Determination of Ligand−Protein Dissociation Constants by Electrospray Mass Spectrometry-Based Diffusion Measurements
Bibliographic record
Abstract
A novel approach for the quantification of ligand-protein interactions is presented. Electrospray ionization mass spectrometry (ESI-MS) is used to monitor the diffusion behavior of noncovalent ligands in the presence of their protein receptors. These data allow the fraction of free ligand in solution to be determined, such that the corresponding dissociation constants can be calculated. A set of conditions is developed that provides an "allowable range" of concentrations for this type of assay. The method is tested by applying it to two different inhibitor-enzyme systems. The dissociation constants measured for benzamidine-trypsin and for N,N',N' '-triacetylchitotriose-lysozyme are (50 +/- 10) and (6 +/- 1) mM, respectively. Both of these results are in good agreement with previous data from the literature. In contrast to traditional ESI-MS-based methods, the approach used in this work does not rely on the preservation of specific solution-type noncovalent interactions in the gas phase. It is shown that this method allows an accurate determination of dissociation constants, even in cases in which the ion abundance ratio of free to ligand-bound protein in ESI-MS does not reflect the corresponding concentration ratio 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 | 0.001 |
| 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".