Unlocking the potential: unveiling tyrphostins with Michael-reactive cyanoacrylate motif as promising inhibitors of human 5-lipoxygenase
Bibliographic record
Abstract
Abstract Human 5-lipoxygenase (5-LO) is the key enzyme in the biosynthesis of leukotrienes, mediators of the innate immune system that also play an important role in inflammatory diseases and cancer. In this study, we present compounds, containing a Michael-reactive cyanoacrylate moiety as potent inhibitors of 5-LO. Representatives of the tyrosine kinase inhibitor family called tyrphostins, structurally related to known 5-LO inhibitors, were screened for their 5-LO inhibitory properties using recombinant human 5-LO, intact human PMNL (polymorphonuclear leukocytes), and PMNL homogenates. Their mode of action was characterized by the addition of glutathione, using a fourfold cysteine 5-LO mutant and mass spectrometry analysis. SAR studies revealed several members of the tyrphostin family containing a Michael-reactive cyanoacrylate to efficiently inhibit 5-LO. We identified degrasyn (IC50 0.11 µM), tyrphostin A9 (IC50 0.8 µM), AG879 (IC50 78 nM), and AG556 (IC50 64 nM) as potent 5-LO inhibitors. Mass spectrometry analysis revealed that degrasyn and AG556 covalently bound to up to four cysteines, including C416 and/or C418 which surround the substrate entry site. Furthermore, the 5-LO inhibitory effect of degrasyn was remarkably impaired by the addition of glutathione or by the mutation of cysteines to serines at the surface of 5-LO. We successfully identified several tyrphostins as potent inhibitors of human 5-LO. Degrasyn and AG556 were able to covalently bind to 5-LO via their cyanoacrylate moiety. This provides a promising mechanism for targeting 5-LO by Michael acceptors, leading to new therapeutic opportunities in the field of inflammation and cancer.
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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) | 0.001 | 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".