Probing the Free Energy Landscape of the Fast-Folding gpW Protein by Relaxation Dispersion NMR
Bibliographic record
Abstract
The topographic features of the free energy landscapes that govern the thermodynamics and kinetics of conformational transitions in proteins, which in turn are integral for function, are not well understood. This reflects the experimental challenges associated with characterizing these multidimensional surfaces, even for small proteins. Here we focus on a 62-residue protein, gpW, that folds very rapidly into a native structure with an α/β topology in which α-helices are at the N- and C-terminal ends of the molecule with a central β-hairpin positioned orthogonally to the helices. Using relaxation dispersion NMR spectroscopy to probe the conformational fluctuations in gpW at 1 °C, we found that the native state interconverts with a transiently formed, sparsely populated second state with a lifetime of 250 μs, consistent with the global folding-unfolding rate under these conditions. In this low-populated state, the β-hairpin is unfolded whereas the α-helices remain predominantly formed. Our results argue for a hierarchical stability of secondary structural elements and demonstrate the existence of a complex free energy landscape even in this small, fast-folding single-domain protein.
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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".