solute concentration at the surface of the sensor chip cause changes in the The specificity of a Biacore analysis is determined through the nature and. Protein concentration, surface plasmon resonance (SPR), Biacore, purification GE Healthcare, , Biacore Concentration Analysis Handbook. 8. Biacore X Handbook BR Edition AB. 3 and (with the optional Biacore X Plus Package) concentration analyses.
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Protein concentration data are required for understanding protein interactions and are a prerequisite for the determination of affinity and kinetic properties.
It is vital for the judgment of protein quality and for monitoring the effect of therapeutic agents. Protein concentration values are typically obtained by comparison to a standard and derived from a standard curve. The use of a protein standard is convenient, but may not give reliable results if samples and standards behave differently. In other cases, a standard preparation may not be available and has to be established and validated.
Using surface plasmon resonance SPR biosensors, an alternative concentration method is possible. This method is called calibration-free concentration analysis CFCA ; it generates active concentration data directly and without the use of a standard. The active concentration of a protein is defined through its interaction with its binding partner.
This concentration can differ from the total protein concentration if some protein fraction is incapable of binding. If a protein has several different binding sites, active concentration data can be established for each binding site using site-specific interaction partners. This review will focus on CFCA analysis. The major part of the review will, however, try to set expectations on CFCA and discuss how CFCA can be further developed for absolute and relative concentration measurements.
The online version of this article doi: Proteins are complex molecules involved in catalysis and signaling, and serve as building blocks in cells. They often function in networks, and a single protein may interact with several other biomolecules. Protein expression may differ in health and disease, and specific proteins have been identified as biomarkers, i.
Antibodies are generated for the detection of biomarkers and both antibodies and vaccines are designed for the treatment or prevention of a range of diseases. Clearly, correct estimates of protein activity and concentration will be important for obtaining a better insight into protein function. The first time a protein is expressed, no standard preparation is available. An immune response can differ from one animal to another or from one person to another, and it may be difficult or even impossible to identify a specific IgG that can be used as a common standard.
Commercial research reagents may not always show the expected activity, and this may impact the quality and cost of research Baker It is in situations like these that calibration-free concentration analysis CFCA can make an impact, as it has the potential to measure active concentrations without the use of a standard.
In Biacore systems, the analyte is injected over the sensor surface under conditions of laminar flow Sjoelander and Urbaniczkyand the SPR response has concwntration shown to correlate with changes in mass on the sensor surface Stenberg et al.
CFCA methodology on the Biacore platform has been developed gradually from early concepts Karlsson et al. Handhook events are generally described by a two-compartment model Myszka et al.
Transport and binding of analyte to a sensor surface. A transport coefficient k m describes transport across the diffusion layer to the sensor surface, where the analyte binds to its ligand circle segment. Binding rates are defined by analyte and ligand concentrations and rate constants k a and k d.
The height of the depletion layer depends on the flow rate, and is reduced at high flow rates. The balance between transport and kinetic rates can be further controlled by altering the immobilization level.
Biosensor binding data and its applicability to the determination of active concentration
For the same interaction, the shape of the binding curves can differ dramatically and depend on the experimental conditions. With low immobilization levels Fig. Transport effects are negligible, and the surface concentration is identical to the bulk concentration of the sample.
At high immobilization levels Biacorr. In parallel, the time to reach a steady state is prolonged. In this situation, the concentration at the sensor surface varies with the flow rate, and is lower than the bulk concentration. Kinetic analysis and CFCA share theoretical concepts, but differ in the way data are generated and analyzed Pol et al. CFCA analysis is focused on initial binding rates, and requires that the transport coefficient can be pre-calculated and used as a constant during data analysis.
CFCA also requires that the interaction is probed with two flow rates. By fitting binding data to the one-to-one interaction model, the concentration of the analyte can then be determined.
This differentiates CFCA from kinetic analysis, concentratikn the concentration has to be entered as a constant and the transport coefficient is calculated during the fitting procedure, along with the rate constants. Unfortunately, it is hhandbook possible to simultaneously fit analyte concentrations and the cocentration coefficient, as these parameters cannot be resolved from each cpncentration.
Sample matrixes range from pure buffer systems to diluted serum samples. In such cases, the resulting data are interpreted as relative concentrations, or CFCA data are regarded as an empirical value that can be correlated to other effects. Examples of relative analysis include comparison of stressed and wild-type protein Hensel et al. CFCA data have been successfully correlated to growth inhibition in vaccine development Spiegel et al.
In contrast to kinetic analysis, which has benefitted from increased sensitivity of SPR systems, CFCA has received less attention from developers and from the user community.
This is surprising, as CFCA can potentially be developed to an absolute concentration method. For this, a detailed understanding of the transport coefficient is necessary Pol anqlysis al.
Biosensor binding data and its applicability to the determination of active concentration
By examining the parameters in the transport coefficient, it is possible to estimate the repeatability and accuracy of CFCA measurements. The transport coefficient can also be used to calculate the degree of mass transport limitation MTL. With this, it may be possible to predict under which circumstances CFCA can be successfully applied. Note that Biacore-specific data vary for different instruments and flow cell designs.
The values provided here are relevant for the Biacore T flow cell FC 2—1 configuration. Biacore-specific data vary between different versions of Biacore systems. The height and width are properties of the flow system and the integrated fluidic cartridge IFC. The distance from the inlet to the center of the detection spot depends on the angle of incident light and on the thicknesses of the optointerface and the glass on the sensor chip.
The length of the detection spot is given by the optical settings. All rate equations are presented in the Supplementary data. Christensen used simulation and re-analysis of binding data, and showed that accurate concentrations could be obtained with MTL as low as 0. They derived concentrations using binding rates rather than full binding curves.
Overlay plot of simulated and fitted data.
Analyte concentrations were 5, 10, and 20 nM a — f and 0. Each curve is identified by the scale to the right and this scale is applicable to both left and right sensorgram panels. As simulated and re-evaluated data are shown in an overlay plot, each subfigure contains 12 sensorgrams.
The resulting binding curves were re-analyzed using the CFCA model, which includes a dilution factor to allow for global analysis of a concentration series. The insert shows the degree of MTL calculated with Eq. Data were simulated with varying k ak dand concentration values, as shown in Fig. Each subfigure in Fig. The fitting procedure takes all dilutions into account and returns one concentration value that corresponds to that of the undiluted sample.
The fitted concentrations agree with concentrations used in simulation and differences in k d values do not impact the results. Clearly, binding curve analysis confirms the findings of Christensen, and demonstrates that CFCA analysis can be expected to return correct concentrations at very low degrees of MTL. The analysis requires the use of two flow rates for accurate analysis of transport effects and simulated data cannot be retrieved if only one flow rate is used. These simulations are in contrast to the experimental data from Pol et al.
Interestingly, experimental data reported by Visentin et al. To obtain more insight into these findings, it may be useful to look into the repeatability and accuracy of CFCA, and factors that influence them. These k t values were compared to nominal k t values. Predicted variation in k t values.
Error spaces were assigned to the nominal values of the height and width of the flow cell, the flow rate, the distance to the detection center spot, and the detection spot length. This value can be compared to a response CV of 1. As the experimental data include chemical noise stability of reagents and sensor chipsit can be assumed that system parameters which influence k t are very stable during a run.
The situation is somewhat different when Biacore systems are compared, as IFCs, pumps, optointerfaces, and optical settings are unique to each system Fig. This conversion factor was obtained by using radioactively labeled proteins Stenberg et al.
In these experiments, proteins were bound to a carboxymethylated dextran sensor surface by electrostatic forces. The protein sample was re-circulated until a steady state was reached, and then transferred to a scintillation counter. Binding experiments are very different. A ligand is already immobilized when the analyte is injected, and the distribution of the analyte in the dextran matrix may not be known.
Different binding scenarios are illustrated in Fig.
Hypothetical distribution of analyte on sensor surfaces and in the surface plasmon resonance SPR evanescent field. The gray bar illustrates that the strength of the SPR signal is reduced with increasing distance from the gold surface. Here, most of the analyte is assumed to remain at the top of the immobilized layer, as diffusion into the matrix may be prevented. The capturing agent is immobilized to a high degree and the ligand is bound on top of the capturing agent.
The analyte is again assumed to remain at the top of the sensor surface. When these scenarios are considered in view analsyis the evanescent field strength at the SPR angle Stenberg et al. The intensity of the SPR signal decays exponentially with increasing distance from the sensor surface. The difference in response per mass unit is, thus, approximately two-fold when scenarios with protein binding to the top of the dextran matrix are compared with binding to a 2D surface without a dextran matrix.
Consequently, the response per mass unit will be lower in the scenarios in Fig. The experimental hancbook used by Pol et al. This may explain why Visentin et al.