Magnetic “Fishing” Assay To Screen Small-Molecule Mixtures for Modulators of Protein−Protein Interactions
Bibliographic record
Abstract
Protein-protein interactions are an intricate part of biological pathways and have become important targets for drug discovery. Here we present a two-stage magnetic bead assay to functionally screen small-molecule mixtures for modulators of protein-based interactions, with simultaneous affinity-based isolation of active compounds and identification by mass spectrometry. Proteins of interest interact in solution prior to the addition of Ni(II)-functionalized magnetic beads to recover an intact protein-protein complex through affinity capture of a polyhistidine-tagged primary target ("protein-complex fishing"). Protein-complex fishing, utilizing His(6)-tagged calmodulin (CaM) as the primary (bait) protein and melittin (Mel) as the target, was used to screen a mass-encoded library of 1000 bioactive compounds (50 mixtures, 20 compounds each) and successfully identified three known antagonists, three naturally occurring phenolic compounds previously reported to disrupt CaM-activated phosphodiesterase activity, and two newly identified modulators of the CaM-Mel interaction, methylbenzethonium and pempidine tartrate. The ability to produce quantitative inhibition data is also shown through the development of dose-dependent response curves and the determination of inhibition constants (K(I)) for the novel compound methylbenzethonium (K(I) = 14-49 nM) and two known antagonists, calmidazolium (K(I) = 1.7-7.5 nM) and trifluoperazine (K(I) = 1.2-3.0 μM), with the latter two values being in close agreement with literature values.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".