Optimization of Search Engines and Postprocessing Approaches to Maximize Peptide and Protein Identification for High-Resolution Mass Data
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- 作者：Chengjian Tu ; Quanhu Sheng ; Jun Li ; Danjun Ma ; Xiaomeng Shen ; Xue Wang ; Yu Shyr ; Zhengping Yi ; Jun Qu
- 刊名：Journal of Proteome Research
- 出版年：2015
- 出版时间：November 6, 2015
- 年：2015
- 卷：14
- 期：11
- 页码：4662-4673
- 全文大小：782K
- ISSN：1535-3907
文摘
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鈥揚ercolator and MS Amanda鈥揚ercolator 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鈥揼roup performs the best for data sets with MS2 produced by collision-induced dissociation (CID) and IT analysis; Mascot鈥揕FDR gives more identifications for data sets generated by higher-energy collisional dissociation (HCD) and analyzed in Orbitrap (HCD鈥揙T) and in Orbitrap Fusion (HCD鈥揑T); MS Amanda鈥揋roup excels for the Q-TOF data set and the Orbitrap Velos HCD鈥揙T 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.