Homology and Molecular Dynamics Models of Toll‐Like Receptor 7 Protein and Its Dimerization
Bibliographic record
Abstract
Toll-like receptor protein 7 is a transmembrane protein playing a crucial role in the signaling pathways involved in innate immunity. Its crystal structure is not yet available, but there are several proteins possessing domains of sufficiently high homology, which enabled us to build a model of the toll-like receptor protein 7 monomer and gain insights into dimer formation. To obtain a reliable structure prediction, we subjected this model to equilibration using molecular dynamics simulations. Furthermore, the equilibrated monomer structure was used to construct models of dimerization and to predict binding sites for small ligands. Docking studies were performed for some of the known toll-like receptor protein 7 ligands. We determined that a new homology model generated by the LOOPP server provides a good alternative to a previously reported model. Our docking results indicate that the addition of either imiquimod or 1V209 to a toll-like receptor protein 7 dimer changes an unfavorable interaction into a favorable one. We found that eight small molecules docked to two pockets in toll-like receptor protein 7 bind to both pockets at pH 7 and at pH 5.5. This work provides a realistic model that could be used for drug discovery aimed at finding toll-like receptor protein 7 dimerization activators, with potential clinical applications to a host of diseases, including cancer.
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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| 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".