Improved Validation of Peptide MS/MS Assignments Using Spectral Intensity Prediction

Department of Chemistry and Biochemistry, University of Colorado at Boulder, Boulder, Colorado, United States
Molecular & Cellular Proteomics (Impact Factor: 6.56). 02/2007; 6(1):1-17. DOI: 10.1074/mcp.M600320-MCP200
Source: PubMed


A major limitation in identifying peptides from complex mixtures by shotgun proteomics is the ability of search programs to accurately assign peptide sequences using mass spectrometric fragmentation spectra (MS/MS spectra). Manual analysis is used to assess borderline identifications; however, it is error-prone and time-consuming, and criteria for acceptance or rejection are not well defined. Here we report a Manual Analysis Emulator (MAE) program that evaluates results from search programs by implementing two commonly used criteria: 1) consistency of fragment ion intensities with predicted gas phase chemistry and 2) whether a high proportion of the ion intensity (proportion of ion current (PIC)) in the MS/MS spectra can be derived from the peptide sequence. To evaluate chemical plausibility, MAE utilizes similarity (Sim) scoring against theoretical spectra simulated by MassAnalyzer software (Zhang, Z. (2004) Prediction of low-energy collision-induced dissociation spectra of peptides. Anal. Chem. 76, 3908-3922) using known gas phase chemical mechanisms. The results show that Sim scores provide significantly greater discrimination between correct and incorrect search results than achieved by Sequest XCorr scoring or Mascot Mowse scoring, allowing reliable automated validation of borderline cases. To evaluate PIC, MAE simplifies the DTA text files summarizing the MS/MS spectra and applies heuristic rules to classify the fragment ions. MAE output also provides data mining functions, which are illustrated by using PIC to identify spectral chimeras, where two or more peptide ions were sequenced together, as well as cases where fragmentation chemistry is not well predicted.

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Available from: Krzysztof Cios, Jan 25, 2016
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    • "Our results confirm that finer-scale spectra predicted from comprehensive fragmentation pathways can provide valuable information for peptide identification [Sun et al. (2007)] and demonstrate the potential to improve the accuracy of spectra matching by modeling these structures. Similar improvement in peptide identification was also observed in Klammer et al. (2008), who developed a probabilistic model of peptide fragmentation chemistry using the dynamic Bayesian network (DBN) and identified peptides using the features learned from DBN using the support vector machine (SVM). "
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    • "Parent mass tolerance was 1.2 Da, MS/MS tolerance was 0.5 Da, and fixed modifications were set to carbamidomethyl cysteine. Peptides were filtered using in-house MSPlus and MAE algorithms previously described (Resing et al., 2004; Sun et al., 2007). "
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