Docking studies and model development of tea polyphenol proteasome inhibitors: Applications to rational drug design
Bibliographic record
Abstract
Previously, we demonstrated that natural and synthetic ester bond-containing green tea polyphenols were potent and specific non-peptide proteasome inhibitors. However, the molecular mechanism of inhibition is currently unknown. Here, we report that inhibition of the chymotrypsin activity of the 20S proteasome by (-)-epigallocatechin-3-gallate (EGCG) is time-dependent and irreversible, implicating acylation of the beta5-subunit's catalytic N-terminal threonine (Thr 1). This knowledge is used, along with in silico docking experiments, to aid in the understanding of binding and inhibition. On the basis of these docking experiments, we propose that (-)-EGCG binds the chymotrypsin site in an orientation and conformation that is suitable for a nucleophilic attack by Thr 1. Consistently, the distance from the electrophilic carbonyl carbon of (-)-EGCG to the hydroxyl group of Thr 1 was measured as 3.18 A. Furthermore, the A ring of (-)-EGCG acts as a tyrosine mimic, binding to the hydrophobic S1 pocket of the beta5-subunit. In the process, the (-)-EGCG scissile bond may become strained, which could lower the activation energy for attack by the hydroxyl group of Thr 1. This model is validated by comparison of predicted and actual activities of several EGCG analogs, either naturally occurring, previously synthesized, or rationally synthesized.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".