Advances in the application of NMR to the study of GPCRs and ligand–GPCR interactions
Bibliographic record
Abstract
INTRODUCTION: GPCRs are targeted by nearly one-third of FDA-approved drugs and are therefore of great interest in drug discovery pursuits. Nuclear magnetic resonance (NMR) builds upon our understanding of the structural biology of GPCRs by helping to identify dynamic facets of ligand engagement, activation, and G protein coupling, often through the identification of an ensemble. AREAS COVERED: The basic facets of NMR spectroscopy and relaxation experiments (e.g. CPMG, WaterLOGSY, STD) are described as they pertain to the study of structure activity relationships (SAR), ligand-fragment interaction dynamics, and fragment-based drug discovery. This article is based on literature searches that have utilized ISI Web of Knowledge, MEDLINE, and Google Scholar. EXPERT OPINION: The structural biology of GPCRs and their associated complexes are rapidly advancing, particularly via cryoEM techniques which provide high-resolution structures and additional insights into minor states represented into the ensemble. Nevertheless, there is still an important niche for NMR methods to capture in terms of the delineation of detailed and physiologically representative ensembles of functional states and their associated dynamics.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.003 |
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".