A Systematic Experimental and Computational Approach to Investigating Phosphotyrosine Signaling Networks
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CitationKoytiger, Grigoriy. 2013. A Systematic Experimental and Computational Approach to Investigating Phosphotyrosine Signaling Networks. Doctoral dissertation, Harvard University.
AbstractMutation and over-expression of Receptor Tyrosine Kinases (RTKs) or the proteins they regulate serve as oncogenic drivers in diverse cancers. RTKs catalyze the transfer of phosphate from ATP to the hydroxyl group on tyrosine. The proximal stretch of amino acids including this post translational modification is then able to be recognized by SH2 and PTB domains. Chapter 1 details our work to better understand RTK signaling and its link to oncogenesis using protein microarrays to systematically and quantitatively measure interactions between virtually every SH2 or PTB domain encoded in the human genome and all known sites of tyrosine phosphorylation on 40 out of the 53 Receptor Tyrosine Kinases. Chapter 2 expands upon this work to study the next layer of binding among SH2 and PTB domain-containing adaptor proteins themselves. We found that adaptor proteins, like RTKs, have many high affinity bindings sites for other adaptor proteins. In addition, proteins driving oncogenesis, including both receptors and adaptor proteins, tend to be highly interconnected via a network of SH2 and PTB domain-mediated interactions. Our results suggest that network topological properties such as connectivity can be used to prioritize new drug targets in these well-studied signaling networks. Despite the extensive work presented here on experimentally determining interactions, we nevertheless are unable to keep up with the discovery of new sites of tyrosine phosphorylation by high throughput mass spectrometry as well as their mutation in cancer discovered by next generation tumor sequencing approaches. Chapter 3 introduces work in progress to build a unified predictive model of SH2 domain interactions via integration of diverse data sets of binding as well as crystal structures of domain-peptide interactions. This model will enable researchers discovering new phosphorylation events or mutations to be able to predict potential interaction partners and thereby elucidate novel functional mechanisms.
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