Predictive approach to MS/MS fragmentation using Density Functional Theory calculations

Abstract

It was hypothesised that significant (above 1.0 Å) bond elongation can occur as a result of protonation at the oxygen of ether or ester groups. The hypothesis was tested on a set of 23 small (under 700 Da) agrochemical and pharmaceutical molecules. The computational data was analysed together with a set of positive ion electrospray mass spectra to identify correlation between product ion and predicted change in C-O bond length. The change of the C-O bond (predicted by computational modelling) close to or above 1.0 Å was in agreement with observed product ions in mass spectra. Density functional theory (DFT)(B3LYP 6-31+G*) and semi-empirical (PM3) methods showed potential to predict fragmentation upon ionisation, which occurs as result of the protonation event and not because of in-source CID. The success rate for the prediction of proton-induced fragmentation is 48% by semi-empirical and 39% by DFT calculation. The observed effect is a proton-induced dissociation (fragmentation upon ionisation). The application of DFT for the prediction of MS/MS fragmentation of diastereomers was also investigated, as the development of a method to identify diastereomers will be beneficial for a high-throughput environment. A combination of high resolution MS/MS data andDFT calculations has the potential to provide such information. Two metabolic diastereomers were successfully distinguished based on the difference in the fragmentation of sodiated and protonated molecules. Computational modelling showed a difference in ground state energies and geometries of those compounds, explaining the observed differences. Application of DFT calculationsto tandem mass spectra of sodiated and protonated molecules allows the assignment of stereochemistry to diastereomers. The performance of the DFT calculationsin predictingMS/MS fragmentation was evaluated using a set of 12 agrochemical compounds. The variety of the chemistries highlighted a need for analysis of a bigger data set. Computational modelling showed better performance for prediction of MS/MS in time (~60% success rate) whereas only ~50% of product ions were correctly predicted for MS/MS in space. The observed difference can be explained by the presence of secondary product ions in MS/MS in space data. A way to improve performance of the predictive approach through modelling of product ions and analysis of their geometries was suggested. DFT calculations have potential to predict differences between MS/MS in time and in space.

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ORCID for Graham Langley: orcid.org/0000-0002-8323-7235

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