Lisacek Frédérique, Schnider Boris, Imberty Anne
SIB Swiss Institute of Bioinformatics CH-1227 Geneva, Switzerland.
Computer Science Department, UniGe CH-1227 Geneva, Switzerland.
BBA Adv. 2025 Mar 6;7:100154. doi: 10.1016/j.bbadva.2025.100154. eCollection 2025.
Lectins are ubiquitous proteins that interact with glycans in a variety of molecular processes and as such, also play a role in diseases, whether infectious, chronic or cancer-related. The systematic study of lectins is therefore essential, in particular for understanding cell-cell communication. Accumulated protein three-dimensional structural data in the past decades boosted advance in AI-based prediction and opened up new options to characterise lectins that are known to often be multimeric and multivalent. This article reviews the methods to obtain structures of lectins, the current data available for lectin 3D structures and their interactions, how this knowledge is used to classify these proteins and shows that the combination of an array of bioinformatics tools should make the prediction of binding specificity possible in a near future.
凝集素是普遍存在的蛋白质，在各种分子过程中与聚糖相互作用，因此在疾病中也发挥作用，无论是感染性疾病、慢性疾病还是癌症相关疾病。因此，对凝集素进行系统研究至关重要，特别是对于理解细胞间通讯而言。过去几十年积累的蛋白质三维结构数据推动了基于人工智能的预测的进展，并为表征通常为多聚体和多价的凝集素开辟了新的选择。本文综述了获得凝集素结构的方法、目前可获得的凝集素三维结构及其相互作用的数据、如何利用这些知识对这些蛋白质进行分类，并表明一系列生物信息学工具的结合应能在不久的将来实现结合特异性的预测。
