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

• 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

• . [12] in the context of photoswitchable ligands to the lectin LecA. Staying in the theme of lectin characterization, Lundstrøm et al. study the glycan binding profile of CMA1 originating from melon [13]. A time that sees great opportunities in computational biology also breeds innovative applications

Improved deconvolution of natural products’ protein targets using diagnostic ions from chemical proteomics linkers

• Andreas Wiest and
• Pavel Kielkowski

Beilstein J. Org. Chem. 2024, 20, 2323–2341, doi:10.3762/bjoc.20.199

• charged pyridinium or imidazolium residues (Figure 7D) [88]. The presence of a stable positively charged moiety in the linker has, in case of imidazolium, increased by 70–90% the relative abundance of modified peptides. In combination with release of the glycan-related oxonium ions during fragmentation

• forms a tight non-covalent complex with a glycoprotein called avidin or its non-glycosylated form streptavidin, from which the latter shows less of an unspecific protein–protein interaction due to missing glycan groups [93]. Streptavidin-coated paramagnetic agarose or acrylamide beads are used for pull

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

• Ferran Nieto-Fabregat Maria Pia Lenza Angela Marseglia Cristina Di Carluccio Antonio Molinaro Alba Silipo Roberta Marchetti Department of Chemical Sciences, University of Naples Federico II, Via Cinthia 4, 80126, Italy 10.3762/bjoc.20.180 Abstract Protein–glycan interactions play pivotal roles in

• , both in free state and in complex with proteins, also with reference to the principles, methodologies, and applications of all-atom molecular dynamics simulations. Herein, we focused on the programs that are generally employed for preparing protein and glycan input files to execute molecular dynamics

• simulations and analyse the corresponding results. The presented computational toolbox represents a valuable resource for researchers studying protein–glycan interactions and incorporates advanced computational methods for building, visualising and predicting protein/glycan structures, modelling protein

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

• numerous enhancements to the findability and interpretability of deposited glycan structures, yet crucial quality indicators are either missing or hard to find on the PDB pages. Without a way to access wider glycochemical context, problematic structures may be taken as fact by keen but inexperienced

• 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

• glycoprotein as pointed out by Crispin and collaborators [8], and more recently the realisation that high-energy ring conformations, a rare event in six-membered pyranosides, were present in ca. 15% of the N-glycan components of glycoproteins in the PDB [3]. Many of these findings originated the development of

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

• 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

• definition of positional isomers. The combination with elements of the condensed IUPAC code allows to describe even rather complex structural elements. Thus, this “proglycan” coding could be the missing link between drawn structures and software-oriented representations of N-glycan structures. On top, it may

• greatly facilitate keyboard-based mining for glycan substructures in glycan repositories. Keywords: N-glycans; nomenclature; structural features; Introduction Virtually any article on protein glycosylation starts with imposing assurances about the biological significance of the various structures. This

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

• garnered attention for their roles as laboratory probes and potential therapeutics. Here, we report the discovery and characterization of Cucumis melo agglutinin (CMA1), a new R-type lectin from melon. Our findings reveal CMA1’s unique glycan-binding profile, mechanistically explained by its 3D structure

• , augmenting our understanding of R-type lectins. We expressed CMA1 recombinantly and assessed its binding specificity using multiple glycan arrays, covering 1,046 unique sequences. This resulted in a complex binding profile, strongly preferring C2-substituted, beta-linked galactose (both GalNAc and Fuca1-2Gal

• groundwork for further exploration of its biological and therapeutic potential. Keywords: carbohydrate; glycan array; melon; plant lectin; R-type; Introduction Lectins have long been the subject of intense scientific scrutiny, serving as molecular bridges that span the realms of biochemistry, cellular

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

• glycan sequences. We showed that, while loading and reaction scale did not significantly influence the AGA outcome, the chemical nature of the linker dramatically altered the isolated yields. We identified that the major determinants of AGA yields are cleavage from the solid support and post-AGA

• purification steps. Keywords: automated glycan assembly; photocleavable linker; polysaccharides; solid-phase synthesis; Introduction Automated glycan assembly (AGA) is a solid-phase method that enables the rapid synthesis of complex oligo- and polysaccharides from protected monosaccharide building blocks

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

• proteomic data into a glycomic context by harvesting information about glyco-related genes and proteins. Glycobioinformatics requires additional information about the expressed glycan, including but not limited to monosaccharide composition, full or partial sequence including linkage and branching structure

• tightly connected to mainstream bioinformatics. For example, databases and tools from genomics can be used for gaining information about genes encoding for glycosyltransferases, glycosidases, and glycan-binding proteins (lectins), and search engines initially designed for the detection of

• ] 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

• approach (also known as fragment coupling) allows connecting pre-assembled oligosaccharide blocks. To decrease the synthetic time required for the chemical synthesis of polysaccharides, automated techniques have been developed [28][29][30][31]. Automated glycan assembly (AGA) connects monosaccharide BBs on

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

• , State University of New York, Buffalo, NY 14260, USA 10.3762/bjoc.17.119 Abstract Glycosylation is a common posttranslational modification, and glycan biosynthesis is regulated by a set of glycogenes. The role of transcription factors (TFs) in regulating the glycogenes and related glycosylation

• health and disease may affect multiple carbohydrate structures. Upon applying the Fisher’s exact test along with glycogene pathway classification, we identified TFs that may specifically regulate the biosynthesis of individual glycan types. Integration with Reactome DB knowledge provided an avenue to

• relationships, the starting point for experimental system-wide validation. Keywords: ChIP-Seq; glycoinformatics; glycosylation; TCGA transcription factor; Introduction The glycan signatures of cells and tissue are controlled by the expression pattern of 300–350 glycosylating-related genes that are together

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

• biomedical applications due to their ability to inhibit the glycan and glycosaminoglycan biosynthesis [34][35][36][37]. The fluorine substituent has typically been introduced into these GlcNAc and GalNAc analogues using nucleophilic fluorination. The primary position (C6 hydroxy group) was fluorinated by

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

• ], Tellurium [39], or other modelling software supporting this format. Glycan structures can be imported or exported as GlycoCT [40], and exported as Linear Code [41] or IUPAC condensed linear formats. Sets of structures can be downloaded as CSV or GlycoCT. A key function of Glycologue is the ability to

• predict the enzymes required for the biosynthesis of a given glycan; the subset of the enzyme activities can then be used to generate all of the ganglioside carbohydrates, starting from ceramide, or any other structure. By setting a baseline knockout of enzyme activities, the effects of further knockouts

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

• complex, reflecting the high structural diversity of peptide and glycan portions. The use of glycopeptide-centered glycoproteomics by mass spectrometry is rapidly evolving in many research areas, leading to a demand in reliable data analysis tools. In recent years, several bioinformatic tools were

• -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

• identify all glycopeptides in a cluster. The clusters are also presented in interactive graphs, which assist in the identification of false-positive connections (unlikely mass/RT shifts) and unexpected glycopeptide clusters (e.g., missed cleaved products and peptide or glycan modifications). This

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

• 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

• 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

• synthesized through the serial addition of monosaccharides to form large polysaccharides. To build computational models of glycan synthesis, the biochemical reactions involved must be defined and described mathematically in a form that can be interpreted by computers [1][2][3]. Several groups have created

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

• 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

• the MUC1 VNTR domain. Thus, a synthetic 8-amino acid peptide (APDTRPAP) and the corresponding Tn glycopeptide were synthesized. It was found from the co-crystallization of the AR20.5 antigen-binding fragment (Fab) with the MUC1 peptide and glycopeptide that the glycan moiety of the glycopeptide did

• not bind to the antibody (Figure 1 and PDB ID:5T6P, 5T78). This is unusual considering that in previous experiments of murine antibody SM3 that Brooks [17] found the glycan forms part of the epitope and binds directly to the antibody. Movahedin et al. hypothesized that the glycan modulates the

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

• [2][3]; the level of N-glycan expression regulates the adhesiveness of a cell [4]; glycosylation also plays a role in immune function [5] and cellular signalling [5][6]. At the forefront, glycosylation plays a significant role in influencing protein–protein interactions. For example, the influenza

• virus uses the haemagglutinin glycoprotein to recognise and bind sialic acid decorations of human cells in the respiratory tract [7]. Glycosylation is also used by pathogens to evade the host’s immune system via glycan shields [8][9][10], and thereby to delay an immune response [11]. The structural

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

• 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

• aspects of glycan identification, quantification and visualisation, some of which will be further developed throughout this article. Biological molecules express their function throughout their three-dimensional structures. For this reason, structural biology places great emphasis on the three-dimensional

• an essential part of the research process, data visualisation allows not only to communicate experimental results but also is a crucial step in the integration of multiple data derived resources, such as thermodynamics and kinetic analysis, glycan arrays, mutagenesis, etc. Data visualisation remains

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

• properties of BODIPY and the biocompatibility of dextran. The BODIPY dextran nanoparticles were characterized regarding their size, morphology, polarity, and toxicity in vitro. We demonstrate the feasibility of mechanochemistry for boronic ester formation [23] to a glycan polymer as a route of conjugation

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

• the mammalian body. They play a vital role in a number of physiologic and pathologic conditions. Glycan microarrays allow a plethora of information to be obtained on protein–glycan binding interactions. In this review, we describe the intricacies of the generation of glycan microarray data and the

• experimental methods for studying binding. We highlight the importance of this knowledge before moving on to the data analysis. We then highlight a number of tools for the analysis of glycan microarray data such as data repositories, data visualization and manual analysis tools, automated analysis tools and

• 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

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

• ]. This heterogeneity can take the form of variable occupancy, also known as macroheterogeneity – the presence or absence of modification at a particular site in a protein, due to inefficient transfer of the initial glycan structure [5]. In addition, the non-template-driven synthesis of glycan structures

• means that there can be multiple different glycan structures attached at the same site in a pool of mature glycoproteins [6]. This structural heterogeneity is also known as microheterogeneity. This heterogeneity in glycan structure and occupancy can be influenced by many genetic and environmental

• packages have been developed for analysis of outputs from MS technology to automate the process of transformation of raw MS data into ion intensities and matching them with appropriate glycan and peptide sequence databases for glycopeptide identification (reviewed in [12][13][14][15][16]). However, there

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

• 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

• optimization study. The key advantage of this computational approach is that all runs can be analyzed simultaneously with high accuracy in glycan identification and quantitation and there is no theoretical limit to the scale of this method. Keywords: capillary electrophoresis; clustering; data analysis

• , migration time is generally calculated by correlation with internal standards that bracket the time of elution of the glycans of interest [10]. This process is used to calculate a glucose unit (GU) which helps to align the datasets so that the GU of each glycan can be used to identify the glycan through

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

• also compare these structural motifs and combine them with mammalian N-glycan motifs to devise strategies for the control of the N-glycan 3D structure through sequence. Our results suggest that the N-glycans’ architecture can be described in terms of the local spatial environment of groups of

• implicated in the cell’s interactions with its environment, facilitating communication and infection [1][2]. These processes are often initiated by molecular recognition involving carbohydrate-binding proteins (lectins) or by glycan–glycan interactions [1][3][4][5], all events that hinge on specific

• structural and dynamic features of the glycans. This makes the 3D complementarity of the glycans architecture key towards the success of these processes and an essential piece of information for us to have in order to understand glycan recognition. Because of their chemical nature, glycans are intrinsically

Synthesis of Streptococcus pneumoniae serotype 9V oligosaccharide antigens

• Sharavathi G. Parameswarappa,
• Claney L. Pereira and
• Peter H. Seeberger

Beilstein J. Org. Chem. 2020, 16, 1693–1699, doi:10.3762/bjoc.16.140

• vaccines. The serotype 9V is part of the currently marketed conjugate vaccine and contains an acetate modification. To better understand the importance of glycan modifications in general and acetylation in particular, defined oligosaccharide antigens are needed for serological and immunological studies

• ManpNAc were synthesized using a convergent [2 + 3] glycosylation strategy. The antigens we prepared will be employed in serological studies using glycan arrays and immunological studies in vivo to probe the significance of the acetate group for immunogenicity and antigenicity. Streptococcus pneumoniae 9V

Automated glycan assembly of arabinomannan oligosaccharides from Mycobacterium tuberculosis

• Alonso Pardo-Vargas,
• Priya Bharate,
• Martina Delbianco and
• Peter H. Seeberger

Beilstein J. Org. Chem. 2019, 15, 2936–2940, doi:10.3762/bjoc.15.288

• the understanding of these polysaccharides and the development of novel therapeutical and diagnostic agents. Automated glycan assembly (AGA) was employed to prepare the core structure of AM from MTB, containing α-(1,6)-Man, α-(1,5)-Ara, and α-(1,2)-Man linkages. The introduction of a capping step

• after each glycosylation and further optimized reaction conditions allowed for the synthesis of a series of oligosaccharides, ranging from hexa- to branched dodecasaccharides. Keywords: arabinomannan; automated glycan assembly; capping; Introduction Bacterial infections caused by MTB killed 1.7

• lipoarabinomannan (LAM) analogs in urine samples using the Alere lateral flow urine (LF)-LAM assay [13]. However, the low sensitivity of the Alere assay limits its scope. Glycan array screening of more than 100 monoclonal antibodies against more than 60 synthetic glycans related to the mycobacterial cell wall

Design, synthesis and biological evaluation of immunostimulating mannosylated desmuramyl peptides

• Rosana Ribić,
• Ranko Stojković,
• Lidija Milković,
• Mariastefania Antica,
• Marko Cigler and
• Srđanka Tomić

Beilstein J. Org. Chem. 2019, 15, 1805–1814, doi:10.3762/bjoc.15.174

• and TLR ligands [48]. Mannose receptors are one group in the CLRs family which exist as soluble and transmembrane receptors [4]. Like TLRs they initiate innate immune responses and activate acquired immunity. Mannose structures on the other hand, are one of the glycan structures that build up tumor