Glycosaminoglycans as Polyelectrolytes: Charge, Interactions, and Applications
Bibliographic record
Abstract
Glycosaminoglycans (GAGs) are linear, negatively charged biopolymers that modulate complex biological processes, such as blood coagulation, immune regulation, or viral entry. Their sulfation pattern and chain length govern how strongly they bind to other physiologically relevant species. Most of these interactions rely on electrostatic forces facilitated by the strong polyanionic properties of GAGs; therefore, considering these from a polyelectrolyte vantage point can help understand how such charge-based, often transient interactions contribute to physiological and pathological processes. While the different GAG classes share key electrostatic properties, they exhibit unique structural features that shape their function. Here, it is highlighted on how modern separation and analytical tools exploit the polyanionic character of GAGs to dissect subtle structural details. For these, the fundamental description of their charge–charge interactions is crucial. With this knowledge, modified GAGs, synthetic GAG mimetics, or GAG-binding molecules can be designed that replicate or refine their key properties and show promise for therapeutic and biomedical applications. Altogether, recognizing the importance of GAGs as polyelectrolytes provides vital knowledge on how their charge distribution mediates crucial biomolecular interactions in health and disease, and thus it helps complete our knowledge on fundamentally important biopolymers.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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".