Mucin‐Inspired, High Molecular Weight Virus Binding Inhibitors Show Biphasic Binding Behavior to Influenza A Viruses
Bibliographic record
Abstract
Multivalent binding inhibitors are a promising new class of antivirals that prevent virus infections by inhibiting virus binding to cell membranes. The design of these inhibitors is challenging as many properties, for example, inhibitor size and functionalization with virus attachment factors, strongly influence the inhibition efficiency. Here, virus binding inhibitors are synthesized, the size and functionalization of which are inspired by mucins, which are naturally occurring glycosylated proteins with high molecular weight (MDa range) and interact efficiently with various viruses. Hyperbranched polyglycerols (hPGs) with molecular weights ranging between 10 and 2600 kDa are synthesized, thereby hitting the size of mucins and allowing for determining the impact of inhibitor size on the inhibition efficiency. The hPGs are functionalized with sialic acids and sulfates, as suggested from the structure of mucins, and their inhibition efficiency is determined by probing the inhibition of influenza A virus (IAV) binding to membranes using various methods. The largest, mucin-sized inhibitor shows potent inhibition at pm concentrations, while the inhibition efficiency decreases with decreasing the molecular weight. Interestingly, the concentration-dependent IAV inhibition shows a biphasic behavior, which is attributed to differences in the binding affinity of the inhibitors to the two IAV envelope proteins, neuraminidase, and hemagglutinin.
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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".