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

Area of Science:

• Mass spectrometry
• Glycoproteomics
• Computational biology

Background:

• Accurate monoisotopic peak assignment is critical for reliable glycopeptide identification in mass spectrometry.
• Inaccurate assignments can significantly compromise the quantity and credibility of glycopeptide identifications.

Purpose of the Study:

• To introduce MAP-Match (Monoisotopic Assignment using Partial Match), a novel method for precise monoisotopic peak assignment in intact N-glycopeptide identification.
• To enhance the accuracy and efficiency of glycopeptide identification pipelines.

Main Methods:

• MAP-Match utilizes a partial matching strategy between observed isotopic clusters (ObIC) and average isotopic clusters (AvgIC).
• AvgIC generation relies on 'average elemental composition per Da' data from the Byonic glycan database.
• Stepwise partial matching of ObIC and AvgIC, guided by mass centroid deviation, determines the true monoisotopic peak.

Main Results:

• Implementation in StrucGP software led to a 22.60% increase in identified glycopeptide spectrum matches (GPSMs) on mouse brain data.
• MAP-Match effectively differentiates glycans with similar molecular weights, such as distinguishing two fucose residues from one N-acetylneuraminic acid (Neu5Ac) residue.

Conclusions:

• MAP-Match significantly improves the accuracy and number of identified glycopeptide spectrum matches.
• The method enhances the capability of glycoproteomics by enabling precise discrimination between structurally similar glycans.