Enrichment and analysis of phosphopeptides under different experimental conditions using titanium dioxide affinity chromatography and mass spectrometry
Bibliographic record
Abstract
Titanium dioxide metal oxide affinity chromatography (TiO(2)-MOAC) is widely regarded as being more selective than immobilized metal-ion affinity chromatography (IMAC) for phosphopeptide enrichment. However, the widespread application of TiO(2)-MOAC to biological samples is hampered by conflicting reports as to which experimental conditions are optimal. We have evaluated the performance of TiO(2)-MOAC under a wide range of loading and elution conditions. Loading and stringent washing of peptides with strongly acidic solutions ensured highly selective enrichment for phosphopeptides, with minimal carryover of non-phosphorylated peptides. Contrary to previous reports, the addition of glycolic acid to the loading solution was found to reduce specificity towards phosphopeptides. Base elution in ammonium hydroxide or ammonium phosphate provided optimal specificity and recovery of phosphorylated peptides. In contrast, elution with phosphoric acid gave incomplete recovery of phosphopeptides, whereas inclusion of 2,5-dihydroxybenzoic acid in the eluant introduced a bias against the recovery of multiply phosphorylated peptides. TiO(2)-MOAC was also found to be intolerant of many reagents commonly used as phosphatase inhibitors during protein purification. However, TiO(2)-MOAC showed higher specificity than immobilized gallium (Ga(3+)), immobilized iron (Fe(3+)), or zirconium dioxide (ZrO(2)) affinity chromatography for phosphopeptide enrichment. Matrix-assisted laser desorption/ionization mass spectrometry (MALDI-MS) was more effective in detecting larger, multiply phosphorylated peptides than liquid chromatography/electrospray ionization tandem mass spectrometry (LC/ESI-MS/MS), which was more efficient for smaller, singly phosphorylated peptides.
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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) | 0.002 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| 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".