Optimization of immobilized gallium (III) ion affinity chromatography for selective binding and recovery of phosphopeptides from protein digests.
Bibliographic record
Abstract
Although widely used in proteomics research for the selective enrichment of phosphopeptides from protein digests, immobilized metal-ion affinity chromatography (IMAC) often suffers from low specificity and differential recovery of peptides carrying different numbers of phosphate groups. By systematically evaluating and optimizing different loading, washing, and elution conditions, we have developed an efficient and highly selective procedure for the enrichment of phosphopeptides using a commercially available gallium(III)-IMAC column (PhosphoProfile, Sigma). Phosphopeptide enrichment using the reagents supplied with the column is incomplete and biased toward the recovery and/or detection of smaller, singly phosphorylated peptides. In contrast, elution with base (0.4 M ammonium hydroxide) gives efficient and balanced recovery of both singly and multiply phosphorylated peptides, while loading peptides in a strong acidic solution (1% trifluoracetic acid) further increases selectivity toward phosphopeptides, with minimal carryover of nonphosphorylated peptides. 2,5-Dihydroxybenzoic acid, a matrix commonly used when analyzing phosphopeptides by matrix-assisted laser desorption/ionization mass spectrometry was also evaluated as an additive in loading and eluting solvents. Elution with 50% acetonitrile containing 20 mg/mL dihydroxybenzoic acid and 1% phosphoric acid gave results similar to those obtained using ammonium hydroxide as the eluent, although the latter showed the highest specificity for 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 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| 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".