Univ. Grenoble Alpes, CNRS, CERMAV, Grenoble, France.
Swiss Institute of Bioinformatics, Geneva, Switzerland.
Methods Mol Biol. 2020;2132:1-14. doi: 10.1007/978-1-0716-0430-4_1.
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.
寻找新的生物分子需要清楚地了解生物合成和降解途径。这种观点适用于大多数代谢物以及其他分子类型，如聚糖，其谱仍然很差。凝集素是特异性识别并非共价相互作用于聚糖的蛋白质。这类蛋白质被认为在生物学中起着重要作用。糖结合基于多价性，这使凝集素具有与表面聚糖相互作用的独特能力，并显著促进细胞-细胞识别和相互作用。多年来，人们使用多种技术研究凝集素，部分研究结果可在在线数据库中获得。不幸的是，这些数据库与最流行的组学数据库（基因组学、蛋白质组学和糖组学）的连接仍然有限。此外，凝集素的多样性得到扩展，需要制定一个灵活的分类，使其与不断发布的新序列和 3D 结构保持兼容。我们设计了 UniLectin，以深入了解凝集素的知识、它们的分类及其生物学作用。该平台包括 UniLectin3D，这是一个凝集素 3D 结构的精心整理的数据库，遵循定期更新的分类，一组比较和可视化工具以及逐渐发布的专门针对序列数据库中预测的特定凝集素的模块。第二个模块是 PropLec，专注于基于五个不同家族特征的所有物种中的β-螺旋桨凝集素预测。本章介绍了如何使用 UniLectin 来探索凝集素的多样性、它们的 3D 结构和相关的功能信息，以及执行可靠的β-螺旋桨凝集素预测。
