%0 Journal Article
%T Identification of a novel Plasmopara halstedii elicitor protein combining de novo peptide sequencing algorithms and RACE-PCR
%A Stephan Jung
%A Claudia Fladerer
%A Frank Braendle
%A Johannes Madlung
%A Otmar Spring
%A Alfred Nordheim
%J Proteome Science
%D 2010
%I BioMed Central
%R 10.1186/1477-5956-8-24
%X The performance order of the algorithms was PEAKS online > PepNovo > CompNovo. In summary, PEAKS online correctly predicted 45% of measured peptides for a protein test data set.All three de novo peptide sequencing algorithms were used to identify MS/MS spectra of tryptic peptides of an unknown 57 kDa protein of P. halstedii. We found ten de novo sequenced peptides that showed homology to a Phytophthora infestans protein, a closely related organism of P. halstedii. Employing a second complementary approach, verification of peptide prediction and protein identification was performed by creation of degenerate primers for RACE-PCR and led to an ORF of 1,589 bp for a hypothetical phosphoenolpyruvate carboxykinase.Our study demonstrated that identification of proteins within minute amounts of sample material improved significantly by combining sensitive LC-MS methods with different de novo peptide sequencing algorithms. In addition, this is the first study that verified protein prediction from MS data by also employing a second complementary approach, in which RACE-PCR led to identification of a novel elicitor protein in P. halstedii.Over the last decade, mass spectrometry has evolved as an indispensable tool in protein analysis [1,2]. Recent improvements enable the elucidation of sequence information from limited amounts of protein by usage of MS/MS which is the most reliable way to identify proteins [3]. However, MS analysis of proteolytic peptides generates thousands of MS/MS spectra in a single experiment [4]. Matching these spectra to peptides manually would be a near impossible task. Consequently, computational solutions were generated and today, automated peptide identification is performed by database search algorithms, the most popular being Sequest [5] and Mascot [6].Although search algorithms perform an automated search for peptide identification and allow a high-throughput mode for modern proteomics laboratories, database searches do not solve all problems. Ba
%U http://www.proteomesci.com/content/8/1/24