Cryo-EM structures of human ZnT8 in both outward- and inward-facing conformations
Abstract
ZnT8 is a Zn2+/H+ antiporter that belongs to SLC30 family and plays an essential role in regulating Zn2+ accumulation in the insulin secretory granules of pancreatic β cells. Dysfunction of ZnT8 is associated with both type 1 and 2 diabetes. However, the Zn2+/H+ exchange mechanism of ZnT8 remains unclear due to the lack of high-resolution structures. Here, we report the cryo-EM structures of human ZnT8 (HsZnT8) in both outward- and inward-facing conformations. HsZnT8 forms a dimeric structure with four Zn2+ binding sites within each subunit: a highly conserved primary site in transmembrane domain (TMD) housing the Zn2+ substrate; an interfacial site between TMD and C-terminal domain (CTD) that modulates the Zn2+ transport activity of HsZnT8; and two adjacent sites buried in the cytosolic domain and chelated by conserved residues from CTD and the His-Cys-His (HCH) motif from the N-terminal segment of the neighboring subunit. A comparison of the outward- and inward-facing structures reveals that the TMD of each HsZnT8 subunit undergoes a large structural rearrangement, allowing for alternating access to the primary Zn2+ site during the transport cycle. Collectively, our studies provide the structural insights into the Zn2+/H+ exchange mechanism of HsZnT8.
Data availability
The Cryo-EM maps of HsZnT8 determined in three different conditions will have been deposited in the Electron Microscopy Data Bank and. The corresponding atomic coordinates will be deposited to the RCSB Protein Data Bank, with the entry ID: EMD-22285 and PDB 6XPD for the structure of HsZnT8-DM, EMD-22286 and PDB 6XPE for the structure of HsZnT8-WT in the presence of zinc, and EMD-22287 and PDB 6XPF for the structure of HsZnT8-WT in the absence of zinc. Source data files have been provided for Figure 2h and 2i.
-
Structural basis for the autoregulation of the zinc transporter YiiPElectron Microscopy Data Bank, 3h90.
-
CryoEM Structure of the Zinc Transporter YiiP from helical crystalsElectron Microscopy Data Bank, 5vrf.
Article and author information
Author details
Funding
National Institute of General Medical Sciences (R01GM136976)
- Xiao-chen Bai
Welch Foundation (I-1944)
- Xiao-chen Bai
Cancer Prevention and Research Institute of Texas (RP160082)
- Xiao-chen Bai
Howard Hughes Medical Institute
- Youxing Jiang
National Institute of General Medical Sciences (GM079179)
- Youxing Jiang
Welch Foundation (I-1578)
- Youxing Jiang
The funders had no role in study design, data collection and interpretation, or the decision to submit the work for publication.
Copyright
© 2020, Xue 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.
Metrics
-
- 5,288
- views
-
- 938
- downloads
-
- 54
- citations
Views, downloads and citations are aggregated across all versions of this paper published by eLife.
Download links
Downloads (link to download the article as PDF)
Open citations (links to open the citations from this article in various online reference manager services)
Cite this article (links to download the citations from this article in formats compatible with various reference manager tools)
Further reading
-
- Structural Biology and Molecular Biophysics
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.
-
- Structural Biology and Molecular Biophysics
Experimental detection of residues critical for protein–protein interactions (PPI) is a time-consuming, costly, and labor-intensive process. Hence, high-throughput PPI-hot spot prediction methods have been developed, but they have been validated using relatively small datasets, which may compromise their predictive reliability. Here, we introduce PPI-hotspotID, a novel method for identifying PPI-hot spots using the free protein structure, and validated it on the largest collection of experimentally confirmed PPI-hot spots to date. We explored the possibility of detecting PPI-hot spots using (i) FTMap in the PPI mode, which identifies hot spots on protein–protein interfaces from the free protein structure, and (ii) the interface residues predicted by AlphaFold-Multimer. PPI-hotspotID yielded better performance than FTMap and SPOTONE, a webserver for predicting PPI-hot spots given the protein sequence. When combined with the AlphaFold-Multimer-predicted interface residues, PPI-hotspotID yielded better performance than either method alone. Furthermore, we experimentally verified several PPI-hotspotID-predicted PPI-hot spots of eukaryotic elongation factor 2. Notably, PPI-hotspotID can reveal PPI-hot spots not obvious from complex structures, including those in indirect contact with binding partners. PPI-hotspotID serves as a valuable tool for understanding PPI mechanisms and aiding drug design. It is available as a web server (https://ppihotspotid.limlab.dnsalias.org/) and open-source code (https://github.com/wrigjz/ppihotspotid/).