Analysis of complex proteoglycans using serial proteolysis and EThcD provides deep N- and O-glycoproteomic coverage

Proteoglycans are a small but diverse family of proteins that play a wide variety of roles at the cell surface and in the extracellular matrix. In addition to their glycosaminoglycan (GAG) chains, they are N- and O-glycosylated. All of these types of glycosylation are crucial to their function but present a considerable analytical challenge. We describe the combination of serial proteolysis followed by the application of higher-energy collisional dissociation (HCD) and electron transfer/higher-energy collisional dissociation (EThcD) to optimize protein sequence coverage and glycopeptide identification from proteoglycans. In many cases, the use of HCD alone allows the identification of more glycopeptides. However, the localization of glycoforms on multiply glycosylated peptides has remained elusive. We demonstrate the use of EThcD for the confident assignment of glycan compositions on multiply glycosylated peptides. Dense glycosylation on proteoglycans is key to their biological function; thus, developing tools to identify and quantify doubly glycosylated peptides is of interest. Additionally, glycoproteomics searches identify glycopeptides in otherwise poorly covered regions of proteoglycans. The development of these and other analytical tools may permit glycoproteomic similarity comparisons in biological samples.

Automated summary

Proteoglycans are a diverse family of proteins that play various roles at the cell surface and extracellular matrix. This study presents a method combining serial proteolysis, higher-energy collisional dissociation (HCD), and electron transfer/higher-energy collisional dissociation (EThcD) to optimize protein sequence coverage and glycopeptide identification from proteoglycans. The use of EThcD allows the confident assignment of glycan compositions on multiply glycosylated peptides. The development of analytical tools for glycoproteomic similarity comparisons in biological samples is of great interest.