Heterobifunctional Protein Binders Enable Cell Type-Specific Killing Through In-cell Enrichment
Bibliographic record
Abstract
Abstract Non-catalytic heterobifunctional molecules promise to expand the range of therapeutic options by establishing complexes between key target proteins and accessory presenter proteins equipped with additional properties. Here, we systematically investigate the rational design of such molecules, explore the biochemical basis of complex formation and determine how they achieve cellular efficacy using the endogenously expressed immunophilin FKBP12 and the transcriptional regulator BRD4 as paradigms. We present classes of bifunctional molecules that enable selective, FKBP12-dependent killing of specific cell types at subnanomolar concentrations and allow to differentiate between closely similar bromodomains. We propose that the strongly potentiated efficacy of these bifunctional compounds is based on cellular enrichment through binding to the highly abundant presenter protein FKBP12, a mechanism we term “CellTrap”. Our findings substantiate the concept that highly expressed, non-essential proteins can be repurposed as selective recruiters to expand therapeutic windows of existing small-molecule inhibitors, opening new avenues for designing targeted drugs with improved cell-type specificity.
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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 | 0.000 | 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) | 0.002 | 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".