Title: Large-scale analysis of protein phosphorylation
Description: Fueled by genome sequencing projects and advances in instrumentation, only recently has the potential to conduct large scale experiments on biological systems become possible. Although major advances have been made over the last decade, there has been little progress towards the large-scale analysis of protein phosphorylation, largely due to a lack of suitable methods. This dissertation aimed at addressing reversible protein phosphorylation on a large scale through three primary objectives. The first objective focused on the development and implementation of methods for the large scale analysis of protein phosphorylation using tandem mass spectrometry, in which we developed a strong-cation exchange (SCX) chromatography based approach to enrich for phosphorylation. Using this method we were able to identify over two thousand human phosphorylation sites, the first dataset to ever identify over a thousand phosphorylation sites in a single experiment. With the ability to generate such large datasets, the second objective focused on the development and implementation of informatics tools to enable robust data collection and interpretation in an automated fashion. The primary constraint on large-scale phosphorylation experiments was now centered on generating an accurate dataset, which included proper phosphorylation site localization and sequence assignment validated only through manual interpretation. To eliminate the peed to manually validate spectra, we developed an algorithm which automated site-localization such that largescale datasets could now be validated in a fully automated fashion.

The third objective focused on the strategic application of there improved and automated technologies to a model of metastatie prostate cancer. Combined with quantitative approaches, we were able to identify nearly 5,000 non-redundant phosphorylation sites and quantified over 2,600 in which we identified several candidates which may play a role in tumor progression.