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.