Assaying Small-Molecule−Receptor Interactions by Continuous Flow Competitive Displacement Chromatography/Mass Spectrometry
Bibliographic record
Abstract
Small-molecule screening techniques that employ mass spectrometry detection have been highly successful. However, the inability of conventional techniques, such as frontal affinity chromatography-mass spectrometry (FAC-MS), to easily identify weak binding molecules (i.e., Kd >or= 1 microM) using small amounts of target protein (subpicomole levels) represents a significant impediment to the widespread use of the method in the routine screening of low-abundance membrane receptors. This limitation is particularly notable in the early stages of the drug discovery process, as weak binding molecules can serve as useful leads for targets with no known ligand or when existing tight binding ligands have little therapeutic value. Competitive assay methods involving the displacement of an indicator ligand offer a more sensitive alternative, as the ability to generate an appreciable signal through various methods, including transient overconcentrations of indicator compounds, provides an unambiguous means for identifying weak affinity ligands. In this work we describe a continuous flow competitive assay based on the principles of FAC-MS that can be widely used to identify and characterize weak affinity ligands using low levels of the nicotinic acetylcholine receptor from Torpedo californica (nAChR). The validity of the assay is shown through the ability to identify nicotine (Kd approximately 1 microM) with columns containing <2 pmol of binding sites. Multiple injections of nicotine on a single column produce reproducible peaks in the signal of the indicator compound, epibatidine (Kd approximately 2 nM) showing minimal degradation in signal intensity between trials. The intensity of the peaks is dependent on the concentration of nicotine being injected, and binding curves can be generated through multiple injections on the same column. We investigate and optimize various parameters, including assay speed and concentrations, and demonstrate an automated assay format with the potential for use as a high-throughput screening tool. The ability to screen for weak binders of more pharmacologically relevant membrane receptors in a high-throughput screening format is discussed.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.002 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.002 |
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".