Improving collision induced dissociation (CID), high energy collision dissociation (HCD), and electron transfer dissociation (ETD) fourier transform MS/MS degradome-peptidome identifications using high accuracy mass information

Abstract

MS dissociation methods, including collision induced dissociation (CID), high energy collision dissociation (HCD), and electron transfer dissociation (ETD), can each contribute distinct peptidome identifications using conventional peptide identification methods (Shen et al. J. Proteome Res. 2011), but such samples still pose significant informatics challenges. In this work, we explored utilization of high accuracy fragment ion mass measurements, in this case provided by Fourier transform MS/MS, to improve peptidome peptide data set size and consistency relative to conventional descriptive and probabilistic scoring methods. For example, we identified 20-40% more peptides than SEQUEST, Mascot, and MS_GF scoring methods using high accuracy fragment ion information and the same false discovery rate (FDR) from CID, HCD, and ETD spectra. Identified species covered >90% of the collective identifications obtained using various conventional peptide identification methods, which significantly addresses the common issue of different data analysis methods generating different peptide data sets. Choice of peptide dissociation and high-precision measurement-based identification methods presently available for degradomic-peptidomic analyses needs to be based on the coverage and confidence (or specificity) afforded by the method, as well as practical issues (e.g., throughput). By using accurate fragment information, >1000 peptidome components can be identified from a single human blood plasma analysis with low peptide-level FDRs (e.g., 0.6%), providing an improved basis for investigating potential disease-related peptidome components.

Figure 1

Array of peptide fragment counts matched for targets (blue solid dots) and decoys (red circles). All top peptide candidates output from SEQUEST for individual FT MS/MS CID spectra are displayed.

Figure 2

Assignments of CID FT MS/MS spectra with CBC and scoring methods for CS 2–5 peptides. CBC_TS and CBC_SL are used for the CBC method; assignments of spectra with the CBC, SEQUEST, and Mascot at a specific FDR as described in the test. (A): CBC vs. SEQUEST; (B): CBC vs. Mascot.

Figure 3

Assignments of HCD FT MS/MS spectra with CBC and scoring methods for CS 2–5 peptides. (A): CBC vs. SEQUEST; (B): CBC vs. Mascot. Other conditions same as Figure 2.

Figure 4

Assignments of ETD FT MS/MS spectra with CBC and scoring methods for CS 2–5 peptides. (A): CBC vs. SEQUEST; (B): CBC vs. Mascot. Other conditions same as Figure 2.

Figure 5

Peptide identification with four versions of CBC method from CID, HCD, and ETD spectra through control of identification peptide-level FDR.

Figure 6

Comparison of CBC, MS_GF probability score, and SEQUEST fragments and SP value for peptide identification from CID spectra with control of peptide-level FDR.

Figure 7

Overlaps of peptides identified with CBC, SEQUEST, and Mascot from CID, HCD, and ETD spectra. (A): CBC_TF, CBC_TS, and CBC_SL; (B): CBC_TS, CBC_SL, and SEQUEST scoring; (C): CBC_TS, CBC_SL, and Mascot scoring; (D) CBC, SEQUEST scoring, and Mascot scoring. All peptides were identified at a 2% spectrum-level FDR.

Figure 8

CBC covers most peptides identified from SEQUEST, Mascot, and MS_GF scoring methods. CBC_TF for the CBC method. SEQUEST and Mascot datasets obtained with 10 ppm mass tolerance for precursors and 0.05 Da for fragments; the MS_GF peptide dataset was identified by filtration of SEQUEST or Mascot top-1 hits with MS_GF probability scores; all peptides were identified from CID spectra at 2% spectrum-level FDR.