Proofreading Is Too Noisy For Effective Ligand Discrimination
Bibliographic record
Abstract
Kinetic proofreading (KPR) has been used as a paradigmatic explanation for the high specificity of important biological processes including ligand discrimination by cellular receptors. Kinetic proofreading enhances the difference in the mean receptor occupancy between different ligands, thus potentially enabling better discrimination. On the other hand, proofreading also attenuates the signal, increasing the relative magnitude of noise in the downstream signal. This can interfere with reliable ligand discrimination. To understand the effect of noise on ligand discrimination beyond the comparison of the mean signals, we formulate the task of ligand discrimination as a problem of statistical estimation of the molecular affinity of ligands. Our analysis reveals that proofreading typically worsens ligand resolution which decreases with the number of proofreading steps under most commonly considered conditions. This contrasts with the usual notion that kinetic proofreading universally improves ligand discrimination with additional proofreading steps. Our results are consistent across a variety of different proofreading schemes, suggesting that they are inherent to the KPR mechanism itself rather than any particular model of molecular noise. Based on our results, we suggest alternative roles for kinetic proofreading schemes such as multiplexing and combinatorial encoding in multi-ligand/multi-output pathways.
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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 | 0.007 | 0.044 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.005 | 0.006 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.005 | 0.003 |
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".