General, Label-Free Method for Determining <i>K</i>_d and Ligand Concentration Simultaneously

Some of the most commonly used affinity reagents (e.g., antibodies) are often developed and used in conditions where their input concentrations ([L]₀) and affinities (<i>K</i>_d) are not known. Here, we have developed a general approach to determine both [L]₀ and <i>K</i>_d values simultaneously for affinity reagents (small molecules, proteins, and antibodies). To do this, we perform quantitative equilibrium exclusion immunoassays with two different concentrations of target and fit the data simultaneously to determine <i>K</i>_d and [L]₀. The results give accurate and reproducible measures of both values compared to established methods. By performing detailed error analysis, we demonstrate that our fitting gives unique solutions and indicates where <i>K</i>_d and [L]₀ measures are reliable. Furthermore, we found that a divalent model of antibody binding gives accurate <i>K</i>_d and [L]₀ values in both the forward (antibody immobilized) and the reverse (target immobilized) assaysaddressing the long-term problem of obtaining quantitative data from reverse assays

Robust, Quantitative Analysis of Proteins using Peptide Immunoreagents, in Vitro Translation, and an Ultrasensitive Acoustic Resonant Sensor

A major benefit of proteomic and genomic data is the potential for developing thousands of novel diagnostic and analytical tests of cells, tissues, and clinical samples. Monoclonal antibody technologies, phage display and mRNA display, are methods that could be used to generate affinity ligands against each member of the proteome. Increasingly, the challenge is not ligand generation, rather the analysis and affinity rank-ordering of the many ligands generated by these methods. Here, we developed a quantitative method to analyze protein interactions using in vitro translated ligands. In this assay, in vitro translated ligands generate a signal by simultaneously binding to a target immobilized on a magnetic bead and to a sensor surface in a commercial acoustic sensing device. We then normalize the binding of each ligand with its relative translation efficiency in order to rank-order the different ligands. We demonstrate the method with peptides directed against the cancer marker Bcl-x_L. Our method has 4- to 10-fold higher sensitivity, using 100-fold less protein and 5-fold less antibody per sample, as compared directly with ELISA. Additionally, all analysis can be conducted in complex mixtures at physiological ionic strength. Lastly, we demonstrate the ability to use peptides as ultrahigh affinity reagents that function in complex matrices, as would be needed in diagnostic applications