Abstract

There are a variety of methods available to calculate the inhibition constant (K_i) that characterizes substrate inhibition by a competitive inhibitor. Linearized versions of the Michaelis-Menten equation (e.g., Lineweaver-Burk, Dixon, etc.) are frequently used, but they often produce substantial errors in parameter estimation. This study was conducted to compare three methods of analysis for the estimation ofK_i: simultaneous nonlinear regression (SNLR); nonsimultaneous, nonlinear regression, “K_M,app” method; and the Dixon method. Metabolite formation rates were simulated for a competitive inhibition model with random error (corresponding to 10% coefficient of variation). These rates were generated for a control (i.e., no inhibitor) and five inhibitor concentrations with six substrate concentrations per inhibitor and control. TheK_M/K_i ratios ranged from less than 0.1 to greater than 600. A total of 3 data sets for each of threeK_M/K_i ratios were generated (i.e., 108 rates/data set perK_M/K_i ratio). The mean inhibition and control data were fit simultaneously (SNLR method) using the full competitive enzyme-inhibition equation. In theK_M,app method, the mean inhibition and control data were fit separately to the Michaelis-Menten equation. The SNLR approach was the most robust, fastest, and easiest to implement. The K_M,app method gave good estimates ofK_i but was more time consuming. Both methods gave good recoveries of K_M andV_MAX values. The Dixon method gave widely ranging and inaccurate estimates of K_i. For reliable estimation of K_i values, the SNLR method is preferred.