RESUMEN
Nonradioactive homogeneous assays are widely used to screen for inhibitors of biomolecular interactions. To ensure optimal sensitivity for the detection of competitive inhibitors, reagent concentrations should be fixed at or below the K(D) of the protein-protein interaction. Accurate measurement of K(D) during assay development is therefore critical. Although conventional methods work well with heterogeneous assays, they are generally unsatisfactory with homogeneous systems. Here the authors describe an alternative method to determine the K(D) of protein-protein interactions in homogeneous assays. The method uses a rearrangement of the Cheng-Prusoff equation: IC50= (([Ki]/K(D)) x [L]) + Ki. A competitive inhibitor is titrated into the ligand-receptor binding assay at a range of ligand concentrations and IC50 values are calculated. Plotting measured IC50 versus concentration of ligand gives a linear plot with y-intercept (Ki) and gradient (Ki/K(D)). K(D) is the affinity constant for the ligand-receptor interaction. Here the authors use homogeneous time-resolved fluorescence (HTRF) in 2 model systems (TRAIL/TRAIL receptor 4 and OX40 ligand/OX40 receptor) and demonstrate that measured K(D) values calculated using the linearized Cheng-Prusoff plot compare favorably with those from independent experiments. The advantages and limitations of the method are discussed.
Asunto(s)
Evaluación Preclínica de Medicamentos/métodos , Proteínas/química , Biotina/química , Biotinilación , Tampones (Química) , Humanos , Concentración 50 Inhibidora , Cinética , Ligandos , Ligando OX40/química , Unión Proteica , Mapeo de Interacción de Proteínas , Proteínas/metabolismo , Receptores del Ligando Inductor de Apoptosis Relacionado con TNF/metabolismo , Proteínas Recombinantes/química , Ligando Inductor de Apoptosis Relacionado con TNF/metabolismoRESUMEN
The authors describe a system for collecting usage metrics from widely distributed automation systems. An application that records and stores usage data centrally, calculates run times, and charts the data was developed. Data were collected over 20 months from at least 28 workstations. The application was used to plot bar charts of date versus run time for individual workstations, the automation in a specific laboratory, or automation of a specified type. The authors show that revised user training, redeployment of equipment, and running complimentary processes on one workstation can increase the average number of runs by up to 20-fold and run times by up to 450%. Active monitoring of usage leads to more effective use of automation. Usage data could be used to determine whether purchasing particular automation was a good investment.