Probing allosteric interactions in homo-oligomeric molecular machines using solution NMR spectroscopy
Bibliographic record
Abstract
Developments in solution NMR spectroscopy have significantly impacted the biological questions that can now be addressed by this methodology. By means of illustration, we present here a perspective focusing on studies of a number of molecular machines that are critical for cellular homeostasis. The role of NMR in elucidating the structural dynamics of these important molecules is emphasized, focusing specifically on intersubunit allosteric communication in homo-oligomers. In many biophysical studies of oligomers, allostery is inferred by showing that models specifically including intersubunit communication best fit the data of interest. Ideally, however, experimental studies focusing on one subunit of a multisubunit system would be performed as an important complement to the more traditional bulk measurements in which signals from all components are measured simultaneously. Using an approach whereby asymmetric molecules are prepared in concert with NMR experiments focusing on the structural dynamics of individual protomers, we present examples of how intersubunit allostery can be directly observed in high-molecular-weight protein systems. These examples highlight some of the unique roles of solution NMR spectroscopy in studies of complex biomolecules and emphasize the important synergy between NMR and other atomic resolution biophysical methods.
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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 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".