Quantum chemical electron impact mass spectrum prediction for de novo structure elucidation: Assessment against experimental reference data and comparison to competitive fragmentation modeling

Abstract

We investigate the success of the quantum chemical electron impact mass spectrum (QCEIMS) method in predicting the electron impact mass spectra of a diverse test set of 61 small molecules selected to be representative of common fragmentations and reactions in electron impact mass spectra. Comparison with experimental spectra is performed using the standard matching algorithms, and the relative ranking position of the actual molecule matching the spectra within the NIST-11 library is examined. We find that the correct spectrum is ranked in the top two matches from structural isomers in more than 50% of the cases. QCEIMS, thus, reproduces the distribution of peaks sufficiently well to identify the compounds, with the RMSD and mean absolute difference between appropriately normalized predicted and experimental spectra being at most 9% and 3% respectively, even though the most intense peaks are often qualitatively poorly reproduced. We also compare the QCEIMS method to competitive fragmentation modeling for electron ionization, a training-based mass spectrum prediction method, and remarkably we find the QCEIMS performs equivalently or better. We conclude that QCEIMS will be very useful for those who wish to identify new compounds which are not well represented in the mass spectral databases.machine learning, mass spectrometry, quantum chemistry, simulation 1 | I N TR ODU C TI ON Mass spectrometry (MS) is of major importance for identifying the presence of small quantities of a compound in a mixture, particularly because it is several orders of magnitude more sensitive than equivalent structure determination methods. [1] Indeed, MS is often the only feasible identification method [2][3][4] in contexts like the identification of semiochemicals, [5][6][7] where abundances of critical compounds may be in the ng range.Electron-impact mass spectrometry (EIMS) coupled to some chromatographic separation techniques is, at present, likely to be the predominant form of MS practiced for small molecules. The typical protocol for identifying unknown compounds using EIMS involves matching against a known mass spectrum (MSp), using spectral libraries in conjunction with software to identify matches. [8][9][10][11][12][13] For example, the National Institute of Standards and Technology (NIST) mass spectral library (version 11) contains electron impact (EI) spectra of more than 2310 5 compounds, and may be readily searched using the NIST MS Search program. However large, such a library may only contain a tiny fraction of the total number of small molecular species found in the universe, which for molecules less than 500 Daltons in weight is estimated to be in excess of 10 60 . [14] Simply put, the huge number of possible structural isomers for each molecular formula renders it practically impossible for a library to be exhaustive. If the identification of a compound whose spectra has yet to be characterized (i.e., its spectra is not in a library) is desired, much of the value provided by MS matching/ library methods is lost (although ...

Software Tools and Approaches for Compound Identification of LC-MS/MS Data in Metabolomics

“…The accuracy of in silico generated peaks and their abundances have to be largely improved. A comparison between QCEIMS and CFM-ID has shown that both algorithms perform well enough to get correct identifications for half of the 61 investigated molecules [ 75 ]. However, certain rearrangement reactions, including McLafferty rearrangements, remain underestimated.…”

Quantum Chemistry Calculations for Metabolomics

“…Similar to the evaluation of machine-learning prediction methods such as CFM-ID, 325 quantum chemical models have to be rigorously tested by comparing theoretical predictions against experimental reference spectra. 326 Similarity match scores and compound rankings should be reported. 294 This can be done with the National Institute of Standards and Technology (NIST) MS Search program and the NIST and MassBank of North America (MoNA) mass spectral databases.…”

Evaluating the Accuracy of the QCEIMS Approach for Computational Prediction of Electron Ionization Mass Spectra of Purines and Pyrimidines

“…The statistics of many fragmentation trajectories are then summarized to yield a list of fragment ions and their associated intensities [24]. While much theoretical work on QCEIMS has been published in the past [27][28][29][30][31][32][33][34], currently only four purines and pyrimidines have been investigated in detail [27]. Density function theory (DFT) has also been used for calculating the partition coefficients in organic solvents [35].…”