Utilization of integrated Michaelis–Menten equations for enzyme inhibition diagnosis and determination of kinetic constants using Solver supplement of Microsoft Office Excel

• Published in: Computer methods and programs in biomedicine 2013-01, Vol.109 (1), p.26-31
• Main Authors: Bezerra, Rui M.F, Fraga, Irene, Dias, Albino A
• Format: Article
• Language: English
• Subjects: Akaike information criterion; Alkaline Phosphatase - antagonists & inhibitors; Alkaline Phosphatase - chemistry; Analytical, structural and metabolic biochemistry; Biological and medical sciences; Catalysis; Diagnosis of enzyme inhibition; Enzyme Inhibitors - chemistry; Enzymes - chemistry; Enzymes - metabolism; Enzymes and enzyme inhibitors; Fundamental and applied biological sciences. Psychology; Integrated Michaelis–Menten equations; Internal Medicine; Kinetics; Nitrophenols - chemistry; Nonlinear regression; Organophosphorus Compounds - chemistry; Other; Software; Solver
• ISSN: 0169-2607; 1872-7565
• DOI: 10.1016/j.cmpb.2012.08.017

Abstract Enzyme kinetic parameters are usually determined from initial rates nevertheless, laboratory instruments only measure substrate or product concentration versus reaction time (progress curves). To overcome this problem we present a methodology which uses integrated models based on Michaelis–Menten equation. The most severe practical limitation of progress curve analysis occurs when the enzyme shows a loss of activity under the chosen assay conditions. To avoid this problem it is possible to work with the same experimental points utilized for initial rates determination. This methodology is illustrated by the use of integrated kinetic equations with the well-known reaction catalyzed by alkaline phosphatase enzyme. In this work nonlinear regression was performed with the Solver supplement (Microsoft Office Excel). It is easy to work with and track graphically the convergence of SSE (sum of square errors). The diagnosis of enzyme inhibition was performed according to Akaike information criterion.