基于知识的 in silico 碎片化和对自然产品的质谱进行注释,使用 MassKG
Bingjie Zhu1,2, Zhenhao Li1,3, Zehua Jin1,2
1College of Pharmaceutical Sciences, Zhejiang University, Hangzhou 310058, China.
Computational and structural biotechnology journal
|September 23, 2024
概括
MassKG是一种新的算法,通过将碎片化数据与深度学习相结合来增强自然产品的发现,以识别已知的化合物并从质谱数据中生成新的结构.
科学领域:
- 分析化学 分析化学
- 计算化学计算化学
- 自然产品化学 自然产品化学
背景情况:
- 液体染色学与双重质谱学 (LC-MS/MS) 结合,对于天然产品的识别至关重要.
- 自然产品的结构多样性在高效的LC-MS/MS数据注释方面带来了重大挑战.
- 由于目前的注释限制,加速发现新型自然产品结构受到阻碍.
研究的目的:
- 引入MassKG,这是一个旨在快速复制和发现新型自然产品 (NP) 结构的算法.
- 为了提高自然产品LC-MS/MS数据注释的效率和准确性.
- 为MS/MS数据注释和分析提供一个用户友好的Web服务器.
主要方法:
- MassKG将基于知识的碎片化策略与基于深度学习的分子生成模型相结合.
- 编制了407,720个已知的NP结构的综合数据库,并通过化学语言模型生成了266,353个新型结构.
- 该算法作为一个具有用户友好的界面,自动报告和碎片树可视化的Web服务器来实现.
主要成果:
- 与现有的最先进的算法相比,MassKG在光谱注释方面表现出卓越的性能.
- 该算法成功编译了已知的大量数据集,并生成了新的NP结构.
- 在Panax notoginseng,Ginkgo biloba,Codonopsis pilosula和Astragalus membranaceus的验证研究证实了MassKG的解释能力.
结论:
- MassKG显著推进了从复杂的生物样本中识别和发现自然产品的研究.
- 开发的算法和Web服务器为自然产品化学和药物发现研究人员提供了强大的工具.
- 马斯克的经过验证的表现凸显了它在加速探索天然产品化学空间方面的潜力.
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