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  • Title: Interaction networks within disease-associated GαS variants characterized by an integrative biophysical approach.
    Author: Anazia K, Koenekoop L, Ferré G, Petracco E, Gutiérrez-de-Terán H, Eddy MT.
    Journal: J Biol Chem; 2024 Aug; 300(8):107497. PubMed ID: 38925329.
    Abstract:
    Activation of G proteins through nucleotide exchange initiates intracellular signaling cascades essential for life processes. Under normal conditions, nucleotide exchange is regulated by the formation of G protein-G protein-coupled receptor complexes. Single point mutations in the Gα subunit of G proteins bypass this interaction, leading to loss of function or constitutive gain of function, which is closely linked with the onset of multiple diseases. Despite the recognized significance of Gα mutations in disease pathology, structural information for most variants is lacking, potentially due to inherent protein dynamics that pose challenges for crystallography. To address this, we leveraged an integrative spectroscopic and computational approach to structurally characterize seven of the most frequently observed and clinically relevant mutations in the stimulatory Gα subunit, GαS. A previously proposed allosteric model of Gα activation linked structural changes in the nucleotide-binding pocket with functionally important changes in interactions between switch regions. We investigated this allosteric connection in GαS by integrating data from variable temperature CD spectroscopy, which measured changes in global protein structure and stability, and molecular dynamics simulations, which observed changes in interaction networks between GαS switch regions. Additionally, saturation-transfer difference NMR spectroscopy was applied to observe changes in nucleotide interactions with residues within the nucleotide binding site. These data have enabled testing of predictions regarding how mutations in GαS result in loss or gain of function and evaluation of proposed structural mechanisms. The integration of experimental and computational data allowed us to propose a more nuanced classification of mechanisms underlying GαS gain-of-function and loss-of-function mutations.
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