Despite the promise of liquid chromatography–mass spectrometry (LC-MS) for interpreting intact glycopeptides, challenges persist. Two issues in particular contribute to this difficulty: peptide backbone characterization and glycan false discovery rate (FDR) estimation.
In a recent report, Zeng et al. (2016) offer a novel pipeline suitable for addressing these challenges: pGlyco.1 Their approach is twofold. First, they identify glycans using complementary fragments derived from two MS methods (higher-energy collisional dissociation tandem MS [HCD-MS/MS] and collision-induced dissociation tandem MS [CID-MS/MS]) and employ a novel target-decoy protocol to estimate FDR. Then, they identify peptide backbones by applying fully automated data-dependent acquisition of MS3 to the Y1 ion. Together, these steps enable identification of intact glycopeptides as well as detailed spectral data for glycans and peptides.
Table 1. pGlyco: A four-step pipeline
Step one: |
Following full scan, Zeng et al. use diagnostic ions to select true glycopeptides. |
Step two: |
They then generate same-precursor spectrum pairs and filtered Y1 ions, which they use to deduce the peptide backbone. They establish FDR using a novel target-decoy method. |
Step three: |
Next, they employ data-dependent MS3 for the three most intense peaks within a set mass range, which includes the Y1 ion. They estimate FDR using a conventional target-decoy method. |
Step four: |
Using the compiled data, they integrate the peptide backbone masses and retention times to produce complete information for glycans and backbones. |
To demonstrate the efficiency of their pipeline, the research team prepared a mixture of six standard glycoproteins (IgG, IgA, IgM, alpha-1-acid glycoprotein, alpha-2-macroglobulin and haptoglobin). After trypsinization and enrichment, they performed two LC-MS/MS runs using an Orbitrap Fusion Tribrid mass spectrometer coupled with an EASY-nLC system (both Thermo Scientific). One run gathered HCD-MS/MS and CID-MS/MS spectra, and the other collected HCD-MS/MS and MS3 spectra.
Zeng et al. highlight their application of the Y1 ion for peptide backbone deduction. First, they filtered unreliable candidate Y1 ions and then compiled a list of remaining candidates. For these, they applied the spectral data to deduce Y1 ion mass (precursor mass less glycan mass and HexNAc mass), ultimately yielding a spectral snapshot of the peptide backbone. In this way, the Y1 ion data served as a link between glycan identification and peptide backbone characterization.
The other important feature of this study was the novel use of a spectrum-based decoy strategy coupled with the finite mixture model (FMM) to estimate glycan FDR. The team constructed a theoretical decoy spectrum and then competitively matched both theoretical target and decoy spectra against the experimental spectrum, employing FMM to account for possible bias. They validated this method using a conventional sequence-based target-decoy protocol with peptide identifications from public data sets with good results.
Using their standard glycoprotein mixture, Zeng et al. observed 765 glycopeptide-spectrum matches (GPSMs), which filtered to 556 GPSMs corresponding to 309 non-redundant glycopeptides at 1% glycan FDR. Further, pGlyco identified 25 of the 46 potential glycosylation sites from the researchers’ standard protein database. When the team manually inspected the GPSM results, they found that one out of 556 GPSMs was a false identification (compared with 73 out of the 765 GPSMs before glycan FDR cutoff) and that, when the reported FDR was 1%, the real (manual) FDR was only 0.18%. Overall, they report their spectrum-based decoy strategy to be an effective method.
The research team offers pGlyco as a robust pipeline that exploits the advanced settings of next-generation mass spectrometers like the Orbitrap Fusion to improve intact glycopeptide identification. They further provide a novel decoy strategy suitable to estimate glycan FDR. Interested parties can find pGlyco as a free download.
Reference
1. Zeng, W.F., et al. (2016) “pGlyco: A pipeline for the identification of intact N-glycopeptides by using HCD- and CID-MS/MS and MS3,” Scientific Reports, 6(25102), doi: 10.1038/srep25102.
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