MetAssimulo 2.0:一个网络应用程序,用于模拟现实的1D和2D代谢1HNMR光谱
Yan Yan1, Beatriz Jiménez2, Michael T Judge1,3
1Section of Bioinformatics, Division of Systems Medicine, Department of Metabolism, Digestion and Reproduction, Faculty of Medicine, Imperial College London, London W12 0NN, United Kingdom.
代谢学研究人员现在可以使用Python工具MetAssimulo 2.0,模拟各种生物流体的1D和2DNMR光谱. 这有助于开发用于代谢物分析的机器学习算法,当标记数据稀缺时.
科学领域:
- 代谢学 代谢学 代谢学
- 计算生物学 计算生物学
- 频谱学是一种光谱学.
背景情况:
- 核磁共振 (NMR) 光谱在代谢学中非常重要,因为它的可重复性和吞吐量.
- 一维和二维NMR光谱中的重叠模式使代谢物识别和量化复杂化.
- 有限的标记光谱数据阻碍了机器学习,特别是深度学习在代谢学中的应用.
研究的目的:
- 介绍MetAssimulo 2.0,一个升级的基于Python的网络应用程序,用于模拟代谢NMR光谱.
- 扩大模拟能力超出1D 1H尿液的NMR,包括血液和脑脊液,以及2D光谱 (J-Res,COSY).
- 为代谢学研究提供支持算法开发的工具,特别是用于深度学习应用.
主要方法:
- 重新实现MetAssimulo作为一个Python网络应用程序.
- 在血液光谱模拟中加入蛋白质背景以增强现实性.
- 包括对2DJ分辨率 (J-Res) 和相关性光谱学 (COSY) 频谱的模拟.
- 单个和分组光谱的模拟,具有离散和连续的结果,包括代谢物之间的相关性.
主要成果:
- MetAssimulo 2.0 模拟了尿液,血液和脑脊液的 1D 和 2D NMR 光谱.
- 血液光谱模拟通过增强的现实主义实现了与真实光谱的Pearson相关性约为0.82.
- 该工具支持各种实验设计和分析与生物医学反应相关的代谢物概况.
结论:
- MetAssimulo 2.0显著提高了NMR光谱模拟在代谢学中的实用性.
- 该工具能够在深度学习和代谢学的交叉点推进研究.
- 代码和光谱数据的可用性有助于进一步的开发和应用.
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