An Optimized Informatics Pipeline for Mass Spectrometry-Based Peptidomics

Abstract

The comprehensive MS analysis of the peptidome, the intracellular and intercellular products of protein degradation, has the potential to provide novel insights on endogenous proteolytic processing and its utility in disease diagnosis and prognosis. Along with the advances in MS instrumentation and related platforms, a plethora of proteomics data analysis tools have been applied for direct use in peptidomics; however, an evaluation of the currently available informatics pipelines for peptidomics data analysis has yet to be reported. In this study, we began by evaluating the results of several popular MS/MS database search engines, including MS-GF+, SEQUEST, and MS-Align+, for peptidomics data analysis, followed by identification and label-free quantification using the well-established accurate mass and time (AMT) tag and newly developed informed quantification (IQ) approaches, both based on direct LC-MS analysis. Our results demonstrated that MS-GF+ outperformed both SEQUEST and MS-Align+ in identifying peptidome peptides. Using a database established from MS-GF+ peptide identifications, both the AMT tag and IQ approaches provided significantly deeper peptidome coverage and less missing data for each individual data set than the MS/MS methods, while achieving robust label-free quantification. Besides having an excellent correlation with the AMT tag quantification results, IQ also provided slightly higher peptidome coverage. Taken together, we propose an optimized informatics pipeline combining MS-GF+ for initial database searching with IQ (or AMT tag) approaches for identification and label-free quantification for high-throughput, comprehensive, and quantitative peptidomics analysis. Graphical Abstract ᅟ.

Keywords: Accurate mass and time tag; Identification; Informed quantitation; MS-GF+; Peptidomics; Quantification.

Figure 1

Comparison of the peptidome peptide identification results from MS-GF+, SEQUEST and MS-Align+. (A) Distribution of precursor monoisotopic mass for MS-GF+ (white), SEQUEST (blue) and MS-Align+ (red). (B) Venn diagram showing the overlap of the identifications resulted from MS-GF+ (red), SEQUEST (yellow) and MS-Align+ (blue) analyses.

Figure 2

Comparison of identification and quantification results from MS-GF+, AMT tag and IQ analyses. (A) Comparison of number of identified unique peptides in each sample for MS-GF+ (blue), AMT (red) and IQ (green). (B) Distribution of quantification frequency among all samples for MS-GF+ (blue), AMT tag (red) and IQ (green).

Figure 3

Comparison of the label-free quantification results from AMT tag and IQ analyses. (A) Distribution of the Pearson correlation values for peptides common in all samples quantified by AMT tag and IQ approaches. (B) Scatter plot showing the correlation of all peptides quantified by AMT tag and IQ approaches from sample A_0.