A novel fusion tool to enable G protein-coupled receptor structure determination

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Authors: Nita R. Shah, Mathieu Oosterlaken, Claudine Bisson, Mattia Bertinelli, Alicia M. Churchill-Angus, Andrew Hutchin, Jola Kopec, Vadim Kotov, Ciaran R. McFarlane, Erika Griss Pascualli, Ana Pavić, Matthias Zebisch
+3 more Cédric Fiez-Vandal, Edoardo Fabini, Stéphanie Duclos
Cited by: 1
References: 54

Abstract

Structure determination of G protein-coupled receptors (GPCRs) plays an important role in accelerating drug development against this medically important protein family. This study outlines the development of a new fusion tool to enable structure determination of GPCRs in inactive conformations by cryo-EM. Initially, a PDB mining approach was applied to select eight naturally occurring proteins with the intention of fusing them into the intracellular loop 3 (ICL3) of GPCRs to create a suitable fiducial marker for cryo-EM workflows. During the selection process, candidates with known high-affinity protein binders were prioritized to enable a further increase in the protein mass of the fiducial marker. Fusion constructs were generated with adenosine receptor A 2A (A 2A R) and were assessed for expression and aggregation levels. For the two best-performing new fusion constructs, ligand binding was characterized to ensure that the fusion tag did not significantly affect protein behaviour. A 2A R with a β-lactamase fusion in ICL3 and binding partner β-lactamase inhibitory protein II (BLIPII) was then selected to solve an antagonist-bound structure. The overall map was resolved to an average of 3.2 Å resolution with continuous helices connecting the β-lactamase to helices 5 and 6 of A 2A R. Focused refinement of the A 2A R region improved the local resolution and map detail in the orthosteric site, thereby allowing confident modelling of the antagonist ligand, ZM241385, which matches previously described X-ray crystallographic structures. This new fusion provides an alternative option for GPCR structure determination, with several potential benefits compared with existing tools, such as a more favourable position relative to the GPCR to reduce potential clashes.

Fields of Study & Subjects

Receptor Mechanisms and Signaling Protein Structure and Dynamics Machine Learning in Bioinformatics

Publication details

Journal / source
Acta Crystallographica Section D Structural Biology
Publisher
International Union of Crystallography (IUCr)
Volume
82
Issue
6
Pages
655–663
Publication date
12 May 2026
Language
EN