Molecular interactions can be discovered and characterized when the stationary phase is constructed around one of the components of a binding event.
Biosensor Alternative: Frontal Affinity Chromatography
David C. Schriemer University of Calgary (Canada)
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ou wouldn’t expect an analytical chemist to get excited about a chromatographic method that exhibits poor resolution and doesn’t actually separate solutes. In fact, one could argue that a low-resolution, nonseparating method shouldn’t be called “chromatography” at all. Nonetheless, chromatography is defined as a set of methods capable of distinguishing two or more compounds on the basis of differential partitioning between mobile and stationary phases. Zonal chromatography translates this discrimination into a physical separation, but frontal chromatography does not. The chief distinction between the two methods is sample introduction. Zonal analysis is characterized by the injection of a small plug of sample in a volume and concentration carefully chosen so as to not overload the capacity of the column. Frontal analysis sets this caution aside and infuses larger volumes of sample that saturate the column. This simple method actually predates zonal chromatography; it was used in the 1800s for crude purification (1). Under constant infusion, early fractions are devoid of dissolved species because they are adsorbed on the column. As Tswett pointed out, even Aristotle applied the method when he converted seawater to potable water by filtering it through layers of earth (2). A large-scale version of frontal affinity chromatography (FAC) was first described in 1975 by Kasai (3). The very basic form of the frontal method can be found in today’s tap-mounted water filters and gas masks. Frontal analysis has been used to characterize solute–sorbent interactions. It is the premier method for generating distribution isotherms, which are plots of the concentration of a sample component in the stationary phase versus its concentration in the mobile phase (4 ). These isotherms facilitate our understanding of the chromatographic process and contribute to the design of more efficient separation systems for zonal applications. This characterization ability suggests an alternative approach to the ubiquitous binding assay. Binding assays are used to study biochemical processes, for high-throughput screening to identify lead compounds, and to optimize these compounds, to name a few applications. The goal of most in vitro binding assays is to
© 2004 AMERICAN CHEMICAL SOCIETY
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FIGURE 1. From linear to nonlinear chromatographic behavior. (a) A given ligand experiences an accelerated breakthrough as its concentration increases (red, right to left). Under linear chromatographic conditions ([A]0 1 µmol of protein (8). Amounts of this magnitude are clearly inappropriate in today’s research and development environment, which could explain why the technique has lain dormant for so long. However, a direct proportionality exists between the volume of sample required to achieve breakthrough and the amount of immobilized biomolecule: Lowering the capacity of the affinity column reduces sample requirements. Our laboratory uses microcartridges incorporating low- to subpicomole amounts of immobilized protein that require only a few microliters of sample. Further miniaturization is indeed possible and will allow FAC to be wielded with greater frugality in columns sized to accommodate sample availability. The breakthrough curves generated by continuous infusion of sample ligand through an affinity column reflect the nature of the binding event under study. The exact shape of these curves depends on both thermodynamic and kinetic considerations, much as it does in conventional zonal chromatography (4, 9). The goal in FAC system design is to use an affinity column in which the shape and elution time of the breakthrough curve is dominated by the actual binding event. From a practical perspective, this involves minimizing extra-column dead volume and swept volume, ensuring sufficiently high column efficiency, and reducing the impact of unwanted retention mechanisms. When these requirements are satisfied, models of varying degrees of complexity can be fitted to the data derived from breakthrough curves and used to generate thermodynamic and kinetic descriptors of the interaction (10, 11). In this article, we will deal only with basic thermodynamics of biomolecular interactions.
Theory The simplest model of a binding event involves the interaction of a ligand with a single class of receptor binding sites. A given receptor molecule may have multiple, equivalent sites, but the model assumes their independence. The basic binding function stems from the familiar law of mass action, and when expressed as a function of the measurable quantity in a FAC experiment, it takes the form V – V0 =
Bt [A]0 + K d
(1)
The breakthrough volume, V, for a ligand is presented as a corrected volume, in which the breakthrough volume of the ligand in the absence of the binding event, V0, is subtracted from V. In practice, V0 is a difficult number to obtain, and thus a structurally related nonligand is often used to assess V0. Bt is the dynamic capacity of the affinity column for the ligand, [A]0 is the infusion concentration of the ligand, and Kd is the dissociation constant for the interaction. This basic model goes by various names and has been reworked into a range of expressions, on the basis of the field of application. Some might more readily recognize the basic bind-
ing function when it is presented as the Langmuir binding isotherm, the rectangular hyperbolic function, or even the Michaelis– Menten function (10). Chromatographers recognize it as but one example of a nonlinear convex binding isotherm— all expressions stem from an equilibrium mass-action treatment of the simple expression A + B ↔ AB. Figure 1a shows the effect of ligand concentration on both the appearance of the curve and V. At high dilution relative to the Kd of a particular interaction, V is insensitive to slight changes in ligand concentration and has actually achieved its maximum value. At this point, it is useful to draw some parallels with zonal chromatography. Under these dilute conditions, FAC operates in the linear region of the binding function, which is also exactly where we like it to be in zonal chromatography. Therefore, breakthrough times and retention times are equivalent. Note that the ideal Gaussian profile of zonal chromatography becomes an “integrated Gaussian” in frontal analysis. Measurements of V are taken at the weighted midpoint of the curve. One way to obtain these volumes is by differentiating the curve into a peak and using standard chromatographic tools for moment analysis or peak centroiding. Although it is too simplified for many biologically relevant associations, this basic binding model is nevertheless a useful starting point because deviations will become apparent upon data treatment. The model works for quite a large range of systems, however, particularly those involving drug–protein interactions or any small-molecule–protein interactions.
Data collection and treatment The easiest way to measure Kd values is by sequential infusion of increasing concentrations of ligand, without washing steps between infusions (12). Measurements of V are then related to the difference in successive ligand concentrations. Once values of V – V0 are obtained for a range of ligand concentrations, classical linearization or nonlinear regression techniques can be used for the simultaneous determination of Bt and Kd. A useful form is [A]0, j + y j = B t
[
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for the jth in a series of injections. The slope provides the column capacity and the negative y intercept provides Kd. Other indirect methods with marker or indicator ligands can also be used (12, 13). The ability to obtain Bt and Kd for the interaction from a single course of experiments is a unique feature of FAC. No independent measurement of the capacity or any assumption regarding the amount of active protein is needed, which is very important because protein preparations are rarely 100% active, and this feature ensures high accuracy in the FAC measurement of Kd. As ligand concentration becomes appreciable relative to the Kd of the binding event (i.e., >0.1 Kd), we enter into the nonlinear region of the binding function. One possible effect of nonlinearity on the shape of the breakthrough curve is that it becomes steeper in its early stages but tends to roll off as it approaches [A]0 (Figure 1a). This is the so-called “self-sharpening” effect and is to be expected under equilibrium, nonlinear conditions when our basic model isotherm is followed (4). The equivalent in zonal chromatography would be a tailing or skewed peak. Another effect of nonlinear behavior occurs when the sample contains two ligands in competition for the same binding site (Figure 1b). If two ligands are infused at low concentrations relative to their respective Kd values, they will have breakthrough times that are independent of one another. Again, this is a comforting conclusion that has its parallel in zonal chromatography, in which we work very hard to ensure that the retention time of one compound does not affect that of a second compound. But when the ligands’ concentrations begin to approach their respective Kd values, two changes are observed. Breakthrough times are accelerated, but ligand displacement also occurs. The concentration of the weaker ligand increases transiently above its infusion level; this is caused by the action of the slower-eluting stronger ligand. When the stronger ligand enters a region through which the weaker ligand has passed, it re-establishes the local equilibrium and displaces the weaker ligand (14, 15). This wave, or roll-up, is a unique and useful feature that can be used to extract information from very complex mixtures.
Characterizing and quantitating molecular interactions HSA is probably the most extensively investigated protein stationary phase for all forms of 444 A
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affinity chromatography. Drug binding to HSA and other plasma proteins significantly affects the overall pharmacological and pharmacokinetic properties of a given drug. Quantitating the extent of binding is critical, given the abundance of such proteins in the human system (16, 17 ). Compounds that are net neutral or anionic at physiological pH are particularly susceptible to binding with HSA; thus, HSA binding studies are commonly performed as a secondary screen in early drug discovery and in the later stages of drug lead optimization. HSA is actually quite a challenging protein for interaction characterization because it presents at least two small-molecule binding sites that are allosterically linked and it undergoes significant conformational change upon ligand binding. Yamaguchi et al. have shown that salicylic acid binds to a large FAC column with a Kd of 37.9 ± 0.1 µM for monomeric HSA and 48.8 µM for dimeric HSA (18). They immobilized the monomeric and dimeric HSA in separate columns and showed that both protein stationary phases exhibited a 3:1 binding stoichiometry (drug to protein monomer). These data compare very favorably with those generated from the classical equilibrium dialysis method (38.8 and 48.8 µM, respectively). Breakthrough curves generally reflect the averaged binding of the ligand to all available sites, thus the Kd determination represents an intrinsic value, and data for each of the proposed three binding sites are not available through the FAC method. As with all homogeneous or two-phase systems used for binding studies, determinations of stoichiometry require an independent quantitation of the number of moles of active protein bound. A binding model must also be proposed, and in this case Yamaguchi used n equivalent, independent sites. The FAC titration method, in which increasing concentrations of ligand are applied to the column and the corresponding breakthrough curves are measured, provides Kd and Bt simultaneously. Bt values are a useful byproduct of the experiment because they allow for stoichiometry measurements if the number of moles of active protein is known. Obtaining Bt and Kd simultaneously via the titration method facilitates the accurate assessment of binding energies. By varying the temperature of the FAC column, Hage’s experiments showed that the active capacity of an HSA stationary phase increased in a reversible manner for both R and S enantiomers of warfarin (19). Because Kd is measured independent of capacity changes, binding energies could be determined in a straightforward manner. Data were modeled with a simple Arrhenius relationship, which showed that the enantiomers have significantly different entropic
2.5 2.0
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rate, the experiment requires only ~25 pmol per run. The use of MS detection with FAC offers 1.0 the potential of even higher rates of Kd de0.5 termination by multiplexing, without requirK d,8 = 8 nM ing labeled ligands. We simultaneously meas0.0 ured the Kd values of three oligosaccharides in a mixture on a protein stationary phase 0 5 10 15 20 25 consisting of a monoclonal antibody raised to Time (min) abequose, a saccharide present in Salmonella paratyphi B O-antigens (7). The values were FIGURE 2. FAC/MS experiment ranks ligands. in good agreement with individual determiEight proprietary ligands, each at a concentration of 1 µM to ensure operation in the nonlinear region of nations. It is easy to appreciate that such the binding isotherm, were infused on a sorbitol dehydrogenase column. Their Kd values span the range of micromolar to nanomolar. multiplexing would work when operating at or near the linear isotherm. Just like in zonal chromatography, the breakthrough volumes would be independand enthalpic contributions to the free energy of binding. The limited opportunity for sensitive protein–carbohydrate ent of one another, and the basic equation applies for each ligand. What would happen in a more challenging, nonlinear enviinteraction analysis has driven much of the renewed interest in FAC. Individual interactions between these two classes of bio- ronment, for example, when dealing with higher ligand conmolecule are typically weak, exhibiting Kd values in the mil- centrations and/or lower Kd values (tighter binders)? Figure 2 limolar and micromolar range; biological systems rely on mul- displays the breakthrough curves for a mixture of eight ligands tivalent interactions and avidity to achieve significant binding on a sorbitol dehydrogenase protein stationary phase. Ligand energies. The characterization of individual associations tradi- concentrations were equal to or greater than the respective Kd tionally required high concentrations of carbohydrate ligand, values in all cases. We have shown that the rank order of bindbut with FAC, these characterizations can now be performed ing strengths is preserved, even under these nonlinear condiindependent of concentration. Hirabayashi and Kasai have ex- tions (12). The figure indicates that although titrating the mixtensively applied FAC to the analysis of a series of galectin–car- ture compresses the breakthrough curves and the displacement bohydrate interactions. Galectins are proteins that exhibit features, it does not affect the ranking. This sort of qualitative, specificity for -galactoside-containing glycoconjugates, such comparative analysis would be very useful in drug lead optias those found in N-acetyllactosamine- and polylactosamine- mization or whenever one ligand is compared with another. containing glycoproteins. At least nine human galectins have The stronger binders and their identities are confirmed via the been discovered and another eight proposed on the basis of se- corresponding m/z values. For simple systems adhering to the quence similarity (20). They are calcium-independent lectins, basic model, these displacements offer additional confirmation and all appear to have a highly conserved core sequence of that the ligands are competing for the same binding site. ~130 amino acids (21). The number of biological processes The derivation of accurate dissociation constants from FAC upon which this family impinges includes the migration and ad- data requires that the analysis occur under equilibrium condihesion of cells, apoptosis, cell growth and proliferation, and im- tions, which can be ensured by altering the flow rate of the exmune response and inflammation (21–23). periment; if the measured Kd values are constant over a range The FAC work of Hirabayashi and Kasai has been instru- of flow rates, then equilibrium conditions can be assumed. Howmental in demonstrating the variability of the galectin carbo- ever, the major limitation for direct analysis of ligand binding via hydrate recognition domain and in suggesting unique roles for FAC appears to be interactions with slow kinetics. For example, each galectin (24). Their FAC system monitors pyridylaminat- slow on-rates exhibiting rate constants ka 10 µM), allowing for infusion under linear-isotherm conditions and rapid indicator kinetics. Therefore, V – V0 = B t /Kd, and the indicator ligand simply functions as a tool to measure the reduction in column capacity caused by the test ligand (hence, the term “indicator” and not “competing” ligand). We have used this approach in the FAC/MS analysis of several receptor–ligand systems (12). For example, nafoxidine, an estrogen antagonist used in the treatment of breast cancer, is a slow, tight binder that targets the ligand-binding domain of estrogen receptor (25). It also exhibits appreciable nonspecific 446 A
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binding to the FAC microcolumn, so its breakthrough volume could not be treated with Equation 1, even though the protein– ligand interaction fits the basic model. With a weak steroid used as an indicator ligand, the equilibrium Kd was accurately determined to be 23 nM. We find this method to be extremely useful for Kd measurements of single ligands in general; weak ligands are easy to obtain for most receptor systems, which usually exhibit rapid kinetics. For our FAC/MS work, we typically select indicators with Kd values in the range 10 µM–1 mM and apply them at low micromolar concentrations. However, the Kd of the indicator ligand can be
