Glycosciences.DB: an annotated data collection linking glycomics and proteomics data (2018 update)

Böhm, Michael; Bohne-Lang, Andreas; Frank, Martin; Loss, Alexander; Rojas-Macias, Miguel A.; Lütteke, Thomas

doi:10.1093/nar/gky994

Cited by 70 publications

(43 citation statements)

References 31 publications

(31 reference statements)

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“…The GlyProt tool (Glycosciences.DB portal 40 (www.glycosciences.de)) was employed to attach carbohydrate chains at each N-linked glycosylation sites onto the full trimeric S-protein structure 41 . Six different glycans were considered for full homogeneously glycosylation patterns that are schematically depicted in Fig.…”

Section: Building Fully Glycosylated Trimer Model Of Sars-cov-2 S-promentioning

confidence: 99%

Impact of glycan cloud on the B-cell epitope prediction of SARS-CoV-2 Spike protein

Wintjens

Bifani

2020

npj Vaccines

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The SARS-CoV-2 outbreak originated in China in late 2019 and has since spread to pandemic proportions. Diagnostics, therapeutics and vaccines are urgently needed. We model the trimeric Spike protein, including flexible loops and all N-glycosylation sites, in order to elucidate accessible epitopes for antibody-based diagnostics, therapeutics and vaccine development. Based on published experimental data, six homogeneous glycosylation patterns and two heterogeneous ones were used for the analysis. The glycan chains alter the accessible surface areas on the S-protein, impeding antibody-antigen recognition. In presence of glycan, epitopes on the S1 subunit, that notably contains the receptor binding domain, remain mostly accessible to antibodies while those present on the S2 subunit are predominantly inaccessible. We identify 28 B-cell epitopes in the Spike structure and group them as nonaffected by the glycan cloud versus those which are strongly masked by the glycan cloud, resulting in a list of favourable epitopes as targets for vaccine development, antibody-based therapy and diagnostics.

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Section: Building Fully Glycosylated Trimer Model Of Sars-cov-2 S-promentioning

confidence: 99%

Impact of glycan cloud on the B-cell epitope prediction of SARS-CoV-2 Spike protein

Wintjens

Bifani

2020

npj Vaccines

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“…Although the glycan repertoire utilised in Eukaryotic systems is thought to be large, the diversity within any given biological sample is constrained by the limited number of monosaccharides used in Eukaryotic systems [7], as well as the expression of proteins required for the construction of glycans such as glycosyltransferases [8]. Experimentally, these constraints lead to only a limited number of glycans being produced across Eukaryotic samples [9, 10] despite the large number of potential glycan structures [11, 12]. This limited diversity within both Eukaryotic N -linked and O -linked glycans has enabled the development of glycan databases which have facilitated high throughput glycoproteomic studies [13] using tools such as Byonic [14] and pGlyco [15].…”

Section: Introductionmentioning

confidence: 99%

Open database searching enables the identification and comparison of bacterial glycoproteomes without defining glycan compositions prior to searching

Izaham

Scott

2020

Preprint

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

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“…Considering efficiency and usability of 3D representation based on SNFG concept, which grows popular among glycoscientists, the development of alternative solutions in carbohydrate 3D structure representations has a potential for application in glycoinformatics projects. Support of colored residues in 3D structures implemented via JSmol on GLYCOSCIENCES.de portal was reported [ 47 ] ( Figure 11 b). Similarly, CSDB project has developed a 3D viewer ( …”

Section: 3d Structure Input and Visualizationmentioning

confidence: 96%

“…Despite being a valuable source of 3D structural data for glycoscientists, PDB lacks convenient search facilities for glycan structures. Some projects have developed data-mining tools capable of retrieving bioglycan molecular geometry data from PDB: Glycan Reader ( ) [ 260 , 261 ] ( ), pdb2linucs (GLYCOSCIENCES.de) [ 47 , 259 , 318 ] ( ), GlycoNAVI TCarp [ 61 ] ( ) (https://gitlab.com/glyconavi/pdb2glycan) and GlyFinder (GLYCAM-Web) [ 257 , 258 ] ( ).…”

Section: Protein Data Bank and Its Validationmentioning

confidence: 99%

Three-Dimensional Structures of Carbohydrates and Where to Find Them

Scherbinina

Toukach

2020

IJMS

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Analysis and systematization of accumulated data on carbohydrate structural diversity is a subject of great interest for structural glycobiology. Despite being a challenging task, development of computational methods for efficient treatment and management of spatial (3D) structural features of carbohydrates breaks new ground in modern glycoscience. This review is dedicated to approaches of chemo- and glyco-informatics towards 3D structural data generation, deposition and processing in regard to carbohydrates and their derivatives. Databases, molecular modeling and experimental data validation services, and structure visualization facilities developed for last five years are reviewed.

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Glycosciences.DB: an annotated data collection linking glycomics and proteomics data (2018 update)

Cited by 70 publications

References 31 publications

Impact of glycan cloud on the B-cell epitope prediction of SARS-CoV-2 Spike protein

Impact of glycan cloud on the B-cell epitope prediction of SARS-CoV-2 Spike protein

Open database searching enables the identification and comparison of bacterial glycoproteomes without defining glycan compositions prior to searching

Three-Dimensional Structures of Carbohydrates and Where to Find Them

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