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J Proteome Res. 2015 Nov 6;14(11):4662-73. doi: 10.1021/acs.jproteome.5b00536. Epub 2015 Sep 30.

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

Tu C1,2, Sheng Q3, Li J1,2, Ma D4, Shen X1,2, Wang X1,2,5, Shyr Y3, Yi Z4, Qu J1,2.

Author information

Department of Pharmaceutical Sciences, State University of New York , 285 Kapoor Hall, Buffalo, New York 14260, United States.
New York State Center of Excellence in Bioinformatics and Life Sciences , 701 Ellicott Street, Buffalo, New York 14203, United States.
Center for Quantitative Sciences, Vanderbilt University School of Medicine , 2220 Pierce Avenue, Nashville, Tennessee 37232, United States.
Department of Pharmaceutical Sciences, Eugene Applebaum College of Pharmacy/Health Sciences, Wayne State University , 259 Mack Avenue, Detroit, Michigan 48202, United States.
Department of Cell Stress Biology, Roswell Park Cancer Institute , Elm and Carlton Streets, Buffalo, New York 14263, United States.


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 (BuildSummary). These results provide valuable guidelines for the optimal interpretation of proteomic results and the development of fit-for-purpose protocols under different situations.


BuildSummary; PeptideProphet; Percolator; database search engine; local false discovery rate; post-processing approach

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