Bioinformatics and Mass Spectrometry for Microorganism Identification:  Proteome-Wide Post-Translational Modifications and Database Search Algorithms for Characterization of Intact H. pylori

• Published in: Analytical chemistry (Washington) Vol. 73; no. 19; pp. 4566 - 4573
• Main Authors: DEMIREV, Plamen A., LIN, Jeffrey S., PINEDA, Fernando J., FENSELAU, Catherine
• Format: Journal Article
• Language: English
• Published: Washington, DC American Chemical Society 01-10-2001
• Subjects: Algorithms; Bacterial Proteins - analysis; Bacterial Proteins - metabolism; Bacteriological methods and techniques used in bacteriology; Bacteriology; Bioinformatics; Biological and medical sciences; Chemistry; Computational Biology - methods; Databases, Factual; Fundamental and applied biological sciences. Psychology; General aspects; Helicobacter pylori - chemistry; Helicobacter pylori - classification; Helicobacter pylori - metabolism; Mathematics in biology. Statistical analysis. Models. Metrology. Data processing in biology (general aspects); Microbiology; Microorganisms; Online data bases; Protein Processing, Post-Translational; Proteome - metabolism; Scientific imaging; Spectrometry, Mass, Matrix-Assisted Laser Desorption-Ionization - methods; Characterization; Spirillales; Helicobacter pylori; Computerized processing; Spirillaceae; Bacteria; Identification; Matrix assisted laser desorption ionization; Microorganism; Method; Algorithm; Mass spectrometry
• ISSN: 0003-2700; 1520-6882
• DOI: 10.1021/ac010466f

MALDI-TOF mass spectrometry has been coupled with Internet-based proteome database search algorithms in an approach for direct microorganism identification. This approach is applied here to characterize intact H. pylori (strain 26695) Gram-negative bacteria, the most ubiquitous human pathogen. A procedure for including a specific and common posttranslational modification, N-terminal Met cleavage, in the search algorithm is described. Accounting for posttranslational modifications in putative protein biomarkers improves the identification reliability by at least an order of magnitude. The influence of other factors, such as number of detected biomarker peaks, proteome size, spectral calibration, and mass accuracy, on the microorganism identification success rate is illustrated as well.