Construction of Polymer–Protein Bioconjugates with Varying Chain Topologies: Polymer Molecular Weight and Steric Hindrance Effects
Bibliographic record
Abstract
We report on the fabrication of well-defined polymer-protein bioconjugates with varying chain architectures, including star polymers, star block copolymers, and heteroarm star copolymers through the specific noncovalent interaction between avidin and biotinylated synthetic polymer precursors. Homopolymer and diblock precursors site-specifically labeled with a single biotin moiety at the chain terminal, chain middle, or diblock junction point were synthesized by a combination of atom-transfer radical polymerization (ATRP) and click reactions. By taking advantage of molecular recognition between avidin and biotin moieties, supramolecular star polymers, star block copolymers, and heteroarm star copolymers were successfully fabricated. This specific binding process was also assessed by using the diffraction optic technology (DOT) technique. We further investigated the effects of polymer molecular weights, location of biotin functionality within the polymer chain, and polymer chain conformations, that is, steric hindrance effects, on the binding numbers of biotinylated polymer chains per avidin within the polymer-protein bioconjugates, which were determined by the standard avidin/2-(4-hydroxyazobenzene)benzoic acid (HABA) assay. The binding numbers vary in the range of 1.9-3.3, depending on the molecular weights, locations of biotin functionality within synthetic polymer precursors, and polymer chain conformations.
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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) | 0.001 | 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".