Multiattribute Glycan Identification and FDR Control for Glycoproteomics

Abstract

Rapidly improving methods for glycoproteomics have enabled increasingly large-scale analyses of complex glycopeptide samples, but annotating the resulting mass spectrometry data with high confidence remains a major bottleneck. We recently introduced a fast and sensitive glycoproteomics search method in our MSFragger search engine, which reports glycopeptides as a combination of a peptide sequence and the mass of the attached glycan. In samples with complex glycosylation patterns, converting this mass to a specific glycan composition is not straightforward, however, as many glycans have similar or identical masses. Here, we have developed a new method for determining the glycan composition of N-linked glycopeptides fragmented by collisional or hybrid activation that uses multiple sources of information from the spectrum, including observed glycan B-(oxonium) and Y-type ions and mass and precursor monoisotopic selection errors to discriminate between possible glycan candidates.Combined with false discovery rate estimation for the glycan assignment, we show this method is capable of specifically and sensitively identifying glycans in complex glycopeptide analyses and effectively controls the rate of false glycan assignments. The new method has been incorporated into the PTM-Shepherd modification analysis tool to work directly with the MSFragger glyco search in the FragPipe graphical user interface, providing a complete computational pipeline for annotation of Nglycopeptide spectra with FDR control of both peptide and glycan components that is both sensitive and robust against false identifications.

MSBooster: Improving Peptide Identification Rates using Deep Learning-Based Features

“…However, this does not mean that ion mobility added information will not be useful in other scenarios. IM may improve the resolution of peptidoforms with isobaric modifications (e.g., in glycopeptide identification workflows [69]) or assist with PTM site localization [70, 71]. The minimal strength of IM features observed in this work may also be due to the limitations of a linear SVM model in Percolator.…”

Highly fucosylated N-glycans at the synaptic vesicle and neuronal plasma membrane

“…2B-E). Glycan peptide spectral matches (GlycoPSMs) were obtained by analysis with MSFragger in glyco mode using the FragPipe analysis suite (Kong et al, 2017;Polasky et al, 2022Polasky et al, , 2020 and filtered by excluding those glycoPSMs with a calculated false discovery rate cut-off (Q-value) of < 0.025. A total of over 2,500 unique glycopeptides from over 550 glycoproteins was identified by this method (Fig.…”

“…For glycoproteomics experiments, runs were grouped by sample type (SV, SV with enrichment, or synaptosomes with enrichment) prior to analysis. The same mouse protein database was used and spectra were searched with MSFragger (v.3.4) using glyco mode (Polasky et al, 2022(Polasky et al, , 2020 with the following settings: precursor mass tolerance, ± 20 ppm; fragment mass tolerance, ± 20 ppm, mass calibration and parameter optimization enabled; isotope error, 0/1/2; enzymatic cleavage, strict trypsin with up to 2 missed cleavages; peptide length, 7-50; peptide mass range, 400-5000 Da. Methionine oxidation and N-terminal acetylation were allowed as variable modifications and cysteine carbamidomethylation included as a fixed modification.…”

HLA-Glyco: A large-scale interrogation of the glycosylated immunopeptidome

“…1b ). We further filtered glyco-PSMs by glycan q-value (q ≤ 0.05) to remove glycopeptides lacking sufficient evidence supporting the glycan composition assignment 29 by PTM-Shepherd 30 . With this improved filtering method, the proportion of PSMs corresponding to known glycosites increased to 96%, and the proportion of identified glycosites corresponding to known glycoproteins increased to 95%, with 79% of sites previously identified in other glycoproteomic analyses (Supplementary Fig.…”

“…CC-BY-ND 4.0 International license perpetuity. It is made available under a preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in The copyright holder for this this version posted December 8, 2022. ; https://doi.org/10.1101/2022.12.05.519200 doi: bioRxiv preprint by glycan q-value (q ≤ 0.05) to remove glycopeptides lacking sufficient evidence supporting the glycan composition assignment 29 by PTM-Shepherd 30 . With this improved filtering method, the proportion of PSMs corresponding to known glycosites increased to 96%, and the proportion of identified glycosites corresponding to known glycoproteins increased to 95%, with 79% of sites previously identified in other glycoproteomic analyses (Supplementary Fig.…”