Identification of a novel Plasmopara halstedii elicitor protein combining de novo peptide sequencing algorithms and RACE-PCR

Abstract

<p>Abstract</p> <p>Background</p> <p>Often high-quality MS/MS spectra of tryptic peptides do not match to any database entry because of only partially sequenced genomes and therefore, protein identification requires <it>de novo </it>peptide sequencing. To achieve protein identification of the economically important but still unsequenced plant pathogenic oomycete <it>Plasmopara halstedii</it>, we first evaluated the performance of three different <it>de novo </it>peptide sequencing algorithms applied to a protein digests of standard proteins using a quadrupole TOF (QStar Pulsar i).</p> <p>Results</p> <p>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.</p> <p>All three <it>de </it>novo peptide sequencing algorithms were used to identify MS/MS spectra of tryptic peptides of an unknown 57 kDa protein of <it>P. halstedii</it>. We found ten <it>de novo </it>sequenced peptides that showed homology to a <it>Phytophthora infestans </it>protein, a closely related organism of <it>P. halstedii</it>. 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.</p> <p>Conclusions</p> <p>Our study demonstrated that identification of proteins within minute amounts of sample material improved significantly by combining sensitive LC-MS methods with different <it>de novo </it>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 <it>P. halstedii</it>.</p