Abstract
Peptides and proteins are routinely identified from peptide fragmentation spectra acquired in a mass spectrometer, analyzed by database search engines. The types of fragments that can be formed are known, and it is also well appreciated that certain fragment types are more common or more informative than others. However, most search engines do not use detailed knowledge of peptide fragmentation, but rather consider a limited range of fragments, giving each an equivalent weighting in their scoring system that decides which results are likely to be correct. This chapter discusses efforts to make use of information about the frequency of observation of different fragment ion types in order to produce more sophisticated and sensitive scoring systems and demonstrates how these new scoring systems are particularly powerful for analysis of electron capture or electron transfer dissociation data.
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Acknowledgments
This work was supported by this grant has now been replaced by the one already listed. The National Institute of General Medical Sciences, NIH NIGMS P41GM103481, from the National Institutes of Health, and the Vincent J. Coates Foundation.
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Chalkley, R.J. (2013). Improving Peptide Identification Using Empirical Scoring Systems. In: Matthiesen, R. (eds) Mass Spectrometry Data Analysis in Proteomics. Methods in Molecular Biology, vol 1007. Humana Press, Totowa, NJ. https://doi.org/10.1007/978-1-62703-392-3_7
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DOI: https://doi.org/10.1007/978-1-62703-392-3_7
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