Name: bioinformatics resources for the study of glycan-mediated protein interactions
Uploaded: 2022-01-20T14:00:00
Description: Watch this Scientific Journal Video about Bioinformatics Resources for the Study of Glycan-Mediated Protein Interactions at JoVE.com

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

Testing Visual Sensitivity to the Speed and Direction of Motion in Lizards

Testing visual sensitivity in any species provides basic information regarding behaviour, evolution, and ecology. However, testing specific features of the visual system provide more empirical evidence for functional applications. Investigation into the sensory system provides information about the sensory capacity, learning and memory ability, and establishes known baseline behaviour in which to gauge deviations (Burghardt, 1977). However, unlike mammalian or avian systems, testing for learning and memory in a reptile species is difficult. Furthermore, using an operant paradigm as a psychophysical measure of sensory ability is likewise as difficult. Historically, reptilian species have responded poorly to conditioning trials because of issues related to motivation, physiology, metabolism, and basic biological characteristics. Here, I demonstrate an operant paradigm used a novel model lizard species, the Jacky dragon (Amphibolurus muricatus) and describe how to test peripheral sensitivity to salient speed and motion characteristics. This method uses an innovative approach to assessing learning and sensory capacity in lizards. I employ the use of random-dot kinematograms (RDKs) to measure sensitivity to speed, and manipulate the level of signal strength by changing the proportion of dots moving in a coherent direction. RDKs do not represent a biologically meaningful stimulus, engages the visual system, and is a classic psychophysical tool used to measure sensitivity in humans and other animals. Here, RDKs are displayed to lizards using three video playback systems. Lizards are to select the direction (left or right) in which they perceive dots to be moving. Selection of the appropriate direction is reinforced by biologically important prey stimuli, simulated by computer-animated invertebrates.

Testing visual sensitivity in lizards using an operant conditioning paradigm that employs video playback of random-dot kinematograms and computer-generated invertebrates.

Testing visual sensitivity in any species provides basic information regarding behaviour, evolution, and ecology. However, testing specific features of ...

Computer-Generated Animal Model Stimuli

Communication between animals is diverse and complex. Animals may communicate using auditory, seismic, chemosensory, electrical, or visual signals. In particular, understanding the constraints on visual signal design for communication has been of great interest. Traditional methods for investigating animal interactions have used basic observational techniques, staged encounters, or physical manipulation of morphology. Less intrusive methods have tried to simulate conspecifics using crude playback tools, such as mirrors, still images, or models. As technology has become more advanced, video playback has emerged as another tool in which to examine visual communication (Rosenthal, 2000). However, to move one step further, the application of computer-animation now allows researchers to specifically isolate critical components necessary to elicit social responses from conspecifics, and manipulate these features to control interactions. Here, I provide detail on how to create an animation using the Jacky dragon as a model, but this process may be adaptable for other species. In building the animation, I elected to use Lightwave  3D to alter object morphology, add texture, install bones, and provide comparable weight shading that prevents exaggerated movement. The animation is then matched to select motor patterns to replicate critical movement features. Finally, the sequence must rendered into an individual clip for presentation. Although there are other adaptable techniques, this particular method had been demonstrated to be effective in eliciting both conspicuous and social responses in staged interactions.

Computer-generated stimuli using the Jacky dragon as a model.

Communication between animals is diverse and complex. Animals may communicate using auditory, seismic, chemosensory, electrical, or visual signals. In ...

Imaging Protein-protein Interactions in vivo

Protein-protein interactions are a hallmark of all essential cellular processes. However, many of these interactions are transient, or energetically weak, preventing their identification and analysis through traditional biochemical methods such as co-immunoprecipitation. In this regard, the genetically encodable fluorescent proteins (GFP, RFP, etc.) and their associated overlapping fluorescence spectrum have revolutionized our ability to monitor weak interactions in vivo using F&ouml;rster resonance energy transfer (FRET)1-3. Here, we detail our use of a FRET-based proximity assay for monitoring receptor-receptor interactions on the endothelial cell surface.

Imaging Protein-protein Interactions in vivo

This protocol describes how to image protein-protein interactions using a FRET-based proximity assay.

Protein-protein interactions are a hallmark of all essential cellular processes. However, many of these interactions are transient, or energetically weak, ...

iCLIP - Transcriptome-wide Mapping of Protein-RNA Interactions with Individual Nucleotide Resolution

The unique composition and spatial arrangement of RNA-binding proteins (RBPs) on a transcript guide the diverse aspects of post-transcriptional regulation1. Therefore, an essential step towards understanding transcript regulation at the molecular level is to gain positional information on the binding sites of RBPs2.
Protein-RNA interactions can be studied using biochemical methods, but these approaches do not address RNA binding in its native cellular context. Initial attempts to study protein-RNA complexes in their cellular environment employed affinity purification or immunoprecipitation combined with differential display or microarray analysis (RIP-CHIP)3-5. These approaches were prone to identifying indirect or non-physiological interactions6. In order to increase the specificity and positional resolution, a strategy referred to as CLIP (UV cross-linking and immunoprecipitation) was introduced7,8. CLIP combines UV cross-linking of proteins and RNA molecules with rigorous purification schemes including denaturing polyacrylamide gel electrophoresis. In combination with high-throughput sequencing technologies, CLIP has proven as a powerful tool to study protein-RNA interactions on a genome-wide scale (referred to as HITS-CLIP or CLIP-seq)9,10. Recently, PAR-CLIP was introduced that uses photoreactive ribonucleoside analogs for cross-linking11,12.
Despite the high specificity of the obtained data, CLIP experiments often generate cDNA libraries of limited sequence complexity. This is partly due to the restricted amount of co-purified RNA and the two inefficient RNA ligation reactions required for library preparation. In addition, primer extension assays indicated that many cDNAs truncate prematurely at the crosslinked nucleotide13. Such truncated cDNAs are lost during the standard CLIP library preparation protocol. We recently developed iCLIP (individual-nucleotide resolution CLIP), which captures the truncated cDNAs by replacing one of the inefficient intermolecular RNA ligation steps with a more efficient intramolecular cDNA circularization (Figure 1)14. Importantly, sequencing the truncated cDNAs provides insights into the position of the cross-link site at nucleotide resolution. We successfully applied iCLIP to study hnRNP C particle organization on a genome-wide scale and assess its role in splicing regulation14.

The spatial arrangement of RNA-binding proteins on a transcript is a key determinant of post-transcriptional regulation. Therefore, we developed individual-nucleotide resolution UV crosslinking and immunoprecipitation (iCLIP) that allows precise genome-wide mapping of the binding sites of an RNA-binding protein.

The unique composition and spatial arrangement of RNA-binding proteins (RBPs) on a transcript guide the diverse aspects of post-transcriptional ...

A Protocol for Computer-Based Protein Structure and Function Prediction

Genome sequencing projects have ciphered millions of protein sequence, which require knowledge of their structure and function to improve the understanding of their biological role. Although experimental methods can provide detailed information for a small fraction of these proteins, computational modeling is needed for the majority of protein molecules which are experimentally uncharacterized. The I-TASSER server is an on-line workbench for high-resolution modeling of protein structure and function. Given a protein sequence, a typical output from the I-TASSER server includes secondary structure prediction, predicted solvent accessibility of each residue, homologous template proteins detected by threading and structure alignments, up to five full-length tertiary structural models, and structure-based functional annotations for enzyme classification, Gene Ontology terms and protein-ligand binding sites. All the predictions are tagged with a confidence score which tells how accurate the predictions are without knowing the experimental data. To facilitate the special requests of end users, the server provides channels to accept user-specified inter-residue distance and contact maps to interactively change the I-TASSER modeling; it also allows users to specify any proteins as template, or to exclude any template proteins during the structure assembly simulations. The structural information could be collected by the users based on experimental evidences or biological insights with the purpose of improving the quality of I-TASSER predictions. The server was evaluated as the best programs for protein structure and function predictions in the recent community-wide CASP experiments. There are currently &gt;20,000 registered scientists from over 100 countries who are using the on-line I-TASSER server.

Guidelines for computer based structural and functional characterization of protein using the I-TASSER pipeline is described. Starting from query protein sequence, 3D models are generated using multiple threading alignments and iterative structural assembly simulations. Functional inferences are thereafter drawn based on matches to proteins with known structure and functions.

Genome sequencing projects have ciphered millions of protein sequence, which require knowledge of their structure and function to improve the ...

The MultiBac Protein Complex Production Platform at the EMBL

Proteomics research revealed the impressive complexity of eukaryotic proteomes in unprecedented detail. It is now a commonly accepted notion that proteins in cells mostly exist not as isolated entities but exert their biological activity in association with many other proteins, in humans ten or more, forming assembly lines in the cell for most if not all vital functions.1,2 Knowledge of the function and architecture of these multiprotein assemblies requires their provision in superior quality and sufficient quantity for detailed analysis. The paucity of many protein complexes in cells, in particular in eukaryotes, prohibits their extraction from native sources, and necessitates recombinant production. The baculovirus expression vector system (BEVS) has proven to be particularly useful for producing eukaryotic proteins, the activity of which often relies on post-translational processing that other commonly used expression systems often cannot support.3 BEVS use a recombinant baculovirus into which the gene of interest was inserted to infect insect cell cultures which in turn produce the protein of choice. MultiBac is a BEVS that has been particularly tailored for the production of eukaryotic protein complexes that contain many subunits.4 A vital prerequisite for efficient production of proteins and their complexes are robust protocols for all steps involved in an expression experiment that ideally can be implemented as standard operating procedures (SOPs) and followed also by non-specialist users with comparative ease. The MultiBac platform at the European Molecular Biology Laboratory (EMBL) uses SOPs for all steps involved in a multiprotein complex expression experiment, starting from insertion of the genes into an engineered baculoviral genome optimized for heterologous protein production properties to small-scale analysis of the protein specimens produced.5-8 The platform is installed in an open-access mode at EMBL Grenoble and has supported many scientists from academia and industry to accelerate protein complex research projects.

Protein complexes catalyze key cellular functions. Detailed functional and structural characterization of many essential complexes requires recombinant production. MultiBac is a baculovirus/insect cell system particularly tailored for expressing eukaryotic proteins and their complexes. MultiBac was implemented as an open-access platform, and standard operating procedures developed to maximize its utility.

Proteomics  research revealed the impressive complexity of eukaryotic proteomes in  unprecedented detail. It is now a commonly accepted notion that ...

Budding Yeast Protein Extraction and Purification for the Study of Function, Interactions, and Post-translational Modifications

Homogenization by bead beating is a fast and efficient way to release DNA, RNA, proteins, and metabolites from budding yeast cells, which are notoriously hard to disrupt. Here we describe the use of a bead mill homogenizer for the extraction of proteins into buffers optimized to maintain the functions, interactions and post-translational modifications of proteins. Logarithmically growing cells expressing the protein of interest are grown in a liquid growth media of choice. The growth media may be supplemented with reagents to induce protein expression from inducible promoters (e.g. galactose), synchronize cell cycle stage (e.g. nocodazole), or inhibit proteasome function (e.g. MG132). Cells are then pelleted and resuspended in a suitable buffer containing protease and/or phosphatase inhibitors and are either processed immediately or frozen in liquid nitrogen for later use. Homogenization is accomplished by six cycles of 20 sec&#160;bead-beating (5.5 m/sec), each followed by one minute incubation on ice. The resulting homogenate is cleared by centrifugation and small particulates can be removed by filtration. The resulting cleared whole cell extract (WCE) is precipitated using 20% TCA for direct analysis of total proteins by SDS-PAGE followed by Western blotting. Extracts are also suitable for affinity purification of specific proteins, the detection of post-translational modifications, or the analysis of co-purifying proteins. As is the case for most protein purification protocols, some enzymes and proteins may require unique conditions or buffer compositions for their purification and others may be unstable or insoluble under the conditions stated. In the latter case, the protocol presented may provide a useful starting point to empirically determine the best bead-beating strategy for protein extraction and purification. We show the extraction and purification of an epitope-tagged SUMO E3 ligase, Siz1, a cell cycle regulated protein that becomes both sumoylated and phosphorylated, as well as a SUMO-targeted ubiquitin ligase subunit, Slx5.

The preparation of high quality yeast cell extracts is a necessary first step in the analysis of individual proteins or entire proteomes. Here we describe a fast, efficient, and reliable homogenization protocol for budding yeast cells that has been optimized to preserve protein functions, interactions, and post-translational modifications.

Homogenization by bead beating is a fast and efficient way to release DNA, RNA, proteins, and metabolites from budding yeast cells, which are notoriously ...

Combining Single-molecule Manipulation and Imaging for the Study of Protein-DNA Interactions

The paper describes the combination of optical tweezers and single molecule fluorescence detection for the study of protein-DNA interaction. The method offers the opportunity of investigating interactions occurring in solution (thus avoiding problems due to closeby surfaces as in other single molecule methods), controlling the DNA extension and tracking interaction dynamics as a function of both mechanical parameters and DNA sequence. The methods for establishing successful optical trapping and nanometer localization of single molecules are illustrated. We illustrate the experimental conditions allowing the study of interaction of lactose repressor (lacI), labeled with Atto532, with a DNA molecule containing specific target sequences (operators) for LacI binding. The method allows the observation of specific interactions at the operators, as well as one-dimensional diffusion of the protein during the process of target search. The method is broadly applicable to the study of protein-DNA interactions but also to molecular motors, where control of the tension applied to the partner track polymer (for example actin or microtubules) is desirable.

Here we describe the instrumentation and methods for detecting single fluorescently-labeled protein molecules interacting with a single DNA molecule suspended between two optically trapped microspheres.

The paper describes the combination of optical tweezers and single molecule fluorescence detection for the study of protein-DNA interaction. The method ...

Probing High-density Functional Protein Microarrays to Detect Protein-protein Interactions

High-density functional protein microarrays containing ~4,200 recombinant yeast proteins are examined for kinase protein-protein interactions using an affinity purified yeast kinase fusion protein containing a V5-epitope tag for read-out. Purified kinase is obtained through culture of a yeast strain optimized for high copy protein production harboring a plasmid containing a Kinase-V5 fusion construct under a GAL inducible promoter. The yeast is grown in restrictive media with a neutral carbon source for 6 hr followed by induction with 2% galactose. Next, the culture is harvested and kinase is purified using standard affinity chromatographic techniques to obtain a highly purified protein kinase for use in the assay. The purified kinase is diluted with kinase buffer to an appropriate range for the assay and the protein microarrays are blocked prior to hybridization with the protein microarray. After the hybridization, the arrays are probed with monoclonal V5 antibody to identify proteins bound by the kinase-V5 protein. Finally, the arrays are scanned using a standard microarray scanner, and data is extracted for downstream informatics analysis1,2 to determine a high confidence set of protein interactions for downstream validation in vivo.

Using protein microarrays containing nearly the entire S. cerevisiae proteome is probed for rapid unbiased interrogation of thousands of protein-protein interactions in parallel. This method can be utilized for protein-small molecule, posttranslational modification, and other assays in high-throughput.

High-density functional protein microarrays containing ~4,200 recombinant yeast proteins are examined for kinase protein-protein interactions using an ...

Electrophoretic Mobility Shift Assay (EMSA) for the Study of RNA-Protein Interactions: The IRE/IRP Example

RNA/protein interactions are critical for post-transcriptional regulatory pathways. Among the best-characterized cytosolic RNA-binding proteins are iron regulatory proteins, IRP1 and IRP2. They bind to iron responsive elements (IREs) within the untranslated regions (UTRs) of several target mRNAs, thereby controlling the mRNAs translation or stability. IRE/IRP interactions have been widely studied by EMSA. Here, we describe the EMSA protocol for analyzing the IRE-binding activity of IRP1 and IRP2, which can be generalized to assess the activity of other RNA-binding proteins as well. A crude protein lysate containing an RNA-binding protein, or a purified preparation of this protein, is incubated with an excess of32 P-labeled RNA probe, allowing for complex formation. Heparin is added to preclude non-specific protein to probe binding. Subsequently, the mixture is analyzed by non-denaturing electrophoresis on a polyacrylamide gel. The free probe migrates fast, while the RNA/protein complex exhibits retarded mobility; hence, the procedure is also called &#8220;gel retardation&#8221; or &#8220;bandshift&#8221; assay. After completion of the electrophoresis, the gel is dried and RNA/protein complexes, as well as free probe, are detected by autoradiography. The overall goal of the protocol is to detect and quantify IRE/IRP and other RNA/protein interactions. Moreover, EMSA can also be used to determine specificity, binding affinity, and stoichiometry of the RNA/protein interaction under investigation.

Electrophoretic Mobility Shift Assay EMSA for the Study of RNA-Protein Interactions: The IRE/IRP Example

Here we present a protocol to analyze RNA/protein interactions. The electrophoretic mobility shift assay (EMSA) is based on the differential migration of RNA/protein complexes and free RNA during native gel electrophoresis. By using a radiolabeled RNA probe, RNA/protein complexes can be visualized by autoradiography.

RNA/protein interactions are critical for post-transcriptional regulatory pathways. Among the best-characterized cytosolic RNA-binding proteins are iron ...