On the Nature of the Multivalency Effect: A Thermodynamic Model
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
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 | 0.001 |
| 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 it