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

• Published in: PLoS computational biology Vol. 6; no. 8; p. e1000908
• Main Authors: Whisenant, Thomas C., Ho, David T., Benz, Ryan W., Rogers, Jeffrey S., Kaake, Robyn M., Gordon, Elizabeth A., Huang, Lan, Baldi, Pierre, Bardwell, Lee
• Format: Journal Article
• Language: English
• Published: United States Public Library of Science 01.08.2010; Public Library of Science (PLoS)
• Subjects: Algorithms; Artificial Intelligence; Binding Sites; Biochemistry; Cell Biology/Cell Signaling; Cellular signal transduction; Computational Biology/Genomics; Computational Biology/Sequence Motif Analysis; Cyclin-dependent kinases; Experiments; Genetic aspects; Genome, Human; Genomes; Humans; Kinases; Knowledge Bases; Kruppel-Like Transcription Factors - chemistry; Mitogen-Activated Protein Kinases - chemistry; Nerve Tissue Proteins - chemistry; Observations; Peptides; Phosphorylation; Properties; Protein Binding; Protein kinases; Proteins; Signal transduction; Substrate Specificity; Substrates (Biochemistry); Transcription Factors - chemistry; Zinc Finger Protein GLI1; Zinc Finger Protein Gli3; United States; Kruppel-Like Transcription Factors; Mitogen-Activated Protein Kinases; Knowledge Bases; Algorithms; Artificial Intelligence; Humans; Protein Binding; Substrate Specificity; Nerve Tissue Proteins; Transcription Factors; Genome, Human; Binding Sites
• ISSN: 1553-7358; 1553-734X; 1553-7358
• DOI: 10.1371/journal.pcbi.1000908

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.