There is a lack of experimental electron ionization high-resolution mass spectra available to assist compound identification. The in silico generation of mass spectra by quantum chemistry can aid annotation workflows, in particular to support the identification of compounds that lack experimental reference spectra, such as environmental chemicals. We present an open-source, semiautomated workflow for the in silico prediction of electron ionization high-resolution mass spectra at 70 eV based on the QCxMS software. The workflow was applied to predict the spectra of 367 environmental chemicals, and the accuracy was evaluated by comparison to experimental reference spectra acquired. The molecular flexibility, number of rotatable bonds, and number of electronegative atoms of a compound were negatively correlated with prediction accuracy. Few analytes are predicted to sufficient accuracy for the direct application of predicted spectra in spectral matching workflows (overall average score 428). The m/z values of the top 5 most abundant ions of predicted spectra rarely match ions in experimental spectra, evidencing the disconnect between simulated fragmentation pathways and empirical reaction mechanisms.

Institute of Computer Science Masaryk University Botanická 554 68a Brno 602 00 Czech Republic

RECETOX Faculty of Science Masaryk University Kotlářská 2 Brno 602 00 Czech Republic

Zobrazit více v PubMed

Price E. J.; Palát J.; Coufaliková K.; Kukučka P.; Codling G.; Vitale C. M.; Koudelka Š.; Klánová J. Open High-Resolution EI+ Spectral Library of Anthropogenic Compounds. Front. Public Health 2021, 9, 622558.10.3389/fpubh.2021.622558. PubMed DOI PMC

MassBank Consortium MassBank EU; http://www.massbank.eu/Contents.

MassBank Consortium MassBank Japan; http://www.massbank.jp/Contents.

Fiehn laboratory at UC Davis MassBank of North America; https://massbank.us/spectra/statistics.

Horai H.; et al. MassBank: a public repository for sharing mass spectral data for life sciences. J. Mass Spectrom. 2010, 45, 703–714. 10.1002/jms.1777. PubMed DOI

Wang M.; et al. Sharing and community curation of mass spectrometry data with Global Natural Products Social Molecular Networking. Nat. Biotechnol. 2016, 34, 828–837. 10.1038/nbt.3597. PubMed DOI PMC

Stettin D.; Poulin R. X.; Pohnert G. Metabolomics Benefits from Orbitrap GC–MS—Comparison of Low- and High-Resolution GC–MS. Metabolites 2020, 10, 143.10.3390/metabo10040143. PubMed DOI PMC

Vinaixa M.; Schymanski E. L.; Neumann S.; Navarro M.; Salek R. M.; Yanes O. Mass spectral databases for LC/MS- and GC/MS-based metabolomics: State of the field and future prospects. TrAC Trends in Anal. Chem. 2016, 78, 23–35. 10.1016/j.trac.2015.09.005. DOI

Krettler C. A.; Thallinger G. G. A map of mass spectrometry-based in silico fragmentation prediction and compound identification in metabolomics. Briefings Bioinf 2021, 22, 1–25. 10.1093/bib/bbab073. PubMed DOI

Wei J. N.; Belanger D.; Adams R. P.; Sculley D. Rapid Prediction of Electron–Ionization Mass Spectrometry Using Neural Networks. ACS Cent. Sci. 2019, 5, 700–708. 10.1021/acscentsci.9b00085. PubMed DOI PMC

Zhu H.; Liu L.; Hassoun S.. Using Graph Neural Networks for Mass Spectrometry Prediction. Machine Learning in Computational Biology, 2020.

Young A.; Röst H.; Wang B. Tandem mass spectrum prediction for small molecules using graph transformers. Nat. Mach. Intell. 2024, 6, 404–416. 10.1038/s42256-024-00816-8. DOI

Murphy M.; Jegelka S.; Fraenkel E.; Kind T.; Healey D.; Butler T.. Efficiently predicting high resolution mass spectra with graph neural networks. Proceedings of the 40th International Conference on Machine Learning; 2023; pp 25549–25562.

Goldman S.; Bradshaw J.; Xin J.; Coley C. Prefix-Tree Decoding for Predicting Mass Spectra from Molecules. Advances in Neural Information Processing Systems 2023, 48548–48572.

Zhu R. L.; Jonas E. Rapid Approximate Subset-Based Spectra Prediction for Electron Ionization-Mass Spectrometry. Anal. Chem. 2023, 95, 2653–2663. 10.1021/acs.analchem.2c02093. PubMed DOI PMC

Grimme S. Towards First Principles Calculation of Electron Impact Mass Spectra of Molecules. Angew. Chem., Int. Ed. 2013, 52, 6306–6312. 10.1002/anie.201300158. PubMed DOI

Koopman J.; Grimme S. From QCEIMS to QCxMS: A Tool to Routinely Calculate CID Mass Spectra Using Molecular Dynamics. J. Am. Soc. Mass Spectrom. 2021, 32, 1735–1751. 10.1021/jasms.1c00098. PubMed DOI

Bauer C. A.; Grimme S. How to Compute Electron Ionization Mass Spectra from First Principles. J. Phys. Chem. A 2016, 120, 3755–3766. 10.1021/acs.jpca.6b02907. PubMed DOI

Bauer C. A.; Grimme S. First principles calculation of electron ionization mass spectra for selected organic drug molecules. Org. Biomol. Chem. 2014, 12, 8737–8744. 10.1039/C4OB01668H. PubMed DOI

Koopman J.; Grimme S. Calculation of Electron Ionization Mass Spectra with Semiempirical GFNn-xTB Methods. ACS Omega 2019, 4, 15120–15133. 10.1021/acsomega.9b02011. PubMed DOI PMC

Grimme S.; Bannwarth C.; Shushkov P. A Robust and Accurate Tight-Binding Quantum Chemical Method for Structures, Vibrational Frequencies, and Noncovalent Interactions of Large Molecular Systems Parametrized for All spd-Block Elements (Z = 1–86). J. Chem. Theory Comput. 2017, 13, 1989–2009. 10.1021/acs.jctc.7b00118. PubMed DOI

Bannwarth C.; Ehlert S.; Grimme S. GFN2-xTB—An Accurate and Broadly Parametrized Self-Consistent Tight-Binding Quantum Chemical Method with Multipole Electrostatics and Density-Dependent Dispersion Contributions. J. Chem. Theory Comput. 2019, 15, 1652–1671. 10.1021/acs.jctc.8b01176. PubMed DOI

Ásgeirsson V.; Bauer C. A.; Grimme S. Quantum chemical calculation of electron ionization mass spectra for general organic and inorganic molecules. Chem. Sci. 2017, 8, 4879–4895. 10.1039/C7SC00601B. PubMed DOI PMC

Wang S.; Kind T.; Bremer P. L.; Tantillo D. J.; Fiehn O. Beyond the Ground State: Predicting Electron Ionization Mass Spectra Using Excited-State Molecular Dynamics. J. Chem. Inf. Model. 2022, 62, 4403–4410. 10.1021/acs.jcim.2c00597. PubMed DOI

Schreckenbach S. A.; Anderson J. S.; Koopman J.; Grimme S.; Simpson M. J.; Jobst K. J. Predicting the Mass Spectra of Environmental Pollutants Using Computational Chemistry: A Case Study and Critical Evaluation. J. Am. Soc. Mass Spectrom. 2021, 32, 1508–1518. 10.1021/jasms.1c00078. PubMed DOI

Lee J.; Kind T.; Tantillo D. J.; Wang L.-P.; Fiehn O. Evaluating the Accuracy of the QCEIMS Approach for Computational Prediction of Electron Ionization Mass Spectra of Purines and Pyrimidines. Metabolites 2022, 12, 68.10.3390/metabo12010068. PubMed DOI PMC

Wang S.; Kind T.; Tantillo D. J.; Fiehn O. Predicting in silico electron ionization mass spectra using quantum chemistry. J. Cheminf. 2020, 12, 63.10.1186/s13321-020-00470-3. PubMed DOI PMC

Wang S.; Kind T.; Bremer P. L.; Tantillo D. J.; Fiehn O. Quantum Chemical Prediction of Electron Ionization Mass Spectra of Trimethylsilylated Metabolites. Anal. Chem. 2022, 94, 1559–1566. 10.1021/acs.analchem.1c02838. PubMed DOI

Sander T.; Freyss J.; von Korff M.; Rufener C. DataWarrior: An Open-Source Program For Chemistry Aware Data Visualization And Analysis. J. Chem. Inf. Model. 2015, 55, 460–473. 10.1021/ci500588j. PubMed DOI

Rojas W. Y.; Hecht H.; Ahmad Z.. RECETOX/ei_spectra_predictions: v0.5; 2024, https://github.com/RECETOX/ei_spectra_predictions.

Troják M.; Hecht H.; Čech M.; Price E. J. MSMetaEnhancer: A Python package for mass spectra metadata annotation. Journal of Open Source Software 2022, 7, 4494.10.21105/joss.04494. DOI

Dral P. O.; Wu X.; Spörkel L.; Koslowski A.; Weber W.; Steiger R.; Scholten M.; Thiel W. Semiempirical Quantum-Chemical Orthogonalization-Corrected Methods: Theory, Implementation, and Parameters. J. Chem. Theory Comput. 2016, 12, 1082–1096. 10.1021/acs.jctc.5b01046. PubMed DOI PMC

Price E. J.; Palát J.; Coufaliková K.; Kukučka P.; Codling G.; Vitale C. M.; Koudelka Š.; Klánová J.. RECETOX Exposome HR-[EI+]-MS library; 2021, 10.5281/zenodo.4471217. PubMed DOI PMC

Barca G. M. J.; et al. Recent developments in the general atomic and molecular electronic structure system. J. Chem. Phys. 2020, 152, 154102.10.1063/5.0005188. PubMed DOI

Djoumbou Feunang Y.; Eisner R.; Knox C.; Chepelev L.; Hastings J.; Owen G.; Fahy E.; Steinbeck C.; Subramanian S.; Bolton E.; Greiner R.; Wishart D. S. ClassyFire: automated chemical classification with a comprehensive, computable taxonomy. J. Cheminf. 2016, 8, 61.10.1186/s13321-016-0174-y. PubMed DOI PMC

Landrum G.; et al. rdkit/rdkit: 2023_09_3 (Q3 2023) Release; 2023.

Huber F.; Verhoeven S.; Meijer C.; Spreeuw H.; Castilla E.; Geng C.; van der Hooft J.; Rogers S.; Belloum A.; Diblen F.; Spaaks J. matchms - processing and similarity evaluation of mass spectrometry data. Journal of Open Source Software 2020, 5, 2411.10.21105/joss.02411. DOI

Virtanen P.; et al. SciPy 1.0: Fundamental Algorithms for Scientific Computing in Python. Nat. Methods 2020, 17, 261–272. 10.1038/s41592-019-0686-2. PubMed DOI PMC

The pandas development team pandas-dev/pandas: Pandas; https://github.com/pandas-dev/pandas.

Stein S. E. Chemical substructure identification by mass spectral library searching. J. Am. Soc. Mass Spectrom. 1995, 6, 644–655. 10.1016/1044-0305(95)00291-K. PubMed DOI

Cooper B. T.; Yan X.; Simón-Manso Y.; Tchekhovskoi D. V.; Mirokhin Y. A.; Stein S. E. Hybrid Search: A Method for Identifying Metabolites Absent from Tandem Mass Spectrometry Libraries. Anal. Chem. 2019, 91, 13924–13932. 10.1021/acs.analchem.9b03415. PubMed DOI PMC

Li Y.; Kind T.; Folz J.; Vaniya A.; Mehta S. S.; Fiehn O. Spectral entropy outperforms MS/MS dot product similarity for small-molecule compound identification. Nat. Methods 2021, 18, 1524–1531. 10.1038/s41592-021-01331-z. PubMed DOI

Rojas W. Y.RECETOX Spectral Similarity Top 5 Peaks Galaxy Workflow and History; 2024, https://zenodo.org/records/10842560.

Rojas W. Y.RECETOX Spectral Similarity All Peaks Galaxy Workflow and History; 2024, https://zenodo.org/records/10842462.

Bannwarth C.; Caldeweyher E.; Ehlert S.; Hansen A.; Pracht P.; Seibert J.; Spicher S.; Grimme S. Extended tight-binding quantum chemistry methods. WIREs Comput. Mol. Sci. 2021, 11, 1–49. 10.1002/wcms.1493. DOI

von Korff M.; Sander T.. About Complexity and Self-Similarity of Chemical Structures in Drug Discovery. Chaos and Complex Systems; Springer-Verlag: Berlin, Heidelberg, 2013; pp 301–306.

Hecht H.QCxMS prediction of alkyl halides comparison of GFN1-xTB and GFN2-xTB; 2024, https://zenodo.org/records/10839047.

Khanna V.; Ranganathan S. Physiochemical property space distribution among human metabolites, drugs and toxins. BMC Bioinf 2009, 10, 1–18. 10.1186/1471-2105-10-S15-S10. PubMed DOI PMC

Nelson T. R.; White A. J.; Bjorgaard J. A.; Sifain A. E.; Zhang Y.; Nebgen B.; Fernandez-Alberti S.; Mozyrsky D.; Roitberg A. E.; Tretiak S. Non-adiabatic Excited-State Molecular Dynamics: Theory and Applications for Modeling Photophysics in Extended Molecular Materials. Chem. Rev. 2020, 120, 2215–2287. 10.1021/acs.chemrev.9b00447. PubMed DOI

Sarojini D.; Burrows-Schilling C.; Thomas K.; Mizumoto C.. Towards Developing a Guide to Choosing National High-Performance Computing Resources. Practice and Experience in Advanced Research Computing; New York, 2023; pp 382–385.

IDC Corporate High Performance Computing in the EU: Progress on the Implementation of the European HPC Strategy; 2015; pp 1–137.

RECETOX Mirrorplots for “Quantum chemistry based prediction of electron ionization mass spectra for environmental chemicals”; 2024, https://zenodo.org/records/12784293. PubMed PMC

Quantum Chemistry-Based Prediction of Electron Ionization Mass Spectra for Environmental Chemicals