MINIREVIEW Computational approaches to understand a-conotoxin interactions at neuronal nicotinic receptors Se ´ bastien Dutertre and Richard J. Lewis Institute for Molecular Bioscience, University of Queensland, Brisbane, Queensland, Australia Recent and increasing use of computational tools in the field of nicotinic receptors has led to the publication of several models of ligand–receptor interactions. These models are all based on the crystal structure at 2.7 A ˚ resolution of a protein related to the extracellular N-terminus of nicotinic acetyl- choline receptors (nAChRs), the acetylcholine binding pro- tein. In the absence of any X-ray or NMR information on nAChRs, this new structure has provided a reliable alter- native to study the nAChR structure. We are now able to build homology models of the binding domain of any nAChR subtype and fit in different ligands using docking programs. This strategy has already been performed suc- cessfully for the docking of several nAChR agonists and antagonists. This minireview focuses on the interaction of a-conotoxins with neuronal nicotinic receptors in light of our new understanding of the receptor structure. Computational tools are expected to reveal the molecular recognition mechanisms that govern the interaction between a-cono- toxins and neuronal nAChRs at the molecular level. An accurate determination of their binding modes on the neuronal nAChR may allow the rational design of a-cono- toxin-based ligands with novel nAChR selectivity. Keywords: a-conotoxins; computational tools; docking simulation; homology modeling; neuronal nicotinic acetyl- choline receptor. Introduction Neuronally active a-conotoxins are disulfide rich mini- proteins produced in the venom of predatory Conus species. They were first discovered in 1994 and represent valuable pharmacological tools for the study of electrophysiological properties of nicotinic acetylcholine receptor (nAChR) subtypes and their distribution in native tissues [1]. Although a lot is known about these a-conotoxins (see other reviews in this series; [1a)c]), including their three- dimensional structure, functional determinants and pairwise interactions with their specific target, the lack of informa- tion on the nAChR three-dimensional structure has prevented attempts to gain molecular insights into the toxin–receptor interaction (Table 1). In 2001, this situation changed dramatically when Sixma and colleagues published the high-resolution crystal struc- ture of an acetylcholine binding protein (AChBP), a soluble protein homologue to the extracellular domain of nAChRs [2]. This structure revealed the acetylcholine (ACh) binding site in great detail and rationalized the interpretation of more than 30 years of research on nAChRs. This new molecule also served as a template for building models of nAChRs, which were subsequently used for docking studies of several nAChR ligands. Indeed, combining experimental data with today’s high-performance computational tools, we can now predict and simulate the ligand–receptor interaction. The accurate identification of a-conotoxin interactions with the neuronal nAChR using homology modeling and docking simulations is expected to provide new information into how these small peptides achieve their remarkable selectivity. Several NMR and X-ray structures of a-cono- toxins are available, and for some of them, identified pairwise interactions can guide the docking process and lead to an accurate solution. Their docking modes on nAChR homol- ogy models will also help to distinguish between distinct toxin binding sites and identify how they acheive their unique selectivity. From experimental data, four microsites have already emerged for the nAChRs: one common a-neuro- toxin microsite and several distinct a-conotoxin microsites. Such distinctions in nAChR binding modes are particularly important as they could represent the specific targets required to produce highly subtype selective drugs with fewer side-effects [3]. a-Conotoxins that bind to nAChRs with very high affinity and selectivity could be used as natural scaffolds in the design of new therapeutic agents based on the structure of neuronal nAChR homology models. Structure of nAChRs Pre-AChBP view of nAChR structure In the past half-century, nAChRs, which are the proto- typical receptors of the ligand-gated ion channel super- family, have led to an impressive number of physiological, Correspondence to R. J. Lewis, Institute for Molecular Bioscience, University of Queensland, Brisbane, Queensland 4072, Australia. Fax: + 61 73346 2101, Tel.: + 61 73346 2984, E-mail: r.lewis@imb.uq.edu.au Abbreviations: ACh, acetylcholine; AChBP, acetylcholine binding protein; nAChR, nicotinic acetylcholine receptor; SAR, structure- activity relationship. (Received 22 January 2004, revised 17 March 2004, accepted 6 April 2004) Eur. J. Biochem. 271, 2327–2334 (2004) Ó FEBS 2004 doi:10.1111/j.1432-1033.2004.04147.x pharmacological and structural studies (reviewed in [4,5]). Most of the work has been performed on the muscle subtype, thanks to the large amount of the receptor obtained from the electric organ of the Torpedo marmorata ray and availability of selective pharmacological tools like the snake a-neurotoxins. The analysis of both muscle and neuronal subunit sequences reveal a high degree of identity and similar hydropathy plots, and therefore, we can extrapolate the majority of these structural data to the neuronal subtypes [5]. The overall structure of the neuronal nAChR is a homo- (only a7, a8ora9) or hetero-pentamer composed from the 12 subunits (a2–a10; b2–b4) that have been identified in mammalian species so far [6]. Each subunit possesses an extracellular N-terminus (ligand binding domain), four transmembrane domains, an intracellular loop and an extracellular C-terminus. a4/b2 Receptors, which mainly control pain [7], and a7 receptors are the most abundant nAChR subtypes in the mammalian brain [3]. Studies also indicate significant expression levels of a3, a5andb4 subunits in different nuclei of the brain [8]. As a result of their role in the propagation of action potentials, cognitive function and involvement in diverse central nervous system pathologies including pain, they are targets for many drugs and toxins. In addition to the ACh binding site, many other binding sites on nAChRs have been identified. There is a binding site for positive allosteric modulators (increased neuronal nAChR-medi- ated ion conductance), two binding sites for noncompet- itive blockers or negative allosteric modulators, and a steroid binding site [8]. Fluorescence measurements using labelled a-neurotoxin first revealed the localization of the competitive binding site close to the outer perimeter of the muscle nAChR at a distance of 39–45 A ˚ from the membrane surface [9]. This was later confirmed by the electron microscopy of the Torpedo receptor at 4.6 A ˚ resolution, showing the location of the putative ACh binding site cavities  30 A ˚ away from the membrane [10]. Intensive mutagenesis studies have shown that competitive agonists and antagonists bind at the interface between a–a or a–b subunits, identifying a vicinal disulfide and several conserved aromatic residues located on six segments (or loops) A–F [4]. Segments A, B and C are part of the principal component (subunits a 1 for muscle and a(+) for the neuronal subtypes), while segments D, E and F are part of the complementary component (c, d or e for muscle and a(–) or b for the neuronal subtypes) [11]. Acetylcholine binding protein Although a large amount of structural information has become available on the nAChR topology during the last decade [4], its three-dimensional structure still remains unknown. Surprisingly, the first molecular insights con- cerning nAChRs structure came from the crystal structure at atomic resolution of an acetylcholine binding protein (AChBP) extracted from the Mollusca Lymnaea stagnalis [2]. AChBP is a soluble, glia-derived protein that modu- lates synaptic transmission in the mollusc’s brain, binds ACh and other nAChR ligands, and resembles the N-terminus ligand binding domain of nAChRs. The structure shows five identical subunits arranged in a cylinder of 80 A ˚ in diameter with a central pore of 18 A ˚ (Fig. 1), in agreement with the dimensions expected for the ligand binding domain of nAChRs from Torpedo electron microscopy data [10]. The rich b strand compo- sition of AChBP is also in accordance with secondary structure prediction for the N-terminus of nAChR subunit [12]. Each AChBP subunit possesses an a helix, two short 3 10 helices and 10 stranded b sheets, revealing an immunoglobulin-like subunit topology (Fig. 2). The bind- ing site is found in a cleft comprised mainly of aromatic residues from loops A–F and a series of b strands at the interface of two subunits, in accordance with the mutation experiments on nAChRs. Homology models of the neuronal nAChR ligand binding domain The template: a high resolution structure of AChBP AChBP is not an ion channel (it is a soluble protein that lacks the transmembrane/intracellular parts compared to nAChRs), but importantly displays many nAChR prop- erties, including binding of nAChR ligands [13] and a conformational change in response to agonist binding [14]. Interestingly, the highest percentage of identity (26.5%) has been found with the ligand binding domain of the a7 neuronal nAChR subtype (Fig. 1). This percentage increa- ses dramatically when considering only the loops forming the ACh binding pocket (40–60%), as expected given the functional homology [15]. The sequence alignment between the rat nAChR subunit and AChBP sequences revealed a very good fit with only few gaps of one or two residues (Fig. 3). A misaligned domain resulting in a gap Table 1. a-Conotoxins active on neuronal nAChR subtypes. *, C-terminal amidation; O, hydroxyproline; Ys, sulfated tyrosine; c, c-carboxyglutamic acid. Conserved residues shaded. 2328 S. Dutertre and R. J. Lewis (Eur. J. Biochem. 271) Ó FEBS 2004 of four residues occurs in loop C for the b subunits, which lack the vicinal disulfide found in AChBP and the a subunits. This effect on structure is not an issue for the analysis of the ACh binding site (and the docking simulation of ligands) because loop C of the b subunit is not involved in the competitive binding site [16]. Finally, the important residues for the binding of ACh and other competitive ligands are conserved in the AChBP sequence, explaining why AChBP also binds nicotine, epibatine, (+)-tubocurarine and a-bungarotoxin [5]. Therefore, AChBP is considered a reliable structure for nAChR homology modeling and docking simulations of compet- itive ligands [17]. Modeling methods The recent and growing literature dealing with nAChR homology models based on the AChBP structure reveals that two main modeling approaches can be used that each produce similar high quality models. The first one will be referenced as the Ômanual methodÕ. The process of building a model for a protein using this method is divided into different steps. The first and critical step in all molecular modeling is to get the alignment between template and target sequences optimised. Some programs exist that do this automatically. However, despite their improvements, the results in some cases still need to be manually refined to further increase the percentage of identity and eliminate aberrations. The following steps are: (a) determine the structurally conserved regions and assign directly the coordinates of these regions from the template to the target sequence; (b) generate random loops for the insertions/ deletions (or use a structural database search); (c) assign the coordinates of the chosen loop to the model, and finally (d) refine/relax the new structure using minimization/dynamic simulations. The second approach will be called the Ôautomated methodÕ in contrast to the first one. It generates a whole structure from the target sequence based on the alignment with the experimentally solved homologue struc- ture. Besides the commercially available software, SWISS - MODEL is a server devoted to this task and is available free of charge at the ExPASY site (http://www.expasy.org/ swissmod/). These methods or derivatives of these have led to the publication of a number of modelled nAChR structures, including: muscle nAChR subtype of human [18], human D -tubocurarine–metocurine complex [19], mouse [20], mouse D -tubocurarine complex [21], torpedo ray [22,23], torpedo a-bungarotoxin complex [24], nAChR DEG-3 mutant [25], a7 nAChR neuronal subtype of human [26], chick [23], chick a-cobratoxin complex [15], a4b2 nAChR neuronal subtype of human [26], rat [23], and a3b2 and a4b4 nAChR neuronal subtype of human [26]. Fig. 2. AChBP subunit structure. Fig. 1. AChBP three-dimensional structure (PDB:1I9B). (A) Side view. (B) Top view. (+)W143, (+)Y89, (+)Y185, (–)W53, (–)Y164 and (+)Y192 are in ball and stick representation. Figures were prepared using the program PYMOL (http://pymol.sourceforge.net/). Ó FEBS 2004 Modeling a-conotoxin–nAChR interactions (Eur. J. Biochem. 271) 2329 Exploration at the molecular level of models of the neuronal nAChRs As neuronal nAChRs are attractive targets for treating many diseases such as cognitive dysfunction, neurodegeneration and other central nervous system pathologies [3,8], exciting developments in drug discovery/drug design focusing on new selective nAChR compounds are now emerging since the AChBP structure was first described. Agonist or antagonist drugs that selectively target receptor subtypes could be designed that maximize the desired effect and minimize the side-effects. While subtype select- ive antagonists, a-conotoxins, may prove to be beneficial in the treatment of certain neuropathology and diseases [27], it is far more challenging to convert them to agonist acting drugs for a wider therapeutic application, whilst maintaining their other remarkable features. We have generated homology models of several nAChR subtypes [27a], so that we can start to analyse in detail the nAChR pharmacophore that binds a-conotoxins at the molecular level. As expected, the nAChR binding pocket contains the same hydrophobic cage as the AChBP structure, formed by six aromatic residues. However, several non- conserved residues lining this conserved pocket are more interesting, as they are likely to be responsible for the observed subtype selectivity of a-conotoxins and other ligands (Fig. 4). Care is needed when analysing models built upon a crystal structure. During the crystallization process, the packing forces fix the molecule (in particular the flexible loops) in a certain conformation, which does not necessarily reflect the true or ideal state for ligand recognition. In addition, the sequence threading of a homology modeling procedure can introduce additional errors, as the loop lengths and the side chains are different. In AChBP, the b9/b10 hairpin covers the binding site, but one can easily imagine it as a flexible loop that could allow a more open binding site in the physiological resting state than that shown in the crystal. However, the most likely access routes to the ligand binding site are from above or below the double cysteine-containing loop C. Indeed, we can identify two cavities at the interface between adjacent subunits from visual inspection of the molecular surface of AChBP and nAChR homology models (Fig. 4). For instance, on an a7 nAChR model, a large cavity appears below the b9/b10 hairpin and is made of loop C (+), the C-terminus of loop F (–), loop A (+) and the C-terminus of the b6 (–) strand while a narrower one exists above, with contributions from loop C (+), loop B (+), loop E (–), the N-terminus of the b6(–)andb1 (–) strands and the C-terminus of the b2 (–) strand. In addition to these observations, several lines of experimental evidence reinforce these cavities as the obvious access routes for the binding of competitive ligands. First, a-neurotoxin docking based on double-mutant cycle ana- lysis and NMR data show that they achieve their antagonist activity by targeting the larger cavity. Indeed, they insert the tip of their loop II into the ACh binding pocket from below the b9/b10 hairpin to occupy the binding pocket [15,28]. Secondly, residues that confer selectivity for smaller antag- onists like the Waglerin have been mapped in the small Fig. 3. Sequence alignment between AChBP and the N-terminal binding domain of rat nAChR subunits. Secondary structures of AChBP (a, a helices; b, b sheet) and previously identified nAChR loops are indicated above the alignment. Dark grey, residues common to AChBP and the nAChR sequences; light grey, residues common only to the nAChR subunits; squares, residues involved in the a-conotoxin binding site. 2330 S. Dutertre and R. J. Lewis (Eur. J. Biochem. 271) Ó FEBS 2004 cavity above the b9/b10 hairpin [20]. Finally, recent docking of metocurine and D -tubocurarine on AChBP structure revealed that they both bind the ACh pocket by extending into the small cavity [29]. In a hypothesis involving a single pharmacophore, competitive antagonists would interact with the same binding site, but via different binding modes. Here, it is strongly suggested that more than one binding site exists for competitive ligands on nAChRs, and probably more than one binding mode for each binding site. From these initial results, it seems probable that smaller ligands might bind preferentially in the small cavity, while large ligands, like three-finger snake toxins, would only bind in the larger cavity. This raises the question Ôhow do the a-conotoxins bind to the different nAChR subtypes?Õ,as they are of intermediate size. a-Conotoxin docking modes on neuronal nAChRs a-Conotoxin binding sites Of the 11 neuronally active a-conotoxins, four have been the subject of more intense investigations: ImI, PnIB, PnIA and MII (Fig. 5). Their structure-activity relationship (SAR) with nAChRs has already been reviewed elsewhere [1,30]. However, the nAChR homology models provide for the first time a three-dimensional visualization of their binding determinants on a7anda3b2. In Fig. 4, these determinants have been mapped and a-conotoxins appear to bind mainly to loop C, but also clearly extend to microsites above this loop and on the b9b10 hairpin. Indeed, a-conotoxins act at the competitive nAChRs binding site, which, in light of the AChBP structure, is mainly defined by a 10 A ˚ hydrophobic Fig. 5. a-Conotoxin structures. The right panel is rotated 90° around the y-axis from the left panel. The figures were prepared using SWISS - PDBVIEWER [50]. Fig. 4. a7 and a3b2 nAChR homology models showing determinants influencing a-conotoxin binding identified by mutagenesis. AChR side- chainsaffecting.ImI,darkblue;PnIB,green;PnIA,pinkandMII, turquoise are indicated. ImI and PnIB share W147 and Y193, while PnIA and MII share I186. The small and large shaded regions repre- sent the location of the small and large cavities, respectively. Ó FEBS 2004 Modeling a-conotoxin–nAChR interactions (Eur. J. Biochem. 271) 2331 pocket. However, as conotoxins represent a larger volume, they must use different subdomains outside the ACh binding site to accommodate their bulky side chains [31]. a-Conotoxin subtype selectivity could therefore arise from the amino acid composition and geometric conformation of these microsites. The microsite hypothesis is supported by different a-conotoxin sequences (Table 1), different a-conotoxin kinetics [32–35], and finally, the involvement of different nAChR residues from different regions of nAChR subunit sequences (Table 2, Figs 4 and 5). ImI, which has provided the most complete SAR with regards to the a7 nAChR subtype, where it delineates a discrete binding site above loop C. From the pairwise interactions identified, the toxin must enter into the ACh pocket with its N-terminus, placing the triad D5-P6-R7 in van der Waals contact with (+)Y193 and (+)W147, while the C-terminus makes contacts with the (–) face of a7 nAChR, bridging the two subunits. All distances measured from the model of a7 are compatible with ImIÕs size and the distance constraints derived from mutagenesis studies, indicating that the docking simulations probably provide an accurate view of the molecular basis of the interaction. PnIB determinants are located lower in the ACh binding site, which is in accordance with different binding sites for ImI and PnIB [31]. The mutagenesis experiments suggest a highly hydrophobic interaction between W147, Y91, Y186 and Y193 in the binding pocket and the hydrophobic patch of PnIB L5, P6, P7, A9 and L10. This deeper interaction would also explain the slower dissociation kinetics of PnIB compared to ImI [34,35]. Three residues on the a3 subunit have recently been identified as specific determinants for PnIA [36]. It is noteworthy that they are all on the b9/10 hairpin containing loop C, which has an obvious structural role for the ACh binding site shape and conformation changes in the receptor [2]. I186, which is shared with MII as a determinant on a3, is situated in the binding pocket, while the two others appear further away. Indeed, a distance of 19 A ˚ has been measured between I186 and P180, making a direct interaction of the toxin with both residues at the same time unlikely. Moreover, the loss or gain of proline, an amino acid known for its structural role, at position 180 and 196, respectively, may alter the C-loop confor- mation and thereby affect the binding of PnIA indirectly [36]. Similarly, three determinants of MII sensitivity have been reported [37], but once again, a distance of 25 A ˚ has been measured between two of them. With the exception of K183, which is on the b9b10 hairpin, I186 and T57 are in, or close to, the ACh binding site. In this view, the MII binding site resembles the PnIB binding site. The binding sites of ImII and ImI exhibit little if any overlap and ImII shows a noticeably slower off-rate despite having nine out of 12 amino acids in common with ImI [35]. Even if ImII appears to be an enigma in terms of its binding site, we can exclude its location in the large cavity as it does not inhibit a-bungarotoxin. Therefore, it probably binds the ACh pocket from a new distinct microsite but still from above the b9b10 hairpin. The binding sites of AuIB, AuIA, AuIC, EpI, GIC and GID remain uncertain, as no SAR has yet been published. Docking studies of these peptides would allow the development of hypotheses that could be tested experimentally. Docking strategy Toxins bind with higher affinity than endogenous ligands, hence their toxicity. This important biological function depends on a very accurate molecular recognition, mostly based on complementary surface shape and electrostatic/ hydrophobic interactions. Therefore, an accurate predic- tion of their binding mode can also provide insight into designing possible leads for drug design. Docking pro- grams can be invaluable tools in the rational drug design as they are now able to predict the ligand–protein interaction. Indeed, tests against protein–ligand complexes from the PDB databank showed up to 80% of correct docking for a particular program [38]. Briefly, protein–ligand interactions are mainly governed by both shape complementary properties and energetic contributions. The search algorithm explores the space to generate different low energy conformations of the ligand molecule. When information on the binding site is known, as for protein with a well-defined pocket, it is possible to limit the search to only part of the receptor and then reduce the computing time. The resulting structures are ranked using a scoring technique. a-Conotoxin docking modes based on homology models of nAChRs will most likely appear soon [39]. The compu- Table 2. a-Conotoxin–nAChR binding interactions. ND, not deter- mined. Residues influencing affinity Pairwise interactions References a7 (+) ()) ImI ImI/a7 W147 W53 D5 R7/Y193 [40–47] Y193 S57 P6 D5/W147 T75 a R7 W10/N109 N109 W10 W10/T75 Q115 a7 (+) ()) PnIB PnIB/a7 W147 S34 S4 L10/W147 [48,49] Y91 P6 P6/W147 R184 P7 P7/Y91 Y186 A9 Y193 L10 a3b2 a3 b2 PnIA PnIA/a3b2 P180 ND A10 ND [31,36] I186 N11 Q196 a3b2 a3 b2 MII MII/a3b2 K183 T57 ND ND [37] I186 a T75 (human) residue is replaced by N75 in the rat sequence. 2332 S. Dutertre and R. J. Lewis (Eur. J. Biochem. 271) Ó FEBS 2004 tational docking of ImI and PnIB, and to a certain extent PnIA and MII, using homology models of neuronal nAChRs would probably produce a reasonable solution as pairwise interactions and determinants can efficiently guide the scoring function. However, docking of other a-conotoxins in the absence of restraints could lead to a number of docking solutions being found within the ACh pocket. Mutagenesis experiments designed from these models could help to discriminate in favour of one conformation. These docking simulations may subsequently be used to guide virtual screening for new a-conotoxin analogues with tailored selectivity. Conclusions Ironically, it seems that key components in understanding mammalian nicotinic synaptic transmission have come from molecules found in other subphyla. In addition to the Torpedo mamorata’s synapses (pisces) and the snake’s a-neurotoxins (reptilia), two molecules extracted from snails (molluscs) have helped to probe the nAChRs structure/ function. The first one (Conus sp.) has provided powerful pharmacological tools, the a-conotoxins; the second one (Lymnaea sp.), thanks to the acetylcholine binding protein, has revealed the structure of the binding domain of nAChRs. Combining this information with the powerful computational tools available today is facilitating drug design at the nAChRs. 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MINIREVIEW Computational approaches to understand a-conotoxin interactions at neuronal nicotinic receptors Se ´ bastien Dutertre and. interaction of a-conotoxins with neuronal nicotinic receptors in light of our new understanding of the receptor structure. Computational tools are expected to reveal