Cysteine Aminoethylation Enables the Site-Specific Glycosylation Analysis of Recombinant Human Erythropoietin using Trypsin.

Recombinant human erythropoietin (rhEPO) is an important biopharmaceutical for which glycosylation is a critical quality attribute. Therefore, robust analytical methods are needed for the in-depth characterization of rhEPO glycosylation. Currently, the protease GluC is widely established for the site-specific glycosylation analysis of rhEPO. However, this enzyme shows disadvantages, such as its specificity and the characteristics of the resulting (glyco)peptides. The use of trypsin, the gold standard protease in proteomics, as the sole protease for rhEPO is compromised, as no natural tryptic cleavage site is located between the glycosylation sites Asn24 and Asn38. Here, cysteine aminoethylation using 2-bromoethylamine was applied as an alternative alkylation strategy to introduce artificial tryptic cleavage sites at Cys29 and Cys33 in rhEPO. The (glyco)peptides resulting from a subsequent digestion using trypsin were analyzed by reverse-phase liquid chromatography-mass spectrometry. The new trypsin-based workflow was easily implemented by adapting the alkylation step in a conventional workflow and was directly compared to an established approach using GluC. The new method shows an improved specificity, a significantly reduced chromatogram complexity, allows for shorter analysis times, and simplifies data evaluation. Furthermore, the method allows for the monitoring of additional attributes, such as oxidation and deamidation at specific sites in parallel to the site-specific glycosylation analysis of rhEPO.

GlycopeptideGraphMS: Improved Glycopeptide Detection and Identification by Exploiting Graph Theoretical Patterns in Mass and Retention Time.

The leading proteomic method for identifying N-glycosylated peptides is liquid chromatography coupled with tandem fragmentation mass spectrometry (LCMS/MS) followed by spectral matching of MS/MS fragment masses to a database of possible glycan and peptide combinations. Such database-dependent approaches come with challenges such as needing high-quality informative MS/MS spectra, ignoring unexpected glycan or peptide sequences, and making incorrect assignments because some glycan combinations are equivalent in mass to amino acids. To address these challenges, we present GlycopeptideGraphMS, a graph theoretical bioinformatic approach complementary to the database-dependent method. Using the AXL receptor tyrosine kinase (AXL) as a model glycoprotein with multiple N-glycosylation sites, we show that those LCMS features that could be grouped into graph networks on the basis of glycan mass and retention time differences were actually N-glycopeptides with the same peptide backbone but different N-glycan compositions. Conversely, unglycosylated peptides did not exhibit this grouping behavior. Furthermore, MS/MS sequencing of the glycan and peptide composition of just one N-glycopeptide in the graph was sufficient to identify the rest of the N-glycopeptides in the graph. By validating the identifications with exoglycosidase cocktails and MS/MS fragmentation, we determined the experimental false discovery rate of identifications to be 2.21%. GlycopeptideGraphMS detected more than 500 unique N-glycopeptides from AXL, triple the number found by a database search with Byonic software, and detected incorrect assignments due to a nonspecific protease cleavage. This method overcomes some limitations of the database approach and is a step closer to comprehensive automated glycoproteomics.