Docking Studies of Hispolon Mediated Human NF-κβ Inhibition and In-Silico Development of Hispolon Derivatives Towards Cancer Treatment
Bibliographic record
Abstract
In-silico drug designing for better anticancer therapeutics targeting human NF-κB, a pivotal enzyme involved in cancer regulating pathway. Hispolon, a black hoof mushroom (Phellinus linteus) or shaggy bracket mushroom (Inonotus hispidus) derived polyphenolic compound is structurally homologous with Curcumin and possess the NF-κB inhibitory efficiency as an anticancer agent. However, the in-silico studies related to Hispolon mediated inhibition of NF-κB activity and its mechanism of action has not yet been investigated so far. The present paper reports in-silico studies carried out to investigate the detailed mechanism of Hispolon mediated inhibition of NF-κB and designing of new potent derivatives of Hispolon having anticancer activity. Docking, Binding free energy analysis, Drug designing ADMET and IC50 has been performed for the fulfilment of above mentioned objective. DRG2 compound (ΔG= -30.180 kcal/mol) is the most potent in binding with human NF-κB among all 10 designed Hispolon derivatives. Met469 and Glu470 of human NF-κB makes an additional interaction with one of the hydrogen atom of 5-methoxy group of ligand’s benzene ring. Methoxy group placed on the -ortho and two -para position of benzene ring and the –meta positioned hydroxyl group of the compound plays crucial role in strengthening the binding energy with human NF-κB. ADMET analysis also confirmed the drug-likeness and efficiency of DRG2 with NF-κB. In-depth structural, molecular modelling, docking and binding energy studies helps to redesign Hispolon to better compound that might have some additional inhibitory effect on human NF-κB thus can be used as anticancer therapeutics.
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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 | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".