Expanding the Scope of Electrophiles Capable of Targeting K-Ras Oncogenes
Bibliographic record
Abstract
There is growing interest in reversible and irreversible covalent inhibitors that target noncatalytic amino acids in target proteins. With a goal of targeting oncogenic K-Ras variants (e.g., G12D) by expanding the types of amino acids that can be targeted by covalent inhibitors, we survey a set of electrophiles for their ability to label carboxylates. We functionalized an optimized ligand for the K-Ras switch II pocket with a set of electrophiles previously reported to react with carboxylates and characterized the ability of these compounds to react with model nucleophiles and oncogenic K-Ras proteins. Here, we report that aziridines and stabilized diazo groups preferentially react with free carboxylates over thiols. Although we did not identify a warhead that potently labels K-Ras G12D, we were able to study the interactions of many electrophiles with K-Ras, as most of the electrophiles rapidly label K-Ras G12C. We characterized the resulting complexes by crystallography, hydrogen/deuterium exchange, and differential scanning fluorimetry. Our results both demonstrate the ability of a noncatalytic cysteine to react with a diverse set of electrophiles and emphasize the importance of proper spatial arrangements between a covalent inhibitor and its intended nucleophile. We hope that these results can expand the range of electrophiles and nucleophiles of use in covalent protein modulation.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 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) | 0.001 | 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".