Identification Of Intact Glycopeptides Research Articles

Autonomous Dissociation-type Selection for Glycoproteomics Using a Real-Time Library Search.

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.

Uncovering missing glycans and unexpected fragments with pGlycoNovo for site-specific glycosylation analysis across species

Precision mapping of site-specific glycans using mass spectrometry is vital in glycoproteomics. However, the diversity of glycan compositions across species often exceeds database capacity, hindering the identification of rare glycans. Here, we introduce pGlycoNovo, a software within the pGlyco3 software environment, which employs a glycan first-based full-range Y-ion dynamic searching strategy. pGlycoNovo enables de novo identification of intact glycopeptides with rare glycans by considering all possible monosaccharide combinations, expanding the glycan search space to 16~1000 times compared to non-open search methods, while maintaining accuracy, sensitivity and speed. Reanalysis of SARS Covid-2 spike protein glycosylation data revealed 230 additional site-specific N-glycans and 30 previously unreported O-glycans. pGlycoNovo demonstrated high complementarity to six other tools and superior search speed. It enables characterization of site-specific N-glycosylation across five evolutionarily distant species, contributing to a dataset of 32,549 site-specific glycans on 4602 proteins, including 2409 site-specific rare glycans, and uncovering unexpected glycan fragments.

PGlycoQuant with a deep residual network for quantitative glycoproteomics at intact glycopeptide level

Large-scale intact glycopeptide identification has been advanced by software tools. However, tools for quantitative analysis remain lagging behind, which hinders exploring the differential site-specific glycosylation. Here, we report pGlycoQuant, a generic tool for both primary and tandem mass spectrometry-based intact glycopeptide quantitation. pGlycoQuant advances in glycopeptide matching through applying a deep learning model that reduces missing values by 19–89% compared with Byologic, MSFragger-Glyco, Skyline, and Proteome Discoverer, as well as a Match In Run algorithm for more glycopeptide coverage, greatly expanding the quantitative function of several widely used search engines, including pGlyco 2.0, pGlyco3, Byonic and MSFragger-Glyco. Further application of pGlycoQuant to the N-glycoproteomic study in three different metastatic HCC cell lines quantifies 6435 intact N-glycopeptides and, together with in vitro molecular biology experiments, illustrates site 979-core fucosylation of L1CAM as a potential regulator of HCC metastasis. We expected further applications of the freely available pGlycoQuant in glycoproteomic studies.

Strategies for Proteome-Wide Quantification of Glycosylation Macro- and Micro-Heterogeneity.

Protein glycosylation governs key physiological and pathological processes in human cells. Aberrant glycosylation is thus closely associated with disease progression. Mass spectrometry (MS)-based glycoproteomics has emerged as an indispensable tool for investigating glycosylation changes in biological samples with high sensitivity. Following rapid improvements in methodologies for reliable intact glycopeptide identification, site-specific quantification of glycopeptide macro- and micro-heterogeneity at the proteome scale has become an urgent need for exploring glycosylation regulations. Here, we summarize recent advances in N- and O-linked glycoproteomic quantification strategies and discuss their limitations. We further describe a strategy to propagate MS data for multilayered glycopeptide quantification, enabling a more comprehensive examination of global and site-specific glycosylation changes. Altogether, we show how quantitative glycoproteomics methods explore glycosylation regulation in human diseases and promote the discovery of biomarkers and therapeutic targets.

Precise, fast and comprehensive analysis of intact glycopeptides and modified glycans with pGlyco3

Great advances have been made in mass spectrometric data interpretation for intact glycopeptide analysis. However, accurate identification of intact glycopeptides and modified saccharide units at the site-specific level and with fast speed remains challenging. Here, we present a glycan-first glycopeptide search engine, pGlyco3, to comprehensively analyze intact N- and O-glycopeptides, including glycopeptides with modified saccharide units. A glycan ion-indexing algorithm developed for glycan-first search makes pGlyco3 5–40 times faster than other glycoproteomic search engines without decreasing accuracy or sensitivity. By combining electron-based dissociation spectra, pGlyco3 integrates a dynamic programming-based algorithm termed pGlycoSite for site-specific glycan localization. Our evaluation shows that the site-specific glycan localization probabilities estimated by pGlycoSite are suitable to localize site-specific glycans. With pGlyco3, we confidently identified N-glycopeptides and O-mannose glycopeptides that were extensively modified by ammonia adducts in yeast samples. The freely available pGlyco3 is an accurate and flexible tool that can be used to identify glycopeptides and modified saccharide units.

GproDIA enables data-independent acquisition glycoproteomics with comprehensive statistical control

Large-scale profiling of intact glycopeptides is critical but challenging in glycoproteomics. Data independent acquisition (DIA) is an emerging technology with deep proteome coverage and accurate quantitative capability in proteomics studies, but is still in the early stage of development in the field of glycoproteomics. We propose GproDIA, a framework for the proteome-wide characterization of intact glycopeptides from DIA data with comprehensive statistical control by a 2-dimentional false discovery rate approach and a glycoform inference algorithm, enabling accurate identification of intact glycopeptides using wide isolation windows. We further utilize a semi-empirical spectrum prediction strategy to expand the coverage of spectral libraries of glycopeptides. We benchmark our method for N-glycopeptide profiling on DIA data of yeast and human serum samples, demonstrating that DIA with GproDIA outperforms the data-dependent acquisition-based methods for glycoproteomics in terms of capacity and data completeness of identification, as well as accuracy and precision of quantification. We expect that this work can provide a powerful tool for glycoproteomic studies.

On-tissue spatially resolved glycoproteomics guided by N-glycan imaging reveal global dysregulation of canine glioma glycoproteomic landscape

Here, we present an approach to identify N-linked glycoproteins and deduce their spatial localization using a combination of matrix-assisted laser desorption ionization (MALDI) N-glycan mass spectrometry imaging (MSI) and spatially resolved glycoproteomics. We subjected glioma biopsies to on-tissue PNGaseF digestion and MALDI-MSI and found that the glycan HexNAc4-Hex5-NeuAc2 was predominantly expressed in necrotic regions of high-grade canine gliomas. To determine the underlying sialo-glycoprotein, various regions in adjacent tissue sections were subjected to microdigestion and manual glycoproteomic analysis. Results identified haptoglobin as the protein associated with HexNAc4-Hex5-NeuAc2, thus directly linking glycan imaging with intact glycopeptide identification. In total, our spatially resolved glycoproteomics technique identified over 400 N-, O-, and S- glycopeptides from over 30 proteins, demonstrating the diverse array of glycosylation present on the tissue slices and the sensitivity of our technique. Ultimately, this proof-of-principle work demonstrates that spatially resolved glycoproteomics greatly complement MALDI-MSI in understanding dysregulated glycosylation.

Comparison of Three Glycoproteomic Methods for the Analysis of the Secretome of CHO Cells Treated with 1,3,4-O-Bu3ManNAc.

Comprehensive analysis of the glycoproteome is critical due to the importance of glycosylation to many aspects of protein function. The tremendous complexity of this post-translational modification, however, makes it difficult to adequately characterize the glycoproteome using any single method. To overcome this pitfall, in this report we compared three glycoproteomic analysis methods; first the recently developed N-linked glycans and glycosite-containing peptides (NGAG) chemoenzymatic method, second, solid-phase extraction of N-linked glycoproteins (SPEG), and third, hydrophilic interaction liquid chromatography (HILIC) by characterizing N-linked glycosites in the secretome of Chinese hamster ovary (CHO) cells. Interestingly, the glycosites identified by SPEG and HILIC overlapped considerably whereas NGAG identified many glycosites not observed in the other two methods. Further, utilizing enhanced intact glycopeptide identification afforded by the NGAG workflow, we found that the sugar analog 1,3,4-O-Bu3ManNAc, a “high flux” metabolic precursor for sialic acid biosynthesis, increased sialylation of secreted proteins including recombinant human erythropoietin (rhEPO).

On-Tissue Spatially-Resolved Glycoproteomics Guided by N-Glycan Imaging Reveal Global Dysregulation of Canine Glioma Glycoproteomic Landscape

We identify N-linked glycoproteins and deduce their spatial localization using spatially-resolved glycoproteomic guided by MALDI mass spectrometry N-glycan imaging on formalin-fixed paraffin embedded (FFPE) canine high-grade glioma tissues. The glycan HexNAc4-Hex5-NeuAc2 was found to be predominantly expressed in necrotic regions, whereas high mannose HexNAc2-Hex5 was predominantly found in benign regions. To determine the underlying sialo-glycoprotein, various regions in adjacent tissue sections were analysed. Haptoglobin is the protein associated with HexNAc4-Hex5-NeuAc2. Additionally, we identified several high-mannose (Hex2-HexNAc5) glycopeptides enriched in benign regions. This is the first report that directly links glycan imaging with intact glycopeptide identification. Over 400 N-glycosylated, O-GalNAcylated, O mannosylated, and S- and O- GlcNAcylated glycopeptides from over 30 proteins were identified. While N-glycosylation and O- mannosylation were similar between benign and tumor/necrotic sections, S- and O-GlcNAc glycopeptides were significantly decreased in tumor/necrotic sections, whereas sialylated O-GalNAc glycopeptides were significantly upregulated.

Examining and Fine-tuning the Selection of Glycan Compositions with GlyConnect Compozitor

A key point in achieving accurate intact glycopeptide identification is the definition of the glycan composition file that is used to match experimental with theoretical masses by a glycoproteomics search engine. At present, these files are mainly built from searching the literature and/or querying data sources focused on posttranslational modifications. Most glycoproteomics search engines include a default composition file that is readily used when processing MS data. We introduce here a glycan composition visualizing and comparative tool associated with the GlyConnect database and called GlyConnect Compozitor. It offers a web interface through which the database can be queried to bring out contextual information relative to a set of glycan compositions. The tool takes advantage of compositions being related to one another through shared monosaccharide counts and outputs interactive graphs summarizing information searched in the database. These results provide a guide for selecting or deselecting compositions in a file in order to reflect the context of a study as closely as possible. They also confirm the consistency of a set of compositions based on the content of the GlyConnect database. As part of the tool collection of the Glycomics@ExPASy initiative, Compozitor is hosted at https://glyconnect.expasy.org/compozitor/ where it can be run as a web application. It is also directly accessible from the GlyConnect database.

Strategic Applications of Negative-Mode LC-MS/MS Analyses to Expedite Confident Mass Spectrometry-Based Identification of Multiple Glycosylated Peptides.

Although recent advances in mass spectrometry (MS) have enabled meaningful glycoproteomic undertakings, many technical limitations remain unsolved. Among these, the ability to efficiently sequence the peptide backbone for de novo identification, delineating multiple N- and O-glycosylation sites on single glycopeptides, and deriving more glycan structure information to discriminate isomeric glycoforms are well acknowledged practical problems to be tackled. To address these issues, we explored the use of negative-mode MS2/MS3 fragmentation to supplement current nanoLC-MS2-based sequencing and identification of intact glycopeptides largely performed in positive mode. Consistent with previous reports by others, we found that sulfation and sialylation drastically alter the MS2 fragmentation pattern of glycopeptides in negative mode and the characteristic features identified can be utilized to program the most informative MS3 on the glycan moiety itself. Importantly, direct elimination of one or more O-glycans under negative-mode MS2 affords an easy way to discover additional O-glycosylations on a multiply glycosylated peptide by virtue of enumerating the dehydration scars imprinted on the O-glycosylated sites. Moreover, the characteristic peptide core ion carrying a ring cleavage remnant of the innermost amino sugar residue of an N-glycan can be relied upon to filter out all related N-glycopeptides carrying additional O-glycans defined by specific mass increments. Such enhanced ability to advance from definitive identification of single to multiple site-specific glycosylation on the same peptide backbones is anticipated to have a significant impact on the level of structural and biological insights one can gain in glycoproteomic applications.

Analysis of PNGase F-Resistant N-Glycopeptides Using SugarQb for Proteome Discoverer 2.1 Reveals Cryptic Substrate Specificities.

SugarQb (http://www.imba.oeaw.ac.at/sugarqb) is a freely available collection of computational tools for the automated identification of intact glycopeptides from high‐resolution HCD MS/MS datasets in the Proteome Discoverer environment. We report the migration of SugarQb to the latest and free version of Proteome Discoverer 2.1, and apply it to the analysis of PNGase F‐resistant N‐glycopeptides from mouse embryonic stem cells. The analysis of intact glycopeptides highlights unexpected technical limitations to PNGase F‐dependent glycoproteomic workflows at the proteome level, and warrants a critical reinterpretation of seminal datasets in the context of N‐glycosylation‐site prediction.

Large-scale intact glycopeptide identification by Mascot database search

Workflows capable of determining glycopeptides in large-scale are missing in the field of glycoproteomics. We present an approach for automated annotation of intact glycopeptide mass spectra. The steps in adopting the Mascot search engine for intact glycopeptide analysis included: (i) assigning one letter codes for monosaccharides, (ii) linearizing glycan sequences and (iii) preparing custom glycoprotein databases. Automated annotation of both N- and O-linked glycopeptides was proven using standard glycoproteins. In a large-scale study, a total of 257 glycoproteins containing 970 unique glycosylation sites and 3447 non-redundant N-linked glycopeptide variants were identified in 24 serum samples. Thus, a single tool was developed that collectively allows the (i) elucidation of N- and O-linked glycopeptide spectra, (ii) matching glycopeptides to known protein sequences, and (iii) high-throughput, batch-wise analysis of large-scale glycoproteomics data sets.

PGlyco 2.0 enables precision N-glycoproteomics with comprehensive quality control and one-step mass spectrometry for intact glycopeptide identification

The precise and large-scale identification of intact glycopeptides is a critical step in glycoproteomics. Owing to the complexity of glycosylation, the current overall throughput, data quality and accessibility of intact glycopeptide identification lack behind those in routine proteomic analyses. Here, we propose a workflow for the precise high-throughput identification of intact N-glycopeptides at the proteome scale using stepped-energy fragmentation and a dedicated search engine. pGlyco 2.0 conducts comprehensive quality control including false discovery rate evaluation at all three levels of matches to glycans, peptides and glycopeptides, improving the current level of accuracy of intact glycopeptide identification. The N-glycoproteome of samples metabolically labeled with 15N/13C were analyzed quantitatively and utilized to validate the glycopeptide identification, which could be used as a novel benchmark pipeline to compare different search engines. Finally, we report a large-scale glycoproteome dataset consisting of 10,009 distinct site-specific N-glycans on 1988 glycosylation sites from 955 glycoproteins in five mouse tissues.

Sequential fragment ion filtering and endoglycosidase-assisted identification of intact glycopeptides.

Detailed characterization of glycoprotein structures requires determining both the sites of glycosylation as well as the glycan structures associated with each site. In this work, we developed an analytical strategy for characterization of intact N-glycopeptides in complex proteome samples. In the first step, tryptic glycopeptides were enriched using ZIC-HILIC. Secondly, a portion of the glycopeptides was treated with endoglycosidase H (Endo H) to remove high-mannose (Man) and hybrid N-linked glycans. Thirdly, a fraction of the Endo H-treated glycopeptides was further subjected to PNGase F treatment in 18O water to remove the remaining complex glycans. The intact glycopeptides and deglycosylated peptides were analyzed by nano-RPLC-MS/MS, and the glycan structures and the peptide sequences were identified by using the Byonic or pFind tools. Sequential digestion by endoglycosidase provided candidate glycosites information and indication of the glycoforms on each glycopeptide, thus helping to confine the database search space and improve the confidence regarding intact glycopeptide identification. We demonstrated the effectiveness of this approach using RNase B and IgG and applied this sequential digestion strategy for the identification of glycopeptides from the HepG2 cell line. We identified 4514 intact glycopeptides coming from 947 glycosites and 1011 unique peptide sequences from HepG2 cells. The intensity of different glycoforms at a specific glycosite was obtained to reach the occupancy ratios of site-specific glycoforms. These results indicate that our method can be used for characterizing site-specific protein glycosylation in complex samples. Graphical abstract Through integrating the information of intact glycopeptide, fragment ions filters and endoglycosidase digestion, the reliability of the identification could be significantly improved. We quantified the site-specific glycoforms occupancy ratios through the MS response signaling of each glycopeptide at the same time.

Isotope-targeted glycoproteomics (IsoTaG) analysis of sialylated N- and O-glycopeptides on an Orbitrap Fusion Tribrid using azido and alkynyl sugars.

Protein glycosylation is a post-translational modification (PTM) responsible for many aspects of proteomic diversity and biological regulation. Assignment of intact glycan structures to specific protein attachment sites is a critical step towards elucidating the function encoded in the glycome. Previously, we developed isotope-targeted glycoproteomics (IsoTaG) as a mass-independent mass spectrometry method to characterize azide-labeled intact glycopeptides from complex proteomes. Here, we extend the IsoTaG approach with the use of alkynyl sugars as metabolic labels and employ new probes in analysis of the sialylated glycoproteome from PC-3 cells. Using an Orbitrap Fusion Tribrid mass spectrometer, we identified 699 intact glycopeptides from 192 glycoproteins. These intact glycopeptides represent a total of eight sialylated glycan structures across 126 N- and 576 O-glycopeptides. IsoTaG is therefore an effective platform for identification of intact glycopeptides labeled by alkynyl or azido sugars and will facilitate further studies of the glycoproteome.

Automated Glycan Sequencing from Tandem Mass Spectra of N-Linked Glycopeptides.

Mass spectrometry has become a routine experimental tool for proteomic biomarker analysis of human blood samples, partly due to the large availability of informatics tools. As one of the most common protein post-translational modifications (PTMs) in mammals, protein glycosylation has been observed to alter in multiple human diseases and thus may potentially be candidate markers of disease progression. While mass spectrometry instrumentation has seen advancements in capabilities, discovering glycosylation-related markers using existing software is currently not straightforward. Complete characterization of protein glycosylation requires the identification of intact glycopeptides in samples, including identification of the modification site as well as the structure of the attached glycans. In this paper, we present GlycoSeq, an open-source software tool that implements a heuristic iterated glycan sequencing algorithm coupled with prior knowledge for automated elucidation of the glycan structure within a glycopeptide from its collision-induced dissociation tandem mass spectrum. GlycoSeq employs rules of glycosidic linkage as defined by glycan synthetic pathways to eliminate improbable glycan structures and build reasonable glycan trees. We tested the tool on two sets of tandem mass spectra of N-linked glycopeptides cell lines acquired from breast cancer patients. After employing enzymatic specificity within the N-linked glycan synthetic pathway, the sequencing results of GlycoSeq were highly consistent with the manually curated glycan structures. Hence, GlycoSeq is ready to be used for the characterization of glycan structures in glycopeptides from MS/MS analysis. GlycoSeq is released as open source software at https://github.com/chpaul/GlycoSeq/ .

PGlyco: a pipeline for the identification of intact N-glycopeptides by using HCD- and CID-MS/MS and MS3.

Confident characterization of the microheterogeneity of protein glycosylation through identification of intact glycopeptides remains one of the toughest analytical challenges for glycoproteomics. Recently proposed mass spectrometry (MS)-based methods still have some defects such as lack of the false discovery rate (FDR) analysis for the glycan identification and lack of sufficient fragmentation information for the peptide identification. Here we proposed pGlyco, a novel pipeline for the identification of intact glycopeptides by using complementary MS techniques: 1) HCD-MS/MS followed by product-dependent CID-MS/MS was used to provide complementary fragments to identify the glycans, and a novel target-decoy method was developed to estimate the false discovery rate of the glycan identification; 2) data-dependent acquisition of MS3 for some most intense peaks of HCD-MS/MS was used to provide fragments to identify the peptide backbones. By integrating HCD-MS/MS, CID-MS/MS and MS3, intact glycopeptides could be confidently identified. With pGlyco, a standard glycoprotein mixture was analyzed in the Orbitrap Fusion, and 309 non-redundant intact glycopeptides were identified with detailed spectral information of both glycans and peptides.

GPQuest: A Spectral Library Matching Algorithm for Site-Specific Assignment of Tandem Mass Spectra to Intact N-glycopeptides.

Glycoprotein changes occur in not only protein abundance but also the occupancy of each glycosylation site by different glycoforms during biological or pathological processes. Recent advances in mass spectrometry instrumentation and techniques have facilitated analysis of intact glycopeptides in complex biological samples by allowing the users to generate spectra of intact glycopeptides with glycans attached to each specific glycosylation site. However, assigning these spectra, leading to identification of the glycopeptides, is challenging. Here, we report an algorithm, named GPQuest, for site-specific identification of intact glycopeptides using higher-energy collisional dissociation (HCD) fragmentation of complex samples. In this algorithm, a spectral library of glycosite-containing peptides in the sample was built by analyzing the isolated glycosite-containing peptides using HCD LC-MS/MS. Spectra of intact glycopeptides were selected by using glycan oxonium ions as signature ions for glycopeptide spectra. These oxonium-ion-containing spectra were then compared with the spectral library generated from glycosite-containing peptides, resulting in assignment of each intact glycopeptide MS/MS spectrum to a specific glycosite-containing peptide. The glycan occupying each glycosite was determined by matching the mass difference between the precursor ion of intact glycopeptide and the glycosite-containing peptide to a glycan database. Using GPQuest, we analyzed LC-MS/MS spectra of protein extracts from prostate tumor LNCaP cells. Without enrichment of glycopeptides from global tryptic peptides and at a false discovery rate of 1%, 1008 glycan-containing MS/MS spectra were assigned to 769 unique intact N-linked glycopeptides, representing 344 N-linked glycosites with 57 different N-glycans. Spectral library matching using GPQuest assigns the HCD LC-MS/MS generated spectra of intact glycopeptides in an automated and high-throughput manner. Additionally, spectral library matching gives the user the possibility of identifying novel or modified glycans on specific glycosites that might be missing from the predetermined glycan databases.

GlycoMaster DB: Software To Assist the Automated Identification of N-Linked Glycopeptides by Tandem Mass Spectrometry

Glycosylation is one of the most commonly observed post-translational modifications (PTMs) in eukaryotes. It is believed that more than 50% eukaryotic proteins are glycosylated. To reveal the biological functions of protein-linked glycans involved in numerous biological processes, the high-throughput identification of both glycoproteins and the attached glycan structures becomes fundamentally important. Tandem mass spectrometry (MS/MS) is an effective method for glycoproteomic analysis because of its high sensitivity and selectivity. Two experimental approaches exist to obtain MS/MS spectral data of glycopeptides. One consists of isolating glycans from glycopeptides and generating MS/MS spectra of the glycans and peptides separately. The other approach produces spectra directly from intact glycopeptides. The latter approach has the advantage of retaining the glycosylation site information. However, the spectral data cannot be readily analyzed because of the lack of software specifically designed for the identification of intact glycopeptides. To address this need, we developed a novel software tool, GlycoMaster DB, to assist the automated and high-throughput identification of intact N-linked glycopeptides from MS/MS spectra. The software simultaneously searches a protein sequence database and a glycan structure database to find the best pair of peptide and glycan for each input spectrum. GlycoMaster DB can analyze mass spectral data produced with HCD/ETD mixed fragmentation, where HCD spectra are used to identify glycans and ETD spectra are used to determine peptide sequences. When only HCD spectra are available, GlycoMaster DB can still help to identify the glycans, and a list of possible peptide sequences are reported according to the accurate precursor mass and the N-linked glycopeptide sequon. GlycoMaster DB is freely accessible at http://www-novo.cs.uwaterloo.ca:8080/GlycoMasterDB .