NMR Spectroscopy and Protein Structure Determination: Applications to Drug Discovery and Development
Bibliographic record
Abstract
Recent technological advances in NMR methods and instrumentation are having a significant impact in structural biology. These innovations are also impacting pharmaceutical biotechnology as it is now possible to use NMR spectroscopy to rapidly characterize a growing number of prospective protein drugs and protein drug targets. This review provides a general summary of how solution-state NMR can be used to determine protein structures. It also focuses on exploring how advances in solution state NMR are changing the way in which protein structures can be determined and protein-ligand interactions can be characterized. Recent innovations in protein sample preparation, in instrumentation and data collection, in spectral assignment and in structure generation are highlighted. The impact of solution-state NMR on pharmaceutical biotechnology is also discussed, with a special emphasis on describing how NMR has been used to study a number of pharmaceutically important proteins and how NMR is currently being used to rapidly screen and to map the binding sites of small molecules to a range of protein targets.
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.005 |
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".