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Structural Database for Lectins and the UniLectin Web Platform

Protocol
Part of the Methods in Molecular Biology book series (MIMB, volume 2132)

Abstract

The search for new biomolecules requires a clear understanding of biosynthesis and degradation pathways. This view applies to most metabolites as well as other molecule types such as glycans whose repertoire is still poorly characterized. Lectins are proteins that recognize specifically and interact noncovalently with glycans. This particular class of proteins is considered as playing a major role in biology. Glycan-binding is based on multivalence, which gives lectins a unique capacity to interact with surface glycans and significantly contribute to cell–cell recognition and interactions. Lectins have been studied for many years using multiple technologies and part of the resulting information is available online in databases. Unfortunately, the connectivity of these databases with the most popular omics databases (genomics, proteomics, and glycomics) remains limited. Moreover, lectin diversity is extended and requires setting out a flexible classification that remains compatible with new sequences and 3D structures that are continuously released. We have designed UniLectin as a new insight into the knowledge of lectins, their classification, and their biological role. This platform encompasses UniLectin3D, a curated database of lectin 3D structures that follows a periodically updated classification, a set of comparative and visualizing tools and gradually released modules dedicated to specific lectins predicted in sequence databases. The second module is PropLec, focused on β-propeller lectin prediction in all species based on five distinct family profiles. This chapter describes how UniLectin can be used to explore the diversity of lectins, their 3D structures, and associated functional information as well as to perform reliable predictions of β-propeller lectins.

Key words

Lectin Carbohydrate-binding protein Database Classification Sequence 3D structure Profile-based prediction 

Notes

Acknowledgments

The authors acknowledge support by the ANR PIA Glyco@Alps (ANR-15-IDEX-02) and the Alliance Campus Rhodanien Co-funds (http://campusrhodanien.unige-cofunds.ch).

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Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2020

Authors and Affiliations

  1. 1.Univ. Grenoble Alpes, CNRS, CERMAVGrenobleFrance
  2. 2.Swiss Institute of BioinformaticsGenevaSwitzerland
  3. 3.Computer Science DepartmentUniGeGenevaSwitzerland
  4. 4.Section of BiologyUniGeGenevaSwitzerland

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