| dc.description.abstract | Carbohydrates on proteins play essential roles in mediating various biological activities, such as cell adhesion, cell signaling, and antigen-antibody recognition. Altered glycosylation profiles on proteins have been found to be closely related to the progression of certain diseases including cancer. Moreover, the majority of biological therapeutics are glycoproteins, and variations in their glycosylation states would impact the efficacy of these pharmaceuticals. In order to gain in-depth understanding of the structure and function relationship of glycoproteins, the glycosylation profile associated with the protein needs to be determined. The workflow for site-specific glycosylation analysis involves the enzymatic digestion of glycoproteins, and the resulting glycopeptides are analyzed by LC-MS and tandem MS. Among different fragmentation modes in tandem MS, electron transfer dissociation (ETD) is extremely useful in revealing the sequence and glycan location of a glycopeptide. However, data analysis is a huge bottleneck for high throughput glycopeptide identifications using ETD. In this dissertation, this analytical challenge is addressed in multiple facets. Firstly, ETD-MS/MS data of N-linked glycopeptides with known peptide sequences and glycan compositions are collected to build a training dataset. By studying the training dataset, the fragmentation patterns of glycopeptides are summarized to develop an effective algorithm for scoring of the glycopeptide ETD data. A software tool is built based on the algorithm to interpret data collected from a clade C HIV envelope glycoprotein, gp140, and no false positive assignment is made by the program. Secondly, the fragmentation of O-linked glycopeptides in ETD is systematically studied, such that useful rules are found to facilitate O-glycopeptide identifications. The rules are implemented into an algorithm to score O-glycopeptide candidates against the ETD data, and the developed algorithm demonstrates superior performance compared to a publicly available data analysis tool. Lastly, a new glycoproteomics software is outlined to evaluate the false discovery rate of automated glycopeptide assignments reported by a computer program. In sum, this dissertation advances the glycoproteomics field by establishing an integral system for expedited glycopeptide analysis with improved accuracy. The other part of the dissertation details an absolute quantitation approach for determining the extent of glycosylation on individual glycosylation sites on a protein, and the developed method quantifies the glycosylation site occupancy more accurately than the conventional approach. | |
