Protein domain mimetics as in vivo modulators of hypoxia-inducible factor signaling
Bibliographic record
Abstract
Selective blockade of gene expression by designed small molecules is a fundamental challenge at the interface of chemistry, biology, and medicine. Transcription factors have been among the most elusive targets in genetics and drug discovery, but the fields of chemical biology and genetics have evolved to a point where this task can be addressed. Herein we report the design, synthesis, and in vivo efficacy evaluation of a protein domain mimetic targeting the interaction of the p300/CBP coactivator with the transcription factor hypoxia-inducible factor-1α. Our results indicate that disrupting this interaction results in a rapid down-regulation of hypoxia-inducible genes critical for cancer progression. The observed effects were compound-specific and dose-dependent. Gene expression profiling with oligonucleotide microarrays revealed effective inhibition of hypoxia-inducible genes with relatively minimal perturbation of nontargeted signaling pathways. We observed remarkable efficacy of the compound HBS 1 in suppressing tumor growth in the fully established murine xenograft models of renal cell carcinoma of the clear cell type. Our results suggest that rationally designed synthetic mimics of protein subdomains that target the transcription factor-coactivator interfaces represent a unique approach for in vivo modulation of oncogenic signaling and arresting tumor growth.
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".