Exploring the Surface Charge on Peptide−Gold Nanoparticle Conjugates by Force Spectroscopy
Bibliographic record
Abstract
The conformation and charge exposure of peptides attached to colloidal gold nanoparticles (AuNPs) are critical for both the colloidal stability and for the recognition of biological targets in biomedical applications such as diagnostics and therapy. We prepared conjugates of AuNPs and three isomer peptides capable of recognizing toxic aggregates of the amyloid beta protein (Abeta) involved in Alzheimer's disease, namely, CLPFFD-CONH(2) (i0), CDLPFF-CONH(2) (i1), and CLPDFF-CONH(2) (i2), where D is the amino acid aspartic acid that is negatively charged at pH = 7.4. We then studied the effect of peptide sequence on the charge exposure through force spectroscopy measurements. The peptide-AuNPs conjugates were fixed on glass surfaces, and their interactions with peptide-functionalized tips were determined. Our results show a higher density of surface charge in the conjugates of the isomers i0 and i2 and a lower density in i1, which is due to the higher degree of functionalization in the first two compared with the third. However, the charge per molecule of the peptide is higher for i1 with respect to i0 and i2, which could be related to the local conformation that the peptides adopt on the surface. The acid-base behavior of the peptide anchored to the AuNPs is different than expected in aqueous solutions of free peptides, which could be related to the low accessibility of the NH(2)-terminal group belonging to the cysteine that is located near the AuNPs surface. In contrast with other techniques, the fixation of the peptide-AuNPs conjugates to a surface allows for characterization of the local charge exposure of peptides anchored to AuNPs over a wide range of pH.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 teacher head, 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".