Toward direct determination of conformations of protein building units from multidimensional NMR experiments. V. NMR chemical shielding analysis of N‐formyl‐serinamide, a model for polar side‐chain containing peptides
Bibliographic record
Abstract
Knowledge of chemical shift-structure relationships could greatly facilitate the NMR chemical shift assignment and structure refinement processes that occur during peptide/protein structure determination via NMR spectroscopy. To determine whether such correlations exist for polar side chain containing amino acid residues the serine dipeptide model, For-L-Ser-NH(2), was studied. Using the GIAO-RHF/6-31+G(d) and GIAO-RHF/TZ2P levels of theory the NMR chemical shifts of all hydrogen ((1)H(N), (1)H(alpha), (1)H(beta1), (1)H(beta2)), carbon ((13)C(alpha), (13)C(beta), (13)C') and nitrogen ((15)N) atoms have been computed for all 44 stable conformers of For-L-Ser-NH(2). An attempt was made to establish correlation between chemical shift of each nucleus and the major conformational variables (omega(0), phi, psi, omega(1), chi,(1) and chi(2)). At both levels of theory a linear correlation can be observed between (1)H(alpha)/phi, (13)C(alpha)/phi, and (13)C(alpha)/psi. These results indicate that the backbone and side-chain structures of For-L-Ser-NH(2) have a strong influence on its chemical shifts.
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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) | 0.000 | 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 | 0.001 | 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".