A conformation-specific ON-switch for controlling CAR T cells with an orally available drug
Bibliographic record
Abstract
Molecular ON-switches in which a chemical compound induces protein-protein interactions can allow cellular function to be controlled with small molecules. ON-switches based on clinically applicable compounds and human proteins would greatly facilitate their therapeutic use. Here, we developed an ON-switch system in which the human retinol binding protein 4 (hRBP4) of the lipocalin family interacts with engineered hRBP4 binders in a small molecule-dependent manner. Two different protein scaffolds were engineered to bind to hRBP4 when loaded with the orally available small molecule A1120. The crystal structure of an assembled ON-switch shows that the engineered binder specifically recognizes the conformational changes induced by A1120 in two loop regions of hRBP4. We demonstrate that this conformation-specific ON-switch is highly dependent on the presence of A1120, as demonstrated by an ∼500-fold increase in affinity upon addition of the small molecule drug. Furthermore, the ON-switch successfully regulated the activity of primary human CAR T cells in vitro. We anticipate that lipocalin-based ON-switches have the potential to be broadly applied for the safe pharmacological control of cellular therapeutics.
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 | 0.000 | 0.000 |
| 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 | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".