Peptide identifications in «shotgun» proteomics using tandem mass spectrometry

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T12N1

Peptide identifications in «shotgun» proteomics using tandem
mass spectrometry: comparison of search engine algorithms

M.V. Ivanov, L.I. Levitsky, A.A. Lobas, I.A. Tarasova, M.L. Pridatchenko,
V.G. Zgoda, S.A. Moshkovskii, G. Mitulovic, M.V. Gorshkov

High-throughput proteomics technologies are gaining popularity in different areas of life sciences. One of the main objectives of proteomics is the characterization of the proteins in the biological samples using liquid chromatography/mass spectrometry analysis of the corresponding proteolytic peptide mixtures. Both the complexity and the scale of experimental data obtained even from a single experi mental run require developing a variety of specialized bioinformatic tools for the automated data mining. One of the most important tools is the so-called proteomics search engine used for identification of proteins present in a sample by comparing experimental and theoretical tandem mass spectra. The latter are generated for the proteolytic peptides obtained from a protein database. Peptide identifications obtained by the search engine are then scored according to the probability of a correct peptide-spectrum match. The purpose of this work was to perform a comparison of different search algorithms using data obtained for complex protein mixtures including both annotated protein standards and clinical samples. The comparison was performed for three popular search engines: commercially available Mascot, open-source X!Tandem, and closed code freely-distributed OMSSA. It was shown that the search engine OMSSA identifies in general smaller number of proteins, while X!Tandem and Mascot deliver similar performances. We found no compelling reasons for using the commercial search engine instead of its open source competitor.

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