| Title: | Quantum Chemical Prediction of Electron Ionization Mass Spectra of Trimethylsilylated Metabolites |
| Author(s): | Wang S; Kind T; Bremer PL; Tantillo DJ; Fiehn O; |
| Address: | "West Coast Metabolomics Center, UC Davis Genome Center, University of California, 451 Health Sciences Drive, Davis, California 95616, United States. Department of Chemistry, University of California, 1 Shields Ave., Davis, California 95616, United States" |
| DOI: | 10.1021/acs.analchem.1c02838 |
| ISSN/ISBN: | 1520-6882 (Electronic) 0003-2700 (Linking) |
| Abstract: | "Chemical derivatization, especially silylation, is widely used in gas chromatography coupled to mass spectrometry (GC-MS). By introducing the trimethylsilyl (TMS) group to substitute active hydrogens in the molecule, thermostable volatile compounds are created that can be easily analyzed. While large GC-MS libraries are available, the number of spectra for TMS-derivatized compounds is comparatively small. In addition, many metabolites cannot be purchased to produce authentic library spectra. Therefore, computationally generated in silico mass spectral databases need to take TMS derivatizations into account for metabolomics. The quantum chemistry method QCEIMS is an automatic method to generate electron ionization (EI) mass spectra directly from compound structures. To evaluate the performance of the QCEIMS method for TMS-derivatized compounds, we chose 816 trimethylsilyl derivatives of organic acids, alcohols, amides, amines, and thiols to compare in silico-generated spectra against the experimental EI mass spectra from the NIST17 library. Overall, in silico spectra showed a weighted dot score similarity (1000 is maximum) of 635 compared to the NIST17 experimental spectra. Aromatic compounds yielded a better prediction accuracy with an average similarity score of 808, while oxygen-containing molecules showed lower accuracy with only an average score of 609. Such similarity scores are useful for annotation of small molecules in untargeted GC-MS-based metabolomics, suggesting that QCEIMS methods can be extended to compounds that are not present in experimental databases. Despite this overall success, 37% of all experimentally observed ions were not found in QCEIMS predictions. We investigated QCEIMS trajectories in detail and found missed fragmentations in specific rearrangement reactions. Such findings open the way forward for future improvements to the QCEIMS software" |
| Keywords: | *Electrons Gas Chromatography-Mass Spectrometry/methods Mass Spectrometry *Metabolomics/methods Software; |
| Notes: | "MedlineWang, Shunyang Kind, Tobias Bremer, Parker Ladd Tantillo, Dean J Fiehn, Oliver eng U2C ES030158/ES/NIEHS NIH HHS/ Research Support, N.I.H., Extramural 2022/01/11 Anal Chem. 2022 Jan 25; 94(3):1559-1566. doi: 10.1021/acs.analchem.1c02838. Epub 2022 Jan 10" |
