A Molecular Docking Study Reveals That Short Peptides Induce Conformational Changes in the Structure of Human Tubulin Isotypes αβI, αβII, αβIII and αβIV
Bibliographic record
Abstract
Microtubules are cylindrical protein polymers assembled in the cytoplasm of all eukaryotic cells by polymerization of aβ tubulin dimers, which are involved in cell division, migration, signaling, and intracellular traffic. These functions make them essential in the proliferation of cancerous cells and metastases. Tubulin has been the molecular target of many anticancer drugs because of its crucial role in the cell proliferation process. By developing drug resistance, tumor cells severely limit the successful outcomes of cancer chemotherapy. Hence, overcoming drug resistance motivates the design of new anticancer therapeutics. Here, we retrieve short peptides obtained from the data repository of antimicrobial peptides (DRAMP) and report on the computational screening of their predicted tertiary structures for the ability to inhibit tubulin polymerization using multiple combinatorial docking programs, namely PATCHDOCK, FIREDOCK, and ClusPro. The interaction visualizations show that all the best peptides from the docking analysis bind to the interface residues of the tubulin isoforms αβl, αβll, αβlll, and αβlV, respectively. The docking studies were further confirmed by a molecular dynamics simulation, in which the computed root-mean-square deviation (RMSD), and root-mean-square fluctuation (RMSF), verified the stable nature of the peptide-tubulin complexes. Physiochemical toxicity and allergenicity studies were also performed. This present study suggests that these identified anticancer peptide molecules might destabilize the tubulin polymerization process and hence can be suitable candidates for novel drug development. It is concluded that wet-lab experiments are needed to validate these findings.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".