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Matrix Thermal Shift Assay for Fast Construction of Multidimensional Ligand-Target Space.

Chengfei RuanYan WangXiaolei ZhangJiawen LyuNa ZhangYanni MaChunzhen ShiGuangbo QuMingliang Ye
Published in: Analytical chemistry (2022)
Existing thermal shift-based mass spectrometry approaches are able to identify target proteins without chemical modification of the ligand, but they are suffering from complicated workflows with limited throughput. Herein, we present a new thermal shift-based method, termed matrix thermal shift assay (mTSA), for fast deconvolution of ligand-binding targets and binding affinities at the proteome level. In mTSA, a sample matrix, treated horizontally with five different compound concentrations and vertically with five technical replicates of each condition, was denatured at a single temperature to induce protein precipitation, and then, data-independent acquisition was employed for quick protein quantification. Compared with previous thermal shift assays, the analysis throughput of mTSA was significantly improved, but the costs as well as efforts were reduced. More importantly, the matrix experiment design allowed simultaneous computation of the statistical significance and fitting of the dose-response profiles, which can be combined to enable a more accurate identification of target proteins, as well as reporting binding affinities between the ligand and individual targets. Using a pan-specific kinase inhibitor, staurosporine, we demonstrated a 36% improvement in screening sensitivity over the traditional thermal proteome profiling (TPP) and a comparable sensitivity with a latest two-dimensional TPP. Finally, mTSA was successfully applied to delineate the target landscape of perfluorooctanesulfonic acid (PFOS), a persistent organic pollutant that is hard to perform modification on, and revealed several potential targets that might account for the toxicities of PFOS.
Keyphrases
  • mass spectrometry
  • high throughput
  • single cell
  • high resolution
  • liquid chromatography
  • emergency department
  • risk assessment
  • protein protein
  • big data