A solution NMR study showing that active site ligands and nucleotides directly perturb the allosteric equilibrium in aspartate transcarbamoylase
Bibliographic record
Abstract
The 306-kDa aspartate transcarbamoylase is a well studied regulatory enzyme, and it has emerged as a paradigm for understanding allostery and cooperative binding processes. Although there is a consensus that the cooperative binding of active site ligands follows the Monod-Wyman-Changeux (MWC) model of allostery, there is some debate about the binding of effectors such as ATP and CTP and how they influence the allosteric equilibrium between R and T states of the enzyme. In this article, the binding of substrates, substrate analogues, and nucleotides is studied, along with their effect on the R-T equilibrium by using highly deuterated, (1)H,(13)C-methyl-labeled protein in concert with methyl-transverse relaxation optimized spectroscopy (TROSY) NMR. Although only the T state of the enzyme can be observed in spectra of wild-type unliganded aspartate transcarbamoylase, binding of active-site substrates shift the equilibrium so that correlations from the R state become visible, allowing the equilibrium constant (L') between ligand-saturated R and T forms of the enzyme to be measured quantitatively. The equilibrium constant between unliganded R and T forms (L) also is obtained, despite the fact that the R state is "invisible" in spectra, by means of an indirect process that makes use of relations that emerge from the fact that ligand binding and the R-T equilibrium are linked. Titrations with MgATP unequivocally establish that its binding directly perturbs the R-T equilibrium, consistent with the Monod-Wyman-Changeux model. This study emphasizes the utility of modern solution NMR spectroscopy in understanding protein function, even for systems with aggregate molecular masses in the hundreds of kilodaltons.
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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 | 0.000 | 0.000 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 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".