Comparison of Dynamics of Extracellular Accesses to the β<sub>1</sub> and β<sub>2</sub> Adrenoceptors Binding Sites Uncovers the Potential of Kinetic Basis of Antagonist Selectivity
Bibliographic record
Abstract
From the molecular mechanism of antagonist unbinding in the β(1) and β(2) adrenoceptors investigated by steered molecular dynamics, we attempt to provide further possibilities of ligand subtype and subspecies selectivity. We have simulated unbinding of β(1)-selective Esmolol and β(2)-selective ICI-118551 from both receptors to the extracellular environment and found distinct molecular features of unbinding. By calculating work profiles, we show different preference in antagonist unbinding pathways between the receptors, in particular, perpendicular to the membrane pathway is favourable in the β(1) adrenoceptor, whereas the lateral pathway involving helices 5, 6 and 7 is preferable in the β(2) adrenoceptor. The estimated free energy change of unbinding based on the preferable pathway correlates with the experimental ligand selectivity. We then show that the non-conserved K347 (6.58) appears to facilitate in guiding Esmolol to the extracellular surface via hydrogen bonds in the β(1) adrenoceptor. In contrast, hydrophobic and aromatic interactions dominate in driving ICI-118551 through the easiest pathway in the β(2) adrenoceptor. We show how our study can stimulate design of selective antagonists and discuss other possible molecular reasons of ligand selectivity, involving sequential binding of agonists and glycosylation of the receptor extracellular surface.
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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 | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".