Choo MS, Wan C, Rudd PM, Nguyen-Khuong T. GlycopeptideGraphMS: Improved Glycopeptide Detection and Identification by Exploiting Graph Theoretical Patterns in Mass and Retention Time. Anal Chem. 2019;91(11):7236–7244. doi:10.1021/acs.analchem.9b00594
Abstract:
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.
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Funding Info:
This research / project is supported by the A*STAR Joint Council Office, under its HighGlycoART funding programme (Award 1355g00086) and by the A*STAR Biomedical Research Council, under its GlycoSing funding programme (Award SPF2013/001).