3D-MPEA:一种以图表注意力模型为导向的计算方法,用于通过聚焦质谱的多层分子网络在互动学中说明未知的代谢物
Zibian Fan1, Wei Jia1,2
1School of Food Science and Engineering, Shaanxi University of Science & Technology, Xi'an710021, China.
Analytical chemistry
|May 3, 2024
概括
一个新的图表注意力和长期短期记忆模型通过将MS2光谱集成到分子网络中来增强多omics注释,改善物质捕获和代谢物识别精度.
科学领域:
- 代谢学 代谢学 代谢学
- 计算化学的计算化学
- 生物信息学是一种生物信息学.
背景情况:
- 质谱学 (MS2) 光谱匹配对于化合物表征至关重要.
- 挑战包括光谱变异,凝聚和不完整的数据库,阻碍了多omics注释.
- 目前的方法与各种离子物种和不断扩大的光谱库作斗争.
研究的目的:
- 使用先进的计算模型开发一个优化的方法,用于多omics注释.
- 提高复杂生物样本中代谢物和物质识别的准确性和范围.
- 解决当前光谱匹配和数据库扩展策略的局限性.
主要方法:
- 使用图表注意力和长短期记忆 (LSTM) 模型将类似的MS2光谱集成到分子网络中.
- 利用同位素离子峰集群间距特征来崩不同的离子物种.
- 扩大光谱参考库,以加强化合物表征.
- 在一项由2,2-bis(4-hydroxyphenyl) propane调解的猪肉刺激研究中的应用.
主要成果:
- 与经典方法相比,实现了123.1%的物质捕获能力.
- 在代谢物注释方面表现出高精度 (83.5-117.1%),超过了以前的精度.
- 鉴定出β-氨酸-氨酸二酸酶是受2,2-bis(4-基) 影响的代谢途径中的关键节点.
- 启用了全球代谢物注释,从已知到未知的代谢物-脂质-蛋白质水平.
结论:
- 拟议的注意力图和LSTM模型有效地注释了生物体中反复出现的未知因素.
- 这种方法有助于破译在多omics数据中未知的物质.
- 该方法显著提高了用于全面生物分析的光谱匹配能力.
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