Identification of a Highly Cooperative PROTAC Degrader Targeting GTP-Loaded KRAS(On) Alleles
Bibliographic record
Abstract
High Resolution Image Download MS PowerPoint Slide Kirsten rat sarcoma viral oncogene homologue (KRAS) is a frequently mutated oncogene in multiple types of cancer and is a high priority target for oncology drug development. There are many different KRAS mutations, including mutations that favor the GTP-loaded hydrolysis-incompetent “active” state of KRAS, KRAS(on), that can lead to tumorigenesis. However, small molecule interventions thus far have predominantly targeted single mutations of “inactive” GDP-loaded KRAS, KRAS(off), such as KRAS G12C . Here, we address this gap through the development of heterobifunctional VHL-based PROTACs capable of engaging and degrading KRAS(on), thus addressing a wider range of KRAS mutations. By studying ternary complex affinity, stability, and binding modes using SPR and X-ray cocrystal structures, we identified PROTACs that exhibit high positive cooperativity in forming ternary complexes with VHL and GCP-loaded KRAS as representative of KRAS(on) variants. Degrader activity profiling in relevant cancer cells supported the discovery of ACBI4, a PROTAC which forms a highly stable and cooperative ternary complex between VHL and GTP-bound KRAS and which potently degrades KRAS G12R, leading to antiproliferative effect in KRAS mutant-driven cancer cells. ACBI4 provides a new chemical tool for studying the impact of degrading KRAS(on) mutants, which is not possible with current pan-KRAS inhibitors or degraders.
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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 | 0.001 |
| 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".