Mass spectrometric characterization of lipid‐modified peptides for the analysis of acylated proteins
Bibliographic record
Abstract
The analysis of acylated proteins by mass spectrometry (MS) has largely been overshadowed in proteomics by the analysis of glycosylated and phosphorylated proteins; however, lipid modifications on proteins are proving to be of increasing importance in biomedical research. In order to identify the marker ions and/or neutral loss fragments that are produced upon collision-induced dissociation, providing a means to identify the common lipid modifications on proteins, peptides containing an N-terminally myristoylated glycine, a palmitoylated cysteine and a farnesylated cysteine were chemically synthesized. Matrix-assisted laser desorption/ionization time-of-flight time-of-flight (MALDI-TOF-TOF), electrospray ionization quadrupole time-of-flight (ESI Q-TOF), and electrospray ionization hybrid triple-quadrupole/linear ion trap (ESI QqQ(LIT)) mass spectrometers were used for the analysis. The peptide containing the N-terminally myristoylated glycine, upon CID, produced the characteristic fragments a1 (240.4 Th) and b1 (268.4 Th) ions as well as a low-intensity neutral loss of 210 Da (C14H26O). The peptides containing a farnesylated cysteine residue fragmented to produce a marker ion at a m/z of 205 Th (C15H25) as well as other intense farnesyl fragment ions, and a neutral loss of 204 Da (C15H24). The peptides containing a palmitoylated cysteine moiety generated neutral losses of 238 Da (C16H30O) and 272 Da (C16H32OS); however, no marker ions were produced. The neutral losses were more prominent in the MALDI-TOF-TOF spectra, whereas the marker ions were more abundant in the ESI QqQ(LIT) and Q-TOF mass spectra.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".