Oscore: a combined score to reduce false negative rates for peptide identification in tandem mass spectrometry analysis

摘要

Tandem mass spectrometry (MS/MS) has been widely used in proteomics studies. Multiple algorithms have been developed for assessing matches between MS/MS spectra and pepticle sequences in databases. However, it is still a challenge to reduce false negative rates without compromising the high confidence of peptide identification. In this study, we developed the score, Oscore, by logistic regression using SEQUEST and AMASS variables to identify fully tryptic peptides. Since these variables showed complicated association with each other, combining them together rather than applying them to a threshold model improved the classification of correct and incorrect pepticle identifications. Oscore achieved both a lower false negative rate and a lower false positive rate than Peptide Prophet on datasets from 18 known protein mixtures and several proteome-scale samples of different complexity, database size and separation methods. By a three-way comparison among Oscore, PeptideProphet and another logistic regression model which made use of Peptide Prophet's variables, the main contributor for the improvement made by Oscore is discussed.