Computational modeling and biological evaluation of methylated (-)-EGCG analogs
Bibliographic record
Abstract
Proc Amer Assoc Cancer Res, Volume 47, 2006 4897 The anti-cancer and cancer-preventive effects of green tea and its main constituent (-)-epigallocatechin-3-gallate [(-)-EGCG] are well documented by a variety of studies, including epidemiological, cell culture, animal, and clinical. While (-)-EGCG remains the most potent polyphenol in green tea, it is particularly unstable under neutral or alkaline conditions ( i.e . physiologic pH) and may be modified by methylation, glucuronidation and sulfonation. In order to understand the significance of methylation on cancer-preventative effects of tea polyphenols, we synthesized several (-)-EGCG and (-)-ECG analogs with various number of methyl groups (mono-, di-, and tri-) attached to the B- and/or D-rings. Additionally, we synthesized putative prodrugs that contain protective peracetate groups (removable by cellular cytosolic esterases) in place of the remaining hydroxyl groups. The structure-activity relationships (SARs) were studied by both in silico computational modeling of the methylated (-)-EGCG analogs to the proteasome active site and their in vitro inhibitory potencies against a purified 20S proteasome. While the number of methyl groups was increased, a gradual decrease was observed in the probability of methylated (-)-EGCG analogs to bind in silico to the active site of the proteasome. Consistently, as the number of methyl groups increased on the (-)-EGCG molecule, the in vitro proteasome-inhibitory potencies were also decreased. When human leukemic Jurkat T cells were tested, the pro-drug of the mono-methylated (-)-EGCG analog caused greater proteasome inhibition and apoptosis than the pro-drug of the tri-methylated (-)-EGCG analog. Our data suggest that methylation of (-)-EGCG lessens its proteasome-inhibitory ability that might lead to decreased cancer preventative effects.
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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 | 0.002 | 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) | 0.000 | 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".