AI Insight
Researchers have identified a common evolutionary mechanism that governs how all class A G-protein-coupled receptors (GPCRs) become activated, despite their structural diversity. Using machine learning and coevolution analysis applied to all known class A GPCR structures, they developed a mathematical model that describes receptor activation independently of specific sequences. They validated this model through molecular dynamics simulations showing activation transitions in diverse GPCRs, including an orphan receptor (GPR183), and demonstrated they could map how ligands affect receptor activation using the beta-2-adrenergic receptor as an example.
Why it matters
This unified framework enables researchers to study poorly understood and orphan GPCRs by comparing them to well-characterized receptors, potentially accelerating drug discovery since GPCRs are major pharmaceutical targets. The approach provides a blueprint for understanding mechanism commonalities across other protein families beyond GPCRs.
Understand the Science
⚠️ Preprint – Noch nicht peer-reviewed
Dieser Artikel wurde noch nicht von unabhängigen Experten begutachtet. Die Ergebnisse sind vorläufig und sollten mit Vorsicht interpretiert werden.
Cells communicate with their environment by integrating signals, often chemical in nature, triggered by specific molecules bind to specific membrane-bound receptors, resulting in a downstream signaling cascade. Arguably, G-protein-coupled receptors (GPCRs) constitute the most pharmacologically important family of such receptors, binding small molecules, peptides, lipids, and hormones with high specificity. However, despite a highly conserved fold and sequence similarity, GPCRs are still mostly studied on a case-by-case basis. Here, we infer a general, evolutionarily conserved mechanism of class A GPCR activation. By leveraging coevolution and machine learning methods applied to all class A GPCRs structures, we derive a mathematical description (a so-called collective variable – CV) of the receptor’s activation state which is independent of its sequence. Then, we bias molecular dynamics simulations along this CV to obtain transitions between activation states of a diverse set of class A GPCR family members. To demonstrate that our model generalizes beyond GPCRs in our training set, we obtain conformational transitions of an orphan receptor, GPR183. Finally, we show that we can model ligand effect on the receptors by converging Free Energy Surfaces of activation of the {beta}2-adrenergic receptor within this common mechanism framework. These results, to our knowledge, prove for the first time the existence of a mechanism uniting all class A GPCRs. Our approach thus facilitates direct comparisons between receptors and opens up the possibility of structural and dynamical studies of many orphan and understudied GPCRs. It also serves as a blueprint for inferring family-wide protein mechanisms.
Source: Who's driving? Common evolutionary mechanism of activation of class A GPCRs