Higher Chemical College, Russian Academy of Sciences, 125047 Moscow, Russia.
N.D. Zelinsky Institute of Organic Chemistry, Russian Academy of Sciences, 119991 Moscow, Russia.
Bioinformatics. 2018 Mar 15;34(6):957-963. doi: 10.1093/bioinformatics/btx696.
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.
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 13C 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.
GRASS is implemented in a free web tool available at http://csdb.glycoscience.ru/grass.html.
kapaev_roman@mail.ru or netbox@toukach.ru.
Supplementary data are available at Bioinformatics online.
碳水化合物在各种生化过程中起着至关重要的作用,并且可用于开发药物和疫苗。然而,在碳水化合物的情况下,其一级结构的阐明通常是一项复杂的任务。因此,它们仍然是结构特征研究最少的一类生物分子,这阻碍了糖化学和糖生物学的发展。开发一种可用于辅助研究人员确定天然碳水化合物结构的工具将推动生物医学和制药应用中的糖化学发展。
我们提出了 GRASS(糖结构的生成、排序和分配),这是一种用于半自动化阐明碳水化合物和衍生物结构的新方法,它使用未分配的 13C NMR 光谱以及从色谱、光学、化学和其他方法获得的信息。该方法基于最近报道的最准确的碳水化合物 NMR 模拟的新方法。它结合了广泛的支持结构特征、高精度和高性能。
GRASS 以免费的网络工具形式实现,可在 http://csdb.glycoscience.ru/grass.html 获得。
kapaev_roman@mail.ru 或 netbox@toukach.ru。
补充数据可在“Bioinformatics”在线获取。