A Computational Framework for Identification and Quantification of Glycopeptides in Complex Proteome Samples
Glycosylation is an important protein modification that involves the 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 are vital for developing indicators of disease. Although computational methods based on mass spectrometry data have proven to be effective in monitoring changes in the glycome, developing such methods for the glycoproteome can be challenging.
This is largely due to the inherent complexity in studying glycan structures in tandem with corresponding glycosylation sites. Here, a framework is introduced for detecting intact glycopeptides in complex samples along with methods that makes quantifying aberrant glycosylation possible across multiple samples. Scoring algorithms designed for different mass spectrometry fragmentation data are used to confidently characterize site-specific glycosylation of proteins. Fragme ntation events from technical replicate data are pooled together for increased confidence in glycopeptide detection following which assignments are made using a unique False Discovery Rate based approach. Further, a statistical model is proposed that enables determination of differential protein glycosylation between healthy and disease samples, not just at the global glycoprotein level but also for site-specific glycosylations.