A Knowledge-Based System for Display and Prediction of O-Glycosylation Network Behaviour in Response to Enzyme Knockouts

In silico simulation of glycosylation and related pathways

Glycans participate in a vast number of recognition systems in diverse organisms in health and in disease. However, glycans cannot be sequenced because there is no sequencer technology that can fully characterize them. There is no "template" for replicating glycans as there are for amino acids and nucleic acids. Instead, glycans are synthesized by a complicated orchestration of multitudes of glycosyltransferases and glycosidases. Thus glycans can vary greatly in structure, but they are not genetically reproducible and are usually isolated in minute amounts. To characterize (sequence) the glycome (defined as the glycans in a particular organism, tissue, cell, or protein), glycosylation pathway prediction using in silico methods based on glycogene expression data, and glycosylation simulations have been attempted. Since many of the mammalian glycogenes have been identified and cloned, it has become possible to predict the glycan biosynthesis pathway in these systems. By then incorporating systems biology and bioprocessing technologies to these pathway models, given the right enzymatic parameters including enzyme and substrate concentrations and kinetic reaction parameters, it is possible to predict the potentially synthesized glycans in the pathway. This review presents information on the data resources that are currently available to enable in silico simulations of glycosylation and related pathways. Then some of the software tools that have been developed in the past to simulate and analyze glycosylation pathways will be described, followed by a summary and vision for the future developments and research directions in this area.

Simulated digestions of free oligosaccharides and mucin-type O-glycans reveal a potential role for Clostridium perfringens

The development of a stable human gut microbiota occurs within the first year of life. Many open questions remain about how microfloral species are influenced by the composition of milk, in particular its content of human milk oligosaccharides (HMOs). The objective is to investigate the effect of the human HMO glycome on bacterial symbiosis and competition, based on the glycoside hydrolase (GH) enzyme activities known to be present in microbial species. We extracted from UniProt a list of all bacterial species catalysing glycoside hydrolase activities (EC 3.2.1.-), cross-referencing with the BRENDA database, and obtained a set of taxonomic lineages and CAZy family data. A set of 13 documented enzyme activities was selected and modelled within an enzyme simulator according to a method described previously in the context of biosynthesis. A diverse population of experimentally observed HMOs was fed to the simulator, and the enzymes matching specific bacterial species were recorded, based on their appearance of individual enzymes in the UniProt dataset. Pairs of bacterial species were identified that possessed complementary enzyme profiles enabling the digestion of the HMO glycome, from which potential symbioses could be inferred. Conversely, bacterial species having similar GH enzyme profiles were considered likely to be in competition for the same set of dietary HMOs within the gut of the newborn. We generated a set of putative biodegradative networks from the simulator output, which provides a visualisation of the ability of organisms to digest HMO and mucin-type O-glycans. B. bifidum, B. longum and C. perfringens species were predicted to have the most diverse GH activity and therefore to excel in their ability to digest these substrates. The expected cooperative role of Bifidobacteriales contrasts with the surprising capacities of the pathogen. These findings indicate that potential pathogens may associate in human gut based on their shared glycoside hydrolase digestive apparatus, and which, in the event of colonisation, might result in dysbiosis. The methods described can readily be adapted to other enzyme categories and species as well as being easily fine-tuneable if new degrading enzymes are identified and require inclusion in the model.

Mammalian milk glycomes: Connecting the dots between evolutionary conservation and biosynthetic pathways

Milk oligosaccharides (MOs) are among the most abundant constituents of breast milk and are essential for health and development. Biosynthesized from monosaccharides into complex sequences, MOs differ considerably between taxonomic groups. Even human MO biosynthesis is insufficiently understood, hampering evolutionary and functional analyses. Using a comprehensive resource of all published MOs from >100 mammals, we develop a pipeline for generating and analyzing MO biosynthetic networks. We then use evolutionary relationships and inferred intermediates of these networks to discover (1) systematic glycome biases, (2) biosynthetic restrictions, such as reaction path preference, and (3) conserved biosynthetic modules. This allows us to prune and pinpoint biosynthetic pathways despite missing information. Machine learning and network analysis cluster species by their milk glycome, identifying characteristic sequence relationships and evolutionary gains/losses of motifs, MOs, and biosynthetic modules. These resources and analyses will advance our understanding of glycan biosynthesis and the evolution of breast milk.

In silico analysis of the human milk oligosaccharide glycome reveals key enzymes of their biosynthesis

Human milk oligosaccharides (HMOs) form the third most abundant component of human milk and are known to convey several benefits to the neonate, including protection from viral and bacterial pathogens, training of the immune system, and influencing the gut microbiome. As HMO production during lactation is driven by enzymes that are common to other glycosylation processes, we adapted a model of mucin-type GalNAc-linked glycosylation enzymes to act on free lactose. We identified a subset of 11 enzyme activities that can account for 206 of 226 distinct HMOs isolated from human milk and constructed a biosynthetic reaction network that identifies 5 new core HMO structures. A comparison of monosaccharide compositions demonstrated that the model was able to discriminate between two possible groups of intermediates between major subnetworks, and to assign possible structures to several previously uncharacterised HMOs. The effect of enzyme knockouts is presented, identifying β-1,4-galactosyltransferase and β-1,3-N-acetylglucosaminyltransferase as key enzyme activities involved in the generation of the observed HMO glycosylation patterns. The model also provides a synthesis chassis for the most common HMOs found in lactating mothers.

Applying transcriptomics to studyglycosylation at the cell type level

The complex multi-step process of glycosylation occurs in a single cell, yet current analytics generally cannot measure the output (the glycome) of a single cell. Here, we addressed this discordance by investigating how single cell RNA-seq data can be used to characterize the state of the glycosylation machinery and metabolic network in a single cell. The metabolic network involves 214 glycosylation and modification enzymes outlined in our previously built atlas of cellular glycosylation pathways. We studied differential mRNA regulation of enzymes at the organ and single cell level, finding that most of the general protein and lipid oligosaccharide scaffolds are produced by enzymes exhibiting limited transcriptional regulation among cells. We predict key enzymes within different glycosylation pathways to be highly transcriptionally regulated as regulatable hotspots of the cellular glycome. We designed the Glycopacity software that enables investigators to extract and interpret glycosylation information from transcriptome data and define hotspots of regulation.

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RNA-seq can provide information on the glycosylation metabolic network state

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It is possible to readout glycosylation capacity from single cell RNA-seq data

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Genes regulating the biosynthesis of common glycan scaffolds show little regulation

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Key enzymes in the glycosylation network are predicted to be regulatable hotspots

Elucidating Human Milk Oligosaccharide biosynthetic genes through network-based multi-omics integration

Human Milk Oligosaccharides (HMOs) are abundant carbohydrates fundamental to infant health and development. Although these oligosaccharides were discovered more than half a century ago, their biosynthesis in the mammary gland remains largely uncharacterized. Here, we use a systems biology framework that integrates glycan and RNA expression data to construct an HMO biosynthetic network and predict glycosyltransferases involved. To accomplish this, we construct models describing the most likely pathways for the synthesis of the oligosaccharides accounting for >95% of the HMO content in human milk. Through our models, we propose candidate genes for elongation, branching, fucosylation, and sialylation of HMOs. Our model aggregation approach recovers 2 of 2 previously known gene-enzyme relations and 2 of 3 empirically confirmed gene-enzyme relations. The top genes we propose for the remaining 5 linkage reactions are consistent with previously published literature. These results provide the molecular basis of HMO biosynthesis necessary to guide progress in HMO research and application with the goal of understanding and improving infant health and development.

Human milk oligosaccharides are fundamental to infant health. Here the authors deploy a multi-omics systems biology approach to elucidate their biosynthetic network, including the associated enzymes and likely structures of ambiguous oligosaccharides.

Computational Modeling of O-Linked Glycan Biosynthesis in CHO Cells

Glycan biosynthesis simulation research has progressed remarkably since 1997, when the first mathematical model for N-glycan biosynthesis was proposed. An O-glycan model has also been developed to predict O-glycan biosynthesis pathways in both forward and reverse directions. In this work, we started with a set of O-glycan profiles of CHO cells transiently transfected with various combinations of glycosyltransferases. The aim was to develop a model that encapsulated all the enzymes in the CHO transfected cell lines. Due to computational power restrictions, we were forced to focus on a smaller set of glycan profiles, where we were able to propose an optimized set of kinetics parameters for each enzyme in the model. Using this optimized model we showed that the abundance of more processed glycans could be simulated compared to observed abundance, while predicting the abundance of glycans earlier in the pathway was less accurate. The data generated show that for the accurate prediction of O-linked glycosylation, additional factors need to be incorporated into the model to better reflect the experimental conditions.

An Interactive View of Glycosylation

The present chapter focuses on the interactive and explorative aspects of bioinformatics resources that have been recently released in glycobiology. The comparative analysis of data in a field where knowledge is scattered, incomplete, and disconnected from main biology requires efficient visualization, integration, and interactive tools that are currently only partially implemented. This overview highlights converging efforts toward building a consistent picture of protein glycosylation.

O-Glycologue: A Formal-Language-Based Generator of O-Glycosylation Networks

The web application O-Glycologue provides an online simulation of the biosynthetic enzymes of O-linked glycosylation, using a knowledge-based system described previously. Glycans can be imported in GlycoCT condensed format, or else as IUPAC condensed names, and passed as substrates to the enzymes, which are modeled as regular-expression-based substitutions on strings. The resulting networks of reactions can be exported as SBML. The effects of knocking out different sets of enzyme activities can be compared. A method is provided for predicting the enzymes required to produce a given substrate, using an O-glycan from human gastric mucin as an example. The system has been adapted to other systems of glycosylation enzymes, and an application to ganglioside oligosaccharide synthesis is demonstrated. O-Glycologue is available at https://glycologue.org/o/ .

Comparative Glycomics Analysis of Mass Spectrometry Data

Glycan profiling is a common strategy that is used to determine the distribution of N-linked glycans, O-linked glycans and glycolipid associated complex carbohydrate structures that are part of various cell and tissue sources. Such data are central to our understanding of functional glycomics, and this knowledge can also be used for pathway construction and other applications in the field of Systems Glycobiology. Glycans released from cell/tissue samples are often studied in their free-form. They can also be functionalized with aglycones like 2-aminobenzamide (2AB) and procainamide to enhance separation and improve ionization during liquid chromatography/mass spectrometry. Additionally, these released glycans may be permethylated in order to improve glycan quantitation. In such work, besides studying the glycans in a single sample, there is also interest in comparing multiple samples in order to determine underlying similarities and differences, for example in terms of specific epitopes that are changed when cells of the same origin differentiate along different pathways. The current chapter describes the development and usage of cGlyco ("comparative Glycomics"), an open-source program that can be used to compare data from multiple mass spectrometry runs. As an example, we apply cGlyco to compare the glycan profile of multiple MALDI-TOF glycomics profiling data collected by core-C of the Consortium for Functional Glycomics (CFG).

ANALYSIS OF CARBOHYDRATES AND GLYCOCONJUGATES BY MATRIX-ASSISTED LASER DESORPTION/IONIZATION MASS SPECTROMETRY: AN UPDATE FOR 2015-2016

This review is the ninth update of the original article published in 1999 on the application of matrix-assisted laser desorption/ionization (MALDI) mass spectrometry to the analysis of carbohydrates and glycoconjugates and brings coverage of the literature to the end of 2016. Also included are papers that describe methods appropriate to analysis by MALDI, such as sample preparation techniques, even though the ionization method is not MALDI. Topics covered in the first part of the review include general aspects such as theory of the MALDI process, matrices, derivatization, MALDI imaging, fragmentation and arrays. The second part of the review is devoted to applications to various structural types such as oligo- and poly-saccharides, glycoproteins, glycolipids, glycosides and biopharmaceuticals. Much of this material is presented in tabular form. The third part of the review covers medical and industrial applications of the technique, studies of enzyme reactions and applications to chemical synthesis. The reported work shows increasing use of combined new techniques such as ion mobility and the enormous impact that MALDI imaging is having. MALDI, although invented over 30 years ago is still an ideal technique for carbohydrate analysis and advancements in the technique and range of applications show no sign of deminishing. © 2020 Wiley Periodicals, Inc.

Big-Data Glycomics: Tools to Connect Glycan Biosynthesis to Extracellular Communication

Characteristically, cells must sense and respond to environmental cues. Despite the importance of cell-cell communication, our understanding remains limited and often lacks glycans. Glycans decorate proteins and cell membranes at the cell-environment interface, and modulate intercellular communication, from development to pathogenesis. Providing further challenges, glycan biosynthesis and cellular behavior are co-regulating systems. Here, we discuss how glycosylation contributes to extracellular responses and signaling. We further organize approaches for disentangling the roles of glycans in multicellular interactions using newly available datasets and tools, including glycan biosynthesis models, omics datasets, and systems-level analyses. Thus, emerging tools in big data analytics and systems biology are facilitating novel insights on glycans and their relationship with multicellular behavior.

Simulating the enzymes of ganglioside biosynthesis with Glycologue

Gangliosides are an important class of sialylated glycosphingolipids linked to ceramide that are a component of the mammalian cell surface, especially those of the central nervous system, where they function in intercellular recognition and communication. We describe an in silico method for determining the metabolic pathways leading to the most common gangliosides, based on the known enzymes of their biosynthesis. A network of 41 glycolipids is produced by the actions of the 10 enzymes included in the model. The different ganglioside nomenclature systems in common use are compared and a systematic variant of the widely used Svennerholm nomenclature is described. Knockouts of specific enzyme activities are used to simulate congenital defects in ganglioside biosynthesis, and altered ganglioside status in cancer, and the effects on network structure are predicted. The simulator is available at the Glycologue website, https://glycologue.org/.

A consensus-based and readable extension of Linear Code for Reaction Rules (LiCoRR)

Systems glycobiology aims to provide models and analysis tools that account for the biosynthesis, regulation, and interactions with glycoconjugates. To facilitate these methods, there is a need for a clear glycan representation accessible to both computers and humans. Linear Code, a linearized and readily parsable glycan structure representation, is such a language. For this reason, Linear Code was adapted to represent reaction rules, but the syntax has drifted from its original description to accommodate new and originally unforeseen challenges. Here, we delineate the consensuses and inconsistencies that have arisen through this adaptation. We recommend options for a consensus-based extension of Linear Code that can be used for reaction rule specification going forward. Through this extension and specification of Linear Code to reaction rules, we aim to minimize inconsistent symbology thereby making glycan database queries easier. With a clear guide for generating reaction rule descriptions, glycan synthesis models will be more interoperable and reproducible thereby moving glycoinformatics closer to compliance with FAIR standards. Here, we present Linear Code for Reaction Rules (LiCoRR), version 1.0, an unambiguous representation for describing glycosylation reactions in both literature and code.

Open Database Searching Enables the Identification and Comparison of Bacterial Glycoproteomes without Defining Glycan Compositions Prior to Searching

Mass spectrometry has become an indispensable tool for the characterization of glycosylation across biological systems. Our ability to generate rich fragmentation of glycopeptides has dramatically improved over the last decade yet our informatic approaches still lag behind. Although glycoproteomic informatics approaches using glycan databases have attracted considerable attention, database independent approaches have not. This has significantly limited high throughput studies of unusual or atypical glycosylation events such as those observed in bacteria. As such, computational approaches to examine bacterial glycosylation and identify chemically diverse glycans are desperately needed. Here we describe the use of wide-tolerance (up to 2000 Da) open searching as a means to rapidly examine bacterial glycoproteomes. We benchmarked this approach using N-linked glycopeptides of Campylobacter fetus subsp. fetus as well as O-linked glycopeptides of Acinetobacter baumannii and Burkholderia cenocepacia revealing glycopeptides modified with a range of glycans can be readily identified without defining the glycan masses before database searching. Using this approach, we demonstrate how wide tolerance searching can be used to compare glycan use across bacterial species by examining the glycoproteomes of eight Burkholderia species (B. pseudomallei; B. multivorans; B. dolosa; B. humptydooensis; B. ubonensis, B. anthina; B. diffusa; B. pseudomultivorans). Finally, we demonstrate how open searching enables the identification of low frequency glycoforms based on shared modified peptides sequences. Combined, these results show that open searching is a robust computational approach for the determination of glycan diversity within bacterial proteomes.

Open Database Searching Enables the Identification and Comparison of Bacterial Glycoproteomes without Defining Glycan Compositions Prior to Searching

Mass spectrometry has become an indispensable tool for the characterization of glycosylation across biological systems. Our ability to generate rich fragmentation of glycopeptides has dramatically improved over the last decade yet our informatic approaches still lag behind. Although glycoproteomic informatics approaches using glycan databases have attracted considerable attention, database independent approaches have not. This has significantly limited high throughput studies of unusual or atypical glycosylation events such as those observed in bacteria. As such, computational approaches to examine bacterial glycosylation and identify chemically diverse glycans are desperately needed. Here we describe the use of wide-tolerance (up to 2000 Da) open searching as a means to rapidly examine bacterial glycoproteomes. We benchmarked this approach using N-linked glycopeptides of Campylobacter fetus subsp. fetus as well as O-linked glycopeptides of Acinetobacter baumannii and Burkholderia cenocepacia revealing glycopeptides modified with a range of glycans can be readily identified without defining the glycan masses before database searching. Using this approach, we demonstrate how wide tolerance searching can be used to compare glycan use across bacterial species by examining the glycoproteomes of eight Burkholderia species (B. pseudomallei; B. multivorans; B. dolosa; B. humptydooensis; B. ubonensis, B. anthina; B. diffusa; B. pseudomultivorans). Finally, we demonstrate how open searching enables the identification of low frequency glycoforms based on shared modified peptides sequences. Combined, these results show that open searching is a robust computational approach for the determination of glycan diversity within bacterial proteomes.

Metabolic engineering challenges of extending N-glycan pathways in Chinese hamster ovary cells

In mammalian cells, N-glycans may include multiple N-acetyllactosamine (poly-LacNAc) units that can play roles in various cellular functions and properties of therapeutic recombinant proteins. Previous studies indicated that β-1,3-N-acetylglucosaminyltransferase 2 (B3GNT2) and β-1,4-galactotransferase 1 (B4GALT1) are two of the primary glycosyltransferases involved in generating LacNAc units. In the current study, knocking out sialyltransferase genes slightly enhanced the LacNAc content (≥4 repeats per glycan) on recombinant EPO protein. Next, the role of single and dual-overexpression of B3GNT2 and B4GALT1 was explored in recombinant EPO-expressing Chinese hamster ovary (CHO) cells. While overexpression of B4GALT1 slightly enhanced the levels of large glycans on recombinant EPO, overexpression of B3GNT2 in EPO-expressing CHO cells significantly decreased the recombinant EPO LacNAc content, resulting in N-glycans terminating primarily with GlcNAc structures, a limited number of Gals, and nearly undetectable sialylation, which was also observed in sialyltransferases knock-out-B3GNT2 overexpression cell lines. Considering the nature of the binding domain motifs present on B3GNT2, which evolved from β1,3-galactosyltransferases, its overexpression may have competed and inhibited endogenous β1,4-galactosyltransferases for exposed GlcNAc residues on the N-glycans, resulting in premature termination of many N-glycans at GlcNAc. Furthermore, B3GNT2 overexpression enhanced intracellular UDP-GlcNAc and CMP-Neu5Ac content while slightly lowering UDP-Gal content. The presence of a sink for UDP-GlcNAc in the form of B3GNT2 with no disposition may have also elevated the intracellular levels of this nucleotide as well as its downstream product, CMP-Neu5Ac. Furthermore, we were unable to overexpress B4GALT1 at either the transcriptional or translational levels following initial B3GNT2 expression. Expression of B3GNT2 following initial expression of B4GALT1 was also problematic in that transcriptional and translational analysis indicated the accumulation of truncated B3GNT2 missing a section of the B3GNT2 trans-Golgi lumen domain while transmembrane and cytoplasmic domains were present. Given that glycosylation is a very complex intra-network process, the addition of one or more recombinant glycosyltransferases may have an unexpected influence on the expression and activities of glycosyltransferases, which can disrupt the nucleotide sugar levels and lead to unexpected modifications of the resulting N-glycan patterns.

A Markov model of glycosylation elucidates isozyme specificity and glycosyltransferase interactions for glycoengineering

Glycosylated biopharmaceuticals are important in the global pharmaceutical market. Despite the importance of their glycan structures, our limited knowledge of the glycosylation machinery still hinders controllability of this critical quality attribute. To facilitate discovery of glycosyltransferase specificity and predict glycoengineering efforts, here we extend the approach to model N-linked protein glycosylation as a Markov process. Our model leverages putative glycosyltransferase (GT) specificity to define the biosynthetic pathways for all measured glycans, and the Markov chain modelling is used to learn glycosyltransferase isoform activities and predict glycosylation following glycosyltransferase knock-in/knockout. We apply our methodology to four different glycoengineered therapeutics (i.e., Rituximab, erythropoietin, Enbrel, and alpha-1 antitrypsin) produced in CHO cells. Our model accurately predicted N-linked glycosylation following glycoengineering and further quantified the impact of glycosyltransferase mutations on reactions catalyzed by other glycosyltransferases. By applying these learned GT-GT interaction rules identified from single glycosyltransferase mutants, our model further predicts the outcome of multi-gene glycosyltransferase mutations on the diverse biotherapeutics. Thus, this modeling approach enables rational glycoengineering and the elucidation of relationships between glycosyltransferases, thereby facilitating biopharmaceutical research and aiding the broader study of glycosylation to elucidate the genetic basis of complex changes in glycosylation.

A mechanism for bistability in glycosylation

Glycosyltransferases are a class of enzymes that catalyse the posttranslational modification of proteins to produce a large number of glycoconjugate acceptors from a limited number of nucleotide-sugar donors. The products of one glycosyltransferase can be the substrates of several other enzymes, causing a combinatorial explosion in the number of possible glycan products. The kinetic behaviour of systems where multiple acceptor substrates compete for a single enzyme is presented, and the case in which high concentrations of an acceptor substrate are inhibitory as a result of abortive complex formation, is shown to result in non-Michaelian kinetics that can lead to bistability in an open system. A kinetic mechanism is proposed that is consistent with the available experimental evidence and provides a possible explanation for conflicting observations on the β-1,4-galactosyltransferases. Abrupt switching between steady states in networks of glycosyltransferase-catalysed reactions may account for the observed changes in glycosyl-epitopes in cancer cells.

Fc gamma receptors: glycobiology and therapeutic prospects

Therapeutic antibodies hold great promise for the treatment of cancer and autoimmune diseases, and developments in antibody–drug conjugates and bispecific antibodies continue to enhance treatment options for patients. Immunoglobulin (Ig) G antibodies are proteins with complex modifications, which have a significant impact on their function. The most important of these modifications is glycosylation, the addition of conserved glycans to the antibody Fc region, which is critical for its interaction with the immune system and induction of effector activities such as antibody-dependent cell cytotoxicity, complement activation and phagocytosis. Communication of IgG antibodies with the immune system is controlled and mediated by Fc gamma receptors (FcγRs), membrane-bound proteins, which relay the information sensed and gathered by antibodies to the immune system. These receptors are also glycoproteins and provide a link between the innate and adaptive immune systems. Recent information suggests that this receptor glycan modification is also important for the interaction with antibodies and downstream immune response. In this study, the current knowledge on FcγR glycosylation is discussed, and some insight into its role and influence on the interaction properties with IgG, particularly in the context of biotherapeutics, is provided. For the purpose of this study, other Fc receptors such as FcαR, FcεR or FcRn are not discussed extensively, as IgG-based antibodies are currently the only therapeutic antibody-based products on the market. In addition, FcγRs as therapeutics and therapeutic targets are discussed, and insight into and comment on the therapeutic aspects of receptor glycosylation are provided.

Multi-level regulation of cellular glycosylation: from genes to transcript to enzyme to structure

Glycosylation is a ubiquitous mammalian post-translational modification that both decorates a majority of expressed proteins and regulates their function. Cellular glycan biosynthesis is facilitated by a few hundred enzymes that are collectively termed 'glycoenzymes'. The expression and activity of these enzymes is controlled at the transcription, translation and post-translation levels. New wet-lab advances are providing analytical methods to collect large-scale data at these multiple levels, relational databases are starting to collate these results, and computer models are beginning to integrate this information across scales in order to gain new knowledge. These activities are likely to enable the qualitative and quantitative mapping of pathways regulating glycan production and function in proteins, cells and tissue.

Comprehensive analysis of the N-glycan biosynthetic pathway using bioinformatics to generate UniCorn: A theoretical N-glycan structure database

Glycan structures attached to proteins are comprised of diverse monosaccharide sequences and linkages that are produced from precursor nucleotide-sugars by a series of glycosyltransferases. Databases of these structures are an essential resource for the interpretation of analytical data and the development of bioinformatics tools. However, with no template to predict what structures are possible the human glycan structure databases are incomplete and rely heavily on the curation of published, experimentally determined, glycan structure data. In this work, a library of 45 human glycosyltransferases was used to generate a theoretical database of N-glycan structures comprised of 15 or less monosaccharide residues. Enzyme specificities were sourced from major online databases including Kyoto Encyclopedia of Genes and Genomes (KEGG) Glycan, Consortium for Functional Glycomics (CFG), Carbohydrate-Active enZymes (CAZy), GlycoGene DataBase (GGDB) and BRENDA. Based on the known activities, more than 1.1 million theoretical structures and 4.7 million synthetic reactions were generated and stored in our database called UniCorn. Furthermore, we analyzed the differences between the predicted glycan structures in UniCorn and those contained in UniCarbKB (www.unicarbkb.org), a database which stores experimentally described glycan structures reported in the literature, and demonstrate that UniCorn can be used to aid in the assignment of ambiguous structures whilst also serving as a discovery database.