Developing an understanding of the mechanism of voltage-gated ion channels in molecular terms requires knowledge of the structure of the active and resting conformations. Although the active-state conformation is known from x-ray structures, an atomic resolution structure of a voltage-dependent ion channel in the resting state is not currently available. This has motivated various efforts at using computational modeling methods and molecular dynamics (MD) simulations to provide the missing information. A comparison of recent computational results reveals an emerging consensus on voltage-dependent gating from computational modeling and MD simulations. This progress is highlighted in the broad context of preexisting work about voltage-gated channels.
"A central pore module composed of the pore-forming S5, P, and S6 transmembrane segments from four homologous subunits or domains (Fig. 4A, left, blue) is surrounded by four symmetrically arranged voltage-sensing modules containing the S1–S4 transmembrane segments (Fig. 4A, left, green) connected by the S4–S5 linkers (Fig. 4A, left, red). Current structure-function models indicate that positive gating charges at intervals of three amino acid residues in the S4 transmembrane segment in each voltage-sensing module move outward under the influence of the electric field and initiate opening of the activation gate at the intracellular end of the pore by exerting a torque on the inner end of the pore-lining S6 segments (Catterall, 2010; Vargas et al., 2012; Yarov-Yarovoy et al., 2012). The structure of Na V Ab captures the preopen state—all voltage sensors are activated while the pore remains closed but poised to open (Payandeh et al., 2011). "
[Show abstract][Hide abstract] ABSTRACT: Allosteric interactions play vital roles in metabolic processes and signal transduction and, more recently, have become the focus of numerous pharmacological studies because of the potential for discovering more target-selective chemical probes and therapeutic agents. In addition to classic early studies on enzymes, there are now examples of small molecule allosteric modulators for all superfamilies of receptors encoded by the genome, including ligand- and voltage-gated ion channels, G protein-coupled receptors, nuclear hormone receptors, and receptor tyrosine kinases. As a consequence, a vast array of pharmacologic behaviors has been ascribed to allosteric ligands that can vary in a target-, ligand-, and cell-/tissue-dependent manner. The current article presents an overview of allostery as applied to receptor families and approaches for detecting and validating allosteric interactions and gives recommendations for the nomenclature of allosteric ligands and their properties.
"In particular, the chick homolog of Ci-VSP appears to provide a scaffold that lead to VSFPs with faster kinetics (Han et al., 2013; St-Pierre et al., 2014). The advantage of chimeras between Ci-VSP and Kv potassium channel subunits, as introduced and employed here, is that Kv channels are among the best studied membrane proteins with a wealth of structural understanding (Villalba-Galea et al., 2008; Vargas et al., 2012). This will likely instigate a more rational approach in the fine-tuning of the voltage sensing portion of GEVIs. "
[Show abstract][Hide abstract] ABSTRACT: Deciphering how the brain generates cognitive function from patterns of electrical signals is one of the ultimate challenges in neuroscience. To this end, it would be highly desirable to monitor the activities of very large numbers of neurons while an animal engages in complex behaviors. Optical imaging of electrical activity using genetically encoded voltage indicators (GEVIs) has the potential to meet this challenge. Currently prevalent GEVIs are based on the voltage-sensitive fluorescent protein (VSFP) prototypical design or on the voltage-dependent state transitions of microbial opsins. We recently introduced a new VSFP design in which the voltage-sensing domain (VSD) is sandwiched between a fluorescence resonance energy transfer pair of fluorescent proteins (termed VSFP-Butterflies) and also demonstrated a series of chimeric VSD in which portions of the VSD of Ciona intestinalis voltage-sensitive phosphatase are substituted by homologous portions of a voltage-gated potassium channel subunit. These chimeric VSD had faster sensing kinetics than that of the native Ci-VSD. Here, we describe a new set of VSFPs that combine chimeric VSD with the Butterfly structure. We show that these chimeric VSFP-Butterflies can report membrane voltage oscillations of up to 200 Hz in cultured cells and report sensory evoked cortical population responses in living mice. This class of GEVIs may be suitable for imaging of brain rhythms in behaving mammalians.
[Show abstract][Hide abstract] ABSTRACT: Quantitative structure-based modeling of voltage activation of ion channels is very challenging. For example, it is very hard to reach converging results, by microscopic simulations while macroscopic treatments involve major uncertainties regarding key features. The current work overcomes some of the above challenges by using our recently developed coarse-grained (CG) model in simulating the activation of the Kv1.2 channel. The CG model has allowed us to explore problems that cannot be fully addressed at present by microscopic simulations, while providing insights on some features that are not usually considered in continuum models, including the distribution of the electrolytes between the membrane and the electrodes during the activation process and thus the physical nature of the gating current. Here, we demonstrate that the CG model yields realistic gating charges and free energy landscapes that allow us to simulate the fluctuating gating current in the activation processes. Our ability to simulate the time dependence of the fast gating current allows us to reproduce the observed trend and provides a clear description of its relationship to the landscape involved in the activation process.
Proceedings of the National Academy of Sciences 01/2014; 111(6). DOI:10.1073/pnas.1324014111 · 9.67 Impact Factor
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