Analysis of Human Serum O-glycoproteomics data with Glycoproteomic Tools
Ontology highlight
ABSTRACT:
ORGANISM(S): Homo Sapiens (human)
SUBMITTER: Mingliang Ye
PROVIDER: PXD039079 | JPOST Repository | Wed May 31 00:00:00 BST 2023
REPOSITORIES: jPOST
Dataset's files
Items per page: 5 1 - 5 of 20 |
Shared Molecules
Only show the datasets with similarity scores above: 0.5
Threshold
0.5
Similar Datasets
Project description:Reanalysis of submissions PXD005411, PXD005413, PXD005412, PXD005553, PXD005555, PXD005565 and PXD019937 using Glyco-Decipher. Identification results of peptide-spectrum matches supporting Glyco-Decipher manuscript (Glyco-Decipher: glycan database-independent peptide matching enables discovery of new glycans and in-depth characterization of site-specific N-glycosylation). Recently, several elegant bioinformatics tools have been developed to identify glycopeptides from tandem mass spectra for site-specific glycoproteomics studies. These glycan database-dependent tools have substantially improved glycoproteomics analysis but fail to identify glycopeptides with unexpected glycans. We present a platform called Glyco-Decipher to interpret the glycoproteomics data of N-linked glycopeptides. It adopts a glycan database-independent peptide matching scheme that allows the unbiased profiling of glycans and the discovery of new glycans linked with modifications. Reanalysis of several large-scale datasets showed that Glyco-Decipher outperformed the open search method in glycan blind searching and the popular glycan database-dependent software tools in glycopeptide identification. Our glycan database-independent search also revealed that modified glycans are responsible for a large fraction of unassigned glycopeptide spectra in shotgun glycoproteomics.
2022-03-04 | PXD031032 | Pride
Project description:Identification results of peptide-spectrum matches supporting Glyco-Decipher manuscript (Glyco-Decipher: Glycan database-independent peptide matching enables discovery of new glycans and in-depth characterization of site-specific N-glycosylation). Recently, several elegant bioinformatics tools have been developed to identify glycopeptides from tandem mass spectra for site-specific glycoproteomics studies. These glycan database-dependent tools have substantially improved glycoproteomics analysis but fail to identify glycopeptides with unexpected glycans. We present a platform called Glyco-Decipher to interpret the glycoproteomics data of N-linked glycopeptides. It adopts a glycan database-independent peptide matching scheme that allows the unbiased profiling of glycans and the discovery of new glycans linked with modifications. Reanalysis of several large-scale datasets showed that Glyco-Decipher outperformed the open search method in glycan blind searching and the popular glycan database-dependent software tools in glycopeptide identification. Our glycan database-independent search also revealed that modified glycans are responsible for a large fraction of unassigned glycopeptide spectra in shotgun glycoproteomics.
2022-03-04 | PXD031025 | Pride
Project description:The isotopic envelope pattern of selenium is guaranteed by its six stable isotopes with distinctive distribution (74Se, 0.89%; 76Se, 9.37%; 77Se, 7.63%; 78Se, 23.77%; 80Se, 49.61%; 82Se, 8.73%). We postulated that the effective incorporation of selenosugar in glycoproteins would achieve direct isotopic pattern prediction for glycopeptides, without the need for secondary tagging procedures. Consequently, we performed comparative studies using a previously reported computational algorithm, the updated version of selenium-encoded isotopic signature targeted profiling (SESTAR++)31, 32, and a glycan-first glycopeptide search engine, pGlyco3 to comprehensively analyze the intact N-glycopeptides in cells treated with SeMOE probes.
2023-11-20 | PXD042137 | Pride
Project description:Tandem mass spectrometry (MS/MS) is the gold standard for intact glycopeptide identification, enabling peptide sequence elucidation and site-specific localization of glycan compositions. Beam-type collisional activation is generally sufficient for N-glycopeptides, while electron-driven dissociation is crucial for site localization in O-glycopeptides. Modern glycoproteomic methods often employ multiple dissociation techniques within a single LC-MS/MS analysis, but this approach frequently sacrifices sensitivity when analyzing multiple glycopeptide classes simultaneously. Here we explore the utility of intelligent data acquisition for glycoproteomics through real-time library searching (RTLS) to match oxonium ion patterns for on-the-fly selection of the appropriate dissociation method. By matching dissociation method with glycopeptide class, this autonomous dissociation-type selection (ADS) generates equivalent numbers of N-glycopeptide identifications relative to traditional beam-type collisional activation methods while also yielding comparable numbers of site-localized O-glycopeptide identifications relative to conventional electron transfer dissociation-based methods. The ADS approach represents a step forward in glycoproteomics throughput by enabling site-specific characterization of both N-and O-glycopeptides within the same LC-MS/MS acquisition.
2024-11-15 | PXD055054 | Pride
Project description:Reanalysis of submissions PXD011533 and PXD009476 using MSFragger-Glyco mode. Processed MSFragger results files (pepXML) and PSM tables (psm.tsv) supporting MSFragger-Glyco manuscript (Fast and Comprehensive N- and O-glycoproteomics analysis with MSFragger-Glyco. Recent advances in methods for enrichment and mass spectrometric analysis of intact glycopeptides have produced large-scale glycoproteomics datasets, but interpreting this data remains challenging. We present MSFragger-Glyco, aa new glycoproteomics mode of the MSFragger search engine, called MSFragger-Glyco, for fast and sensitive identification of N- and O-linked glycopeptides and open glycan searches. Reanalysis of recent N-glycoproteomics data resulted in annotation of 80% more glycopeptide-spectrum matches (glycoPSMs) than previously reported. In published O-glycoproteomics data, our method more than doubled the number of glycoPSMs annotated when searching the same glycans as the original search and yielded 4-6-fold increases when expanding searches to include additional glycan compositions and other modifications. Expanded searches also revealed many sulfated and complex glycans that remained hidden to the original search.
2020-10-06 | PXD021196 | Pride
Project description:Identification results of peptide-spectrum matches supporting Glyco-Decipher manuscript (Glyco-Decipher: Glycan database-independent peptide matching enables discovery of new glycans and in-depth characterization of site-specific N-glycosylation). Recently, several elegant bioinformatics tools have been developed to identify glycopeptides from tandem mass spectra for site-specific glycoproteomics studies. These glycan database-dependent tools have substantially improved glycoproteomics analysis but fail to identify glycopeptides with unexpected glycans. We present a platform called Glyco-Decipher to interpret the glycoproteomics data of N-linked glycopeptides. It adopts a glycan database-independent peptide matching scheme that allows the unbiased profiling of glycans and the discovery of new glycans linked with modifications. Reanalysis of several large-scale datasets showed that Glyco-Decipher outperformed the open search method in glycan blind searching and the popular glycan database-dependent software tools in glycopeptide identification. Our glycan database-independent search also revealed that modified glycans are responsible for a large fraction of unassigned glycopeptide spectra in shotgun glycoproteomics.
2023-09-25 | PXD045630 |
Project description:We adopted the method used in the O-Pair article to validate the FDR calculations of O-glycopeptide search in O-Search-Pattern, O-Pair Search, MSFragger-Glyco, and pGlyco. The detailed description of the method was provided in the ‘Methods’ and ‘Evaluating O-Pair Search performance’ parts of O-Pair article (Lu et al., 2020).
2023-05-31 | PXD039078 | JPOST Repository
Project description:Protein N-glycosylation plays critical roles in controlling brain function, but little is known about human brain N-glycoproteome and its alterations in Alzheimer's disease (AD). Here, we report the first, large-scale, site-specific N-glycoproteome profiling study of human AD and control brains using mass spectrometry-based quantitative N-glycoproteomics. The study provided a system-level view of human brain N-glycoproteins and in vivo N-glycosylation sites and identified disease signatures of altered N-glycopeptides, N-glycoproteins, and N-glycosylation site occupancy in AD. Glycoproteomics-driven network analysis showed 13 modules of co-regulated N-glycopeptides/glycoproteins, 6 of which are associated with AD phenotypes. Our analyses revealed multiple dysregulated N-glycosylation-affected processes and pathways in AD brain, including extracellular matrix dysfunction, neuroinflammation, synaptic dysfunction, cell adhesion alteration, lysosomal dysfunction, endocytic trafficking dysregulation, endoplasmic reticulum dysfunction, and cell signaling dysregulation. Our findings highlight the involvement of N-glycosylation aberrations in AD pathogenesis and provide new molecular and system-level insights for understanding and treating AD.
| S-EPMC7852392 | biostudies-literature
Project description:Interpreting large-scale glycoproteomic data for intact glycopeptide identification has been tremendously advanced by software tools. However, software tools for quantitative analysis of intact glycopeptides remain lagging behind, which greatly hinders exploring the differential expression and functions of site-specific glycosylation in organisms. Here, we report pGlycoQuant, a generic software tool for quantitative intact glycopeptide analysis, supporting both primary and tandem mass spectrometry quantitation for multiple quantitative strategies. pGlycoQuant advances in glycopeptide evidence matching through applying a deep learning model that reduces missing values for glycopeptide quantification by over 60% compared with Byologic, MSFragger-Glyco and Skyline, as well as an optional function of Match-In-Run (MIR) algorithm for more quantitative coverage of glycopeptides, thus greatly expanding the quantitative function of several powerful search engines, currently including pGlyco 2.0, pGlyco3, Byonic and MSFragger-Glyco. The pGlycoQuant-based site-specific N-glycoproteomic study conducted here quantifies 6435 intact N-glycopeptides in three hepatocellular carcinoma cell lines with different metastatic potentials and, together with in vitro molecular biology experiments, illustrates core fucosylation at site 979 of the L1 cell adhesion molecule (L1CAM) as a potential regulator of HCC metastasis. pGlycoQuant is freely available at https://github.com/Power-Quant/pGlycoQuant/releases. We have demonstrated pGlycoQuant to be a powerful tool for the quantitative analysis of site-specific glycosylation and the exploration of potential glycosylation-related biomarker candidates, and we expect further applications in glycoproteomic studies.
2022-05-16 | MSV000089484 | MassIVE
Project description:We report a LC-MS/MS O-glycoproteomics strategy using Data Independent Acquisition (DIA) mode that holds the potential for enabling direct analysis of O-glycoproteins with characterization of sites and structures of O-glycans on a proteome-wide scale with quantification of stoichiometries. To explore the use of a DIA strategy for O-glycoproteomics, we built a spectral library of O-glycopeptides with the most common core1 O-glycan structures. This Glyco-DIA library consists of sublibraries obtained from cell lines and human serum, and it currently covers 2,076 O-glycoproteins (11,452 unique glycopeptide sequences) and the five most common core1 O-glycan structures. Applying the Glyco-DIA library to human serum without enrichment for glycopeptides enabled us to identify and quantify nearly 293 distinct glycopeptide sequences bearing up to 5 different core1 O-glycans from 159 glycoproteins in a singleshot analysis. The DIA method is expandable and widely applicable to different glycoproteomes, and it may represent the first direct and comprehensive approach to glycoproteomics.
2019-11-19 | PXD011063 | Pride