Computational Prediction and Experimental Verification of New MAP Kinase Docking Sites and Substrates Including Gli Transcription Factors

In order to fully understand protein kinase networks, new methods are needed to identify regulators and substrates of kinases, especially for weakly expressed proteins. Here we have developed a hybrid computational search algorithm that combines machine learning and expert knowledge to identify kinase docking sites, and used this algorithm to search the human genome for novel MAP kinase substrates and regulators focused on the JNK family of MAP kinases. Predictions were tested by peptide array followed by rigorous biochemical verification with in vitro binding and kinase assays on wild-type and mutant proteins. Using this procedure, we found new ‘D-site’ class docking sites in previously known JNK substrates (hnRNP-K, PPM1J/PP2Czeta), as well as new JNK-interacting proteins (MLL4, NEIL1). Finally, we identified new D-site-dependent MAPK substrates, including the hedgehog-regulated transcription factors Gli1 and Gli3, suggesting that a direct connection between MAP kinase and hedgehog signaling may occur at the level of these key regulators. These results demonstrate that a genome-wide search for MAP kinase docking sites can be used to find new docking sites and substrates.Author Summary: Protein kinases are enzymes that regulate key cellular processes by covalently attaching a phosphate group to substrate proteins; they are crucial components of signaling pathways involved in cancer, diabetes, and many other diseases. Identifying the substrates of particular protein kinases is challenging, and many existing biochemical methods are biased against weakly expressed proteins like transcription factors. Here we exploited the observation that mitogen-activated protein kinases (MAPKs) briefly attach to many of their substrates before phosphorylating them, docking onto a sequence known as the ‘D-site’. We developed D-finder, a computational tool that uses a combination of expert knowledge and machine learning to search genome databases for D-sites. We then verified several of D-finder's predictions using rigorous and well-established biochemical assays. The most intriguing predicted and verified substrates were the Gli1 and Gli3 transcription factors of the ‘hedgehog’ signaling pathway. Gli transcription factors are involved in embryonic development and stem cell differentiation, and have also been found to be hyperactive in several types of cancer. There is emerging evidence that crosstalk with MAPK pathways is important in Gli-mediated regulation. Our study, however, is the first to show that MAPKs directly phosphorylate Gli transcription factors.