Protein–Protein Binding Affinities in Solution Determined by Electrospray Mass Spectrometry
Bibliographic record
Abstract
Electrospray ionization (ESI) allows the transfer of multi-protein complexes into the gas phase, thereby providing a simple approach for monitoring the stoichiometry of these noncovalent assemblies by mass spectrometry (MS). It remains unclear, however, whether the measured ion abundance ratios of free and bound species are suitable for determining solution-phase binding affinities (K(d) values). Many types of mass spectrometers employ rf-only quadrupoles as ion guides. This work demonstrates that the settings used for these devices are a key factor for ensuring uniform transmission behavior, which is a prerequisite for meaningful affinity measurements. Using bovine β-lactoglobulin and hemoglobin as model systems, it is demonstrated that under carefully adjusted conditions the "direct" ESI-MS approach is capable of providing K(d) values that are in good agreement with previously published solution-phase data. Of the several ion sources tested, a regular ESI emitter operated with pressure-driven flow at 1 μL min(-1) provided the most favorable results. Potential problems in these experiments include conformationally-induced differences in ionization efficiencies, inadvertent collision-induced dissociation, and ESI-induced clustering artifacts. A number of simple tests can be conducted to assess whether or not these factors are prevalent under the conditions used. In addition, the fidelity of the method can be scrutinized by performing measurements over a wide concentration range. Overall, this work supports the viability of the direct ESI-MS approach for determining binding affinities of protein-protein complexes in solution.
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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 | 0.002 |
| 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".