Trans-Proteomic Pipeline supports and improves analysis of electron transfer dissociation data sets - PubMed (original) (raw)

Trans-Proteomic Pipeline supports and improves analysis of electron transfer dissociation data sets

Eric W Deutsch et al. Proteomics. 2010 Mar.

Abstract

Electron transfer dissociation (ETD) is an alternative fragmentation technique to CID that has recently become commercially available. ETD has several advantages over CID. It is less prone to fragmenting amino acid side chains, especially those that are modified, thus yielding fragment ion spectra with more uniform peak intensities. Further, precursor ions of longer peptides and higher charge states can be fragmented and identified. However, analysis of ETD spectra has a few important differences that require the optimization of the software packages used for the analysis of CID data or the development of specialized tools. We have adapted the Trans-Proteomic Pipeline to process ETD data. Specifically, we have added support for fragment ion spectra from high-charge precursors, compatibility with charge-state estimation algorithms, provisions for the use of the Lys-C protease, capabilities for ETD spectrum library building, and updates to the data formats to differentiate CID and ETD spectra. We show the results of processing data sets from several different types of ETD instruments and demonstrate that application of the ETD-enhanced Trans-Proteomic Pipeline can increase the number of spectrum identifications at a fixed false discovery rate by as much as 100% over native output from a single sequence search engine.

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Figures

Figure 1

Figure 1

Example screenshot of a spectrum from Dataset 1 in the TPP interface for 4+ ALPIRRDDEVLVVRGSK. Each identified peak is labeled with its ion series, series number, and charge identification. Charge reduced precursor peaks are labeled with the prefix M++++. On the left are some user-settable parameters for the spectrum display. On the right, the m/z values for expected c, z•, and y series ions are listed, and shaded if detected in the spectrum.

Figure 2

Figure 2

Schematic overview of the workflow of the post-processing and validation of the results of the SEQUEST and OMSSA sequence database searches using the TPP tools. PeptideProphet was used to model and validate the peptide spectrum matches (PSMs). The iProphet tool was used to coalesce the results to the distinct peptide sequence level. ProteinProphet was used to infer the identified proteins and assign probabilities to each protein based on the upstream analysis.

Figure 3

Figure 3

Number of PSMs as a function of FDR for the SEQUEST search before (red dotted) and after TPP processing (red solid), OMSSA search before (blue dotted) and after TPP processing (blue solid), combined (orange), and SpectraST spectral library search (black) for the three datasets. All three datasets are from different yeast whole-cell lysate samples; Dataset 1 was obtained with an LTQ Orbitrap ETD, while Datasets 2 and 3 were obtained with LTQ ETD instruments in different labs. In all three cases, applying two search engines and the TPP tools nearly doubled the number of identified spectra at a 1% FDR over using SEQUEST raw scores.

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