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

• huge interest in the study of ‘Glycomics’ or ‘Carbohydrate Chemistry.’ These truly elevated the recognition of carbohydrates from being mere building blocks to a frontier research topic towards the development of drugs and pharmaceuticals [12][13], diagnostic tools [14], artificial sweeteners [15

• ] and corresponding automated oligosaccharide synthesis [63][64][65]. These procedures pave the way for the new generation of glycomics and glycochemistry which promises to show new paths towards deciphering the unsolved mysteries of nature [66]. There have been many reviews in literature showcasing the

Discovery of ianthelliformisamines D–G from the sponge Suberea ianthelliformis and the total synthesis of ianthelliformisamine D

• Sasha Hayes,
• Yaoying Lu,
• Bernd H. A. Rehm and
• Rohan A. Davis

Beilstein J. Org. Chem. 2024, 20, 3205–3214, doi:10.3762/bjoc.20.266

• Sasha Hayes Yaoying Lu Bernd H. A. Rehm Rohan A. Davis Institute for Biomedicine and Glycomics, Griffith University, Don Young Road, Brisbane, 4111, Australia NatureBank, Griffith University, Don Young Road, Brisbane, 4111, Australia Centre for Cell Factories and Biopolymers, Griffith University

• from either Sigma or Aaron Chemicals. Sponge material The sponge sample was obtained from the NatureBank [23] biota library housed at the Institute for Biomedicine and Glycomics, Griffith University, Australia. A voucher specimen of Suberea ianthelliformis (NB6014998; phylum Porifera, class

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

• networks (NN), that allows the user to predict protein-glycan binding. Taking a glycan structure as input, this model is able to predict the strength of the interaction based on the relative fluorescence units (RFUs) measured in the Consortium for Functional Glycomics glycan arrays and extrapolating these

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

• sugar model [3], checks that structures match the nomenclature used for deposition in the PDB [14], compares glycan compositions to known structures as reported by glycomics (e.g., GlyConnect [19]) and glyco-informatics (e.g., GlyTouCan [20]) databases and repositories [15], and checks how close the

• unlikeliness for a human to read and understand the WURCS code. It is much more likely that the WURCS code would be copied and searched for in a glycomics database, hence we provide that functionality in a streamlined way. The final section of the validation report includes all of the validation metrics

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

• and ask yourself, how one could label an Eppendorf vial with the most condensed approved version of the IUPAC code (Figure 1). The graphical depictions are far more illustrative. On top, the “Symbol Nomenclature for Glycans” (SNFG) format, which was pioneered by the Consortium of Functional Glycomics

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

• Glycomics (NCFG, Figure 2a) and the Glycosciences Laboratory at Imperial College London (ICL, Figure 2b). We note that, together, this encompasses 1,046 unique glycan sequences, spanning all major glycan classes and substantial taxonomic diversity. Next to these unique sequences, even more effects stem from

• kDa MWCO centrifugal filter prior to TEV cleavage. Glycan array experiments NCFG array For the NCFG array, data was collected by the National Center for Functional Glycomics (NCFG) at Beth Israel Deaconess Medical Center, Harvard Medical School. For experiments, a standard binding buffer (20 mM Tris

• File 1, table “imperial” and “rca_imperial”, with the array generation in Supporting Information File 3 according to the MIRAGE guidelines (Minimum Information Required for A Glycomics Experiment) [32]. For both array types, data were transformed into z-scores by subtracting the mean value across the

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

• intelligence in combination with spectroscopy-augmented mass spectrometry for carbohydrates sequencing and glycomics applications. Keywords: Bayesian neural network; deep learning; glycomics; IR; spectroscopy; Introduction DNA and protein sequencing technologies that aim at determining the structure of a

• dedicated analytical tools (glycomics) is clearly identified as a critical bottleneck, impeding the full development of glycosciences despite their relevance for various strategic fields such as pharmaceutical and food industry; bio-based materials and renewable energy, and their considerable potential

• spectroscopic fingerprints database. In MS–IR experiments, the IR data as well as the mass of the molecule are simultaneously acquired, therefore the mass could readily be used as a prefilter. More generally, all experimental data obtained in a glycomics workflow – such as MS/MS; HPLC; ion mobility; … – could

Synthesis, α-mannosidase inhibition studies and molecular modeling of 1,4-imino-ᴅ-lyxitols and their C-5-altered N-arylalkyl derivatives

• Martin Kalník,
• Sergej Šesták,
• Juraj Kóňa,
• Maroš Bella and
• Monika Poláková

Beilstein J. Org. Chem. 2023, 19, 282–293, doi:10.3762/bjoc.19.24

• Martin Kalnik Sergej Sestak Juraj Kona Maros Bella Monika Polakova Institute of Chemistry, Center for Glycomics, Slovak Academy of Sciences, Dúbravská cesta 9, 845 38 Bratislava, Slovakia Medical Vision, Civic Research Association, Záhradnícka 4837/55, 82108 Bratislava, Slovakia 10.3762/bjoc

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

• , degree of experimental evidence, and the statistical approaches taken by other investigators can influence the set of TF–gene relationships found. In addition to in silico validation, perturbational experiments, such as performing CRISRP-Cas9 knockouts with single-cell RNA-Seq, followed by glycomics

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

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

• possible to get deeper insights on precisely the information that is most difficult to recover by structure solution methods: the full-length glycan composition, including linkage details for the glycosidic bonds. The developments have given rise to glycomics. Thankfully, several large scale glycomics

• initiatives have stored results in publicly available databases, some of which can be accessed through API interfaces. In the present work, we will describe how the Privateer carbohydrate structure validation software has been extended to harness results from glycomics projects, and its use to greatly improve

• the validation of 3D glycoprotein structures. Keywords: electron cryomicroscopy; glycoinformatics; glycomics; Privateer; X-ray crystallography; Introduction Glycosylation-related processes are prevalent in life. The attachment of carbohydrates to macromolecules extends the capabilities of cells to

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

• greyscale colouring as the so-called Oxford nomenclature (UOXF) [4][5], and even fully coloured schemes later on. Among these, some of the proposed representation forms have been accepted and implemented by several groups and initiatives, namely the Consortium for Functional Glycomics (CFG) [6]. Whereas the

• Glycomics@ExPASy (available at https://glycoproteome.expasy.org/sugarsketcher/) for online drawing of glycan structures. The interactive graphical interface (Figure 4, top) allows glycan drawing by glycobiologists and non-expert users. In particular, a “Quick Mode” helps users with limited knowledge of

• sequence converter from one format to another. Original GlycanBuilder. GlycanBuilder [10][20] was originally part of the GlycoWorkbench platform [49]. This interface is integrated in most tools of the Glycomics@ExPASy collection that require a drawing interface to query data. GlycanBuilder is written in

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

• available on the National Center for Functional Glycomics website (https://ncfg.hms.harvard.edu/protocols), for the multi-well chamber method (e.g., NCFG slides) and for the coverslip method (e.g., CFG slides). Data acquisition Once the slides are dried post sample incubation, the slides are scanned using a

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

• , Faculty of Engineering, National University of Singapore (NUS), Singapore 117575 10.3762/bjoc.16.176 Abstract The accurate assessment of antibody glycosylation during bioprocessing requires the high-throughput generation of large amounts of glycomics data. This allows bioprocess engineers to identify

• qualitatively curate large cohort CE-LIF glycomics data. For glycan identification, a previously reported method based on internal triple standards is used. For determining the glycan relative quantities our method uses a clustering algorithm to ‘divide and conquer’ highly heterogeneous electropherograms into

• . Here, we describe a computational solution for the identification and quantitation of glycans in a large glycomics CE dataset generated during process development of an anti-HER-2 antibody. The method is a semi-automated approach and improved accurate glycan assignments and quantitation compared to

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

• represents an essential resource to advance glycomics [11][12][13][14][15], while molecular simulations fit in very well as complementary and orthogonal techniques to support and advance structural glycobiology research. Indeed, current high performance computing (HPC) technology allows us to study realistic

Synthesis of 1,4-imino-L-lyxitols modified at C-5 and their evaluation as inhibitors of GH38 α-mannosidases

• Maroš Bella,
• Sergej Šesták,
• Ján Moncoľ,
• Miroslav Koóš and
• Monika Poláková

Beilstein J. Org. Chem. 2018, 14, 2156–2162, doi:10.3762/bjoc.14.189

• the Slovak Research and Development Agency (Grant no. APVV-0484-12) and Scientific Grant Agency (Grant no. VEGA 2/0064/15) is gratefully acknowledged. This contribution is the result of the project implementation: Centre of Excellence for Glycomics, ITMS26240120031, supported by the Research

Intramolecular glycosylation

• Xiao G. Jia and
• Alexei V. Demchenko

Beilstein J. Org. Chem. 2017, 13, 2028–2048, doi:10.3762/bjoc.13.201

• glycosylation reactions wherein the facial stereoselectivity is achieved by tethering of the glycosyl donor and acceptor counterparts. Keywords: carbohydrates; glycosylation; intramolecular reactions; oligosaccharides; Introduction With recent advances in glycomics [1][2], we now know that half of the

Biomimetic molecular design tools that learn, evolve, and adapt

• David A Winkler

Beilstein J. Org. Chem. 2017, 13, 1288–1302, doi:10.3762/bjoc.13.125

• deep scientific questions [26]. Examples include gene expression microarray technologies, rapid development of glycomics technologies, large-scale use of proteomics, and the proliferation of mathematical descriptions of molecules and more complex materials. Analogous to biological feature detection

Towards inhibitors of glycosyltransferases: A novel approach to the synthesis of 3-acetamido-3-deoxy-D-psicofuranose derivatives

• Maroš Bella,
• Miroslav Koóš and
• Chun-Hung Lin

Beilstein J. Org. Chem. 2015, 11, 1547–1552, doi:10.3762/bjoc.11.170

• Excellence for Glycomics, ITMS26240120031, supported by the Research & Development Operational Programme funded by the ERDF. The technical assistance afforded by Ms Beáta Kalivodová is gratefully acknowledged.

Expanding the scope of cyclopropene reporters for the detection of metabolically engineered glycoproteins by Diels–Alder reactions

• Anne-Katrin Späte,
• Verena F. Schart,
• Julia Häfner,
• Andrea Niederwieser,
• Thomas U. Mayer and
• Valentin Wittmann

Beilstein J. Org. Chem. 2014, 10, 2235–2242, doi:10.3762/bjoc.10.232

• biological events, 1 represents a promising probe for future glycomics studies. Of special interest is the fact that cyclopropene tags can be combined with azide–alkyne cycloaddition to achieve dual labeling of two different (sugar) moieties as was shown earlier [19][21][23][24][30]. Experimental General

Reaction of selected carbohydrate aldehydes with benzylmagnesium halides: benzyl versus o-tolyl rearrangement

• Maroš Bella,
• Bohumil Steiner,
• Vratislav Langer and
• Miroslav Koóš

Beilstein J. Org. Chem. 2014, 10, 1942–1950, doi:10.3762/bjoc.10.202

• Maros Bella Bohumil Steiner Vratislav Langer Miroslav Koos Institute of Chemistry, Center for Glycomics, Slovak Academy of Sciences, Dúbravská cesta 9, 845 38 Bratislava, Slovakia Environmental Inorganic Chemistry, Department of Chemical and Biological Engineering, Chalmers University of

• financial support from the Slovak Research and Development Agency (Grant no. APVV-0484-12) and Scientific Grant Agency (Grant nos. VEGA 2/0101/11 and 1/0962/12) is gratefully acknowledged. This contribution is the result of the project implementation: Centre of Excellence for Glycomics, ITMS 26240120031

Molecular recognition of surface-immobilized carbohydrates by a synthetic lectin

• Melanie Rauschenberg,
• Eva-Corrina Fritz,
• Christian Schulz,
• Tobias Kaufmann and
• Bart Jan Ravoo

Beilstein J. Org. Chem. 2014, 10, 1354–1364, doi:10.3762/bjoc.10.138

• sequencing, automated synthesis and high-throughput microarray screening are lacking in glycobiology [5]. However, during the last decade, these methods have also been adapted to carbohydrates [6][7][8]. Carbohydrate microarrays on chips proved to be a particularly useful tool in glycomics [9][10][11] since

Synthesis of a derivative of α-D-Glcp(1->2)-D-Galf suitable for further glycosylation and of α-D-Glcp(1->2)-D-Gal, a disaccharide fragment obtained from varianose

• Carla Marino,
• Carlos Lima,
• Karina Mariño and
• Rosa M. de Lederkremer

Beilstein J. Org. Chem. 2012, 8, 2142–2148, doi:10.3762/bjoc.8.241

• , establishing the connection between the glycan structure and its biological function, a discipline called functional glycomics, is one of the most challenging areas nowadays and requires a broad interdisciplinary approach. Penicillium varians elaborates an unusual polysaccharide called varianose [3] that

Studies on the substrate specificity of a GDP-mannose pyrophosphorylase from Salmonella enterica

• Lu Zou,
• Ruixiang Blake Zheng and
• Todd L. Lowary

Beilstein J. Org. Chem. 2012, 8, 1219–1226, doi:10.3762/bjoc.8.136

• Lu Zou Ruixiang Blake Zheng Todd L. Lowary Alberta Glycomics Centre and Department of Chemistry, The University of Alberta, Edmonton, AB T6G 2G2, Canada 10.3762/bjoc.8.136 Abstract A series of methoxy and deoxy derivatives of mannopyranose-1-phosphate (Manp-1P) were chemically synthesized, and

• the Alberta Glycomics Centre, the University of Alberta and the Natural Sciences and Engineering Research Council of Canada. We thank Dr. Warren Wakarchuk at the National Research Council of Canada for providing the plasmid containing the GDP-ManPP gene, and Mr. Myles B. Poulin for technical

The use of glycoinformatics in glycochemistry

• Thomas Lütteke

Beilstein J. Org. Chem. 2012, 8, 915–929, doi:10.3762/bjoc.8.104

• aid with quality control. Specific problems of glycoinformatics compared to bioinformatics for genomics or proteomics, especially concerning integration and long-term maintenance of the existing glycan databases, are also discussed. Keywords: carbohydrates; databases; glycomics; software

• resources have also been started. Formats and protocols for data exchange have been specified [9][10]. Recently, the MIRAGE (Minimum Information Related to A Glycomics Experiment) consortium was founded to define checklists for the standardization of experimental glycomics data and meta information [11

• weekly with new PDB entries. Other databases that implemented CarbBank data are KEGG GLYCAN [35], EUROCarbDB [24], and the Glycan Structure Database of the Consortium for Functional Glycomics (CFG) [21]. KEGG GLYCAN is part of the Kyoto Encyclopedia of Genes and Genomes (KEGG) and integrates carbohydrate