Virtual and Biophysical Screening Targeting the γ-Tubulin Complex – A New Target for the Inhibition of Microtubule Nucleation
Bibliographic record
Abstract
Microtubules are the main constituents of mitotic spindles. They are nucleated in large amounts during spindle assembly, from multiprotein complexes containing γ-tubulin and associated γ-tubulin complex proteins (GCPs). With the aim of developing anti-cancer drugs targeting these nucleating complexes, we analyzed the interface between GCP4 and γ-tubulin proteins usually located in a multiprotein complex named γ-TuRC (γ-Tubulin Ring Complex). 10 ns molecular dynamics simulations were performed on the heterodimers to obtain a stable complex in silico and to analyze the residues involved in persistent protein-protein contacts, responsible for the stability of the complex. We demonstrated in silico the existence of a binding pocket at the interface between the two proteins upon complex formation. By combining virtual screening using a fragment-based approach and biophysical screening, we found several small molecules that bind specifically to this pocket. Sub-millimolar fragments have been experimentally characterized on recombinant proteins using differential scanning fluorimetry (DSF) for validation of these compounds as inhibitors. These results open a new avenue for drug development against microtubule-nucleating γ-tubulin complexes.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 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".