Background: Sporulation, characteristic for some bacteria such as Bacillus subtilis, has not been entirely defined yet. Protein phosphatase E (PrpE) and small, acid soluble spore proteins (SASPs) influence this process. Nevertheless, direct result of PrpE interaction on SASPs content in spore coat of B. subtilis has not been evidenced so far. As proteomic approach enables global analysis of occurring proteins, therefore it was chosen in this experiment to compare SASPs occurrence in two strains of B. subtilis, standard 168 and ΔprpE, lacking PrpE phosphatase. Proteomic analysis is still a challenge, and despite of big approach in mass spectrometry (MS) field, the identification reliability remains unsatisfactory. Therefore there is a rising interest in new methods, particularly bioinformatic tools that would harden protein identification. Most of currently applied algorithms are based on MS-data. Information from separation steps is not still in routine usage, even though they also provide valuable facts about analyzed structures. The aim of this research was to apply a model for peptides retention times prediction, based on quantitative structure-retention relationships (QSRR) in SASPs analysis, obtained from two strains of B. subtilis proteome digests after separation and identification of the peptides by LC-ESI-MS/MS. The QSRR approach was applied as the additional constraint in proteomic research verifying results of MS/MS ion search and confirming the correctness of the peptides identifications along with the indication of the potential false positives and false negatives.
Results: In both strains of B. subtilis, peptides characteristic for SASPs were found, however their identification confidence varied. According to the MS identity parameter Xcorr and difference between predicted and experimental retention times (ΔtR) four groups could be distinguished: correctly and incorrectly identified, potential false positives and false negatives. The ΔprpE strain was characterized by much higher amount of SASPs peptides than standard 168 and their identification confidence was, mostly for alpha- and beta-type SASP, satisfactory.
Conclusions: The QSRR-based model for predicting retention times of the peptides, was a useful additional to MS tool, enhancing protein identification. Higher content of SASPs in strain lacking PrpE phosphatase suggests that this enzyme may influence their occurrence in the spores, lowering levels of these proteins.