Mechanistic inhibition of human papillomavirus-associated cervical cancer and DNA polymerase theta receptor by natural Apigenin derivatives: An extensive drug design and computational approach
Bibliographic record
Abstract
Abstract The present study deals with the advanced in-silico analyses of several Apigenin derivatives to explore human papillomavirus-associated cervical cancer and DNA polymerase theta inhibitor properties by molecular docking, molecular dynamics, QSAR, drug-likeness, PCA, a dynamic cross-correlation matrix and quantum calculation properties. Initially, the literature study revealed that Apigenin has potent antimicrobial and anticancer properties. So, their potential derivatives are picked up to determine human papillomavirus-associated cervical cancer and DNA polymerase theta inhibitor properties . The findings were streamlined by exposing the in silico molecular docking, highlighting that all the Apigenin derivatives exhibit prospective interactions of energy binding with targeted proteins. The Apigenin 4'-O-Rhamnoside and Apigenin-4'-Alpha-L-Rhamnoside were more potent against HPV45 oncoprotein E7 (PDB ID 2EWL), where the Apigenin and Apigenin 5-O-Beta-D-Glucopyranoside showed significant binding energy against L1 protein of human. Similarly, the binding affinity range is achieved from -7.5 kcal/mol to -8.8 kcal/mol against DNA polymerase theta, representing that Apigenin derivatives should inhibit the DNA polymerase theta (PDB ID 8E23). Then, this finding was confirmed by molecular dynamic simulation at 100ns and analysis of different parameters such as RMSD, RMSF, SASA, H-bond, and RoG profiles and it is found that our selected compounds were found to be potentially stable. When stability testing is passed, the ADMET, pharmacokinetics, and drug-likeness properties are screened and fulfilled by all the criteria. Moreover, QSAR, PCA, Dynamic cross-correlation matrix, and quantum calculation were also conducted and the outcome was satisfied. As this study was carried out using in silico computational approaches and obtained outstanding results. So, to validate these results, further wet-lab experiments might be carried out under in vivo and in vitro conditions.
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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 | 0.001 | 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".