N‐(2,4)‐dinitrophenyl‐L‐arginine Interacts with EphB4 and Functions as an EphB4 Kinase Modulator
Bibliographic record
Abstract
The erythropoietin-producing hepatocellular carcinoma receptor B4 is a receptor tyrosine kinase whose expression is preserved in various malignancies, including colon, gastric, and breast carcinoma. Hepatocellular carcinoma receptor B4 presence in tumor cells and involvement in cancer suppression makes it a potential therapeutic target for activating compounds. Moreover, modulators of its activity also have a strong potential to be used in diagnosis and therapy monitoring. We used virtual ligand screening to identify novel hepatocellular carcinoma receptor B4 kinase modulators for experimental testing. Three independent assay platforms confirmed that dinitrophenyl-L-arginine is likely to affect the kinase activity of hepatocellular carcinoma receptor B4. An enzyme-coupled spectrophotometric assay has been used to examine this possibility and may prove to be useful for assessing other novel kinase modulator candidates. Overall, our observations suggest that dinitrophenyl-L-arginine has an activating effect on hepatocellular carcinoma receptor B4 and, therefore, more efficient derivatives may have therapeutic effects in tumors where hepatocellular carcinoma receptor B4 exhibits antimalignant properties. The hepatocellular carcinoma receptor B4-activating effect is discussed with respect to previously described mechanisms, using predicted and experimental structures for docked ligands. As a novel kinase activity modulator, dinitrophenyl-L-arginine may provide new insights into molecular mechanisms by which kinases are activated or regulated, and may serve as a lead compound for the generation of novel hepatocellular carcinoma receptor B4-activating therapeutic compounds.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| 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) | 0.000 | 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 teacher head, 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".