What is the primary use of an Eadie-Hofstee plot in enzyme kinetics?

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Multiple Choice

What is the primary use of an Eadie-Hofstee plot in enzyme kinetics?

Explanation:
Eadie-Hofstee plots are used to extract the kinetic parameters Km and Vmax from enzyme data by linearizing the Michaelis-Menten equation in a way that keeps the data in a familiar, low-error form. Start with the Michaelis-Menten relationship v = (Vmax [S])/(Km + [S]). By rearranging to a linear form, you get v = Vmax − Km · (v/[S]), which is a straight line when you plot v (the reaction velocity) versus v/[S]. In this line, the intercept on the v axis is Vmax and the slope is −Km. So fitting a line to the data yields both Km and Vmax directly. This approach is advantageous because it uses data in a form closer to the original measurements than some other linearizations, making parameter estimation straightforward. It’s not used to determine the activation energy, the rate law, or substrate specificity, which come from different analyses and experimental designs. Keep in mind that because v appears on both axes, experimental error in v can influence the fit, so careful data quality is important.

Eadie-Hofstee plots are used to extract the kinetic parameters Km and Vmax from enzyme data by linearizing the Michaelis-Menten equation in a way that keeps the data in a familiar, low-error form. Start with the Michaelis-Menten relationship v = (Vmax [S])/(Km + [S]). By rearranging to a linear form, you get v = Vmax − Km · (v/[S]), which is a straight line when you plot v (the reaction velocity) versus v/[S]. In this line, the intercept on the v axis is Vmax and the slope is −Km. So fitting a line to the data yields both Km and Vmax directly.

This approach is advantageous because it uses data in a form closer to the original measurements than some other linearizations, making parameter estimation straightforward. It’s not used to determine the activation energy, the rate law, or substrate specificity, which come from different analyses and experimental designs. Keep in mind that because v appears on both axes, experimental error in v can influence the fit, so careful data quality is important.

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