The interaction of β2-microglobulin with gold nanoparticles: impact of coating, charge and size
Bibliographic record
Abstract
Gold nanoparticles (AuNPs) have been proved to be ideal scaffolds to build nanodevices whose performance can be tuned by changing their coating. In particular, the interaction of AuNPs with proteins was revealed to be highly dependent on the physico-chemical properties of the gold cluster protecting monolayer. In this work we studied the behavior of three different alkanethiolate-coated AuNPs (AT-AuNPs) when they are incubated with a model amyloidogenic protein, β2-microglobulin (β2m), whose clinical relevance in dialysis-related amyloidosis (DRA) and structural properties are well known. To the aim we synthesized 6-mercaptohexanoic acid-coated AuNPs (MHA-AuNPs) and (11-mercaptoundecyl)-N,N,N-trimethylammonium bromide-coated AuNPs (MUTAB-AuNPs) of 7.5 nm diameter and 3-mercaptopropionic acid-coated AuNPs (MPA-AuNPs) of 3.6 nm diameter. To study the effects of the incubation with β2m of these NPs that differ in charge and dimension, we employed NMR, UV-vis and fluorescence spectroscopy, along with transmission electron microscopy (TEM). The three tested AuNP systems gave different results. We found that MHA-AuNPs precipitate with the protein into large agglomerates inducing β2m unfolding, MUTAB-AuNP precipitation is triggered by the protein that remains unchanged in solution, at least at the higher considered protein/NP ratio, and MPA-AuNPs interact preferentially with a localized region of the protein that stays essentially stably dissolved. These results stress the complexity of the bio-nano interface and the relevance and viability of the fine control of NP properties to master protein-NP interactions.
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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) | 0.000 | 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".