Optimization of Search Engines and Postprocessing Approaches to Maximize Peptide and Protein Identification for High-Resolution Mass Data

Abstract

The two key steps for analyzing proteomic data generated by high-resolution MS are database searching and postprocessing. While the two steps are interrelated, studies on their combinatory effects and the optimization of these procedures have not been adequately conducted. Here, we investigated the performance of three popular search engines (SEQUEST, Mascot, and MS Amanda) in conjunction with five filtering approaches, including respective score-based filtering, a group-based approach, local false discovery rate (LFDR), PeptideProphet, and Percolator. A total of eight data sets from various proteomes (e.g., E. coli, yeast, and human) produced by various instruments with high-accuracy survey scan (MS1) and high- or low-accuracy fragment ion scan (MS2) (LTQ-Orbitrap, Orbitrap-Velos, Orbitrap-Elite, Q-Exactive, Orbitrap-Fusion, and Q-TOF) were analyzed. It was found combinations involving Percolator achieved markedly more peptide and protein identifications at the same FDR level than the other 12 combinations for all data sets. Among these, combinations of SEQUEST–Percolator and MS Amanda–Percolator provided slightly better performances for data sets with low-accuracy MS2 (ion trap or IT) and high accuracy MS2 (Orbitrap or TOF), respectively, than did other methods. For approaches without Percolator, SEQUEST–group performs the best for data sets with MS2 produced by collision-induced dissociation (CID) and IT analysis; Mascot–LFDR gives more identifications for data sets generated by higher-energy collisional dissociation (HCD) and analyzed in Orbitrap (HCD–OT) and in Orbitrap Fusion (HCD–IT); MS Amanda–Group excels for the Q-TOF data set and the Orbitrap Velos HCD–OT data set. Therefore, if Percolator was not used, a specific combination should be applied for each type of data set. Moreover, a higher percentage of multiple-peptide proteins and lower variation of protein spectral counts were observed when analyzing technical replicates using Percolator-associated combinations; therefore, Percolator enhanced the reliability for both identification and quantification. The analyses were performed using the specific programs embedded in Proteome Discoverer, Scaffold, and an in-house algorithm (Build Summary). These results provide valuable guidelines for the optimal interpretation of proteomic results and the development of fit-for-purpose protocols under different situations.

Performance Investigation of Proteomic Identification by HCD/CID Fragmentations in Combination with High/Low-Resolution Detectors on a Tribrid, High-Field Orbitrap Instrument

“…We also evaluated 0.35 Da tolerance for fragment ion masses analyzed by HCD-IT as suggested by previous reports[ 27 ]. Lower peptide/protein identifications were achieved relative to the use of 1.0 Da tolerance when using SEQUEST-PeptideProphet as shown in S1D Fig and no difference was observed when using SEQUEST-Percolator as we previously studied [ 28 ]. Thus, 1.0 Da tolerance for fragment ion masses analyzed by ion trap was applied in this study as we previously reported[ 28 ].…”

Using the entrapment sequence method as a standard to evaluate key steps of proteomics data analysis process

“…However, this flaw can be overcome when combined with the followed quality control methods by considering the distributions of target and decoy hits and reanalyzing the original scores. The SVM-based percolator algorithm has been proved to be an ideal QC method [ 11 , 29 ]. Thus, we further analyzed Mascot’s results by PepDistiller [ 11 ] (a bulit-in Percolator classifier), X!Tandem’s results and MS-GF+’s results by Percolator [ 27 , 30 ], Tide’s results and Comet’s results by Percolator intergrated in Crux [ 25 , 26 ].…”

Comparison of data‐acquisition methods for the identification and quantification of histone post‐translational modifications on a Q Exactive HF hybrid quadrupole Orbitrap mass spectrometer

“…MS Amanda places an emphasis on high-accuracy MS2 data and is therefore optimized for high resolution and mass accuracy at both the MS1 and MS2 levels. 24 As the MS2 resolution increased from 30,000 to 60,000 fewer peptides were identified (40, 29, 26, respectively) (see Figure 1C). The average top score of the peptides did not significantly increase as the resolution increased (see Figure 1D).…”

“…In addition to processing the data with Mascot, an alternative search engine, MS Amanda, was also used. MS Amanda places an emphasis on high‐accuracy MS2 data and is therefore optimized for high resolution and mass accuracy at both the MS1 and MS2 levels . As the MS2 resolution increased from 30,000 to 60,000 fewer peptides were identified (40, 29, 26, respectively) (see Figure C).…”