Self-Assembled Ligands Targeting TLR7: A Molecular Level Investigation
Bibliographic record
Abstract
Toll-like receptors (TLRs) are pattern recognition transmembrane proteins that play an important role in innate immunity. In particular, TLR7 plays a role in detecting nucleic acids derived from viruses and bacteria. The huge number of pathologies in which TLR7 is involved has led to an increasing interest in developing new compounds targeting this protein. Several conjugation strategies were proposed for TLR7 agonists to increase the potency while maintaining a low toxicity. In this work, we focus the attention on two promising classes of TLR7 compounds derived from the same pharmacophore conjugated with phospholipid and polyethylene glycol (PEG). A multidisciplinary investigation has been carried out by molecular dynamics (MD), dynamic light scattering (DLS), electron paramagnetic resonance (EPR), and cytotoxicity assessment. DLS and MD indicated how only the phospholipid conjugation provides the compound abilities to self-assemble in an orderly fashion with a maximal pharmacophore exposition to the solvent. Further EPR and cytotoxicity experiments highlighted that phospholipid compounds organize in stable aggregates and well interact with TLR7, whereas PEG conjugation was characterized by poorly stable aggregates at the cells surface. The methodological framework proposed in this study may be used to investigate, at a molecular level, the interactions generally occurring between aggregated ligands, to be used as drugs, and protein receptors.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| 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) | 0.001 | 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".