Improved Approach to Protein Identification Using Peptide Mass Fingerprint

Won-A Joo1, Kap-Soon Noh2, Chan-Wha Kimm, Graduate School of Life Sciences and Biotechnology; 2, Graduate School of Life Sciences and Biotechnology

Peptide mass fingerprint (PMF) has been a useful method for rapid and high-throughput protein identification. Automated database search softwares such as Ms-Fit, Mascot, ProFound, and PeptIdent made the protein identification possible. These softwares compared to the theoretical list of masses, can give information usually only on a few peptides provide well-defined peaks that are easily identifiable. The majority of peptides, however, provide small peaks or no peak at all. Existing schemes for determining the best match include ranking by number of matches and a scoring system. This system based on the observed frequency of peptides from all proteins of database in a given molecular weight range (MOWSE score). When the mass spectral data are incomplete and/or of low mass accuracy, the “number of matches” approach may be inadequate to make a useful identification. Several researchers addressed the question of the factors influencing the quality of a mass spectrometry (MS) experiment. Missed cleavages, post-translational modifications of peptides and contaminants are important factors affecting the results of the MS analysis, however these factors have an influence the identification process rather than the quality of the MS spectra. In our study, we compared softwares used frequently to identify the proteins of Homo sapience and Halobacterium salinarum. These attempts could provide more effective algorithm in the protein identification as each species using PMF.