Molecular Dynamics Simulations Reveal Fundamental Role of Water As Factor Determining Affinity of Binding of β-Blocker Nebivolol to β<sub>2</sub>-Adrenergic Receptor
Bibliographic record
Abstract
The beta-adrenergic antagonists (beta-blockers) constitute a class of drugs that have well-established roles in treatments of various cardiovascular diseases. Despite a 50 year history, there are two clinically important subtypes of beta-adrenergic receptors (betaARs) called beta(1)AR and beta(2)AR that still are promising drug targets. Our study maps the interactions between nebivolol-one of the most efficient beta-blocking agents-and the beta(2)-adrenergic receptor by simulating two optical isomers of nebivolol: ssss-nebivolol and srrr-nebivolol. The srrr-configuration binds preferentially to beta(1)AR and beta(2)AR. The ssss-form has much lower binding affinity to both of them. Our work indicates that water is a very important component of the binding site of the beta(2)AR receptor. We found that the higher stereoselectivity of the srrr-configuration is due to interactions with water molecules, which extensively hydrate the binding site of beta(2)AR. By lowering the energy of binding, water enhanced the affinity of the srrr-form to beta(2)AR. We also address the problem of beta(1)AR/beta(2)AR selectivity. At higher concentrations, all beta-blocking agents lose their specificity and bind nonselectively, causing many adverse effects. Our simulations indicate that PHE194, TYR308, and ILE309 of the beta(2)AR and the corresponding residues of the beta(1)AR receptor may be important determinants of beta(1)AR versus beta(2)AR selectivity.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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 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".