Afoullouss Sam, Balsam Agata, Allcock A Louise, Thomas Olivier P
School of Biological and Chemical Sciences, Ryan Institute, National University of Ireland Galway (NUI Galway), University Road, H91TK33 Galway, Ireland.
School of Natural Sciences, Ryan Institute, National University of Ireland Galway (NUI Galway), University Road, H91TK33 Galway, Ireland.
Metabolites. 2022 Mar 14;12(3):245. doi: 10.3390/metabo12030245.
Since the introduction of the online open-source GNPS, molecular networking has quickly become a widely applied tool in the field of natural products chemistry, with applications from dereplication, genome mining, metabolomics, and visualization of chemical space. Studies have shown that data dependent acquisition (DDA) parameters affect molecular network topology but are limited in the number of parameters studied. With an aim to optimize LC-MS parameters for integrating GNPS-based molecular networking into our marine natural products workflow, a design of experiment (DOE) was used to screen the significance of the effect that eleven parameters have on both Classical Molecular Networking workflow (CLMN) and the new Feature-Based Molecular Networking workflow (FBMN). Our results indicate that four parameters (concentration, run duration, collision energy and number of precursors per cycle) are the most significant data acquisition parameters affecting the network topology. While concentration and the LC duration were found to be the two most important factors to optimize for CLMN, the number of precursors per cycle and collision energy were also very important factors to optimize for FBMN.
自从在线开源的全球天然产物分子网络系统（GNPS）推出以来，分子网络已迅速成为天然产物化学领域广泛应用的工具，应用范围涵盖了化合物的快速鉴定、基因组挖掘、代谢组学以及化学空间可视化等方面。研究表明，数据依赖型采集（DDA）参数会影响分子网络拓扑结构，但所研究的参数数量有限。为了优化液相色谱-质谱（LC-MS）参数，以便将基于GNPS的分子网络整合到我们的海洋天然产物研究流程中，我们采用了实验设计（DOE）方法来筛选11个参数对经典分子网络工作流程（CLMN）和新的基于特征的分子网络工作流程（FBMN）的影响的显著性。我们的结果表明，四个参数（浓度、运行时间、碰撞能量和每个循环的前体数量）是影响网络拓扑结构的最重要的数据采集参数。虽然发现浓度和液相色谱运行时间是优化CLMN的两个最重要因素，但每个循环的前体数量和碰撞能量也是优化FBMN的非常重要的因素。
