Quick and Simple Detection Technique to Assess the Binding of Antimicrotubule Agents to the Colchicine-Binding Site
Bibliographic record
Abstract
Development of antimitotic binding to the colchicine-binding site for the treatment of cancer is rapidly expanding. Numerous antimicrotubule agents are prepared every year, and the determination of their binding affinity to tubulin requires the use of purified tubulins and radiolabeled ligands. Such a procedure is costly and time-consuming and therefore is limited to the most promising candidates. Here, we report a quick and inexpensive method that requires only usual laboratory resources to assess the binding of antimicrotubules to colchicine-binding site. The method is based on the ability of N,N'-ethylene-bis(iodoacetamide) (EBI) to crosslink in living cells the cysteine residues at position 239 and 354 of β-tubulin, residues which are involved in the colchicine-binding site. The β-tubulin adduct formed by EBI is easily detectable by Western blot as a second immunoreacting band of β-tubulin that migrates faster than β-tubulin. The occupancy of colchicine-binding site by pertinent antimitotics inhibits the formation of the EBI: β-tubulin adduct, resulting in an assay that allows the screening of new molecules targeting this binding site.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".