一个扩展的高分辨率MS/MS光谱的数据库,用于藻衍生的天然产品
Joe Bracegirdle1, John A Elix2, Udayangani Mawalagedera3
1School of Molecular Sciences, The University of Western Australia, Perth, WA, 6009, Australia.
Scientific data
|February 11, 2025
概括
这项研究通过增加534个新的Orbitrap MS/MS光谱,包括399种独特的代谢物,扩展了类化合物识别数据库. 这显著提高了识别类衍生未知物的能力,并提升了化学分类学.
科学领域:
- * 代谢学 代谢学
- * 化学系统学 化学系统学
- * * 自然产品化学 自然产品化学
背景情况:
- *传统的类化合物识别方法,如点位测试和TLC,缺乏现代代谢技术的灵敏度和准确性.
- *2019年建立的鱼数据库 (LDB) 为251种代谢物提供Q-TOFMS/MS光谱,有助于识别鱼衍生未知物.
- * 需要扩大光谱库,以改善类代谢物的全面识别.
研究的目的:
- * 为了生成和添加新的Orbitrap MS/MS光谱数据,用于大量的藻衍生化合物.
- * 增加光谱数据库的覆盖范围,以识别类代谢物.
- *促进化物化学分类学和天然产品发现的进步.
主要方法:
- *生成Orbitrap MS/MS光谱,用于来自杰克·埃利克斯代谢物库的534种藻衍生化合物.
- *包括399种以前没有在LDB中存在的独特代谢物.
- *使用GNPS平台上的图书馆搜索和分子网络工具进行技术验证,使用了三种澳大利亚枝提取物.
主要成果:
- *增加了534种新的光谱,将综合数据库扩展到650种化合物.
- * 将399种新型化合物引入光谱库.
- *成功技术验证了扩展的数据库,用于识别澳大利亚枝提取物中的化合物.
结论:
- *扩展的光谱数据库显著提高了识别藻衍生化合物的能力.
- * 这一资源预计将完善的化学分类学框架.
- *增加的数据将有助于发现新的菌体代谢产物.
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