The effect of neighbouring group participation and possible long range remote group participation in O-glycosylation

• Rituparna Das and
• Balaram Mukhopadhyay

Beilstein J. Org. Chem. 2025, 21, 369–406, doi:10.3762/bjoc.21.27

• achieve better stereocontrol and to facilitate the formation of synthetically challenging glycosidic linkages. Keywords: chemical O-glycosylation; neighbouring group participation; remote group participation; solvent effect; stereocontrol; Introduction Cell surface glycans in living cells have

• significantly spurred the scientific curiosity of researchers over the past few decades [1]. Intensive studies revealed that this subtle code of cell surface glycans is extensively responsible for monitoring a plethora of diverse biological phenomena like post-translational modification of proteins [2][3

• ], cosmetics, and detergents. Carbohydrates are the most abundant and versatile renewable sources of energy on earth. However, extraction of naturally occurring complex glycans from natural sources is a laborious and cumbersome task. Moreover, the isolated samples provide a low-yield heterogenous mixture of

Chemical glycobiology

• Elisa Fadda,
• Rachel Hevey,
• Benjamin Schumann and
• Ulrika Westerlind

Beilstein J. Org. Chem. 2025, 21, 8–9, doi:10.3762/bjoc.21.2

• applicable to glycans. Today, the most amazing tools in genome engineering are used to great effect to disrupt or alter the glycan biosynthetic machinery, but they still cannot be used to, for instance, mutate one glycan into another in the same manner as nucleic acids can be mutated. Methods in molecular

• biology are facile and quantitative. But they do not tell us the function of a particular glycoform on a specific glycoprotein. To put glycans on the map, chemists needed to be inventive. At the time of writing this Editorial article, we are all early- and mid-career investigators who have learned from

• glycan assembly [9]. Target-directed synthetic strategies are being developed by Reihill et al. [10] and Karak et al. [11], exploring the syntheses of the linker-displaying, sulfated TF disaccharide and lipid II analogues, respectively. The direct application of synthetic glycans is shown by Fan et al

Computational toolbox for the analysis of protein–glycan interactions

• Ferran Nieto-Fabregat,
• Maria Pia Lenza,
• Angela Marseglia,
• Cristina Di Carluccio,
• Antonio Molinaro,
• Alba Silipo and
• Roberta Marchetti

Beilstein J. Org. Chem. 2024, 20, 2084–2107, doi:10.3762/bjoc.20.180

• as powerful tools that can help in drawing, building and visualising complex biomolecules and provide insights into their dynamic behaviour at atomic and molecular levels. This review provides an overview of the main computational tools useful for studying biomolecular systems, particularly glycans

• determinants that govern specific molecular interactions. Keywords: computational tools; glycan–protein interactions; MD; molecular recognition; Review Introduction Carbohydrates also referred to as saccharides, sugars, or glycans, constitute one of the main building blocks of biomolecules, alongside lipids

• physical functions [3]. Glycoscience encompasses the comprehensive study of glycans focusing on their structural, biosynthetic, biological and evolutionary aspects [4], thus playing a central role in the identification and characterisation of the glycome’ structure and function, and in unveiling its

Monitoring carbohydrate 3D structure quality with the Privateer database

• Jordan S. Dialpuri,
• Haroldas Bagdonas,
• Lucy C. Schofield,
• Phuong Thao Pham,
• Lou Holland and
• Jon Agirre

Beilstein J. Org. Chem. 2024, 20, 931–939, doi:10.3762/bjoc.20.83

• complement the growing glycan content of the PDB. Keywords: carbohydrates; database; N-glycans; N-glycosylation; polysaccharides; validation; website; Introduction Carbohydrate modelling is an important but often cumbersome stage in the macromolecular X-ray structure solution workflow. The accurate

• overall conformation of N-glycans comes to that of validated deposited structures [16]. The PDB-REDO [21] database is a separate resource, albeit linked to the PDB in that the entries that compound PDB-REDO are those original PDB crystallographic entries that included experimental data (i.e., reflection

• validation report is the beginning of the carbohydrate information, listed as ‘glycans’ in the JSON format. Within this ‘glycan’ scope, information is segmented into glycan types, that is, ‘n-glycan’, ‘o-glycan’, ‘s-glycan’, ‘c-glycan’, and 'ligand'. Each of these glycan types contains an array of individual

Introduction of a human- and keyboard-friendly N-glycan nomenclature

• Friedrich Altmann,
• Johannes Helm,
• Martin Pabst and
• Johannes Stadlmann

Beilstein J. Org. Chem. 2024, 20, 607–620, doi:10.3762/bjoc.20.53

• -glycans, at least, no simple words. Next to chemical formulas, the IUPAC code can be regarded as the best, most reliable and yet immediately comprehensible annotation of oligosaccharide structures of any type from any source. When it comes to N-glycans, the venerable IUPAC code has, however, been widely

• supplanted by highly simplified terms for N-glycans that count the number of antennae or certain components such as galactoses, sialic acids and fucoses and give only limited room for exact structure description. The highly illustrative – and fortunately now standardized – cartoon depictions gained much

• alphanumeric descriptions of the hundreds and thousands of N-glycan structures. Here, we present a system that describes N-glycans by defining their terminal elements. To minimize redundancy and length of terms, the common elements of N-glycans are taken as granted. The preset reading order facilitates

Elucidating the glycan-binding specificity and structure of Cucumis melo agglutinin, a new R-type lectin

• Jon Lundstrøm,
• Emilie Gillon,
• Valérie Chazalet,
• Nicole Kerekes,
• Antonio Di Maio,
• Ten Feizi,
• Yan Liu,
• Annabelle Varrot and
• Daniel Bojar

Beilstein J. Org. Chem. 2024, 20, 306–320, doi:10.3762/bjoc.20.31

• unique binding profile of specifically recognizing C2-substituted galactose in the context of glycans. Results and Discussion Identification and production of a new lectin from the melon Cucumis melo CMA1 is a predicted protein from whole-genome shotgun sequencing of leaves from the melon plant Cucumis

• answer the question whether CMA1 was a functional lectin and, if yes, what its binding specificity was. The standard approach to elucidate lectin binding specificity is via glycan array experiments. Here, tagged soluble lectin is added to, often, immobilized glycans and bound lectin is quantified via

• fluorescence scanners, which can be paired with glycan information due to the known arrangements of immobilized glycans on the plate. To cover the broadest possible sequence space, we tested our eukaryotically produced CMA1 protein against the two largest glycan arrays at the National Center for Functional

Synthesis of the 3’-O-sulfated TF antigen with a TEG-N₃ linker for glycodendrimersomes preparation to study lectin binding

• Mark Reihill,
• Hanyue Ma,
• Dennis Bengtsson and
• Stefan Oscarson

Beilstein J. Org. Chem. 2024, 20, 173–180, doi:10.3762/bjoc.20.17

• project with groups from Universities in Munich and Pennsylvania we are investigating carbohydrate–lectin interactions using programmable glycodendrimersomes based on synthetic glycans. We have earlier synthesized 2-[2-(2-azidoethoxy)ethoxy]ethyl (TEG-N3) glycosides of lactose, 3’-Su-lactose and LacdiNAc

• cell. Samples were prepared at the concentration (g/100 mL) in the solvent indicated. Deprotected glycans were lyophilised using a freeze-dryer Alpha 1-2 LDplus (Christ Ltd): pressure: 0.055 mbar; ice-condenser temperature: −55 °C. 2-[2-(2-Azidoethoxy)ethoxy]ethyl 3-O-acetyl-2-deoxy-4,6-O-di-tert

GlAIcomics: a deep neural network classifier for spectroscopy-augmented mass spectrometric glycans data

• Thomas Barillot,
• Baptiste Schindler,
• Baptiste Moge,
• Elisa Fadda,
• Franck Lépine and
• Isabelle Compagnon

Beilstein J. Org. Chem. 2023, 19, 1825–1831, doi:10.3762/bjoc.19.134

• biopolymer have been established decades ago and are commonly used in a routine and automated manner. However, the development of such technology for the sequencing of the third class of biological polymer – glycans, also known as carbohydrates, saccharides, or "sugars" – lags far behind. This lack of

• have evaluated the performances of a Bayesian deep neural network for automatic analysis and classification tasks on glycans MS–IR fingerprints. It showed robust prediction accuracies on an exogeneous dataset. We observed that it is capable to generalize as it could categorize more noisy and distorted

• experimental data pipeline between the experiment raw spectra recording and the sequencing algorithm. Rejected spectra would be manually reviewed and fed back to the model as new training samples which in turn would reduce the epistemic error. It will therefore speed up the construction of glycans

Linker, loading, and reaction scale influence automated glycan assembly

• Marlene C. S. Dal Colle,
• Manuel G. Ricardo,
• Nives Hribernik,
• José Danglad-Flores,
• Peter H. Seeberger and
• Martina Delbianco

Beilstein J. Org. Chem. 2023, 19, 1015–1020, doi:10.3762/bjoc.19.77

• Universität Berlin, Arnimallee 22, 14195 Berlin, Germany 10.3762/bjoc.19.77 Abstract Automated glycan assembly (AGA) affords collections of well-defined glycans in a short amount of time. We systematically analyzed how parameters connected to the solid support affect the AGA outcome for three different

• , photocleavage, hydrogenolysis of the remaining PGs, and purification (Figure 2C). The latter is commonly employed for compounds synthesized on L2 because of the poor stability of free-reducing glycans in basic conditions needed for the methanolysis step [33]. The isolated yields of the fully protected compound

• showed the presence of capped linker (*), capped dimer (†), and Lev-containing dimer (‡). The monosaccharides are represented following the symbol nomenclature for glycans (SNFG). Supporting Information Supporting Information File 120: Experimental procedures and characterization data. Funding We thank

Anomeric 1,2,3-triazole-linked sialic acid derivatives show selective inhibition towards a bacterial neuraminidase over a trypanosome trans-sialidase

• Peterson de Andrade,
• Sanaz Ahmadipour and
• Robert A. Field

Beilstein J. Org. Chem. 2022, 18, 208–216, doi:10.3762/bjoc.18.24

• differences in sialidases that need to be addressed in order to achieve selective inhibition. Keywords: inhibition; neuraminidase; sialic acid; trans-sialidase; 1,2,3-triazole; Introduction Amongst the diversity of glycans present in living organisms, N-acetylneuraminic acid (Neu5Ac, sialic acid) is

GlycoBioinformatics

• Kiyoko F. Aoki-Kinoshita,
• Frédérique Lisacek,
• Niclas Karlsson,
• Daniel Kolarich and
• Nicolle H. Packer

Beilstein J. Org. Chem. 2021, 17, 2726–2728, doi:10.3762/bjoc.17.184

• for current global health challenges and to understand just about every biological process. Molecular dynamic modeling to understand how glycans interact with biomolecules visualizes and allows the development of hypotheses regarding the function of glycans to be tested at a molecular level. The

• article by Barnett et al. [2] uses molecular dynamics to show that O-linked glycosylation alters peptide conformation, which influences the binding of the peptides to antibodies, despite the fact that glycans are not directly involved in the binding. Another molecular modeling article by Fogarty et al. [3

• ] suggests a new concept of glycoblocks, which are subunits of 3D glycan structures. This concept may become useful in describing specific epitopes and functional units of glycans. With the recent pandemic experience, the need for glycobioinformatics for global health was highlighted, where the laboratory of

Progress and challenges in the synthesis of sequence controlled polysaccharides

• Giulio Fittolani,
• Theodore Tyrikos-Ergas,
• Denisa Vargová,
• Manishkumar A. Chaube and
• Martina Delbianco

Beilstein J. Org. Chem. 2021, 17, 1981–2025, doi:10.3762/bjoc.17.129

• polysaccharides. For each class of polysaccharides, we discuss the available synthetic approaches and their current limitations. Keywords: enzymes; glycans; polysaccharides; synthesis; well-defined polymers; Introduction Polysaccharides are an abundant class of natural polymers that play important roles in the

A systems-based framework to computationally describe putative transcription factors and signaling pathways regulating glycan biosynthesis

• Theodore Groth,
• Rudiyanto Gunawan and
• Sriram Neelamegham

Beilstein J. Org. Chem. 2021, 17, 1712–1724, doi:10.3762/bjoc.17.119

• A and B together) and basal breast cancer. Here, glycans potentially affected by the enriched TFs are shown in SNFG format [18][19]. The analysis suggests that TF transformations accompanying cancer progression may impact all four major classes of glycans: O- and N-glycans found on glycoproteins

• tumors. Sialyl Lewis-X is considered to be an important regulator of cancer metastasis as it binds the selectins on various vascular and blood cell types. Regulation of GalNAc-type O-linked glycans by SMAD2: SMAD2 was found to significantly affect core 1 and 2 O-linked glycan structures (enrichment p

• specific tissue type, supporting ChIP-Seq evidence is not cell-type-specific. Regardless of these limitations, the current study presents a framework for thinking in the glycosciences, so that knowledge of genes and transcripts can be linked to glycans and their function [2]. Conclusion A majority of

Synthesis of multiply fluorinated N-acetyl-D-glucosamine and D-galactosamine analogs via the corresponding deoxyfluorinated glucosazide and galactosazide phenyl thioglycosides

• Vojtěch Hamala,
• Lucie Červenková Šťastná,
• Martin Kurfiřt,
• Petra Cuřínová,
• Martin Dračínský and
• Jindřich Karban

Beilstein J. Org. Chem. 2021, 17, 1086–1095, doi:10.3762/bjoc.17.85

• ]. Unprotected multiply-deoxyfluorinated N-acetyl-ᴅ-glucosamine (GlcNAc) and N-acetyl-ᴅ-galactosamine (GalNAc) have not yet been described except for a 4,6-difluoro-GalNAc analog [22], although GlcNAc is the most abundant monosaccharide component of mammalian glycans [23], and GalNAc occurs in important glycan

Simulating the enzymes of ganglioside biosynthesis with Glycologue

• Andrew G. McDonald and
• Gavin P. Davey

Beilstein J. Org. Chem. 2021, 17, 739–748, doi:10.3762/bjoc.17.64

• acting as a wildcard character. Double branches are used to form symmetric core structures, such as the trimannosyl core of N-glycans, or O-linked glycan cores based on GalNAc (Equation 3): Capping of branches and linearly extended chains is achieved through termination, of which sialylation is a typical

• on the number and type of glycans formed can be predicted. In the web application version of Table 2, reactants and reactions are linked to ChEBI [42] and Rhea [43] by their identifiers, where these are available. Future development In addition to biosynthesis, the biochemistry of gangliosides

Semiautomated glycoproteomics data analysis workflow for maximized glycopeptide identification and reliable quantification

• Steffen Lippold,
• Arnoud H. de Ru,
• Jan Nouta,
• Peter A. van Veelen,
• Magnus Palmblad,
• Manfred Wuhrer and
• Noortje de Haan

Beilstein J. Org. Chem. 2020, 16, 3038–3051, doi:10.3762/bjoc.16.253

• quantification. Keywords: bioinformatics; cysteine oxidation; glycoproteomics; immunoglobulins; mass spectrometry; Introduction Protein glycosylation mainly occurs in the form of N- and O-glycosylation. N-Glycans are attached to Asn within an amino acid consensus sequence (Asn-Xxx-Ser/Thr, Xxx ≠ Pro) and O

• -glycans are attached to Ser or Thr. Glycan compositions can range from monosaccharides (e.g., Tn antigen for O-glycans [1]) to large polysaccharides (e.g., N-glycans of recombinant human erythropoietin [2]). The most common building blocks of human protein glycans are hexoses (glucose, galactose, and

• glycan composition. The low intensity of these glycopeptide signals resulted in a decreased likelihood for MS/MS selection. Of note, the IgA2 HYT glycopeptide covers a sequence stretch homologous to the hinge region of IgA1, carrying O-glycans. In a previous study the IgA1 peptide has been referred to as

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

• Benjamin P. Kellman,
• Yujie Zhang,
• Emma Logomasini,
• Eric Meinhardt,
• Karla P. Godinez-Macias,
• Austin W. T. Chiang,
• James T. Sorrentino,
• Chenguang Liang,
• Bokan Bao,
• Yusen Zhou,
• Sachiko Akase,
• Isami Sogabe,
• Thukaa Kouka,
• Elizabeth A. Winzeler,
• Iain B. H. Wilson,
• Matthew P. Campbell,
• Sriram Neelamegham,
• Frederick J. Krambeck,
• Kiyoko F. Aoki-Kinoshita and
• Nathan E. Lewis

Beilstein J. Org. Chem. 2020, 16, 2645–2662, doi:10.3762/bjoc.16.215

• 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. Keywords: glycoinformatics; linear code; systems glycobiology; Introduction Glycans are predominantly

• possible SUs, Linear Code uses “ // ” to separate them (i.e., Ab4//Ga2Aa3 represents Ab4Aa3 or Ga2Aa3) (UR5). When analyzing fragmented glycans, an “< index number>%” is used to store fragmented structures as a variable. For example, NNa6=1%|1%Ab4GNb2Ma3(1%Ab4GNb2Ma6)Mb4Gb is a glycan containing a terminal

• Rules Linear Code was first used to represent reaction rules in 2009. A reaction network, specifying glycans with condensed IUPAC and Linear Code, was trained on mass spectrometry abundance to learn biosynthetic enzyme activities [10]. Their reaction rules table contained four features: enzyme, reactant

Comparative ligand structural analytics illustrated on variably glycosylated MUC1 antigen–antibody binding

• Christopher B. Barnett,
• Tharindu Senapathi and
• Kevin J. Naidoo

Beilstein J. Org. Chem. 2020, 16, 2540–2550, doi:10.3762/bjoc.16.206

• molecules for the well-known system of mucin binding to the AR20.5 murine antibody. The binding of glycosylated biomolecules is of increasing interest as glycans are found to be involved in cellular functioning and messaging. The mucins, which are cell surface-associated glycoproteins, are found in mucous

• sequence of 20 amino acids (–His-Gly-Val-Thr-Ser-Ala-Pro-Asp-Thr-Arg-Pro-Ala-Pro-Gly-Ser-Thr-Ala-Pro-Pro-Ala–)n, and there are five sites where O-glycosylation may occur (indicated in bold). In cancerous cells, the glycans tend to be truncated or have additional sialylation [14]. For example, in mammary

• epithelial cells, the mixture of O-glycans that glycosylate mucins are extended core 2 structures, while in breast cancer cells, O-glycan mass decreases (hypoglycosylation), and there is an increase in abundance of sialylated core 1 [15]. The upregulation of Tn (αGalNAc) and STn (αNeuAc-2,6-αGalNAc) antigens

Leveraging glycomics data in glycoprotein 3D structure validation with Privateer

• Haroldas Bagdonas,
• Daniel Ungar and
• Jon Agirre

Beilstein J. Org. Chem. 2020, 16, 2523–2533, doi:10.3762/bjoc.16.204

• complexity of glycans in glycoproteins have been, and currently remain, significant challenges in structural biology. These aspects present unique problems to the two most prolific techniques: X-ray crystallography and cryo-electron microscopy. At the same time, advances in mass spectrometry have made it

• in the compositions of the glycans added to specific glycosylation sites – microheterogeneity. This variation in the microheterogeneous composition patterns arises due to the competition of glycan-processing enzymes in biosynthesis pathways [23]. Implications for the structure determination of

• heterogeneous [22]. Moreover, oligosaccharides often significantly interfere with the formation of crystal contacts that allow the formation of well-diffracting crystals. Because of this, glycans are often truncated in MX samples to aid crystal formation [27]. In cryo-EM, samples of glycoproteins are vitrified

Computational tools for drawing, building and displaying carbohydrates: a visual guide

• Kanhaya Lal,
• Rafael Bermeo and
• Serge Perez

Beilstein J. Org. Chem. 2020, 16, 2448–2468, doi:10.3762/bjoc.16.199

• carried out in structural glycobiology, typically using various software. In this perspective article, we outline developments in the computational tools for the sketching, visualisation and modelling of glycans. The article also provides details on the standard representation of glycans, and

• glycoconjugates, which helps the communication of structure details within the scientific community. We highlight the comparative analysis of the available tools which could help researchers to perform various tasks related to structure representation and model building of glycans. These tools can be useful for

• glycobiologists or any researcher looking for a ready to use, simple program for the sketching or building of glycans. Keywords: bioinformatics; carbohydrate; glycan; glycobiology; nomenclature; oligosaccharide; polysaccharide; representation; structure; Introduction Glycoscience is a rapidly surfacing and

The B & B approach: Ball-milling conjugation of dextran with phenylboronic acid (PBA)-functionalized BODIPY

• Patrizia Andreozzi,
• Lorenza Tamberi,
• Elisamaria Tasca,
• Gina Elena Giacomazzo,
• Marta Martinez,
• Mirko Severi,
• Marco Marradi,
• Stefano Cicchi,
• Sergio Moya,
• Giacomo Biagiotti and
• Barbara Richichi

Beilstein J. Org. Chem. 2020, 16, 2272–2281, doi:10.3762/bjoc.16.188

• . Acknowledgements We thank COST Action INNOGLY: Innovation with Glycans: new frontiers from synthesis to new biological targets (CA18013). We thank Maria de Los Angeles Ramirez, CIC biomaGUNE, for TEM analysis. Funding B.R., G.B., S.C., M.M., P.A. and G.G. thank MIUR-Italy ("Progetto Dipartimenti di Eccellenza 2018

Tools for generating and analyzing glycan microarray data

• Akul Y. Mehta,
• Jamie Heimburg-Molinaro and
• Richard D. Cummings

Beilstein J. Org. Chem. 2020, 16, 2260–2271, doi:10.3762/bjoc.16.187

• Akul Y. Mehta Jamie Heimburg-Molinaro Richard D. Cummings Department of Surgery, Beth Israel Deaconess Medical Center, National Center for Functional Glycomics, Harvard Medical School, Boston, MA, 02215, USA 10.3762/bjoc.16.187 Abstract Glycans are one of the major biological polymers found in

• structural informatics tools. Keywords: data analysis; glycan binding; glycan microarray; glycomics; informatics; Introduction Glycans represent a major type of biomolecule in all living things, along with DNA, RNA, lipids and proteins [1]. In mammals, glycans commonly occur as post-translational

• modifications of proteins (glycoproteins), but they are also linked to lipids (glycolipids) and occur as free molecules. Such glycomolecules have vital roles in a wide range of physiological functions and also participate in many pathologic conditions [2]. Some classic examples of important glycans include the

GlypNirO: An automated workflow for quantitative N- and O-linked glycoproteomic data analysis

• Toan K. Phung,
• Cassandra L. Pegg and
• Benjamin L. Schulz

Beilstein J. Org. Chem. 2020, 16, 2127–2135, doi:10.3762/bjoc.16.180

• agalactosylated N-glycans on IgG were increased in abundance in HCC (Figure 1a), and the relative abundance of the HexNAc(5)Hex(6)NeuAc(3) composition at multiple sites on alpha-1-antichymotrypsin was decreased in HCC (Figure 1b). N-Glycoproteome analysis To extend our analysis, we next investigated the full

• different glycan structures at specific sites in diverse glycoproteins. Examining the data in more detail identified several sites with multiple glycoforms with statistically significant changes in abundance. Specifically, HCC patients had decreased abundance of disialylated N-glycans at alpha-1-antitrypsin

• N271 and haptoglobin N184 (Figure 3a and 3b), with increased abundance of non-sialylated N-glycans at fibrinogen N78 (Figure 3c), and decreased abundance of trisialylated N-glycans at alpha-2-HS-glycoprotein N176 (Figure 3d). Together, this suggests an overall decrease in sialylation of N-glycans

Clustering and curation of electropherograms: an efficient method for analyzing large cohorts of capillary electrophoresis glycomic profiles for bioprocessing operations

• Ian Walsh,
• Matthew S. F. Choo,
• Sim Lyn Chiin,
• Amelia Mak,
• Shi Jie Tay,
• Pauline M. Rudd,
• Yang Yuansheng,
• Andre Choo,
• Ho Ying Swan and
• Terry Nguyen-Khuong

Beilstein J. Org. Chem. 2020, 16, 2087–2099, doi:10.3762/bjoc.16.176

• critical process parameters that control the glycosylation critical quality attributes. The advances made in protocols for capillary electrophoresis-laser-induced fluorescence (CE-LIF) measurements of antibody N-glycans have increased the potential for generating large datasets of N-glycosylation values

• problems are often due to fluctuations introduced by varying process conditions resulting in heterogeneous peak shapes. Additionally, peaks with co-eluting glycans can produce peaks of a non-Gaussian nature in some process conditions and not in others. Here, we describe an approach to quantitatively and

• to assess how glycans behave under these diverse conditions. During this process development of antibody-based drugs, the N-glycosylation of an antibody can deviate from their expected glycomic profiles as a result of fluctuations in culture conditions and operating parameters. Therefore, to assess

How and why plants and human N-glycans are different: Insight from molecular dynamics into the “glycoblocks” architecture of complex carbohydrates

• Carl A. Fogarty,
• Aoife M. Harbison,
• Amy R. Dugdale and
• Elisa Fadda

Beilstein J. Org. Chem. 2020, 16, 2046–2056, doi:10.3762/bjoc.16.171

• , implicated in protein folding and structural stability, and mediating interactions with receptors and with the environment. All N-glycans share a common core from which linear or branched arms stem from, with functionalization specific to different species and to the cells’ health and disease state. This

• diversity generates a rich collection of structures, all diversely able to trigger molecular cascades and to activate pathways, which also include adverse immunogenic responses. These events are inherently linked to the N-glycans’ 3D architecture and dynamics, which remain for the large part unresolved and

• undetected because of their intrinsic structural disorder. In this work we use molecular dynamics (MD) simulations to provide insight into N-glycans’ 3D structure by analysing the effects of a set of very specific modifications found in plants and invertebrate N-glycans, which are immunogenic in humans. We