Structural Insights into Human Acid-sensing Ion Channel 1a Inhibition by Snake Toxin Mambalgin1
Abstract
Acid-sensing ion channels (ASICs) are proton-gated cation channels that are involved in diverse neuronal processes including pain sensing. Peptide toxin Mambalgin1 (Mamba1) from black mamba snake venom can reversibly inhibit the conductance of ASICs, showing an analgesic effect. However, the detailed inhibitory mechanism of Mamba1 on ASIC1s, especially how Mamba1 binding to extracellular domain affects the conformational changes of the transmembrane domain of ASICs remains elusive. Here, we present single-particle cryo-EM structures of human ASIC1a (hASIC1a) and hASIC1a-Mamba1 complex at resolutions of 3.56 and 3.90 Å, respectively. The structures revealed the inhibited conformation of hASIC1a upon Mamba1 binding. The combination of the structural and physiological data indicates that Mamba1 prefers to bind hASIC1a in a closed state and reduces the proton sensitivity of the channel, representing a closed-state trapping mechanism.
Data availability
The EM maps for hASIC1a and hASIC1a-Mamba1 complex have been deposited in EMDB (www.ebi.ac.uk/pdbe/emdb/) with accession codes EMD-30346 and EMD-30347. The atomic coordinates for hASIC1a and hASIC1a-Mamba1 complex have been deposited in the Protein Data Bank (www.rcsb.org) with accession codes 7CFS and 7CFT respectively
Article and author information
Author details
Funding
Ministry of Science and Technology of the People's Republic of China (National Key Research and Development Project,2017YFA0505201,2017YFA0505403 and 2016YFA0400903)
- Changlin Tian
Chinese Academy of Sciences (Queensland-Chinese Academy of Sciences (Q-CAS) Collaborative Science Fund,GJHZ201946)
- Changlin Tian
Ministry of Science and Technology of the People's Republic of China (National Key Research and Development Project,2017YFA0505200)
- Lei Liu
National Natural Science Foundation of China (31600601,21778051)
- Demeng Sun
National Natural Science Foundation of China (91753205,21532004)
- Lei Liu
The funders had no role in study design, data collection and interpretation, or the decision to submit the work for publication.
Copyright
© 2020, Tian 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
-
- 3,615
- views
-
- 658
- downloads
-
- 36
- 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/).