Microorganism Identification by Mass Spectrometry and Protein Database Searches

A method for rapid identification of microorganisms is presented, which exploits the wealth of information contained in prokaryotic genome and protein sequence databases. The method is based on determining the masses of a set of ions by MALDI TOF mass spectrometry of intact or treated cells. Subsequent correlation of each ion in the set to a protein, along with the organismic source of the protein, is performed by searching an Internet-accessible protein database. Convoluting the lists for all ions and ranking the organisms corresponding to matched ions results in the identification of the microorganism. The method has been successfully demonstrated on B. subtilis and E. coli, two organisms with completely sequenced genomes. The method has been also tested for identification from mass spectra of mixtures of microorganisms, from spectra of an organism at different growth stages, and from spectra originating at other laboratories. Experimental factors such as MALDI matrix preparation, spectral reproducibility, contaminants, mass range, and measurement accuracy on the database search procedure are addressed too. The proposed method has several advantages over other MS methods for microorganism identification. The massive effort to sequence the human genome has brought about a rapid increase in the speed with which DNA sequences from all species are being accumulated in publicly available computer databases. As a result, the complete genomes of 18 microorganisms are now known, 1 with the entire sequence of a multicellular organismsthe nematode Caenorhabditis eleganss very recently unveiled. 2 The rate with which complete genome sequences of new microorganisms are unraveled is constantly accelerating, and it is predicted that the completed genomes of more than 50 prokaryotes will be available in less than a year. 3 One of the major tasks of the emerging discipline of bioinformatics 4 is mapping the interconnections and correlations between DNA sequences and protein sequences in an effort to understand cellular function at the molecular level. There exists complementarity between the genome of an organism and its respective proteome, i.e., the dynamic entity set of all expressed proteins. In databases, such complementarity is realized via assignment of an amino acid sequence to each "open reading frame" (ORF) in a DNA sequence. By using bioinformatics tools, the complete proteomes of microorganisms with established DNA sequences are also made available and accessible through the Internet. Unequivocal characterization of such organisms can be achieved through knowledge of their complete genomes or complementary proteomes. Concurrently, sequences of many individual proteins and genes from various organisms have also been obtained and entered into databases. Such a wealth of sequence information has prompted scientists to address previously intractable questions about the structure and functions of the proteome. [5][6][7][8] Recent technical and methodological advances in mass spectrometry (MS) have promoted its wider use for structural elucidation of biopolymers. The expanding requirements in proteomics, e.g., for rapid identification of proteins present in a mixture in picomolar amounts, have resulted in the development of powerful MS-based procedures for identity assignment of individual proteins. 9-14 They are based on chemical/enzymatic digestion of material (obtained from a single spot in a twodimensional gel electropherogram or by other suitable chromatographic techniques) and mass spectral determination of the molecular masses of the protein and resulting peptides (peptide mapping). Making use of already available information in protein sequence databases, a comparison is made between proteolytic peptide mass patterns generated "in silico" and experimentally observed peptide masses. A "hit-list" is compiled, ranking candidate proteins in the database, based on (among other criteria) number of matches between the proteolytic fragments. Several Web sites are accessible that provide software for protein identification on-line, based on peptide mapping and sequenc