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.
Dworzanski
Jacek P
JP
Geo-Centers, Inc., Aberdeen Proving Ground, Maryland 21010-0068, USA. jacek.dworzanski@us.army.mil
Deshpande
Samir V
SV
Chen
Rui
R
Jabbour
Rabih E
RE
Snyder
A Peter
AP
Wick
Charles H
CH
Li
Liang
L
D000595:Amino Acid Sequence; D001419:Bacteria / Q000145:classification*; D001426:Bacterial Proteins / Q000032:analysis*; D019295:Computational Biology; D013058:Mass Spectrometry; D008969:Molecular Sequence Data; D010446:Peptide Fragments / Q000032:analysis; D010449:Peptide Mapping; D010802:Phylogeny; D020543:Proteome / Q000032:analysis*; D040901:Proteomics / Q000379:methods*
D001426:Bacterial Proteins; D010446:Peptide Fragments; D020543:Proteome