Glycomics@ExPASy: Bridging the Gap

Abstract

Glycomics@ExPASy (https://www.expasy.org/glycomics) is the glycomics tab of ExPASy, the server of SIB Swiss Institute of Bioinformatics. It was created in 2016 to centralize web-based glycoinformatics resources developed within an international network of glycoscientists. The hosted collection currently includes mainly databases and tools created and maintained at SIB but also links to a range of reference resources popular in the glycomics community. The philosophy of our toolbox is that it should be {glycoscientist AND protein scientist}-friendly with the aim of (1) popularizing the use of bioinformatics in glycobiology and (2) emphasizing the relationship between glycobiology and protein-oriented bioinformatics resources. The scarcity of data bridging these two disciplines led us to design tools as interactive as possible based on database connectivity to facilitate data exploration and support hypothesis building. Glycomics@ExPASy was designed, and is developed, with a long-term vision in close collaboration with glycoscientists to meet as closely as possible the growing needs of the community for glycoinformatics.

Keywords: Bioinformatics; Bioinformatics software; Glycomics; Glycoproteomics; Glycosylation.

Fig. 1.

The glycomics tab of the ExPASy website. This figure captures the Glycomics tab of ExPASy website. Glycomics resources are divided in two sections: Databases on the left and Tools on the right. In addition, resources developed by SIB are identified with the SIB logo whereas a gray icon precedes external tools. This screenshot reflects the content as of July 2018. The range of databases and tools is destined to expand and this image is likely to differ in the years to come.

Fig. 2.

The essential entities described in the resources of Glycomics@ExPASy. One of the main purposes of Glycomics@ExPASy is the integration of the tools in the collection shown in Fig. 1. To that end, GlyConnect is used as a dashboard for using in-house and cross-referenced resources. It is designed to ease navigation between entities named in red: protein, peptide, site, glycan, composition and ligand. A protein, a glycan composition or a glycan structure (through its structural properties) is an entry point in the database. Then structures, peptides and sites can be listed and compared and possible correlations brought out.

Fig. 3.

From Mass Spectrometry data to glycoprotein profile. A representative scenario of the possible combination of PepSweetener and Glynsight in order to support the manual annotation of MS1 mass spectra of intact N-glycopeptides and integrate quantitative information when available. Users can process MS1 Spectra using PepSweetener to identify all the possible N-glycan compositions on a single human protein. Intact glycopeptide masses are broken into the respective contributions of the peptide and the glycan masses. Compositions in PepSweetener are in the detailed format shown in supplemental Fig. S1. Then, when quantitative data on each composition is available, Glynsight can be used to identify specific glycosylation patterns. The procedure can be repeated with a second protein and Glynsight will automatically generate the differential analysis of glycan profiles on the proteins. The integration with Glyconnect leads to displaying the potential glycan structures known to match the differentially expressed monosaccharide compositions.

Fig. 4.

From composition to glycoprotein features. An interactive way of extracting glycoprotein features from glycan compositions combining published data and ad hoc tools (1). A list of compositions is input in GlyConnect, which retrieves all the proteins reported as having these compositions attached to them (on the left) and reported glycan structures corresponding to this composition (on the right) annotated in this knowledge base. Glycan structures can be further processed to extract contained glycan epitopes using EpitopeXtractor (2a). Glycoepitope results can be mapped on Glydin', an interactive epitope network (3a). Glydin' aggregates glycan epitopes from four different sources (databases and literature reviews) and provides links to the original information. When epitopes are taken from SugarBindDB, further information on the pathogens can be browsed (4a).

Fig. 5.

Glycan mediated protein-protein interactions. This figure shows how a new hypothesis on glycan mediated protein-protein interaction can be built using published data in Glyconnect and SugarBindDB: A, In this scenario glycan binding protein (GBP) is selected in SugarBind. The information on the glycan ligand recognized by the GBP is used to perform a substructure search on all the structures in Glyconnect with the GlyS3 glycan substructure search tool. The structures identified by GlyS3 are used in Glyconnect to create a list of target proteins that can interact with the initial GBP. In the example of the blood group B antigen triose, there are 35 full structure types in GlyConnect that contain this glycoepitope (B) In this scenario a glycoprotein in GlyConnect is selected with its list of associated glycan structures. Glycans are processed with EpitopeXtractor to single out all the glycan epitopes contained. The Glydin' interactive map of structurally related glycoepitopes helps visualizing the potential common substructures in the complete set of glycoepitopes. Then, extracted epitopes are used in SugarBind to identify all the reported GBPs that can possibly interact with the initial glycoprotein. In this example, the VP1 capsid protein of the Norwalk virus is known to bind the blood group B antigen triose. Note that protein structures shown above UniProt and those shown above GlyConnect are not related to the example but simply illustrating the difference in the information that is stored on the unglycosylated protein in contrast with the stored information on intact glycoproteins.