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(Cantor and Schimmel, 1980). Thus, if the above approximation is valid,
then the affinity of the competitor results directly from the slopes of
the two straight lines and the total concentration of the inhibitor.
However, if the competitive inhibitor is not present in great excess
over the total concentration of binding sites, then the above
approximation will not be valid, the resulting Scatchard plot will be
curved (Feldman, 1972), and the error in the derived equilibrium
constant caused by making the approximation may be substantial. In the
present communication we suggest a simple procedure for eliminating this
error by linearizing the curved Scatchard plot resulting from this
experimental design.
The very popular competition displacement experimental design
(known as the "ED^q" method) also generates data that are somewhat
difficult to analyze in the laboratory without the aid of computerized
nonlinear regression techniques. Within this design one measures the
fraction of initially bound labeled ligand remaining bound at
equilibrium in the presence of increasing concentrations of the
unlabeled competitive inhibitor whose affinity is to be measured (e.g.,
Abrass & Scarpace, 1981; Lindenbaum & Chatterton, 1981; for review, see
Rodbard, 1973). The total concentration of inhibitor that displaces
half the initially bound labeled ligand ("ED^g") is determined, and one
then attempts to relate this inhibitor concentration to its actual
affinity for the binding sites. This design presents two major
problems: the curvature of the displacement plot makes the precise
determination of ED^g difficult, and the ED5Q itself is often quite
different from, and difficult to relate to, the actual equilibrium
dissociation constant of the competing ligand. In many situations the