| The concept of signature peptide approach to proteomics was evaluated in this work. By study E. coli, yeast and human protein databases, it was concluded that using multiple properties of peptides to recognize signature peptides is better for protein identification. Combination of peptide mass fingerprinting and chromatographic behavior could greatly increase confidence in identified proteins. A software, S&barbelow;imulation A&barbelow;ssisted O&barbelow;nline P&barbelow;rotein I&barbelow;dentification/Quantification (SAOPI), was also developed for data mining of signature peptide approach. By communicating with local genome database, SAOPI virtually digests genome proteins. After selecting peptides with rare amino acid residues or post translational modifications, the signature peptides are recognized by database searching, and proteins are identified from their signature peptides. Theoretically predicted peptide separation behaviors, such as retention time in reversed-phase chromatography (RPC) and electrophoretic mobility in capillary electrophoresis (CE), are also used to aid protein identification. After this, protein regulation is studied by calculating ratio of the peak intensity of the same signature peptide but from control and sample. |
