A simulation-guided “swapping” protocol for NMR titrations to study protein–protein interactions
Bibliographic record
Abstract
Solution nuclear magnetic resonance (NMR) spectroscopy is a powerful technique for characterizing protein–protein interactions. NMR-monitored titration proves to be effective in determining dissociation constants, particularly for Kd values in the micromolar to millimolar range. In conventional NMR titrations, planning for optimal titration conditions requires a prior estimate of Kd. In addition, a highly concentrated ligand stock solution is often required, posing challenges when the ligand exhibits limited solubility and stability at elevated concentrations. To overcome these constraints, we propose a simulation-guided “swapping” protocol for NMR titrations. Guided by simulations of the binding curves, two samples, one with zero and the other with maximum ligand concentration, but both containing identical protein concentrations, initiate the titration. Using a “swapping” strategy, intermediate ligand concentrations in between those of the two initial samples are generated without the need of a concentrated ligand stock, while maintaining constant protein concentration. More importantly, this protocol facilitates estimation of Kd by early titration points and allows on-the-fly optimization of the titration points. The proposed approach enhances the efficiency of NMR titrations and provides a straightforward means to optimize the experimental conditions for the titrations.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".