Dissociation kinetics of G proteins from G protein–coupled receptors and effects of allosteric modulation
Bibliographic record
Abstract
G protein–coupled receptors (GPCRs), the largest superfamily of human membrane proteins with >800 members, are primary targets for ~1/3 of all marketed drugs. Recent fluorescence experiments underscored the pivotal role of GPCR–G protein complex lifetime in their coupling efficiency and selectivity. However, these experiments are often expensive, time-consuming, and limited to a small number of GPCR–G protein systems. On the other hand, it is challenging to simulate GPCR–G protein dissociation using molecular dynamics (MD) methods. Here, we have employed Protein–Protein Interaction Gaussian accelerated MD (PPI-GaMD) simulations and experiments to probe the kinetics and pathways of G protein dissociation from GPCRs. For five systems with published experimental kinetic data, PPI-GaMD simulations successfully captured G protein dissociation from the GPCRs, including the adrenergic, adenosine, and muscarinic receptors. The simulations allowed identification of two distinct dissociation pathways and calculation of the G protein dissociation rates, which were in good agreement with experimental data. Additionally, we simulated the effect of positive allosteric modulators (PAMs) of the adenosine A 1 receptor (A 1 R) in Gi protein dissociation and supported simulation findings with bioluminescence resonance energy transfer biosensor experiments evaluating G βγ kinetics following A 1 R activation. A 1 R PAMs were found to strengthen the agonist–receptor and receptor–G protein interactions and significantly reduce dissociation rates of the Gi protein. In summary, complementary PPI-GaMD simulations and kinetic assays have enabled detailed characterization of the kinetics and pathways of G protein dissociation, a critical event in the GPCR signaling cascade, and the effects of GPCR allosteric modulators.
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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 | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".