Elucidating the Mechanism of Action of the Clinically Approved Taxanes: A Comprehensive Comparison of Local and Allosteric Effects
Bibliographic record
Abstract
The clinically approved taxanes (paclitaxel, docetaxel and cabazitaxel) target the tubulin protein in microtubules. Despite the clinical success of these agents, the mechanism of action of this class of drugs remains elusive, making rational design of taxanes difficult. Molecular dynamics simulations of these three taxanes with the αβ-tubulin heterodimer examine the similarities and differences in the effects of the drugs on tubulin, probing both local and allosteric effects. Despite their structural similarity, the drugs adopt different conformations in the binding site on β-tubulin. The taxanes similarly increase the helical character of α- and β-tubulins. No correlations are found between microtubule assembly and (i) binding affinity or (ii) the role of the M-loop in enhancing lateral contacts. Instead, changes in intra- and interdimer longitudinal contacts are indicative of the mechanism of action of the taxanes. We find β:H1-S1', and more importantly β:H9 and β:H10, play a role translating the effect of local drug binding in β-tubulin to an allosteric effect in α-tubulin and propose that the displacement of these secondary structures towards α-tubulin may be used as a predictor of the effect of taxanes on the tubulin heterodimers in rational drug design approaches.
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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) | 0.000 | 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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".