Free Energy Calculations on the Binding of Colchicine and Its Derivatives with the α/β-Tubulin Isoforms
Bibliographic record
Abstract
Tubulin is the target for numerous small molecule ligands which alter microtubule dynamics leading to cell cycle arrest and apoptosis. Many of these ligands are currently used clinically for the treatment of several types of cancer, and they bind to one of three distinct binding sites within beta-tubulin (paclitaxel, vinca, and colchicine), all of which have been identified crystallographically. Unfortunately, serious side effects always accompany chemotherapy since these drugs bind to tubulin indiscriminately, leading to the death of both cancerous and healthy cells. However, the existence and distribution of divergent tubulin isoforms provide a platform upon which we may build novel chemotherapeutic drugs that can differentiate between different cell types and therefore reduce undesirable side effects. We report results of computational analysis that aims at predicting differences between the binding energies of a family of colchicine derivatives against 10 human alpha/beta-tubulin isoforms. Free energy perturbation method has been used in our calculations and the results provide a proof of principle by indicating significant differences both among the derivatives and between tubulin isoforms.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".