Comparison of HPLC/ESI-FTICR MS versus MALDI-TOF/TOF MS for glycopeptide analysis of a highly glycosylated HIV envelope glycoprotein

  • Janet Irungu
  • Eden P. Go
  • Ying Zhang
  • Dilusha S. Dalpathado
  • Hua-Xin Liao
  • Barton F. Haynes
  • Heather Desaire


Defining the structures and locations of the glycans attached on secreted proteins and virus envelope proteins is important in understanding how glycosylation affects their biological properties. Glycopeptide mass spectrometry (MS)-based analysis is a very powerful, emerging approach to characterize glycoproteins, in which glycosylation sites and the corresponding glycan structures are elucidated in a single MS experiment. However, to date there is not a consensus regarding which mass spectrometric platform provides the best glycosylation coverage information. Herein, we employ two of the most widely used MS approaches, online high performance liquid chromatography-electrospray ionization mass spectrometry (HPLC/ESI-MS) and offline HPLC followed by matrix-assisted laser desorption/ionization mass spectrometry (MALDI-MS), to determine which of the two approaches provides the best glycosylation coverage information of a complex glycoprotein, the group M consensus HIV-1 envelope, CON-S gp140ΔCFI, which has 31 potential glycosylation sites. Our results highlight differences in the informational content obtained between the two methods such as the overall number of glycosylation sites detected, the numbers of N-linked glycans present at each site, and the type of confirmatory information obtained about the glycopeptide using MS/MS experiments. The two approaches are quite complementary, both in their coverage of glycopeptides and in the information they provide in MS/MS experiments. The information in this study contributes to the field of mass spectrometry by demonstrating the strengths and limitations of two widely used MS platforms in glycoprotein analysis.


Glycopeptide Glycosylation Site Tryptic Peptide Collision Induce Dissociation Mass Spectrometry Technique 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Supplementary material

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Supplementary material, approximately 866 KB.


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Copyright information

© American Society for Mass Spectrometry 2008

Authors and Affiliations

  • Janet Irungu
    • 1
  • Eden P. Go
    • 1
  • Ying Zhang
    • 1
  • Dilusha S. Dalpathado
    • 1
  • Hua-Xin Liao
    • 2
  • Barton F. Haynes
    • 2
  • Heather Desaire
    • 1
  1. 1.Department of ChemistryUniversity of KansasLawrenceUSA
  2. 2.Duke University Medical CenterDurhamUSA

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