Precise Monoisotopic Peak Assignment of Intact Glycopeptides Using Partial Average Mass Matching

Accurate assignment of monoisotopic peaks plays a crucial
role
in mass spectrometry-based glycoproteomics, as inaccurate assignment
can severely impair both the quantity and credibility of glycopeptide
identifications. In this work, we introduce a method that utilizes
a partial match between the observed isotopic cluster (ObIC) and the
average isotopic cluster (AvgIC) to enable precise monoisotopic peak
assignment for intact N-glycopeptide identification,
MAP-Match (Monoisotopic Assignment using Partial Match). The key aspects of MAP-Match
are (1) generation of the AvgIC using the “average elemental
composition per Da” data from the Byonic glycan database and
(2) stepwise partial matching of the ObIC and AvgIC based on the deviation
of their mass centroids to determine which section of the AvgIC matches
most closely to the ObIC, thereby deducing the true monoisotopic peak.
The method presented in this work is implemented in our previously
developed N-glycopeptide identification software,
StrucGP, and results in a 22.60% increase in the number of identified
glycopeptide spectrum matches (GPSMs) when analyzing the mouse brain
data set. Additionally, we find that MAP-Match can effectively discriminate
between glycans with similar molecular weights at the monosaccharide
level, such as two fucose residues versus one N-acetylneuraminic
acid (Neu5Ac) residue.