Measurement of State-Specific Association Constants in Allosteric Sensors through Molecular Stapling and NMR
Bibliographic record
Abstract
Allostery is a ubiquitous mechanism to control biological function and arises from the coupling of inhibitory and binding equilibria. The extent of coupling reflects the inactive vs active state selectivity of the allosteric effector. Hence, dissecting allosteric determinants requires quantification of state-specific association constants. However, observed association constants are typically population-averages, reporting on overall affinities but not on allosteric coupling. Here we propose a general method to measure state-specific association constants in allosteric sensors based on three key elements, i.e., state-selective molecular stapling through disulfide bridges, competition binding saturation transfer experiments and chemical shift correlation analyses to gauge state populations. The proposed approach was applied to the prototypical cyclic adenosine monophosphate (cAMP)-dependent protein kinase (PKA-RIα), for which the structures of the inactive and active states are available, as needed to design the state-selective disulfide bridges. Surprisingly, the PKA-RIα state-specific association constants are comparable to those of a structurally homologous domain with ∼10(3)-fold lower cAMP-affinity, suggesting that the affinity difference arises primarily from changes in the position of the dynamic apo inhibitory equilibrium.
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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 | 0.001 |
| 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 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".