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Enhanced peptide identification by electron transfer dissociation using an improved Mascot Percolator.

Wright JC, Collins MO, Yu L, Käll L, Brosch M, Choudhary JS - Mol. Cell Proteomics (2012)

Bottom Line: We have previously interfaced the Mascot search engine with Percolator and demonstrated sensitivity and specificity benefits with CID data.Using a data set of CID and ETcaD spectral pairs, we find that at a 1% false discovery rate, the overlap in peptide identifications by CID and ETD is 83%, which is significantly higher than that obtained using either stand-alone Mascot (69%) or OMSSA (39%).We conclude that Mascot Percolator is a highly sensitive and accurate post-search algorithm for peptide identification and allows direct comparison of peptide identifications using multiple alternative fragmentation techniques.

View Article: PubMed Central - PubMed

Affiliation: Proteomic Mass Spectrometry, Wellcome Trust Sanger Institute, Hinxton, Cambridge.

ABSTRACT
Peptide identification using tandem mass spectrometry is a core technology in proteomics. Latest generations of mass spectrometry instruments enable the use of electron transfer dissociation (ETD) to complement collision induced dissociation (CID) for peptide fragmentation. However, a critical limitation to the use of ETD has been optimal database search software. Percolator is a post-search algorithm, which uses semi-supervised machine learning to improve the rate of peptide spectrum identifications (PSMs) together with providing reliable significance measures. We have previously interfaced the Mascot search engine with Percolator and demonstrated sensitivity and specificity benefits with CID data. Here, we report recent developments in the Mascot Percolator V2.0 software including an improved feature calculator and support for a wider range of ion series. The updated software is applied to the analysis of several CID and ETD fragmented peptide data sets. This version of Mascot Percolator increases the number of CID PSMs by up to 80% and ETD PSMs by up to 60% at a 0.01 q-value (1% false discovery rate) threshold over a standard Mascot search, notably recovering PSMs from high charge state precursor ions. The greatly increased number of PSMs and peptide coverage afforded by Mascot Percolator has enabled a fuller assessment of CID/ETD complementarity to be performed. Using a data set of CID and ETcaD spectral pairs, we find that at a 1% false discovery rate, the overlap in peptide identifications by CID and ETD is 83%, which is significantly higher than that obtained using either stand-alone Mascot (69%) or OMSSA (39%). We conclude that Mascot Percolator is a highly sensitive and accurate post-search algorithm for peptide identification and allows direct comparison of peptide identifications using multiple alternative fragmentation techniques.

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Related in: MedlinePlus

Experimental setups—Experimental setups for combinations of CID and ETD fragmentation. Parallel experiments use only one fragmentation method, sequential experiments collect both CID and ETD fragmented spectra for each precursor. A third type known as decision tree experiments can use a logic tree to select CID or ETD fragmentation for each precursor ion.
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Figure 1: Experimental setups—Experimental setups for combinations of CID and ETD fragmentation. Parallel experiments use only one fragmentation method, sequential experiments collect both CID and ETD fragmented spectra for each precursor. A third type known as decision tree experiments can use a logic tree to select CID or ETD fragmentation for each precursor ion.

Mentions: Here we benchmark the new version of Mascot Percolator and validate its FDR estimations using CID and ETcaD data obtained from universal protein standards (UPS). Then using a large collection of publicly available yeast data, we evaluate the sensitivity and selectivity performance of Mascot Percolator with ETD and ETcaD data, against both Mascot and OMSSA. A detailed assessment of Mascot Percolator's performance using in-house E.Coli data sets was conducted examining the two different fragmentation methods using parallel and sequential experimental workflows, described Fig. 1. The parallel experiments allow us to evaluate CID and ETD/ETcaD fragmentation as standalone techniques, whereas the sequential experiments enable direct comparison of fragmentation methods at the peptide level. In addition to low resolution ion trap fragmentation data, we also evaluate FT-ETcaD experiments in which high resolution MS/MS spectra are acquired. Overall, using a range of CID and ETD data sets we demonstrate substantial increase in the number of peptide spectrum matches (PSMs) using Mascot Percolator.


Enhanced peptide identification by electron transfer dissociation using an improved Mascot Percolator.

Wright JC, Collins MO, Yu L, Käll L, Brosch M, Choudhary JS - Mol. Cell Proteomics (2012)

Experimental setups—Experimental setups for combinations of CID and ETD fragmentation. Parallel experiments use only one fragmentation method, sequential experiments collect both CID and ETD fragmented spectra for each precursor. A third type known as decision tree experiments can use a logic tree to select CID or ETD fragmentation for each precursor ion.
© Copyright Policy - open-access
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC3412976&req=5

Figure 1: Experimental setups—Experimental setups for combinations of CID and ETD fragmentation. Parallel experiments use only one fragmentation method, sequential experiments collect both CID and ETD fragmented spectra for each precursor. A third type known as decision tree experiments can use a logic tree to select CID or ETD fragmentation for each precursor ion.
Mentions: Here we benchmark the new version of Mascot Percolator and validate its FDR estimations using CID and ETcaD data obtained from universal protein standards (UPS). Then using a large collection of publicly available yeast data, we evaluate the sensitivity and selectivity performance of Mascot Percolator with ETD and ETcaD data, against both Mascot and OMSSA. A detailed assessment of Mascot Percolator's performance using in-house E.Coli data sets was conducted examining the two different fragmentation methods using parallel and sequential experimental workflows, described Fig. 1. The parallel experiments allow us to evaluate CID and ETD/ETcaD fragmentation as standalone techniques, whereas the sequential experiments enable direct comparison of fragmentation methods at the peptide level. In addition to low resolution ion trap fragmentation data, we also evaluate FT-ETcaD experiments in which high resolution MS/MS spectra are acquired. Overall, using a range of CID and ETD data sets we demonstrate substantial increase in the number of peptide spectrum matches (PSMs) using Mascot Percolator.

Bottom Line: We have previously interfaced the Mascot search engine with Percolator and demonstrated sensitivity and specificity benefits with CID data.Using a data set of CID and ETcaD spectral pairs, we find that at a 1% false discovery rate, the overlap in peptide identifications by CID and ETD is 83%, which is significantly higher than that obtained using either stand-alone Mascot (69%) or OMSSA (39%).We conclude that Mascot Percolator is a highly sensitive and accurate post-search algorithm for peptide identification and allows direct comparison of peptide identifications using multiple alternative fragmentation techniques.

View Article: PubMed Central - PubMed

Affiliation: Proteomic Mass Spectrometry, Wellcome Trust Sanger Institute, Hinxton, Cambridge.

ABSTRACT
Peptide identification using tandem mass spectrometry is a core technology in proteomics. Latest generations of mass spectrometry instruments enable the use of electron transfer dissociation (ETD) to complement collision induced dissociation (CID) for peptide fragmentation. However, a critical limitation to the use of ETD has been optimal database search software. Percolator is a post-search algorithm, which uses semi-supervised machine learning to improve the rate of peptide spectrum identifications (PSMs) together with providing reliable significance measures. We have previously interfaced the Mascot search engine with Percolator and demonstrated sensitivity and specificity benefits with CID data. Here, we report recent developments in the Mascot Percolator V2.0 software including an improved feature calculator and support for a wider range of ion series. The updated software is applied to the analysis of several CID and ETD fragmented peptide data sets. This version of Mascot Percolator increases the number of CID PSMs by up to 80% and ETD PSMs by up to 60% at a 0.01 q-value (1% false discovery rate) threshold over a standard Mascot search, notably recovering PSMs from high charge state precursor ions. The greatly increased number of PSMs and peptide coverage afforded by Mascot Percolator has enabled a fuller assessment of CID/ETD complementarity to be performed. Using a data set of CID and ETcaD spectral pairs, we find that at a 1% false discovery rate, the overlap in peptide identifications by CID and ETD is 83%, which is significantly higher than that obtained using either stand-alone Mascot (69%) or OMSSA (39%). We conclude that Mascot Percolator is a highly sensitive and accurate post-search algorithm for peptide identification and allows direct comparison of peptide identifications using multiple alternative fragmentation techniques.

Show MeSH
Related in: MedlinePlus