Application of <sup>15</sup>N-Edited <sup>1</sup>H–<sup>13</sup>C Correlation NMR Spectroscopy─Toward Fragment-Based Metabolite Identification and Screening via HCN Constructs
Bibliographic record
Abstract
Many key building blocks of life contain nitrogen moieties. Despite the prevalence of nitrogen-containing metabolites in nature, 15 N nuclei are seldom used in NMR-based metabolite assignment due to their low natural abundance and lack of comprehensive chemical shift databases. However, with advancements in isotope labeling strategies, 13 C and 15 N enriched metabolites are becoming more common in metabolomic studies. Simple multidimensional nuclear magnetic resonance (NMR) experiments that correlate 1 H and 15 N via single bond 1 J NH or multiple bond 2–3 J NH couplings using heteronuclear single quantum coherence (HSQC) or heteronuclear multiple bond coherence are well established and routinely applied for structure elucidation. However, a 1 H– 15 N correlation spectrum of a metabolite mixture can be difficult to deconvolute, due to the lack of a 15 N specific database. In order to bridge this gap, we present here a broadband 15 N-edited 1 H– 13 C HSQC NMR experiment that targets metabolites containing 15 N moieties. Through this approach, nitrogen-containing metabolites, such as amino acids, nucleotide bases, and nucleosides, are identified based on their 13 C, 1 H, and 15 N chemical shift information. This approach was tested and validated using a [ 15 N, 13 C] enriched Daphnia magna (water flea) metabolite extract, where the number of clearly resolved 15 N-containing peaks increased from only 11 in a standard HSQC to 51 in the 15 N-edited HSQC, and the number of obscured peaks decreased from 59 to just 7. The approach complements the current repertoire of NMR techniques for mixture deconvolution and holds considerable potential for targeted metabolite NMR in 15 N, 13 C enriched 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 | 0.000 | 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 | 0.001 |
| 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".