Structures of invisible, excited protein states by relaxation dispersion NMR spectroscopy
Bibliographic record
Abstract
Molecular function is often predicated on excursions between ground states and higher energy conformers that can play important roles in ligand binding, molecular recognition, enzyme catalysis, and protein folding. The tools of structural biology enable a detailed characterization of ground state structure and dynamics; however, studies of excited state conformations are more difficult because they are of low population and may exist only transiently. Here we describe an approach based on relaxation dispersion NMR spectroscopy in which structures of invisible, excited states are obtained from chemical shifts and residual anisotropic magnetic interactions. To establish the utility of the approach, we studied an exchanging protein (Abp1p SH3 domain)-ligand (Ark1p peptide) system, in which the peptide is added in only small amounts so that the ligand-bound form is invisible. From a collection of (15)N, (1)HN, (13)C(alpha), and (13)CO chemical shifts, along with (1)HN-(15)N, (1)H(alpha)-(13)C(alpha), and (1)HN-(13)CO residual dipolar couplings and (13)CO residual chemical shift anisotropies, all pertaining to the invisible, bound conformer, the structure of the bound state is determined. The structure so obtained is cross-validated by comparison with (1)HN-(15)N residual dipolar couplings recorded in a second alignment medium. The methodology described opens up the possibility for detailed structural studies of invisible protein conformers at a level of detail that has heretofore been restricted to applications involving visible ground states of proteins.
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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 | 0.001 | 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".