Professor Jessica Prenni, Director of the CSU Proteomics and Metabolomics Facility, is leading an effort in proteomics. Proteomics is the large-scale study of proteins, in particular the structures and functions of proteins. Professor Prenni makes use of two software packages—Mascot and SEQUEST—in her research. Mascot is a software search engine that uses mass spectrometry data to identify proteins from primary sequence databases. Mass spectrometry, on the other hand, is an analytical technique that measures the mass-to-charge ratio of charged particles. Mascot implements a wide variety of proven searching methods to analyze mass spectrometry data. Using Mascot, the general approach for searching is to take a small sample of the protein of interest and digest it with a proteolytic enzyme such as trypsin. The resulting digest mixture is then analyzed by a mass spectrometer. Afterwards, the experimental mass values are compared with entries in a comprehensive primary sequence database (e.g., MSDB, NCBInr, SwissProt, dbEST) to find out the closest match(es).

SEQUEST is a tandem mass spectrometry data analysis program used for protein identification. In contrast to mass spectrometry, tandem mass spectrometry involves multiple steps of mass spectrometry selection. SEQUEST identifies collections of tandem mass spectra to peptide sequences that have been generated from databases of protein sequences. It identifies each tandem mass spectrum individually, and evaluates protein sequences from a database to compute the list of peptides that could result from each. Since the peptide's intact mass is known from the mass spectrum, using this information SEQUEST can determine the set of candidate peptides sequences that could meaningfully be compared to the spectrum. Specifically, for each candidate peptide, SEQUEST projects a theoretical tandem mass spectrum and compares these theoretical spectra to the observed tandem mass spectrum using a cross correlation approach. The candidate sequence with the best matching theoretical tandem mass spectrum is then reported as the best identification for this spectrum.