Domain- and state-specific shape of the electric field tunes voltage sensing in voltage-gated sodium channels.

The ability to sense transmembrane voltage underlies most physiological roles of voltage-gated sodium (Nav) channels. Whereas the key role of their voltage-sensing domains (VSDs) in channel activation is well established, the molecular underpinnings of voltage coupling remain incompletely understood. Voltage-dependent energetics of the activation process can be described in terms of the gating charge that is defined by coupling of charged residues to the external electric field. The shape of the electric field within VSDs is therefore crucial for the activation of voltage-gated ion channels. Here, we employed molecular dynamics simulations of cardiac Nav1.5 and bacterial NavAb, together with our recently developed tool g_elpot, to gain insights into the voltage-sensing mechanisms of Nav channels via high-resolution quantification of VSD electrostatics. In contrast to earlier low-resolution studies, we found that the electric field within VSDs of Nav channels has a complex isoform- and domain-specific shape, which prominently depends on the activation state of a VSD. Different VSDs vary not only in the length of the region where the electric field is focused but also differ in their overall electrostatics, with possible implications in the diverse ion selectivity of their gating pores. Due to state-dependent field reshaping, not only translocated basic but also relatively immobile acidic residues contribute significantly to the gating charge. In the case of NavAb, we found that the transition between structurally resolved activated and resting states results in a gating charge of 8e, which is noticeably lower than experimental estimates. Based on the analysis of VSD electrostatics in the two activation states, we propose that the VSD likely adopts a deeper resting state upon hyperpolarization. In conclusion, our results provide an atomic-level description of the gating charge, demonstrate diversity in VSD electrostatics, and reveal the importance of electric-field reshaping for voltage sensing in Nav channels.

Molecular mechanisms of ion conduction and ion selectivity in TMEM16 lipid scramblases.

TMEM16 lipid scramblases transport lipids and also operate as ion channels with highly variable ion selectivities and various physiological functions. However, their molecular mechanisms of ion conduction and selectivity remain largely unknown. Using computational electrophysiology simulations at atomistic resolution, we identified the main ion-conductive state of TMEM16 lipid scramblases, in which an ion permeation pathway is lined by lipid headgroups that directly interact with permeating ions in a voltage polarity-dependent manner. We found that lipid headgroups modulate the ion-permeability state and regulate ion selectivity to varying degrees in different scramblase isoforms, depending on the amino-acid composition of the pores. Our work has defined the structural basis of ion conduction and selectivity in TMEM16 lipid scramblases and uncovered the mechanisms responsible for the direct effects of membrane lipids on the conduction properties of ion channels.

g_elpot: A Tool for Quantifying Biomolecular Electrostatics from Molecular Dynamics Trajectories.

Electrostatic forces drive a wide variety of biomolecular processes by defining the energetics of the interaction between biomolecules and charged substances. Molecular dynamics (MD) simulations provide trajectories that contain ensembles of structural configurations sampled by biomolecules and their environment. Although this information can be used for high-resolution characterization of biomolecular electrostatics, it has not yet been possible to calculate electrostatic potentials from MD trajectories in a way allowing for quantitative connection to energetics. Here, we present g_elpot, a GROMACS-based tool that utilizes the smooth particle mesh Ewald method to quantify the electrostatics of biomolecules by calculating potential within water molecules that are explicitly present in biomolecular MD simulations. g_elpot can extract the global distribution of the electrostatic potential from MD trajectories and measure its time course in functionally important regions of a biomolecule. To demonstrate that g_elpot can be used to gain biophysical insights into various biomolecular processes, we applied the tool to MD trajectories of the P2X3 receptor, TMEM16 lipid scramblases, the secondary-active transporter GltPh, and DNA complexed with cationic polymers. Our results indicate that g_elpot is well suited for quantifying electrostatics in biomolecular systems to provide a deeper understanding of its role in biomolecular processes.