Ligand induced receptor multimerization achieves the specificity enhancement of kinetic proofreading without associated costs
Bibliographic record
Abstract
Kinetic proofreading (KPR) is a commonly invoked mechanism for specificity enhancement of receptor signaling. However, specificity enhancement comes at a cost of non-equilibrium energy input and signal attenuation. We show that ligand induced multimeric receptor assembly can enhance receptor specificity to the same degree as KPR, yet without the need for out-of-equilibrium energy expenditure and signal loss. We show how multimeric receptor specificity enhancement arises from the amplification of affinity differences via sequential progression down a free energy landscape. We also show that multimeric receptor ligand recognition is more robust to stochastic fluctuations and molecular noise than KPR receptors. Finally, we show that multimeric receptors perform signaling tasks beyond specificity enhancement like absolute discrimination and aspects of ligand antagonism. Our results suggest that multimeric receptors may serve as a potent mechanism of ligand discrimination comparable to and potentially with more advantages than traditional proofreading.
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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 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".