On the Nature of the Multivalency Effect: A Thermodynamic Model
Bibliographic record
Abstract
A quantitative model is proposed for the analysis of the thermodynamic parameters of multivalent interactions in dilute solutions or with immobilized multimeric receptor. The model takes into account all bound species and describes multivalent binding via two microscopic binding energies corresponding to inter- and intramolecular interactions (Delta G(o)inter and Delta G(o)intra), the relative contributions of which depend on the distribution of complexes with different numbers of occupied binding sites. The third component of the overall free energy, which we call the "avidity entropy" term, is a function of the degeneracy of bound states, Omega(i), which is calculated on the basis of the topology of interaction and the distribution of all bound species. This term grows rapidly with the number of receptor sites and ligand multivalency, it always favors binding, and explains why multivalency can overcome the loss of conformational entropy when ligands displayed at the ends of long tethers are bound. The microscopic parameters and may be determined from the observed binding energies for a set of oligovalent ligands by nonlinear fitting with the theoretical model. Here binding data obtained from two series of oligovalent carbohydrate inhibitors for Shiga-like toxins were used to verify the theory. The decavalent and octavalent inhibitors exhibit subnanomolar activity and are the most active soluble inhibitors yet seen that block Shiga-like toxin binding to its native receptor. The theory developed here in conjunction with our protocol for the optimization of tether length provides a predictive approach to design and maximize the avidity of multivalent ligands.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".