Multiplexed Post-Experimental Monoisotopic Mass Refinement (mPE-MMR) to Increase Sensitivity and Accuracy in Peptide Identifications from Tandem Mass Spectra of Cofragmentation

Abstract

Mass spectrometry (MS)-based proteomics, which uses high-resolution hybrid mass spectrometers such as the quadrupole-orbitrap mass spectrometer, can yield tens of thousands of tandem mass (MS/MS) spectra of high resolution during a routine bottom-up experiment. Despite being a fundamental and key step in MS-based proteomics, the accurate determination and assignment of precursor monoisotopic masses to the MS/MS spectra remains difficult. The difficulties stem from imperfect isotopic envelopes of precursor ions, inaccurate charge states for precursor ions, and cofragmentation. We describe a composite method of utilizing MS data to assign accurate monoisotopic masses to MS/MS spectra, including those subject to cofragmentation. The method, "multiplexed post-experiment monoisotopic mass refinement" (mPE-MMR), consists of the following: multiplexing of precursor masses to assign multiple monoisotopic masses of cofragmented peptides to the corresponding multiplexed MS/MS spectra, multiplexing of charge states to assign correct charges to the precursor ions of MS/MS spectra with no charge information, and mass correction for inaccurate monoisotopic peak picking. When combined with MS-GF+, a database search algorithm based on fragment mass difference, mPE-MMR effectively increases both sensitivity and accuracy in peptide identification from complex high-throughput proteomics data compared to conventional methods.

Figure 1

(A) Overall schematic of the mPE-MMR process. MS data were used for UMC generation. MS/MS data were used for original MGF generation. The MS/MS information on the original MGF were further processed by charge state multiplexing, C13 mass correction and deconvolution of cofragmented precursors of the dotted box. (B) Graphical workflow of the dotted box in (A). An MS/MS event targeting 621.79 Th led to cofragmentation of three different precursor ions within the isolation window. Among the 105 candidate masses that mPE-MMR calculated using three peaks (out of 10 peaks) in the isolation window, three different monoisotopic masses were found matches with UMC masses and the UMC masses were assigned to the corresponding MS/MS spectrum. More detailed information on the mPE-MMR procedures can be found in the text.

Figure 2

(A) Expanded MS spectrum showing peaks within the isolation window (red arrow, targeted precursor ion). (B) Expanded MS spectrum with the two corresponding isotopic envelopes (+2 and +3 as assigned by the conventional method) indicated by purple and dark green filled circles. (C) Expanded MS spectrum with the three mPE-MMR-assigned isotopic envelopes indicated by dark brown, dark blue, and green filled circles. (D) Annotated MS/MS spectra using the precursor masses assigned by the conventional method. (E) Annotated MS/MS spectrum using the three precursor masses determined by mPE-MMR.

Figure 3

(A) Target-decoy score distribution of PSMs using the conventional method on one-fraction data set. (B) Target-decoy score distribution of PSMs using the mPE-MMR method on the same data set. (C) Venn diagram of PSMs at 1% FDR in the conventional and mPE-MMR methods. (D) Distributions of mass measurement accuracies for the conventional method only (blue) and the mPE-MMR method only PSMs (red).

Figure 4

(A) Charge state distribution of all MS/MS data in the conventional MGF using the one-fraction data set. (B) Charge state distribution of all MS/MS data in mPE-MMR MGF using the one-fraction data set. mPE-MMR increases not only the sensitivity of identification for the peptides of high charge states (≥+4), but also those of low change states (Figure S2).

Figure 5

Bar graph showing the number of precursor masses per MS/ MS scan in the mPE-MMR MGF file. About 75% of MS/MS scans were assigned with two or more precursors. The stacked columns show the proportion of MS/MS scans that identified the indicated number of peptides.

Figure 6

Example of a “filtered-out” peptide (A) by UMC filtering and its corresponding UMC peptide (B).

Figure 7

(A) Venn diagram of peptides at 1% FDR in the single-round and iterative database search methods. (B) Distributions of peptide score differences between the single-round and iterative database searches for the common peptides. The MS-GF+ search time for the single-round and the iterative methods were 149 and 563 min, respectively.

Figure 8

(A) Venn diagram of mutated peptides identified by the conventional and mPE-MMR methods. (B) Annotated MS/MS spectrum of MQLQHLVEGEHITSDGLK (+4) with the V22I mutation, which was only identified by the mPE-MMR method. (C) Annotated MS/MS spectrum of cofragmentation of DALSDLALHFLNK and IDGITIHQSLAIIEYLEETRPTPR. The inset shows the expanded MS spectrum, illustrating two precursor ions within the isolation window.