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Glycan-to-glycan binding was shown by biochemical and biophysical measurements to mediate xenogeneic self-recognition and adhesion in sponges, stage-specific cell compaction in mice embryos, and in vitro tumor cell adhesion in mammals. This intermolecular recognition process is accepted as the new paradigm accompanying high-affinity and low valent protein-to-protein and protein-to-glycan binding in cellular interactions. Glycan structures in sponges have novel species-specific sequences. Their common features are the large size >100 kD, polyvalency >100 repeats of the specific self-binding oligosaccharide, the presence of fucose, and sulfated and/or pyruvylated hexoses. These structural and functional properties, different from glycosaminoglycans, inspired their classification under the glyconectin name. The molecular mechanism underlying homophilic glyconectin-to-glyconectin binding relies on highly polyvalent, strong, and structure-specific interactions of small oligosaccharide motifs, possessing ultra-weak self-binding strength and affinity. Glyconectin localization at the glycocalyx outermost cell surface layer suggests their role in the initial recognition and adhesion event during the complex and multistep process. In mammals, Lex-to-Lex homophilic binding is structure-specific and has ultra-weak affinity. Cell adhesion is achieved through highly polyvalent interactions, enabled by clustering of small low valent structure in plasma membranes.

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

The level of ambiguity in describing glycan structure has significantly increased with the upsurge of large-scale glycomics and glycoproteomics experiments. Consequently, an ontology-based model appears as an appropriate solution for navigating these data. However, navigation is not sufficient and the model should also enable advanced search and comparison. A new ontology with a tree logical structure is introduced to represent glycan structures irrespective of the precision of molecular details. The model heavily relies on the GlycoCT encoding of glycan structures. Its implementation in the GlySTreeM knowledge base was validated with GlyConnect data and benchmarked with the Glycowork library. GlySTreeM is shown to be fast, consistent, reliable and more flexible than existing solutions for matching parts of or whole glycan structures. The model is also well suited for painless future expansion.

Protein glycosylation is of great importance in many biological processes. Glycosylation has been increasingly analyzed at the intact glycopeptide level using mass spectrometry to study site-specific glycosylation changes under different physiological and pathological conditions. StrucGP is a glycan database-independent search engine for the structural interpretation of -glycoproteins at the site-specific level. To ensure the accuracy of results, two collision energies are implemented in instrument settings for each precursor to separate fragments of peptides and glycans. In addition, the false discovery rates (FDR) of peptides and glycans as well as probabilities of detailed structures are estimated. In this protocol, the use of StrucGP is demonstrated, including environment configuration, data preprocessing as well as result inspection and visualization using our in-house software \"GlycoVisualTool\". The described workflow should be able to be performed by anyone with basic proteomic knowledge.

Molluscs display a sophisticated N-glycan pattern on their proteins, which is, in terms of involved structural features, even more diverse than that of vertebrates. This review summarises the current knowledge of mollusc N-glycan structures, with a focus on the functional aspects of the corresponding glycoproteins. Furthermore, the potential of mollusc-derived biomolecules for medical applications is addressed, emphasising the importance of mollusc research.

O-linked α-N-acetylgalactosamine (α-GalNAc) in the Gc protein is essential for macrophage activation; thus, the GalNAc-attached form of Gc protein is called Gc macrophage activating factor (GcMAF). O-linked glycans in Gc proteins from human plasma mainly consist of trisaccharides. GcMAF is produced when glycans on the Gc protein are hydrolyzed by α-Sia-ase and β-Gal-ase, leaving an α-GalNAc. Upon hydrolysis of α-GalNAc present on GcMAF, the protein loses the macrophage-activating effect. In contrast, our synthesized pyrrolidine-type iminocyclitol possessed strong in vitro α-GalNAc-ase inhibitory activity. In this study, we examined the protective effects of iminocyclitol against GcMAF via inhibition of α-GalNAc-ase activity. Detailed mass spectrometric analyses revealed the protective effect of the inhibitor on GcMAF. Furthermore, structural information regarding the glycosylation site and glycan structure was obtained using tandem mass spectrometric (MS/MS) analysis of the glycosylated peptides after tryptic digestion.

In life science fields, database integration is progressing and contributing to collaboration between different research fields, including the glycosciences. The integration of glycan databases has greatly progressed collaboration worldwide with the development of the international glycan structure repository, GlyTouCan. This trend has increased the need for a tool by which researchers in various fields can easily search glycan structures from integrated databases. We have developed a web-based glycan structure search tool, SugarDrawer, which supports the depiction of glycans including ambiguity, such as glycan fragments which contain underdetermined linkages, and a database search for glycans drawn on the canvas. This tool provides an easy editing feature for various glycan structures in just a few steps using template structures and pop-up windows which allow users to select specific information for each structure element. This tool has a unique feature for selecting possible attachment sites, which is defined in the Symbol Nomenclature for Glycans (SNFG). In addition, this tool can input and output glycans in WURCS and GlycoCT formats, which are the most commonly-used text formats for glycan structures.

The use of liquid chromatography-electrospray ionization-tandem mass spectrometry (LC-ESI-MS n ) for the glycoproteomic characterization of glycopeptides is a growing field of research. The N- and O-glycosylated peptides (N- and O-glycopeptides) analyzed typically originate from protease-digested glycoproteins where many of them are expected to be biomedically important. Examples of LC-MS 2 and MS 3 fragmentation strategies used to pursue glycan structure, peptide identity and attachment-site identification analyses of glycopeptides are described in this review. MS 2 spectra, using the CID and HCD fragmentation techniques of a complex biantennary N-glycopeptide and a core 1 O-glycopeptide, representing two examples of commonly studied glycopeptide types, are presented. A few practical tips for accomplishing glycopeptide analysis using reversed-phase LC-MS n shotgun proteomics settings, together with references to the latest glycoproteomic studies, are presented.

Human CD52 is a small glycopeptide (12 amino acid residues) with one linked glycosylation site at asparagine 3 (Asn3) and several potential glycosylation serine/threonine sites. Soluble CD52 is released from the surface of activated T cells and mediates immune suppression via its glycan moiety. In suppressing activated T cells, it first sequesters the pro-inflammatory high mobility group Box 1 (HMGB1) protein, which facilitates its binding to the inhibitory sialic acid-binding immunoglobulin-like lectin-10 (Siglec-10) receptor. We aimed to identify the features of CD52 glycan that underlie its bioactivity. Analysis of native CD52 purified from human spleen revealed extensive heterogeneity in glycosylation and multi-antennary sialylated glycans with abundant polyLacNAc extensions, together with mainly di-sialylated glycosylation type structures. Glycomic (porous graphitized carbon-ESI-MS/MS) and glycopeptide (C8-LC-ESI-MS) analysis of recombinant soluble human CD52-immunoglobulin Fc fusion proteins revealed that CD52 bioactivity was correlated with a high abundance of tetra-antennary α-2,3/6 sialylated glycans. Removal of α-2,3 sialylation abolished bioactivity, which was restored by re-sialylation with α-2,3 sialyltransferases. When glycoforms of CD52-Fc were fractionated by anion exchange MonoQ-GL chromatography, bioactive fractions displayed mainly tetra-antennary, α-2,3 sialylated glycan structures and a lower relative abundance of bisecting GlcNAc structures compared to non-bioactive fractions. In addition, glycan core type-2 di-sialylated structures at Ser12 were more abundant in bioactive CD52 fractions. Understanding the structural features of CD52 glycan required for its bioactivity will aid its development as an immunotherapeutic agent.

Chloroviruses are large double-stranded DNA (dsDNA) viruses that infect certain isolates of chlorella-like green algae. They contain up to approximately 400 protein-encoding genes and 16 transfer RNA (tRNA) genes. This review summarizes the unexpected finding that many of the chlorovirus genes encode proteins involved in manipulating carbohydrates. These include enzymes involved in making extracellular polysaccharides, such as hyaluronan and chitin, enzymes that make nucleotide sugars, such as GDP-L-fucose and GDP-D-rhamnose and enzymes involved in the synthesis of glycans attached to the virus major capsid proteins. This latter process differs from that of all other glycoprotein containing viruses that traditionally use the host endoplasmic reticulum and Golgi machinery to synthesize and transfer the glycans.