Structural Database for Lectins and the UniLectin Web Platform.

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

Journal

Methods in molecular biology (Clifton, N.J.)
ISSN: 1940-6029
Titre abrégé: Methods Mol Biol
Pays: United States
ID NLM: 9214969

Informations de publication

Date de publication:
2020
Historique:
entrez: 20 4 2020
pubmed: 20 4 2020
medline: 18 2 2021
Statut: ppublish

Résumé

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.

Identifiants

pubmed: 32306309
doi: 10.1007/978-1-0716-0430-4_1
doi:

Substances chimiques

Lectins 0
Polysaccharides 0

Types de publication

Journal Article Research Support, Non-U.S. Gov't

Langues

eng

Sous-ensembles de citation

IM

Pagination

1-14

Auteurs

François Bonnardel (F)

Univ. Grenoble Alpes, CNRS, CERMAV, Grenoble, France.
Swiss Institute of Bioinformatics, Geneva, Switzerland.
Computer Science Department, UniGe, Geneva, Switzerland.

Serge Perez (S)

Univ. Grenoble Alpes, CNRS, CERMAV, Grenoble, France.

Frédérique Lisacek (F)

Swiss Institute of Bioinformatics, Geneva, Switzerland. frederique.lisacek@sib.swiss.
Computer Science Department, UniGe, Geneva, Switzerland. frederique.lisacek@sib.swiss.
Section of Biology, UniGe, Geneva, Switzerland. frederique.lisacek@sib.swiss.

Anne Imberty (A)

Univ. Grenoble Alpes, CNRS, CERMAV, Grenoble, France. anne.imberty@cermav.cnrs.fr.

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