bioRxivBiophysics
Energy landscapes reveal agonist control of GPCR activation via microswitches
Abstract
Agonist binding to G protein-coupled receptors (GPCRs) leads to conformational changes in the transmembrane region that activate cytosolic signaling pathways. Al-though high resolution structures of different receptor states are available, atomistic details of the allosteric signalling across the membrane remain elusive. We calculated free energy landscapes of the {beta}2 adrenergic receptors activation using atomistic molecular dynamics simulations in an optimized string of swarms framework, which sheds new light on how microswitches govern the equilibrium between conformational states. Contraction of the extracellular binding site in the presence of the agonist BI-167107 is obligatorily coupled to conformational changes in a connector motif located in the core of the transmembrane region. The connector is probabilistically coupled to the conformation of the intracellular region. An active connector promotes desolvation of a buried cavity, a twist of the conserved NPxxY motif, and an interaction between two conserved tyrosines in transmembrane helices 5 and 7 (Y-Y motif), which leads to a larger population of active-like states at the G protein binding site. This coupling is augmented by protonation of the strongly conserved Asp792.50. The agonist binding site hence communicates with the intracellular region via a cascade of locally connected microswitches. Characterization of these can be used to understand how ligands stabilize distinct receptor states and contribute to development drugs with specific signaling properties. The developed simulation protocol is likely transferable to other class A GPCRs. Graphical TOC Entry O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=82 SRC="FIGDIR/small/627026v3_ufig1.gif" ALT="Figure 1"> View larger version (24K): org.highwire.dtl.DTLVardef@1dfcfb5org.highwire.dtl.DTLVardef@bdaf81org.highwire.dtl.DTLVardef@14d74e6org.highwire.dtl.DTLVardef@35cea9_HPS_FORMAT_FIGEXP M_FIG C_FIG
- assay: structural biology
- research focus: protein folding & dynamics
- approach: deep learning
- approach: statistical model
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