Tài liệu Báo cáo khoa học: Computational approaches to understand a-conotoxin interactions at neuronal nicotinic receptors doc

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 acetylacet

M I N I R E V I E W

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 acetylacetyl-choline 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-conoa-neuro-toxin 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)

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, a8 or a9) or hetero-pentamer composed

from the 12subunits (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/b2Receptors,

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,

a5 and b4 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

Torpedoreceptor 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 a1for 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

310 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.

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

-MODELis 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], a4b2nAChR neuronal subtype of human [26], rat [23], and a3b2and 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

(+)Y192are in ball and stick representation Figures were prepared

using the program PYMOL (http://pymol.sourceforge.net/).

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 non-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 (–) and b1 (–) 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.

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 a7 and a3b2 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-chains affecting ImI, dark blue; PnIB, green; PnIA, pink and MII,

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.

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 12amino 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

Q115

a7

a3b2

Q196

a3b2

I186

a

T75 (human) residue is replaced by N75 in the rat sequence.

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

Acknowledgements

We thank Joel Tyndall, Christina Schroeder and Ivana Saska for their

comments on the manuscript This work was supported by a grant from

the Australian Research Council.

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