Probing the Kinetic Landscape of Transient Peptide−Protein Interactions by Use of Peptide <sup>15</sup>N NMR Relaxation Dispersion Spectroscopy: Binding of an Antithrombin Peptide to Human Prothrombin
Bibliographic record
Abstract
Protein-ligand interactions may lead to the formation of multiple molecular complexes in dynamic exchange, affecting the kinetic and thermodynamic characteristics of the binding equilibrium. We followed the dissociation kinetics of the transient and specific complex of an antithrombotic peptide N-acetyl-Asp(55)-Phe-Glu-Glu-Ile-Pro(60)-Glu-Glu-Tyr-Leu-Gln(65) with human prothrombin by use of (15)N NMR relaxation dispersion spectroscopy of the peptide. Every one of the five (15)N-labeled adjacent residues of the peptide exhibited apparently different kinetic exchange and relaxation behaviors, which were especially evident at different concentrations of prothrombin. Binding-induced (15)N relaxation dispersion of residues Phe(56), Glu(57), Glu(58), and Ile(59) can be fitted phenomenologically to a two-site on-and-off exchange mechanism with physically feasible relaxation and kinetic parameters obtained for residues Phe(56), Glu(58), and Ile(59), independent of the prothrombin concentration. The apparent kinetic parameters of Glu(57) show some dependence on the concentration of prothrombin and the extracted transverse relaxation rate for Glu(57) in the bound state was severalfold higher than that expected for a protein-peptide complex with a size of approximately 72 kDa. In addition, the equilibrium population of the bound peptide obtained for Glu(57) was inconsistent with those for Phe(56), Glu(58), and Ile(59) and with the prothrombin/peptide ratios used in the experiments. These discrepancies can be explained by the presence of two conformations for the peptide-protein complex exchanging at a rate of approximately 100 s(-)(1). In all, our study shows that fast dissociation of protein-peptide complexes can be studied quantitatively using peptide (15)N NMR relaxation dispersion measurements without a precise knowledge of the peptide and protein concentrations. In addition, protein titration was found to improve the accuracy of quantitative analysis and may make it possible to determine the rate of conformational changes within the protein-peptide complex.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".