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Proteomics researchers today face an interesting challenge: how to choose among the dozens of data processing and analysis pipelines available for converting tandem mass spectrometry files to protein identifications. Due to the dominance of Orbitrap technology in proteomics in recent history, many researchers have defaulted to the vendor software Proteome Discoverer. Over the fourteen years since the initial release of the software, it has evolved in parallel with the increasingly complex demands faced by proteomics researchers. Today, Proteome Discoverer exists in two distinct forms with both powerful commercial versions and fully functional free versions in use in many labs today. Throughout the 11 main versions released to date, a central theme of the software has always been the ability to easily view and verify the spectra from which identifications are made. This ability is, even today, a key differentiator from other data analysis solutions. In this review I will attempt to summarize the history and evolution of Proteome Discoverer from its first launch to the versions in use today.

The lack of efficient biomarkers for the early detection of gastric cancer (GC) contributes to its high mortality rate, so it is crucial to discover novel diagnostic targets for GC. Recent studies have implicated the potential of site‐specific glycans in cancer diagnosis, yet it is challenging to perform highly reproducible and sensitive glycoproteomics analysis on large cohorts of samples. Here, a highly robust N‐glycoproteomics (HRN) platform comprising an automated enrichment method, a stable microflow LC‐MS/MS system, and a sensitive glycopeptide‐spectra‐deciphering tool is developed for large‐scale quantitative N‐glycoproteome analysis. The HRN platform is applied to analyze serum N‐glycoproteomes of 278 subjects from three cohorts to investigate glycosylation changes of GC. It identifies over 20 000 unique site‐specific glycans from discovery and validation cohorts, and determines four site‐specific glycans as biomarker candidates. One candidate has branched tetra‐antennary structure capping with sialyl‐Lewis antigen, and it significantly outperforms serum CEA with AUC values > 0.89 compared against < 0.67 for diagnosing early‐stage GC. The four‐marker panel can provide improved diagnostic performances. Besides, discrimination powers of four candidates are also testified with a verification cohort using PRM strategy. This findings highlight the value of this strong tool in analyzing aberrant site‐specific glycans for cancer detection. Aberrant glycosylation is recognized as a hallmark of cancers; however, no site‐specific glycan biomarker is available for clinical use. In this study, a highly robust N‐glycoproteomics (HRN) platform with remarkable stability is developed for biomarker discovery. A branched site‐specific glycan capping with sialyl‐Lewis antigen has been found to be a promising biomarker for the early detection of gastric cancer.

Being a widely occurring protein post-translational modification, N-glycosylation features unique multi-dimensional structures including sequence and linkage isomers. There have been successful bioinformatics efforts in N-glycan structure identification using N-glycoproteomics data; however, symmetric “mirror” branch isomers and linkage isomers are largely unresolved. Here, we report deep structure-level N-glycan identification using feature-induced structure diagnosis (FISD) integrated with a deep learning model. A neural network model is integrated to conduct the identification of featured N-glycan motifs and boosts the process of structure diagnosis and distinction for linkage isomers. By adopting publicly available N-glycoproteomics datasets of five mouse tissues (17,136 intact N-glycopeptide spectrum matches) and a consideration of 23 motif features, a deep learning model integrated with a convolutional autoencoder and a multilayer perceptron was trained to be capable of predicting N-glycan featured motifs in the MS/MS spectra with previously identified compositions. In the test of the trained model, a prediction accuracy of 0.8 and AUC value of 0.95 were achieved; 5701 previously unresolved N-glycan structures were assigned by matched structure-diagnostic ions; and by using an explainable learning algorithm, two new fragmentation features of m/z  = 674.25 and m/z  = 835.28 were found to be significant to three N-glycan structure motifs with fucose, NeuAc, and NeuGc, proving the capability of FISD to discover new features in the MS/MS spectra. Graphical Abstract

Glycosylation is the most abundant and complex protein modification, and can have a profound structural and functional effect on the conjugate. The oligosaccharide fraction is recognized to be involved in multiple biological processes, and to affect proteins physical properties, and has consequentially been labeled a critical quality attribute of biopharmaceuticals. Additionally, due to recent advances in analytical methods and analysis software, glycosylation is targeted in the search for disease biomarkers for early diagnosis and patient stratification. Biofluids such as saliva, serum or plasma are of great use in this regard, as they are easily accessible and can provide relevant glycosylation information. Thus, as the assessment of protein glycosylation is becoming a major element in clinical and biopharmaceutical research, this review aims to convey the current state of knowledge on the N -glycosylation of the major plasma glycoproteins alpha-1-acid glycoprotein, alpha-1-antitrypsin, alpha-1B-glycoprotein, alpha-2-HS-glycoprotein, alpha-2-macroglobulin, antithrombin-III, apolipoprotein B-100, apolipoprotein D, apolipoprotein F, beta-2-glycoprotein 1, ceruloplasmin, fibrinogen, immunoglobulin (Ig) A, IgG, IgM, haptoglobin, hemopexin, histidine-rich glycoprotein, kininogen-1, serotransferrin, vitronectin, and zinc-alpha-2-glycoprotein. In addition, the less abundant immunoglobulins D and E are included because of their major relevance in immunology and biopharmaceutical research. Where available, the glycosylation is described in a site-specific manner. In the discussion, we put the glycosylation of individual proteins into perspective and speculate how the individual proteins may contribute to a total plasma N -glycosylation profile determined at the released glycan level.

Mucin domains are densely O-glycosylated modular protein domains that are found in a wide variety of cell surface and secreted proteins. Mucin-domain glycoproteins are known to be key players in a host of human diseases, especially cancer, wherein mucin expression and glycosylation patterns are altered. Mucin biology has been difficult to study at the molecular level, in part, because methods to manipulate and structurally characterize mucin domains are lacking. Here, we demonstrate that secreted protease of C1 esterase inhibitor (StcE), a bacterial protease from Escherichia coli, cleaves mucin domains by recognizing a discrete peptide- and glycan-based motif. We exploited StcE’s unique properties to improve sequence coverage, glycosite mapping, and glycoform analysis of recombinant human mucins by mass spectrometry. We also found that StcE digests cancer-associated mucins from cultured cells and from ascites fluid derived from patients with ovarian cancer. Finally, using StcE, we discovered that sialic acid-binding Ig-type lectin-7 (Siglec-7), a glycoimmune checkpoint receptor, selectively binds sialomucins as biological ligands, whereas the related receptor Siglec-9 does not. Mucin-selective proteolysis, as exemplified by StcE, is therefore a powerful tool for the study of mucin domain structure and function.

Glycopeptide enrichment is a crucial step in glycoproteomics for which hydrophilic interaction chromatography (HILIC) has extensively been applied due to its low bias towards different glycan types. A systematic evaluation of applicable HILIC mobile phases on glycopeptide enrichment efficiency and selectivity is, to date, however, still lacking. Here, we present a novel, simplified technique for HILIC enrichment termed “Drop-HILIC”, which was applied to systematically evaluate the mobile phase effect on ZIC-HILIC (zwitterionic type of hydrophilic interaction chromatography) glycopeptide enrichment. The four most commonly used MS compatible organic solvents were investigated: (i) acetonitrile, (ii) methanol, (iii) ethanol and (iv) isopropanol. Glycopeptide enrichment efficiencies were evaluated for each solvent system using samples of increasing complexity ranging from well-defined synthetic glycopeptides spiked into different concentrations of tryptic BSA peptides, followed by standard glycoproteins, and a complex sample derived from human (depleted and non-depleted) serum. ZIC-HILIC glycopeptide efficiency largely relied upon the used solvent. Different organic mobile phases enriched distinct glycopeptide subsets in a peptide backbone hydrophilicity-dependant manner. Acetonitrile provided the best compromise for the retention of both hydrophilic and hydrophobic glycopeptides, whereas methanol was confirmed to be unsuitable for this purpose. The enrichment efficiency of ethanol and isopropanol towards highly hydrophobic glycopeptides was compromised as considerable co-enrichment of unmodified peptides occurred, though for some hydrophobic glycopeptides isopropanol showed the best enrichment properties. This study shows that even minor differences in the peptide backbone and solvent do significantly influence HILIC glycopeptide enrichment and need to be carefully considered when employed for glycopeptide enrichment. Graphical Abstract The organic solvent plays a crucial role in ZIC-HILIC glycopeptide enrichment

Glycoproteomics is a rapidly developing field, and data analysis has been stimulated by several technological innovations. As a result, there are many software tools from which to choose; and each comes with unique features that can be difficult to compare. This work presents a head-to-head comparison of five modern analytical software: Byonic, Protein Prospector, MSFraggerGlyco, pGlyco3, and GlycoDecipher. To enable a meaningful comparison, parameter variables were minimized. One potential confounding variable is the glycan database that informs glycoproteomic searches. We performed glycomic profiling of the samples and used the output to construct matched glycan databases for each software. Up to 17,000 glycopeptide spectra were identified across three replicates of wild-type SH-SY5Y cells. There was overlap among all software for glycoproteins identified, locations of glycosites, and glycans; but there was no clear winner. Incorporation of several comparative criteria was critically important for learning the most information in this study and should be used more broadly when assessing software. A single criterion, such as number of glycopeptide spectra found, is not sufficient. We present evidence that suggests Byonic reports many spurious results at the glycoprotein and glycosite level. Overall, our results indicate that glycoproteomic searches should involve more than one software, excluding the current version of Byonic, to generate confidence by consensus. It may be useful to consider software with peptide-first approaches and with glycan-first approaches. Graphical Abstract

N-glycosylation is essential for many biological processes in mammals. A variety of N-glycan structures exist, of which, the formation of bisecting N-acetylglucosamine (GlcNAc) is catalyzed by N-acetylglucosaminyltransferase-III (GnT-III, encoded by the Mgat3 gene). We previously identified various bisecting GlcNAc-modified proteins involved in Alzheimer’s disease and cancer. However, the mechanisms by which GnT-III acts on the target proteins are unknown. Here, we performed comparative glycoproteomic analyses using brain membranes of wild type (WT) and Mgat3-deficient mice. Target glycoproteins of GnT-III were enriched with E4-phytohemagglutinin (PHA) lectin, which recognizes bisecting GlcNAc, and analyzed by liquid chromatograph-mass spectrometry. We identified 32 N-glycosylation sites (Asn-Xaa-Ser/Thr, Xaa ≠ Pro) that were modified with bisecting GlcNAc. Sequence alignment of identified N-glycosylation sites that displayed bisecting GlcNAc suggested that GnT-III does not recognize a specific primary amino acid sequence. The molecular modeling of GluA1 as one of the good cell surface substrates for GnT-III in the brain, indicated that GnT-III acts on N-glycosylation sites located in a highly flexible and mobile loop of GluA1. These results suggest that the action of GnT-III is partially affected by the tertiary structure of target proteins, which can accommodate bisecting GlcNAc that generates a bulky flipped-back conformation of the modified glycans.

Cancer early detection and treatment response prediction continue to pose significant challenges. Cancer liquid biopsies focusing on detecting circulating tumor cells (CTCs) and DNA (ctDNA) have shown enormous potential due to their non-invasive nature and the implications in precision cancer management. Recently, liquid biopsy has been further expanded to profile glycoproteins, which are the products of post-translational modifications of proteins and play key roles in both normal and pathological processes, including cancers. The advancements in chemical and mass spectrometry-based technologies and artificial intelligence-based platforms have enabled extensive studies of cancer and organ-specific changes in glycans and glycoproteins through glycomics and glycoproteomics. Glycoproteomic analysis has emerged as a promising tool for biomarker discovery and development in early detection of cancers and prediction of treatment efficacy including response to immunotherapies. These biomarkers could play a crucial role in aiding in early intervention and personalized therapy decisions. In this review, we summarize the significant advance in cancer glycoproteomic biomarker studies and the promise and challenges in integration into clinical practice to improve cancer patient care.

Cell surface glycosylation has a variety of functions, and its dysregulation in cancer contributes to impaired signaling, metastasis and the evasion of the immune responses. Recently, a number of glycosyltransferases that lead to altered glycosylation have been linked to reduced anti-tumor immune responses: B3GNT3, which is implicated in PD-L1 glycosylation in triple negative breast cancer, FUT8, through fucosylation of B7H3, and B3GNT2, which confers cancer resistance to T cell cytotoxicity. Given the increased appreciation of the relevance of protein glycosylation, there is a critical need for the development of methods that allow for an unbiased interrogation of cell surface glycosylation status. Here we provide an overview of the broad changes in glycosylation at the surface of cancer cell and describe selected examples of receptors with aberrant glycosylation leading to functional changes, with emphasis on immune checkpoint inhibitors, growth-promoting and growth-arresting receptors. Finally, we posit that the field of glycoproteomics has matured to an extent where large-scale profiling of intact glycopeptides from the cell surface is feasible and is poised for discovery of new actionable targets against cancer.