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Title: Correlation of Multiple Peptide Mass Spectra for Phosphoprotein Identification

Author
item FENG, JIAN - JOHNS HOPKINS UNIVERSITY
item Garrett, Wesley
item NAIMAN, DANIEL - JOHNS HOPKINS UNIVERSITY
item Cooper, Bret

Submitted to: Journal of Proteome Research
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 2/1/2009
Publication Date: 5/15/2009
Citation: Feng, J., Garrett, W.M., Naiman, D., Cooper, B. 2009. Correlation of Multiple Peptide Mass Spectra for Phosphoprotein Identification. Journal of Proteome Research. 8(11):1021.

Interpretive Summary: What happens to proteins inside a plant cell can affect many things that happen to the cell and thus to the plant. One thing that happens to proteins is called phosphorylation. Phosphorylation controls protein activity and may even determine if a plant has disease resistance. However, it has been difficult to detect phosphorylated proteins in plants. We developed a software program that uses a mathematical model to improve the identification of these important proteins in soybeans. This software is expected to be useful when trying to identify the soybean proteins that become phosphorylated and regulate disease resistance responses to soybean rust. This software is most likely to be useful to scientists at universities and government agencies who need resources to interpret their protein data.

Technical Abstract: When collision induced dissociation is used to fragment phosphorylated peptides during tandem mass spectrometry (MS2), an ion exhibiting the neutral loss of phosphoric acid can be the major product. The neutral loss ion can then be fragmented during MS3 for additional resolution of the peptide sequence. Together, MS2 and MS3 spectral pairs can offer supporting identification of phosphorylated peptides and proteins. Here, the software program PANORAMICS has been modified to make use of Mascot results for MS2 and MS3 spectral data sets. For pairs, the algorithm considers the number of shared m/z peaks used for peptide assignment and then adjusts the score evaluating that a peptide was correctly matched to these spectra using a mathematical model. The algorithm then calculates peptide probabilities for paired and unpaired spectra, and deduces a probability that a protein was identified given the set of matched peptides. The output provides information useful for determining whether peptides and proteins are phosphorylated. The program can process large result files often generated by multidimensional protein identification technology (MudPIT).