A comparison of seven methods for fitting the Michaelis-Menten equation

Area of Science:

• Biochemistry
• Enzyme kinetics
• Data analysis

Background:

• The Michaelis-Menten equation is fundamental for describing enzyme kinetics.
• Accurate parameter estimation (Vmax and Km) is crucial for understanding enzyme mechanisms.
• Simulated data with various error types were used to rigorously test fitting methods.

Purpose of the Study:

• To compare the efficacy of different linear transformation methods for fitting the Michaelis-Menten equation.
• To identify the most robust methods for enzyme kinetic data analysis, particularly in the presence of experimental error.

Main Methods:

• Fitted the Michaelis-Menten equation to simulated enzyme kinetic data.
• Employed three linear transformation techniques.
• Evaluated established methods including those by Cohen, Eisenthal & Cornish-Bowden, Merino, and Wilkinson.

Main Results:

• The methods of Eisenthal & Cornish-Bowden (1974) and Wilkinson (1961) demonstrated superior performance.
• These methods provided more accurate estimations of kinetic parameters compared to others tested.
• Linear transformations are effective for Michaelis-Menten analysis but method choice impacts accuracy.

Conclusions:

• The Eisenthal & Cornish-Bowden and Wilkinson methods are recommended for fitting the Michaelis-Menten equation to enzyme kinetic data.
• Careful selection of data transformation and fitting method is essential for reliable enzyme kinetic studies.
• These findings aid in improving the accuracy of enzyme kinetic parameter determination.