<p>Chromatography based mass spectrometry approaches (xC-MS)
are commonly used in untargeted metabolomics, providing retention time, m/z
values and metabolite specific-fragments all of which are used to identify and validate
an unknown analyte. Ion mobility-mass spectrometry (IM-MS) is emerging as an
enhancement to classic xC-MS strategies, by offering additional separation as
well as collision cross section (CCS) determination. In order to apply such an
approach to a synthetic biology workflow, verified data from metabolite
standards is necessary. In this work we present experimental <sup>DT</sup>CCS<sub>N2</sub>
values for a range of metabolites in positive and negative ionisation modes
using drift time-ion mobility-mass spectrometry (DT-IM-MS) with nitrogen as the
buffer gas. Creating a useful database containing <sup>DT</sup>CCS<sub>N2</sub>
measurements for application in metabolite identification relies on a robust
technique that acquires measurements of high reproducibility. We report that 86%
of the metabolites measured in replicate have a relative standard deviation
lower than 0.2 %. Examples of
metabolites with near identical mass are demonstrated to be separated by ion
mobility with over 4% difference in <sup>DT</sup>CCS<sub>N2</sub> values. We conclude
that the integration of ion mobility into current LC-MS workflows can aid in
small molecule identification for both targeted and untargeted metabolite
screening which is commonly performed in synthetic biology.</p>
Artificial neural networks for the prediction of peptide drift time in ion mobility mass spectrometry
