Protein Identification by PMF Tools Using MS Data

currently the most common method used to identify proteins in a high throughpnt environment, is based on the comparison of a list of experimental peptide masses with theoretical peptide masses. The experimental masses are generated from the MS measurement of an enzymatically digested protein sample. The theoretical masses are obtained from an in silico digestion of all sequences in a database. The goal is to find the protein(s) whose peptide masses show the best match with the experimental fingerprint. The method can be divided into 3 steps. The first step is pe detection, i.e. the selection of the most relevant masses for protein identification from the mass spectra. Frequently, only few experimental peptide 

In the comparison phase, apart from the experimental peptide masses and the proteinase used to digest the proteins, some optional attributes may be specified to reflect experimental conditions and to reduce the search space. These optional attributes may include information coming from the sample such as species of origin, M, or pi of the whole protein with the accepted error range, possible chemical or artefactual modifications like carboxymethylation of cysteines or oxidation of methionines. Other parameters to be specified include the mass tolerance or the minimum number of matching peptides required for a protein to be suggested as a possible match. Providing a maximum of information available about the sample helps to decrease the number of candidate proteins, to reduce the probability of false positive matches, and thus to increase the confidence of the identification. However, one must be careful not to miss the correct protein either. 

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