Relaxation-optimized correlation spectroscopy ROCSY for assigning 1H or 13C spin systems in large proteins
Bibliographic record
Abstract
• Relaxation-optimized correlation spectroscopy, ROCSY. • ROCSY minimizes NMR signal decay for J-coupled spins in large systems. • ROCSY is the most efficient way to transfer 1 H– 1 H or 13 C– 13 C magnetization. • Hard pulses separated by delays keep magnetization along z-axis as long as possible. • Magnetization transfer occurs via strong J coupling and homonuclear NOE. Solution NMR studies of large systems are hampered by rapid signal decay. We hereby introduce ROCSY (relaxation-optimized total correlation spectroscopy), which maximizes transfer efficiency across J-coupling-connected spin networks by minimizing the amount of time magnetization spends in the transverse plane. Hard pulses are substituted into the Clean-CITY TOCSY pulse element first developed by Ernst and co-workers, allowing for longer delays in which magnetization is aligned along the z-axis. This has the following consequences: 1) transverse relaxation is minimized, 2) resonance offset effects are minimized, 3) and through-space nuclear Overhauser enhancement (NOE) adds to J-coupling-mediated magnetization transfer. The major drawbacks of the technique are additional heat generation produced by the hard pulses and complication of analysis by the additional through-space NOE magnetization transfer. We demonstrate H alpha -to-HN correlations not possible using conventional 1 H-TOCSY in a 15 N-enriched sample of PagP (a 161-residue integral membrane protein) in dodecylphosphocholine detergent micelles. We also demonstrate enhanced signal-to-noise compared to 13 C-TOCSY in a 15 N, 13 C-enriched sample of cardiac troponin C N-terminal domain. We thus propose that ROCSY can be used to boost signal in any protein NMR experiment that utilizes TOCSY, with greater enhancements seen in higher molecular weight systems.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".