Identification and quantification of intact glycopeptides

• Main Author: Mayampurath, Anoop M
• Format: Dissertation
• Language: English
• Published: ProQuest Dissertations & Theses 01-01-2013
• Subjects: Bioinformatics
• ISBN: 9781303659614; 1303659611

Glycosylation is an important protein modification that involves enzymatic attachment of sugars to amino acid residues. The functional roles of glycoproteins include cell signaling and immune response. Thus, understanding the structure of sugars and effects of glycosylation might be vital for developing indicators of disease development and progression. Although computational methods based on mass spectrometric data have proven to be effective in monitoring changes in the glycome, developing such methods for the glycoproteome could be challenging, largely due to the inherent complexity in simultaneously studying glycan structures and corresponding glycosylation sites. We believe that studying intact N-linked glycopeptides, i.e. glycopeptides with N-linked glycans attached to glycosylation sites, has advantages over traditional methods that cleave the glycan from their respective glycopeptides. This thesis presents computational methods for identifying intact glycopeptides in complex proteome samples. Scoring algorithms are presented for tandem mass spectra of glycopeptides resulting from collision-induced dissociation (CID), high-energy C-trap dissociation (HCD) and electron transfer dissociation (ETD) fragmentation methods. An empirical false-discovery rate estimation method, based on a target-decoy search approach, is derived to assign confidence. The power of our method is further enhanced when multiple datasets are pooled together from replicates. We present a computational framework that encompasses these algorithms. Analysis of complex human serum samples revealed 103 highly confident N-linked glycopeptides from 53 sites across 33 glycoproteins using conventional proteomic platforms without any glycopeptide enrichment. To utilize the power of retaining intact glycosylation information, this thesis also describes a novel statistical method for quantifying and comparing site-specific glycosylation events across multiple samples. On application to an esophageal cancer study, the model detected several Nlinked glycoproteins such as Vitronectin that show significantly different abundances at site-specific levels within cancer/control samples. The results depicted here validate our speculation that site-specific glycosylation provides essential information for disease marker discovery, and thus intact glycopeptides should be studied as a whole. From an informatics perspective, our method is ready to be used for the discovery of biomarkers on site-specific glycosylations in complex proteomic samples.