Robust Prediction of the MASCOT Score for an Improved Quality Assessment in Mass Spectrometric Proteomics

Abstract

Protein identification by tandem mass spectrometry is based on the reliable processing of the acquired data. Unfortunately, the generation of a large number of poor quality spectra is commonly observed in LC-MS/MS, and the processing of these mostly noninformative spectra with its associated costs should be avoided. We present a continuous quality score that can be computed very quickly and that can be considered an approximation of the MASCOT score in case of a correct identification. This score can be used to reject low quality spectra prior to database identification, or to draw attention to those spectra that exhibit a (supposedly) high information content, but could not be identified. The proposed quality score can be calibrated automatically on site without the need for a manually generated training set. When this score is turned into a classifier and when features are used that are independent of the instrument, the proposed approach performs equally to previously published classifiers and feature sets and also gives insights into the behavior of the MASCOT score.