Solution-Phase Chelators for Suppressing Nonspecific Protein−Metal Interactions in Electrospray Mass Spectrometry
Bibliographic record
Abstract
Protein-metal complexes may be transferred from solution into the gas phase by electrospray ionization (ESI), such that they can be directly analyzed by mass spectrometry (MS). In principle, therefore, ESI-MS represents a simple and elegant approach for gaining insights into the binding stoichiometry and affinity of these assemblies. Unfortunately, the formation of nonspecific metal adducts during ESI can be a severe problem, often leading to binding levels that are dramatically higher than those in bulk solution. Focusing on several calcium binding proteins as test systems, this work explores the suitability of different salts to serve as metal source. Despite their widespread use in previous ESI-MS studies, calcium chloride and acetate induce extensive nonspecific adduction. In contrast, much lower levels of artifactual metal binding are observed in the presence of calcium tartrate. In the case of high and intermediate affinity proteins, the resulting ESI-MS data are in excellent agreement with the calcium binding behavior in bulk solution. The situation is more challenging when studying proteins with very low affinities, but in the presence of tartrate qualitative information on protein-metal interactions can still be obtained. The beneficial effects of tartrate also extend to zinc binding experiments. This work does not directly explore the mechanism by which tartrate suppresses nonspecific metalation. However, it seems likely that weak chelators such as tartrate sequester metal ions within rapidly shrinking droplets during the final stages of ESI, thereby reducing nonspecific metal adduction to protein carboxylates. The use of tartrate and possibly other weak chelators will greatly enhance the reliability of future ESI-MS studies on the interactions of proteins with divalent metal ions.
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| 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) | 0.002 | 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".