GRASS: semi-automated NMR-based structure elucidation of saccharides
GRASS: semi-automated NMR-based structure elucidation of saccharides
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DOI：
10.1093/bioinformatics/btx696
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发表时间：
2018-03-15
期刊：
影响因子：
5.8
通讯作者：
Toukach, Philip V.
中科院分区：
文献类型：
--
作者：
Kapaev, Roman R.;Toukach, Philip V.
动机：碳水化合物在各种生物化学过程中起着至关重要的作用，并可用于开发药物和疫苗。然而，在碳水化合物的情况下，一级结构解析通常是一项复杂的任务。因此，它们仍然是结构特征最少的一类生物分子，并且它阻碍了糖化学和糖生物学的进展。创建一个有用的仪器，旨在帮助研究人员在天然碳水化合物结构的测定将推进糖化学在生物医学和制药应用。我们提供草（Generation，Ranking and Assignment of candidate Structures），一种半自动解析碳水化合物和衍生物结构的新方法，使用未分配的C-13 NMR光谱和从色谱、光学、化学和其他方法。这种方法是基于最近报道的最准确的碳水化合物NMR模拟的新方法。它结合了广泛多样的支持结构特征、高精度和高性能。
Motivation: Carbohydrates play crucial roles in various biochemical processes and are useful for developing drugs and vaccines. However, in case of carbohydrates, the primary structure elucidation is usually a sophisticated task. Therefore, they remain the least structurally characterized class of biomolecules, and it hampers the progress in glycochemistry and glycobiology. Creating a usable instrument designed to assist researchers in natural carbohydrate structure determination would advance glycochemistry in biomedical and pharmaceutical applications.Results: We present GRASS (Generation, Ranking and Assignment of Saccharide Structures), a novel method for semi-automated elucidation of carbohydrate and derivative structures which uses unassigned C-13 NMR spectra and information obtained from chromatography, optical, chemical and other methods. This approach is based on new methods of carbohydrate NMR simulation recently reported as the most accurate. It combines a broad diversity of supported structural features, high accuracy and performance.