Mass spectrometric characterization of lipid‐modified peptides for the analysis of acylated proteins
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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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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.006 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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 it