PeaksDB: New Software for Substantially Improved Peptide Identification from Orbitrap ETD Mass Spectrometry

摘要

Objective: To substantially improve the peptide identification sensitivity and accuracy from the Orbitrap ETD data with computational methods. Method: The algorithm takes full advantage of the characteristics of the Orbitrap ETD data, including: (1) high mass resolution of the precursor ions, and (2) the distributions of different fragment ion types in the MS/MS scans. For the first characteristic, a pre-search step is conducted to determine the precursor mass error distribution. This does not only make the precursor mass more accurate by a software recalibration, but also allows the use of the mass error as an important feature in the peptide-spectrum matching score function. For the second characteristic, the frequencies of different fragment ion types at different precursor charge states are statistically learned, and used in the score calculation. Moreover, the precursor-related ions in the MS/MS spectra are removed. Additionally, the score function makes use of the similarity between a database peptide and the de novo sequencing result. Result: PeaksDB was compared against three other search engines: MSGF-DB, Mascot, and ZCore. The same shuffled decoy database was appended to the target database and searched together to estimate the false discovery rate (FDR) of each individual engine. The same search parameters were used for all engines except that MSGFDB does not support variable PTMs. If no variable PTM is allowed, the numbers of identified peptides of different engines at 1% FDR are: PeaksDB (2356) > MSGF-DB (2147) > Mascot (1459) > ZCore (1030). If a few common PTMs are allowed, the numbers change to PeaksDB (3501) > Mascot (2677) > MSGF-DB (2147) > ZCore(1125). Conclusion: PeaksDB substantially improved the sensitivity and accuracy of peptide identifications on Orbitrap ETD data. At 1% false discovery rate, PeaksDB identified 1.3 to 1.6 times as many peptides as Mascot 2.3.