TREK-2 (KCNK10/K2P10), a two-pore domain potassium (K2P) channel, is gated by multiple stimuli such as stretch, fatty acids, and pH and by several drugs. However, the mechanisms that control channel gating are unclear. Here we present crystal structures of the human TREK-2 channel (up to 3.4 angstrom resolution) in two conformations and in complex with norfluoxetine, the active metabolite of fluoxetine (Prozac) and a state-dependent blocker of TREK channels. Norfluoxetine binds within intramembrane fenestrations found in only one of these two conformations. Channel activation by arachidonic acid and mechanical stretch involves conversion between these states through movement of the pore-lining helices. These results provide an explanation for TREK channel mechanosensitivity, regulation by diverse stimuli, and possible off-target effects of the serotonin reuptake inhibitor Prozac.

No abstract

Cell membranes are complex multicomponent systems, which are highly heterogeneous in the lipid distribution and composition. To date, most molecular simulations have focussed on relatively simple lipid compositions, helping to inform our understanding of in vitro experimental studies. Here we describe on simulations of complex asymmetric plasma membrane model, which contains seven different lipids species including the glycolipid GM3 in the outer leaflet and the anionic lipid, phosphatidylinositol 4,5-bisphophate (PIP2), in the inner leaflet. Plasma membrane models consisting of 1500 lipids and resembling the in vivo composition were constructed and simulations were run for 5 µs. In these simulations the most striking feature was the formation of nano-clusters of GM3 within the outer leaflet. In simulations of protein interactions within a plasma membrane model, GM3, PIP2, and cholesterol all formed favorable interactions with the model α-helical protein. A larger scale simulation of a model plasma membrane containing 6000 lipid molecules revealed correlations between curvature of the bilayer surface and clustering of lipid molecules. In particular, the concave (when viewed from the extracellular side) regions of the bilayer surface were locally enriched in GM3. In summary, these simulations explore the nanoscale dynamics of model bilayers which mimic the in vivo lipid composition of mammalian plasma membranes, revealing emergent nanoscale membrane organization which may be coupled both to fluctuations in local membrane geometry and to interactions with proteins.

G-protein-coupled receptors (GPCRs) are involved in many physiological processes and are therefore key drug targets. Although detailed structural information is available for GPCRs, the effects of lipids on the receptors, and on downstream coupling of GPCRs to G proteins are largely unknown. Here we use native mass spectrometry to identify endogenous lipids bound to three class A GPCRs. We observed preferential binding of phosphatidylinositol-4,5-bisphosphate (PtdIns(4,5)P) over related lipids and confirm that the intracellular surface of the receptors contain hotspots for PtdIns(4,5)P binding. Endogenous lipids were also observed bound directly to the trimeric Gαβγ protein complex of the adenosine A receptor (AR) in the gas phase. Using engineered Gα subunits (mini-Gα mini-Gα and mini-Gα), we demonstrate that the complex of mini-Gα with the β adrenergic receptor (βAR) is stabilized by the binding of two PtdIns(4,5)P molecules. By contrast, PtdIns(4,5)P does not stabilize coupling between βAR and other Gα subunits (mini-Gα or mini-Gα) or a high-affinity nanobody. Other endogenous lipids that bind to these receptors have no effect on coupling, highlighting the specificity of PtdIns(4,5)P. Calculations of potential of mean force and increased GTP turnover by the activated neurotensin receptor when coupled to trimeric Gαβγ complex in the presence of PtdIns(4,5)P provide further evidence for a specific effect of PtdIns(4,5)P on coupling. We identify key residues on cognate Gα subunits through which PtdIns(4,5)P forms bridging interactions with basic residues on class A GPCRs. These modulating effects of lipids on receptors suggest consequences for understanding function, G-protein selectivity and drug targeting of class A GPCRs.

Potassium channels enable K(+) ions to move passively across biological membranes. Multiple nanosecond-duration molecular dynamics simulations (total simulation time 5 ns) of a bacterial potassium channel (KcsA) embedded in a phospholipid bilayer reveal motions of ions, water, and protein. Comparison of simulations with and without K(+) ions indicate that the absence of ions destabilizes the structure of the selectivity filter. Within the selectivity filter, K(+) ions interact with the backbone (carbonyl) oxygens, and with the side-chain oxygen of T75. Concerted single-file motions of water molecules and K(+) ions within the selectivity filter of the channel occur on a 100-ps time scale. In a simulation with three K(+) ions (initially two in the filter and one in the cavity), the ion within the central cavity leaves the channel via its intracellular mouth after approximately 900 ps; within the cavity this ion interacts with the Ogamma atoms of two T107 side chains, revealing a favorable site within the otherwise hydrophobically lined cavity. Exit of this ion from the channel is enabled by a transient increase in the diameter of the intracellular mouth. Such "breathing" motions may form the molecular basis of channel gating.

A dimeric structure of the sodium–proton antiporter NhaA provides insight into the roles of Asp163 and Lys300 in the transport mechanism.

1. Introduction 4751.1 Ion channels 4751.1.1 Gramicidin 4761.1.2 Helix bundle channels 4771.1.3 K channels 4801.1.4 Porins 4831.1.5 Nicotinic acetylcholine receptor 4831.1.6 Physiological properties 4831.2 Simulations 4841.2.1 Atomistic versus mean-field simulations 4842. Atomistic simulations 4852.1 Modelling of ion-interaction parameters 4852.1.1 Interatomic distances and the problem of ionic radii 4862.1.2 Solvation energy 4872.1.3 Hydration shells and coordination numbers 4892.1.4 Parameters in common use and transferability 4912.1.5 Summary 4912.2 Water in pores versus bulk 4912.2.1 Simple pore models 4942.2.2 gA 4952.2.3 Alm 4962.2.4 LS36 (and LS24) 4962.2.5 Nicotinic receptor M2δ5 4972.2.6 Influenza A M2 4972.2.7 K channels 4972.2.8 nAChR 4982.2.9 Porins 4982.2.10 Relevance 4992.2.11 Problems with simulations 5012.3 Dynamics of ions in pores 5032.3.1 Simple pore models 5032.3.2 Helix bundles 5042.3.3 gA and KcsA 5052.4 Energetics of permeation and ion selectivity 5092.4.1 Potential and free energy profiles 5092.4.2 gA 5102.4.3 α-Helix bundles 5112.4.4 KcsA 5122.4.5 Ion selectivity 5142.4.6 Problems of estimating energetic profiles 5152.5 Conformational changes 5162.5.1 gA 5162.5.2 Alm and LS3 5162.5.3 KcsA 5172.6 Protonation states 5233. Coarse-grained simulations 5243.1 Introduction 5243.1.1 Predicting conductance magnitudes 5253.2 Electro-diffusion: the Nernst–Planck approach 5263.2.1 Calculating the potential profile from Poisson and PB theory 5283.2.2 Calculating the potential profile from BD simulations 5303.2.3 Combining Nernst–Planck and Poisson: PNP 5303.3 Beyond PNP 5323.4 BD simulations 5323.4.1 Basic theory in ion channels 5323.4.2 Incorporating the environment 5333.5 Applications 5353.5.1 Model systems 5353.5.1.1 Solving the Poisson and PB equation for channel-like geometries 5353.5.1.2 Comparing PB, PNP and BD 5363.5.2 Applications to known structures 5373.5.2.1 gA 5373.5.2.2 Porin 5393.5.2.3 LS3 5403.5.2.4 Alm 5423.5.2.5 nAChR 5423.5.2.6 KcsA 5433.6 pKa calculations 5433.7 Selectivity 5443.7.1 Anion/cation selectivity 5453.7.2 Monovalent/divalent ion selectivity 5454. Problems 5464.1 Atomistic simulations 5464.1.1 Problems 5464.1.2 Parameters 5484.2 BD 5494.3 Mean-field simulations 5495. Conclusions 5505.1 Progress 5505.2 The future 5506. Acknowledgements 5517. References 551Ion channels are proteins that form ‘holes’ in membranes through which selected ions move passively down their electrochemical gradients. The ions move quickly, at (nearly) diffusion limited rates (ca. 107 ions s−1 per channel). Ion channels are central to many properties of cell membranes. Traditionally they have been the concern of neuroscientists, as they control the electrical properties of the membranes of excitable cells (neurones, muscle; Hille, 1992). However, it is evident that ion channels are present in many types of cell, not all of which are electrically excitable, from diverse organisms, including plants, bacteria and viruses (where they are involved in functions such as cell homeostasis) in addition to animals. Thus ion channels are of general cell biological importance. They are also of biomedical interest, as several dizeases (‘channelopathies’) have been described which are caused by changes in properties of a specific ion channel (Ashcroft, 2000). Moreover, passive diffusion channels for substances other than ions are common (porins, aquaporins), as are active membrane transport processes coupled to ion gradients or ATP hydrolysis. An understanding of ion channels may also provide a gateway to understanding these processes.

A rapid and easy-to-use method of predicting the conductance of an ion channel from its three-dimensional structure is presented. The method combines the pore dimensions of the channel as measured in the HOLE program with an Ohmic model of conductance. An empirically based correction factor is then applied. The method yielded good results for six experimental channel structures (none of which were included in the training set) with predictions accurate to within an average factor of 1.62 to the true values. The predictive r2 was equal to 0.90, which is indicative of a good predictive ability. The procedure is used to validate model structures of alamethicin and phospholamban. Two genuine predictions for the conductance of channels with known structure but without reported conductances are given. A modification of the procedure that calculates the expected results for the effect of the addition of nonelectrolyte polymers on conductance is set out. Results for a cholera toxin B-subunit crystal structure agree well with the measured values. The difficulty in interpreting such studies is discussed, with the conclusion that measurements on channels of known structure are required.