Chemoproteomics is a key technology to characterize the mode of action of drugs, as it directly identifies the protein targets of bioactive compounds and aids in the development of optimized small-molecule compounds. Current approaches cannot identify the protein targets of a compound and also detect the interaction surfaces between ligands and protein targets without prior labeling or modification. To address this limitation, we here develop LiP-Quant, a drug target deconvolution pipeline based on limited proteolysis coupled with mass spectrometry that works across species, including in human cells. We use machine learning to discern features indicative of drug binding and integrate them into a single score to identify protein targets of small molecules and approximate their binding sites. We demonstrate drug target identification across compound classes, including drugs targeting kinases, phosphatases and membrane proteins. LiP-Quant estimates the half maximal effective concentration of compound binding sites in whole cell lysates, correctly discriminating drug binding to homologous proteins and identifying the so far unknown targets of a fungicide research compound.

Obesity is a well-known risk factor for the development of secondary complications such as type 2 diabetes. However, only a part of the obese population develops secondary metabolic disorders. Here, we identify the transcription factor retinoid-related orphan receptor gamma (RORγ) as a negative regulator of adipocyte differentiation through expression of its newly identified target gene matrix metalloproteinase 3. In vivo differentiation of adipocyte progenitor cells from Rorγ-deficient mice is enhanced and obese Rorγ−/− mice show decreased adipocyte sizes. These small adipocytes are highly insulin sensitive, leading to an improved control of circulating free fatty acids. Ultimately, Rorγ−/− mice are protected from hyperglycemia and insulin resistance in the state of obesity. In adipose stromal-vascular fraction from obese human subjects, Rorγ expression is correlated with adipocyte size and negatively correlated with adipogenesis and insulin sensitivity. Taken together, our findings identify RORγ as a factor, which controls adipogenesis as well as adipocyte size and modulates insulin sensitivity in obesity. RORγ might therefore serve as a novel pharmaceutical target to treat obesity-associated insulin resistance.

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