Probing Dynamic Conformations of the High-Molecular-Weight αB-Crystallin Heat Shock Protein Ensemble by NMR Spectroscopy
Bibliographic record
Abstract
Solution- and solid-state nuclear magnetic resonance (NMR) spectroscopy are highly complementary techniques for studying supra-molecular structure. Here they are employed for investigating the molecular chaperone αB-crystallin, a polydisperse ensemble of between 10 and 40 identical subunits with an average molecular mass of approximately 600 kDa. An IxI motif in the C-terminal region of each of the subunits is thought to play a critical role in regulating the size distribution of oligomers and in controlling the kinetics of subunit exchange between them. Previously published solid-state NMR and X-ray results are consistent with a bound IxI conformation, while solution NMR studies provide strong support for a highly dynamic state. Here we demonstrate through FROSTY (freezing rotational diffusion of protein solutions at low temperature and high viscosity) MAS (magic angle spinning) NMR that both populations are present at low temperatures (<0 °C), while at higher temperatures only the mobile state is observed. Solution NMR relaxation dispersion experiments performed under physiologically relevant conditions establish that the motif interchanges between flexible (highly populated) and bound (sparsely populated) states. This work emphasizes the importance of using multiple methods in studies of supra-molecules, especially for highly dynamic ensembles where sample conditions can potentially affect the conformational properties observed.
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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 | 0.000 |
| 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".