PROFILING AND DATA PROCESSING STRATEGIES FOR PEPTIDOMIC ANALYSISBySiobhan Shay Peptide functions have been underappreciated compared to proteins. With a few notable exceptions, peptides have been thought to function largely as degradation intermediates between proteins and individual amino acids, but there is growing recognition that peptides have their own significant biological functions. Peptides, from both endogenous and exogenous sources, including diet, play essential roles regulating beneficial and detrimental physiological functions. Some bioactive peptides are unusual in that to exhibit physiological functions, they show resistance to proteolytic enzymes. The elucidation of identities of bioactive peptides is essential for determination of their abundances, structures and functions. Peptidomics, the analysis and description of total peptide content within a biological sample, however, is often hindered both by the nature of the peptide and current analytical approaches. Peptides are exposed in vivo to multiple proteolytic enzymes, and the constituents of the resulting digestion-resistant and bioactive peptidome are not guaranteed to have a specific N- or C-termini. As a result, digestion-resistant peptides often lack terminal amino acids with basic side chains that yield mass spectrometric fragments sufficient for their identification. In addition, bioactive peptides are also hindered by the effects of chromatographic co-elution, as peptidomes are more complex than can be resolved in a single liquid chromatographic separation. To mitigate the challenges that inhibit peptidomic analyses from detecting, identifying and characterizing the maximum number of peptides within a complex biological sample, this dissertation describes an alternate method that applies metabolomic-like data processing strategies to non-targeted profiling of the peptidome using a simulated digestion procedure to generate a peptide mixture of great complexity. The application of a data-independent LC/TOF MS analysis followed by multivariate statistical analysis allows for the survey of the entire complement of a peptidome, providing abundances are above limits of detection. Multivariate analysis also allows for the recognition of peptides within a peptidome that differentiate physiological states, genotypes, treatments, or temporal changes. In this work, the extent of post-translational deamidation of glutamine was found to be a distinguishing characteristic of protein grains of wheat (Triticum aestivum) and its relative spelt (Triticum spelta). In the model digestion-resistant peptidome generated from proteolysis of wheat storage proteins using gastrointestinal enzymes, surviving peptides exhibit a narrow range of hydrophobicity, and chromatographic co-elution hinders the analysis. The use of non-traditional stationary phase/solvent system combinations for peptide separation can spread peptide elution over a wider range of retention times. This dissertation describes use of a pentafluorophenyl propyl HPLC stationary phase that provides for orthogonal separations relative to octadcecylsilyl phases that are obtained through manipulation of the stationary phase/solvent system. Finally, while the application of a data-independent HPLC-MS approach can detect thousands of peptides in a single sample, confirmation of peptide identity relies on additional information, most notably, MS/MS. However, many of the digestion-resistant peptides derived from wheat are rich in glutamate, and collision-induced dissociation yielded peptides that were not identified using sequence database searching. In some of these peptides, fragment ion spectra were dominated by internal sequence fragment ions including some with masses consistent with dehydrative cyclization. These findings highlight the need for continued improvement to peptidomic technologies.
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