Probability-based protein identification by searching sequence databases using mass spectrometry data

Several algorithms have been described in the literature for protein identi fication by searching a sequence database using mass spectrometry data. In some approaches, the experimental data are peptide molecular weights from t he digestion of a protein by an enzyme. Other approaches use tandem mass sp ectrometry (MS/MS) data from one or more peptides. Still others combine mas s data with amino acid sequence data. We present results from a new compute r program, Mascot, which integrates all three types of search. The scoring algorithm is probability based, which has a number of advantages: (i) A sim ple rule can be used to judge whether a result is significant or not. This is particularly useful in guarding against false positives. (ii) Scores can be compared with those from other types of search, such as sequence homolo gy. (iii) Search parameters can be readily optimised by iteration. The stre ngths and limitations of probability-based scoring are discussed, particula rly in the context of high throughput, fully automated protein identificati on.