Docking Simulations of G-Protein Coupled Receptors Uncover Crossover Binding Patterns of Diverse Ligands to Angiotensin, Alpha-Adrenergic and Opioid Receptors: Implications for Cardiovascular Disease and Addiction
Bibliographic record
Abstract
Recent bioassay studies have unexpectedly supported the high (computationally predicted) binding affinities of angiotensin receptor blockers (ARBs) at α-adrenergic receptors (αARs) in isolated smooth muscle. Computational predictions from ligand docking studies are consistent with very low concentrations of ARBs (e.g., sartans or bisartans) that partially reduce (20–50%) the contractile response to phenylephrine, suggesting that some ARBs may function as partial inverse agonists at αARs. Virtual ligand screening (docking) and molecular dynamics (MD) simulations were carried out to explore the binding affinities and stabilities of selected non-peptide ligands (e.g., ARBs and small-molecule opioids) for several G-protein coupled receptor (GPCR) types, including angiotensin II (AngII) type 1 receptor (AT1R), α1AR, α2AR, and μ-(µOR) and ժ-opioid receptors (ժOR). Results: All ligands docked preferentially to the binding pocket on the cell surface domain of the GPCR types investigated. Drug binding was characterized by weak interactions (hydrophobic, hydrogen bonding, pi-pi) and stronger ionic and salt-bridge interactions (cation-pi and cation-anion interactions). Ligands specific to each GPCR category showed considerable cross-binding with alternative GPCRs, with small-molecule medications appearing less selective than their peptide or ARB functional equivalents. ARBs that exhibit higher affinities for AT1R also demonstrate higher affinities for µORs and ժORs than opiate ligands, such as fentanyl and naltrexone. Moreover, ARBs had a higher affinity for αARs than either alpha agonists (epinephrine and phenylephrine) or inhibitors (prazosin and doxazosin). MD simulations of membrane-embedded ARB-GPCR complexes proved stable over nanosecond time scales and suggested that some ARBs may behave as agonists or antagonists depending on the GPCR type. Based on the results presented in this and related investigations, we propose that agonists bind to the resting A-site of GPCRs, while inverse agonists occupy the desensitizing D-site, which partial agonists like morphine and fentanyl share, contributing to addiction. ARBs block both AngII and alpha receptors, suggesting that they are more potent antihypertensive drugs than ACE inhibitors. ARBs have the potential to inhibit morphine tolerance and appear to disrupt receptor desensitization processes, potentially by competing at the D-site. Our results suggest the possible therapeutic potential of ARBs in treating methamphetamine and opiate addictions.
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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".