A novel algorithm for validating peptide identification from a shotgun proteomics search engine

Abstract

Liquid chromatography coupled with tandem mass spectrometry (LC-MS/MS) has revolutionized the proteomics analysis of complexes, cells, and tissues. In a typical proteomic analysis, the tandem mass spectra from a LC-MS/MS experiment are assigned to a peptide by a search engine that compares the experimental MS/MS peptide data to theoretical peptide sequences in a protein database. The peptide spectra matches are then used to infer a list of identified proteins in the original sample. However, the search engines often fail to distinguish between correct and incorrect peptides assignments. In this study, we designed and implemented a novel algorithm called De-Noise to reduce the number of incorrect peptide matches and maximize the number of correct peptides at a fixed false discovery rate using a minimal number of scoring outputs from the SEQUEST search engine. The novel algorithm uses a three-step process: data cleaning, data refining through a SVM-based decision function, and a final data refining step based on proteolytic peptide patterns. Using proteomics data generated on different types of mass spectrometers, we optimized the De-Noise algorithm on the basis of the resolution and mass accuracy of the mass spectrometer employed in the LC-MS/MS experiment. Our results demonstrate De-Noise improves peptide identification compared to other methods used to process the peptide sequence matches assigned by SEQUEST. Because De-Noise uses a limited number of scoring attributes, it can be easily implemented with other search engines.

Figure 1

Pseudocode for the De-Noise algorithm.

Figure 2

Venn diagrams for the seven datasets showing the number of overlapping validated PSMs from De-Noise, PeptidePropheet, and Percolator. An FDR of 0.05 was used for all three approaches.

Figure 3

Plots of target PSM hits for the seven datasets validated under a series of FDRs for De-Noise, PeptideProphet, and Percolator. The number of target peptide hits is plotted for a FDR range from 0.01 to 0.1. (A) Gcn4 LCQ (B) UPS1 LTQ (C) Tal08 LTQ-Orbitrap XL MiPS (D) PBMC LTQ-Orbitrap XL MiPS (E) PBMC LTQ-Orbitrap XL MiPS-off (F) PBMC LTQ-Orbitrap Velos MiPS (G) PBMC LTQ-Orbitrap Velos MiPS-off.

Figure 3

Plots of target PSM hits for the seven datasets validated under a series of FDRs for De-Noise, PeptideProphet, and Percolator. The number of target peptide hits is plotted for a FDR range from 0.01 to 0.1. (A) Gcn4 LCQ (B) UPS1 LTQ (C) Tal08 LTQ-Orbitrap XL MiPS (D) PBMC LTQ-Orbitrap XL MiPS (E) PBMC LTQ-Orbitrap XL MiPS-off (F) PBMC LTQ-Orbitrap Velos MiPS (G) PBMC LTQ-Orbitrap Velos MiPS-off.

Figure 4

ROC curves for the seven datasets showing the validation performance of De-Noise, PeptideProphet, and Percolator. (A) Gcn4 LCQ (B) UPS1 LTQ (C) Tal08 LTQ-Orbitrap XL MiPS-off (D) PBMC LTQ Orbitrap XL MiPS (E) PBMC LTQ-Orbitrap XL MiPS-off (F) PBMC LTQ-Orbitrap Velos MiPS (G) PBMC LTQ-Orbitrap Velos MiPS-off.

Figure 4

ROC curves for the seven datasets showing the validation performance of De-Noise, PeptideProphet, and Percolator. (A) Gcn4 LCQ (B) UPS1 LTQ (C) Tal08 LTQ-Orbitrap XL MiPS-off (D) PBMC LTQ Orbitrap XL MiPS (E) PBMC LTQ-Orbitrap XL MiPS-off (F) PBMC LTQ-Orbitrap Velos MiPS (G) PBMC LTQ-Orbitrap Velos MiPS-off.

Figure 4

ROC curves for the seven datasets showing the validation performance of De-Noise, PeptideProphet, and Percolator. (A) Gcn4 LCQ (B) UPS1 LTQ (C) Tal08 LTQ-Orbitrap XL MiPS-off (D) PBMC LTQ Orbitrap XL MiPS (E) PBMC LTQ-Orbitrap XL MiPS-off (F) PBMC LTQ-Orbitrap Velos MiPS (G) PBMC LTQ-Orbitrap Velos MiPS-off.