Using Nanoscale Substrate Curvature to Control the Dimerization of a Surface-Bound Protein
Bibliographic record
Abstract
The influence of surface geometry on adsorbed proteins offers new possibilities for controlling quaternary structure by manipulating protein-protein interactions at a surface, with applications that are relevant to protein aggregation, fibrillation, ligand binding, and surface catalysis. To understand the effect of surface curvature on the structure of the surface-bound protein β-lactoglobulin (β-LG), we have used a combination of polystyrene (PS) nanoparticles (NPs) and ultrathin PS films to fabricate chemically pure, hydrophobic surfaces that have nanoscale curvature and are stable in aqueous buffer. We have used single molecule force spectroscopy to measure the detachment contour lengths L(c) for β-LG adsorbed on the highly curved PS surfaces, and we compare these values in situ to those measured for β-LG adsorbed on flat PS surfaces on the same samples. The L(c) distributions measured on all flat PS surfaces show a large monomer peak near 60 nm and a smaller dimer peak at 120 nm. For 190 and 100 nm diameter NPs, which are effectively flat on the scale of the β-LG molecules, there is no measurable difference between the L(c) distributions obtained for the flat and curved surfaces. However, for 60 nm diameter NPs the dimer peak is smaller, and for 25 nm diameter NPs the dimer peak is absent, indicating that the number of surface-bound dimers is significantly reduced by an increase in the curvature of the underlying surface. These results indicate that surface curvature provides a new method of manipulating protein-protein interactions and controlling the quaternary structure of adsorbed proteins.
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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".