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Comparative Study
. 2009 Aug;20(8):1435-40.
doi: 10.1016/j.jasms.200903006. Epub 2009 Mar 14.

Post-acquisition ETD spectral processing for increased peptide identifications

Affiliations
Comparative Study

Post-acquisition ETD spectral processing for increased peptide identifications

David M Good et al. J Am Soc Mass Spectrom. 2009 Aug.

Abstract

Tandem mass spectra (MS/MS) produced using electron transfer dissociation (ETD) differ from those derived from collision-activated dissociation (CAD) in several important ways. Foremost, the predominant fragment ion series are different: c- and z(*)-type ions are favored in ETD spectra while b- and y-type ions comprise the bulk of the fragments in CAD spectra. Additionally, ETD spectra possess charge-reduced precursors and unique neutral losses. Most database search algorithms were designed to analyze CAD spectra, and have only recently been adapted to accommodate c- and z(*)-type ions; therefore, inclusion of these additional spectral features can hinder identification, leading to lower confidence scores and decreased sensitivity. Because of this, it is important to pre-process spectral data before submission to a database search to remove those features that cause complications. Here, we demonstrate the effects of removing these features on the number of unique peptide identifications at a 1% false discovery rate (FDR) using the open mass spectrometry search algorithm (OMSSA). When analyzing two biologic replicates of a yeast protein extract in three total analyses, the number of unique identifications with a approximately 1% FDR increased from 4611 to 5931 upon spectral pre-processing--an increase of approximately 28.6%. We outline the most effective pre-processing methods, and provide free software containing these algorithms.

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Figures

Figure 1
Figure 1
The effect of removing interfering peaks from the MS/MS scan #3118 from SCX fraction 6, biological replicate #1, first analysis. Panel A shows the unprocessed scan, where panels B–D show the step-wise removal of the precursor, charge-reduced precursors, and neutral losses, respectively. Note how while all scans were identified by OMSSA, as the interfering peaks were removed, the confidence of the identification increased, and only that which was completely processed remained after trimming to a 1% FDR.
Figure 2
Figure 2
The effect of spectral pre-processing on total ID’s from a proteomics experiment. Panel A provides the average number of unique identifications (sequence exclusive) within a 1% FDR for the three analyses based upon the amount of spectral processing. Panel B illustrates the percent decrease for these identifications as compared performing the entire processing routine.

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