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Optimization algorithm for de novo analysis of tandem mass spectrometry data

Protein identification is usually achieved by tandem mass spectrometry (MS/MS). Because of the difficulty in measuring complete proteins using MS/MS, typically a protein is enzymatically digested into peptides and the MS/MS spectrum of each peptide is measured. The database searching methods are predominant in the task of peptide identification. Their aim is to find the best match between model spectra generated from the peptides stored in the database and the experimental mass spectrum obtained for an unidentified peptide. In this approach one assumes that the peptide under investigation belongs to the scanned database. Otherwise, so called de novo methods have to be applied to determine the peptide sequence. Unfortunately, de novo sequencing algorithms are fragile in the presence of missing peaks, background noise or post-translational protein modifications. In this paper, we propose a post-processing method for optimizing the results obtained from de novo sequencing algorithms. Our approach in the reconstruction of amino acid sequences employs only spectral features and is robust with respect to missing data. We demonstrate the significant improvement achieved using our method applied to sequences reconstructed using a popular de novo sequencing method. The tool is freely available at http://pepygen. sourceforge.net.