An NMR Method for the Determination of Protein-Binding Interfaces Using Dioxygen-Induced Spin−Lattice Relaxation Enhancement
Bibliographic record
Abstract
Using oxygen as a paramagnetic probe, researchers can routinely study topologies and protein-binding interfaces by NMR. The paramagnetic contribution to the amide (1)H spin-lattice relaxation rates (R(1)(P)) have been studied for uniformly (2)H,(15)N-labeled FB protein, a 60-residue three-helix bundle, constituting the B domain of protein A. Through TROSY versions of inversion-recovery experiments, R(1)(P) could be determined. R(1)(P) was then measured in the presence of a stoichiometric equivalent of an unlabeled Fc fragment of immunoglobulin (Ig) G, and the ratio of R(1)(P) of the FB-Fc complex to that of free FB [i.e., R(1)(P)(complex)/R(1)(P)(free)] was determined for each observable residue. Regions of helix I and helix II, which were previously known to interact with Fc, were readily identified as belonging to the binding interface by their characteristically reduced values of R(1)(P)(complex)/R(1)(P)(free). The method of comparing oxygen-induced spin-lattice relaxation rates of free protein and protein-protein complexes, to detect binding interfaces, offers greater sensitivity than chemical shift perturbation, while it is not necessary to heavily deuterate the labeled protein, as is the case in cross saturation experiments.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".