Insight into the key interactions of bromodomain inhibitors based on molecular docking, interaction fingerprinting, molecular dynamics and binding free energy calculation
Bibliographic record
Abstract
The bromodomain is a key protein-protein interaction module that specifically reads the acetylation marks of histones in epigenetic regulation. Currently, lots of inhibitors targeting the bromodomain have been reported as therapeutic agents. To better understand the interaction mechanism of bromodomain inhibitors, 20 diverse bromodomain inhibitors were studied using a combination of computational methods, including molecular docking, interaction fingerprinting, molecular dynamics simulation and binding free energy calculation. As a result, interactions important for the activity were critically analyzed, and the energy contribution in terms of individual residues was explored. These integrated results provided insights into two hot spots in the active site of the bromodomain, where the hydrophobic hot spot formed by Trp81, Val87, Leu92 and Ile146 played a central role in the interaction, and the hydrogen-bond hot spot mediated by Asn140 exhibited a moderate contribution to the binding affinity of the bromodomain inhibitors. This interaction mechanism study may facilitate the rational design of novel small-molecule bromodomain inhibitors.
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 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".