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Glycoproteomics is a powerful yet analytically challenging research tool. Software packages aiding the interpretation of complex glycopeptide tandem mass spectra have appeared, but their relative performance remains untested. Conducted through the HUPO Human Glycoproteomics Initiative, this community study, comprising both developers and users of glycoproteomics software, evaluates solutions for system-wide glycopeptide analysis. The same mass spectrometrybased glycoproteomics datasets from human serum were shared with participants and the relative team performance for N - and O-glycopeptide data analysis was comprehensively established by orthogonal performance tests. Although the results were variable, several high-performance glycoproteomics informatics strategies were identified. Deep analysis of the data revealed key performance-associated search parameters and led to recommendations for improved ‘high-coverage’ and ‘high-accuracy’ glycoproteomics search solutions. This study concludes that diverse software packages for comprehensive glycopeptide data analysis exist, points to several high-performance search strategies and specifies key variables that will guide future software developments and assist informatics decision-making in glycoproteomics., This analysis presents the results of a community-based evaluation of existing software for large-scale glycopeptide data analysis.

The α-galactosyl epitope is a terminal N-glycan moiety of glycoproteins found in mammals except in humans, and thus, it is recognized as an antigen that provokes an immunogenic response in humans. Accordingly, it is necessary to analyze the α-galactosyl structure in biopharmaceuticals or organ transplants. Due to an identical glycan composition and molecular mass between α-galactosyl N-glycans and hybrid/high-mannose-type N-glycans, it is challenging to characterize α-galactosyl epitopes in N-glycoproteins using mass spectrometry. Here, we describe a method to identify α-galactosyl N-glycopeptides in mice glycoproteins using liquid chromatography with tandem mass spectrometry with higher-energy collisional dissociation (HCD). The first measure was an absence of [YHM] ion peaks in the HCD spectra, which was exclusively observed in hybrid and/or high-mannose-type N-glycopeptides. The second complementary criterion was the ratio of an m/z 528.19 (Hex2HexNAc1) ion to m/z 366.14 (Hex1HexNAc1) ion (Im/z528/Im/z366). The measure of [Im/z528/Im/z366 > 0.3] enabled a clear-cut determination of α-galactosyl N-glycopeptides with high accuracy. In Ggta1 knockout mice, we could not find any α-galactosyl N-glycoproteins identified in WT mice plasma. Using this method, we could screen for α-galactosyl N-glycoproteins from mice spleen, lungs, and plasma samples in a highly sensitive and specific manner. Conclusively, we suggest that this method will provide a robust analytical tool for determination of α-galactosyl epitopes in pharmaceuticals and complex biological samples.

The N-glycosylation of proteins is one of the most important post-translational modifications relevant to various biological functions. The identification and quantification of N-glycoproteins in liquid chromatography-mass spectrometry (LC-MS) is challenging because of their low analytical sensitivity and selectivity. This is due to their microheterogeneity and the difficulty of synthesizing N-glycopeptides as an internal standard. Parallel reaction monitoring (PRM) is widely used in targeted LC-MS. The key advantage of LC-PRM is that it can identify N-glycopeptides using tandem mass spectrometry (MS/MS) fragmentation, even without an internal standard. We investigated the feasibility of analyzing N-glycoproteins using multiplex immunoprecipitation to improve sensitivity and selectivity. We targeted N-glycoproteins [α-fetoprotein (AFP), vitronectin (VTN), and α-1-antichymotrypsin (AACT)] that are abnormally glycosylated in hepatocellular carcinoma (HCC). Their tryptic N-glycopeptides were selected to determine the percentages of fucosylated N-glycopeptides using Y ions, which include glycopeptide fragments with amino acid sequences. Finally, we confirmed that the area under the receiver operating characteristic curve (AUC = 0.944) for the combination of AFP and VTN increased more so than for a single glycopeptide (AUC = 0.889 for AFP and 0.792 for VTN) with respect to discriminating between HCC and cirrhosis serum. This study shows that an LC-PRM method using multiplex N-glycoproteins immunoprecipitated from serum could be applied to develop and verify cancer biomarkers. Graphical abstract.

Rationale Glycoprotein fucosylation, one of the major posttranslational modifications, is known to be highly involved in proteins related to various cancers. Fucosylation occurs in the core and/or outer sites of N-glycopeptides. Elucidation of the fucosylation type of N-glycoproteins is therefore important. However, it has remained a challenge to classify the fucosylation types of N-glycopeptides using collision-induced dissociation (CID) tandem mass (MS/MS) spectra. Methods The relative intensities of the Y1 F, Y2 F, Y3 F, and Y4 F product ions in the CID-MS/MS spectra of the IgG N-glycopeptides were measured for core fucosylation. The Core Fucose Index (CFI) was then calculated by multiplication of the relative intensities with a weight factor from logistic regression to differentiate between the core and none fucosylation. From the relative intensities of the B2 F and B3 SF ions of the MS/MS spectra of the AGP N-glycopeptides for outer fucosylation, the Outer Fucose Index (OFI) was calculated to differentiate between the outer and none fucosylation. Results In order to classify core and/or outer fucosylation of N-glycoproteins, we defined the fucosylation score (F-score) by a sigmoidal equation using a combination of the CFI and the OFI. For application, we classified the fucosylation types of N-glycoproteins in human plasma with 99.7% accuracy from the F-score. Human plasma samples showed 54.4%, 33.3%, 10.3%, and 1.6% for none, core, outer, and dual fucosylated N-glycopeptides, respectively. Core fucosylation was abundant at mono- and bi-antennary N-glycopeptides. Outer fucosylation was abundant at tri- and tetra-antennary N-glycopeptides. In total, 113 N-glycopeptides of 29 glycoproteins from 3365 glycopeptide spectral matches (GPSMs) were classified for different types of fucosylation. Conclusions We established an F-score to classify three different fucosylation types: core, outer, and dual types of N-glycopeptides. The fucosylation types of 20 new N-glycopeptides from 11 glycoproteins in human plasma were classified using the F-score. Therefore, the F-score can be useful for the automatic classification of different types of fucosylation in N-glycoproteins of biological fluids including plasma, serum, and urine.

The α-galactosyl epitope is a terminal

Protein glycosylation is known to be involved in biological progresses such as cell recognition, growth, differentiation, and apoptosis. Fucosylation of glycoproteins plays an important role for structural stability and function of N-linked glycoproteins. Although many of biological and clinical studies of protein fucosylation by fucosyltransferases has been reported, structural classification of fucosylated N-glycoproteins such as core or outer isoforms remains a challenge. Here, we report for the first time the classification of N-glycopeptides as core- and outer-fucosylated types using tandem mass spectrometry (MS/MS) and machine learning algorithms such as the deep neural network (DNN) and support vector machine (SVM). Training and test sets of more than 800 MS/MS spectra of N-glycopeptides from the immunoglobulin gamma and alpha 1-acid-glycoprotein standards were selected for classification of the fucosylation types using supervised learning models. The best-performing model had an accuracy of more than 99% against manual characterization and area under the curve values greater than 0.99, which were calculated by probability scores from target and decoy datasets. Finally, this model was applied to classify fucosylated N-glycoproteins from human plasma. A total of 82N-glycopeptides, with 54 core-, 24 outer-, and 4 dual-fucosylation types derived from 54 glycoproteins, were commonly classified as the same type in both the DNN and SVM. Specifically, outer fucosylation was dominant in tri- and tetra-antennary N-glycopeptides, while core fucosylation was dominant in the mono-, bi-antennary and hybrid types of N-glycoproteins in human plasma. Thus, the machine learning methods can be combined with MS/MS to distinguish between different isoforms of fucosylated N-glycopeptides.

Fucosylation of N-glycoproteins has been implicated in various diseases, such as hepatocellular carcinoma (HCC). However, few studies have performed site-specific analysis of fucosylation in liver-secreted proteins. In this study, we characterized the fucosylation patterns of liver-secreted proteins in HCC plasma using a workflow to identify site-specific N-glycoproteins, where characteristic B- and/or Y-ion series with and without fucose in collision-induced dissociation were used in tandem mass spectrometry. In total, 71 fucosylated N-glycopeptides from 13 major liver-secreted proteins in human plasma were globally identified by LC-MS/MS. Additionally, 37 fucosylated N-glycopeptides were newly identified from nine liver-secreted proteins, including alpha-1-antichymotrypsin, alpha-1-antitrypsin, alpha-2-HS-glycoprotein, ceruloplasmin, alpha-1-acid glycoprotein 1/2, alpha-2-macroglobulin, serotransferrin, and beta-2-glycoprotein 1. Of the fucosylated N-glycopeptides, bi- and tri-antennary glycoforms were the most common ones identified in liver-secreted proteins from HCC plasma. Therefore, we suggest that this analytical method is effective for characterizing fucosylation in liver-secreted proteins. Graphical abstract A global map of fucosylated and non-fucosylated glycopeptides from 13 liver-secreted glycoproteins in hepatocellular carcinoma plasma.

The Bioinformatics & Molecular Design Research Center Mass Spectral Library - Natural Products (BMDMS-NP) is a library containing the mass spectra of natural compounds, especially plant specialized metabolites. At present, the library contains the electrospray ionization tandem mass spectrometry (ESI-MS/MS) spectra of 2739 plant metabolites that are commercially available. The contents of the library were made comprehensive by incorporating data generated under various experimental conditions for compounds with diverse molecular structures. The structural diversity of the BMDMS-NP data was evaluated using molecular fingerprints, and it was sufficiently exhaustive enough to represent the structures of the natural products commercially available. The MS/MS spectra of each metabolite were obtained with different types/brands of ion traps (tandem-in-time) or combinations of mass analyzers (tandem-in-space) at multiple collision energies. All spectra were measured repeatedly in each environment because variations can occur in spectra, even under the same conditions. Moreover, the probability, separability of searching, and transferability of this spectral library were evaluated against those of MS/MS libraries, namely: NIST17 and MoNA.

Protein glycosylation is known to be involved in biological progresses such as cell recognition, growth, differentiation, and apoptosis. Fucosylation of glycoproteins plays an important role for structural stability and function of N-linked glycoproteins. Although many of biological and clinical studies of protein fucosylation by fucosyltransferases has been reported, structural classification of fucosylated N-glycoproteins such as core or outer isoforms remains a challenge. Here, we report for the first time the classification of N-glycopeptides as core- and outer-fucosylated types using tandem mass spectrometry (MS/MS) and machine learning algorithms such as the deep neural network (DNN) and support vector machine (SVM). Training and test sets of more than 800 MS/MS spectra of N-glycopeptides from the immunoglobulin gamma and alpha 1-acid-glycoprotein standards were selected for classification of the fucosylation types using supervised learning models. The best-performing model had an accuracy of more than 99% against manual characterization and area under the curve values greater than 0.99, which were calculated by probability scores from target and decoy datasets. Finally, this model was applied to classify fucosylated N-glycoproteins from human plasma. A total of 82N-glycopeptides, with 54 core-, 24 outer-, and 4 dual-fucosylation types derived from 54 glycoproteins, were commonly classified as the same type in both the DNN and SVM. Specifically, outer fucosylation was dominant in tri- and tetra-antennary N-glycopeptides, while core fucosylation was dominant in the mono-, bi-antennary and hybrid types of N-glycoproteins in human plasma. Thus, the machine learning methods can be combined with MS/MS to distinguish between different isoforms of fucosylated N-glycopeptides.

Analyses of intact glycopeptides using mass spectrometry is challenging due to the numerous types of isomers of glycan moieties attached to the peptide backbone. Here, we demonstrate that high-temperature reversed-phase liquid chromatography (RPLC) can be used to separate isomeric O- and N-linked glycopeptides. In general, high column temperatures enhanced the resolution for separation of sialylated O- and N-linked glycopeptide isomers with decreased retention times. Using the high-temperature RPLC method, α2-6-linked sialylated N-glycopeptides were eluted first, followed by α2-3-linked isomers. However, highly sialylated N-glycopeptides containing hydrophobic amino acids exhibited increased retention times at high temperature. The separation of sialylated O- and N-glycopeptides with different glycan isoforms using a high-temperature RPLC method was demonstrated. This study indicates that reversed-phase chromatographic separation at high column temperatures is suitable for the separation of glycopeptide structural isomers.

The goal of the Chromosome-Centric Human Proteome Project (C-HPP) is to fully provide proteomic information from each human chromosome, including novel proteoforms, such as novel protein-coding variants expressed from noncoding genomic regions, alternative splicing variants (ASVs), and single amino acid variants (SAAVs). In the 144 LC/MS/MS raw files from human hippocampal tissues of control, epilepsy, and Alzheimer's disease, we identified the novel proteoforms with a workflow including integrated proteomic pipeline using three different search engines, MASCOT, SEQUEST, and MS-GF+. With a

Dried blood spot (DBS) samples have a number of advantages, especially with respect to ease of collection, transportation, and storage and to reduce biohazard risk. N-glycosylation is a major post-translational modification of proteins in human blood that is related to a variety of biological functions, including metastasis, cell–cell interactions, inflammation, and immunization. Here, we directly analyzed tryptic N-glycopeptides from glycoproteins in DBS samples using liquid chromatography-tandem mass spectrometry (LC-MS/MS) without centrifugation of blood samples, depletion of major proteins, desalting of tryptic peptides, and enrichment of N-glycopeptides. Using this simple method, we identified a total of 41 site-specific N-glycopeptides from 16 glycoproteins in the DBS samples, from immunoglobulin gamma 1 (IgG-1, 10 mg/mL) down to complement component C7 (50 μg/mL). Of these, 32 N-glycopeptides from 14 glycoproteins were consistently quantified over 180 days stored at room temperature. The major abundant glycoproteins in the DBS samples were IgG-1 and IgG-2, which contain nine asialo-fucosylated complex types of 16 different N-glycopeptide isoforms. Sialo-non-fucosylated complex types were primarily detected in the other glycoproteins such as alpha-1-acid glycoprotein 1, 2, alpha-1-antitypsin, alpha-2-macroglobulin, haptoglobin, hemopexin, Ig alpha 1, 2 chain C region, kininogen-1, prothrombin, and serotransferrin. We first report the characterization of site-specific N-glycoproteins in DBS samples by LC-MS/MS with minimal sample preparation.

O-glycans in saliva and tear isolated from patients suffering from ocular rosacea, a form of inflammatory ocular surface disease, were profiled and their structures were elucidated using high resolution mass spectrometry. We have previously shown that certain structures, particularly sulfated oligosaccharides, increased in the tear and saliva of rosacea patients. In this study, the structures of these glycans were elucidated using primarily tandem mass spectrometry. There were important similarities in the glycan profiles of tears and saliva with the majority of the structures in common. The structures of the most abundant species common to both tear and saliva, which were also the most abundant species in both, were elucidated. For sulfated species, the positions of the sulfate groups were localized. The majority of the structures were new, with the sulfated glycans comprising of mucin core 1 and core 2-typestructures. As both saliva and tear are rich in mucins, it is suggested that the O-glycans are mainly components of mucins. The study further illustrates the strong correspondence between the glycans in the tear and saliva of ocular rosacea patients.

Glycoprotein conformations are complex and heterogeneous. Currently, site-specific characterization of glycopeptides is a challenge. We sought to establish an efficient method of N-glycoprotein characterization using mass spectrometry (MS). Using alpha-1-acid glycoprotein (AGP) as a model N-glycoprotein, we identified its tryptic N-glycopeptides and examined the data reproducibility in seven laboratories running different LC-MS/MS platforms. We used three test samples and one blind sample to evaluate instrument performance with entire sample preparation workflow. 165 site-specific N-glycopeptides representative of all N-glycosylation sites were identified from AGP 1 and AGP 2 isoforms. The glycopeptide fragmentations by collision-induced dissociation or higher-energy collisional dissociation (HCD) varied based on the MS analyzer. Orbitrap Elite identified the greatest number of AGP N-glycopeptides, followed by Triple TOF and Q-Exactive Plus. Reproducible generation of oxonium ions, glycan-cleaved glycopeptide fragment ions, and peptide backbone fragment ions was essential for successful identification. Laboratory proficiency affected the number of identified N-glycopeptides. The relative quantities of the 10 major N-glycopeptide isoforms of AGP detected in four laboratories were compared to assess reproducibility. Quantitative analysis showed that the coefficient of variation was25% for all test samples. Our analytical protocol yielded identification and quantification of site-specific N-glycopeptide isoforms of AGP from control and disease plasma sample.

Human glycoproteins exhibit enormous heterogeneity at each N-glycosite, but few studies have attempted to globally characterize the site-specific structural features. We have developed Integrated GlycoProteome Analyzer (I-GPA) including mapping system for complex N-glycoproteomes, which combines methods for tandem mass spectrometry with a database search and algorithmic suite. Using an N-glycopeptide database that we constructed, we created novel scoring algorithms with decoy glycopeptides, where 95 N-glycopeptides from standard α1-acid glycoprotein were identified with 0% false positives, giving the same results as manual validation. Additionally automated label-free quantitation method was first developed that utilizes the combined intensity of top three isotope peaks at three highest MS spectral points. The efficiency of I-GPA was demonstrated by automatically identifying 619 site-specific N-glycopeptides with FDR ≤ 1%, and simultaneously quantifying 598 N-glycopeptides, from human plasma samples that are known to contain highly glycosylated proteins. Thus, I-GPA platform could make a major breakthrough in high-throughput mapping of complex N-glycoproteomes, which can be applied to biomarker discovery and ongoing global human proteome project.

The high-throughput identification and accurate quantification of proteins are essential strategies for exploring cellu- lar functions and processes in quantitative proteomics. Stable isotope tagging is a key technique in quantitative proteomic research, accompanied by automated tandem mass spectrometry. For the differential proteome analysis of mouse neuronal cell lines, we used a multiplexed isobaric tagging method, in which a four-plex set of amine-reactive isobaric tags are available for peptide derivatization. Using the four-plex set of isobaric tag for relative and absolute quantitation (iTRAQ) reagents, we analyzed the differential proteome in several stroke time pathways (0, 4, and 8 h) after the mouse neuronal cells have been stressed using a glutamate oxidant. In order to obtain a list of the differentially expressed proteins, we selected those proteins which had appar- ently changed significantly during the stress test. With 95% of the peptides showing only a small variation in quantity before and after the test, we obtained a list of eight up-regulated and four down-regulated proteins for the stroke time pathways. To validate the iTRAQ approach, we studied the use of oxidant stresses for mouse neuronal cell samples that have shown differential proteome in several stroke time pathways (0, 4, and 8 h). Results suggest that histone H1 might be the key protein in the oxidative injury caused by glutamate-induced cytotoxicity in HT22 cells.

Acinetobacter baumannii is a Gram-negative, nonmotile aerobic bacterium that has emerged as an important nosocomial pathogen. Multidrug-resistant (MDR) A. baumannii is difficult to treat with antibiotics, and treatment failure in infected patients is of great concern in clinical settings. To investigate proteome regulation in A. baumannii under antibiotic stress conditions, quantitative membrane proteomic analyses of a clinical MDR A. baumannii strain cultured in subminimal inhibitory concentrations of tetracycline and imipenem were performed using a combination of label-free (one-dimensional electrophoresis-liquid chromatography-tandem mass spectrometry) and label (isobaric tag for relative and absolute quantitation) approaches. In total, 484 proteins were identified, and 302 were classified as outer membrane, periplasmic, or plasma membrane proteins. The clinical A. baumannii strain DU202 responded specifically and induced different cell wall and membrane protein sets that provided resistance to the antibiotics. The induction of resistance-nodulation-cell division transporters and protein kinases, and the repression of outer membrane proteins were common responses in the presence of tetracycline and imipenem. Induction of a tetracycline resistant pump, ribosomal proteins, and iron-uptake transporters appeared to be dependent on tetracycline conditions, whereas β-lactamase and penicillin-binding proteins appeared to be dependent on imipenem conditions. These results suggest that combined liquid chromatography-based proteomic approaches can be used to identify cell wall and membrane proteins involved in the antibiotic resistance of A. baumannii.

A protein identified in multiple separate bands of a 1-D gel reflects variation in the molecular weight caused by alternative splicing, endoproteolytic cleavage, or PTMs, such as glycosylation or ubiquitination. To characterize such a protein distribution over the bands, we defined an entity called an 'island' as the band region including the bands of the same protein identified sequentially. We quantified the island distribution using a new variable called an Iscore. Previously, as described in Park et al.. (Proteomics 2006, 6, 4978-4986.), we analyzed human brain tissue using a multidimensional MS/MS separation method. Here, the new method of island analysis was applied to the previous proteome data. The soluble and membrane protein fractions of human brain tissue were reanalyzed using the island distribution. The proteome of the soluble fraction exhibited more variation in island positions than that of the membrane fraction. Through the island analysis, we identified protein modifications and protein complexes over the 1-D gel bands.

Gram-negative bacteria constitutively secrete native outer membrane vesicles (OMVs) into the extracellular milieu. Although recent progress in this area has revealed that OMVs are essential for bacterial survival and pathogenesis, the mechanism of vesicle formation and the biological roles of OMVs have not been clearly defined. Using a proteomics approach, we identified 141 protein components of Escherichia coli-derived native OMVs with high confidence; two separate analyses yielded identifications of 104 and 117 proteins, respectively, with 80 proteins overlapping between the two trials. In the group of identified proteins, the outer membrane proteins were highly enriched, whereas inner membrane proteins were lacking, suggesting that a specific sorting mechanism for vesicular proteins exists. We also identified proteins involved in vesicle formation, the removal of toxic compounds and attacking phage, and the elimination of competing organisms, as well as those involved in facilitating the transfer of genetic material and protein to other bacteria, targeting host cells, and modulating host immune responses. This study provides a global view of native bacterial OMVs. This information will help us not only to elucidate the biogenesis and functions of OMV from nonpathogenic and pathogenic bacteria but also to develop vaccines and antibiotics effective against pathogenic strains.

The characterization of site-specific microheterogeneity in glycoprotein is very important for understanding cell biology and disease processes. Vitronectin is well known to be a multifunctional glycoprotein in the blood and the extracellular matrix, which is related to hepatocellular carcinoma (HCC). Here, we systematically analyzed the site-specific N-glycopeptides of vitronectin in human plasma by tandem mass spectrometry combined with immunoprecipitation and hydrophilic interaction liquid chromatography (HILIC) enrichment. Vitronectin was purified with immunoprecipitation by monoclonal antibody from plasma and digested to tryptic N-glycopeptides.Then, enrichment with HILIC materials was used and followed by analysis with nano-LC/MS/MS. The sequences of N-glycopeptides were identified from the mass spectra by high-energy C-trap dissociation (HCD) and collision-induced dissociation (CID). In HCD mode, oxonium ions were used for recognizing glycopeptides and y ions for sequencing the peptide backbone. In CID mode, Y ions were used for characterizing their glycoforms. As a result, a total of 17 site-specific N-glycopeptides were completely identified in all of the three N-glycosylation sites of vitronectin in human plasma, including 12 N-glycopeptides first reported. Finally, we specifically found that three hybrid and four complex glycopeptides of triantennary forms with outer fucosylation increased in HCC human plasma.

A simple mass spectrometric approach for the discovery and validation of biomarkers in human plasma was developed by targeting nonglycosylated tryptic peptides adjacent to glycosylation sites in an N-linked glycoprotein, one of the most important biomarkers for early detection, prognoses, and disease therapies. The discovery and validation of novel biomarkers requires complex sample pretreatment steps, such as depletion of highly abundant proteins, enrichment of desired proteins, or the development of new antibodies. The current study exploited the steric hindrance of glycan units in N-linked glycoproteins, which significantly affects the efficiency of proteolytic digestion if an enzymatically active amino acid is adjacent to the N-linked glycosylation site. Proteolytic digestion then results in quantitatively different peptide products in accordance with the degree of glycosylation. The effect of glycan steric hindrance on tryptic digestion was first demonstrated using alpha-1-acid glycoprotein (AGP) as a model compound versus deglycosylated alpha-1-acid glycoprotein. Second, nonglycosylated tryptic peptide biomarkers, which generally show much higher sensitivity in mass spectrometric analyses than their glycosylated counterparts, were quantified in human hepatocellular carcinoma plasma using a label-free method with no need for N-linked glycoprotein enrichment. Finally, the method was validated using a multiple reaction monitoring analysis, demonstrating that the newly discovered nonglycosylated tryptic peptide targets were present at different levels in normal and hepatocellular carcinoma plasmas. The area under the receiver operating characteristic curve generated through analyses of nonglycosylated tryptic peptide from vitronectin precursor protein was 0.978, the highest observed in a group of patients with hepatocellular carcinoma. This work provides a targeted means of discovering and validating nonglycosylated tryptic peptides as biomarkers in human plasma, without the need for complex enrichment processes or expensive antibody preparations.