Tandem mass spectrometry (MSMS) has become a powerful tool for the analysis of biomolecules. To reveal molecular identity, experimental mass (m/z) data are either matched to appropriate databases or processed de-novo. Both approaches are essentially one-dimensional, because the m/z values of fragment ions play dominant role, while the intensities (the second dimension) are being neglected, despite their potential to corroborate or contradict the identification. Unlike the trivial case of m/z values, predicting the ion intensities has not been mastered yet beyond empirical observation and statistical treatment. This dissertation presents a fundamental, structure-based algorithm for the prediction of fragment ion intensities in MSMS spectra of peptides and metabolites. The algorithm builds on the central hypothesis that the fragment ion intensities reflect the relative abundances of respective protonated precursor isomers prior to fragmentation. The hypothesis is supported by extensive experimental evidence showing that in ion trap mass detectors, relative ion intensities do not depend on the energy or activation time of fragmentation when commonly used ranges of conditions are explored. The multi-step algorithm developed includes molecular mechanics Monte Carlo conformational space sampling, semi-empirical calculations, and Density Functional Theory (DFT) quantum chemistry computations for structure refinement and energy calculations. A Boltzmann distribution determined from energy values of pertinent precursors accurately corresponded with relative ion intensities in MSMS spectra of three model pentapeptides. Reproducibility of the algorithm was tested, and while substantial differences were revealed among the multiple Monte Carlo samplings started from the same initial structures, the inconsistency was mitigated in following semi-empirical and DFT steps. The algorithm was optimized for efficiency as well. Computational costs were lowered (by more than 50%) by narrowing the energy window in which the conformers were taken to the following steps in the algorithm and finally to the Boltzmann distribution. For metabolites, ion intensity orders for nine out of eleven molecules were predicted correctly. However, the accuracy of the prediction of relative ion intensities was not satisfactory. Nevertheless, predicting the most intensive ion alone could be invaluable for preliminary metabolite identification and selecting good candidate standards for ultimate identification based on matched properties of analyte and standard.
| Identifer | oai:union.ndltd.org:MSSTATE/oai:scholarsjunction.msstate.edu:td-3352 |
| Date | 14 December 2013 |
| Creators | Pechan, Tibor |
| Publisher | Scholars Junction |
| Source Sets | Mississippi State University |
| Detected Language | English |
| Type | text |
| Format | application/pdf |
| Source | Theses and Dissertations |
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