Identification of Contaminants in Proteomics Mass Spectrometry Data

Mass spectrometry (MS) is a widely used method for protein identification. Peptide mass fingerprinting is the protein identification technique in which MS is employed to determine the masses of peptide fragments generated following enzymatic digestion of proteins. The masses of peptides are then submitted to a recognition program, e.g., MASCOT or MSFIT, for identification of a protein. The strategy is hampered, however, because not only are the peptide masses determined, but also the masses of multiple contaminants that are also present in the sample. Although the masses of some common and known contaminants are removed (e.g., peptides generated by trypsin autolysis), many others are inadvertently incorporated into the analysis. In this paper we present an approach for automatic identification of contaminant masses so that they can be removed prior to the identification process. For this purpose we have developed an algorithm that clusters mass values. We calculate the frequencies of all masses and then identify contaminants. We propose that masses with frequency higher than a given value are contaminants. In our analysis of 3,029 digested proteins, yielding 78,384 masses, we identified 16 possible contaminants. Of these 16, four are known trypsin autolysis peptides. Removing these contaminant masses from the database search will lead to more accurate and reliable protein identification.