| Abstract
| - Detection and identification of pathogenic bacteria andtheir protein toxins play a crucial role in a properresponse to natural or terrorist-caused outbreaks ofinfectious diseases. The recent availability of whole genome sequences of priority bacterial pathogens opens newdiagnostic possibilities for identification of bacteria byretrieving their genomic or proteomic information. Wedescribe a method for identification of bacteria based ontandem mass spectrometric (MS/MS) analysis of peptidesderived from bacterial proteins. This method involvesbacterial cell protein extraction, trypsin digestion, liquidchromatography MS/MS analysis of the resulting peptides, and a statistical scoring algorithm to rank MS/MSspectral matching results for bacterial identification. Tofacilitate spectral data searching, a proteome database wasconstructed by translating genomes of bacteria of interestwith fully or partially determined sequences. In this work,a prototype database was constructed by the automatedanalysis of 87 publicly available, fully sequenced bacterialgenomes with the GLIMMER gene finding software. MS/MS peptide spectral matching for peptide sequence assignment against this proteome database was done bySEQUEST. To gauge the relative significance of theSEQUEST-generated matching parameters for correctpeptide assignment, discriminant function (DF) analysisof these parameters was applied and DF scores were usedto calculate probabilities of correct MS/MS spectra assignment to peptide sequences in the database. Thepeptides with DF scores exceeding a threshold valuedetermined by the probability of correct peptide assignment were accepted and matched to the bacterial proteomes represented in the database. Sequence filteringor removal of degenerate peptides matched with multiplebacteria was then performed to further improve identification. It is demonstrated that using a preset criterion withknown distributions of discriminant function scores andprobabilities of correct peptide sequence assignments, atest bacterium within the 87 database microorganismscan be unambiguously identified.
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