Mass Spectrometry-Based Proteomics Combined with Bioinformatic Tools for Bacterial Classification

Timely classification and identification of bacteria is of vital importance in many areas of public health.
We present a mass spectrometry (MS)-based proteomics approach for bacterial classification. In this
method, a bacterial proteome database is derived from all potential protein coding open reading frames
(ORFs) found in 170 fully sequenced bacterial genomes. Amino acid sequences of tryptic peptides
obtained by LC−ESI MS/MS analysis of the digest of bacterial cell extracts are assigned to individual
bacterial proteomes in the database. Phylogenetic profiles of these peptides are used to create a matrix
of sequence-to-bacterium assignments. These matrixes, viewed as specific assignment bitmaps, are
analyzed using statistical tools to reveal the relatedness between a test bacterial sample and the
microorganism database. It is shown that, if a sufficient amount of sequence information is obtained
from the MS/MS experiments, a bacterial sample can be classified to a strain level by using this
proteomics method, leading to its positive identification.
Keywords: classification of bacteria • proteomics • tandem mass spectrometry • LC−MS/MS • bioinformatics