Protein‐metal ion interactions, stoichiometries and relative affinities determined by on‐line size exclusion gel filtration mass spectrometry
Bibliographic record
Abstract
The modulation of metal ions on protein function is well recognized and of paramount importance in protein biochemistry. To date, very few methods allow direct determination of protein-metal ion interactions, as well as exact stoichiometric binding ratios. In this work we demonstrate the usefulness of two on-line size exclusion gel filtration mass spectrometry approaches to directly detect protein-metal ion adducts, as well as determine exact protein-metal ion stoichiometries. We show that on-line size exclusion column chromatography (SEC) and rapid in-line desalting (RILED) coupled to microelectrospray mass spectrometry (microESI-MS) can be used for such analyses. The SEC approach can be effectively used to both separate proteins in a complex mixture and exchange buffers prior to the electrospray process. While RILED does not allow for protein separation, it provides a much faster high-throughput desalting procedure than the conventional SEC technique. Specifically, we show that SEC/microESI-MS and RILED/MS can be used to determine calcium ion binding stoichiometries to a high-affinity, metal ion binding protein, calbindin D(28K). Furthermore, the same approaches can also be used to determine metal ion binding stoichiometries of low-affinity metal-binding proteins such as Spo0F.
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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) | 0.001 | 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 | 0.000 |
| 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".