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Potential application of compliance constants in predicting the mass spectral fragmentation of metabolites
Author(s) -
Miriyala Vijay M.,
Sitha Sanyasi,
Steenkamp Paul A.,
Madala Ntakadzeni E.
Publication year - 2015
Publication title -
rapid communications in mass spectrometry
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.528
H-Index - 136
eISSN - 1097-0231
pISSN - 0951-4198
DOI - 10.1002/rcm.7281
Subject(s) - chemistry , metabolite , fragmentation (computing) , mass spectrometry , metabolomics , electrospray ionization , chromatography , computational chemistry , analytical chemistry (journal) , biochemistry , computer science , operating system
Rationale Metabolomics is a qualitative and quantitative measurement of the metabolite content of any biological system under a given physiological status. Due to the chemically diverse nature of these samples, metabolite identification is a difficult task, and development of alternative approaches, such as those based on mass spectrometry (MS), aimed at proper metabolite identification is required. Methods Compliance constants, a direct measure of mechanical bond strength, were used for the first time to predict the MS fragmentation patterns of different regional isomers of a ubiquitous plant metabolite, caffeoylquinic acid (CQA). The compliance constant of an ester bond linking caffeic acid and a quinic acid molecule in CQA was calculated using density functional theory and Wilson's G‐matrix formalism to distinguish the isomers. The predicted fragmentation patterns were compared with mass spectra obtained using negative ion electrospray ionization ultra‐high‐performance liquid chromatography quadrupole time‐of‐flight mass spectrometry (UHPLC/QTOFMS). Results Our in silico results were found to be in correlation with our UHPLC/QTOFMS results, suggesting a potential application of compliance constant algorithms for the rationalization of complex mass spectrometric data. The results also show that the different configurations in stereochemistry that exist between different regional isomers contribute to the underlying energy of the surrounding bonds and the fragmentation thereof. Conclusions The results of our pilot study suggest that computational modelling can be applied for metabolite identification during metabolomic data mining and Natural Product research in general. Copyright © 2015 John Wiley & Sons, Ltd.