Inactivation of Protein Tyrosine Phosphatases by Peracids Correlates with the Hydrocarbon Chain Length
Bibliographic record
Abstract
BACKGROUND/AIMS: Protein tyrosine phosphatases are crucial enzymes controlling numerous physiological and pathophysiological events and can be regulated by oxidation of the catalytic domain cysteine residue. Peracids are highly oxidizing compounds, and thus may induce inactivation of PTPs. The aim of the present study was to evaluate the inhibitory effect of peracids with different length of hydrocarbon chain on the activity of selected PTPs. METHODS: The enzymatic activity of human CD45, PTP1B, LAR, bacterial YopH was assayed under the cell-free conditions, and activity of cellular CD45 in human Jurkat cell lysates. The molecular docking and molecular dynamics were performed to evaluate the peracids binding to the CD45 active site. RESULTS: Here we demonstrate that peracids reduce enzymatic activity of recombinant CD45, PTP1B, LAR, YopH and cellular CD45. Our studies indicate that peracids are more potent inhibitors of CD45 than hydrogen peroxide (with an IC50 value equal to 25 nM for peroctanoic acid and 8 µM for hydrogen peroxide). The experimental data show that the inactivation caused by peracids is dependent on hydrocarbon chain length of peracids with maximum inhibitory effect of medium-chain peracids (C8-C12 acyl chain), which correlates with calculated binding affinities to the CD45 active site. CONCLUSION: Peracids are potent inhibitors of PTPs with the strongest inhibitory effect observed for medium-chain peracids.
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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) | 0.002 | 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".