The author warmly acknowledges past and present members of the Proteome Informatics Group involved in developing the resources used in this tutorial, specifically, Julien Mariethoz and Catherine Hayes for GlyConnect, Fran&#231;ois Bonnardel for UniLectin, Davide Alocci, and Frederic Nikitin for the Octopus, and Thibault Robin for Compozitor and final touch on Octopus.
The development of the glyco@Expasy project is supported by the Swiss Federal Government through the State Secretariat for Education, Research and Innovation (SERI) and is currently complemented by the Swiss National Science Foundation (SNSF: 31003A_179249). ExPASy is maintained by the Swiss Institute of Bioinformatics and hosted at the Vital-IT Competency Center. The author also acknowledges Anne Imberty for outstanding cooperation on the UniLectin platform jointly supported by ANR PIA Glyco@Alps (ANR-15-IDEX-02), Alliance Campus Rhodanien Co-funds (http://campusrhodanien.unige-cofunds.ch) Labex Arcane/CBH-EUR-GS (ANR-17-EURE-0003).

NOTE: A device with an Internet connection (larger screen preferred) and an up-to-date Web browser such as Chrome or Firefox is required. Using Safari or Edge may not be as reliable.
1. From a protein N-glycome in GlyConnect to a lectin of UniLectin
<ol>
	<li>Accessing resources from Glyco@Expasy 
	NOTE: The procedure described here is to access GlyConnect but can be applied to accessing any resource recorded in the platform.
	<ol>
		<li>Go to https://g...

<ol>
	<li>Spring Harbor Laboratory Press. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=.">.</a> Essentials of Glycobiology. , (2015).</li><li>Gray, C. J., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Advancing+solutions+to+the+carbohydrate+sequencing+challenge.">Advancing solutions to the carbohydrate sequencing challenge.</a> Journal of the American Chemical Society. 141 (37), 14463-14479 (2019).</li><li>Tsuchiya, S., Yamada, I., Aoki-Kinoshita, K. F. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=GlycanFormatConverter:+a+conversion+tool+for+translating+the+complexities+of+glycans.">GlycanFormatConverter: a conversion tool for translating the complexities of glycans.</a> Bioinformatics. 35 (14), 2434-2440 (2018).</li><li>Fujita, A., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=The+international+glycan+repository+GlyTouCan+version+3.0.">The international glycan repository GlyTouCan version 3.0.</a> Nucleic Acids Research. 49, 1529-1533 (2021).</li><li>Alocci, D., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=GlyConnect:+glycoproteomics+goes+visual,+interactive,+and+analytical.">GlyConnect: glycoproteomics goes visual, interactive, and analytical.</a> Journal of Proteome Research. 18 (2), 664-677 (2019).</li><li>York, W. S., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=GlyGen:+computational+and+informatics+resources+for+glycoscience.">GlyGen: computational and informatics resources for glycoscience.</a> Glycobiology. 30 (2), 72-73 (2020).</li><li>Watanabe, Y., Aoki-Kinoshita, K. F., Ishihama, Y., Okuda, S. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=GlycoPOST+realizes+FAIR+principles+for+glycomics+mass+spectrometry+data.">GlycoPOST realizes FAIR principles for glycomics mass spectrometry data.</a> Nucleic Acids Research. 49, 1523-1528 (2020).</li><li>Campbell, M. P., Aoki-Kinoshita, K. F., Lisacek, F., York, W. S., Packer, N. H. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Glycoinformatics.">Glycoinformatics.</a> Essentials of Glycobiology. , (2015).</li><li>Cao, W., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Recent+advances+in+software+tools+for+more+generic+and+precise+intact+glycopeptide+analysis.">Recent advances in software tools for more generic and precise intact glycopeptide analysis.</a> Molecular &amp; Cellular Proteomics. 20, 100060 (2021).</li><li>Mariethoz, J., Hayes, C., Lisacek, F. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Glycan+compositions+with+Compozitor+to+enhance+glycopeptide+identification.">Glycan compositions with Compozitor to enhance glycopeptide identification.</a> Proteomics Data Analysis. 2361, 109-127 (2021).</li><li>Kawahara, R., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Communityevaluation+of+glycoproteomics+informatics+solutions+reveals+high-performance+search+strategies+of+serum+glycopeptide+analysis.">Communityevaluation of glycoproteomics informatics solutions reveals high-performance search strategies of serum glycopeptide analysis.</a> Nature Methods. 18, 1304-1316 (2021).</li><li>Lisacek, F., Aoki-Kinoshita, K. F., Vora, J. K., Mazumder, R., Tiemeyer, M. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Glycoinformatics+resources+integrated+through+the+GlySpace+Alliance.">Glycoinformatics resources integrated through the GlySpace Alliance.</a> Comprehensive Glycoscience. 1, 507-521 (2021).</li><li>Mariethoz, J., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Glycomics@ExPASy:+bridging+the+gap.">Glycomics@ExPASy: bridging the gap.</a> Molecular &amp; Cellular Proteomics. 17 (11), 2164-2176 (2018).</li><li>Duvaud, S., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Expasy,+the+swiss+bioinformatics+resource+portal,+as+designed+by+its+users.">Expasy, the swiss bioinformatics resource portal, as designed by its users.</a> Nucleic Acids Research. 49, 216-227 (2021).</li><li>The UniProt Consortium et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=UniProt:+the+universal+protein+knowledgebase+in+2021.">UniProt: the universal protein knowledgebase in 2021.</a> Nucleic Acids Research. 49, 480-489 (2021).</li><li>Bonnardel, F., Perez, S., Lisacek, F., Imberty, A. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Structural+database+for+lectins+and+the+UniLectin+web+platform.">Structural database for lectins and the UniLectin web platform.</a> Lectin Purification and Analysis. 2132, 1-14 (2020).</li><li>Neelamegham, S., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Updates+to+the+symbol+nomenclature+for+glycans+guidelines.">Updates to the symbol nomenclature for glycans guidelines.</a> Glycobiology. 29 (9), 620-624 (2019).</li><li>Sharon, N. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=IUPAC-IUB+Joint+Commission+on+Biochemical+Nomenclature+(JCBN).+Nomenclature+of+glycoproteins,+glycopeptides+and+peptidoglycans:+JCBN+recommendations+1985.">IUPAC-IUB Joint Commission on Biochemical Nomenclature (JCBN). Nomenclature of glycoproteins, glycopeptides and peptidoglycans: JCBN recommendations 1985.</a> Glycoconjugate Journal. 3 (2), 123-133 (1986).</li><li>Harvey, D. J., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Proposal+for+a+standard+system+for+drawing+structural+diagrams+of+N-+and+O-linked+carbohydrates+and+related+compounds.">Proposal for a standard system for drawing structural diagrams of N- and O-linked carbohydrates and related compounds.</a> Proteomics. 9 (15), 3796-3801 (2009).</li><li>Song, E., Mayampurath, A., Yu, C. -. Y., Tang, H., Mechref, Y. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Glycoproteomics:+identifying+the+glycosylation+of+prostate+specific+antigen+at+normal+and+high+isoelectric+points+by+LC-MS/MS.">Glycoproteomics: identifying the glycosylation of prostate specific antigen at normal and high isoelectric points by LC-MS/MS.</a> Journal of Proteome Research. 13 (12), 5570-5580 (2014).</li><li>Moran, A. B., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Profiling+the+proteoforms+of+urinary+prostate-specific+antigen+by+capillary+electrophoresis+-+mass+spectrometry.">Profiling the proteoforms of urinary prostate-specific antigen by capillary electrophoresis - mass spectrometry.</a> Journal of Proteomics. 238, 104148 (2021).</li><li>Wang, W., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=High-throughput+glycopeptide+profiling+of+prostate-specific+antigen+from+seminal+plasma+by+MALDI-MS.">High-throughput glycopeptide profiling of prostate-specific antigen from seminal plasma by MALDI-MS.</a> Talanta. 222, 121495 (2021).</li><li>wwPDB consortium metal. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Protein+Data+Bank:+the+single+global+archive+for+3D+macromolecular+structure+data.">Protein Data Bank: the single global archive for 3D macromolecular structure data.</a> Nucleic Acids Research. 47, 520-528 (2019).</li><li>Sehnal, D., Grant, O. C. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Rapidly+display+glycan+symbols+in+3D+structures:+3D-SNFG+in+LiteMol.">Rapidly display glycan symbols in 3D structures: 3D-SNFG in LiteMol.</a> Journal of Proteome Research. 18 (2), 770-774 (2019).</li><li>Bonnardel, F., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=UniLectin3D,+a+database+of+carbohydrate+binding+proteins+with+curated+information+on+3D+structures+and+interacting+ligands.">UniLectin3D, a database of carbohydrate binding proteins with curated information on 3D structures and interacting ligands.</a> Nucleic Acids Research. 47, 1236-1244 (2019).</li><li>Sehnal, D., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=LiteMol+suite:+interactive+web-based+visualization+of+large-scale+macromolecular+structure+data.">LiteMol suite: interactive web-based visualization of large-scale macromolecular structure data.</a> Nature Methods. 14 (12), 1121-1122 (2017).</li><li>Salentin, S., Schreiber, S., Haupt, V. J., Adasme, M. F., Schroeder, M. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=PLIP:+fully+automated+protein-ligand+interaction+profiler.">PLIP: fully automated protein-ligand interaction profiler.</a> Nucleic Acids Research. 43, 443-447 (2015).</li><li>Robin, T., Mariethoz, J., Lisacek, F. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Examining+and+fine-tuning+the+selection+of+glycan+compositions+with+GlyConnect+Compozitor.">Examining and fine-tuning the selection of glycan compositions with GlyConnect Compozitor.</a> Molecular &amp; Cellular Proteomics. 19 (10), 1602-1618 (2020).</li><li>Compagno, D., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Glycans+and+galectins+in+prostate+cancer+biology,+angiogenesis+and+metastasis.">Glycans and galectins in prostate cancer biology, angiogenesis and metastasis.</a> Glycobiology. 24 (10), 899-906 (2014).</li><li>Gentilini, L. D., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Stable+and+high+expression+of+Galectin-8+tightly+controls+metastatic+progression+of+prostate+cancer.">Stable and high expression of Galectin-8 tightly controls metastatic progression of prostate cancer.</a> Oncotarget. 8 (27), 44654-44668 (2017).</li><li>Schw&#228;mmle, V., Verano-Braga, T., Roepstorff, P. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Computational+and+statistical+methods+for+high-throughput+analysis+of+post-translational+modifications+of+proteins.">Computational and statistical methods for high-throughput analysis of post-translational modifications of proteins.</a> Journal of Proteomics. 129, 3-15 (2015).</li><li>Khatri, K., Klein, J. A., Zaia, J. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Use+of+an+informed+search+space+maximizes+confidence+of+site-specific+assignment+of+glycoprotein+glycosylation.">Use of an informed search space maximizes confidence of site-specific assignment of glycoprotein glycosylation.</a> Analytical and Bioanalytical Chemistry. 409 (2), 607-618 (2017).</li><li>Sztain, T., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=A+glycan+gate+controls+opening+of+the+SARS-CoV-2+spike+protein.">A glycan gate controls opening of the SARS-CoV-2 spike protein.</a> Nature Chemistry. 13, 963-968 (2021).</li></ol>

GlyConnect Octopus as a tool for revealing unexpected correlations 
GlyConnect Octopus was originally designed to query the database with a loose definition of glycans. Indeed, the literature often reports the main characteristics of glycans in a glycome such as being fucosylated or sialylated, being made of two or more antennae, etc. Furthermore, glycans whether N- or O-linked are classified in cores, as detailed in the reference manual Essentials of Glycobiology1, that are ...

Glycans, proteins to which they are attached (glycoproteins) and proteins to which they bind (lectins or carbohydrate-binding proteins) are the main molecular actors at the cell surface1. Despite this central role in cell-cell communication, large-scale studies, including glycomics, glycoproteomics, or glycan-interactomics data are still scarce compared to their counterpart in genomics and proteomics.
Until recently, methods for characterizing the branching structures of complex carbohydrates while still being conjugated to the carrier protein had not been developed. The biosynthesis of glycoproteins is a non-template-driven process in which the monosaccharide donors, the accepting glycoprotein substrates, and the glycosyltransferases and glycosidases play an interactive role. The resulting glycoproteins can bear complex structures with multiple branching points where each monosaccharide component can be one of the several types present in nature1. The non-template-driven process imposes biochemical analysis as the only option for generating oligosaccharide structural data. The analytical process of glycan structures attached to a native protein is often challenging as it requires sensitive, quantitative, and robust technologies to determine monosaccharide composition, linkages, and branching sequences2.
In this context, mass spectrometry (MS) is the most widely used technique in glycomics and glycoproteomics experiments. As time goes, these are carried out in higher throughput settings and data is now accumulating in databases. Glycan structures in various formats3, populate GlyTouCan4, the universal glycan data repository where each structure is associated with a stable identifier irrespective of the level of precision with which the glycan is defined (e.g., possibly missing linkage type or ambiguous composition). Very similar structures are collected but their minor differences are clearly reported. Glycoproteins are described and curated in GlyConnect5 and GlyGen6, two databases cross-referencing each other. MS data supporting structural pieces of evidence are increasingly stored in GlycoPOST7. For a wider coverage of online resources, chapter 52 of the reference manual, Essentials of Glycobiology, is dedicated to glycoinformatics8. Interestingly, glycopeptide identification software has proliferated in recent years9,10 though not to the benefit of reproducibility. The latter concern prompted the leaders of the HUPO GlycoProteomics Initiative (HGI) to set a software challenge in 2019. The MS data obtained from processing complex mixtures of N- and O-glycosylated human serum proteins in CID, ETD, and EThcD fragmentation modes, were made available to competitors whether software users or developers. The full report on the results of this challenge11 is only outlined here. To begin with, a spread of identifications was observed. It was mainly interpreted as caused by the diversity of methods implemented in search engines, of their settings, and how outputs were filtered, and peptide "counted". The experimental design may also have put some software and approaches at a (dis)advantage. Importantly, participants using the same software reported inconsistent results, thereby highlighting serious reproducibility issues. It was concluded by comparing different submissions that some software solutions perform better than others and some search strategies yield better results. This feedback is likely to guide the improvement of automated glycopeptide data analysis methods and will in turn, impact database content.
The expansion of glycoinformatics led to creating web portals that provide information and access to multiple similar or complementing resources. The most recent and up-to-date are described in a chapter of the Comprehensive Glycoscience book series12 and through cooperation, a solution to data sharing and information exchange is offered in an open access mode. One such portal was developed which was originally called Glycomics@ExPASy13 and renamed Glyco@Expasy, following the major overhaul of the Expasy platform14 that has hosted a large collection of tools and databases used across several -omics for decades, the most popular item being UniProt15-the universal protein knowledgebase. Glyco@Expasy offers a didactic discovery of the purpose and usage of databases and tools, based on a visual categorization and a display of their interdependencies. The following protocol illustrates procedures to explore glycomics and glycoproteomics data with a selection of resources from this portal that makes the connection between glycoproteomics and glycan-interactomics explicit via glycomics. As it is, glycomics experiments produce structures where monosaccharides are fully defined and linkages partially or fully determined, but their protein site attachment is poorly, if at all, characterized. In contrast, glycoproteomics experiments generate precise site attachment information but with a poor resolution of glycan structures, often limited to monosaccharide compositions. This information is pieced together in the GlyConnect database. Furthermore, search tools in GlyConnect can be used to detect potential glycan ligands which are described along with the proteins recognizing them in UniLectin16, linked to GlyConnect via glycans. The protocol presented here is divided into two sections to cover questions specific to N-linked and O-linked glycans and glycoproteins.

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			<td>recent version of web browser</td>
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bioinformatics resources for the study of glycan-mediated protein interactions

The Glyco@Expasy initiative was launched as a collection of interdependent databases and tools spanning several aspects of knowledge in glycobiology. In particular, it aims at highlighting interactions between glycoproteins (such as cell surface receptors) and carbohydrate-binding proteins mediated by glycans. Here, major resources of the collection are introduced through two illustrative examples centered on the N-glycome of the human Prostate Specific Antigen (PSA) and the O-glycome of human serum proteins. Through different database queries and with the help of visualization tools, this article shows how to explore and compare content in a continuum to gather and correlate otherwise scattered pieces of information. Collected data are destined to feed more elaborate scenarios of glycan function. Glycoinformatics introduced here is, therefore, proposed as a means to either strengthen, shape, or refute assumptions on the specificity of a protein glycome in a given context.

The first part of the protocol (section 1) showed how to investigate the specificity or the commonality of N-glycans attached on Asn-69 of the human Prostate Specific Antigen (PSA) using the GlyConnect platform. Tissue-dependent (urine and seminal fluid), as well as isoform-dependent (normal and high pI) variations in glycan expression, were emphasized using two visualizing tools (Figure 4 and Figure 5).
First, GlyCon...

Watch this Scientific Journal Video about Bioinformatics Resources for the Study of Glycan-Mediated Protein Interactions at JoVE.com

Bioinformatics Resources for the Study of Glycan-Mediated Protein Interactions

This protocol illustrates how to explore, compare, and interpret human protein glycomes with online resources.

The Glyco@Expasy initiative was launched as a collection of interdependent databases and tools spanning several aspects of knowledge in glycobiology. In ...

Bioformatics is about using computers to solve questions in biology. Glycoinformatics is about using computers to solve questions in glyco biology. With glycoinformatics, we develop databases that store glycomics or glycoproteomics data that can be browsed or searched, and we also develop tools to visualize and compare this data.The role of glycans is increasingly recognized as being important in health and disease, and glycoinformatics is trying to bring that forward as well. Catherine Hayes is trained in glyco biology and works as a data scientist. Julien Mariethoz is trained in computer science and coordinates the developments of databases and tools.Go to the website glycoproteome.expasy. org/glycomics-expasy and in the leftmost menu, check the glycoproteins box. The bubble chart on the right will zoom in in the bubble matching that category, then click on the GlyConnect bubble to open the GlyConnect homepage in a new tab.Select the protein button and in the protein view page, type prostate in the search window. Click on 790 corresponding to the common isoform of prostate-specific antigen or PSA. Next, on the top multicolored bar, click on the source button in green to display the sample types from which the published data were processed.Click on the disease button to check the health-related content of the database. Then click on the structure button to view the full list of 135 structures associated with PSA from glycomics data. Click on the composition button for the associated 78 compositions determined by glycoproteomics experiments.Click on any structure or composition to obtain further details. To reduce the ambiguity of compositions, click on suggested struct below a selected composition. A suggestion is made each time the monosaccharide count coincides with that of a listed structure.To fully explore the protein page, view further details on the right side of the page. Go to the Octopus homepage to confirm the presence of common structural traits in the diversity of glycans attached to PSA, keep the N-Linked tab selected by default, move to the cores subtab and click on the hybrid icon. Then move to the properties subtab, select the sialylated icon and click on the green search button.In the displayed graph of relationships, hover over H6N4F1S1 to highlight links to seven proteins in three structures. Contrast this by hovering over H6N4F2S1 that singles out the two isoforms of PSA. Hover over the structure ID to show its SNFG representation and click on it to open the corresponding page.Change the center nodes to tissues and then place the cursor on urine or seminal fluid in the middle of the graph to view different associations. Change the center nodes to disease to display 13 options, one of which is prostate cancer. The only protein associated is PSA.Next, click on the clear button to refresh the search. Move to the properties subtab and click on the bi-antennary icon. Then move to the determinants subtab, select the 3-sialyl-LN type two icon and click on the green search button.Check the Octopus-retrieved associations with bi-antennary glycans containing a terminal 3-sialyl-LN type two motif. Change the center nodes to tissues for easier reading and hover over KLK3_human to directly connect seminal fluid with PSA common isoform and seven structures. Go back to the protein page, in this case PSA, to perform the scan of potential relationships between each composition in a list thereof.On the right side of the PSA entry page, click on the Compozitor link. Ensure that the Compozitor search fields are pre-filled with the details of the ID 790 entry in the protein tab. Click on the add to selection button to retrieve data from the database.Deselect the include virtual nodes option and then click on the compute graph button to display a graph showing a well-connected set of 78 compositions representing the PSA N-glycome and a bar plot showing the main characteristics of the glycans. Remain in the main protein tab and select prostate-specific antigen high Pi isoform in the protein field. Click on the add to selection button to retrieve data from the database that amounts to 57 compositions.Click on the compute graph button to generate the superimposed graphs of both isoforms and assess the differences in glycomes of the two PSA isoforms. Go to the website www.unilectin. eu and click on the UniLectin3D button.Click on the glycan search button, then click on the purple diamond representing a sialic acid which prompts the display of all glycan-binding motifs ending with a sialic acid stored in the database. Click on the 3-sialyl-LN type two motif to prompt the display of all lectins for which a 3D structure confirming the interaction with 3-sialyl-LN type two is known. The search by field option.In the species field, type Homo sapiens. Click on the explore x-ray structures button to filter out the original list. Only one entry remains, that is the human galectin-8.Click on the view the 3D structure and information button to display detailed information of human galectin-8 interacting with 3-sialyl-LN type two. Access the structural information on human galectin-8 displayed on the page with two different viewers. Hold the mouse to turn the molecule around and bring the ligand to the fore with the LiteMol software.Mouse over the listed interactions on the left to update the view on the right and locate where that particular interaction acts in the structure with the PLIP software. Browse the HGI dataset from the GlyConnect homepage by going directly to the referenced article on this page. Click on the Compozitor link on the right side of the reference entry page to assess the consistency of the dataset.The search field will be already filled with reference equals to the DOI number in the advanced tab of the tool. Type glycan_type=O-linked after the DOI number to narrow down the search to O-linked glycans. Then click on the add to selection button to retrieve the data from the database.Keep the include virtual nodes option selected and click on the compute graph button to display the graph of connected compositions. Go to the protein tab of GlyConnect Compozitor and from the protein list, select inter-alpha trypsin inhibitor heavy chain H4.Ensure that the species selection is Homo sapiens by default. Deselect N-Linked in the glycan type.Select only THR 725 in the site list and click on the add to selection button. Then click on the compute graph button to display the graph of connected compositions. To make sense of the virtual nodes, click on the export button below the graph.Only select virtual and click on the clipboard icon to copy the selection of eight compositions. Paste the selection in the query window of Compozitor's custom tab. Set the selection label in the compositions field, select O-Linked in the glycan type field and click on the add to selection button.Finally, click on the compute graph button. The tissue-dependent associations between proteins and glycans are shown in this output of GlyConnect Octopus. All human proteins carrying hybrid and sialylated glycan structures with the tissues in which they are expressed are displayed in this output.The associations with urine are highlighted showing two proteins, choriogonadotropin or GLHA human and PSA common isoform or KLK3 human, connected to the scattered glycan structures. Similarly, the associations with seminal fluid are highlighted showing two protein isoforms of PSA connected to the grouped glycan structures. The superimposed N-glycomes of the two isoforms of PSA are shown in the output of GlyConnect Compozitor.Blue nodes represent the glycans associated with the common isoform and those of the high Pi isoform are represented as red nodes. The overlap between glycomes is shown as magenta nodes. Numbers inside the nodes represent the number of glycan structures matching the labeled composition according to the content of the GlyConnect database regarding PSA.The PSA glycome displayed in GlyConnect was shown to correlate to galectin-8 displayed in UniLectin3D via the 3-sialyl-LN type two terminal epitope. This provides a likely but not guaranteed scenario for protein-protein interactions mediated by glycans. A high-quality set of O-glycan compositions associated with human serum was examined and compared to the GlyConnect database content, thereby offering the option of customizing a glycan composition file for the refined identification of the glycopeptides.It could rely on the minimal set of 20 compositions available from one dataset or be enhanced with 23 to 26 items rationally collected in GlyConnect to strengthen the consistency of the set. From this protocol, it is important to remember that a glycome cannot be limited to a list of items. And that precisely with glycoinformatics tools, you can show the dependencies between these items which ultimately will explain their function.

bioinformatics-resources-for-study-glycan-mediated-protein

Research

JoVE Journal

Biology

3.1K Aufrufe.  SIB Swiss Institute of Bioinformatics. In der Bioinformatik geht es darum, mit Hilfe von Computern Fragen in der Biologie zu lösen. Bei Glycoinformatics geht es darum, mit Computern Fragen in der Glykobiologie zu lösen. Mit Glykoinformatik entwickeln wir Datenbanken, die Glykomik oder Glykoproteomik-Daten speichern, die durchsucht oder durchsucht werden können, und wir entwickeln auch Werkzeuge, um diese Daten zu visualisieren und zu vergleichen.Die Rolle von Glykanen wird zunehmend als wichtig für Gesundheit und Krankh...