Extending the Range of Rate Constants Available from BIACORE: Interpreting Mass Transport-Influenced Binding Data

Abstract

Surface-based binding assays are often influenced by the transport of analyte to the sensor surface. Using simulated data sets, we test a simple two-compartment model to see if its description of transport and binding is sufficient to accurately analyze BIACORE data. First we present a computer model that can generate realistic BIACORE data. This model calculates the laminar flow of analyte within the flow cell, its diffusion both perpendicular and parallel to the sensor surface, and the reversible chemical reaction between analyte and immobilized reactant. We use this computer model to generate binding data under a variety of conditions. An analysis of these data sets with the two-compartment model demonstrates that good estimates of the intrinsic reaction rate constants are recovered even when mass transport influences the binding reaction. We also discuss the conditions under which the two-compartment model can be used to determine the diffusion coefficient of the analyte. Our results illustrate that this model can significantly extend the range of association rate constants that can be accurately determined from BIACORE.

Biosensor binding data and its applicability to the determination of active concentration

“…Binding events are generally described by a two-compartment model (Myszka et al 1998), where transport of analyte to the sensor surface and binding at the sensor surface are regarded as linked processes, as illustrated in Fig. 1a.…”

The Effect of a Receptor Layer on the Measurement of Rate Constants

“…With few exceptions (Goldstein and Wofsy, in preparation;Schuck, 1996;Yarmush et al, 1996) it has been customary to model such reactions as occurring on the biological surface by imposing a reacting boundary condition on a transport equation for the analyte (Davis et al, 1995;Myszka et al, 1998;Edwards, 1999Edwards, , 2000Edwards et al, 1999;Mason et al, 1999). A truer model would include a small receptor layer in which the receptor sites reside and through which the ligand must diffuse to reach them.…”

Structural identifiability of surface binding reactions involving heterogeneous analyte: Application to surface plasmon resonance experiments

“…Three core model types are applied to the problem of estimating kinetic rate constants from SPR data: the first model is the well-mixed Langmuir model, with the second model being the two-compartment model proposed by Myszka et al (1998) that includes transport effects, while the third model, the effective rate constant approximation, can be derived from the second via a quasisteady state assumption (or directly from consideration of the fluid dynamics of the analyte in the flow and subsequent binding (Edwards, 2001)). …”

“…One approach is to model the reaction as occurring on the sensor surface via a reacting boundary condition on a transport equation for the analyte (Myszka et al, 1998;Edwards et al, 1999). Another approach has been to include a receptor layer in the model, to allow for the dextran layer in which the receptor is immobilised, and to also consider analyte diffusion into the layer to reach binding sites (Edwards, 2001).…”

“…Therefore, if the rapid mixing model is not appropriate then systematic errors in the estimates for the rate constants can arise (Chaiken et al, 1992). Myszka et al (1998) propose a two-compartment model that includes transport effects while modelling the binding process using Langmuir-type mass action kinetics. Under a quasisteady state (QSS) assumption this two-compartment model can be reduced to a single differential equation for the bound analyte.…”