Probing nascent structures in peptides using natural abundance <sup>13</sup>C NMR relaxation and reduced spectral density mapping
Bibliographic record
Abstract
The main chain motional properties for a series of peptides that appear to have preferred conformations in solution have been systematically studied using solution-state nuclear magnetic resonance spectroscopy. The series of peptides were derived from the N-termini of pro-inflammatory chemokine proteins and HoxB1, a transcriptional regulator. As an unstructured control, a ten residue peptide was designed, synthesized, and found to be minimally structured from solution NMR data. The dynamic properties of the main chain for the peptides were assessed through longitudinal and transverse main chain (13)Calpha relaxation rates and the heteronuclear nuclear Overhauser effect. Motional parameters were interpreted using reduced spectral density mapping and compared with those derived from an extended Lipari-Szabo model in which the rotational correlation time was calculated for each main chain site of the peptide. Comparison of spectral density and Lipari-Szabo analyses for the peptides to those of the unstructured control peptide reveals significant differences in the dynamic behavior of the peptides. The amplitude of picosecond to nanosecond timescale motions for the main chain is observed to decrease for all of the chemokine peptides and HoxB1 over the regions that show partial structure at low temperatures. Comparatively, changes in picosecond to nanosecond timescale motions for the unstructured control peptide show no correlation with sequence position. These results indicate that there are distinguishable low temperature motional differences between an intrinsically unstructured peptide and peptides that have an inherent propensity to structure.
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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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".