A heuristic method for assigning a false-discovery rate for protein identifications from mascot database search results

MS/MS and database searching has emerged as a valuable technology for rapidly analyzing protein expression, localization, and post-translational modifications. The probability-based search engine Mascot has found widespread use as a tool to correlate tandem mass spectra with peptides in a sequence database. Although the Mascot scoring algorithm provides a probability-based model for peptide identification, the independent peptide scores do not correlate with the significance of the proteins to which they match. Herein, we describe a heuristic method for organizing proteins identified at a specified false-discovery rate using Mascot-matched peptides. We call this method PROVALT, and it uses peptide matches from a random database to calculate false-discovery rates for protein identifications and reduces a complex list of peptide matches to a nonredundant list of homologous protein groups. This method was evaluated using Mascot-identified peptides from a Trypanosoma cruzi epimastigote whole-cell lysate, which was separated by multidimensional LC and analyzed by MS/MS. PROVALT was then compared with the two traditional methods of protein identification when using Mascot, the single peptide score and cumulative protein score methods, and was shown to be superior to both in regards to the number of proteins identified and the inclusion of lower scoring nonrandom peptide matches.