GIPS-Mix for Accurate Identification of Isomeric Components in Glycan Mixtures Using Intelligent Group-Opting Strategy.

Accurate identification of glycan structures is highly desirable as they are intimately linked to their different functions. However, glycan samples generally exist as mixtures with multiple isomeric structures, making assignment of individual glycan components very challenging, even with the aid of multistage mass spectrometry (MSn). Here, we present an approach, GIPS-mix, for assignment of isomeric glycans within a mixture using an intelligent group-opting strategy. Our approach enumerates all possible combinations (groupings) of candidate glycans and opts in the best-matched glycan group(s) based on the similarity between the simulated spectra of each glycan group and the acquired experimental spectra of the mixture. In the case that a single group could not be elected, a tie break is performed by additional MSn scanning using intelligently selected precursors. With 11 standard mixtures and 6 human milk oligosaccharide fractions, we demonstrate the application of GIPS-mix in assignment of individual glycans in mixtures with high accuracy and efficiency.

NIST Interlaboratory Study on Glycosylation Analysis of Monoclonal Antibodies: Comparison of Results from Diverse Analytical Methods.

Glycosylation is a topic of intense current interest in the development of biopharmaceuticals because it is related to drug safety and efficacy. This work describes results of an interlaboratory study on the glycosylation of the Primary Sample (PS) of NISTmAb, a monoclonal antibody reference material. Seventy-six laboratories from industry, university, research, government, and hospital sectors in Europe, North America, Asia, and Australia submitted a total of 103 reports on glycan distributions. The principal objective of this study was to report and compare results for the full range of analytical methods presently used in the glycosylation analysis of mAbs. Therefore, participation was unrestricted, with laboratories choosing their own measurement techniques. Protein glycosylation was determined in various ways, including at the level of intact mAb, protein fragments, glycopeptides, or released glycans, using a wide variety of methods for derivatization, separation, identification, and quantification. Consequently, the diversity of results was enormous, with the number of glycan compositions identified by each laboratory ranging from 4 to 48. In total, one hundred sixteen glycan compositions were reported, of which 57 compositions could be assigned consensus abundance values. These consensus medians provide community-derived values for NISTmAb PS. Agreement with the consensus medians did not depend on the specific method or laboratory type. The study provides a view of the current state-of-the-art for biologic glycosylation measurement and suggests a clear need for harmonization of glycosylation analysis methods.

Causal link between immunoglobulin G glycosylation and cancer: A potential glycobiomarker for early tumor detection.

Changes in immunoglobulin G (IgG) glycan structures are currently believed to closely related to the emergence of cancer. In this review, we summarize the current body of evidence suggesting that differences in serum IgG glycosylation patterns correspond to changes in multiple types of cancer. Modifications include IgG terminal N-link galactosylation, IgG core fucosylation, IgG terminal sialylation, and IgG terminal bisecting N-acetylglucosamine. IgG N-glycomic alterations represent promising novel biomarkers for non-invasive-cancer diagnosis, prognosis, and progression monitoring; they are characterized by high sensitivity and specificity, compensating for previously identified glycobiomarkers.

Best-first search guided multistage mass spectrometry-based glycan identification.

MOTIVATION: Glycan identification has long been hampered by complicated branching patterns and various isomeric structures of glycans. Multistage mass spectrometry (MSn) is a promising glycan identification technique as it generates multiple-level fragments of a glycan, which can be explored to deduce branching pattern of the glycan and further distinguish it from other candidates with identical mass. However, the automatic glycan identification still remains a challenge since it mainly relies on expertise to guide a MSn instrument to generate spectra. RESULTS: Here, we proposed a novel method, named bestFSA, based on a best-first search algorithm to guide the process of spectrum producing in glycan identification using MSn. BestFSA is able to select the most appropriate peaks for next round of experiments and complete the identification using as few experimental rounds. Our analysis of seven representative glycans shows that bestFSA correctly distinguishes actual glycans efficiently and suggested bestFSA could be used in practical glycan identification. The combination of the MSn technology coupled with bestFSA should greatly facilitate the automatic identification of glycan branching patterns, with significantly improved identification sensitivity, and reduce time and cost of MSn experiments. AVAILABILITY AND IMPLEMENTATION: http://glycan.ict.ac.cn. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Distribution of abnormal IgG glycosylation patterns from rheumatoid arthritis and osteoarthritis patients by MALDI-TOF-MSn.

Glycosylation is a post-translational modification essential for maintaining the structure and function of proteins. Abnormal N-glycan patterns have been found in various diseases compared to healthy controls. A decrease in terminal galactosylated N-glycans of serum IgG in rheumatoid arthritis (RA) and osteoarthritis (OA) may be involved in their immunopathogenesis. However, how glycan patterns differ between RA and OA remains unclear. Here, we identified 15 glycan forms of serum IgG from RA and OA using MALDI-TOF MS. We found that IgG galactosylation represented a suitable candidate for differentiating RA from healthy controls (AUC > 0.9). Then, we performed binary logistic regression to screen out three bisecting N-acetylglucosamine (GlcNAc) glycoforms for distinguishing between OA and RA. Combined ROC analysis of the selected glycans yielded an AUC of 0.81 between OA and RA and an AUC of 0.79 between OA and RF/ACPA negative RA. Similar results were found in the validation set. In conclusion, our analysis demonstrates that RA and OA are distinguished on the basis of their different IgG glycan patterns, which thus serve as suitable candidates as biomarkers for reliably identifying clinical conditions such as RA and OA.

Toward Automated Identification of Glycan Branching Patterns Using Multistage Mass Spectrometry with Intelligent Precursor Selection.

Glycans play important roles in a variety of biological processes. Their activities are closely related to the fine details of their structures. Unlike the simple linear chains of proteins, branching is a unique feature of glycan structures, making their identification extremely challenging. Multistage mass spectrometry (MS n) has become the primary method for glycan structural identification. The major difficulty for MS n is the selection of fragment ions as precursors for the next stage of scanning. Widely used strategies are either manual selection by experienced experts, which requires considerable expertise and time, or simply selecting the most intense peaks by which the product-ion spectrum generated may not be structurally informative and therefore fail to make the assignment. We here report a glycan "intelligent precursor selection" strategy (GIPS) to guide MS n experiments. Our approach consists of two key elements, an empirical model to calculate candidate glycan's probability and a statistical model to calculate fragment ion's distinguishing power in order to select the structurally most informative peak as the precursor for next-stage scanning. Using 15 glycan standards, including three pairs with isomeric sequences and eight variously fucosylated oligosaccharides on linear or branched hexasaccharide backbones isolated from a human milk oligosaccharide fraction by HPLC, we demonstrate its successful application to branching pattern analysis with improved efficiency and sensitivity and also the potential for automated operation.

Mass spectrometric profiling reveals association of N-glycan patterns with epithelial ovarian cancer progression.

Aberrant changes of N-glycan modifications on proteins have been linked to various diseases including different cancers, suggesting possible avenue for exploring their etiologies based on N-glycomic analysis. Changes in N-glycan patterns during epithelial ovarian cancer development have so far been investigated mainly using serum, plasma, ascites, and cell lines. However, changes in patterns of N-glycans in tumor tissues during epithelial ovarian cancer progression have remained largely undefined. To investigate whether changes in N-glycan patterns correlate with oncogenesis and progression of epithelial ovarian cancer, we profiled N-glycans from formalin-fixed paraffin-embedded tissue slides using matrix-assisted laser desorption/ionization time-of-flight mass spectrometry and quantitatively compared among different pathological grades of epithelial ovarian cancer and healthy controls. Our results show that among the 80 compositions of N-glycan detected, expression levels of high-mannose type were higher in epithelial ovarian cancer samples than that observed in healthy controls, accompanied by reduced levels of hybrid-type glycans. By applying receiver operating characteristic analysis, we show that a combined panel composed of four high-mannose and three fucosylated neutral complex N-glycans allows for good discrimination of epithelial ovarian cancer from healthy controls. Furthermore, using a statistical analysis of variance assay, we found that different N-glycan patterns, including 2 high-mannose-type, 2 fucosylated and sialylated complex structures, and 10 fucosylated neutral complex N-glycans, exhibited specific changes in N-glycan abundance across epithelial ovarian cancer grades. Together, our results provide strong evidence that N-glycomic changes are a strong indicator for epithelial ovarian cancer pathological grades and should provide avenues to identify novel biomarkers for epithelial ovarian cancer diagnosis and monitoring.

Characterization of IgG glycosylation in rheumatoid arthritis patients by MALDI-TOF-MSn and capillary electrophoresis.

An analytical method based on the combination of multistage mass spectrometry (MSn) and capillary electrophoresis (CE) was developed for the analysis of immunoglobulin G (IgG) glycosylation in rheumatoid arthritis (RA) patients. It has been recently suggested that IgG glycosylation defect may be involved in RA immunopathogenesis. Complete characterization of glycans, including both qualitative and quantitative analysis, requires a combination of different techniques, and accurate, robust, sensitive, and high-throughput methodologies are important for analysis of clinical samples. In the present study, N-glycosylation of IgG in RA patients and in healthy people was characterized through identification of the released glycans using multistage matrix-assisted laser desorption/ionization time of flight mass spectrometry (MALDI-TOF-MSn), and quantitation by CE. Assignment of the IgG N-glycan structures was made through branching pattern analysis by MSn with high-throughput. Further accurate quantitation indicated that galactosylation and sialylation of IgG N-glycans in RA cases were significantly lower than in healthy subjects. The results indicate that CE coupled with MSn can identify abnormal glycosylation of IgG in RA patients compared with healthy people, and that the present work is useful for RA mechanism studies and RA diagnosis. Graphical Abstract Qualitative and quantitative analysis of IgG glycosylation in rheumatoid arthritis patients by MALDI-TOF-MSn and capillary electrophoresis.

Glycomic profiling of carcinoembryonic antigen isolated from human tumor tissue.

BACKGROUND: Carcinoembryonic antigen (CEA) is a protein commonly found in human serum, with elevated CEA levels being linked to the progression of a wide range of tumors. It is currently used as a biomarker for malign tumors such as lung cancer and colorectal cancer [Urol Oncol: Semin Orig Invest 352: 644-648, 2013 and Lung Cancer 80: 45-49, 2013]. However, due to its low specificity in clinical applications, CEA can be used for monitoring only, rather than tumor diagnosis. The function of many glycoproteins is critically dependent on their glycosylation pattern, which in turn has the potential to serve in tumor detection. However, little is known about the detailed glycan patterns of CEA. METHODS: To determine these patterns, we isolated and purified CEA proteins from human tumor tissues using immunoaffinity chromatography. The glycan patterns of CEA were then analyzed using a Matrix-Assisted Laser Desorption/Ionization-Time of Flight-Mass Spectrometry(3) (MALDI-TOF-MS(3)) approach. RESULTS: We identified 61 glycoforms in tumor tissue, where CEA is upregulated. These glycosylation entities were identified as bi-antennary, tri-antennary and tetra-antennary structures carrying sialic acid and fucose residues, and include a multitude of glycans previously not reported for CEA. CONCLUSION: Our findings should facilitate a more precise tumor prediction than currently possible, ultimately resulting in improved tumor diagnosis and treatment.

Protocol for automated N-glycan sequencing using mass spectrometry and computer-assisted intelligent fragmentation.

Biological functions of glycans are intimately linked to fine details in branches and linkages, which make structural identification extremely challenging. Here, we present a protocol for automated N-glycan sequencing using multi-stage mass spectrometry (MSn). We describe steps for release/purification and derivation of glycans and procedures for MSn scanning. We then detail "glycan intelligent precursor selection" to computationally guide MSn experiments. The protocol can be used for both discrete individual glycans and isomeric glycan mixtures. For complete details on the use and execution of this protocol, please refer to Sun et al.,1 Huang et al.,2 and Huang et al.3.

Human milk oligosaccharides in milk of mothers with term and preterm delivery at different lactation stage.

Human milk oligosaccharides (HMOs) are structurally diverse unconjugated glycans, and play crucial roles in protecting infants from infections. Preterm birth is one of the leading causes of neonatal mortality, and preterm infants are particularly vulnerable and are in need of improved outcomes from breast-feeding due to the presence of bioactive HMOs. However, studies on specific difference in HMOs as a function of gestation time have been very limited. We established an approach to extract and analyze HMOs based on 96-well plate extraction and mass spectrometry, and determined maternal phenotypes through distinctive fragments in product-ion spectra. We enrolled 85 women delivering at different gestation times (25-41 weeks), and observed different HMOs correlating with gestation time based on 233 samples from the 85 donors. With the increase of postpartum age, we observed a regular changing trajectory of HMOs in composition and relative abundance, and found significant differences in HMOs secreted at different postpartum times. Preterm delivery induced more variations between participants with different phenotypes compared with term delivery, and more HMOs varied with postpartum age in the population of secretors. The sialylation level in mature milk decreased for women delivering preterm while such decrease was not observed for women delivering on term.

A stepwise mutagenesis approach using histidine and acidic amino acid to engineer highly pH-dependent protein switches.

Antibody-based drugs can be highly toxic, because they target normal tissue as well as tumor tissue. The pH value of the extracellular microenvironments around tumor tissues is lower than that of normal tissues. Therefore, antibodies that engage in pH-dependent binding at slightly acidic pH are crucial for improving the safety of antibody-based drugs. Thus, we implemented a stepwise mutagenesis approach to engineering pH-dependent antibodies capable of selective binding in the acidic microenvironment in this study. The first step involved single-residue histidine scanning mutagenesis of the antibody's complementarity-determining regions to prescreen for pH-dependent mutants and identify ionizable sensitive hot-spot residues that could be substituted by acidic amino acids to obtain pH-dependent antibodies. The second step involved single-acidic amino acid residue substitutions of the identified residues and the assessment of pH-dependent binding. We identified six ionizable sensitive hot-spot residues using single-histidine scanning mutagenesis. Nine pH-dependent antibodies were isolated using single-acidic amino acid residue mutagenesis at the six hot-spot residue positions. Relative to wild-type anti-CEA chimera antibody, the binding selectivity of the best performing mutant was improved by approximately 32-fold according to ELISA and by tenfold according to FACS assay. The mutant had a high affinity in the pH range of 5.5-6.0. This study supports the development of pH-dependent protein switches and increases our understanding of the role of ionizable residues in protein interfaces. The stepwise mutagenesis approach is rapid, general, and robust and is expected to produce pH-sensitive protein affinity reagents for various applications.

Caveolin-1 knockout mice have altered serum N-glycan profile and sialyltransferase tissue expression.

Caveolin-1 (Cav-1) is a constitutive protein within caveolar membranes. Previous studies from our group and others indicated that Cav-1 could mediate N-glycosylation, α2,6-sialylation, and fucosylation in mouse hepatocarcinoma cells in vitro. However, little is known about the effect of Cav-1 expression on glycosylation modifications in vivo. In this study, the N-glycan profiles in serum from Cav-1-/- mice were investigated by lectin microarray and mass spectrometric analysis approaches. The results showed that levels of multi-antennary branched, α2,6-sialylated, and galactosylated N-glycans increased, while high-mannose typed and fucosylated N-glycans decreased in the serum of Cav-1-/- mice, compared with that of wild-type mice. Furthermore, the real-time quantitative PCR analysis indicated that α2,6-sialyltransferase gene expression decreased significantly in Cav-1-/- mouse organ tissues, but α2,3- and α2,8-sialyltransferase did not. Of them, both mRNA and protein expression levels of the ß-galactoside α2,6-sialyltransferase 1 (ST6Gal-I) had dramatically reduced in Cav-1-/- mice organ tissues, which was consistent with the α2,6-sialyl Gal/GalNAc level reduced significantly in tissues instead of serum from Cav-1-/- mice. These results provide for the first time the N-glycans profile of Cav-1-/- mice serum, which will facilitate understanding the function of Cav-1 from the perspective of glycosylation.

Flow injection measurements of S-nitrosothiols species in biological samples using amperometric nitric oxide sensor and soluble organoselenium catalyst reagent.

A novel flow injection analysis (FIA) system suitable for measurement of S-nitrosothiols (RSNOs) in blood plasma is described. In the proposed (FIA) system, samples and standards containing RSNO species are injected into a buffer carrier stream that is mixed with the reagent stream containing 3,3'-dipropionicdiselenide (SeDPA) and glutathione (GSH). SeDPA has been shown previously to catalytically decompose RSNOs in the presence of a reducing agent, such as GSH, to produce nitric oxide (NO). The liberated NO is then detected downstream by an amperometric NO sensor. This sensor is prepared using an electropolymerized m-phenylenediamine (m-PD)/resorcinol and Nafion composite films at the surface of a platinum electrode. Using optimized flow rates and reagent concentrations, detection of various RSNOs at levels in the range of 0.25-20 µM is possible. For plasma samples, detection of background sensor interference levels within the samples must first be carried out using an identical FIA arrangement, but without the added SeDPA and GSH reagents. Subtraction of this background sensor current response allows good analytical recovery of RSNOs spiked into animal plasma samples, with recoveries in the range of 90.4-101.0%.

HepParser: An Intelligent Software Program for Deciphering Low-Molecular-Weight Heparin Based on Mass Spectrometry.

Low-molecular-weight heparins (LMWHs) are considered to be the most successful carbohydrate-based drugs because of their wide use as anticoagulants in clinics. The efficacy of anticoagulants made by LMWHs mainly depends on the components and structures of LMWHs. Therefore, deciphering the components and identifying the structures of LMWHs are critical to developing high-efficiency anticoagulants. However, most LMWHs are mixtures of linear polysaccharides which are comprised of several disaccharide repeating units with high similarity, making it extremely challenging to separate and decipher each component in LMWHs. Here, we present a new algorithm named hepParser to decipher the main components of LMWHs automatically and precisely based on the liquid chromatography/mass spectrometry (LC/MS) data. When tested on the general LMWH using hepParser, profiling of the oligosaccharides with different degrees of polymerization (dp's) was completed with high accuracy within 1 minute. When compared with the results of GlycReSoft on heparan sulfate samples, hepParser achieved more comprehensive and reasonable results automatically.

The effect of N-glycosylation of SARS-CoV-2 spike protein on the virus interaction with the host cell ACE2 receptor.

The densely glycosylated spike (S) protein highly exposed on severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) surface mediates host cell entry by binding to the receptor angiotensin-converting enzyme 2 (ACE2). However, the role of glycosylation has not been fully understood. In this study, we investigated the effect of different N-glycosylation of S1 protein on its binding to ACE2. Using real-time surface plasmon resonance assay the negative effects were demonstrated by the considerable increase of binding affinities of de-N-glycosylated S1 proteins produced from three different expression systems including baculovirus-insect, Chinese hamster ovarian and two variants of human embryonic kidney 293 cells. Molecular dynamic simulations of the S1 protein-ACE2 receptor complex revealed the steric hindrance and Coulombic repulsion effects of different types of N-glycans on the S1 protein interaction with ACE2. The results should contribute to future pathological studies of SARS-CoV-2 and therapeutic development of Covid-19, particularly using recombinant S1 proteins as models.

Identification of carbohydrate peripheral epitopes important for recognition by positive-ion MALDI multistage mass spectrometry.

Carbohydrate sequences are important for various biological processes. It has recently been estimated to have 100,000-500,000 carbohydrate structures in mammalian glycome. However, the peripheral carbohydrate determinants on N- and O-glycoproteins, glycolipids, polysaccharides and secreted free sugars are limited in numbers. Among these blood-group-related antigens the ABO(H)- and Lewis-types are particularly important. Negative-ion MS/MS has been successfully used in assignment of these epitopes on free reducing sugars but cannot be applied to reduced sugars, e.g. O-glycans typically released from mucins as alditols, or in positive-ion detection of either reducing or reduced oligosaccharides. In the present study, we investigate the fragmentation features of permethylated reducing and reduced sugars under positive-ion conditions of multi-stage MALDI-MS, and propose the concept of epitope ion and epitope spectrum for determination of peripheral blood-group related epitopes on secreted human milk oligosaccharides and N-glycans as reducing sugars and O-glycans as reduced alditols in conjunction with MALDI-MS glycan profiling.

Establishment and characterization of patient-derived primary cell lines as preclinical models for gallbladder carcinoma.

BACKGROUND: Gallbladder carcinoma (GBC) is one of the most lethal malignancies which do not have a targeted drug in the clinic. Patient-derived primary cell lines (PDCs) are useful in assessment of cancer complexity and heterogeneity, drug-sensitivity tests, and personalized-drug-selection guidance. The aim of this study is to establish GBC PDCs and characterize their biological features. METHODS: The characterization of PDCs was defined by morphology, growth kinetics, chromosomal analysis, short tandem repeat (STR) analysis, RNA-seq and tumorigenicity. Glycosylation of PDCs derived from GBC was first studied, and the PDC model's performance were also tested and evaluated using seven molecular target inhibitors. RESULTS: Three novel GBC cell lines from three GBC patients were successfully established and denoted as JXQ-3D-902R4, JXQ-3D-4494R, and JXQ-3D-4786R. These cell lines demonstrated the heterogeneous characteristics of tumor morphology and phenotypes which are consistent with primary GBC, such as irregular cell shape, varied chromosomal numbers, and different STR patterns. Moreover, the growth activity and tumorigenicity ability varied among the cell lines, of which JXQ-3D-4494R exhibited the best growth rate. Furthermore, glycan profiling of whole proteins were detected and characterized. Unique N-glycans of each PDC were identified, JXQ-3D-902R4, JXQ-3D-4494R and JXQ-3D-4786R contained ten, four and seven unique glycans, respectively. The epithelial origins of three PDCs were confirmed using RNA-seq based on the highly expressed typical epithelial marker genes. Moreover, the drug-sensitivity results demonstrated that the three PDCs exhibited different responses to the seven-most commonly used targeted medicines belonging to three groups: cell-cycle inhibitors, PI3K/AKT/mTOR signaling-pathway inhibitors, and ErbB inhibitors. JXQ-3D-4494R was sensitive to most of the inhibitors, JXQ-3D-4786R was sensitive to ErbB inhibitors, and JXQ-3D-902R4 was sensitive to PI3K/AKT/mTOR inhibitors. CONCLUSIONS: These results indicate that PDCs may be efficient preclinical models for further investigation of the biological behaviors and potential targeted therapies of human GBC.

Multistage mass spectrometry with intelligent precursor selection for N-glycan branching pattern analysis.

Biological functions of N-glycans are frequently related to their unique branching patterns. Multistage mass spectrometry (MSn) has become the primary method for glycan structural analysis. However, selection of the best fragment as the precursor for the next round of product-ion scanning is important but difficult. We have previously proposed the concept and designed the approach of glycan intelligent precursor selection (GIPS) to guide MSn experiments, but its use in N-glycans is not straightforward as some N-glycans are of high similarity in branching patterns. In the present work we introduced new elements to GIPS to improve its performance in N-glycan branching pattern analysis. These include a hypothesis and significance test, based on Bayes factor, and DPbiased as a new precursor selection strategy. The improved GIPS was successfully applied to identification of individual N-glycans, and incorporated into MALDI-MS N-glycan profiling for assignment of N-glycans obtained from glycoproteins and complex human serum.

Identification of glycan branching patterns using multistage mass spectrometry with spectra tree analysis.

Glycans are crucial to a wide range of biological processes, and their biological activities are closely related to the branching patterns of structures. Different from the simple linear chains of proteins, branching patterns of glycans are more complicated, making their identification extremely challenging. Tandem mass spectrometry (MS2) cannot provide sufficient structural information to deduce glycan branching patterns even with the assistance of various bioinformatic tools and algorithms.The promising technology to identify glycan branching patterns is multi-stage mass spectrometry (MSn). The production-relationship among MSn spectra of a glycan is essentially a tree, making deducing glycan structures from MSn spectra a great challenge. In the present study, we report an approach called glyBranch (glycan Branching pattern identification based on spectra tree) to fully exploit the information contained in the MSn spectra tree for glycan identification. Using 14 glycan standards, including 2 pairs with isomeric sequence, and 16 complex N-glycans isolated from RNase B and IgG, we demonstrated the successful application of glyBranch to branching pattern analysis. The source code of glyBranch is available at https://github.com/bigict/glyBranch/. We have also developed a web-server, which is freely accessible at http://glycan.ict.ac.cn/glyBranch/. SIGNIFICANCE: Glycans are crucial in various biological processes and their functions are closely related to the details of their structures; thus, the identification of glycan branching patterns is of great significance to biological studies. Multistage mass spectrometry (MSn) can provide detailed structural information by generating multiple-level fragments through consecutive fragmentation; however, the interpretation of numerous MSn spectra is extremely challenging. In this study, we present an approach called glyBranch (glycan Branching pattern identification based on spectra tree) to exploit the information contained in MSn spectra tree for glycan identification. This approach will greatly facilitate the automated identification of glycan structures and related biological studies.