The intrinsic substrate specificity of the human tyrosine kinome.

Phosphorylation of proteins on tyrosine (Tyr) residues evolved in metazoan organisms as a mechanism of coordinating tissue growth1. Multicellular eukaryotes typically have more than 50 distinct protein Tyr kinases that catalyse the phosphorylation of thousands of Tyr residues throughout the proteome1-3. How a given Tyr kinase can phosphorylate a specific subset of proteins at unique Tyr sites is only partially understood4-7. Here we used combinatorial peptide arrays to profile the substrate sequence specificity of all human Tyr kinases. Globally, the Tyr kinases demonstrate considerable diversity in optimal patterns of residues surrounding the site of phosphorylation, revealing the functional organization of the human Tyr kinome by substrate motif preference. Using this information, Tyr kinases that are most compatible with phosphorylating any Tyr site can be identified. Analysis of mass spectrometry phosphoproteomic datasets using this compendium of kinase specificities accurately identifies specific Tyr kinases that are dysregulated in cells after stimulation with growth factors, treatment with anti-cancer drugs or expression of oncogenic variants. Furthermore, the topology of known Tyr signalling networks naturally emerged from a comparison of the sequence specificities of the Tyr kinases and the SH2 phosphotyrosine (pTyr)-binding domains. Finally we show that the intrinsic substrate specificity of Tyr kinases has remained fundamentally unchanged from worms to humans, suggesting that the fidelity between Tyr kinases and their protein substrate sequences has been maintained across hundreds of millions of years of evolution.

Substrate displacement of CK1 C-termini regulates kinase specificity.

CK1 kinases participate in many signaling pathways, and their regulation is of meaningful biological consequence. CK1s autophosphorylate their C-terminal noncatalytic tails, and eliminating these tails increases substrate phosphorylation in vitro, suggesting that the autophosphorylated C-termini act as inhibitory pseudosubstrates. To test this prediction, we comprehensively identified the autophosphorylation sites on Schizosaccharomyces pombe Hhp1 and human CK1ε. Phosphoablating mutations increased Hhp1 and CK1ε activity toward substrates. Peptides corresponding to the C-termini interacted with the kinase domains only when phosphorylated, and substrates competitively inhibited binding of the autophosphorylated tails to the substrate binding grooves. Tail autophosphorylation influenced the catalytic efficiency with which CK1s targeted different substrates, and truncating the tail of CK1δ broadened its linear peptide substrate motif, indicating that tails contribute to substrate specificity as well. Considering autophosphorylation of both T220 in the catalytic domain and C-terminal sites, we propose a displacement specificity model to describe how autophosphorylation modulates substrate specificity for the CK1 family.

Illuminating the Dark Cancer Phosphoproteome Through a Machine-Learned Co-Regulation Map of 26,280 Phosphosites.

Mass spectrometry-based phosphoproteomics offers a comprehensive view of protein phosphorylation, but limited knowledge about the regulation and function of most phosphosites restricts our ability to extract meaningful biological insights from phosphoproteomics data. To address this, we combine machine learning and phosphoproteomic data from 1,195 tumor specimens spanning 11 cancer types to construct CoPheeMap, a network mapping the co-regulation of 26,280 phosphosites. Integrating network features from CoPheeMap into a machine learning model, CoPheeKSA, we achieve superior performance in predicting kinase-substrate associations. CoPheeKSA reveals 24,015 associations between 9,399 phosphosites and 104 serine/threonine kinases, including many unannotated phosphosites and under-studied kinases. We validate the accuracy of these predictions using experimentally determined kinase-substrate specificities. By applying CoPheeMap and CoPheeKSA to phosphosites with high computationally predicted functional significance and cancer-associated phosphosites, we demonstrate the effectiveness of these tools in systematically illuminating phosphosites of interest, revealing dysregulated signaling processes in human cancer, and identifying under-studied kinases as putative therapeutic targets.