Abstract
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-MS2 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.
Highlights
Molecular networking is an informatics tool that allows visualization of non-targeted tandem mass spectrometer data (MS2), to highlight structure similarities between metabolites of a complex mixture and help in the annotation of the detected metabolites [1]
Molecular networking is broadly used in the field of natural products (NP) with the introduction of online molecular networking Global Natural Products Social (GNPS) platform, developed by Wang et al [3] in 2016
Parazoanthus axinellae models were an outlier with response models for the number of nodes, neighbors and average cosine being excluded from further analysis due to poor fit with data
Summary
Molecular networking is an informatics tool that allows visualization of non-targeted tandem mass spectrometer data (MS2), to highlight structure similarities between metabolites of a complex mixture and help in the annotation of the detected metabolites [1]. DDA is an autonomous data acquisition mode and works by first taking an MS1 scan and collecting the m/z and relative abundance of analytes. This is instantly followed by multiple MS2 scans, targeting the major analytes selected from the MS1 scan. Molecular networking is broadly used in the field of natural products (NP) with the introduction of online molecular networking Global Natural Products Social (GNPS) platform, developed by Wang et al [3] in 2016. GNPS has been applied to a wide range of applications including dereplication [4–7], metabolomics [8–11], and genome mining [12,13]
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