Structural determinants of voltage-gating properties in calcium channels
- Medical University Innsbruck, Austria
- University of Innsbruck, Austria
Abstract
Voltage-gated calcium channels control key functions of excitable cells, like synaptic transmission in neurons and the contraction of heart and skeletal muscles. To accomplish such diverse functions, different calcium channels activate at different voltages and with distinct kinetics. To identify the molecular mechanisms governing specific voltage-sensing properties we combined structure modeling, mutagenesis, and electrophysiology to analyze the structures, free energy, and transition kinetics of the activated and resting states of two functionally distinct voltage-sensing domains (VSDs) of the eukaryotic calcium channel CaV1.1. Both VSDs displayed the typical features of the sliding helix model; however, they greatly differed in ion-pair formation of the outer gating charges. Specifically, stabilization of the activated state enhanced the voltage-dependence of activation, while stabilization of resting states slowed the kinetics. This mechanism provides a mechanistic model explaining how specific ion-pair formation in separate VSDs can realize the characteristic gating properties of voltage-gated cation channels.
Data availability
All data generated or analysed during this study are included in the manuscript and supporting files. The pdb structures of the models of the activated and the resting states of both the WT VSDs and the mutants are available from the Dryad server https://doi.org/10.5061/dryad.hhmgqnkfd.
-
Structural determinants of voltage-gating properties in calcium channelsDryad Digital Repository, 10.5061/dryad.hhmgqnkfd.
Article and author information
Author details
Monica L Fernández-Quintero
Bernhard E Flucher
Funding
Austrian Science Fund (P30402)
- Bernhard E Flucher
Austrian Science Fund (DOC30)
- Bernhard E Flucher
Austrian Science Fund (T855)
- Marta Campiglio
The funders had no role in study design, data collection and interpretation, or the decision to submit the work for publication.
Reviewing Editor
- Toby W Allen, RMIT University, Australia
Version history
- Received: October 16, 2020
- Accepted: March 29, 2021
- Accepted Manuscript published: March 30, 2021 (version 1)
- Version of Record published: May 5, 2021 (version 2)
Copyright
© 2021, Fernández-Quintero 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
-
- 3,857
- views
-
- 489
- downloads
-
- 19
- citations
Views, downloads and citations are aggregated across all versions of this paper published by eLife.
Download links
A two-part list of links to download the article, or parts of the article, in various formats.
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)
Structural determinants of voltage-gating properties in calcium channels
eLife 10:e64087.
https://doi.org/10.7554/eLife.64087
Further reading
-
- Neuroscience
Subpopulations of neurons in the subthalamic nucleus have distinct activity patterns that relate to the three hypotheses of the Drift Diffusion Model.
-
- Neuroscience
The placebo and nocebo effects highlight the importance of expectations in modulating pain perception, but in everyday life we don’t need an external source of information to form expectations about pain. The brain can learn to predict pain in a more fundamental way, simply by experiencing fluctuating, non-random streams of noxious inputs, and extracting their temporal regularities. This process is called statistical learning. Here, we address a key open question: does statistical learning modulate pain perception? We asked 27 participants to both rate and predict pain intensity levels in sequences of fluctuating heat pain. Using a computational approach, we show that probabilistic expectations and confidence were used to weigh pain perception and prediction. As such, this study goes beyond well-established conditioning paradigms associating non-pain cues with pain outcomes, and shows that statistical learning itself shapes pain experience. This finding opens a new path of research into the brain mechanisms of pain regulation, with relevance to chronic pain where it may be dysfunctional.
