UC San Diego

Chemical genetics seeks to use chemical entities to identify novel proteins and cellular processes that are responsible for complex biological processes. Unlike classical forward genetics, however, the identification of the proteins and signaling pathways targeted by small molecules remains extremely challenging. Affinity approaches are limited by their inherent low throughput and high incidence of identifying irrelevant proteins that interact with the chemical probe. Here an approach is described that attempts to discover targets based on the chemical-induced changes in phosphoproteins. As a test case, the small molecule benzimidazole derivative termed 206A that synergizes with Wnt signaling by an unknown mechanism was chosen. Initial studies focused on the quantitative western blotting-based Kinexus' Phosphosite Profiling service, but the small proportion of the phosphoproteome probed and the inherent low sensitivity of the western blotting limits antibody-based methods. Label- free mass spectrometry analysis with phosphopeptide enrichment provided a vastly greater number of proteins, resulting in semi-quantitative phosphoprotein data for 206A-treated RKO cells over a time course. Custom software was developed to process the data, including modifications to the Trans-Proteomic Pipeline software suite to convert manual phosphopeptide validation to an automated system. Furthermore, a novel binning algorithm was used to detect 742 statistically significant differentially phosphorylated protein "hits" despite the lack of experimental replicates. The protein set was expanded using a protein-protein interaction database, and analysis with the DAVID Bioinformatics Resources website demonstrated that 243 of the original 742 proteins interacted directly with Wnt signaling proteins. These were assessed by selective siRNA knockdown, revealing RPLP0 as a Wnt pathway modulator induced by 206A. Taken together, these data indicate that mass spectroscopy-based phosphoproteomics analysis coupled with systems-level network analysis can be used to identify unknown targets of small molecules in a complex biological setting