Paramagnetic Ligand Tagging To Identify Protein Binding Sites
Bibliographic record
Abstract
Transient biomolecular interactions are the cornerstones of the cellular machinery. The identification of the binding sites for low affinity molecular encounters is essential for the development of high affinity pharmaceuticals from weakly binding leads but is hindered by the lack of robust methodologies for characterization of weakly binding complexes. We introduce a paramagnetic ligand tagging approach that enables localization of low affinity protein-ligand binding clefts by detection and analysis of intermolecular protein NMR pseudocontact shifts, which are invoked by the covalent attachment of a paramagnetic lanthanoid chelating tag to the ligand of interest. The methodology is corroborated by identification of the low millimolar volatile anesthetic interaction site of the calcium sensor protein calmodulin. It presents an efficient route to binding site localization for low affinity complexes and is applicable to rapid screening of protein-ligand systems with varying binding affinity.
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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 | 0.000 | 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 | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".