bioRxivBiophysicsPreprint
A Rapid and Universal Pipeline for High-Resolution GPCR Structure Determination through In Silico Construct Optimization and de novo Protein Design
Asato KojimaiD, Kouki KawakamiiD, Naoya KobayashiiD, Kazuhiro KobayashiiD, Toshiki E. MatsuiiD, Kohei Uemoto, Yuzhong GuiD, Masahiro FukudaiD, Hideaki E. KATOiD
Abstract
G protein-coupled receptors (GPCRs) are critical regulators of human physiology and major drug targets. Although structural studies have provided valuable insights, determining GPCR structures remains challenging, especially for inactive state receptors. Recent advances in cryo-electron microscopy (cryo-EM) have enabled structural determination of small GPCRs by using fusion partner proteins and binders to increase molecular weight. However, current methods require extensive experimental screening of fusion constructs. Widely adopted strategies, such as BRIL-Fab complexes, also face limitations due to inherent flexibility. Here, we introduce a streamlined and universal pipeline that integrates an in silico fusion construct screening program, NOAH (NOAH: NOn-experimental, AI-assisted High-throughput construct screening), with a de novo designed fusion protein called ARK1 (ARtificially-designed fiducial marKer). We validate the efficacy of NOAH by determining the structures of the vasopressin V2 receptor (V2R) bound to the clinical antagonist tolvaptan and the partial agonist OPC51803, as well as the bradykinin B2 receptor (B2R) bound to the clinical antagonist icatibant, thereby elucidating their activation and deactivation mechanisms. Furthermore, we demonstrate the capability of NOAH-ARK1 by solving the tolvaptan-bound V2R structure at higher resolution and showcase the methods versatility by determining the structure of lysophosphatidic acid receptor 2 (LPA2) bound to the antagonist Ki16425. This approach eliminates the need for time-consuming and labor-intensive construct optimization, providing a rapid and widely applicable solution for high-resolution GPCR structure determination and drug discovery.
- organism: human
- assay: cryo-EM
- research focus: protein structure prediction
- approach: deep learning
- approach: experimental assay
- Citations
- 2
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