Revisiting Peptide Identification by High-Accuracy Mass Spectrometry: Problems Associated with the Use of Narrow Mass Precursor Windows

Abstract

Peptide identification is increasingly achieved through database searches in which mass precursor tolerance is set in the ppm range. This trend is driven by the high resolution and accuracy of modern mass spectrometers and the belief that the quality of peptide identification is fully controlled by estimating the false discovery rate (FDR) using the decoy-target approach. However, narrowing mass tolerance decreases the number of sequence candidates, and several authors have raised concerns that these search conditions can introduce inaccuracies. Here, we demonstrate that when scores that only depend on one sequence candidate are used, decoy-based estimates of the number of false positive identifications are accurate even with an average number of candidates of just 200, to the point that remarkably accurate FDR predictions can be made in completely different search conditions. However, when scores that are constructed taking information from additional sequence candidates are used together with low precursor mass tolerances, the proportion of peptides incorrectly identified may become significantly higher than the FDR estimated by the target-decoy approach. Our results suggest that with this kind of score the high mass accuracy of modern mass spectrometers should be exploited by using wide mass windows followed by postscoring mass filtering algorithms.

Muscle molecular adaptations to endurance exercise training are conditioned by glycogen availability: a proteomics‐based analysis in the McArdle mouse model

“…) and false discovery rate (FDR) was calculated using inverted databases and the refined method (Navarro & Vazquez, ) with an additional filtering for precursor mass tolerance of 15 ppm (Bonzon‐Kulichenko et al . ).…”

“…For database searching at the Uniprot database containing all sequences from mouse and contaminants (27April 2016; 48,644 entries), the parameters were selected as follows: trypsin digestion with two maximum missed cleavage sites, precursor and fragment mass tolerances of 2 Da and 0.02 Da, respectively, carbamidomethyl cysteine and TMT modifications at N-terminal and Lys residues as fixed modifications, and methionine oxidation as dynamic modification. Peptide identification was performed using the probability ratio method (Martinez-Bartolome et al 2008) and false discovery rate (FDR) was calculated using inverted databases and the refined method (Navarro & Vazquez, 2009) with an additional filtering for precursor mass tolerance of 15 ppm (Bonzon-Kulichenko et al 2015).…”

Comprehensive quantification of the modified proteome reveals oxidative heart damage in mitochondrial heteroplasmy

“…Peptide identification from MS/MS data was performed using the probability ratio method (Martinez-Bartolome et al, 2008). False discovery rates (FDR) of peptide identifications were calculated using the refined method (Bonzon-Kulichenko et al, 2015; Navarro and Vazquez, 2009); 1% FDR was used as the default criterion for peptide identification.…”

Quantitative proteomics reveals Piccolo as a candidate serological correlate of recovery from Guillain-Barré syndrome

“…Peptide identification from MSMS data was performed using the probability ratio method [44]. False discovery rates (FDR) of peptide identifications were calculated using the refined method [45, 46], and 5% FDR was used as criterion for peptide identification. Each peptide was assigned only to the best protein proposed by the Proteome Discoverer algorithm.…”