadaptation of the experimental conditions, e.g. by lowering the bindingcapacity on the sensor surface or increasing the diffusion rate by increasingflow or agitation of the bulk solution in the cell.The global kinetic analysis with model 2 yields Lr¼ 1.72� 10�3m s�1

and Lm¼ 6.3� 10�6m s�1 for the interaction with v-Src SH2 as analyte14

1310�3 times kon in lmol�1 s�1.14MW¼ 12.3 kDa.

142 Chapter 5

(see Figure 5.5). Applying eq. (5.9), under these conditions, MTL is practically1 for this rather high binding capacity surface (Bmax¼ 320m1). To reduce MTLto 0.5 (Lr¼Lm), the binding capacity Bmax should be reduced to about 1m1,which is not a feasible assay condition. Increasing flow and agitation will not besufficient, either. Assuming that a 5-fold increase in Lm can be obtained, theinteraction will still be completely transport limited. In practice, the increase inLm will be rather limited due to hydrodynamics (stagnant layer) and thedimensions of the (flow) cells and because diffusion within the dextran matrixon the sensor is not sensitive for the flow conditions [23].In general, for an analyte protein of approximately 40 kDa molecular weight,

Lm is around 3� 106m s�1. This number can vary somewhat depending on typeof sensor chip. Applying eq. (5.9), this implies that for a surface withBmax¼ 100m1 (corresponding to 2� 10�8molm�2) that Lm4Lr ifkono2� 102m3mol�1 s�1, corresponding to kono2� 105 lmol�1 s�1. Thisnumber agrees very well with predictions from various MTL models [17,29].The size of the analyte, of course, influences the diffusion rate and the kon value,but as a rule of thumb, transport limitation will affect binding kinetics to adextran-based sensor surface, if kon is larger than 105 lmol�1 s�1.In summary, lowering the binding capacity and increasing the flow rate can

prevent MTL only in the case of slightly transport-influenced kinetics. Inpractice, we assume that the Lm/Lr ratio can be improved at most by about afactor 5 on changing the experimental conditions. As a consequence, inmoderately and severely transport-limited cases an effect of MTL on thekinetics cannot be avoided.

5.3.3.1 Flow or Cuvette?

One can ask whether a flow or cuvette instrument is to be preferred when itcomes to transport limitation. Although differences in hydrodynamic behaviormay exist between a flow and a cuvette instrument and detailed hydrodynamicmodels have been derived for flow cells [23,24], in practice, no significantdifferences in transport fluxes between flow and cuvette systems have beenobserved. This is illustrated by the agreement of the Lm value of9.8� 10�6m s�1 for IL-2 (MW 14kDa) obtained in a flow-instrument (Biacore)using a model similar to model 2 [30] and the value of Lm¼ 6.5� 10�6m s�1

for the v-Src SH2 domain (MW 12.3 kDa) obtained in a cuvette instrument(Table 5.1). We conclude that in practice no significant differences exist in theextent of MTL between cuvette and flow instruments.

5.4 Global Kinetic Analysis of Complex Binding

Models

After describing simple bimolecular interactions, we shift to more complexbinding mechanisms with conformational changes, dimerization, multi-component interactions, multivalent binding, etc. In addition to structural


information as derived from NMR and X-ray analysis, kinetic information andinsight into the mechanism is valuable for understanding the binding processand is of special interest for rational drug design. We describe examples ofapplications of global kinetic analysis with more complex models to illustratethis point.

5.4.1 Global Kinetic Analysis Including Mass Transport and a

Conformational Change

For better understanding of our first example, the bivalent binding of Syktandem SH2 domain (Syk tSH2) with a surface loaded with ITAM-peptide, westart with the description of the structural aspects. The interaction of an ITAM-derived ligand with Syk tSH2 involves bivalent binding of two phosphotyrosinecontaining sequences on the ITAM-peptide with the two SH2 domains of theSyk protein. This interaction plays an important role in, amongst others, signaltransduction of the IgE receptor (FceRI) and the B-cell antigen receptor [31].An X-ray structure of Syk tSH2 with an ITAM peptide is shown in Figure 5.12.The linker part in the ITAM, between the two phosphotyrosine-containingsequences, hardly interacts with the protein [32]. The two SH2 domains in the

Figure 5.12 X-ray structure of Syk tandem SH2 domain (ribbon) with doublytyrosine phosphorylated ITAM (sticks). Both phosphotyrosines areindicated in red. PDB entry 1A81 [32]. Reprinted with adaptation fromref. [14], with permission from Wiley-VCH.

144 Chapter 5

Syk protein (labeled N-SH2 and C-SH2) are connected by a flexible coiled coillinker, giving some flexibility in the inter-SH2 distance.The association phase of this interaction was subjected to a global kinetic

analysis (Figure 5.13). The association phase was assayed at different tempera-tures as part of a complete thermodynamic analysis (see Section 5.6 forthermodynamic analysis based on SPR).At 11 1C, deviation is observed using model 2 (Scheme 5.1) in global analysis

in the association phase as when the signal approaches equilibrium. It isconceivable that a bivalent interaction occurs in two discrete steps [29], asindicated in Box 5.2. After initial monovalent binding, the second step involvesa conformational (intramolecular) change, leading to a high-affinity bivalentlybound complex AB* (model 3, Scheme 5.2). This interaction is certainlytransport limited, as we see a strong effect of added ligand on rebinding inthe dissociation phase (Figure 5.10B). Also indicative of transport limitation isthe initially linear association phase, especially at higher analyte concentrations[21]. Therefore, a transport step is included in model 3.

Figure 5.13 Association phase sensorgrams for the interaction of Syk tandemSH2 domain with an immobilized ITAM peptide. Global kineticanalysis of the experimental data using CLAMP, according to theindicated models in red. Reprinted from ref. [14], with permission fromWiley-VCH.

Model 3: transport

conformation model

Model 4: transport

dimer model

Model 5: 1PP-bivalency

model

*

0

ABAB

ABBA

AA

k

k

k

k

k

k

+

BAAAB

ABBA

AA

k

k

k

k

2

0

+

+ 2ABBAB

ABBA

k

k

k

k

+

+

Scheme 5.2 Complex binding models used in global kinetic analysis of associationphases.


Box 5.2 Relation of Kb, Kconf and Kobs in the conformation

change model

Conformation change model 3 consists of two binding steps (see also Scheme 5.2): an

initial binding event occurs, characterized by the equilibrium association constant Kb.

Second, a structural change in the bound state occurs, characterized by equilibrium

constant Kconf, leading to a higher affinity complex. For a bivalent interaction as in

the case of Syk t SH2 binding to doubly phosphorylated ITAM peptides, this last step

includes a structural arrangement of the complex with a second intramolecular

binding event.

Kb and Kconf are defined by eqs. (5.12) and (5.13):

Kb ¼ ½AB1�½A�½B� ð5:12Þ

Kconf ¼kconf

k�conf¼ ½AB2�

½AB1� ð5:13Þ

The observed equilibrium association constant Kobs is defined by eq. (5.14):

Kobs ¼½AB1� þ ½AB2�

½A�½B� ð5:14Þ

Note that [AB1]+ [AB2] corresponds to the total amount of bound analyte, which is

proportional to the change in SPR signal R. Substitution of eqs. (5.12) and (5.13) in

eq. (5.14) yields

Kobs ¼ Kb 1þ Kconfð Þ ð5:15Þ

A plot of Req vs. [A] will also for this case obey a binding isotherm fit as demonstrated

in Figure 5.3, and from the fit Bmax and Kobs are obtained.

Applying model 3 gives an excellent fit (Figure 5.13B); in the fits, Bmax and koffwere kept at experimental values.15 The fit yields the parameters ktr, kon, kconfand k�conf; the values especially of kon, kconf and k�conf may not be reliable asthey are strongly correlated (see Table 5.2). According to model 3, Kobs containscontributions of the initial binding step characterized by association constant Kb

15Bmax is derived from the Langmuir binding isotherm [eq. (5.2)], which also holds for model 3 (seeBox 5.2); koff is derived from the experiment shown in Figure 5.11B.

146 Chapter 5

and of the second intramolecular binding step or conformation change Kconf

(see Box 5.2). Notwithstanding uncertainty in the rate constants, a Monte Carlorun16 with CLAMP gives consistent values for Kb (¼ kon/koff) and Kconf (¼ kconf/k�conf) of 1.9� 107 lmol�1 and 18.5, respectively. This value of Kb is significantlyhigher than the 7.7� 105 lmol�1 found for the monovalent binding of mono-phosphorylated ITAM peptide [33]. It is likely that the two binding steps inmodel 3 are not completely resolved. First, no obvious biphasic associationphase exists in Figure 5.13; second, by increasing the temperature above30 1C, we obtain an excellent fit using the bimolecular transport model 2

(Figure 5.13C), indicating that the two binding steps can no longer be discerned.Calculation of Kobs from the fit parameters using eq. (5.15) (Box 5.2) yields avalue of 3.6� 108 lmol�1, in excellent agreement with Kobs obtained from thebinding isotherm at 11 1C (3.4� 108 lmol�1). Although the values obtained forKb and Kconf may not be physically meaningful, they can be used to calculatesolid Kobs values with eq. (5.15).

5.4.2 Unusual Kinetics: Intermolecular Bivalent Binding to the

Sensor Surface

A second example from our work where global kinetic analysis plays a centralrole in elucidating the binding mechanism to a SPR sensor surface is Grb2protein binding to an immobilized bivalent polyproline (PP) peptide (2PP). 2PPcontains two PP binding epitopes derived from the SOS protein17 separated by ashort linker moiety. The Grb2 protein exists of two SH3 domains and one SH2domain connected by two flexible linkers [34]. The two SH3 domains can eachbind to one of the PP epitopes of 2PP in a bivalent mode [34,35]. In Figure 5.14,a model of the bivalent complex of Grb2 protein with 2PP is shown.It was expected that the kinetics of this bivalent interaction could be

described by a model similar to that used for the Syk tSH2-ITAM interaction(Section 5.4.1); instead, a different binding model emerged. The binding ofGrb2 to immobilized 2PP SPR sensor surfaces with different binding capacitiesis shown in Figure 5.15. The form of the curve describing the association phase

Table 5.2 Parameters and their correlation from calculations used in Figure5.13B.

Parameter Value Correlation 1 Correlation 2 Correlation 3

1 ktr (m1 s�1 lmol�1) 5.4� 107 – – –2 kon (lmol�1 s�1) 1.19� 106 �0.74 – –3 kconf (s

�1) 2.81 0.72 �1.000 –4 k�conf (s

�1) 0.16 0.72 �1.000 1.000

16 In a Monte Carlo run the fit is repeated for a defined number of cycles with variation of thestart parameters within a defined range.

17The SOS-Grb2 interaction plays an important role in the signal transduction cascade of numerousreceptors, controlling among others cell proliferation and differentiation, platelet aggregation andT-cell activation [36].


appears to be sensitive to the binding capacity: at high binding capacities, theassociation phase looks conventional with a steady increase until equilibrium isreached. Lowering the binding capacity yields biphasic association, with a veryfast initial increase, taking only a few seconds, followed by a slower increase(Figure 5.15B and C). The SPR signals at equilibrium apparently comply withthe Langmuir binding isotherm [eq. (5.2)], as shown in Figure 5.16 for mediumbinding capacity. At high and medium capacity surfaces the binding isotherm

Figure 5.14 Model based on X-ray structure of Grb2 (PDB entry: 1GRI, ribbons)which a double polyproline peptide docked on the SH3 domains (sticks).

Figure 5.15 Sensorgrams of Grb2 protein (range 330–2000 nmol l�1) binding toimmobilized 2PP-peptide with various binding capacities (Table 5.3).(A) and (B) net signal (reference cell subtracted from sample cell); (C)data from the sample cell only due to non-specific binding in thereference cell; lowest curve is the baseline not containing Grb2 protein.

148 Chapter 5

gave comparable KD values (Table 5.3). At low binding capacities equilibriumis not reached within 20 min (Figure 5.15C).The data from binding of Grb2 to the 2PP surfaces with various binding

capacities have been subjected to global kinetic analysis, exploring severalmodels. As shown in Figure 5.17A, the data from very high binding capacity(Figure 5.15A) could be readily fitted with a conformation change model(model 3, Scheme 5.2). Interestingly, the transport step could be omitted fromthe model, giving identical results. This suggests that in this case the interactionis not transport limited, in agreement with the calculated value ofkon¼ 5.8� 104 lmol�1 s�1, which is below the indicated value for transportlimitation (Section 5.3.3).At medium high and low capacity surfaces, model 3 shows systematic devia-

tions from the experimental data as shown in Figure 5.17B: the slope of the slowphase in the fits changes much less with the concentration than experimentallyobserved. X-ray structures suggest that Grb2 dimers can be formed [37,38] andtherefore the data were fitted with dimer model 4 (Scheme 5.2), as can be seen in

Figure 5.16 Equilibrium analysis with binding isotherm of Grb2 protein to a mediumhigh capacity surface of 2PP peptide (data from Figure 5.15B).

Table 5.3 Data from binding isotherm of Grb2 binding to 2PP surfaces.

Binding capacity Bmax (m1) KD (nmol l�1)

High 1520� 55 535� 40Medium 218� 3 460� 30Low 73� 3 NDa

a ND Not Determined (equilibrium not reached).


Figure 5.17C. The residual sum of squares parameter from the fit with model 4was 1.71, compared with 2.50 for model 3. Also for the low capacity surfacefitting with model 4 gave good results (Figure 5.17D). According to model 4binding of the first Grb2 molecule (A in the model) to immobilized 2PP (B in themodel) facilitates binding of a second Grb2 molecule. However, we doubt that onthe surface such physical Grb2 dimer will be formed, because we cannotdemonstrate the formation of dimers in solution upon addition of 2PP-peptideto Grb2, either by chemical cross-linking or dynamic light scattering, in line withpublished results [37].An alternative to bivalent intramolecular binding is intermolecular bivalent

binding (see Scheme 5.3b). In the flexible dextran matrix of the sensor chip, thedistance between 2PP epitopes could easily adapt to facilitate intermolecular

Figure 5.17 Global kinetic analysis of Grb2 binding to 2PP surfaces with CLAMP.(A) High binding capacity surface, fitted with conformation changemodel 3. (B) Medium capacity surface, fitted with conformation changemodel 3. (C) Medium capacity surface, fitted with dimer model 4. (D)Low binding capacity surface, fitted with dimer model 4. Details of thesemodels are given in Scheme 5.2.

150 Chapter 5

binding. If intermolecular bivalent binding occurs, this should be observed for amonovalent 1PP surface (Scheme 5.3c).In Figure 5.18, the association kinetics of 1PP surfaces with low and high

binding capacity are shown. The curves can be readily approximated with abivalent binding model: first monovalent binding of Grb2 protein to immobilized1PP, followed by bivalent binding to a second free 1PP ligand (model 5, Scheme5.2). As expected, the rate of the slow bivalent binding step depends on thebinding capacity: with a high capacity it will be easier to find a second 1PP ligand

Scheme 5.3 Schematic representation of various binding modes of Grb2 protein to1PP and 2PP surfaces.


and the rate will be higher. Interestingly, the affinity of the initial fast binding stepas derived from the kinetic analysis is approximately 12mmol l�1, which is similarto the affinity of monovalent binding of 1PP to Grb2 in solution [35].How can the intermolecular bivalent binding mode to the 2PP surface be

reconciled with the observed association kinetics which can be well described bythe dimer model 4 (Figure 5.17C and D)?We propose a three-step mechanism as outlined in Scheme 5.4: the first step is

monovalent (or bivalent) binding of Grb2 to one 2PP ligand; after this, bivalentbinding with another 2PP ligand occurs (model 5). This intramolecular bindingprepares a perfect second docking site for another Grb2 molecule as the inter-PPdistance is already optimal for bivalent binding. This model also explains whythe slow phase is much more delayed in case of low binding capacity (Figure5.17): it is more difficult to find a second partner for divalent binding. Theproposed binding model for the 2PP surface cannot be defined in all detail in theCLAMP program, e.g. no bivalent ligands can be defined and no discriminationbetween single and double occupation of two 2PP ligands can be made.Actually, model 4 is a simplified approximation of the possible binding modes.It is possible that in the dimer complex (step 3, Scheme 5.4) dimeric interactions,as found in the X-ray structure of Grb2 are involved, as the Grb2 molecules areforced to be together. For the monovalent 1PP surface, such dimer formationcannot take place and the kinetics can be adequately described with model 5.

5.4.3 Global Kinetic Analysis: Concluding Remarks

At first sight, in both examples in this section we have a protein that bindsbivalently to an immobilized ligand. However, the outcome is surprisinglydifferent. This is illustrative for the use of models in global kinetic analysis: one

Figure 5.18 Association phase sensorgrams of Grb2 protein binding to a low (A) andhigh (B) capacity monovalent 1PP surface. Global kinetic analysis withCLAMP according to bivalent binding model 5 (see Scheme 5.2).

152 Chapter 5

should be very careful in the interpretation of the applied binding model. Incomplex situations, good fits of the experimental curves can be derived frommore than one model. This raises the question of how far the physical reality isreflected by these models. A model will only provide an approximation: not allreactions can be included in detail, as described above. Unless the kinetic stepsare well resolved, the calculated kinetic parameters may not be reliable, as theywill also be strongly correlated. Hence it is useful to introduce all possibleexperimental values in the calculations.Other complications may arise from the fact that not all ligands are equally

accessible: the sensor with immobilized ligand comprises a three-dimensional

Scheme 5.4 Sequential steps in the proposed model for binding of Grb2 to the 2PPsurface, ultimately leading to the formation of ‘‘dimers’’.


volume. Especially for high binding capacity sensors, partition into thisvolume may be hampered near saturation of binding due to crowding [23].With multivalent binding analytes the hydrogel of the dextran on the sensormay become more ‘‘cross-linked’’, leading to a more compact structure duringthe binding process. As the SPR signal decreases exponentially with distancefrom the gold surface (Chapter 2), this might lead to an accounted signalincrease vs. the model. In spite of all these considerations, sometimes thequality of the fits with complicated binding models can be stunning (Figures5.17 and 5.18).As a rule, a proposed binding model should be simple and supported by

experimental evidence; additionally, it should include all possible fixed experi-mental parameters. Global kinetic analysis is a unique tool, providing insight intothe binding mechanism, the kinetics of an interaction and the role of proteindynamics. It can inspire new ideas for molecular design and drug development, forexample, the length and rigidity of the linker between the two phosphotyrosine-containing binding epitopes in ITAM-mimetic constructs binding to Syk tSH2 [14].Ample examples exist using the simple bimolecular models 1 or 2; applica-

tions of more complicated models are rather scarce. Such examples are thebinding of IL-2 to the heterodimeric IL-2 receptor [30], binding to a hetero-geneous surface with two different ligands [39] and the kinetic analysis ofamyloid fibril elongation [40]. Deviation from the simple 1:1 model is alreadyindicative of a more complex binding mechanism.

5.5 Affinity in Solution Versus Affinity at the Surface

In SPR measurements, interactions take place at the sensor surface, which is notalways representative of interactions in solution. This is certainly true for divalentanalytes, such as antibodies and GST fusion proteins that form dimers and showan avidity effect when binding to a surface [41]. The amount of analyte binding tothe sensor surface in the presence of a competing ligand in solution is influencedby the affinity of the analyte for this ligand. If the affinity is high, a relatively largeamount of analyte will be in complex with the ligand in solution and only a smallamount of analyte will be available for binding to the surface, resulting in a lowershift in SPR angle. Using this model, Morelock et al. developed a method toobtain thermodynamic binding constants in solution [42]. Based on this, wederived a fitting model for data from competition experiments with constantanalyte and varying ligand concentrations in solution (Box 5.3) [43].An example of competition experiments is shown in Figure 5.19. Experi-

ments were performed at various pH values to determine the shift in pKa of thephosphotyrosine upon binding [43]. The equilibrium dissociation constant atthe chip (KC) was determined at each pH and these values were used in the fits.The experimental data was fitted with eq. (5.19) (Box 5.3), using experimentalvalues for [A]tot and KC, while the independent variable in the fit is the ligandconcentration [B]tot.

154 Chapter 5

Box 5.3 Thermodynamic binding constants for binding in

solution

In an SPR competition experiment with ligands for the analyte present both on the

sensor surface and in solution, the two binding equilibria are as follows:

1. Interaction between analyte A and immobilized ligand B on the sensor chip

(Bc), yielding complex ABc on the sensor. The dissociation constant (KC) is

KC ¼ ½A�½Bc�½ABc� ð5:16Þ

[Bc] and [ABc] are in millidegrees; when all Bc sites are occupied [ABc]¼Bmax.

2. Interaction in solution between ligand B in solution with A to form complex

AB. The dissociation constant (KS) is

KS ¼ ½A�½B�½AB� ð5:17Þ

Note that KS is a thermodynamic binding constant.In analogy with eq. (5.2), the amount of binding onto the surface can be described by

a binding isotherm:

Req ¼ Z½ �KC þ Z½ �

� �Bmax ð5:18Þ

where [Z] is the total concentration of analyte [A]tot minus the amount of analyte in

the complex AB ([Z]¼ [A]tot – [AB]), and eq. (5.18) changes to

Req ¼ A½ �tot�½AB�KC þ A½ �tot�½AB�

� � !

Bmax ð5:19Þ

The amount of complex AB in solution is a function of the affinity in solution (KS)

and eq. (5.17) can be rewritten:

KS ¼½A�tot � ½AB��

½B�tot � ½AB��

½AB� ð5:20Þ

From this equation, it appears that [AB] is a quadratic function of the type

ax2+ bx+ c¼ 0, for which the solution is given by the square root equation

½AB� ¼KS þ ½A�tot þ ½B�tot� �

�ffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiKS þ ½A�tot þ ½B�tot� �2þ4½A�tot½B�tot

q2

ð5:21Þ

Substituting eq. (5.21) for [AB] into eq. (5.19) yields an equation that fits data from

competition experiments. Fitting with [A]tot kept at the experimental value and [B]totas independent variable provides KS and Bmax. Note that eq. (5.19) contains KC. The

value of KC is obtained from a separate experiment. Best fit is expected when

[A]tot Z KC. Bmax is the maximum binding capacity upon complete saturation, and

not the binding capacity in the absence of competing ligand.


In order to verify the reliability of our approach for obtaining affinity data insolution and to see if affinity at the sensor surface is significantly different fromthat in solution, KC and KS values are compared in Table 5.4. The data illustratethat the affinity of dimer proteins (the GST fusion protein and not-heatedGrb2-SH2; see below) at the surface is larger than in solution. This can beexplained by the avidity effect, occurring when the dimer binds bivalently to twoligands at the surface. The case of the Grb2-SH2 protein without GST part isinteresting. It has been reported that this protein occurs as a dimer.18 Probablythis is an artifact due to the expression as a GST fusion protein, which is knownto form dimers through the GST part [44]. From size-exclusion chromatographywe estimate that our GST-cleaved Grb2-SH2 protein contains B60% dimer.The dimer is metastable and upon heating to 50 1C the monomer is irreversiblyformed [38]. Before heating, the affinity to the sensor surface is higher, due to thelarge amount of dimer. KS is higher before heating, suggesting that the affinity ofthe ligand for the dimer in solution is lower than for the monomer.For the pYVNV-peptide binding to monomer Grb2-SH2, consistent values

for KS are obtained (230–260 nmol l�1), notwithstanding large differences in KC

(7.9 for the GST fusion protein to 790 for full-length Grb2) used in the

Figure 5.19 Data from SPR competition experiments to determine the bindingconstant KS in solution. The analyte is Lck-SH2 GST fusion protein(50 nmol l�1), the immobilized ligand and the ligand in solution areidentical [a phosphotyrosine 11-mer peptide derived from the hamstermiddle-T-antigen (hmT)]. Experiments were at different pH: from leftto right pH 9, 6.8 and 5. The lines are the fits with the substitutedeq. (5.19) (see Box 5.3). Reprinted from ref. [43], Copyright (2002), withpermission from Elsevier.

18Dimer formation by domain swapping of an a-helix [38].

156 Chapter 5

calculations to obtain KS. Moreover, the solution affinities agree with literaturevalues obtained using other techniques. This strengthens our confidence in thecompetition approach.As a rule, the affinity of monomer proteins in solution is the same as at the

sensor surface, even for the bivalent binding Syk tSH2! An exception seems tobe the binding of the full-length Grb2 protein to the SH2 domain.19

In the case of a bivalent binding analyte with two (identical) binding sites suchas an antibody, the expression for [AB] will be different from eq. (5.21). Now wehave to take into account that not all occupied antibody remains in solution, asmonovalently occupied antibodies are able to bind to the sensor surface. When acertain fraction of all binding sites are occupied, a statistically determineddistribution exists over double-bound, single-bound and unbound antibodies insolution. Unbound antibodies, and also a single-bound antibody with a ligandfrom solution, can bind to the sensor surface. We have adapted the expressionfor [AB] (Box 5.3) to the statistical distribution [45] and it appears that thiscorrection has only a modest effect on the resulting KS value (o10%).In summary, the approach derived in Box 5.3 cannot be used for every

binding model. Especially when the Langmuir binding isotherm is not suitablefor fitting Req as a function of analyte concentration, this approach will not bevalid. However, for more complicated binding models obeying the Langmuirbinding isotherm, such as the two-step model proposed for Syk tSH2 (Box 5.2),reliable KS values can be obtained. In this case KS will be an apparent bindingconstant, containing the various contributions to Kobs (see Box 5.2). Thecompetition experiments as described in this section are very attractive in drugresearch: the affinity of a range of potential drug candidates can be assayed atthe same surface! In general the standard error in KS is larger than in KC.Processes in solution may not always be representative for processes at

sensor surfaces or in biological systems. We are convinced that in some casesinteraction at a surface might be a better model than interaction in solution,

Table 5.4 Comparison of affinity at the sensor surface (KC) and in solution(KS), calculated from competition experiments using the substitutedeq. (5.19) (see Box 5.3).

Protein+peptide KC (nmol l�1) KS (nmol l�1)

Lck-SH2 GST fusion proteina+hmT-peptide 6 60Grb2-SH2 GST fusion protein+pYVNV-peptide 7.9 260Grb2-SH2 not heatedb+pYVNV-peptide 134 1800Grb2-SH2 heated+pYVNV-peptide 220 255Full length Grb2 protein+pYVNV-peptide 790 230Syk tSH2+ g-ITAM-peptide 5.4 5.7v-Src SH2+hmT-peptide 220 234

a If explicitly indicated the dimer forming GST moiety is present.b Heating to 50 1C converts the Grb2-SH2 dimer irreversibly to the monomer (see text).

19A possible explanation for this difference is discussed in ref. [12].


especially with multivalent interactions. For example, the Sos-protein20

contains multiple (six) polyproline sequences to recognize Grb2 SH3 domains[36]. Several Grb2 molecules might bind bivalently to these sequences in oneSos molecule in different combinations. For this a surface loaded with polypro-line ligands might be a better model than 1:1 interactions in solution.

5.6 Thermodynamic van’t Hoff Analysis Using SPR

Data

As described in the Introduction, it is no longer opportune to describe ligand–receptor interactions in terms of a rigid lock-and-key concept. Binding of areceptor by a ligand can influence the dynamics, induce allosteric changes of thereceptor or, very importantly, have an effect on bound water molecules. All thiscan be vital for the biological effects in a biomolecular interaction. In thissection we will concentrate on SPR-based assay of thermodynamic parameters,to reveal the biomolecular recognition process, to help understand it and toexploit it for improved rational drug design (see Box 5.4) [5,6,8].

5.6.1 van’t Hoff Thermodynamic Analysis

van’t Hoff thermodynamic analysis requires the measurement of the affinity ata range of temperatures. As the SPR signal is extremely sensitive to temperaturechanges, complications may arise when measuring at temperatures deviatingfrom room temperature, as some time may be needed before complete thermalequilibration is reached. An example is shown in Figure 5.20: in the referencecell a temperature effect is observed in addition to a bulk effect. It takes about100 s to reach thermal equilibrium. The temperature effect is not visible in thenet signal (Figure 5.20). The best approach is to use Req for the affinity assay asby the time Req is reached the system will be in thermal equilibrium. It isimportant, especially when kinetics are assayed, that the sample is at the correcttemperature and that the injection system is well thermostated. The design ofnewer generations of SPR instruments, e.g. Autolab ESPRIT, is optimized foraffinity assays in a temperature range 10–45 1C.The simplest case of binding is a bimolecular interaction, such as that

between v-Src SH2 and hmT-peptide as described above. The van’t Hoffthermodynamic analysis of this interaction is shown in Figure 5.21. The datacan be readily fitted with eq. (5.26) and the resulting thermodynamic para-meters are included in Table 5.5. The data show that the affinity at the sensorsurface (KC) matches that in solution (KS), using the method described inSection 5.5. The convex form of the curve indicates a negative value for the heatcapacity (DCp) and this interaction appears to be enthalpy driven. A concisedescription of how to interpret thermodynamic parameters in terms of mole-cular events during the binding process is given in Box 5.5. A more detailedinterpretation can be found in ref. [12].

20This is a crucial interaction in the activation of the Ras signaling pathway described in ref. [36].

158 Chapter 5

Box 5.4 Thermodynamics of binding

For the simple bimolecular interaction between A and B yielding the complex AB, the

change in Gibbs free energy (DG) is related to the standard Gibbs energy change

under standard conditions (1mol l�1 of A and 1mol l�1 of B, at 25 1C):

DG ¼ DG� þ RT ln½AB�½A�½B�

� �¼ �RT lnKa ¼ RT lnKd ð5:22Þ

where R is the gas constant and T is the absolute temperature. DG1 consists of a heat

component released or taken up during the binding process (enthalpy, DH1), and an

entropy (DS1) component related to the change in the degree of ‘‘order’’ of the system

due to binding:

DG� ¼ DH� � TDS� ¼ �RT lnKa ð5:23Þ

For protein–protein interactions and other biomolecular interactions DH1 and DS1change with temperature. The temperature dependence of DH1 and DS1 can be

described in terms of the heat capacity (DCp) as given in eqs. (5.24) and (5.25):

DH� ¼ DH�ðT�Þ þ DCpðT � T�Þ ð5:24Þ

DS� ¼ DS�ðT�Þ þ DCp lnðT=T�Þ ð5:25Þ

DCp is assumed constant within the applied temperature range. T1 is the reference

temperature, usually 25 1C, and DH1(T1) and DS1(T1) are the values of DH1 and DS1at this temperature.

From eqs. (5.23)–(5.25) follows the integrated van’t Hoff equation, eq. (5.26),

which describes the temperature dependence of the affinity constant KA:

lnKA ¼ �DH�ðT�ÞRT

þ DS�ðT�ÞR

þ DCp

R

T � T�

T

� �� ln

T

T�

� �� ð5:26Þ

This expression can be used to fit KA values derived at various temperatures versus

1/T, and yields the thermodynamic parameters DH1, DS1 and DCp. A concise

description of the interpretation of these parameters in terms of molecular events

related to the binding process is given in Box 5.5.

Box 5.5 How to interpret thermodynamic binding parameters?

It is important to realize that in a thermodynamic analysis two situations are

compared: the situation after the process (e.g. binding) is completed, vs. the situation

before the process. This is why we look at the difference in the thermodynamic

parameters (indicated as D) including the whole of the process, e.g. also the solvent.

The thermodynamic analysis of a single interaction usually tells us whether the

binding is entropy or enthalpy driven, but it is not possible to interpret molecular

processes more in detail, e.g. whether replacement of water molecules is involved or

whether protein dynamics decreases. The power of thermodynamic analysis for drug

design lies in the combination of 3D structural information and the study of


structurally related compounds. This can give detailed insight on how specific

structural features contribute to binding energetics. The most important thermo-

dynamic parameters in molecular structural events are:

DH binding enthalpy represents the heat effects involved in the interaction. It can be

directly experimentally determined with calorimetric measurements. The heat effects

are caused by the formation and disruption of non-covalent bonds (hydrogen and

ionic bonds and van der Waals interactions) and can involve bonds between the

reactants, but also bonds of solvent reorganization and conformational rearrange-

ments of the reactants during the binding process. A large part of DH is due to bulk

hydration. In drug design, more water molecules at the interaction interface may

extend the complementarity of the surfaces and H-bond networks [9]. This is

favorable for enthalpy, but disadvantageous due to a loss in entropy, and contributes

to the phenomenon of entropy–enthalpy compensation (see text).

DS binding entropy can in general be interpreted in terms of degree of order and

disorder of the system. This might comprise designed restriction of conformational

freedom and rotation of chemical bonds involved in binding. Also, hydration can be

a major factor for entropy, e.g. in hydrophobic binding: the burial of water-accessible

surfaces and resulting release of water molecules can contribute to binding due to

increases in entropy.

DCp heat capacity is almost entirely ascribed to solvent effects and is considered of

high information content. DCp can be determined directly in calorimetric experiments

over a temperature range. DCp can be interpreted in terms of solvent-accessible

hydrophobic and polar surface areas, buried in the binding process [5]. A decrease in

accessible hydrophobic surface upon binding has a negative effect on DCp; that of

polar surface a positive effect. Experimental DCp values have been related to different

degrees of success to 3D structural information on these surface changes upon

binding. Problems arise due to solvation effects and release or ordering of water

molecules during binding. Ordering of water molecules in the binding interface has a

large negative contribution to DCp [4].

Figure 5.20 Temperature effect on SPR sensorgrams. Left: signals of sample cell(solid line) and reference cell (broken line). Right: net signal (referencecell minus sample cell). Reprinted from ref. [46], Copyright (2003), withpermission from Elsevier.

160 Chapter 5

5.6.2 Comparison of SPR Thermodynamics with Calorimetry

The heat effects of an interaction can be directly measured using calorimetry.Especially the introduction of isothermal titration microcalorimetry (ITC)instruments with improved sensitivity has greatly advanced the use of calori-metry in biomolecular interactions [9]. A debate is ongoing on the equivalenceof enthalpy values from van’t Hoff analysis (DH1vH), compared to those fromcalorimetry (DH1cal): discrepancies between DH1vH, from several techniques foraffinity assay and DH1cal have frequently been observed [47–50]. In calorimetricassays the total of the heat effects is assayed, e.g. heat of dilution, of mixing andheat effects due to changes in buffer protonation and solvent equilibria linkedwith the binding process [47], which go beyond the intrinsic enthalpy contribu-tion of the simple equilibrium A+B"AB. On the other hand, linkedequilibria like that of buffers and solvent will also influence the affinity and

Figure 5.21 van’t Hoff plot for binding of hmT-peptide with v-Src SH2 domain. (J)Affinity at the sensor surface, KC; (K) in solution, KS. The lines arecalculated using eq. (5.26). Reprinted with permission from ref. [12],Copyright (2005) American Chemical Society.

Table 5.5 Thermodynamic parameters derived from van’t Hoff thermo-dynamic analysis shown in Figure 5.21.

Parameter v-Src SH2 with 11-mer hmT-peptide

DH1 (kcalmol�1)a �9.4� 0.6TDS1 (kcalmol�1)a �0.3� 0.6DCp (calmol�1 K�1) �920� 160DG1 (kcalmol�1)a �9.1KD (nmol l�1) 220� 30

a At reference temperature 25 1C.


DH1vH. The situation may even become more complicated when DCp is notconstant with temperature which can occur in multi-step binding processes.Horn et al. demonstrated that, when experimental setup and analysis areperformed correctly, there is no statistically significant difference betweenDH1 values [51]. This holds even for complicated binding models including aconformational equilibrium as shown in Box 5.2.It should be remarked that van’t Hoff analysis is peculiar in its error

estimation. Recently, Tellinghuisen demonstrated that the usual way of errorestimation in van’t Hoff analysis is actually not correct and that the errors inDG1, DH1, DS1 and DCp are a function of temperature, leading to relativelylarge errors in DCp [52]. The number of thermodynamic studies using SPR israther limited [12,14,50,53–55]. In our experience, the thermodynamic para-meters from SPR van’t Hoff analysis often compare fairly well with those fromITC [12,14]. As an example, in Figure 5.22 we show the match between ourSPR data and ITC data from various studies in an entropy–enthalpy compen-sation (EEC) plot for a wide range of ligands for the Lck and v-Src SH2domains [46].EEC is a universal phenomenon in biomolecular interactions in water and is

generally a problem for the medicinal chemist as a gain in enthalpy, e.g. byadding hydrogen bonds to strengthen the binding, will be counteracted by aloss in entropy [56].

Figure 5.22 Entropy–enthalpy plot for binding of various ligands to the Src- orLck-SH2 domains. Open symbols: data derived from various ITCstudies. Closed circles: data derived from SPR competition experiments,with peptide and peptoid ligands. Reprinted from ref. [46], Copyright(2003), with permission from Elsevier.

162 Chapter 5

Combining SPR and calorimetry to explore fully the thermodynamics andkinetics of interacting systems might provide an optimal approach. To explorethe information content of especially DCp values fully, ITC is generally a betterchoice than SPR thermodynamic analysis. A disadvantage of ITC is that itrequires much more material (at least 100 times as much as is needed for SPR).The strong point of the SPR technique is the affinity data and kinetics derivedfrom the same data set.

5.6.3 Transition State Analysis Using Eyring Plots

SPR analysis has the unique feature that kinetic and affinity information can beobtained from one experiment. This implies that thermodynamic experimentscan be performed by analyzing the temperature dependence of kon and koff.Eyring’s transition state theory provides the fundamental conceptual frame-work for understanding rates of chemical processes [57]. The transition state(AB#) is the high energy state along the pathway of reactants to product (for abinding process, the unbound species to the complex).

Aþ B Ðk1

k�1

AB# �!k2 AB ð5:27Þ

Based on eq. (5.27), the thermodynamic equilibrium constant for formationof the transition state is defined as K#¼ [AB#]/[A][B]. Applying statisticalmechanics, we obtain the Eyring equation state that holds for a rate constant k:

k ¼ kBT

hK# ð5:28Þ

where kB is Boltzmann’s constant (1.381� 10�23 JK�1) and h is Plank’sconstant (6.626� 10�34 J s). K# is related to DH# and DS# (the activationenthalpy and entropy, respectively) in the same way as KA is related to DH1

and DS1 [eqs. (5.22), (5.23) and (5.26)]. This implies that for a linear Eyring plot[ln(kh/kBT) vs. 1/T] the data can be fitted with eq. (5.29).

Inkh

kBT

� �¼ �DH#

R

1

Tþ DS#

Rð5:29Þ

A non-linear Eyring plot can be fitted with eq. (5.26); such fits yield theactivation parameters DH#, DC#

p and DS#.Transition state analysis using Eyring plots derived from SPR data have been

published elsewhere [12,14,53,54]; an example is given in Figure 5.23. The koffvalues at various temperatures were determined in the presence of highconcentrations of competing ligand to prevent rebinding (Section 5.3.2.2).The kon values were derived from kon¼ koff/KD. It appears that the Eyringplot for koff is linear, indicating that DC#

p is zero between the complex and thetransition state (vice versa). The plot for kon shows a convex curvature,indicating that DC#

p for formation of the transition state from the reactants isnot zero. DC#

p has the same absolute value as that derived for DCp from the


van’t Hoff analysis of KA for the same interaction (Figure 5.21), because goingfrom the complex to the transition state (koff) DC

#p is zero. The Eyring plot for

kon (Figure 5.23a) is interesting as it displays non-Arrhenius kinetics above20 1C, i.e. at higher temperature kon decreases. Non-Arrhenius kinetics havebeen frequently found for protein folding. A general explanation for thisphenomenon is that at higher temperatures a wide region of conformationalspace is visited and the probability of a flexible ligand or part of a proteinhaving the proper conformation for binding or folding, decreases [58]. Such amodel makes sense for the binding of a pYEEI-peptide to an SH2 domain, asthe high-affinity binding can be regarded as a ‘‘two-pronged plug into a two-holed socket’’ in need of suitable positioning of the pY and I residue for binding[59]. If, for instance, binding starts with the pY moiety in its binding pocket, athigher temperatures it will be more difficult to have the I residue in the correctposition to allow high-affinity binding.A transition state analysis can give additional information, as is also

illustrated by the comparison of binding phosphotyrosine ligands to the v-SrcSH2 domain vs.Grb2 protein. The affinity and DG1 values are comparable, eventhe activation energies DG# are nearly identical (Figure 5.24). However, transi-tion state analysis reveals large differences in DH# and DS#. Both DH# values arenegative, indicating that upon formation of the transition state, heat is released.The high barrier of activation energy is caused by unfavorable activationentropy contribution. For binding to v-Src SH2 this contribution is about4 kcalmol�1 more unfavorable. This means that formation of the transitionstate of the Src SH2 domain from the reactants involves a higher degree ofordering than that of Grb2 SH2. Upon binding, the dynamics of Grb2 and v-SrcSH2 domains is decreased to the same extent, leaving the large difference in DS#

unexplained [12]. The difference in thermodynamic behavior can probably be

Figure 5.23 Eyring plots for the interaction of v-Src SH2 domain with hmT11-mer phosphopeptide. (a) kon, fitted with eq. (5.26); (b) koff, fittedwith equation (5.29). Reprinted with permission from ref. [12],Copyright (2005) American Chemical Society.

164 Chapter 5

attributed to the role of water molecules, which form a hydrogen bondingnetwork at the binding interface between ligand and v-Src SH2 protein [7] uponformation of the transition state, which has an entropy price. On the other hand,the water molecules in the network make a favorable enthalpy contribution tothe transition state, explaining the favorable DH#. For binding to Grb2 SH2,such a role of water molecules is not inferred, only direct contact exists betweenthe ligand and the protein. The above described transition state analysis hasbeen criticized, because it is assumed that every activation leads to complexformation [60]. Alternatively, Arrhenius analysis is advocated [60].In the interpretation of the data, 3D information from X-ray and NMR

analysis is essential. However, the 3D structures alone cannot provide informa-tion on energetic contributions determining the binding process. Especially incases where dynamics of ligand and receptor or solvent effects are involved,results of computational chemistry can be expected to be disappointing. Thecontribution of entropy to the free binding energy can be very large and mayinfluence the affinity by several orders of magnitude. Thermodynamic andkinetic analysis can help to quantify the extent of these contributions and togenerate ideas to exploit them in molecular design.

5.7 SPR Applications in Pharma Research: Concluding

Remarks and Future Perspectives

In this chapter, we have emphasized the role of kinetics and thermodynamics inbiomolecular interactions. Notwithstanding the impressive contributions ofstructural biology and computational chemistry, our understanding and theability to predict affinities of receptor–ligand interactions remain poor. It is

Figure 5.24 Energy transitions at 25 1C as a function of the binding coordinate forphosphorylated peptide binding to v-Src SH2 domain (solid lines) and toGrb2 SH2 domain (dotted lines). Reprinted with permission fromref. [12], Copyright (2005) American Chemical Society.


increasingly acknowledged that for a more accurate notion, thermodynamicand kinetic studies of biomolecular interactions are needed. Modern calori-metric and SPR techniques are the tools to perform such studies and deserve aplace in the toolbox of rational design used by the medicinal chemist andchemical biologist.The high information content of SPR data with kinetic and affinity informa-

tion is unique and allows full thermodynamic and kinetic characterization of aninteraction, including transition state analysis, as shown in Section 5.6.3. Thereare also limitations to the use of van’t Hoff analysis: even with affinity data withrelatively small standard errors, DCp has a large error. DCp is important as itcan give insight into the nature of the surface area buried upon binding and onthe role of water molecules in the binding process (Box 5.5). Using ITC, ingeneral DCp can be more accurately determined by measuring DH at a widerange of temperatures.The best of both worlds is to use SPR and ITC for thermodynamic analysis,

with special emphasis on careful experimental design and on the limitations ofeach method. A concern associated with SPR data is that affinity for a ligandimmobilized on a sensor surface might not be identical with that for the ligandin solution. In this respect it is relevant to introduce linkers between the bindingepitope and the dextran matrix of the sensor chip, as described in this chapter.In many cases no difference appears, especially for monovalent binding.Using the approach for assays of KS with competition experiments as outlinedin Box 5.3, we find comparable affinities at the surface and in solution (seeTable 5.4 and Figure 5.21). On the other hand, binding to a surface mightbe a better model for a biological interaction involving multivalency than(monovalent) interactions in solution.Apart from drug–receptor studies, SPR is also useful for other aspects of drug

research. Studies on binding to serum proteins are relevant for distributionproperties of drugs [61]. Many important drug targets are membrane-boundproteins, e.g.G-protein coupled receptors (GPCRs). Technology to follow passiveand active absorption to membrane interfaces using SPR is under development,as is drug binding to metabolizing enzymes [62]. SPR biosensor systems withsupported monolayers and tethered bilayer membranes are under development,but not standard technology yet [63]. In an approach called ligand fishing,crude tissue extracts and cell homogenates are screened for potential ligands ortargets using SPR [62]. In such approaches, identification of bound species iscrucial, with mass spectrometry (MS) as the ideal platform. In particular, matrix-assisted laser desorption/ionization time-of-flight MS (MALDI-TOFMS) andelectrospray ionizationMS (ESI-MS) are powerful tools for protein identification.It is therefore not surprising that it has been attempted to integrate SPR and MSfor proteome analysis, as reviewed in ref. [64]. SPR serves two main purposes inproteome analysis: (1) to confirm and possibly quantify specific binding and (2) toact as a micro purification support for further analysis. MALDI analysis directlyon a chip surface is possible [65]. Problems may arise due to the small amount ofcaptured protein on the chip, the many handling steps of the procedure and the

166 Chapter 5

acidity of the matrix material. In another approach, analytes are eluted andcollected in a recovery system. In principle then the whole range of MStechniques, including analysis of digested samples, is available for identification.In drug research, high-throughput screening (HTS) plays an essential role in

screening large libraries of compounds. Until recently, the use of SPR technol-ogy was hampered by the limited number of surfaces on one sensor chip. Inview of the high commercial potential, in the slipstream of the development ofarray technologies, SPR imaging applications are emerging using microarrayson chips. Examples include Biacore’s Flexchip microarray device [66] and IBISTechnologies IBIS-iSPR instrument (see Chapter 6) and other SPR imagingtechniques [67,68] (Genoptics and K-MAC, respectively). This field is devel-oping rapidly and is extremely promising. We expect an increasing impact ofSPR technology on drug research. This will be enhanced by further develop-ments of SPR technology for pharma applications, such as high-throughputscreening, further integration of SPR and MS and mimicking membraneenvironments and protein ensembles on SPR surfaces.

5.8 Questions

1. What causes mass transport limitation (MTL) in the kinetics of SPRexperiments?

2. How can MTL be diminished by experimental design?3. How can one perform kinetic analysis under MTL conditions?4. How can depletion of the analyte in a cuvette-based system be calculated?5. What is the strength of SPR as a tool in drug development research?6. Explain why a high loading of the ligand affects the determination of the

affinity constant. Describe at least two ways to solve this and determinefrom the sensorgram given below the affinity constant of an antibodyantigen reaction in nmol l�1.


5.9 Symbols

[A] concentration of analyte A at the sensor surface (mol l�1)[A]0 initially added analyte concentration in the bulk (mol l�1)[A]free analyte concentration corrected for depletion (mol l�1)[A]tot total analyte concentration used in a competition experiment (mol l�1)[B] concentration of free analyte binding sites on the sensor surface

(molm�2 or m1)A molecule A (usually analyte)AB# transition state on formation of the AB complexB molecule B (usually ligand)Bmax maximum binding capacity (in m1)D diffusion coefficient (m2 s�1)DCp heat capacity (Jmol�1K�1)DCp

# activation heat capacity (Jmol�1K�1)DG Gibbs free energy (of binding) under non-standard conditions

(Jmol�1)DG# Gibbs activation free energy for formation of the transition state

(Jmol�1)DG0 Gibbs free energy (of binding) under standard conditions (Jmol�1)DH# activation enthalpy (Jmol�1)DH0 change of enthalpy (upon binding) under standard conditions

(Jmol�1)DS# activation entropy (Jmol�1K�1)DS0 change of entropy (upon binding) under standard conditions

(Jmol�1K�1)h Planck’s constant (6.6262� 10�34 J s)K# equilibrium association constant for formation of the transition state

(lmol�1)KA equilibrium association constant (lmol�1)kb Boltzman’s constant (1.381� 1023 JK�1)KC equilibrium dissociation constant for affinity at the chip (mol l�1)kconf rate constant for conformation change AB -AB* (s�1)Kconf equilibrium constant for conformation change (kconf/k-conf)k-conf rate constant for conformation change AB* -AB (s�1)KD equilibrium dissociation constant (mol l�1)kobs kon[A]+ koff (s�1)koff dissociation rate constant (s�1)kon association rate constants for formation of the complex AB

(lmol�1 s�1)KS equilibrium dissociation constant in solution (mol l�1)ktr transport rate for diffusion from bulk to sensor surface (in

m1 s�1 lmol�1)Lm transport coefficient from bulk solution to sensor surface (m s�1)Lr Onsager coefficient for reaction flux (m s�1)MW molecular weight of analyte (Da)

168 Chapter 5

R gas constant (8.3143 JK�1mol�1)Req net increase in SPR angle at binding equilibrium (reference cell

subtracted from sample cell (m1)Rt net increase in SPR angle at time t (reference cell subtracted from

sample cell (m1)S surface of the sensor in the cells (mm2)T absolute temperature (K)T0 reference temperature (298K)Vbulk volume of the bulk solution in the cells (l)Z viscosity (cP)

5.10 Acknowledgements

The examples presented in this chapter originate from work in the Departmentof Medicinal Chemistry and Chemical Biology, Utrecht Institute of Pharma-ceutical Sciences, Utrecht University, The Netherlands. We wish to thank theHead of the Department, Professor Rob M.J. Liskamp, for making it possibleto study exciting new compounds in our SPR assays. Many members ofthe Department have contributed: we especially thank Dr. Frank J. Dekker,Dr. Rob Ruijtenbeek and Dr. Ir. John A.W. Kruijtzer. We are grateful for thecooperation with Dr Isabelle Broutin (Universite Rene Descartes, Paris) andProfessor Albert Heck (Biomolecular Mass Spectrometry, Utrecht University).We thank The Netherlands Organization for Scientific Research (NWO) forfinancial support.

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Kinetic and Thermodynamic Analysis of Ligand–Receptor ...

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