An In-Silico, In-Vitro and In-Vivo Combined Approach to Identify NMNATs as Potential Protein Targets of ProEGCG for Treatment of Endometriosis
Bibliographic record
Abstract
Endometriosis is defined as endometrial tissues found outside the uterine cavity. ProEGCG is a prodrug of Epigallocatechin gallate (EGCG), a potent polyphenol found in green tea. It inhibits the development of endometriotic lesions of mouse model in vivo , with higher efficacy and more remarkable anti-oxidative ability than EGCG. Our study aims to identify the molecular binding targets and pharmacological actions of ProEGCG in treating endometriosis. Protein target interaction study is essential to fully characterize the mechanism of actions, related therapeutic effects, and side effects. We employed a combined approach, starting with an in silico reverse screening of protein targets and molecular docking, followed by in vitro cellular thermal shift assay (CESTA) to assess the stability of protein-small molecule complexes. Then microarray and immunostaining of endometriotic lesions in mice in vivo confirmed the molecular interaction of the selected targets after treatment. Our study identified enzymes nicotinamide nucleotide adenylyltransferase (NMNAT)1 and NMNAT3 as protein targets of ProEGCG in silico and in vitro and were overexpressed after ProEGCG treatment in vivo . These findings suggested that participation in nicotinate and nicotinamide metabolism potentially regulated the redox status of endometriosis via its antioxidative capacities through binding to the potential therapeutic targets of ProEGCG.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| 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".