Protein Identification Via Tandem Mass Spectra

Recent progress in the sequencing of genomes has had significant impact on biomedical research leading to the development of a new field called proteomics. Shotgun MS/MS has become a key technology in current high-throughput proteomics, especially in studies of protein complex, protein-protein interaction, signal transduction, metabolic pathway reconstruction and simulation of cell behaviors etc. It's a basic but challenging project to identify proteins via tandem mass spectra.Peptide sequencing via tandem mass spectrometry (MS/MS) is one of the most powerful tools in proteomics to identify proteins. We developed a new algorithm for de novo interpretation of MS/MS spectra using graph theory and dynamic alignment between experimental spectrum and theoretical spectrum. The trustworthy peptides from de novo interpretation were used in protein identification via database searching. A high throughput statistical analysis of SwissProt and TrEMBL protein databases showed that it's enough to identify a protein in database with three sequence tags of four amino acid residues, two sequence tags of five amino acid residues or one sequence tag of eight amino acid residues.High throughput scoring algorithms have been applied in shotgun proteomics that was used to find the match of a tandem mass spectrum to a predicted mass spectrum of a peptide within a database. However, these algorithms could produce a significant number of incorrect peptide identifications. We developed a novel approach to scoring tandem mass spectra against a peptide database, in which fragment ion probabilities, number of enzymatic termini of candidate peptides, matching quality and match pattern between experimental and theoretical spectrum were considered. Benchmarking the novel scorer on a large set of experimental MS/MS spectra, it was demonstrated that PepSearch performs significantly better than the widely used software SEQUEST.