Glycosaminoglycans: What Remains To Be Deciphered?

Glycosaminoglycans: What Remains To Be Deciphered?
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DOI:
10.1021/jacsau.2c00569
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发表时间:
2023-03-27
期刊:
影响因子:
8
通讯作者:
Ricard Blum, Sylvie
Ricard Blum, Sylvie
中科院分区:
其他
文献类型:
--
作者:
Perez, Serge;Makshakova, Olga;Angulo, Jesus;Bedini, Emiliano;Bisio, Antonella;de Paz, Jose Luis;Fadda, Elisa;Guerrini, Marco;Hricovini, Michal;Hricovini, Milos;Lisacek, Frederique;Nieto, Pedro M;Pagel, Kevin;Paiardi, Giulia;Richter, Ralf;Samsonov, Sergey A;Vives, Romain R;Nikitovic, Dragana;Ricard Blum, Sylvie

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糖胺聚糖(GAG)是复杂的多糖,其表现出巨大的结构多样性,并实现由细胞外基质、细胞表面和细胞内的数千种相互作用介导的各种功能,其中它们已在细胞核中被检测到。已知与GAG和GAG构象连接的化学基团包含尚未完全破译的“糖密码”。GAG结构和功能的分子背景也很重要,蛋白聚糖核心蛋白的结构和功能对硫酸化GAG的影响,反之亦然,值得进一步研究。缺乏专门的生物信息学工具,挖掘GAG数据集的结构和功能的景观和相互作用的GAG的部分特征。这些悬而未决的问题将受益于本文综述的新方法的发展,即(i)合成GAG寡糖以构建大的和多样化的GAG文库,(ii)通过质谱法进行GAG分析和测序(例如,离子迁移率-质谱法),气相红外光谱法,识别隧道纳米孔和分子建模,以确定生物活性的GAG序列,生物物理学方法,以研究结合界面,并扩大我们的知识和理解的糖编码管理GAG分子识别,和(iii)人工智能的深入调查的GAG组学数据集及其与蛋白质组学的整合。
Glycosaminoglycans (GAGs) are complex polysaccharides exhibiting a vast structural diversity and fulfilling various functions mediated by thousands of interactions in the extracellular matrix, at the cell surface, and within the cells where they have been detected in the nucleus. It is known that the chemical groups attached to GAGs and GAG conformations comprise “glycocodes” that are not yet fully deciphered. The molecular context also matters for GAG structures and functions, and the influence of the structure and functions of the proteoglycan core proteins on sulfated GAGs and vice versa warrants further investigation. The lack of dedicated bioinformatic tools for mining GAG data sets contributes to a partial characterization of the structural and functional landscape and interactions of GAGs. These pending issues will benefit from the development of new approaches reviewed here, namely (i) the synthesis of GAG oligosaccharides to build large and diverse GAG libraries, (ii) GAG analysis and sequencing by mass spectrometry (e.g., ion mobility-mass spectrometry), gas-phase infrared spectroscopy, recognition tunnelling nanopores, and molecular modeling to identify bioactive GAG sequences, biophysical methods to investigate binding interfaces, and to expand our knowledge and understanding of glycocodes governing GAG molecular recognition, and (iii) artificial intelligence for in-depth investigation of GAGomic data sets and their integration with proteomics.
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