Department of Computer Science, Faculty of Computer Science and Management, Wroclaw University of Science and Technology, 50-370 Wroclaw, Poland.
Department of Biosciences, University of Salzburg, Salzburg, Austria.
Bioinformatics. 2019 Jan 15;35(2):293-300. doi: 10.1093/bioinformatics/bty465.
A better understanding of oligosaccharides and their wide-ranging functions in almost every aspect of biology and medicine promises to uncover hidden layers of biology and will support the development of better therapies. Elucidating the chemical structure of an unknown oligosaccharide remains a challenge. Efficient tools are required for non-targeted glycomics. Chemical shifts are a rich source of information about the topology and configuration of biomolecules, whose potential is however not fully explored for oligosaccharides. We hypothesize that the chemical shifts of each monosaccharide are unique for each saccharide type with a certain linkage pattern, so that correlated data measured by NMR spectroscopy can be used to identify the chemical nature of a carbohydrate.
We present here an efficient search algorithm, GlycoNMRSearch, which matches either a subset or the entire set of chemical shifts of an unidentified monosaccharide spin system to all spin systems in an NMR database. The search output is much more precise than earlier search functions and highly similar matches suggest the chemical structure of the spin system within the oligosaccharide. Thus, searching for connected chemical shift correlations within all electronically available NMR data of oligosaccharides is a very efficient way of identifying the chemical structure of unknown oligosaccharides. With an improved database in the future, GlycoNMRSearch will be even more efficient deducing chemical structures of oligosaccharides and there is a high chance that it becomes an indispensable technique for glycomics.
The search algorithm presented here, together with a graphical user interface, is available at http://glyconmrsearch.nmrhub.eu.
Supplementary data are available at Bioinformatics online.
更好地理解寡糖及其在生物学和医学几乎各个方面的广泛功能，有望揭示生物学的隐藏层面，并支持更好的治疗方法的开发。阐明未知寡糖的化学结构仍然是一个挑战。非靶向糖组学需要有效的工具。化学位移是生物分子拓扑结构和构象的丰富信息来源，但对于寡糖，其潜力尚未得到充分探索。我们假设每个单糖的化学位移对于具有特定连接模式的每种糖类型都是独特的，因此通过 NMR 光谱测量的相关数据可用于鉴定碳水化合物的化学性质。
我们在这里提出了一种有效的搜索算法 GlycoNMRSearch，它可以将未识别的单糖自旋系统的一部分或全部化学位移与 NMR 数据库中的所有自旋系统进行匹配。搜索结果比早期的搜索功能更精确，高度相似的匹配表明了寡糖中自旋系统的化学结构。因此，在所有电子可用的寡糖 NMR 数据中搜索连接的化学位移相关性是识别未知寡糖化学结构的非常有效的方法。随着未来数据库的改进，GlycoNMRSearch 将更有效地推断寡糖的化学结构，并且它很有可能成为糖组学不可或缺的技术。
此处提出的搜索算法以及图形用户界面可在 http://glyconmrsearch.nmrhub.eu 上获得。
补充数据可在“Bioinformatics”在线获得。
