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.