Development of a Magnetic Microbead Affinity Selection Screen (MagMASS) Using Mass Spectrometry for Ligands to the Retinoid X Receptor-α.

Abstract

To overcome limiting factors in mass spectrometry-based screening methods such as automation while still facilitating the screening of complex mixtures such as botanical extracts, magnetic microbead affinity selection screening (MagMASS) was developed. The screening process involves immobilization of a target protein on a magnetic microbead using a variety of possible chemistries, incubation with mixtures of molecules containing possible ligands, a washing step that removes non-bound compounds while a magnetic field retains the beads in the microtiter well, and an organic solvent release step followed by LC-MS analysis. Using retinoid X receptor-α (RXRα) as an example, which is a nuclear receptor and target for anti-inflammation therapy as well as cancer treatment and prevention, a MagMASS assay was developed and compared with an existing screening assay, pulsed ultrafiltration (PUF)-MS. Optimization of MagMASS involved evaluation of multiple protein constructs and several magnetic bead immobilization chemistries. The full-length RXRα construct immobilized with amylose beads provided optimum results. Additional enhancements of MagMASS were the application of 96-well plates to enable automation, use of UHPLC instead of HPLC for faster MS analyses, and application of metabolomics software for faster, automated data analysis. Performance of MagMASS was demonstrated using mixtures of synthetic compounds and known ligands spiked into botanical extracts. Graphical Abstract ᅟ.

DOI: 10.1007/s13361-016-1564-0

Cite this paper

@article{Rush2017DevelopmentOA, title={Development of a Magnetic Microbead Affinity Selection Screen (MagMASS) Using Mass Spectrometry for Ligands to the Retinoid X Receptor-α.}, author={Michael D. Rush and Elisabeth M Walker and Gerd Prehna and Tristesse Burton and Richard B van Breemen}, journal={Journal of the American Society for Mass Spectrometry}, year={2017}, volume={28 3}, pages={479-485} }