Skaggs School of Pharmacy and Pharmaceutical Sciences, University of California San Diego, La Jolla, San Diego, California 92093, United States.
Collaborative Mass Spectrometry Innovation Center, University of California San Diego, La Jolla, San Diego, California 92093, United States.
Anal Chem. 2021 Sep 28;93(38):12833-12839. doi: 10.1021/acs.analchem.1c01520. Epub 2021 Sep 17.
Molecular networking of non-targeted tandem mass spectrometry data connects structurally related molecules based on similar fragmentation spectra. Here, we report the ical portionality (ChemProp) contextualization of molecular networks. ChemProp scores the changes of abundance between two connected nodes over sequential data series (e.g., temporal or spatial relationships), which can be displayed as a direction within the network to prioritize potential biological and chemical transformations or proportional changes of (biosynthetically) related compounds. We tested the ChemProp workflow on a ground truth data set of a defined mixture and highlighted the utility of the tool to prioritize specific molecules within biological samples, including bacterial transformations of bile acids, human drug metabolism, and bacterial natural products biosynthesis. The ChemProp workflow is freely available through the Global Natural Products Social Molecular Networking (GNPS) environment.
基于相似的碎裂谱,非靶向串联质谱数据的分子网络将结构相关的分子连接起来。在这里,我们报告了分子网络的化学比例(ChemProp)语境化。ChemProp 对两个连接节点之间的丰度变化进行评分,这些节点跨越连续的数据序列(例如,时间或空间关系),这可以显示为网络中的一个方向,以优先考虑潜在的生物和化学转化或(生物合成)相关化合物的比例变化。我们在一个定义混合物的真实数据集中测试了 ChemProp 工作流程,并强调了该工具在优先考虑生物样本中特定分子方面的效用,包括胆汁酸的细菌转化、人体药物代谢和细菌天然产物生物合成。ChemProp 工作流程可通过全球天然产物社会分子网络(GNPS)环境免费获得。