Studying “Invisible” Excited Protein States in Slow Exchange with a Major State Conformation
Bibliographic record
Abstract
Ever since its initial development, solution NMR spectroscopy has been used as a tool to study conformational exchange. Although many systems are amenable to relaxation dispersion approaches, cases involving highly skewed populations in slow chemical exchange have, in general, remained recalcitrant to study. Here an experiment to detect and characterize "invisible" excited protein states in slow exchange with a visible ground-state conformation (excited-state lifetimes ranging from ∼5 to 50 ms) is presented. This method, which is an adaptation of the chemical exchange saturation transfer (CEST) magnetic resonance imaging experiment, involves irradiating various regions of the spectrum with a weak B(1) field while monitoring the effect on the visible major-state peaks. The variation in major-state peak intensities as a function of frequency offset and B(1) field strength is quantified to obtain the minor-state population, its lifetime, and excited-state chemical shifts and line widths. The methodology was validated with (15)N CEST experiments recorded on an SH3 domain-ligand exchanging system and subsequently used to study the folding transition of the A39G FF domain, where the invisible unfolded state has a lifetime of ∼20 ms. Far more accurate exchange parameters and chemical shifts were obtained than via analysis of Carr-Purcell-Meiboom-Gill relaxation dispersion data.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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".