On-cell nuclear magnetic resonance spectroscopy to probe cell surface interactions
Bibliographic record
Abstract
Nuclear magnetic resonance (NMR) spectroscopy allows the determination of atomic-level information on intermolecular interactions, molecular structure, and molecular dynamics in the cellular environment. This may be broadly divided into studies focused on obtaining detailed molecular information in the intracellular context ("in-cell") or those focused on characterizing molecules or events at the cell surface ("on-cell"). In this review, we outline some key NMR techniques applied for on-cell NMR studies through both solution- and solid-state NMR and survey studies that have used these techniques to uncover key information. In particular, we focus on the application of on-cell NMR spectroscopy to characterize ligand interactions with cell surface membrane proteins such as G-protein coupled receptors (GPCRs) and receptor tyrosine kinases. These techniques allow for quantification of binding affinities, competitive binding assays, delineation of ligands involved in binding, ligand bound-state conformational determination, evaluation of receptor structuring and dynamics, and inference of distance constraints characteristic of the ligand-receptor bound state. Interestingly, it is possible to avoid the barriers of production and purification of membrane proteins while obtaining directly physiologically relevant information through on-cell NMR. We also provide a brief survey of the applicability of on-cell NMR approaches to other classes of cell surface molecules.
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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 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.006 |
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".