UniLectin, A One-Stop-Shop to Explore and Study Carbohydrate-Binding Proteins

Stereoselective Synthesis of the Gal-α-(1→3)-Gal-β-(1→3)-GlcNAc Trisaccharide: a new Ligand for DCAR and Mincle C-Type Lectin Receptors

In this work, we have discovered that the Gal-α-(1→3)-Gal-β-(1→3)-GlcNAc trisaccharide, a fragment of the B antigen Type-1, is a new ligand of two C-type lectin receptors (CLRs) i. e. DCAR and Mincle which are key players in different types of autoimmune diseases. Accordingly, we report here on a straightforward methodology to access pure Gal-α-(1→3)-Gal-β-(1→3)-GlcNAc trisaccharide. A spacer with a terminal primary amine group was included at the reducing end of the GlcNAc residue thus ensuring the further functionalization of the trisaccharide Gal-α-(1→3)-Gal-β-(1→3)-GlcNAc.

BiotechLec: an interactive guide of commercial lectins for glycobiology and biomedical research applications

For decades, lectins have been used as probes in glycobiology and this usage has gradually spread to other domains of Life Science. Nowadays, researchers investigate glycan recognition with lectins in diverse biotechnology and clinical applications, addressing key questions regarding binding specificity. The latter is documented in scattered and heterogeneous sources, and this situation calls for a centralized and easy-access reference. To address this need, an on-line solution called BiotechLec (https://www.unilectin.eu/biotechlec) is proposed in a new section of UniLectin, a platform dedicated to lectin molecular knowledge.

Worldwide Glycoscience Informatics Infrastructure: The GlySpace Alliance

The GlySpace Alliance was formed in 2018 among the principal investigators of three major glycoscience portals: Glyco@Expasy, GlyCosmos, and GlyGen, representing Europe, Asia, and the United States, respectively. While each of these portals has its unique user interface, the aim is to provide the same basic data set of glycan-related omics data. These portals will be introduced with the aim to enable users to find their target information in the most efficient manner, in particular, in terms of the chemical structures of glycans and their functions.

Glycoinformatics in the Artificial Intelligence Era

Artificial intelligence (AI) methods have been and are now being increasingly integrated in prediction software implemented in bioinformatics and its glycoscience branch known as glycoinformatics. AI techniques have evolved in the past decades, and their applications in glycoscience are not yet widespread. This limited use is partly explained by the peculiarities of glyco-data that are notoriously hard to produce and analyze. Nonetheless, as time goes, the accumulation of glycomics, glycoproteomics, and glycan-binding data has reached a point where even the most recent deep learning methods can provide predictors with good performance. We discuss the historical development of the application of various AI methods in the broader field of glycoinformatics. A particular focus is placed on shining a light on challenges in glyco-data handling, contextualized by lessons learnt from related disciplines. Ending on the discussion of state-of-the-art deep learning approaches in glycoinformatics, we also envision the future of glycoinformatics, including development that need to occur in order to truly unleash the capabilities of glycoscience in the systems biology era.