3/2011
vol. 92
abstract:
RESEARCH PAPER Optimization algorithm for de novo analysis
of tandem mass spectrometry data
BioTechnologia vol. 92(3) C pp. 296-300 C 2011
Online publish date: 2014/10/28
PlumX metrics:
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.
keywords:
tandem mass spectrometry, de novo sequencing, genetic algorithm
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