Microorganism Identification by Matrix-Assisted Laser/Desorption Ionization Mass Spectrometry and Model-Derived Ribosomal Protein Biomarkers

An improved data analysis method is described for rapid
identification of intact microorganisms from MALDI-TOF-MS data. The method makes no use of mass spectral
fingerprints. Instead, a microorganism database is automatically generated that contains biomarker masses derived from ribosomal protein sequences and a model of
N-terminal Met loss. We quantitatively validate the method
via a blind study that seeks to identify microorganisms
with known ribosomal protein sequences. We also include
in the database microorganisms with incompletely known
sets of ribosomal proteins to test the specificity of the
method. With an optimal MALDI protocol, and at the 95%
confidence level, microorganisms represented in the
database with 20 or more biomarkers (i.e., those with
complete or nearly completely sequenced genomes) are
correctly identified from their spectra 100% of the time,
with no incorrect identifications. Microorganisms with
seven or less biomarkers (i.e., incompletely sequenced
genomes) are either not identified or misidentified. Robustness with respect to variations in sample preparation
protocol and mass analysis protocol is demonstrated by
collecting data with two different matrixes and under two
different ion-mode configurations. Statistical analysis suggests that, even without further improvement, the method
described here would successfully scale up to microorganism databases with roughly 1000 microorganisms.
The results demonstrate that microorganism identification
based on proteome data and modeling can perform as well
as methods based on mass spectral fingerprinting.