基于特征的分子网络和MassQL的应用,用于MS/MS碎片化研究的desipeptides
Denise M Selegato1, Ana C Zanatta2, Alan C Pilon1
1Nucleus of Bioassays, Biosynthesis, and Ecophysiology of Natural Products (NuBBE), Institute of Chemistry, São Paulo State University (UNESP), Araraquara, Brazil.
Frontiers in molecular biosciences
|August 18, 2023
概括
结合基于特征的分子网络 (FBMN) 和大规模特征查询语言 (MassQL),可以加速识别贝奥维里辛类似物. 这种综合方法通过分析碎片化模式和添加物类型来增强分子结构阐释.
科学领域:
- 代谢学 代谢学 代谢学
- 自然产品化学 自然产品化学
- 质谱测量质量谱测量
背景情况:
- 基于特征的分子网络 (FBMN) 对于绘制和识别分子结构和类似物至关重要.
- 手动验证FBMN集群是耗时的,特别是如果没有先前对分子类的了解.
研究的目的:
- 证明结合FBMN和Mass Spec查询语言 (MassQL) 的有效性,以加快大规模碎片化路径的解码.
- 识别具有相似碎片化模式的分子,如贝奥维里辛及其类似物.
主要方法:
- 从ESI-MS/MS对不同碰撞能量的*Fusarium oxysporum*的实验中构建一个光谱相似性网络.
- 配对FBMN分析与MassQL寻找保存的贝奥维里辛离子.
- 分析化和质子化贝奥维里辛添加物的碎片化模式.
主要成果:
- 在FBMN分析中,化和质子化贝奥维里辛添加物的明显聚类和碎片化模式被揭示出来.
- 鉴定了用于六合标识的关键片段: ([M + H]+) m/z 134, 244, 262, 362 和 ([M + Na]+) m/z 266, 284, 384.
- MassQL成功地识别了类似物,并注释了单独FBMN错过的 beauvericins.
- 差异化布奥维里辛子类和确定了具有甲氨酸硫氧化物残留物的新型结构异构体.
结论:
- 集成的FBMN和MassQL方法有效地将道类型与碎片化模式相关联.
- 这种方法可以更轻松地检测贝奥维里辛集群,包括已知和新型类似物.
- 该方法允许结构异构体的分化,增强自然产品的发现.
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