Structural insights into hormone recognition by the human glucose-dependent insulinotropic polypeptide receptor
- Fudan University, China
- ShanghaiTech University, China
- Huazhong University of Science and Technology, China
- Shanghai Institute of Materia Medica, China
- Zhejiang University School of Medicine, China
Abstract
Glucose-dependent insulinotropic polypeptide (GIP) is a peptide hormone that exerts crucial metabolic functions by binding and activating its cognate receptor, GIPR. As an important therapeutic target, GIPR has been subjected to intensive structural studies without success. Here, we report the cryo-EM structure of the human GIPR in complex with GIP and a Gs heterotrimer at a global resolution of 2.9 Å. GIP adopts a single straight helix with its N terminus dipped into the receptor transmembrane domain (TMD), while the C-terminus is closely associated with the extracellular domain and extracellular loop 1. GIPR employs conserved residues in the lower half of the TMD pocket to recognize the common segments shared by GIP homologous peptides, while uses non-conserved residues in the upper half of the TMD pocket to interact with residues specific for GIP. These results provide a structural framework of hormone recognition and GIPR activation.
Data availability
Atomic coordinates of the GIP-GIPR-Gs complex have been deposited in the Protein Data Bank under accession code 7DTY and Electron Microscopy Data Bank (EMDB) accession code EMD-30860.All data generated or analysed during this study are included in the manuscript and supporting files. Source data files have been provided for Figure 2, Figure 1-figure supplement 1 and Figure 4-figure supplement 4.
Article and author information
Author details
Funding
National Natural Science Foundation of China (81872915)
- Ming-Wei Wang
Shanghai Municipal Science and Technology Major Project (2019SHZDZX02)
- Eric Xu
Strategic Priority Research Program of Chinese Academy of Sciences (XDB37030103)
- Eric Xu
Shanghai Municipality Science and Technology Development Fund (18430711500)
- Ming-Wei Wang
Novo Nordisk-CAS Research Fund (NNCAS-2017-1-CC)
- Dehua Yang
Shanghai Science and Technology Development Foundation (18ZR1447800)
- Dehua Yang
The Young Innovator Association of CAS (2018325)
- Lihua Zhao
SA-SIBS Scholarship Program
- Dehua Yang
- Lihua Zhao
National Natural Science Foundation of China (32071203)
- Lihua Zhao
National Natural Science Foundation of China (81773792)
- Dehua Yang
National Natural Science Foundation of China (81973373)
- Dehua Yang
National Natural Science Foundation of China (21704064)
- Qingtong Zhou
National Science and Technology Major Project of China (2018ZX09735-001)
- Ming-Wei Wang
National Science and Technology Major Project of China (2018ZX09711002-002-005)
- Dehua Yang
National Key Basic Research Program of China (2018YFA0507000)
- Ming-Wei Wang
Ministry of Science and Technology of the People's Republic of China (2018YFA0507002)
- Eric Xu
The funders had no role in study design, data collection and interpretation, or the decision to submit the work for publication.
Copyright
© 2021, Zhao et al.
This article is distributed under the terms of the Creative Commons Attribution License permitting unrestricted use and redistribution provided that the original author and source are credited.
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Structural insights into hormone recognition by the human glucose-dependent insulinotropic polypeptide receptor
eLife 10:e68719.
https://doi.org/10.7554/eLife.68719
Further reading
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Under physiological conditions, proteins continuously undergo structural fluctuations on different timescales. Some conformations are only sparsely populated, but still play a key role in protein function. Thus, meaningful structure–function frameworks must include structural ensembles rather than only the most populated protein conformations. To detail protein plasticity, modern structural biology combines complementary experimental and computational approaches. In this review, we survey available computational approaches that integrate sparse experimental data from electron paramagnetic resonance spectroscopy with molecular modeling techniques to derive all-atom structural models of rare protein conformations. We also propose strategies to increase the reliability and improve efficiency using deep learning approaches, thus advancing the field of integrative structural biology.
