Amino Acid-Dependent Attenuation of Toll-like Receptor Signaling by Peptide-Gold Nanoparticle Hybrids
Bibliographic record
Abstract
Manipulation of immune responsiveness using nanodevices provides a potential approach to treat human diseases. Toll-like receptor (TLR) signaling plays a central role in the pathophysiology of many acute and chronic human inflammatory diseases, and pharmacological regulation of TLR responses is anticipated to be beneficial in many of these inflammatory conditions. Here we describe the discovery of a unique class of peptide-gold nanoparticle hybrids that exhibit a broad inhibitory activity on TLR signaling, inhibiting signaling through TLRs 2, 3, 4, and 5. As exemplified using TLR4, the nanoparticles were found to inhibit both arms of TLR4 signaling cascade triggered by the prototypical ligand, lipopolysaccharide (LPS). Through structure-activity relationship studies, we identified the key chemical components of the hybrids that contribute to their immunomodulatory activity. Specifically, the hydrophobicity and aromatic ring structure of the amino acids on the peptides were essential for modulating TLR4 responses. This work enhances our fundamental understanding of the role of nanoparticle surface chemistry in regulating innate immune signaling, and identifies specific nanoparticle hybrids that may represent a unique class of anti-inflammatory therapeutics for human inflammatory diseases.
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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".