Formation of phosphopeptide‐metal ion complexes in liquid chromatography/electrospray mass spectrometry and their influence on phosphopeptide detection
Bibliographic record
Abstract
Despite major advances in mass spectrometry, the detection of phosphopeptides by liquid chromatography with electrospray mass spectrometry (LC/ES-MS) still remains very challenging in proteomics analysis. Phosphopeptides do not protonate efficiently due to the presence of one or more acidic phosphate groups, making their detection difficult. However, other mechanisms also contribute to the difficulties in phosphopeptide analysis by LC/ES-MS. We report here on one such undocumented problem: the formation of phosphopeptide-metal ion complexes during LC/ES-MS. It is demonstrated that both synthetic phosphopeptides and phosphopeptides from bovine beta-casein and alpha-casein form phosphopeptide-metal ion complexes containing iron and aluminum ions, resulting in a dramatic decrease in signal intensity of the protonated phosphopeptides. The interaction of phosphopeptides with metal ions on the surface of the C18 stationary phase is also shown to alter their chromatographic behavior on reversed-phase columns such that the phosphopeptides, especially multiply phosphorylated peptides, become strongly retained and very difficult to elute. The sources of iron and aluminum are from the solvents, stainless steel, glassware and C18 material. It was also found that, upon addition of EDTA, the formation of the phosphopeptide-metal ion complex is diminished, and the phosphopeptides that did not elute from the LC column can now be detected efficiently as protonated molecules. The sensitivity of detection was greatly increased such that a tetra-phosphorylated peptide, RELEELNVPGEIVEpSLpSpSpSEESITR from the tryptic digestion of bovine beta-casein, was detected at a limit of detection of 25 fmol, which is 400 times lower than without EDTA.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".