Binding Affinity of Concanavalin A to Native and Acid-Hydrolyzed Phytoglycogen Nanoparticles
Bibliographic record
Abstract
Phytoglycogen (PG) is a polysaccharide produced in the kernels of sweet corn as soft, highly branched, compact nanoparticles. Its tree-like or dendritic architecture, combined with a high-safety profile, makes PG nanoparticles attractive for use in biological applications, many of which rely on the association or binding of small biomolecules. We have developed a methodology to functionalize surface plasmon resonance (SPR) sensor surfaces with PG nanoparticles, and we demonstrate the utility of the PG-functionalized SPR sensor by measuring the binding affinity of the tetrameric concanavalin A (ConA) protein to both native PG nanoparticles and smaller, softer acid-hydrolyzed PG nanoparticles. We measure comparable values of the equilibrium association constant K for native and acid-hydrolyzed PG, with a slightly smaller value for the acid-hydrolyzed particles that we attribute to unfavorable lateral interactions between the tetrameric subunits of ConA due to the increase in surface curvature of the smaller acid-hydrolyzed PG particles. We also use infrared reflection-absorption spectroscopy (IRRAS) to show that ConA maintains a large fraction of its native conformation, and thus its bioactivity, upon binding to PG, representing an important step toward the realization of PG as a novel bioactive delivery vehicle.
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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".