Synthesis of Biotinylated α-<scp>d</scp>-Mannoside or <i>N-</i>Acetyl β-<scp>d</scp>-Glucosaminoside Decorated Gold Nanoparticles: Study of Their Biomolecular Recognition with Con A and WGA Lectins
Bibliographic record
Abstract
Gold nanoparticles (NPs) functionalized with a mixed shell of well-defined biotinylated glycopolymers and polyethylene glycol (PEG) provide an effective platform for the biomolecular recognition of proteins both in solution and on surfaces. Well-defined biotinylated glycopolymers were first synthesized by the reversible addition-fragmentation chain transfer (RAFT) process. They contain two types of carbohydrate residues either N-acetyl β-D-glucosaminopyranoside (GlcNAc) or α-D-mannopyranoside (Man) as pendent groups. The biotinylated glycopolymers and polyethylene glycol were subsequently used in the in situ formation of gold glyconanoparticles via an easy photochemical process. The obtained biotinylated glyconanoparticles were characterized by dynamic light scattering (DLS) and transmission electron microscopy (TEM). The bioavailability of the biotin and specific carbohydrate residues at the periphery of the NPs were assessed using the diffraction optic technology (DOT) system. The studies showed the accessibility of the biotin ligands for conjugation to immobilized avidin on the DOTLab biosensor. Furthermore, these avidin conjugated glyconanoparticles were found to selectively immobilize lectins. The specificity of lectin binding was dependent on the type of carbohydrate residues. As such, N-acetyl β-D-glucosaminoside decorated gold nanoparticles were found to specifically interact with wheat germ agglutinin (WGA) lectin, whereas α-D-mannoside ones were found to specifically interact with Concanavalin A (Con A) lectin.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| 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".