In-solution digestion of glycoproteins for glycopeptide-based mass analysis.

Glycopeptides are generated from the enzymatic digestion of glycoproteins with a specific or nonspecific protease. Whether this enzymatic conversion of glycoproteins into glycopeptides and peptides is done in-solution or in-gel, an efficient digestion protocol is one of the key components of a successful outcome in a mass spectrometry-based experimental workflow. This chapter outlines an optimized in-solution digestion protocol to prepare samples for glycopeptide-based mass analysis.

GlycoPep grader: a web-based utility for assigning the composition of N-linked glycopeptides.

GlycoPep grader (GPG) is a freely available software tool designed to accelerate the process of accurately determining glycopeptide composition from tandem mass spectrometric data. GPG relies on the identification of unique dissociation patterns shown for high mannose, hybrid, and complex N-linked glycoprotein types, including patterns specific to those structures containing fucose or sialic acid residues. The novel GPG scoring algorithm scores potential candidate compositions of the same nominal mass against MS/MS data through evaluation of the Y(1) ion and other peptide-containing product ions, across multiple charge states, when applicable. In addition to evaluating the peptide portion of a given glycopeptide, the GPG algorithm predicts and scores product ions that result from unique neutral losses of terminal glycans. GPG has been applied to a variety of glycoproteins, including RNase B, asialofetuin, and transferrin, and the HIV envelope glycoprotein, CON-S gp140ΔCFI. The GPG software is implemented predominantly in PostgreSQL, with PHP as the presentation tier, and is publicly accessible online. Thus far, the algorithm has identified the correct compositional assignment from multiple candidate N-glycopeptides in all tests performed.

A general protease digestion procedure for optimal protein sequence coverage and post-translational modifications analysis of recombinant glycoproteins: application to the characterization of human lysyl oxidase-like 2 glycosylation.

Using recombinant DNA technology for expression of protein therapeutics is a maturing field of pharmaceutical research and development. As recombinant proteins are increasingly utilized as biotherapeutics, improved methodologies ensuring the characterization of post-translational modifications (PTMs) are needed. Typically, proteins prepared for PTM analysis are proteolytically digested and analyzed by mass spectrometry. To ensure full coverage of the PTMs on a given protein, one must obtain complete sequence coverage of the protein, which is often quite challenging. The objective of the research described here is to design a protocol that maximizes protein sequence coverage and enables detection of post-translational modifications, specifically N-linked glycosylation. To achieve this objective, a highly efficient proteolytic digest protocol using trypsin was designed by comparing the relative merits of denaturing agents (urea and Rapigest SF), reducing agents [dithiothreitol (DTT) and tris(2-carboxyethyl)phophine (TCEP)], and various concentrations of alkylating agent [iodoacetamide (IAM)]. After analysis of human apo-transferrin using various protease digestion protocols, ideal conditions were determined to contain 6 M urea for denaturation, 5 mM TCEP for reduction, 10 mM IAM for alkylation, and 10 mM DTT, to quench excess IAM before the addition of trypsin. This method was successfully applied to a novel recombinant protein, human lysyl oxidase-like 2. Furthermore, the glycosylation PTMs were readily detected at two glycosylation sites in the protein. These digestion conditions were specifically designed for PTM analysis of recombinant proteins and biotherapeutics, and the work described herein fills an unmet need in the growing field of biopharmaceutical analysis.

Label-free quantitation: a new glycoproteomics approach.

We demonstrate herein a method for quantifying glycosylation changes on glycoproteins. This novel method uses MS data of characterized glycopeptides to analyze glycosylation profiles, and several quality control tests were done to demonstrate that the method is reproducible, robust, applicable to different types of glycoproteins, and tolerant of instrumental variability during ionization of the analytes. This method is unique in that it is the first label-free quantitative method specifically designed for glycopeptide analysis. It can be used to monitor changes in glycosylation in a glycosylation site-specific manner on a single glycoprotein, or it can be used to quantify glycosylation in a glycoprotein mixture. During mixture analysis, the method can discriminate between changes in glycosylation of a given protein, and changes in the glycoprotein's concentration in the mixture. This method is useful for quantitative analyses in biochemical studies of glycoproteins, where changes in glycosylation composition can be linked to functional differences; it could also be implemented in the pharmaceutical industry, where glycosylation profiles of glycoprotein-based therapeutics must be quantified. Finally, quantification of glycopeptides is an important aspect of glycopeptide-based biomarker discovery, and our quantitative approach could be a valuable asset to this field as well, provided the compositions of the glycopeptides to be quantified are identifiable using other methods.

GlycoPep DB: a tool for glycopeptide analysis using a "Smart Search".

Mass spectrometry is emerging as a versatile analytical tool for profiling glycan and glycopeptide structures. While the interpretation of MS data remains a challenging and difficult task, substantial efforts have been made to develop informatics tools to alleviate MS data interpretation. Here, we present a web-based tool, GlycoPep DB, designed to facilitate compositional assignment for glycopeptides by comparing experimentally measured masses to all calculated glycopeptide masses from a carbohydrate database with N-linked glycans. GlycoPep DB is an advance over current tools to assign N-linked glycans because it uses a concept of "smart searching", where only biologically relevant carbohydrate compositions are searched, when matching carbohydrate compositions with the MS data making glycopeptide compositional assignment more efficient. This is in contrast to currently used tools, where many implausible glycan structures are present in the search output, but fewer biologically relevant glycan motifs are predicted. The utility of GlycoPep DB is illustrated in the analysis of glycopeptides derived from a proteolytic digest of follicle stimulating hormone.