Proof of concept: Pull down assay using bovine serum albumin and an immunomodulator small molecule
Bibliographic record
Abstract
In the past decade, there has been increasing interest in use of small molecules for immunomodulation. The affinity-based pull-down purification is an essential tool for target identification of small molecules and drug discovery. This study presents our recent efforts to investigate the cellular target(s) of Compound A, a small molecule with demonstrated immunomodulatory properties in human peripheral blood mononuclear cells (PBMCs). While we have previously observed the immunomodulatory activity of Compound A in PBMCs, the specific molecular targets underlying its effects remains elusive. To address this challenge, we synthesized a trifluoromethyl phenyl diazirine (TPD)-bearing trifunctional Probe 1 based on the chemical structure of Compound A, which could be used in a pull-down assay to efficiently bind to putative cellular targets via photoaffinity labelling. In this report, we utilized bovine serum albumin (BSA) as a model protein to establish a proof-of-concept in order to assess the suitability of Probe 1 for binding to an endogenous target. By the successful synthesis of Probe 1 and demonstrating the efficient binding of Probe 1 to BSA, we propose that this method can be used as a tool for further identification of potential protein targets of small molecules in living cells. Our findings provide a valuable starting point for further investigations into the molecular mechanisms underlying the immunomodulatory effects of Compound A.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| 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 | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".