A Simulated MS/MS Library for Spectrum-to-spectrum Searching in Large Scale Identification of Proteins

Abstract

Identifying peptides from mass spectrometric fragmentation data (MS/MS spectra) using search strategies that map protein sequences to spectra is computationally expensive. An alternative strategy uses direct spectrum-tospectrum matching against a reference library of previously observed MS/MS that has the advantage of evaluating matches using fragment ion intensities and other ion types than the simple set normally used. However, this approach is limited by the small sizes of the available peptide MS/MS libraries and the inability to evaluate the rate of false assignments. In this study, we observed good performance of simulated spectra generated by the kinetic model implemented in MassAnalyzer We also demonstrate the use of simulated spectra for searching against decoy sequences to estimate false discovery rates. Although we found lower score discrimination with spectrum-to-spectrum searches than with Mascot, particularly for higher charge forms, comparable peptide assignments with low false discovery rate were achieved by examining consensus between X!Hunter and Mascot, filtering results by mass accuracy, and ignoring score thresholds. Protein identification results are comparable to those achieved when evaluating consensus between Sequest and Mascot. Run times with large scale data sets using X!Hunter with the simulated spectral library are 7 times faster than Mascot and 80 times faster than Sequest with the human International Protein Index (IPI) database. We conclude that simulated spectral libraries greatly expand the search space available for spectrum-to-spectrum searching while enabling principled analyses and that the approach can be used in consensus strategies for large scale studies while reducing search times. Identification of proteins in complex samples is a major new area in bioinformatics. The most successful method currently available is shotgun proteomics where proteins are proteolyzed into peptides (usually by trypsin) followed by large scale sequencing of peptides by on-line chromatographic separation and fragmentation in a mass spectrometer (LC-MS/MS). The fragmentation process generates spectra (referred to as MS/MS spectra) from which peptide sequences consistent with the observed fragment ions can be identified (1). A common computational strategy for matching an MS/MS spectrum to a peptide sequence involves interconverting spectral and sequence information (2). Spectrum-to-sequence database search programs match peptide sequences to spectra in one of two ways: by 1) extracting sequence information from an observed spectrum and matching the sequences against peptides contained in a protein database or 2) converting peptide sequences from the protein database into simple spectra (e.g. predicting a subset of possible b and y fragment ions generated by peptide bond cleavage) and matching the predicted fragment ions to those observed. Various scoring methods are then used to evaluate overlap between observed and predicted fragments, including use of probability functions or spectral similar...

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