Neutron encoded labeling for peptide identification

Abstract

Metabolic labeling of cells using heavy amino acids is most commonly used for relative quantitation; however, partner mass shifts also detail the number of heavy amino acids contained within the precursor species. Here, we use a recently developed metabolic labeling technique, NeuCode (neutron encoding) stable isotope labeling with amino acids in cell culture (SILAC), which produces precursor partners spaced ~40 mDa apart to enable amino acid counting. We implement large scale counting of amino acids through a program, "Amino Acid Counter", which determines the most likely combination of amino acids within a precursor based on NeuCode SILAC partner spacing and filters candidate peptide sequences during a database search using this information. Counting the number of lysine residues for precursors selected for MS/MS decreases the median number of candidate sequences from 44 to 14 as compared to an accurate mass search alone (20 ppm). Furthermore, the ability to co-isolate and fragment NeuCode SILAC partners enables counting of lysines in product ions, and when the information is used, the median number of candidates is reduced to 7. We then demonstrate counting leucine in addition to lysine results in a 6-fold decrease in search space, 43 to 7, when compared to an accurate mass search. We use this scheme to analyze a nanoLC-MS/MS experiment and demonstrate that accurate mass plus lysine and leucine counting reduces the number of candidate sequences to one for ~20% of all precursors selected, demonstrating an ability to identify precursors without MS/MS analysis.

Figure 1

Amino acid counting with NeuCode SILAC. (A) Experimental design of lysine NeuCode SILAC utilizing either “heavy 1” lysine (Lys-¹³C₆,¹⁵N₂) or “heavy 2” lysine (Lys-D₈). (B) A survey scan acquired in the Orbitrap was used to select a triply charged precursor at m/z 360.197. (C) A precursor at m/z 630.197 displays the presence of two distinct peaks separated by 23.4 mTh, created by the incorporation of two lysine isotopologues. This was confirmed by a database search, which matched it to sequence, MKPTSIEKE.

Figure 2

Theoretical calculations using amino acid counting. (A) LysC theoretical calculations: 100 000 random yeast LysC peptides were searched against an entire in-silico LysC digest of the yeast proteome with a precursor search tolerance of 20 ppm. The list of resulting peptides was then narrowed if the candidate peptide contained the same number of lysines or lysines and leucines as the peptides used for the search. (B) GluC theoretical calculations: Identical analysis to (A) was performed, but GluC was used as the enzyme for in-silico digest of the yeast proteome.

Figure 3

Lysine NeuCode SILAC enabled amino acid counting. (A) A survey scan acquired in the Orbitrap was used to select a triply charged precursor at m/z 442.970. (B) A precursor at m/z 442.970 displays the presence of two distinct peaks separated by 23.6 mTh, created by the incorporation of 2 lysine isotopologues. (C) A search of an in-silico digest of the yeast proteome with a 20 ppm precursor tolerance returned 11 candidate peptides, only one of which contained two lysines, LNVPKSKALVLE. (D) The number of peptides remaining after accurate mass (20 ppm) and subsequent lysine filtering of 6678 precursors were plotted as a histogram. (E) Median number of peptides considered after filtering.

Figure 4

MS² analysis further narrows precursor search space. (A) Annotated high resolution MS² spectrum of the peptide TGVIKPGMVVTFAPAGVTTE. (B) Enlarged view of lysine-containing product ions. Three doubly charged product ions (b₁₁, b₁₇, b₁₉) exhibit m/z shifts of ~18 mTh or ~36 mDa when corrected for charge state. (C) Lysine and MS² filtering applied to unique peptide spectral matches. For 1652 unique peptide spectral matches, the number of candidates remaining after accurate mass (20 ppm) plus lysine and subsequent MS² filtering were plotted as a histogram.

Figure 5

Lysine and leucine NeuCode SILAC enabled amino acid counting. (A) Experimental design of lysine and leucine NeuCode SILAC. (B) A survey scan acquired in the Orbitrap at a resolving power of 30 000 was used to select a +4 precursor at m/z 525.310. (C) Enlarged view of precursor at m/z 525.310. The presence of two distinct peaks separated by 23.9 mTh is created by the incorporation of 2 lysines and 1 leucine residues. (D) A search of an in-silico digest of the yeast proteome with a 20 ppm precursor tolerance returned 17 candidate peptides, only one of which contained two lysines and one leucine, AKAQGVAVQLKRQPAQPRE. (E) The number of candidate sequences remaining after accurate mass (20 ppm) and subsequent lysine and leucine filtering of 5571 precursors. (F) Median number of candidate sequences considered after filtering.

Figure 6

Applying lysine and leucine counting to a nanoLC-MS/MS experiment. (A) Sensitivity and median number of candidate sequences considered vs precursor search tolerance. (B) Number of candidate sequences with one match vs precursor search tolerance. The number of these precursors that identified the same peptide as a database search is plotted in red. (C) Lysine and leucine counting applied to all precursors selected in a nanoLC-MS/MS experiment. AAC returned one sequence candidate for 916 precursors, while returning zero sequence candidates for 1358 precursors. (D) High resolution MS¹ spectrum of a one match precursor at m/z 620.023. Two peaks spaced 52.4 mTh or 156.9 mDa apart signified the presence of three lysines and 2 leucine residues, mapping this precursor to the peptide GERAKTKDNNLLGKE. (E) An annotated high resolution MS² belonging to the precursor at m/z 620.023 confirms the peptide GERAKTKDNNLLGKE. Product ions produced by HCD containing lysine or leucine demonstrate the appropriate partners in the MS² spectrum (insets).