Protein Specificity of Charged Sequences in Polyanions and Heparins
Bibliographic record
Abstract
Long-range electrostatic interactions are generally assigned a subordinate role in the high-affinity binding of proteins by glycosaminoglycans, the most highly charged biopolyelectrolytes. The discovery of high and low sulfation domains in heparan sulfates, however, suggests selectivity via complementarity of their linear sulfation patterns with protein charge patterns. We examined how charge sequences in anionic/nonionic copolymers affect their binding to a protein with prominent charge anisotropy. Experiments and united-atom Monte Carlo simulations, together with Delphi electrostatic modeling for the protein, confirm strongest binding when polyanion sequences allow for optimization of repulsive and attractive electrostatics. Simulations also importantly identified retention of considerable polyion conformational freedom, even for strong binding. The selective affinity for heparins of high and low charge density found for this protein is consistent with nonspecific binding to distinctly different protein charge domains. These findings suggest a more nuanced view of specificity than previously proposed for heparinoid-binding proteins.
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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".