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Theoretical and Experimental Considerations for a Rapid and High Throughput Measurement of Catalase In Vitro.

ABSTRACT: A rapid and high throughput protocol to measure the catalase activity in vitro has been designed. Catalase is an enzyme with unusual kinetic properties because it does not follow the standard Michaelis-Menten model and is inactivated by H₂O₂. This makes the analysis of the two rate equations of the second-ordered reactions of the kinetic model rather complex. A two-degree polynomial fitting of the experimental data is proposed after transforming the exponential form of the integrated rate equation of the [H₂O₂] into a polynomial using the Taylor series. The fitting is validated by establishing an experimental linear relationship between the initial rate of the H₂O₂ decomposition and the protein concentration, regardless of the suicide inactivation that catalase might undergo beyond t > 0. In addition, experimental considerations are taken into account to avoid statistical bias in the analysis of the catalase activity. ANOVA analyses show that the proposed protocol can be utilized to measure the initial rate of the H₂O₂ decomposition by catalase in 32 samples in triplicates if kept below 8 mM min^-1 in the microplate wells. These kinetic and statistical analyses can pave the way for other antioxidant enzyme activity assays in microplate readers at small scale and low cost.

SUBMITTER: Bendou O

PROVIDER: S-EPMC8773236 | biostudies-literature |

REPOSITORIES: biostudies-literature

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Project description:BackgroundHigh-throughput screening (HTS) is a key part of the drug discovery process during which thousands of chemical compounds are screened and their activity levels measured in order to identify potential drug candidates (i.e., hits). Many technical, procedural or environmental factors can cause systematic measurement error or inequalities in the conditions in which the measurements are taken. Such systematic error has the potential to critically affect the hit selection process. Several error correction methods and software have been developed to address this issue in the context of experimental HTS 1234567. Despite their power to reduce the impact of systematic error when applied to error perturbed datasets, those methods also have one disadvantage - they introduce a bias when applied to data not containing any systematic error 6. Hence, we need first to assess the presence of systematic error in a given HTS assay and then carry out systematic error correction method if and only if the presence of systematic error has been confirmed by statistical tests.ResultsWe tested three statistical procedures to assess the presence of systematic error in experimental HTS data, including the ?2 goodness-of-fit test, Student's t-test and Kolmogorov-Smirnov test 8 preceded by the Discrete Fourier Transform (DFT) method 9. We applied these procedures to raw HTS measurements, first, and to estimated hit distribution surfaces, second. The three competing tests were applied to analyse simulated datasets containing different types of systematic error, and to a real HTS dataset. Their accuracy was compared under various error conditions.ConclusionsA successful assessment of the presence of systematic error in experimental HTS assays is possible when the appropriate statistical methodology is used. Namely, the t-test should be carried out by researchers to determine whether systematic error is present in their HTS data prior to applying any error correction method. This important step can significantly improve the quality of selected hits.

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Theoretical and Experimental Considerations for a Rapid and High Throughput Measurement of Catalase In Vitro.

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