MS/NMR: A Structure-Based Approach for Discovering Protein Ligands and for Drug Design by Coupling Size Exclusion Chromatography, Mass Spectrometry, and Nuclear Magnetic Resonance Spectroscopy
Bibliographic record
Abstract
A protocol is described for rapidly screening small organic molecules for their ability to bind a target protein while obtaining structure-related information as part of a structure-based drug discovery and design program. The methodology takes advantage of and combines the inherent strengths of size exclusion gel chromatography, mass spectrometry, and NMR to identify bound complexes in a relatively universal high-throughput screening approach. Size exclusion gel chromatography in the spin column format provides the high-speed separation of a protein-ligand complex from free ligands. The spin column eluent is then analyzed under denaturing conditions by electrospray ionization mass spectrometry (MS) for the presence of small molecular weight compounds formerly bound to the protein. Hits identified by MS are then individually assayed by chemical shift perturbations in a 2D 1H-15N HSQC NMR spectrum to verify specific interactions of the compound with the protein and identification of the binding site on the protein. The utility of the MS/NMR assay is demonstrated with the use of the catalytic fragment of human fibroblast collagenase (MMP-1) as a target protein and the screening of a library consisting of approximately 32 000 compounds for the identification of molecules that exhibit specific binding to the RGS4 protein.
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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) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".