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Permanent link (DOI): https://doi.org/10.7939/R3NV99N4Z
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Development of a Spectral Searching Strategy for Peptide and Protein Identification Open Access
- Other title
- Type of item
- Degree grantor
University of Alberta
- Author or creator
- Supervisor and department
Li, Liang (Chemistry)
- Examining committee member and department
Campbell, Robert (Chemistry)
Lubman, David (Surgery)
Lin, Guohui (Computer Science)
Clive, Derrick (Chemistry)
Serpe, Michael (Chemistry)
Department of Chemistry
- Date accepted
- Graduation date
Doctor of Philosophy
- Degree level
The overall goal of this thesis research is to develop a spectral searching strategy capable of identifying peptide sequences from MS/MS spectra with high sensitivity and accuracy.
First, a shotgun proteome analysis method was developed and successfully applied to the identification of proteins from thousands of cancer cells. This work illustrated that proteome profiling of a small number of cells isolated from blood can be achieved. By comparing the obtained profile to a standard profile, cell typing might also be possible. This method may prove to be useful for cancer diagnosis or prognosis. From this study, we realized that sequence database searching strategy is one of the bottlenecks to achieve better sensitivity of protein identification for proteome profiling work.
As a promising alternative, spectral searching strategy is believed to be able to provide more sensitive and accurate peptide and protein identification. In spectral searching strategy, there are two main components: spectral libraries and the searching algorithm.
Since an accurate identification by spectral searching strategy is built on the premise of a reliable MS/MS spectral library, 15N-metabolic labeling and 18O-labeling approaches were developed to experimentally validate all the peptide matches from sequence database search results.
With those validated matches, the sensitivity and accuracy of commonly used search engines (Mascot and X!Tandem) and two popular statistical approaches (PeptideProphet and Percolator) were carefully examined. Moreover, two strategies were designed to identify single-hit protein identifications (proteins identified by only one peptide) with high reliability. In addition, Percolator was successfully interfaced with X!Tandem to enhance its performance.
Finally, a spectral searching algorithm called SpecMatching was developed to utilize the experimentally validated spectral library. In analyzing a digest of an E. coli extract using both Mascot and SpecMatching, it was shown that SpecMatching provided better sensitivity and specificity even with this small-size spectral library.
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