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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| 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".