Detection of competitive enzyme inhibition with end point progress curve data

Major Improvements in Robustness and Efficiency during the Screening of Novel Enzyme Effectors by the 3-Point Kinetics Assay

The throughput level currently reached by automatic liquid handling and assay monitoring techniques is expected to facilitate the discovery of new modulators of enzyme activity. Judicious and dependable ways to interpret vast amounts of information are, however, required to effectively answer this challenge. Here, the 3-point method of kinetic analysis is proposed as a means to significantly increase the hit success rates and decrease the number of falsely identified compounds (false positives). In this post-Michaelis-Menten approach, each screened reaction is probed in three different occasions, none of which necessarily coincide with the initial period of constant velocity. Enzymology principles rather than subjective criteria are applied to identify unwanted outliers such as assay artifacts, and then to accurately distinguish true enzyme modulation effects from false positives. The exclusion and selection criteria are defined based on the 3-point reaction coordinates, whose relative positions along the time-courses may change from well to well or from plate to plate, if necessary. The robustness and efficiency of the new method is illustrated during a small drug repurposing screening of potential modulators of the deubiquinating activity of ataxin-3, a protein implicated in Machado-Joseph disease. Apparently, intractable Z factors are drastically enhanced after (1) eliminating spurious results, (2) improving the normalization method, and (3) increasing the assay resilience to systematic and random variability. Numerical simulations further demonstrate that the 3-point analysis is highly sensitive to specific, catalytic, and slow-onset modulation effects that are particularly difficult to detect by typical endpoint assays.

Kinetic-based high-throughput screening assay to discover novel classes of macrophage migration inhibitory factor inhibitors

Macrophage migration inhibitory factor (MIF) is a major mediator of innate immunity and inflammation and presents a potential therapeutic target for various inflammatory, infectious, and autoimmune diseases, including cancer. Although a number of inhibitors have been identified and designed based on the modification of known nonphysiological substrates, the lack of a suitable high-throughput assay has hindered the screening of chemical libraries and the discovery of more diverse inhibitors. Herein the authors report the development and optimization of a robust high-throughput kinetic-based activity assay for the identification of new MIF inhibitors. Using this assay, they screened 80,000 small molecules and identified and validated 13 novel inhibitors of MIF catalytic activity. These small molecules demonstrated inhibition constant (K(i,app)) values ranging from 0.5 to 13 microM.

Sensitivity analysis and error structure of progress curves

Both the sensitivity of the monitored signal in progress curves to variations in enzyme concentration and the standard deviation of this signal were analyzed as a function of the proportion of transformed substrate. Three enzymes catalyzing essentially irreversible reactions were used as model systems: HIV-1 protease, glutathione reductase, and glutathione transferase. For all enzymes analyzed, the sensitivity was maximal when 60-80% of the substrate had been transformed. The standard deviation of reaction progress curve data replicates was also maximal at these substrate conversion levels, a result that was attributed to the influence of the sensitivity to random dispersion of the enzyme concentration. On this basis, we developed a model for the standard deviation of reaction progress curves that gave a good description of the experimental data and efficiently reduced the heteroscedasticity of residuals in a weighted fit of progress curves. This standard deviation model can be used for obtaining more efficient parameter estimates, to simulate noise in Monte Carlo procedures, and to delineate detection limits of enzyme inhibition. The transient increases in the sensitivity and in the standard deviation in progress curves are proposed to be features common to most enzymatic assays.