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

Qiudi Ye1,2, Jiechen Shen3, Bojing Zhu3

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|August 29, 2025
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Summary
This summary is machine-generated.

Accurate monoisotopic peak assignment is vital for glycoproteomics. MAP-Match (Monoisotopic Assignment using Partial Match) improves intact N-glycopeptide identification by matching observed and average isotopic clusters, increasing identifications by 22.60%.

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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.