Peptide identification in 'shotgun' proteomics using tandem mass spectrometry: Comparison of search engine algorithms.

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

High-throughput proteomics technologies are gaining popularity in different areas of life sciences. One of the main objectives of proteomics is characterization of the proteins in 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 experimental run require specialized bioinformatic tools for automated data mining. One of the most important tools is a 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 derived from a protein database. Peptide identifications obtained with 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 acquired for complex protein mixtures, including both annotated protein standards and clinical samples. The comparison was performed for three popular search engines: commercially available Mascot, as well as open-source X!Tandem and OMSSA. It was shown that the search engine OMSSA identifies in general a smaller number of proteins, while X!Tandem and Mascot deliver similar performance. We found no compelling reasons for using the commercial search engine instead of its open source competitor.

PEPTIDE analysis; PROTEOMICS; SEARCH engines; LIQUID chromatography-mass spectrometry; DATA mining

Journal of Analytical Chemistry, 2015, Vol 70, Issue 14, p1614

1061-9348

Academic Journal

10.1134/S1061934815140075