Human Molecular Genetics, 2002, Vol. 11, No. 20 2425-2433
© 2002 Oxford University Press
Ion channels: structural bioinformatics and modelling
Laboratory of Molecular Biophysics, Department of Biochemistry, University of Oxford, South Parks Road, Oxford, OX1 3QU, UK
Received July 3, 2002; Accepted July 22, 2002
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ABSTRACT |
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 TOP ABSTRACT K CHANNELSnAChRCONCLUSIONSREFERENCES |
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Ion channels are membrane proteins of key physiological and pharmacological importance. As is the case for many integral membrane proteins, X-ray structures are known for a few bacterial channels, yet structures of human homologues are required for analysis of channel-associated diseases and for drug design. Homology modelling can be used to help remedy this deficit. In combination with molecular dynamics simulations and associated calculations, modelling provides a powerful approach to understanding structure/function relationships in human ion channels. Modelling techniques have been applied to two classes of potassium channels: voltage-gated (Kv) and inward rectifier (Kir) channels. Kir channel models, based on the structure of the bacterial channel KcsA, have been used as a starting point for detailed simulation studies that have increased our understanding of ion permeation and selectivity mechanisms. The transmembrane domain of GluR0, a bacterial homologue of mammalian glutamate receptors, also may be modelled using the KcsA structure as a template. Models of the nicotinic acetylcholine receptor may be constructed in a modular fashion. The snail acetylcholine-binding protein provides a template for the extracellular ligand-binding domain. The transmembrane pore region can be modelled on the basis of NMR structures of the pore-lining M2 helix.
Ion channels comprise a major functional class of membrane proteins. They form pores in biological membranes through which selected inorganic ions pass rapidly (at near-diffusion rates, 
108 ions s-1 through a single channel). Channels are central to the function of excitable cells (1), for example the nervous system and the heart (2), yet are also present in non-excitable cells and in organisms including bacteria, yeast and plants in which they undertake a diversity of physiological roles. Mutations in ion channels are associated with a number of diseases of, for example, the nervous system and the cardiac system [channelopathies (3)]. Ion channels are also of interest as a major class of targets for novel drugs (4).
Ion channels are integral membrane proteins. Membrane proteins are thought to account for 30% of genes; thus there may be at least 10 000 membrane proteins encoded in the human genome. Despite the large number of membrane proteins and their evident biological importance, we remain relatively ignorant of their three-dimensional structures. High-resolution structures are know for only 40 membrane proteins (see http://blanco.biomol.uci.edu/membrane_proteins_xtal.html for a summary), and of these structures, only 5 are of human or mammalian membrane proteins. This small number reflects problems of overexpression and crystallization of membrane proteins. In recent years, a new paradigm for the study of biomedically important human membrane proteins has emerged, whereby bacterial genomes are searched to find homologues of human proteins that are more amenable to structural studies. This strategy has yielded structures of two bacterial potassium channels [KcsA (5) and MthK (6)], two chloride channels (7) and two ABC transporters (8,9). It has also led to identification of a bacterial homologue of mammalian ionotropic glutamate receptors (10).
Whilst this approach is powerful, and has yielded significant data for ion channels, it does not yield structures of mammalian channel proteins. Here we review how a combination of structural bioinformatics, homology modelling and molecular simulations can be used to generate and evaluate models of homologues of bacterial ion channels. We shall illustrate this via two examples: (i) homology models of the transmembrane (TM) pore-forming domains of potassium channels and related channels based on the structure of the bacterial K channel KcsA; and (ii) models of the extracellular ligand-binding domain of the nicotinic acetylcholine receptor (nAChR) based on the structure of a water-soluble binding protein.
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K CHANNELS |
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TOPABSTRACTK CHANNELSnAChRCONCLUSIONSREFERENCES |
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Potassium (K) channels comprise a large family of ion channels. They are selective for potassium ions over sodium ions and share a common core topology and fold. They play a range of physiological roles, concerned with maintaining a negative voltage inside cells relative to outside. K channels may be classified according to their gating mechanism, i.e. the control of opening and closing of the channel. For example, voltage-gated potassium (Kv) channels respond to changes in TM voltage, whereas inward rectifier potassium (Kir) channels are gated by interactions with other membrane proteins and intracellular ligands.
All K channels share the same core topology and structure (Fig. 1A and B). They differ in the presence or absence of additional TM helices, and of additional non-membrane domains and/or subunits that control their gating. The channel-forming core is composed of two TM helices separated by a re-entrant loop made up of a short pore (P) helix plus a more extended region of polypeptide that forms the selectivity filter (F).
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The structure of the K-channel core domain was revealed by X-ray diffraction studies of KcsA (5) and more recently of a bacterial calcium-activated K channel, MthK (6). That region of the sequence associated with the selectivity filter (i.e. the P and F regions) is highly conserved between different K channels (Fig. 1C). The M1 and M2 helices (called S5 and S6 in Kv channels) are always present, but their level of sequence similarity can be quite low between different K channels. In addition to K channels per se, it is thought that the basic architecture and filter sequence is conserved in more distant homologues, such as GluR0 (10). The latter is a K+-selective glutamate-receptor channel whose extracellular glutamate-binding domain is structurally homologous to that of mammalian ionotropic glutamate receptor channels (11–13).
The filter region of K channels contains a highly conserved sequence motif (TVGYG; Fig. 1C). The conformation adopted by this motif in the tetrameric channel protein is essential to the permeation and selectivity mechanism of the channel (5,14,15). There is also a conserved glycine residue in the M2 helix (G99 in KcsA; Fig. 1C) that plays an important role in helix flexibility during channel gating (16,17).
Based on the structure of KcsA and alignment of the KcsA and ‘target’ channel sequences, it is possible to construct homology models of the pore-forming core domains of a range of K and related channels. However, in order to proceed beyond ‘click and go’ modelling, we must generate accurate homology models that can be verified and refined by comparison with experimental data. This requires careful attention to initial sequence alignments, alongside the use of structural bioinformatics analyses and biomolecular simulations to evaluate and refine initial models. In this way, it is possible to arrive at an optimal homology model that may be used in further investigations of structure/function relations. In particular, such models may be used to examine the relationship between channel structure and open-channel properties (e.g. conductance and selectivity), and to aid the design of drugs intended to selectively block ion channels (18).
Kv channels
There have been a number of studies of homology models of Kv channels (18–20), especially of the Shaker Kv channel from Drosophila (the most intensively investigated member of this channel family). These studies have focused on the core pore-forming domain, since structural templates are not available for the outer ring of 4x4=16 TM helices that surround this domain and control channel gating. For example, Ranatunga et al. (21) generated a homology model of the pore domain of the Shaker Kv channel using KcsA as a template structure. The model was in agreement with mutagenesis and sequence variability data. A number of structural features were shown to be conserved between KcsA and Shaker: a ring of tryptophan side-chains on the outer surface of the pore domain at the extracellular end of the helix bundle; and rings of acidic sidechains at the extracellular mouth of the channel (Fig. 2). One of these rings, formed by four aspartic acid (D447) side-chains at the mouth of the Shaker pore, was shown by pKA calculations to be incompletely ionized at neutral pH. The potential energy profile for a K+ ion moved along the central axis of the Shaker pore domain model selectivity filter revealed a shallow well, the depth of which is modulated by the ionization state of the D447 ring.
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In a somewhat different approach, Rauer et al. (18) used homology models of two channels in T lymphocytes—a Kv channel (Kv1.3) and a calcium-activated channel (IKCa1)—to investigate the molecular basis of the specificity of channel–toxin interactions via docking calculations and mutagenesis studies. Their studies were of particular importance in providing strong evidence for topological similarity of the external vestibules of diverse K channels and suggesting that structure-based strategies may be used to design selective blockers of mammalian K channels.
Kir channels
A second class of K channels that have been studied in some detail by homology modelling is the inward rectifier (Kir) family. The Kirs have the simplest transmembrane topology of the mammalian K channels, which they share with KcsA (i.e. two TM helices plus an intervening P and F loop), but have extensive cytoplasmic domains N- and C-terminal to the transmembrane domain. Kir channel functions include, for example, regulation of cardiac electrical activity (Kir3 subfamily) and of insulin release (Kir6.2) (22). A number of groups have generated homology models of Kir channels, either as a component of experimental studies of channel structure–function studies (23) or for more detailed theoretical analyses.
Capener et al. (24) generated a homology model of the pore-forming domain of Kir6.2, a component of an ATP-sensitive K channel. The lipid-exposed and pore-lining surfaces of the model were shown to be compatible with the known features of membrane proteins and of Kir channels respectively. The Kir6.2 homology model was used as the starting point for nanosecond-duration molecular dynamics (MD) simulations in a solvated phospholipid bilayer. Such simulations allow one to explore the short-timescale mobility of ions and water molecules within the channel, and thus provide additional insights into permeation and selectivity mechanisms (25–27). Analysis of the interactions of the Kir6.2 channel model with K+ ions and water molecules during these simulations suggested that there was a concerted single-file motion of K+ ions and water through the selectivity filter. This is similar to such motion observed in simulations of KcsA (28,29). This suggests that a single-filing mechanism for K+ ion permeation is conserved between different K-channel species. Comparison of Kir6.2 and KcsA simulations suggested a degree of flexibility in the filter, thus complicating models of ion selectivity based upon a rigid filter. A subsequent study (30) explored the conformational dynamics of the Kir6.2 model structure under different simulation conditions. Structural drift from the initial model was small, and there was little effect of simulation conditions on this drift. In a long (10 ns) simulation, it was shown that significant conformational changes were restricted to the water-exposed loops, whilst the core transmembrane structure remained unchanged. A flexibility gradient was observed in the channel molecule along the channel axis, with flexibility increasing from the extracellular to the intracellular end. This correlates with biological function, ion selectivity being located in the filter at the extracellular end of the molecule, whereas gating is thought to be located at the intracellular mouth of the channel. The latter is supported by a recent study (23) combining experimental (chemical modification of introduced cysteines) and homology modelling approaches.
Although Kir channels share the same TM topology as the bacterial channel KcsA, it has been suggested (31) that the two classes of protein may not share the same three-dimensional fold in terms of the packing of the M1 and M2 helices. Homology models based on two alternative sequence alignments for Kir6.2 relative to KcsA were compared in 10 ns MD simulations of both models (embedded in a membrane-mimetic octane slab) (32). The more KcsA-like model revealed significantly less structural drift over the course of the simulation. We therefore conclude that Kir and KcsA share the same TM domain fold. This is supported by the recent identification of a bacterial homologue of mammalian Kir channels (33) that provides an unambiguous alignment between the KcsA and Kir sequences. Furthermore, the alignment used by Capener et al. (24) in generating their initial model preserves the conserved glycine residue (G99 in KcsA; Fig. 1C) that has been suggested to play a key role in the gating of K channels (16).
Models of mutant Kir6.2 channels have been to probe conductance mechanisms at the atomic level. Simulations of three mutant Kir channels (modelled on the previously studied Kir6.2 wild type) were compared with wild-type Kir simulations. These mutations have been shown experimentally to reduce K+ conductance levels (34). In simulations, these mutations resulted in small but significant differences in the conformations of the selectivity filter region (35). In particular, the V127T mutation (located just before the selectivity filter and responsible for the greatest reduction in channel conductance relative to the wild-type channel) resulted in a high frequency of a ‘flipped’ conformation of the selectivity filter. In this conformation, ion permeation is prevented, since some of the carbonyl oxygens that would normally form ion-binding sites are directed away from the pore. Thus the time-averaged behaviour of this mutant channel would be predicted to result in a decrease in conductance, which is exactly what is observed.
GluR0—a distant homologue?
It has been suggested that the TM pore-forming core domain of ionotropic glutamate receptors may resemble an inverted K-channel pore domain (36–38). This was supported by studies (10) in which a 
-BLAST search starting from a mammalian GluR yielded a bacterial protein (subsequently named GluR0) that shared significant sequence identity in its TM domain with KcsA (Fig. 1C), and that was subsequently shown to form glutamate-activated K+-selective channels. It has also been demonstrated that the three-dimensional structure of the extracellular glutamate-binding domain of GluR0 has a similar fold to that of rat GluR2 (13).
Based on the homology of the TM domain of GluR0 with that of KcsA, Arinaminpathy et al. (39) generated a homology model for the TM region of GluR0 based on the recent higher-resolution X-ray structure of KcsA (15). Two initial models were generated, based on slightly different (in the loop/P-helix region) sequence alignments. On the basis of initial analysis of two MD simulations, the model that gave the lowest structural drift over 6 ns (corresponding to the alignment shown in Fig. 1C) was selected for further analysis. Comparison of the distribution of charged residues at the mouth close to the filter of the channel (i.e. the intracellular mouth in GluRs and the extracellular mouth in K channels) between GluR0 and KcsA indicates a similarity between the models (Fig. 2). Over the course of a 6 ns MD simulation of GluR0 in an octane slab, the structural drift from the initial conformation revealed no significant structural deviations in the filter and TM helices from the KcaA-derived starting model. Analysis of hydrogen bonding revealed long-lived interactions of residues in close proximity to the filter region, in addition to four inter-subunit salt bridges. Similar interactions are observed in KcsA, and are thought to help maintain the conformation of the filter—an essential component of K+ selectivity. Thus a homology modelling and simulation study of GluR0 supports the proposal that the M1, P and M2 pore-forming regions in GluR0 and KcsA share a common fold and thus supports a degree of functional similarity between these two classes of channel.
Homology models have also been used to help explain structure–function relationships in mammalian GluRs (40–42). However, it should be noted that the degree of sequence identity between mammalian GluRs and KcsA is much lower. In particular, the TVGYG motif is not conserved, and mammalian GluRs are not K+-selective (unlike GluR0). Thus the rather distant homology between mammalian GluRs and KcsA may challenge current homology modelling techniques. This is supported by preliminary simulation studies of a mammalian GluR2 TM domain model based on KcsA (T. Arinaminpathy and M.S.P. Sansom, unpublished results).
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nAChR |
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TOPABSTRACTK CHANNELSnAChRCONCLUSIONSREFERENCES |
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A major class of ion channels is provided by a superfamily of the ligand-gated ion channels (LGICs), as exemplified by the nicotinic acetylcholine receptor (nAChR) (43). The nAChR is one of the Cys-loop receptors that exhibit a pair of disulfide-bonded cysteines in their N-terminal domain, separated by typically 13 amino acid residues. The nAChR is the major neurotransmitter receptor at vertebrate neuromuscular junctions, and is also present within the vertebrate central nervous system (44). Other neurotransmitter receptors belonging to this family include the glycine receptor, the GABAA and GABAC (
-aminobutyric acid type A and type C) receptors, and the 5HT3 (5-hydroxytryptamine type 3) serotonin receptor (45). An atomic-resolution structure for the nAChR remains elusive, although continued progress in cryoelectron microscopy studies offers the prospect of an 4 Å resolution model in the near future (46–50). These studies, and others (51), indicate that the nAChR is pentameric, each subunit containing an extracellular (EC) domain known to contain ligand-binding sites and four transmembrane helices, of which the second (M2) forms the principal component of the pore lining. There are five known subunit types found in muscle-type nAChRs (
1, ß1, , 
and 
). These nAChRs are heteropentameric (e.g. 2ß) and thus are only approximately 5-fold symmetric. For neuronal nAChRs, there are 12 known subunits (2, ... , 10 and ß2, ... , ß4). Whilst many of them still exist as heteropentamers, some neuronal nAChRs (e.g. 7, 8 and 9) are functional as homopentamers, with 5-fold symmetry. The N-terminal extracellular (ligand-binding) domain contains the proposed neurotransmitter/agonist binding site; the M2 helix bundle forms the channel pore.
Ligand-binding domain
Studies of structure–function relationships of the N-terminal EC (ligand-binding) domain of nAChR have been greatly aided by the discovery and structure determination of a snail (Lymnaea stagnalis) acetylcholine-binding protein (AChBP), homologous to the ligand-binding domain of nAChR (52). This acetylcholine-binding protein is pentameric, and its structure can be docked into the corresponding region of the EM map of the nAChR (50). The AChBP subunit displays 20–27% sequence identity to the N-terminal, extracellular domains of various 7 members. Thus it can be used as a template for modelling the EC domain of the nAChR and related channels.
Le Novere et al. (53) generated a three-dimensional model of the EC domain of chick 7 nAChR. Docking of agonists in the ligand-binding pocket of the model led to positions consistent with labelling and mutagenesis data. In particular, the quaternary ammonium head group of nicotine was suggested to interact with a tryptophan residue (W148 of chick 7) via a 
–cation (54) interaction. Binding affinities inferred from docking give a rank order in agreement with experimental values, lending further support to the model.
We have also generated homology models of chick 7 nAChR (Fig. 3) (H.J. Kim, D. Sattelle and M.S.P. Sansom, manuscript in preparation). Analysis of electrostatic potentials reveals that the interior of the channel vestibule formed by the pentameric assembly of ligand-binding domains has a very negative potential (Fig. 3B). This would be expected to lead to a high local concentration of permeant cations (i.e. Na+ and K+ ions), and is consistent with earlier suggestions of such an effect from sequence analysis (55) and modelling (56) studies.
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The success of these preliminary modelling studies leads to greater confidence in the use of the AChBP structure as a template for modelling the LB domain of the nAChR and related (45) receptors. However, it should be noted that many such receptors are heteropentameric and therefore include subunits that do not take part in binding interactions with agonists. Thus, some breakdown in exact 5-fold symmetry must be accommodated in such models. Furthermore, it is evident that conformational changes occur in response to agonist binding (57). This is supported by the recent docking analysis carried out by Le Novere et al. (53), who suggested that models built upon the template of AChBP are likely to correspond to a desensitized state of the nAChR. Thus analysis of interactions of compounds that interact with a basal/resting form of the ligand-binding domains of nAChRs may require a receptor model with a slightly different conformation of the ligand-binding site from that present in the current AChBP-derived models.
M2 helix bundle
The pore of the nAChR is formed by a bundle of five M2 helices (43,51,58). Low-resolution images of the M2 bundle have been obtained by cryoelectron microscopy (46,47,49) and models have been generated on the basis of such images (59,60). More recently, solid state NMR studies of synthetic M2 peptide [which self-assembles in lipid bilayers to form channels (61)] have been used to explore the conformation and orientation of the M2 helices (62,63).
Modelling and simulation studies of M2 helices have also been performed. For example, Law et al. (64) performed MD simulations on the pore-lining M2 helix of the nAChR to explore how its structure and dynamics changed as a function of environment. In water, the M2 helix partially unfolds to form a molecular hinge in the vicinity of a central Leu residue that has been implicated in the mechanism of ion-channel gating (65,66). In a phospholipid bilayer, either as a single transmembrane helix or as part of a pentameric helix bundle, the M2 helix shows less flexibility, but still exhibited a kink in the vicinity of the central Leu. The single M2 helix tilted relative to the bilayer normal by 12°, in agreement with solid state NMR data (63).
More recently Law et al. (67) have performed an extended (>15 ns) MD simulation of a pentameric bundle (M25) model of the pore-lining region of the nAChR in a POPC bilayer to explore the conformational dynamics of the channel assembly. On the timescale of the simulation, the bundle remains stable, with the polar pore-ling side-chains remaining exposed to the lumen of the channel. Fluctuations at the helix termini, and in the helix curvature, result in closing/opening transitions at both mouths of the channel, on a timescale of 10 ns. Recent NMR and modelling studies of the M2 helix bundle from the GlyR (68) have also suggested that changes in M2 helix packing may correlate with channel opening and closing.
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CONCLUSIONS |
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TOPABSTRACTK CHANNELSnAChRCONCLUSIONSREFERENCES |
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From the studies described above, it is evident that by combining carefully constructed homology models with simulations and related computational studies (e.g. drug docking calculations), one may obtain useful insights into structure–function relationships in mammalian ion channels. This approach, in general, provides a potential route to understanding human membrane transport proteins on the basis of structures of their bacterial homologues.
The success or otherwise of this overall endeavour is dependent on the accuracy of the homology models constructed. It is important to consider how one might evaluate the accuracy of a given model, and how such information could be used to refine initial homology models. Obviously a model should be compared with all available experimental data for the ion channel in question. However, it should also be possible to assess the quality of a membrane protein model in a more general sense. The expanding (although still rather small) database of membrane protein structures means that structural bioinformatics approaches may be used to formulate general rules of membrane protein structure against which a model may be evaluated. For example, a number of studies have revealed the propensity of amphipathic aromatic side-chains (i.e. tyrosine and tryptophan) to form distinct bands on the surface of a membrane protein in the vicinity of the water–lipid bilayer interface (69–71). The presence of similar bands of tyrosine and tryptophan side-chains on the surface of a membrane protein model thus increases our confidence in the validity of the model. Similarly, our understanding of the nature of helix–helix interactions within membrane proteins (72,73) may provide another criterion by which to evaluate models. As the number of experimentally determined membrane protein structures grows, so will our confidence in formulating such statistically derived criteria for judging models.
A further challenge is evident from, for example, the studies of GluR and nAChR described above. Our understanding of membrane protein structure is expanding not only in term of our knowledge of structures of membrane proteins per se, but also in terms of increasing numbers of structures for water-soluble domains and/or subunits of complex membrane proteins. A major challenge will be to integrate structural data from diverse sources on transmembrane and extramembraneous domains/subunits, possibly via modelling approaches combined with, for example, low-resolution images from electron microscopy (74).
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ACKNOWLEDGEMENTS |
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Our thanks are due to our many colleagues for conversations concerning ion channels, in particular Frances Ashcroft and David Sattelle. Work in M.S.P.S.'s laboratory is supported by the Wellcome Trust and the UK BBSRC. H.J.K. and Y.A. are supported by the UK MRC, and C.E.C. by the BBSRC.
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FOOTNOTES |
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* To whom correspondence should be addressed. Tel: +44 1865275371; Fax: +44 1865275182; Email: mark{at}biop.ox.ac.uk
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