Two-Dimensional Mass Spectra Generated from the Analysis of <sup>15</sup>N-Labeled and Unlabeled Peptides for Efficient Protein Identification and de novo Peptide Sequencing
Bibliographic record
Abstract
Protein identification has been greatly facilitated by database searches against protein sequences derived from product ion spectra of peptides. This approach is primarily based on the use of fragment ion mass information contained in a MS/MS spectrum. Unambiguous protein identification from a spectrum with low sequence coverage or poor spectral quality can be a major challenge. We present a two-dimensional (2D) mass spectrometric method in which the numbers of nitrogen atoms in the molecular ion and the fragment ions are used to provide additional discriminating power for much improved protein identification and de novo peptide sequencing. The nitrogen number is determined by analyzing the mass difference of corresponding peak pairs in overlaid spectra of (15)N-labeled and unlabeled peptides. These peptides are produced by enzymatic or chemical cleavage of proteins from cells grown in (15)N-enriched and normal media, respectively. It is demonstrated that, using 2D information, i.e., m/z and its associated nitrogen number, this method can, not only confirm protein identification results generated by MS/MS database searching, but also identify peptides that are not possible to identify by database searching alone. Examples are presented of analyzing Escherichia coli K12 extracts that yielded relatively poor MS/MS spectra, presumably from the digests of low abundance proteins, which can still give positive protein identification using this method. Additionally, this 2D MS method can facilitate spectral interpretation for de novo peptide sequencing and identification of posttranslational or other chemical modifications. We envision that this method should be particularly useful for proteome expression profiling of organelles or cells that can be grown in (15)N-enriched media.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".