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Learning score function parameters for improved spectrum identification in tandem mass spectrometry experiments.

ABSTRACT: The identification of proteins from spectra derived from a tandem mass spectrometry experiment involves several challenges: matching each observed spectrum to a peptide sequence, ranking the resulting collection of peptide-spectrum matches, assigning statistical confidence estimates to the matches, and identifying the proteins. The present work addresses algorithms to rank peptide-spectrum matches. Many of these algorithms, such as PeptideProphet, IDPicker, or Q-ranker, follow a similar methodology that includes representing peptide-spectrum matches as feature vectors and using optimization techniques to rank them. We propose a richer and more flexible feature set representation that is based on the parametrization of the SEQUEST XCorr score and that can be used by all of these algorithms. This extended feature set allows a more effective ranking of the peptide-spectrum matches based on the target-decoy strategy, in comparison to a baseline feature set devoid of these XCorr-based features. Ranking using the extended feature set gives 10-40% improvement in the number of distinct peptide identifications relative to a range of q-value thresholds. While this work is inspired by the model of the theoretical spectrum and the similarity measure between spectra used specifically by SEQUEST, the method itself can be applied to the output of any database search. Further, our approach can be trivially extended beyond XCorr to any linear operator that can serve as similarity score between experimental spectra and peptide sequences.

SUBMITTER: Spivak M

PROVIDER: S-EPMC3436966 | biostudies-literature | 2012 Sep

REPOSITORIES: biostudies-literature

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Learning score function parameters for improved spectrum identification in tandem mass spectrometry experiments.

Spivak Marina M Bereman Michael S MS Maccoss Michael J MJ Noble William Stafford WS

Journal of proteome research 20120815 9

The identification of proteins from spectra derived from a tandem mass spectrometry experiment involves several challenges: matching each observed spectrum to a peptide sequence, ranking the resulting collection of peptide-spectrum matches, assigning statistical confidence estimates to the matches, and identifying the proteins. The present work addresses algorithms to rank peptide-spectrum matches. Many of these algorithms, such as PeptideProphet, IDPicker, or Q-ranker, follow a similar methodol ...[more]

PMID: 22866926

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Project description:In a bottom-up shotgun approach, the proteins of a mixture are enzymatically digested, separated, and analyzed via tandem mass spectrometry. The mass spectra relating fragment ion intensities (abundance) to the mass-to-charge are used to deduce the amino acid sequence and identify the peptides and proteins. The variables that influence intensity were characterized using a multi-factorial mixed-effects model, a ten-fold cross-validation, and stepwise feature selection on 6,352,528 fragment ions from 61,543 peptide ions. Intensity was higher in fragment ions that did not have neutral mass loss relative to any mass loss or that had a +1 charge state. Peptide ions classified for proton mobility as non-mobile had lowest intensity of all mobility levels. Higher basic residue (arginine, lysine or histidine) counts in the peptide ion and low counts in the fragment ion were associated with lower fragment ion intensities. Higher counts of proline in peptide and fragment ions were associated with lower intensities. These results are consistent with the mobile proton theory. Opposite trends between peptide and fragment ion counts and intensity may be due to the different impact of factor under consideration at different stages of the MS/MS experiment or to the different distribution of observations across peptide and fragment ion levels. Presence of basic residues at all three positions next to the fragmentation site was associated with lower fragment ion intensity. The presence of proline proximal to the fragmentation site enhanced fragmentation and had the opposite trend when located distant from the site. A positive association between fragment ion intensity and presence of sulfur residues (cysteine and methionine) on the vicinity of the fragmentation site was identified. These results highlight the multi-factorial nature of fragment ion intensity and could improve the algorithms for peptide identification and the simulation in tandem mass spectrometry experiments.

Dataset Information

Learning score function parameters for improved spectrum identification in tandem mass spectrometry experiments.

Publications

Learning score function parameters for improved spectrum identification in tandem mass spectrometry experiments.

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