In Silico Exploration of Microtubule Agent Griseofulvin and Its Derivatives Interactions with Different Human β-Tubulin Isotypes
Bibliographic record
Abstract
Tubulin isotypes are known to regulate microtubule stability and dynamics, as well as to play a role in the development of resistance to microtubule-targeted cancer drugs. Griseofulvin is known to disrupt cell microtubule dynamics and cause cell death in cancer cells through binding to tubulin protein at the taxol site. However, the detailed binding mode involved molecular interactions, and binding affinities with different human β-tubulin isotypes are not well understood. Here, the binding affinities of human β-tubulin isotypes with griseofulvin and its derivatives were investigated using molecular docking, molecular dynamics simulation, and binding energy calculations. Multiple sequence analysis shows that the amino acid sequences are different in the griseofulvin binding pocket of βI isotypes. However, no differences were observed at the griseofulvin binding pocket of other β-tubulin isotypes. Our molecular docking results show the favorable interaction and significant affinity of griseofulvin and its derivatives toward human β-tubulin isotypes. Further, molecular dynamics simulation results show the structural stability of most β-tubulin isotypes upon binding to the G1 derivative. Taxol is an effective drug in breast cancer, but resistance to it is known. Modern anticancer treatments use a combination of multiple drugs to alleviate the problem of cancer cells resistance to chemotherapy. Our study provides a significant understanding of the involved molecular interactions of griseofulvin and its derivatives with β-tubulin isotypes, which may help to design potent griseofulvin analogues for specific tubulin isotypes in multidrug-resistance cancer cells in future.
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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) | 0.002 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 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) | 0.005 | 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".