Novel computational and drug design strategies for the inhibition of human T-cell leukemia virus 1-associated lymphoma by Astilbin derivatives
Bibliographic record
Abstract
Human T-cell leukemia virus 1 (HTLV-1) associated lymphoma is a devastating malignancy triggered by HTLV-1 infections. We employeda comprehensive drug design and computational strategy in this work to explore the inhibitory activitiesof Astilbin derivatives against HTLV-1-associated lymphoma. We evaluated the stability, binding affinities, and various computational analysis of Astilbin derivatives against target proteins, such as HTLV-1 main protease and HTLV-1 capsid protein. The root mean square deviation (RMSD), root mean square fluctuation, radius of gyration, hydrogen bond analysis, principal component analysis (PCA) and dynamic cross-correlation matrix (DCCM) were applied to characterize these protein-ligand interactions further. Ligand-03 and ligand-04 exhibited notable binding affinity to HTLV-1 capsid protein, while ligand-05 displayed high binding affinity to HTLV-1 protease. MD simulation analysis revealed that ligand-03, bound to HTLV-1 capsid protein, demonstrated enhanced stability with lower RMSD values and fewer conformational changes, suggesting a promising binding orientation. Ligand-04, despite stable binding, exhibited increased structural deviations, making it less suitable. Ligand-05 demonstrated stable binding to HTLV-1 protease throughout the simulation period at 100 nanoseconds. Hydrogen bond analysis indicated that ligand-05 formed persistent hydrogen bonds with significantresidues, contributing to its stability. PCA highlighted ligand-03's more remarkable conformational changes, while DCCM showed ligand-05's distinct dynamics, indicating its different behavior in the complex. Furthermore, binding free energy calculations supported the favorable interactions of ligand-03 and ligand-04 with HTLV-1 capsid protein, while ligand-05 showed weaker interactions with HTLV-1 protease. Molecular electrostatic potential and frontier molecular orbital analyses provided insights into these compounds' charge distribution and stability. In conclusion, this research found Astilbin derivatives as potential inhibitors of HTLV-1-associated lymphoma. Future attempts at drug development will benefit from the steady interaction landscape provided by Ligand-03, Ligand-04 and Ligand-05, which showed the most attractive binding profile with the target protein. These results open up new opportunities for innovative drug development, and more experimental testing should be done between Astilbin derivatives and HTLV-1-associated lymphoma.
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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 | 0.000 | 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".