Mass Spectrometry-Based Protein Identification by Correlation with Sequence Database

Abstract

The progress in genome sequencing projects of a large number of organisms and the advance in mass spectrometry of protein analysis have been significant driving forces in the formation of the field proteomics. The advance in protein mass spectrometry includes development of computer algorithms that use mass spectrometric data to identify proteins in sequence databases. The computer algorithms for protein identification by correlation with sequence databases rely on the availability of constraining parameters that distinguish specific matches from all the other sequences in the database. They can be categorized into three strategies. One of strategies is called “peptide mass fingerprinting,” which is based upon correlating measured masses of peptides derived from digestion of proteins by a residue-specific protease with theoretically calculated peptide masses derived from proteins registered in sequence database. Two strategies for protein identification using tandem mass spectrometry (MS/MS) data are distinguished by demand for interpretation of product ion mass spectra. Product ion mass fingerprinting using uninterpreted MS/MS data of peptides is conceptually similar approach to peptide mass fingerprinting. SEQEUST^® and MS/MS Ions Search in MASCOT^® are the most widely used algorithms for protein identification by searching sequence database using uninterpreted product ion mass spectra. Another strategy using MS/MS data employs the search algorithm by using parameters, such as “peptide sequence tag,” found by manual inspection of product ion mass spectra.