Nature MethodsBioinformaticsPeer-reviewed journal article
Fast and Comprehensive N- and O-glycoproteomics analysis with MSFragger-Glyco
Daniel A. PolaskyiD, Fengchao YuiD, Guo Ci TeoiD, Alexey I. NesvizhskiiiD
Abstract
Glycosylation is a ubiquitous and heterogeneous post-translational modification (PTM) used to accomplish a wide variety of critical cellular tasks. Recent advances in methods for enrichment and mass spectrometric analysis of intact glycopeptides have produced large-scale, high-quality glycoproteomics datasets, but interpreting this data remains challenging. In addition to being large, complex, and heterogeneous, glycans undergo fragmentation during vibrational activation, making common PTM search strategies ineffective for their identification. We present a computational tool called MSFragger-Glyco for fast and highly sensitive identification of N- and O-linked glycopeptides using open and glycan mass offset search strategies. Reanalysis of recently published N-glycoproteomics data resulted in annotation of 83% more glycopeptide-spectrum matches (glycoPSMs) than in previous results, which translated to substantial increases in the numbers of glycoproteins and glycosites that could be identified. In published O-glycoproteomics data, our method more than doubled the number of glycoPSMs annotated when searching the same peptides as the original search and resulted in up to a 6-fold increase when expanding searches to include large numbers of possible glycan compositions and other modifications. Expanded searches revealed trends in glycan composition and crosstalk with phosphorylation that remained hidden to the original search. With greatly improved spectral annotation, coupled with the fast speed of fragment ion index-based scoring, MSFragger-Glyco makes it possible to comprehensively interrogate glycoproteomics data and illuminate the many roles of glycosylation.
- assay: metagenomics
- assay: proteomics
- research focus: benchmarking
- research focus: variant calling
- approach: statistical model
- approach: experimental assay
- Citations
- 296
- FWCI
- 10.70
- Field percentile
- top 1%
Reproducibility ledger
Nobody has reported trying to reproduce this paper yet. Did you replicate an experiment or run the code? Your report saves the next lab a week.
Sign in to file a reproducibility report.
Related papers
- Multi-attribute Glycan Identification and FDR Control for Glycoproteomics
Daniel A. Polasky, Daniel Geiszler, … Alexey I. Nesvizhskii·Molecular & Cellular ProteomicsPeer-reviewed·Jan 26, 2022
- Expanding Glycopeptide Identification with Match-Between-Glycans in FragPipe
Jiechen Shen, Daniel A. Polasky, … Alexey I. Nesvizhskii·bioRxiv·Feb 19, 2026
- Expanding N-Glycopeptide Identifications by Fragmentation Prediction and Glycome Network Smoothing
Joshua Klein, Luis Eduardo Carvalho, Joseph Zaia·bioRxiv·Feb 14, 2021
- A systematic comparison of current bioinformatic tools for glycoproteomics data
Valentina Rangel-Angarita, Keira E. Mahoney, … Stacy Alyse Malaker·bioRxiv·Mar 15, 2022
- Glycoproteomics-compatible MS/MS-based quantification of glycopeptide isomers
J. C. L. Maliepaard, J. M. A. Damen, … K. R. Reiding·Analytical ChemistryPeer-reviewed·Jun 15, 2023
- Extracting informative glycan-specific ions from glycopeptide MS/MS spectra with GlyCounter
Kathryn Kothlow, Haley M. Schramm, … Nicholas McIlvain Riley·Molecular & Cellular ProteomicsPeer-reviewed·Oct 15, 2025