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Chemical Proteomic Approaches Toward Understanding Cell Signaling and Arsenic Toxicity

Abstract

Advances in mass spectrometry (MS) have greatly facilitated protein identification and quantification in complex sample matrices, it can combined with chemical proteomic method to selectively label, identify, and characterize target proteins in the entire human proteome which has been widely used to study functional subgroups of proteins, and provided valuable resources for systematic study of biology pathways. Cell signaling pathways can be largely effected by biological small molecules through their binding to target important proteins, so the discovery and understanding of molecular interactions between small molecules and their targets is critical in drug discovery. However, some of the observed effects of small molecule cannot be attributed to its interactions with their known binding proteins. Thus, there are likely other yet identified ligand-binding proteins. With this in mind, we focused on the discovery of novel binding proteins for several important biological small molecules. Our first study described a strategy used the synthesized affinity chemical lysophosphatidic acid(LPA) probe conjugated with LC-MS/MS analysis to enrich and identify putative LPA-binding proteins in the human proteome, and we further validated that ANXA5 and PGK1 can bind directly with LPA. We next relies on SILAC-based metabolic labeling, biotin-As probe pull-down and LC-MS/MS analysis, achieved the proteome-wide identification of arsenic-binding proteins in human cells and quantitatively characterized the arsenic-protein interactions, revealed HSPA4 as a potential new molecular target underlying the proteotoxic effects of arsenic exposure. Lastly, we employed isotope-coded ATP acyl-phosphate probes, in conjunction with a multiple-reaction monitoring (MRM)-based targeted proteomic method, for proteome-wide identifications of endogenous kinases that can bind to two N6-modified ATP derivatives. Each study shows MS-based chemical proteomic approaches can serve as a efficient tool for the discovery of ligand binding proteins and also provides valuable database for cell signaling mechanism study.

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