Common Internal Allosteric Network Links Anesthetic Binding Sites in a Pentameric Ligand-Gated Ion Channel

Spontaneous lipid binding to the nicotinic acetylcholine receptor in a native membrane

The nicotinic acetylcholine receptor (nAChR) and other pentameric ligand-gated ion channels are native to neuronal membranes with an unusual lipid composition. While it is well-established that these receptors can be significantly modulated by lipids, the underlying mechanisms have been primarily studied in model membranes with few lipid species. Here, we use coarse-grained molecular dynamics simulation to probe specific binding of lipids in a complex quasi-neuronal membrane. We ran a total of 50 μs of simulations of a single nAChR in a membrane composed of 36 species of lipids. Competition between multiple lipid species produces a complex distribution. We find that overall, cholesterol selects for concave inter-subunit sites and polyunsaturated fatty acids select for convex M4 sites, while monounsaturated and saturated lipids are unenriched in the nAChR boundary. We propose the "density-threshold affinity" as a metric calculated from continuous density distributions, which reduces to a standard affinity in two-state binding. We find that the density-threshold affinity for M4 weakens with chain rigidity, which suggests that flexible chains may help relax packing defects caused by the conical protein shape. For any site, PE headgroups have the strongest affinity of all phospholipid headgroups, but anionic lipids still yield moderately high affinities for the M4 sites as expected. We observe cooperative effects between anionic headgroups and saturated chains at the M4 site in the inner leaflet. We also analyze affinities for individual anionic headgroups. When combined, these insights may reconcile several apparently contradictory experiments on the role of anionic phospholipids in modulating nAChR.

Structural Basis for a Bimodal Allosteric Mechanism of General Anesthetic Modulation in Pentameric Ligand-Gated Ion Channels

Ion channel modulation by general anesthetics is a vital pharmacological process with implications for receptor biophysics and drug development. Functional studies have implicated conserved sites of both potentiation and inhibition in pentameric ligand-gated ion channels, but a detailed structural mechanism for these bimodal effects is lacking. The prokaryotic model protein GLIC recapitulates anesthetic modulation of human ion channels, and it is accessible to structure determination in both apparent open and closed states. Here, we report ten X-ray structures and electrophysiological characterization of GLIC variants in the presence and absence of general anesthetics, including the surgical agent propofol. We show that general anesthetics can allosterically favor closed channels by binding in the pore or favor open channels via various subsites in the transmembrane domain. Our results support an integrated, multi-site mechanism for allosteric modulation, and they provide atomic details of both potentiation and inhibition by one of the most common general anesthetics.

Mutagenesis computer experiments in pentameric ligand-gated ion channels: the role of simulation tools with different resolution

Pentameric ligand-gated ion channels (pLGICs) are an important class of widely expressed membrane neuroreceptors, which play a crucial role in fast synaptic communications and are involved in several neurological conditions. They are activated by the binding of neurotransmitters, which trigger the transmission of an electrical signal via facilitated ion flux. They can also be activated, inhibited or modulated by a number of drugs. Mutagenesis electrophysiology experiments, with natural or unnatural amino acids, have provided a large body of functional data that, together with emerging structural information from X-ray spectroscopy and cryo-electron microscopy, are helping unravel the complex working mechanisms of these neuroreceptors. Computer simulations are complementing these mutagenesis experiments, with insights at various levels of accuracy and resolution. Here, we review how a selection of computational tools, including first principles methods, classical molecular dynamics and enhanced sampling techniques, are contributing to construct a picture of how pLGICs function and can be pharmacologically targeted to treat the disorders they are responsible for.

Ketamine Inhibition of the Pentameric Ligand-Gated Ion Channel GLIC

Ketamine inhibits pentameric ligand-gated ion channels (pLGICs), including the bacterial pLGIC from Gloeobacter violaceus (GLIC). The crystal structure of GLIC shows R-ketamine bound to an extracellular intersubunit cavity. Here, we performed molecular dynamics simulations of GLIC in the absence and presence of R- or S-ketamine. No stable binding of S-ketamine in the original cavity was observed in the simulations, largely due to its unfavorable access to residue D154, which provides important electrostatic interactions to stabilize R-ketamine binding. Contrary to the symmetric binding shown in the crystal structure, R-ketamine moved away from some of the binding sites and was bound to GLIC asymmetrically at the end of simulations. The asymmetric binding is consistent with the experimentally measured negative cooperativity of ketamine binding to GLIC. In the presence of R-ketamine, all subunits showed changes in structure and dynamics, irrespective of binding stability; the extracellular intersubunit cavity expanded and intersubunit electrostatic interactions involved in channel activation were altered. R-ketamine binding promoted a conformational shift toward closed GLIC. Conformational changes near the ketamine-binding site were propagated to the interface between the extracellular and transmembrane domains, and further to the pore-lining TM2 through two pathways: pre-TM1 and the β1-β2 loop. Both signaling pathways have been predicted previously using the perturbation-based Markovian transmission model. The study provides a structural and dynamics basis for the inhibitory modulation of ketamine on pLGICs.