Voltage-gated sodium (Nav) channels play critical roles in propagating action potentials and otherwise manipulating ionic gradients in excitable cells. These channels open in response to membrane depolarization, selectively permeating sodium ions until rapidly inactivating. Structural characterization of the gating cycle in this channel family has proved challenging, particularly due to the transient nature of the open state. A structure from the bacterium Magnetococcus marinus Nav (NavMs) was initially proposed to be open, based on its pore diameter and voltage-sensor conformation. However, the functional annotation of this model, and the structural details of the open state, remain disputed. In this work, we used molecular modeling and simulations to test possible open-state models of NavMs. The full-length experimental structure, termed here the cc-model, was consistently dehydrated at the activation gate, indicating an inability to conduct ions. Based on a spontaneous transition observed in extended simulations, and sequence/structure comparison to other Nav channels, we built an alternative p-model featuring a helix transition and the rotation of a conserved asparagine residue into the activation gate. Pore hydration, ion permeation, and state-dependent drug binding in this model were consistent with an open functional state. This work thus offers both a functional annotation of the full-length NavMs structure and a detailed model for a stable Nav open state, with potential conservation in diverse ion-channel families.

Despite the sequence homology between acid-sensing ion channels (ASICs) and epithelial sodium channel (ENaCs), these channel families display very different functional characteristics. Whereas ASICs are gated by protons and show a relatively low degree of selectivity for sodium over potassium, ENaCs are constitutively active and display a remarkably high degree of sodium selectivity. To decipher if some of the functional diversity originates from differences within the transmembrane helices (M1 and M2) of both channel families, we turned to a combination of computational and functional interrogations, using statistical coupling analysis and mutational studies on mouse ASIC1a. The coupling analysis suggests that the relative position of M1 and M2 in the upper part of the pore domain is likely to remain constant during the ASIC gating cycle, whereas they may undergo relative movements in the lower part. Interestingly, our data suggest that to account for coupled residue pairs being in close structural proximity, both domain-swapped and nondomain-swapped ASIC M2 conformations need to be considered. Such conformational flexibility is consistent with structural work, which suggested that the lower part of M2 can adopt both domain-swapped and nondomain-swapped conformations. Overall, mutations to residues in the middle and lower pore were more likely to affect gating and/or ion selectivity than those in the upper pore. Indeed, disrupting the putative interaction between a highly conserved Trp/Glu residue pair in the lower pore is detrimental to gating and selectivity, although this interaction might occur in both domain-swapped and nonswapped conformations. Finally, our results suggest that the greater number of larger, aromatic side chains in the ENaC M2 helix may contribute to the constitutive activity of these channels at a resting pH. Together, the data highlight differences in the transmembrane domains of these closely related ion channels that may help explain some of their distinct functional properties.

Biomolecular simulations are intrinsically high dimensional and generate noisy data sets of ever-increasing size. Extracting important features from the data is crucial for understanding the biophysical properties of molecular processes, but remains a big challenge. Machine learning (ML) provides powerful dimensionality reduction tools. However, such methods are often criticized as resembling black boxes with limited human-interpretable insight. We use methods from supervised and unsupervised ML to efficiently create interpretable maps of important features from molecular simulations. We benchmark the performance of several methods, including neural networks, random forests, and principal component analysis, using a toy model with properties reminiscent of macromolecular behavior. We then analyze three diverse biological processes: conformational changes within the soluble protein calmodulin, ligand binding to a G protein-coupled receptor, and activation of an ion channel voltage-sensor domain, unraveling features critical for signal transduction, ligand binding, and voltage sensing. This work demonstrates the usefulness of ML in understanding biomolecular states and demystifying complex simulations.

Pulsed electric fields are increasingly used in medicine to transiently increase the cell membrane permeability via electroporation to deliver therapeutic molecules into the cell. One type of event that contributes to this increase in membrane permeability is the formation of pores in the membrane lipid bilayer. However, electrophysiological measurements suggest that membrane proteins are affected as well, particularly voltage-gated ion channels (VGICs). The molecular mechanisms by which the electric field could affects these molecules remain unidentified. In this study, we used molecular dynamics simulations to unravel the molecular events that take place in different VGICs when exposing them to electric fields mimicking electroporation conditions. We show that electric fields can induce pores in the voltage-sensor domains (VSDs) of different VGICs and that these pores form more easily in some channels than in others. We demonstrate that poration is more likely in VSDs that are more hydrated and are electrostatically more favorable for the entry of ions. We further show that pores in VSDs can expand into socalled complex pores, which become stabilized by lipid headgroups. Our results suggest that such complex pores are considerably more stable than conventional lipid pores, and their formation can lead to severe unfolding of VSDs from the channel. We anticipate that such VSDs become dysfunctional and unable to respond to changes in transmembrane voltage, which is in agreement with previous electrophysiological measurements showing a decrease in the voltage-dependent transmembrane ionic currents after pulse treatment. Finally, we discuss the possibility of activation of VGICs by submicrosecond-duration pulses. Overall, our study reveals a new, to our knowledge, mechanism of electroporation through membranes containing VGICs.

Genetically encoded (GE) contrast agents detectable by magnetic resonance imaging (MRI) enable non-invasive visualization of gene expression and cell proliferation at virtually unlimited penetration depths. Using hyperpolarized Xe-129 in combination with chemical exchange saturation transfer, an MR contrast approach known as hyper-CEST, enables ultrasensitive protein detection and biomolecular imaging. GE MRI contrast agents developed to date include nanoscale proteinaceous gas vesicles as well as the monomeric bacterial proteins TEM-1 beta-lactamase (bla) and maltose binding protein (MBP). To improve understanding of hyper-CEST NMR with proteins, structural and computational studies were performed to further characterize the Xe-bla interaction. X-ray crystallography validated the location of a high-occupancy Xe binding site predicted by MD simulations, and mutagenesis experiments confirmed this Xe site as the origin of the observed CEST contrast. Structural studies and MD simulations with representative bla mutants offered additional insight regarding the relationship between local protein structure and CEST contrast.

In contrast to most voltage-gated ion channels, hyperpolarization- and cAMP gated (HCN) ion channels open on hyperpolarization. Structure-function studies show that the voltagesensor of HCN channels are unique but the mechanisms that determine gating polarity remain poorly understood. All-atom molecular dynamics simulations (similar to 20 mu s) of HCN1 channel under hyperpolarization reveals an initial downward movement of the S4 voltage-sensor but following the transfer of last gating charge, the S4 breaks into two sub-helices with the lower sub-helix becoming parallel to the membrane. Functional studies on bipolar channels show that the gating polarity strongly correlates with helical turn propensity of the substituents at the breakpoint. Remarkably, in a proto-HCN background, the replacement of breakpoint serine with a bulky hydrophobic amino acid is sufficient to completely flip the gating polarity from inward to outward-rectifying. Our studies reveal an unexpected mechanism of inward rectification involving a linker sub-helix emerging from HCN S4 during hyperpolarization.

Voltage-sensitive membrane proteins are united by their ability to transform changes in membrane potential into mechanical work. They are responsible for a spectrum of physiological processes in living organisms, including electrical signaling and cell-cycle progression. Although the mechanism of voltage-sensing has been well characterized for some membrane proteins, including voltage-gated ion channels, even the location of the voltage-sensing elements remains unknown for others. Moreover, the detection of these elements by using experimental techniques is challenging because of the diversity of membrane proteins. Here, we provide a computational approach to predict voltage-sensing elements in any membrane protein, independent of its structure or function. It relies on an estimation of the propensity of a protein to respond to changes in membrane potential. We first show that this property correlates well with voltage sensitivity by applying our approach to a set of voltage-sensitive and voltage-insensitive membrane proteins. We further show that it correctly identifies authentic voltage-sensitive residues in the voltage-sensor domain of voltage-gated ion channels. Finally, we investigate six membrane proteins for which the voltage-sensing elements have not yet been characterized and identify residues and ions that might be involved in the response to voltage. The suggested approach is fast and simple and enables a characterization of voltage sensitivity that goes beyond mere identification of charges. We anticipate that its application before mutagenesis experiments will significantly reduce the number of potential voltage-sensitive elements to be tested. 

Viral potassium channels (Kcv) are homologous to the pore module of complex -selective ion channels of cellular organisms. Due to their relative simplicity, they have attracted interest towards understanding the principles of conduction and channel gating. In this work, we construct a homology model of the open state, which we validate by studying the binding of known blockers and by monitoring ion conduction through the channel. Molecular dynamics simulations of this model reveal that the re-orientation of selectivity filter carbonyl groups coincides with the transport of potassium ions, suggesting a possible mechanism for fast gating. In addition, we show that the voltage sensitivity of this mechanism can originate from the relocation of potassium ions inside the selectivity filter. We also explore the interaction of with the surrounding bilayer and observe the binding of lipids in the area between two adjacent subunits. The model is available to the scientific community to further explore the structure/function relationship of Kcv channels.