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Mass spectrometry provides a high-throughput approach to identify proteins inbiological samples. A key step in the analysis of mass spectrometry data is toidentify the peptide sequence that, most probably, gave rise to each observedspectrum. This is often tackled using a database search: each observed spectrumis compared against a large number of theoretical "expected" spectra predictedfrom candidate peptide sequences in a database, and the best match isidentified using some heuristic scoring criterion. Here we provide a moreprincipled, likelihood-based, scoring criterion for this problem. Specifically,we introduce a probabilistic model that allows one to assess, for eachtheoretical spectrum, the probability that it would produce the observedspectrum. This probabilistic model takes account of peak locations andintensities, in both observed and theoretical spectra, which enablesincorporation of detailed knowledge of chemical plausibility in peptideidentification. Besides placing peptide scoring on a sounder theoreticalfooting, the likelihood-based score also has important practical benefits: itprovides natural measures for assessing the uncertainty of each identification,and in comparisons on benchmark data it produced more accurate peptideidentifications than other methods, including SEQUEST. Although we focus hereon peptide identification, our scoring rule could easily be integrated into anydownstream analyses that require peptide-spectrum match scores.

A likelihood-based scoring method for peptide identification using mass spectrometry