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Abstract
Several algorithms have been described in the literature for protein identification
by searching a sequence database using mass spectrometry data. In some approaches,
the experimental data are peptide molecular weights from the digestion of a protein
by an enzyme. Other approaches use tandem mass spectrometry (MS/MS) data from one
or more peptides. Still others combine mass data with amino acid sequence data. We
present results from a new computer program, Mascot, which integrates all three types
of search. The scoring algorithm is probability based, which has a number of advantages:
(i) A simple 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 homology. (iii) Search parameters
can be readily optimised by iteration. The strengths and limitations of probability-based
scoring are discussed, particularly in the context of high throughput, fully automated
protein identification.