Accepted Manuscript

Modelling the interactions between animal peptides and membrane proteins

Andrew Hung, Serdar Kuyucak, Christina I. Schroeder, Quentin Kaas

PII: S0028-3908(17)30367-2 DOI: 10.1016/j.neuropharm.2017.07.036 Reference: NP 6805

To appear in: Neuropharmacology

Received Date: 27 March 2017 Revised Date: 18 July 2017 Accepted Date: 31 July 2017

Please cite this article as: Hung, A., Kuyucak, S., Schroeder, C.I., Kaas, Q., Modelling the interactions between animal venom peptides and membrane proteins, Neuropharmacology (2017), doi: 10.1016/ j.neuropharm.2017.07.036.

This is a PDF file of an unedited manuscript that has been accepted for publication. As a service to our customers we are providing this early version of the manuscript. The manuscript will undergo copyediting, typesetting, and review of the resulting proof before it is published in its final form. Please note that during the production process errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain. ACCEPTED MANUSCRIPT Modelling the interactions between animal venom peptides and membrane proteins

Andrew Hung 1, Serdar Kuyucak 2, Christina I Schroeder 3, Quentin Kaas 3*

1School of Science, RMIT University, GPO Box 2476, Melbourne, Victoria 3001, Australia

2School of Physics, University of Sydney, Sydney, New South Wales 2006, Australia

3Institute for Molecular Bioscience, The University of Queensland, Brisbane 4072, Australia

*To whom correspondence should be addressed

Institute for Molecular Bioscience

The University of Queensland

Brisbane 4072, Queensland, Australia

Tel: +61-7-3346-2021

Fax: +61-7-334-2101

E-mail: [email protected] MANUSCRIPT

Highlights:

• This review highlights the use of molecular modelling in recent discoveries in the animal

field.

• A particular focus was made on membrane active as well as on inhibition of voltage-gated

ion channels and nicotinic acetylcholine receptors.

• The biological insights gained from using modern modelling technique are described on a

number of chosenACCEPTED examples.

• An analysis of new molecular modelling possibilities arising from a number of recent crystal and

electron microscopy structures of animal toxin targets is presented.

Keywords: animal toxins; ion channels; molecular dynamics; molecular modelling; voltage-gated ion channels; nicotinic acetylcholine receptors ACCEPTED MANUSCRIPT Abstract

The active components of animal are mostly peptide toxins, which typically target ion channels and receptors of both the central and peripheral nervous system, interfering with action potential conduction and/or synaptic transmission. The high degree of sequence conservation of their molecular targets makes a range of these toxins active at human receptors. The high selectivity and potency displayed by some of these toxins have prompted their use as pharmacological tools as well as drugs or drug leads. Molecular modelling has played an essential role in increasing our molecular-level understanding of the activity and specificity of animal toxins, as well as engineering them for biotechnological and pharmaceutical applications. This review focuses on the biological insights gained from computational and experimental studies of animal venom toxins interacting with membranes and ion channels. A host of recent X-ray crystallography and electron- microscopy structures of the toxin targets has contributed to a dramatic increase in the accuracy of the molecular models of toxin binding modes greatly advancing this exciting field of study. MANUSCRIPT

ACCEPTED ACCEPTED MANUSCRIPT 1 Introduction

Animal venoms are a rich source of peptide toxins mainly acting on the nervous system (Kalia et al., 2015). A number of peptides from this vast and complex pool of bioactive peptides have shown potential therapeutic applications, and the study of animal venoms attract considerable interest

(Calvete, 2017; Kaas and Craik, 2015; Oldrati et al., 2016; Nasiripourdori et al., 2011). Venoms of snakes, spiders, cone snails, , anemones, bees and wasps have been the most investigated, resulting in the identification of several thousands of toxins (Jungo et al., 2012), but only a fraction of these toxins has been functionally characterised. For example, less than 200 out of the 2,500 cone snail toxins currently identified have been pharmacologically characterised (Kaas et al., 2012); with the estimated total number of cone snail toxins being in the range of hundreds of thousand to millions (Akondi et al., 2014; Kaas and Craik, 2014). Animal peptide toxins typically target large membrane proteins, making the structural characterisation of these interactions by nuclear magnetic resonance (NMR), X-ray crystallography or electron microscopy (EM) challenging. To date, only four experimental structures of a complex between MANUSCRIPT a toxin and its molecular target have been determined, two are shown in Figure 1, and the other two are the bound to the voltage-gated K V1.2 (Banerjee et al., 2013) and the spider toxin DkTx bound to the TRPV1 channel (Cao et al., 2013; Gao et al., 2016). The snake α- was crystallised in complex with the ligand-binding domain of an isolated nicotinic acetylcholine receptor (nAChR) α1 subunit (nAChR) (Dellisanti et al., 2007) as well as with an isolated ligand- binding domain of a human α9 nAChR subunit (Zouridakis et al., 2014). The binding site in these crystal structures isACCEPTED largely incomplete because the binding site is located at the interface between two subunits. A number of experimental structures of complexes between toxin and structural homologues to the molecular target have also been determined, for example, the acetylcholine binding protein (AChBP), a structural surrogate for nAChRs, has been crystallised in complex with a range of toxins (Dutertre and Lewis, 2006). Molecular modelling therefore plays an essential role in building hypotheses on toxin binding modes and mode of action, and in guiding the engineering of toxin activity. This review willA focusCCEPTED on recent MANUSCRIPT findings resulting from the molecular modelling of interactions between animal toxins and their membrane targets.

Some toxins, such as the bee melittin, directly target cellular membranes (Section 1), but the vast majority of peptide toxins act on ion channels in the nervous systems. The most common classes of ion channels targeted by animal toxins are the voltage-gated ion channels (Section 2) and the nicotinic acetylcholine receptors (nAChRs) (Section 3). The high degree of sequence conservation of these channels across animal phyla explains the potent activity of these toxins in humans (Jegla et al., 2009; Liebeskind et al., 2015, 2011). Beyond voltage-gated ion channels and nAChRs, a diverse range of membrane proteins in the nervous system is also targeted by toxins (Section 4), such as transporters and some G-protein-coupled receptors.

Toxins are typically selective toward a small subset of receptor subtypes, and the molecular modelling of toxin interactions typically begins with the prediction of the three-dimensional structure of the receptors using an experimentally-determined structure of a related protein. The recent expansion of our knowledge on the structures MANUSCRIPT of some receptor subtypes has the potential to dramatically increase the accuracy of the predicted structures of toxin targets. The most significant examples are the crystal structure of the pentameric nAChRs (Morales-Perez et al.,

2016), the crystal structures of bacterial voltage-gated sodium channels (Payandeh et al., 2012,

2011; Zhang et al., 2012), the EM structure of an insect voltage-gated (Shen et al.,

2017) and the EM structures of a mammalian voltage-gated (Wu et al., 2016,

2015). Structural information on most of the toxin targets is nevertheless lacking because ion channels exist as aACCEPTED very large number of subtypes, which are linked to different functions and are expressed differentially in the nervous system (Catterall et al., 2005; Gotti et al., 2009).

Additionally, some toxins trap their target into a particular functional state (Cestèle et al., 2006;

Stevens et al., 2011; Walker et al., 2009), and it is therefore essential to model the toxin interaction with a certain receptor subtype in a precise activation state. The giant steps recently made in our understanding of the structural features linked to some of these states (Cecchini and Changeux, 2015; Clairfeuille et al., 2016; Plested,ACCEPTED 2016; Sul MANUSCRIPTa et al., 2017) can be used in the modelling of the receptors as well as orient the docking. It should be noted that ion channels are complex systems, and a range of items are often overlooked in the molecular models, most notably protein glycosylation, the membrane lipid composition and the interactions with auxiliary proteins. As illustrated in Figure 1, the molecular surface of the membrane proteins is considerably larger than that of the toxins, and determining the toxin binding mode without any experimental data to orient the computational docking is difficult. Receptor residues potentially involved at the binding interface can be suggested from single point mutation studies (Leipold et al., 2005; Smith et al.,

2007), loop-swapping exercise (Bosmans et al., 2008) or by analysing sequence conservation among orthologous or paralogous sequences (Li-Smerin and Swartz, 1998). Discovering a molecule competing with the toxin of interest and with a known binding mode is highly valuable (Corzo et al., 2003). Ultimately, molecular interactions could also be suggested from double-mutant cycle analysis (Choudhary et al., 2007; Quiram et al., 2000; Yu et al., 2011). Practically, docking is performed using rigid body docking or flexible dockMANUSCRIPTing and/or using molecular dynamics (MD) simulations. Several examples of docking simulation s are described in Section 2. A model of the complex could also be built by homology using as template the structure of a complex between a related toxin and a related molecular target. This latter strategy is often employed for modelling toxins binding to the nAChR orthosteric binding site (Section 3). Beyond the qualitative description of the molecular interactions, some computational techniques can be used to predict the energetics of the interactions.

2 Venom peptidesACCEPTED interacting with membranes Although most peptide toxins exert their effect by interacting with a target protein only, some peptides act by forming a ternary complex with a membrane and receptor in concert or even target the membrane directly, leading to lysis of the cells. One of the most studied toxins of the latter class is melittin. Another well-known class of membrane active peptides are the cytotoxins, the activity of which has been reviewedACCEPTED recently MANUSCRIPT (Gasanov et al., 2014) and will not be further discussed here.

Membrane-active toxins. Melittin is a 26 amino acid residue amphipathic peptide derived from the European honey bee Apis mellifera (Habermann, 1972). Melittin’s membrane lytic activity

(Tosteson and Tosteson, 1981) has been studied extensively using a variety of different techniques, including MD simulation, solid-state NMR spectroscopy, X-ray crystallography, fluorescence spectroscopy, electron microscopy, and CD spectroscopy (Bernèche et al., 1998; Lauterwein et al.,

1979; Lee et al., 2013; Lin and Baumgaertner, 2000; Takahashi et al., 2013). Despite intensive research efforts, the molecular mechanism that underpins melittin activity is still controversial (Lee et al., 2013). Melittin-membrane interaction has been shown to depend on peptide concentration, lipid composition, membrane thickness, membrane potential, pH and hydration level (Bradrick et al., 1995; Bradshaw et al., 1994; Dempsey, 1990; Lin and Baumgaertner, 2000). MD trajectories were used at an early stage to study the interaction of melittin with fully hydrated dimyristoyl phosphatidylcholine (DMPC) bilayer (Bernèche et al. MANUSCRIPT, 1998). This study suggested that the presence of melittin in the upper layer has an effect across both layers of the membrane causing a thinning of the bilayer, allowing water to pass through. Furthermore, it has been reported that at low concentrations, melittin can bind to lipid membranes as a monomer and induce transient leakage, whereas at higher concentrations, it can form stable pores, and at higher concentrations still, melittin can disintegrate the membrane by acting as a detergent (Tosteson and Tosteson, 1981;

Ladokhin and White, 2001; Last et al., 2013). This concentration-dependent phenomenon was originally suggested to be due to the “wedge and edge effect” described by Terwilliger in 1982

(Terwilliger et al., ACCEPTED1982). However, this model was only based on the structure of melittin and did not consider the lipid bilayer. A recent study now suggests that melittin translocates across the lipid bilayer by transient pore fluctuation, allowing it to form stable pores in the presence of an excess of peptide/lipid ratio (Lee et al., 2013).

Trimolecular complexes. The gating-modifier toxins (GMTs) are typically isolated from spider venoms and display an amphipathicACCEPTED surface profile, MANUSCRIPT comprising a hydrophobic patch surrounded by a charged ring, which is similar to melittin and other antimicrobial membrane-binding peptides.

These toxins alter nerve conduction by interacting with voltage-sensor domains of various ion channels. The first GMT reported binding phospholipids was VsTx1, a toxin isolated from the

Chilean rose tarantula ( Grammostola spatulata ) (Lee and MacKinnon, 2004). It was reported that there was a 104-fold difference in inhibition of the archaebacterial voltage-dependent potassium channel KVAP once the channel was extracted from the membrane with detergent. Subsequent spin- down and tryptophan fluorescence studies showed that this was most likely due to the peptides ability to partition in membranes, a feature which had not previously reported for this class of peptides (Lee and MacKinnon, 2004).

Lee and MacKinnon originally reported that VsTx1 access KVAP via the lipid bilayer and binds to both zwitterionic (POPC) and anionic charged (POPE/POPG) phospholipids (Lee and

MacKinnon, 2004). It was later suggested that VsTx1-membrane interaction is dependent on anionic phospholipids (Jung et al., 2005). MD simulMANUSCRIPTations suggest that VsTx1 preferentially partitions at the water/membrane interface. Although it does appear that VsTx1 can interact with both zwitterionic and anionic phospholipids, there is a notable difference in how the peptide interacts with the different lipid systems. This difference is most likely brought about by the formation of the electrostatic interactions stabilising the peptide in the membrane, the number of H- bonds able to be formed between the peptide hydrophilic residues and anionic phospholipids headgroups compared to zwitterionic headgroups, as well as the formation of favourable interactions with the hydrophobic core of the bilayer through the hydrophobic residues of VsTx1, which are orientatedACCEPTED towards the core of the lipid bilayer (Bemporad et al., 2006).

Five other GMTs that have been studied for their ability to bind to membranes using MD simulations are HaTx1 (Nishizawa and Nishizawa, 2006), GsMTx4 (Nishizawa and Nishizawa,

2007), SGTx1 (Wee et al., 2007), ProTx-I, Hd1a (Deplazes et al., 2016) and ProTx-II (Henriques et al., 2016). HaTx1 and GsMTx4 have been described as able to enter deeply into lipid bilayers, interacting with the acyl chains ofA phospholipidsCCEPTED MANUSCRIPTand being pulled in by electrostatic forces between the charged residues on the peptide followed by membrane thinning or dimpling (Nishizawa and

Nishizawa, 2007, 2006). MD simulation studies of other GMTs, including SGTx1, ProTx-I and

ProTx-II, show that these peptides have a shallow binding mode, remaining at the water/membrane interface but stabilised by the same electrostatic forces that pulled the peptides deep into the membrane in the Nishizawa studies (Björklund et al., 2007; Deplazes et al., 2016; Henriques et al.,

2016). Interestingly, the GMT peptides HwTx-IV and Hd1a were evidenced not to bind phospholipid membranes, indicating that membrane interaction is not required for activity (Agwa et al., 2017; Deplazes et al., 2016). These recent reports are surprising since the GMTs display a remarkably conserved amphipathic surface profile, including several charged Arg and Lys residues as well as a number of Trp residues, known for being involved in membrane-binding event.

Therefore, one pressing question in the GMT research field is: why do not all GMTs interact with membrane? However, the rapid advancement in recent years of 3D and 4D NMR and the ever- increasing sensitivity of cryo-EM will hopefully allowMANUSCRIPT us to study the molecular interactions driving the trimolecular complex experimentally in great de tail.

3 Venom peptide blockers of voltage-gated ion channels

Voltage-gated sodium and potassium channels are responsible for electrical signalling in the nervous system. Blocking of these ion channels by venom peptides results in rapid paralysis of a prey, making them ideal targets for venomous animals. There is a huge library of venom peptides blocking sodium and potassium channels with high affinity, which may be developed as therapeutics to treatACCEPTED diseases caused by dysfunctional ion channels (Lewis and Garcia, 2003; Wulff et al., 2009). The main hurdle in drug design from venom peptides is that they usually lack selectivity for the intended channel subtype, which can cause serious side effects. Engineering analogues that have the desired selectivity is a non-trivial task in the lab as it is mostly based on guess work. Computational modelling of ion channel-peptide complexes could help to solve such design problems by providing an AaccurateCCEPTED map of MANUSCRIPT the molecular interactions in a complex structure.

This, of course, assumes that the crystal structure (or an accurate homology model) of the target ion channel is available (venom peptides being smaller their structures are more easily determined from

NMR). Recent reports have shown that accurate protein-peptide complex structures can be obtained by combining docking methods with MD simulations (Alonso et al., 2006; Gordon et al., 2013;

Kuyucak and Norton, 2014). In this scheme, the top binding poses predicted by a docking program are refined in MD simulations until a stable complex structure is obtained. Use of a high-end docking program such as HADDOCK (Dominguez et al., 2003), which allows ensemble docking and flexibility of the protein/peptide, is imperative to avoid false complex structures and getting poses close to the final structure. This will also ensure faster equilibration during MD simulations, which is computationally much more expensive than docking.

Proper validation of a complex model is essential before the results can be used for drug design purposes. Alanine scanning mutagenesis data provide the best means for testing the accuracy of a predicted binding mode, though mutations of a few MANUSCRIPTcritical residues involved in the binding would also be sufficient. If mutation data are not available, one can use the measured binding constant for this purpose by calculating the binding free energy of the peptide from umbrella sampling MD simulations (Chen and Kuyucak, 2011). It should be stressed that the computation of the binding free energy of peptides from umbrella sampling simulations is not trivial, requiring significant expertise and computational resources as well as the availability of an experimental crystal structure of the target receptor or of a highly homologous receptor (Chen and Kuyucak, 2011; Rashid and

Kuyucak , 2012; Rashid et al., 2013; Rashid and Kuyucak, 2014; Rashid et al., 2014; Mahdavi and

Kuyucak, 2014). ValidatedACCEPTED complex structures of a peptide with target and off-target ion channels provide valuable guidance for improving the affinity and selectivity properties of this peptide.

Positions that are only important for binding to off-targets but not the target can be mutated to alanine. Similarly, the affinity of a peptide for a target channel can be increased by inspecting its binding mode and mutating those residues on the peptide that could lead to more contacts with the channel residues. Here one can alsoACCEPTED turn the qualit MANUSCRIPTative predictions gleaned from the binding modes into quantitative ones by performing free energy calculations. The change in the binding free energy due to a mutation can be determined accurately using the free energy perturbation or thermodynamic integration methods (Kuyucak and Norton, 2014). Below we describe a few selected examples of peptide blockers of potassium and sodium channels that illustrate how the proposed methods for modelling work in practice. More comprehensive discussions can be found in recent review articles (Gordon et al., 2013; Kuyucak and Norton, 2014).

Peptide toxin blockers of potassium channels. The potassium channels were the first voltage- gated ion channels to have their structure determined (Doyle et al., 1998). The availability of this structural information, which was rare at the time, prompted a great deal of work focusing on peptides that block these channels, and as part of these studies an extensive number of molecular models were generated (Chen and Chung, 2015). Blockers of K V1 channels have attracted the most attention because they are linked to several diseases. A prime example is the K V1.3 subtype, which is a well-established target for the treatment of aMANUSCRIPTutoimmune diseases (Chi et al., 2012). The discovery that ShK toxin from sea anemone binds to K V1.3 with a picomolar affinity has focused efforts to develop a therapeutic agent from ShK (Chi et al., 2012). The main hurdle was its lack of selectivity for K V1.1 in the nervous system. Over 400 ShK analogues were generated to improve selectivity, but the limited success of this endeavour highlights the difficulty of engineering analogues based on purely guess work. Construction of accurate models for the K V1.1–ShK and

KV1.3–ShK complexes, which were validated using the available mutation data and binding free energies, provided fresh insights on how to design ShK analogues with improved K V1.3/K V1.1 selectivity (Rashid ACCEPTEDand Kuyucak, 2012). The success of binding free energy computations on these receptors can be accounted by the availability of an experimental crystal structure of KV1.2, which is highly homologous to KV1.3 and KV1.1. Comparison of the binding modes indicated that some simple mutations, such as K18A and R29A, could improve the selectivity. These have been ignored hitherto because an existing patent on ShK mutations claimed that they reduced the K V1.3/K V1.1 selectivity. Binding free energy computationsACCEPTED indic MANUSCRIPTated that the K18A mutation would improve the

KV1.3/K V1.1 selectivity by more than 2 kcal/mol (~30-fold of K d) without affecting the affinity for

KV1.3, and this was later confirmed by experiments (Rashid et al., 2013).

A second example where computational modelling of peptides has played a guiding role is the scorpion toxin HsTx1, which also has a picomolar affinity for K V1.3 and exhibits some

KV1.3/K V1.1 selectivity (Regaya et al., 2004). HsTx1 has a more stable structure than ShK and may offer a better alternative as a therapeutic for autoimmune diseases. Models of K V1.1–HsTx1 and

KV1.3–HsTx1 complexes (Figure 2), validated through comparison of the calculated binding free energies with experiment, have revealed that the R14A mutation could improve the KV1.3/K V1.1 selectivity substantially without affecting its affinity for K V1.3 (Rashid and Kuyucak, 2014). This prediction was confirmed in subsequent functional assay experiments, where a selectivity margin of more than 2000-fold in IC 50 was measured for K V1.3 over K V1.1 (Rashid et al., 2014). Thus,

HsTx1[R14A] provides the most selective and potent lead so far in the development of therapeutics for autoimmune diseases. MANUSCRIPT

Peptide blockers of sodium channels . Crystal structures of bacterial Na V channels were determined only recently (Payandeh et al., 2011), and the first eukaryote structure of a Na V channel was determined only this year (Shen et al., 2017), but there are no structures yet for the mammalian

Na V channels. Thus efforts have been focused mainly on constructing accurate homology models for the mammalian Na V channels using the large amount data available on the binding of toxin peptides (Korkosh et al., 2014; Mahdavi and Kuyucak, 2014a). The pore domain is reasonably well described by these ACCEPTEDhomology models. For example, the model of the complex between Na V1.4 and µ- GIIIA provides an accurate representation of the extensive mutation data available for this system (Mahdavi and Kuyucak, 2014a). This model of the pore of Na V1.4 was also validated using mutational data of other µ- (Mahdavi and Kuyucak, 2014b). The binding mode of

GIIIA in the pore is broadly similar to that of toxins interacting with the pore of potassium channels, which is illustrated in Figure 2. But problems remain in the modelling of the S5-P1 and P2-S6 linkers in the extracellularA turretCCEPTED regions MANUSCRIPT because they differ substantially across the four domains and had until recently no similar counterparts in the Protein Data Bank for homology modelling. The 3.8 Å EM structure of the American cockroach putative Na V channel recently unravelled the structure of these turrets, which are heavily glycosylated, but to our knowledge, this very recent structure has not yet been used in any molecular modelling study of toxins (Shen et al.,

2017). As the pore domains of the Na V1 subtypes are very similar, accurate modelling of these regions is essential in order to explain the observed variations in the affinities of peptide toxins among the Na V1 subtypes (Wilson et al., 2011).

Gating-modifier toxins (GMTs). GMTs are spider toxins that inhibit the function of a voltage- gated channel by binding to the voltage-sensor domain (VSD) (Catterall, 2010). As already mentioned in Section 2, some of these toxins form a trimolecular complex involving the membrane and a VSD, and in this case the lipid environment and VSD should both be modelled. GMTs typically bind and lock VSDs into a conformation corresponding to the closed channel, but the crystal structures of K V and bacterial Na V channels onlyMANUSCRIPT display VSDs in conformations associated with the open channel. Several strategies have been used to model the Na V VSDs in a conformational state potentially targeted by the toxins, such as unbiased MD simulations (Jensen et al., 2012), steered-MD simulations (Tarek and Delemotte, 2013), metadynamics (Delemotte et al.,

2015) or Rosetta modelling (Yarov-Yarovoy et al., 2012). Accurate modelling of the conformation of the extracellular loops, namely the S1–S2 and especially the S3–S4 segments of the VSDs, is also critical for determining the binding modes of GMTs, but the structure of the bacterial channels are of little help in this regard. The recent EM and X-ray crystallography structures of the cockroach Na V andACCEPTED rabbit Ca V channels (Shen et al., 2017; Wu et al., 2016, 2015) display VSDs in different activation states and have higher homology of extracellular loops to most mammalian

VSDs, offering exciting opportunities to generate more accurate models of the binding mode of

GMTs. To our knowledge, the only study reporting the full binding process of a GMT interacting with the membrane was reported by the Sansom group (Wee et al., 2010). In this study, the ACCEPTED MANUSCRIPT interaction between the spider toxin VSTx1, the VSD of K VAP and a POPC bilayer was modelled using coarse-grained MD simulations. The toxin was observed to bind spontaneously to the membrane and diffuse toward the VSD. The coarse-grained model was then used in an all-atom modelling of the binding of VSTx1 with the S3–S4 segment of the VSD. The binding mode of the spider toxin HaTx1 on the K V2.1 VSD was modelled using a combination of docking using

ZDOCK in vacuum and MD simulations in a membrane environment, identifying a unique orientation of HaTx1 compatible with the mutagenesis data (Chen et al., 2012). Few models featuring interaction of toxins with Na V and Ca V VSDs have been generated so far (Minassian et al.,

2013), but it is expected that recent experimental structures will prompt more extensive modelling studies. An important target from a pharmacological point of view is the sensory Na V1.7 channel, which plays a key role in inflammatory and neuropathic pain (Vetter et al., 2017). Computational studies of the binding of selected toxin peptides to the voltage sensor of Na V1.7 would therefore be very useful in order to characterise their binding modes and design analogues with improved affinity/selectivity profiles for Na V1.7. MANUSCRIPT 4 Nicotinic acetylcholine receptors

Nicotinic acetylcholine receptors (nAChRs) are pentameric ligand-gated ion channels that mediate rapid synaptic signal transduction (Taly et al., 2009; Cecchini and Changeux, 2015) and exist as different subtypes depending on subunit composition. Neuronal subunits include α2–10 and

β1–4, while the α, γ and δ/ε subunits are found in neuromuscular junctions (Gotti et al., 2007). Each subunit has an extracellular domain (ECD) ( α subunits contain a conserved CC motif at the tip of loop C), a transmembraneACCEPTED and an intracellular domain (Unwin, 2005). The agonist binding site of nAChRs lies between two ECDs of adjacent subunits and is highly conserved within the nAChR family (Brejc et al., 2000). In this regard, peptide inhibitors have a larger contact surface area with the receptor, with multiple contacting residues that can be modified to modulate selectivity. One such source of nAChR-binding peptides is the family of conotoxins, which are 10–50 residue long disulfide-rich peptides isolated fromACCEPTED the venoms MANUSCRIPT of marine cone snails (Akondi et al., 2014). The best-characterised members of this family are α-conotoxins ( α-CTXs), which mainly act as antagonists of nAChRs (Lebbe et al., 2014). Most of these toxins have very similar, rigid structures, displaying a small helical section and two disulfide bonds. They are classified according to the number of residues in the two inter-Cys segments, and most α-CTXs belong to the 3/5, 4/3 and 4/7 subtypes. Due to their selective inhibition of nAChR subtypes, they have therapeutic potential in the treatment of neurological diseases, such as Alzheimer's disease, Parkinson's disease or schizophrenia, or conditions such as neuropathic pain.

α-Conotoxin inhibition of nAChRs: current models and their limitations. Major breakthroughs enabling an understanding of α-CTX/nAChR interactions have come from crystal structures of the homologous protein AChBP in complex with several different α-CTXs: ImI (4/3)

(Ulens et al., 2006; Hansen et al., 2005), PnIA variant (4/7) (Celie et al., 2005), TxIA variant (4/7)

(Dutertre et al., 2007) and BuIA (4/4) (Kim and McIntosh, 2012) (PDB: 4ez1). In all these crystal structures, the α-CTXs have similar binding modes, MANUSCRIPTsuggesting that this binding mode should also be conserved for all α-CTXs displaying a similar structure (Figure 3 AC). These structures have served as templates for homology modelling of nAChR due to the high sequence similarity between

AChBP and the ECD of nAChRs (Sine and Engel, 2006). MD simulations have been used to refine the structures and examine the motions of many α-CTX/nAChR complexes built by homology modelling, and these studies have been extensively surveyed elsewhere (Dutertre and Lewis, 2004;

Tabassum et al., 2016). In a recent study, a homology model of α-conotoxin GIC bound to α3β2 nAChR was generated but could not be used to explain all mutational data (Lin et al., 2016). This model was not refinedACCEPTED using MD simulation, which contrast with all studies hereafter cited that produced molecular models with significant explanatory and predictive power. In the current review, we will mention several illustrative studies, and subsequently focus on prospects for novel investigations.

Amongst the first MD simulation studies of α-CTXs was that of the (4/3) RgIA/ α9α10 complex (Pérez et al., 2009), where RgIA-R7ACCEPTED was found toMANUSCRIPT form multiple contacts with α9-D198 and -P197.

A recent MD study of the same complex (Azam et al., 2015) combined with electrophysiological recording of receptor mutants revealed that the interface between the α10 and α9 subunits is the likely target of RgIA. As an example of a study of 4/7 α-CTXs, MD simulations of the AuIB/ α3β4 complex identified AuIB-F9 as a key residue to its inhibitory activity, this residue being

“sandwiched” between β4-W59 and -K61 (Grishin et al., 2013). Alanine substitutions of these two residues in the receptor resulted in dramatic loss of inhibition, confirming that they play a crucial role at the interface. Molecular model of the interaction between conotoxins and nAChRs provided qualitative insight of important pairwise interactions, but the low resolution of such models prevented the quantitative predictions of absolute binding affinities, for example using umbrella sampling. Nevertheless, several studies have used mutational energy prediction to support the validity of computational models (Tabassum et al., 2016). For example, Yu and colleagues used a combination of molecular mechanics (MM) simulations and Poisson-Boltzmann/Surface Area (PBSA) free energy ( ∆G) calculations to estimate theMANUSCRIPT mutational free energies of a range of ImI mutants bound to the homopentameric α7 nAChR (Yu et al., 2011). Mutational data for Vc1.1 inhibition of the α9α10 nAChR have also been rationalised using MM-PBSA, and this method was employed to discover the binding mode and design more potent mutants (Yu et al., 2013). Protein surface topography is a recently developed visualisation tool that represents on a map of the average properties of the interactions between α-conotoxins and nAChR binding sites recorded during an

MD simulation (Kasheverov et al. 2016). This method was used to design more potent and selective

α-conotoxin PnIA analogues targeting the α7 and nAChR.

MD simulationsACCEPTED have also been applied to explain species-dependant differences in nAChR response to α-CTXs, motivated by the potential development of conotoxins as therapeutics.

Although nAChR orthologues have a high degree of sequence identity, markedly different responses to some α-CTXs have been reported between species. A recent study of the activity of

RegIIA showed that it potently inhibits rat, but not human, α3β2 (Kompella et al., 2015a), with the sole species determining residueA CCEPTED being β2-Q198 MANUSCRIPT (rat) compared with β2-P198 (human). This position, 198, is not in direct contact with RegIIA in the binding mode derived from the AChBP-

PnIA variant structural template. However, MM-PBSA calculations suggest that the amino acid at position 198 of the β2 subunit indirectly influences the interaction between RegIIA-N11/N12 and

α3-E195. The N11 and N12 residues of RegIIA were shown to be crucial for RegIIA activity, with their Ala mutants exhibiting greater selectivity for α3β4 over α3β2 (Kompella et al., 2015b).

Chemical modifications of α-CTX have been employed to impart receptor selectivity. Studies of chemically-modified α-CTX demonstrated that dicarba-Vc1.1 (van Lierop et al., 2013) and RgIA

(Kompella et al., 2013) are active at either nAChR (ion channel) or GABA B receptor (G-protein coupled receptor, GPCR) depending on the position of disulfide substitution, thus causing a switch in selectivity between two entirely different classes of receptors. MD simulations of the native and dicarba-modified Vc1.1 and RgIA bound to the ECD of α9α10 suggested that dicarba substitution at positions 2 and 8 of the conotoxins results in loss of contacts of these residues with the cystine sulfurs at the tip of loop C. The disruption in conotoxin-receptor MANUSCRIPT contact at these positions may be partly responsible for the loss of activity of the 2,8-dicarba conotoxins at nAChR.

The presently-available AChBP/CTX complex structures display a similar binding mode in the orthosteric binding site, but the development of selective inhibitors of certain nAChR subtypes might require targeting other binding sites. This is starkly illustrated by the α4β2 nAChR subtype, for which there is presently no known potent (let alone specific) conotoxin inhibitor, despite efforts at designing one based, for example, on α-conotoxin GID (Banerjee et al., 2014, p. 201). A study of TxIA interaction withACCEPTED α4β2 showed that differential activity between the α3β2 and α4β2 subtypes can be attributed to the occlusion of the agonist binding site by α4-R185 and α4-P195 (Beissner et al., 2012). Thus, the shape of the α4β2 subtype illustrates a potentially insurmountable obstacle to

α-CTXs inhibition, precluding design of novel toxins targeting its orthosteric site. The α5 subunit also has R185 and P195 residues, and could similarly be difficult to target by α-CTXs. Engineering or discovering conotoxins able to inhibit nAChRs by interacting with an allosteric binding site ACCEPTED MANUSCRIPT would offer potential alternatives to design inhibitors of α4β2 or α5- containing nAChR subtypes.

Beyond the classical binding site: prospects from new conotoxins and nAChR structures.

Potential allosteric binding sites can be identified using MD simulations of α-CTX unbinding from the orthosteric binding site of nAChRs (Yu et al., 2012). The computation of the potential of mean force using the Jarzynski equation was used to identify sites of potential interest in the unbinding pathway of α-conotoxin ImI from the α7 nAChR (Yu et al., 2012). A more accurate but computationally intensive method to compute the potential of mean force is umbrella sampling, in which several simulations are carried out with the ligand restrained at different distances from the initial binding site (Woo and Roux, 2005). This approach has been used to study the unbinding of

ImI and PnIA from AChBP (Yu et al., 2016) as well as GID unbinding from α4β2 and α7 nAChRs

(Suresh and Hung, 2016). The increased computational power only recently enabled such tedious computations because the conformational landscape of the relative flexible C-loop needs to be modelled during the unbinding process from this large receptor. Although a number of sites along the unbinding pathways have been identified in thes eMANUSCRIPT studies, their potential as allosteric inhibitory binding sites is presently speculative. A more promising avenue is the recent discovery of a novel dimeric nAChR-inhibiting αD-conotoxin, GeXXA (S. Xu et al., 2015), which can inhibit nAChRs non-competitively. Each chain includes a C-terminal domain (CTD, residues 21–50) which, in monomeric form, retains inhibitory activity against α9α10 nAChR. Dimeric αD-GeXXA exhibited a stronger preference for rat α9α10 compared to the human subtype, with rat α10-H7 (L7 in human) determining the toxin’s species preference. Position 7 was therefore proposed to be part of the binding site of αD-GeXXA. The binding mode was suggested to involve cooperative binding of

αD-GeXXA to the ACCEPTEDECD α-helices, spanning two inter-subunit interfaces (Figure 3 BD). Although the GeXXA residues responsible for its activity have yet to be identified, they are likely to be in the

CTD, since the monomeric GeXXA CTD peptide (comprising residues 21–50 only) was shown to have nAChR-inhibitory activity. We further propose a speculative model, shown in Figure 3, in which rat α10-H7 is in contact with two GeXXA-D33 residues, the latter being the only acidic residue in GeXXA and therefore AmostCCEPTED likely to MANUSCRIPTcontribute to electrostatic interactions between the toxin and the crucial H7 of rat α9α10. Such a (potentially) strong interaction would be lacking in human α9α10, and might explain the lower potency of GeXXA against the human subtype. Another

α-CTX called GeXIVA was recently discovered to non-competitively inhibit nAChRs, and MD simulations suggested a binding site located on the α-helices of the ECD, similarly to αD-GeXXA

(Luo et al., 2015). This novel interaction mechanism opens up new opportunities for the rational design of toxins which target nAChRs, an approach especially valuable for those subtypes which have proved resistant to competitive antagonism by α-conotoxins.

Another major breakthrough in understanding neuronal nAChRs is the recent publication of the crystal structures of the α2 (Kouvatsos et al., 2016) and α4β2 (Morales-Perez et al., 2016) nAChRs.

These are the first neuronal nAChR structures to be solved in pentameric form, and both were crystallised in the classical “open-like” ECD conformation in which loop C is enclosed around the agonist. The folds of the ECDs were found to be broadly similar to that of AChBP. For α4β2, the crystal structure sheds light on side chain conform ationsMANUSCRIPT at the agonist binding α(+) β(-) and non- binding β(+) α(-) interfaces which explain the inability of the latter to bind acetylcholine and other agonists. In the context of inhibitor design, the α4β2 channel structure is in a desensitised, non- conducting state, which raises the possibility of using this structure to aid the design of α-CTXs which can trap the nAChR in a desensitised state in the absence of agonists, rather than directly targeting the resting state structure as in conventional antagonists. Receptor desensitisation is readily achieved by prolonged exposure to agonist; however, cotinine (a choline metabolite) and novel choline analogues have been shown to desensitise nAChRs without a preceding agonist action

(Buccafusco et al.,ACCEPTED 2009). These “silent desensitisers” were proposed to act allosterically. Lastly, the availability of the α2 and α4β2 agonist-bound structures may open up opportunities for improved rational design of CTX positive allosteric modulators. Although conotoxins mainly act as inhibitors, the 4/7 α-CTXs SrIA, SrIB and EI showed potentiation of α4β2 if briefly applied at low concentrations (López-Vera et al., 2007). The newly-available structures of the agonist-bound α2 and α4β2 receptors, coupled withA advancesCCEPTED in molecular MANUSCRIPT modelling and simulation techniques, can serve to greatly enhance our understanding of the intriguing potentiation mechanism of these conotoxins, and perhaps lead to the design of analogues with markedly improved selectivity as positive modulators.

5 Other targets

Besides voltage-gated ion channels and nAChRs, animal toxins also modulate the activity of a range of membrane proteins, and a selection of these targets are highlighted in this section. The

ASIC channels are ion channels activated by pH. Several crystal structures of ASICs in complex with the spider toxin PcTx1 ( 1) or the snake toxin MiTX have been determined, where the toxins were used to stabilise the channels into open states (Baconguis et al., 2014; Baconguis and Gouaux, 2012; Dawson et al., 2012). The binding sites of both toxins overlap and are located at the interface between adjacent subunits. A hydrophobic patch and a cluster of charge interactions were initially hypothesised as essential for PcTx1 bindingMANUSCRIPT at the interface between adjacent subunits (Figure 1). MD simulations of the PcTx1/ASIC1a complex revealed that 31 molecular interactions at the interface were potentially stable, narrowing down the number of the residues to study by mutagenesis, resulting in the identification of only six essential PcTX1 positions (Saez et al., 2015).

The binding mode of snake toxins mambalgin-1 and -2 to ASIC1 were studied by rigid body docking oriented by results of double-mutant cycle analysis (Mourier et al., 2016) or substitutions of the channel for the amino acid of corresponding residues of ASIC2a (Salinas et al., 2014).

Considerable overlap of binding modes of the mambalgin peptides with that of PxTx1 was suggested from theACCEPTED models.

GABA A is a Cys-loop ligand-gated ion channel, similarly to nAChRs, and it is the target of benzodiazepine and several anaesthetics. It was recently discovered that α-conotoxin ImI and bungarotoxin, which inhibits nAChRs, were also active at GABA A (Kudryavtsev et al., 2015). A model of the interaction between ImI and GABA A α1β3 was generated by homology with the ACCEPTED MANUSCRIPT crystal structures of GABA A (Miller and Aricescu, 2014) as well as of ImI/AChBP complex

(Hansen et al., 2005). This model was refined by 100 ns MD simulation in implicit solvent, revealing a similar binding of ImI in the GABA A binding site as in that of AChBP (Kudryavtsev et al., 2015).

GPCRs, or seven-transmembrane-helix receptors, transduce various type of signal received extracellularly and this large class of receptors is the most successful drug target. For example the

α1-adrenoreceptors activation causes vasoconstriction and its inhibitors can be used to treat hypertension. The conotoxin TIA is a negative allosteric modulator of this GPCR (Sharpe et al.,

2003) and a molecular model of its interaction with a human subtype was built (Ragnarsson et al.,

2013). This model was generated by HADDOCK (Dominguez et al., 2003) using a homology model of the human α1B-adrenoreceptor and mutants built by homology with the crystal structure of the turkey β1-adrenoreceptor (Warne et al., 2008). The molecular models helped identify a crucial salt bridge and cation–π interactions at the interface, both involving TIA Arg-4.

The muscarinic acetylcholine receptors are the MANUSCRIPT second major class of receptors activated by acetylcholine at the synapse beside the nAChRs. They are linked to Alzheimer's disease, schizophrenia and chronic obstructive pulmonary disease and exist as five subtypes M1–M5. Four crystal structures of these receptors have been published: the rat M3 subtype (Kruse et al., 2012), the human M2 subtype (Kruse et al., 2013) and recently the human M1 and M4 subtypes were reported (Thal et al., 2016). Some snake three-finger toxins are subtype-selective allosteric modulators of the muscarinic receptors. A model of the interaction of the green mamba muscarinic toxins MT7, whichACCEPTED is subtype-selective for the M1 receptor, was created by docking the NMR solution structure of MT7 with homology models of human muscarinic receptors using patchdock and firedock (J. Xu et al., 2015). Mutational energies were carried out using the MM-GBSA energy function applied to 1,000 snapshots extracted from a 20 ns MD simulation, resulting in a reasonable correlation between the predicted energies and experimental activity. The decomposition of energy terms per residues was used to identify residue hotspots, which were supported by published mutational data. The interactionsA ofCCEPTED the “weak” MANUSCRIPT toxin WT from the snake Naja kaouthia were docked on the human M1 and M3 subtypes using Rosetta and ZDOCK (Lyukmanova et al., 2015).

The stability of the complex observed during a 200 ns simulation carried out in a lipid membrane correlated with activity.

6 Conclusion and outlook

In this review, we have described using a range of examples how molecular modelling has been used as a guide in studies of animal venom toxins interacting with cell membranes and membrane receptors. Regarding membrane interaction, molecular dynamics simulations of spider GMTs helped study their partition in membranes as well as build hypotheses on the mechanism of membrane poration by the bee toxin melittin. The vast majority of molecular models of the binding mode of toxins with membrane receptors focused on providing a rationale to mutagenesis data. In some instance, the models were in turn used to suggest modification to the toxins to improve affinity or selectivity, for example for the anemone MANUSCRIPTtoxin ShK (Rashid et al., 2013), the cone snail toxin Vc1.1 (Yu et al., 2013) or the scorpion toxin HsTx1 (Rashid et al., 2014). Molecular models also helped designing toxins with improved stability, which is required for potential medical or biotechnological applications, and at the same time retain a biological activity. To this regard we cited attempts at replacing the disulfide bonds of cone snail toxins RgIA (Kompella et al., 2013) and

Vc1.1 (van Lierop et al., 2013; Yu et al., 2015).

The research landscape focusing on animal peptides toxins active at membrane proteins is rapidly changing because of the increasing pace at which high-resolution structures of these proteins are discovered,ACCEPTED mainly due to the advent of improved EM techniques. This is an exciting time for the study of animal toxin structure-activity relationship studies, as these new structures can be used to generate highly accurate models of the interactions of these peptides. The recent experimental structures of eukaryote voltage-gated ion channels, ie rabbit Ca V1.1 and cockroach

Na V, will especially benefit the modelling of pore-blocker toxins because these new structures give information on the so-called P-loops,ACCEPTED which are MANUSCRIPTlarge loops hovering over the entrance of the pore of the channel with a structure which could not be hitherto predicted. The crystal structure of the

α4β2 nAChR provides atomic resolution details of the orthosteric binding site, which is targeted by numerous spider, cone snail, and snake toxins and which was typically modelled by homology with distant invertebrate homologues. It should be nevertheless noted that the activation state in which the nAChR, Ca V and Na V have been captured is not directly relevant to the modelling of all toxins, and that experimental structures of an nAChR in closed state or a Na V/Ca V in active state would help build even more accurate models of the binding mode of toxins, which are central to structure- activity relationships studies.

Interestingly, the experimental structures of the molecular targets of certain class of toxins have been determined but to our knowledge no binding mode was yet proposed; for example for the cone snail conantokins, which inhibit the NMDA receptor (Karakas and Furukawa, 2014; Lee et al.,

2014), or for the scorpion maurocalcin, which inhibits the ryanodine receptor (Zalk et al., 2015). The increased accuracy of molecular models enables MANUSCRIPT more reliable estimates of binding energies. Increasing computational power enables increasing conformational exploration and therefore increased accuracy of predicted energies. The computation of energy have been used to support binding modes proposed in molecular models as well as to rationalise mutations studies, but the major challenge remains the prediction of toxins selectivity. Most toxins act on protein targets that exist as multiple closely-related subtypes, and determining the activity at a large number of subtypes is experimentally challenging. The development of computational methods to predict the selectivity of toxins between target subtypes would constitute a major breakthrough for toxin research. ACCEPTED

Acknowledgements.

This work was funded by the Australian Research Council (ARC) Discovery Project

(DP150103990) and a National Health and Medical Research Council (NHMRC) Project Grant

(APP1080405). CIS is an ARC Future Fellow (FT160100055). ACCEPTED MANUSCRIPT Conflicts of Interest

The authors declare no conflict of interest.

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ACCEPTED ACCEPTED MANUSCRIPT Figure legends

Figure 1: Examples of membrane proteins targeted by venom toxins: the G-protein coupled receptors (GPCRs), the voltage-gated calcium/sodium/potassium ion channels

(Ca V/Na V/K V), the nicotinic acetylcholine receptors (nAChR), the acid-sensing ion channels

(ASIC) and the α-amino-3-hydroxy-5-methyl-4-isoxazolepropionic acid receptor (AMPAR).

All these targets beside the GPCRs are ion channels. Representative toxins are shown in orange, and experimental structures of the targets are represented in blue colours. For each target, a view across the lipid membrane (pink) is at the top and a view from the extracellular side of the membrane is at the bottom. Red arrows indicate the flux of ions when the ion channels are open.

The mamba snake toxin MT7 antagonises selectively the M1 muscarinic receptor, which is a

GPCR. The crystal structure of the human M2 muscarinic receptor is here represented for illustration (Protein Data Bank PDB identifier: 4mqs) (Kruse et al., 2013). The marine cone snail toxin MVIIA specifically blocks the pore of human Ca V2.2. The electron microscopy structure of the rabbit Ca V1.1 is here represented for illustration MANUSCRIPT (PDB: 5gjv) (Wu et al., 2016). Most toxins targeting voltage-gated channels act by blocking the pore (arrow numbered 1 on the bottom) or by blocking the voltage sensor domains in various states (arrows 2). The cone snail toxin PeIA inhibits the α9α10 nAChR by competing with acetylcholine in the orthosteric sites, which are located between subunits in the ligand binding domain (indicated by arrows on the bottom). The nAChRs are pentamers and there is consequently a maximum of five binding sites. The crystal structure of the human α4β2 nAChR is here represented for illustration (PDB: 5kxi) (Morales-Perez et al., 2016). The spider psalmotoxin-1,ACCEPTED or PcTx1, binds at the interface between each subunit of ASIC 1a, stabilising the open state. The crystal structure of the complex between PcTx1 and chicken ASIC 1a is here represented (PDB: 4fz0) (Baconguis and Gouaux, 2012). The cone snail toxin con-ikot-ikot blocks the desensitisation of AMPAR by binding as a dimer on top of the ligand-binding domain, plying open the pore of the channel (Chen et al., 2014).

Figure 2. Superposition of molecular models of Kv1.1–HsTx1[R14A] with that of Kv1.1– HsTx1. The backbone of HsTx1[R14A]ACCEPTED (Green) MANUSCRIPT overlaps well with the backbone of HsTx1

(transparent red), indicating that the mutation does not alter the binding mode. The affinity of

HsTx1[R14A] for Kv1.1 is reduced by the loss of the ionic interaction between E353 of the channel and R14 of the toxin. This interaction does not occur for Kv1.3–HsTx1 and therefore its affinity is not affected by the substitution.

Figure 3. Illustrative molecular models of the binding modes of α-conotoxins and αD- conotoxins. Top views of α-conotoxin Vc1.1 bound at nAChR inter-subunit interfaces (A), and of

αD-conotoxin GeXXA bound to the α-helices of nAChR ECD (B). Side views of α-CTX Vc1.1 (C) and αD-CTX GeXXA (D) bound to nAChR. Conotoxins are shown in yellow, nAChR subunits in blue and red. Boxes in panel (D) indicate proposed GeXXA-D33 interaction with rat α10-H7.

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