bioRxivBiophysics
Allosteric effect of nanobody binding on ligand-specific active states of the β2-Adrenergic Receptor
Abstract
Nanobody binding stabilizes the active state of G-protein-coupled receptor (GPCR) and modulates its affinity for bound ligands. However, the atomic level basis for this allosteric regulation remains elusive. Here, we investigate the conformational changes induced by the binding of a nanobody (Nb80) on the active-like {beta}2 adrenergic receptor ({beta}2AR) via enhanced sampling molecular dynamics simulations. Dimensionality reduction analysis shows that Nb80 stabilizes a highly active state of the {beta}2AR with a ~14 [A] outward movement of transmembrane helix 6 and close proximity of transmembrane (TM) helices 5 and 7. This is further supported by the residues located at hotspots located on TMs 5, 6 and 7, as shown by supervised machine learning methods. Besides, ligand-specific subtle differences in the conformations assumed by intercellular loop 2 and extracellular loop 2 are captured from the trajectories of various ligand-bound receptors in the presence of Nb80. Dynamic network analysis further reveals that Nb80 binding can enhance the communication between the binding sites of Nb80 and of the ligand. We identify unique allosteric signal transmission mechanisms between the Nb80-binding site and the extracellular domains in presence of full agonist and G-protein biased partial agonist, respectively. Altogether, our results provide insights into the effect of intracellular binding partners on the GPCR activation mechanism, which could be useful for structure-based drug discovery. TOC O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=153 SRC="FIGDIR/small/451885v1_ufig1.gif" ALT="Figure 1"> View larger version (31K): org.highwire.dtl.DTLVardef@16f8ebforg.highwire.dtl.DTLVardef@104a47dorg.highwire.dtl.DTLVardef@1cb1659org.highwire.dtl.DTLVardef@284699_HPS_FORMAT_FIGEXP M_FIG Graphical Table of Contents C_FIG
- assay: structural biology
- research focus: protein folding & dynamics
- approach: deep learning
- approach: statistical model
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