Selective Extraction and Characterization of a Histidine-Phosphorylated Peptide Using Immobilized Copper(II) Ion Affinity Chromatography and Matrix-Assisted Laser Desorption/Ionization Time-of-Flight Mass Spectrometry
Bibliographic record
Abstract
Phosphorylation is the predominant posttranslational modification involved in regulating enzymatic activity and mediating signal transduction in prokaryotic and eukaryotic cells. Selective enrichment of phosphorylated peptides prior to mass spectrometric analysis facilitates identification of phosphorylated proteins, determination of specific phosphorylated residues, and characterization of the conditions under which phosphorylation occurs. Such protocols have been established for peptides containing residues that form phosphoesters, such as serine and threonine, using immobilized metal-ion affinity chromatography. Despite the importance of histidine phosphorylation in two-component signal transduction pathways, similar protocols for peptides containing phosphorylated histidine (P-His) residues have proven elusive, due to the instability of these modifications and the propensity of unphosphorylated histidines to interact with immobilized metals ions. We describe a method for the selective extraction of a P-His-containing peptide using immobilized copper(II) ions and disposable metal-chelating pipet tips (ZipTipMC, Millipore). The method is contingent upon pH-dependent interactions between the phosphate group and immobilized copper(II) ions. Matrix-assisted laser desorption/ionization time-of-flight mass spectrometry with postsource decay confirms the identity and phosphorylation state of the extracted peptide. Peptides containing unphosphorylated histidine residues or other phosphorylated amino acids are not retained, demonstrating the specificity of the method for P-His-containing 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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 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".