Temperature dependence of NMR chemical shifts: Tracking and statistical analysis
Why this work is in the frame
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Bibliographic record
Abstract
Abstract Isotropic chemical shifts measured by solution nuclear magnetic resonance (NMR) spectroscopy offer extensive insights into protein structure and dynamics. Temperature dependences add a valuable dimension; notably, the temperature dependences of amide proton chemical shifts are valuable probes of hydrogen bonding, temperature‐dependent loss of structure, and exchange between distinct protein conformations. Accordingly, their uses include structural analysis of both folded and disordered proteins, and determination of the effects of mutations, binding, or solution conditions on protein energetics. Fundamentally, these temperature dependences result from changes in the local magnetic environments of nuclei, but correlations with global thermodynamic parameters measured via calorimetric methods have been observed. Although the temperature dependences of amide proton and nitrogen chemical shifts are often well approximated by a linear model, deviations from linearity are also observed and may be interpreted as evidence of fast exchange between distinct conformational states. Here, we describe computational methods, accessible via the Shift‐T web server, including an automated tracking algorithm that propagates initial (single temperature) 1 H 15 N cross peak assignments to spectra collected over a range of temperatures. Amide proton and nitrogen temperature coefficients (slopes determined by fitting chemical shift vs. temperature data to a linear model) are subsequently calculated. Also included are methods for the detection of systematic, statistically significant deviation from linearity (curvature) in the temperature dependences of amide proton chemical shifts. The use and utility of these methods are illustrated by example, and the Shift‐T web server is freely available at http://meieringlab.uwaterloo.ca/shiftt .
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 it