Literature DB >> 22357554

Target identification by chromatographic co-elution: monitoring of drug-protein interactions without immobilization or chemical derivatization.

Janet N Y Chan1, Dajana Vuckovic, Lekha Sleno, Jonathan B Olsen, Oxana Pogoutse, Pierre Havugimana, Johannes A Hewel, Navgeet Bajaj, Yale Wang, Marcel F Musteata, Corey Nislow, Andrew Emili.   

Abstract

Bioactive molecules typically mediate their biological effects through direct physical association with one or more cellular proteins. The detection of drug-target interactions is therefore essential for the characterization of compound mechanism of action and off-target effects, but generic label-free approaches for detecting binding events in biological mixtures have remained elusive. Here, we report a method termed target identification by chromatographic co-elution (TICC) for routinely monitoring the interaction of drugs with cellular proteins under nearly physiological conditions in vitro based on simple liquid chromatographic separations of cell-free lysates. Correlative proteomic analysis of drug-bound protein fractions by shotgun sequencing is then performed to identify candidate target(s). The method is highly reproducible, does not require immobilization or derivatization of drug or protein, and is applicable to diverse natural products and synthetic compounds. The capability of TICC to detect known drug-protein target physical interactions (K(d) range: micromolar to nanomolar) is demonstrated both qualitatively and quantitatively. We subsequently used TICC to uncover the sterol biosynthetic enzyme Erg6p as a novel putative anti-fungal target. Furthermore, TICC identified Asc1 and Dak1, a core 40 S ribosomal protein that represses gene expression, and dihydroxyacetone kinase involved in stress adaptation, respectively, as novel yeast targets of a dopamine receptor agonist.

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Year:  2012        PMID: 22357554      PMCID: PMC3394955          DOI: 10.1074/mcp.M111.016642

Source DB:  PubMed          Journal:  Mol Cell Proteomics        ISSN: 1535-9476            Impact factor:   5.911


  41 in total

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