Signaling pathways represent a central
regulatory mechanism of biological systems where a key event in their correct
functioning is the reversible phosphorylation of proteins. Protein
phosphorylation affects at least one-third of all proteins and is the most
widely studied posttranslational modification. Phosphorylation analysis is
still perceived, in general, as difficult or cumbersome and not readily attempted
by many, despite the high value of such information. Specifically, determining
the exact location of a phosphorylation site is currently considered a major
hurdle, thus reliable approaches are necessary for the detection and
localization of protein phosphorylation. The goal of this PhD thesis was to
develop computation methods and tools for mass spectrometry-based protein
phosphorylation analysis, particularly validation of phosphorylation sites. In
the first two studies, we developed methods for improved identification of
phosphorylation sites in MALDI-MS. In the first study it was achieved through
the automatic combination of spectra from multiple matrices, while in the
second study, an optimized protocol for sample loading and washing conditions was
suggested. In the third study, we proposed and evaluated the hypothesis that in
ESI-MS, tandem CID and HCD spectra of phosphopeptides can be accurately
predicted and used in spectral library searching. This novel strategy for
phosphosite validation and identification offered accuracy that outperformed
the other currently existing popular methods and proved applicable to complex
biological samples. And finally, we significantly improved the performance of
our command-line prototype tool, added graphical user interface, and options
for customizable simulation parameters and filtering of selected spectra,
peptides or proteins. The new software, SimPhospho, is open-source and can be
easily integrated in a phosphoproteomics data analysis workflow. Together, these
bioinformatics methods and tools enable confident phosphosite assignment and
improve reliable phosphoproteome identification and reporting