On the Importance of Well-Calibrated Scores for Identifying Shotgun Proteomics Spectra

Identifying the peptide responsible for generating an observed fragmentation spectrum requires scoring a collection of candidate peptides and then identifying the peptide that achieves the highest score. However, analysis of a large collection of such spectra requires that the score assigned to one spectrum be well-calibrated with respect to the scores assigned to other spectra. In this work, we define the notion of calibration in the context of shotgun proteomics spectrum identification, and we introduce a simple, albeit computationally intensive, technique to calibrate an arbitrary score function. We demonstrate that this calibration procedure yields an increased number of identified spectra at a fixed false discovery rate (FDR) threshold. We also show that proper calibration of scores has a surprising effect on a previously described FDR estimation procedure, making the procedure less conservative. Finally, we provide empirical results suggesting that even partial calibration, which is much less computationally demanding, can yield significant increases in spectrum identification. Overall, we argue that accurate shotgun proteomics analysis requires careful attention to score calibration.