Abstract:
Protein tyrosine kinase and phosphatase domains have binding specificities that depend on the amino acid sequence surrounding the target (phospho)tyrosine residue on their substrates. Although the preferred recognition motifs of many kinase and phosphatase domains have been characterized, we lack a quantitative description of sequence specificity that could guide predictions about signaling pathways or be used to design sequences for biomedical applications. Here, we present a platform that combines genetically-encoded peptide libraries and deep sequencing to profile sequence recognition by tyrosine kinases. We screened several tyrosine kinases against a million-peptide random library and used the resulting profiles to design high-activity sequences and predict phosphorylation efficiencies of substrates. We then screened several kinases against a library containing thousands of human proteome-derived peptides and their naturally-occurring variants. These screens recapitulated independently measured phosphorylation rates and revealed hundreds of phosphosite-proximal mutations that impact phosphosite recognition by tyrosine kinases. Finally, we have made progress towards extending this platform to the analysis of tyrosine phosphatase domains, by optimizing methods to produce tyrosine-phosphorylated bacterial display libraries and implementing methods to detect peptide dephosphorylation on the cell surface. Collectively, these experiments demonstrate the utility of our platform for rapid profiling of sequence specificity by tyrosine kinases and will shed new light on phosphotyrosine signaling.
