A Gating Mechanism of the Serotonin 5-HT3 Receptor

Theory

A Gating Mechanism of the Serotonin 5-HT3 Receptor Graphical Abstract

Authors Shuguang Yuan, Slawomir Filipek, Horst Vogel

Correspondence [email protected] (S.Y.), [email protected] (H.V.)

In Brief Yuan et al. employ all-atom longtimescale molecular dynamics simulations to reveal activation mechanism of the serotonin 5-HT3 receptor. Serotonin binding first induces distinct changes in the highly conserved ligand-binding cage, followed by tiltingtwisting movements of the extracellular domain, which leads to opening of the transmembrane helices and the hydrophobic gate. Finally, the intracellular helix bundle opens lateral ports for ion passage.

Highlights d

Rotamer change of W156 in the highly conserved aromatic cage

d

Tilting-twisting movements captured at the extracellular domain

d

Rotamer change of L260 lead to formation of a continuous water pathway

d

Chlorine ions penetrate into the intracellular vestibule

Yuan et al., 2016, Structure 24, 1–10 May 3, 2016 ª2016 Elsevier Ltd All rights reserved http://dx.doi.org/10.1016/j.str.2016.03.019

Please cite this article in press as: Yuan et al., A Gating Mechanism of the Serotonin 5-HT3 Receptor, Structure (2016), http://dx.doi.org/10.1016/ j.str.2016.03.019

Structure

Theory A Gating Mechanism of the Serotonin 5-HT3 Receptor Shuguang Yuan,1,* Slawomir Filipek,2 and Horst Vogel1,* 1Institute

of Chemical Sciences and Engineering, Ecole Polytechnique Fe´de´rale de Lausanne (EPFL), 1015 Lausanne, Switzerland of Biomodeling, Faculty of Chemistry & Biological and Chemical Research Centre, University of Warsaw, Pasteura 1, 02-093 Warsaw, Poland *Correspondence: [email protected] (S.Y.), [email protected] (H.V.) http://dx.doi.org/10.1016/j.str.2016.03.019 2Laboratory

SUMMARY

Our recently solved high-resolution structure of the serotonin 5-HT3 receptor (5-HT3R) delivered the first detailed structural insights for a mammalian pentameric ligand-gated ion channel. Based on this structure, we here performed a total of 2.8-ms all-atom molecular dynamics simulations to unravel at atomic detail how neurotransmitter binding on the extracellular domain induces sequential conformational transitions in the receptor, opening an ion channel and translating a chemical signal into electrical impulses across the membrane. We found that serotonin binding first induces distinct conformational fluctuations at the side chain of W156 in the highly conserved ligandbinding cage, followed by tilting-twisting movements of the extracellular domain which couple to the transmembrane TM2 helices, opening the hydrophobic gate at L260 and forming a continuous transmembrane water pathway. The structural transitions in the receptor’s transmembrane part finally couple to the intracellular MA helix bundle, opening lateral ports for ion passage. INTRODUCTION The complex mental capacity and motor behavior of mammals rely on the evolution of a distinguished central and peripheral nervous system in which ligand-gated ion channels mediate fast intercellular signaling at synapses. Pentameric ligand- (i.e., neurotransmitter)-gated ion channels (pLGIC) of the Cys-loop receptor family are the most prominent representatives, and are also important targets for treating many neuronal disorders (Corringer et al., 2012; Hurst et al., 2013; Lemoine et al., 2012; Smart and Paoletti, 2012; Thompson et al., 2010; Walstab et al., 2010). These receptors function as allosteric signal transducers across the plasma membrane: at a synapse, a released neurotransmitter binds specifically at the extracellular site of its LGIC receptor, inducing multiple conformational changes to open an intrinsic ion-selective channel (Auerbach, 2014; Changeux, 2014; Corringer et al., 2012; daCosta and Baenziger, 2013; Schmauder et al., 2011). However, structural and mechanistic details of this central process remain speculative, as directly determined high-resolution structures of mammalian Cys-loop receptors are rare due to difficulties in heterologous

expression, purification, and crystallization of these proteins in a functional state (Bill et al., 2011). Present models of Cys-loop receptor structures are based on electron microscopy images of Torpedo acetylcholine receptor (AChR) (Unwin, 2013), crystal structures of the bacterial channel homologs GLIC and ELIC (Bocquet et al., 2009; Hilf and Dutzler, 2008, 2009), a Caenorhabditis elegans glutamate-gated chloride channel, GluCl (Althoff et al., 2014), and most recently, the first high-resolution structure of a mammalian pLGIC, the serotonin 5-HT3 receptor (5-HT3R) (Hassaine et al., 2014). Hence, a prototypical pLGIC is composed of three distinct structural and functional entities: a large b-sheet structured extracellular (EC) domain comprising the ligand-binding site(s), a helical transmembrane (TM) channel-forming part, and an intracellular (IC) domain that influences ion conductance and interacts with cellular scaffolding proteins (Thompson et al., 2010). The intracellular domain is missing in the non-mammalian receptor structures such as GLIC, ELIC, and GluCl and is only poorly resolved in the Torpedo AChR structure (Unwin, 2013), but is well resolved in the 5-HT3R structure (Hassaine et al., 2014). Although the recent high-resolution 3D structures of bacterial and eukaryotic pLGICs resulted in a common structure fold of the Cys-loop receptor family, the central question remains unresolved, namely, how the binding of an activating ligand at the EC receptor domain induces the opening of a gate about 60 A˚ away in the TM region, allowing the translocation of ions across the receptor. As experimental details about the structural transitions during channel activation are lacking, allosteric models have been derived from crystallographic data and molecular dynamics (MD) simulations of the different states of the channel structures of GLIC, ELIC, and GluCl (Aryal et al., 2015; Calimet et al., 2013; Nury et al., 2010; Sauguet et al., 2014; Zhu and Hummer, 2012). Here we have used a total of 2.8-ms all-atom MD simulations to elucidate at atomic resolution the structural transitions of the 5-HT3R in a lipid bilayer during activation by binding its natural neurotransmitter serotonin. Our computer experiments started with the crystal structure of the 5-HT3R in the closed-channel form (Hassaine et al., 2014), removing the five stabilizing nanobodies, embedding the receptor in a lipid bilayer, then inserting five serotonin molecules into the binding sites of the homopentameric 5-HT3R before finally observing the structural transitions of the receptor during the activation process toward the openchannel state. Cryoelectron tomography has shown that the crystal structure of the 5-HT3R is preserved in lipid bilayers (Kudryashev et al., 2016). To distinguish ligand-induced conformational changes from stochastic conformational fluctuations, we performed in addition MD simulations of the apo-5-HT3R,

Structure 24, 1–10, May 3, 2016 ª2016 Elsevier Ltd All rights reserved 1

Please cite this article in press as: Yuan et al., A Gating Mechanism of the Serotonin 5-HT3 Receptor, Structure (2016), http://dx.doi.org/10.1016/ j.str.2016.03.019

Figure 1. W156 Conformational Switches and Interaction Network between Residues in the 5-HT3R (A) Serotonin in the ‘‘aromatic cage’’ (dotted circle) at the interface between two neighboring extracellular domains. (B) Configuration of serotonin in the aromatic cage of the 5-HT3R after 30 ns of MD equilibration. (C) Configuration of serotonin in the aromatic cage of the 5-HT3R after 700 ns of MD simulation.

i.e., the receptor in the absence of an activating ligand. On this basis our MD simulations delivered a realistic description of how the binding of the activating neurotransmitter on the extracellular site of the 5-HT3R induces a sequence of conformational changes leading to the opening of the transmembrane ion channel, and finally to structural changes at the intracellular site of the receptor, and are therefore of direct relevance for the understanding of neuronal signal transmission on an atomic scale. RESULTS Binding of the Activating Neurotransmitter Serotonin to the 5-HT3R: The Role of W156 and Conserved Motifs All MD simulations have been performed on the 5-HT3R integrated into a bilayer of 1-palmitoyl-2-oleoyl-phosphatidylcholine (POPC), as it was shown elsewhere that the channel properties of the receptor are properly reconstituted in such membranes (Hassaine et al., 2014). By performing 2 3 700-ns all-atom MD simulations for both the agonist-free (apo) and the agonist-bound 5-HT3R in a lipid bilayer, we elucidated at atomic resolution the structural transitions of the receptor during ligand-induced activation. A wealth of functional studies on the 5-HT3R (Lummis, 2012), the recent high-resolution crystal structures of the 5-HT3R (Hassaine et al., 2014), and the serotoninbinding protein in complex with serotonin (Kesters et al., 2013) allowed us to establish through dedicated docking a highly reliable model on the binding of serotonin in its 5-HT3R. We found the bound neurotransmitter to be located in the highly conserved ‘‘aromatic ligand-binding cage’’ of pLGICs, formed by Y126, Y207, F199, W156, and W63, at the interface between adjacent receptor subunits (Hassaine et al., 2014) (Figures 1 and 2), which was very similar to the structure found in the serotonin-binding protein (Figure S1). There is no consensus on how many serotonin molecules are required to activate the 5-HT3R; between two and five serotonin molecules have been reported (Rayes et al., 2009). As the pentameric serotonin-binding protein 2 Structure 24, 1–10, May 3, 2016

(5HTBP) in the crystal structure comprised serotonin in each of the five binding sites (PDB: 2YMD, 2YME) (Kesters et al., 2013), we also included five serotonin molecules in the 5-HT3R structure for simulations. The insertion of serotonin into the ligandfree 5-HT3R structure (Hassaine et al., 2014) created conformational stress in the receptor’s binding cage, which initiated during the first 30-ns equilibration phase structural rearrangements in each of the five ligand-binding cages (Figure 1A): The serotonin molecule was stabilized via p-p stacking with the aromatic side chains of Y126 and W156; a cation-p stacking was established between R65 and serotonin; the aromatic side chain of W63 was oriented perpendicularly to the aromatic plane of serotonin, forming a s-p interaction; and a salt bridge was formed between serotonin’s amine group and E209. During the subsequent MD simulation period, serotonin and residues in the aromatic cage underwent further conformational transitions. While the aromatic moiety of serotonin was partially p-p stacking between F199 and Y207, the side chain of W156 switched toward a stable position (Figures 1A–1C). Such conformational switches, which are represented in the time course of c1/c2 angles of W156, took place consecutively in all subunits of the serotonin-bound receptor (Figure S2). Notably, for the apo-5-HT3R both c1 and c2 remained unchanged during the entire MD simulation period (Figure S2). The 5-HT3R is a prototypical representative of an allosterically regulated pLGIC where binding of the neurotransmitter serotonin activates an ion channel via a network of internal conformational changes (Schmauder et al., 2011; Lummis, 2012). To characterize the dynamic coupling of the serotonin-binding pocket to other regions of the receptor, we calculated the atomic displacement correlations for each residue pair in MD simulations for the 5-HT3R without and with bound serotonin. In the correlation network representation (Figure 3), nodes correspond to protein residues connected by edges and weighted by the strength of their respective correlation values. The relation of the correlation network to the 3D protein structure is also depicted in Figure 3. This method has been applied elsewhere to dissect allosteric couplings in various systems (Scarabelli and Grant, 2014; Sethi et al., 2009). Correlation matrices (Figure S3) show that all loops of the binding pocket (Figure 2) strongly couple to the extracellular b strands. As indicated in Figure 3, important correlated motions occur between distinct structural regions of the extracellular part of the receptor. Binding of the neurotransmitter serotonin affects collective motions, especially in loop B (where W156 is located), loop C, the bottom portions of b7-b9-b10, b1-b2-b6, and b10 -b70 -b90 -b100 . While in the apo-5-HT3R (Figure 3A), the bottom portions of b1-b2-b6 (yellow) and b7-b9-b10 (orange) form in both depicted subunits one highly compiled community, they are spread into several groups in the agonist-bound receptor (cyan, orange, yellow, pink, and white in Figure 3B). Ligand binding furthermore increased the size of the black group consisting of loop B, loop C, and the top portion of b7-b9-b10. These observations strongly indicate that the binding of serotonin plays a fundamental role in the space of these regions, inducing an overall looser coupling in the extracellular vestibule. Importance of Domain Tilting and Twisting for Receptor Activation Our MD simulations revealed that activation of the 5-HT3R toward an open channel proceeds in multiple domain movements

Please cite this article in press as: Yuan et al., A Gating Mechanism of the Serotonin 5-HT3 Receptor, Structure (2016), http://dx.doi.org/10.1016/ j.str.2016.03.019

Figure 2. The Serotonin Binding Pocket of the 5-HT3 Receptor Residues lining the serotonin-binding pocket of the 5-HT3R are depicted, together with a conserved residues logo based on multiple sequence alignment on all cationic Cys-loop receptors, including four 5-HT3 serotonin receptors, 17 nicotinic acetylcholine receptors, and one zinc-activated ion channel. The presence of conserved aromatic residues including W63, Y126, W156, and Y207 imply that p-p interactions play a central role for binding of the neurotransmitter. Furthermore, the conserved negatively charged residues at position 209 indicate the importance of electrostatic interactions for the binding of serotonin in the 5-HT3R and beyond for ligand binding in the case of the other anionic Cysloop receptors. The size of the single letters in the depicted sequence segments scales with its conservation at this position. Conserved residues participating in binding serotonin are highlighted by dashed rectangles.

in each subunit (Figure 4). To visualize the overall motion within the receptor we performed a principal components analysis (PCA), extracting data for each subunit from 1,000 snapshots evenly distributed over time in the MD simulations (Figure S3). The channel-gating process visualized by the PCA is best described as a superposition of radial tilting and tangential twisting of domains which results in a blooming-like opening of the receptor channel (Figure 4; Movies S1 and S2). Several motifs including loop A, loop B, loop C, b1-b2 loop, Cys loop, M2-M3 loop, and the M2 and MX helices follow these motions (Figure 2A), which are also reflected by the root-mean-square fluctuations (RMSF) over the backbones of the receptor’s subunits (Figure S4). Interestingly, the M2-M3 loops show by far the largest RMSF amplitudes in subunits A and E; the M2 helices in their center show comparable RMSF amplitudes especially in subunits B, C, and D. This demonstrates the direct coupling of the motions of M2-M3 loops with the fluctuations of the M2 helices. The importance of ligand-induced loop motions in the function of pLGICs has been noted by numerous structural and functional data, as summarized elsewhere (Changeux, 2014; Corringer et al., 2012; Lummis, 2012; Thompson et al., 2010). The MD simulations have resolved in detail the temporal sequence of distinct local structural transitions in the 5-HT3R during agonist-induced activation. About 30 ns after agonist binding (during the equilibration period), the b-structured EC domains have finished a rigid-body reorientation resulting in subunit tilting of Dq = 2 –7 out toward helix M4 (Figures 4 and S5). The tilt angles are slightly different in each subunit but remain stable during the entire simulation time period, with fluctuations of about ±1 around the mean. The outward tilting induces a substantial repositioning of the b1-b2 loops (5 A˚) at the interface between the receptor’s EC and transmembrane regions, and of loops C (3 A˚), which move upward together with the other extracellular loops (Figure 4). However, in the MD simulations of the inactive apo form, the tilting angles only fluctuated within 1 –2 (Figure S5). The structural transitions in the activated EC domains are complemented by additional twisting motions (Figure 4B) with characteristic individual time traces. Subunits A and B reached a final stable state of a twist-angle change of

D4 = 5 –7 after 350 ns, subunit C after 500 ns, and subunits D and E after 700 ns (Figure S5). By contrast, twist-angle fluctuations in the apo form are distinctly smaller (1 –3 ). Residues interaction correlation network analysis (Figure S3) shows that the motions of extracellular part of the receptor are highly correlated with the M2-M3 transmembrane movements. In addition, the motions within a subunit couple to the motions of adjacent subunits (Figure S3). This indicates that the tilting and twisting in the extracellular receptor part leads to the gating of the TM channel. Our results are consistent with previous studies of pLGICs (Calimet et al., 2013) and GABAA receptor (Miller and Aricescu, 2014). Pore Expansion during Receptor Activation In the crystal structure (Hassaine et al., 2014), a constricted ring of hydrophobic amino acid residues in the central M2 region of the 5-HT3R blocks the pathway for translocation of water molecules and hydrated ions across the receptor (Figure 5). To allow the passage of hydrated cations such as Na+, the hydrophobic constriction site has to function as a gate, which would be able to open and thus provide more internal space. We compared the flexibilities of residues in several conserved motifs with the changes of the inner pore size of the receptor and found that they are correlated during our MD simulations (Figure 5A). In the extracellular vestibule of the receptor, structural changes of loop A and loop B lead to considerable expansion (Figure 5C). In the closed state of the 5-HT3R the extracellular vestibule harbors a 4-A˚ wide constriction, which, after binding of serotonin, increased by 8 A˚ in diameter during the MD simulations (Figures 5B and 5C). These changes expand to the interface connecting the receptor’s EC domain via b1-b2 and the Cys loops with M2-M3 loops of the TM domain (Figures 5A and 5B). In consequence, the central part of the TM pore increases by 2–3 A˚ in diameter after binding of serotonin (Figures 5C and S7). The intracellular region of the 5-HT3R is composed of five MA helices extending from the M4 transmembrane helices. They form a closed vestibule in the non-activated receptor (Hassaine et al., 2014) (Figures 5A, 5B, and S8). The binding of serotonin induces an outward tilting and twisting of the M4 helices, which Structure 24, 1–10, May 3, 2016 3

Please cite this article in press as: Yuan et al., A Gating Mechanism of the Serotonin 5-HT3 Receptor, Structure (2016), http://dx.doi.org/10.1016/ j.str.2016.03.019

the volume of the EC vestibule expanded from 39 3 103 A˚3 (initial 10-ns MD simulations) by 18%, to 46 3 103 A˚3 (690–700-ns MD simulations), the volume of the TM pore from 2.7 3 103 A˚3 by 26%, to 3.4 3 103 A˚3, and the volume of the IC vestibule from 7.8 3 103 A˚3 by 32%, to 10.3 3 103 A˚3.

Figure 3. Simultaneous View of Community Residue Interaction Network and 3D Structure of the Neurotransmitter Binding Pocket of the 5-HT3R (A) Apo form without serotonin. (B) With bound serotonin. Colors of the dots in the community analysis correspond to colors in the indicated regions of the 3D protein structure.

couple to the MA helices to open 10-A˚ wide lateral portals in the intracellular vestibule (Figure 5B, right panel; Figures 5C and S7), allowing the entrance of Cl ions (Movie S3). The Cl ions are first captured by the positively charged residues K415, R424, and R428 on the intracellular surface of the receptor, and afterward move toward R416 inside the intracellular vestibule (Figures S6 and S7). Interestingly, even after binding Cl ions, the pKa values of these residues remain in the range of 11–13, implying that they are still protonated. In addition, movements of the ends of the MA helices form a hydrophobic tunnel, which becomes partly accessible by water molecules (Figure 5B). In detail, the following conformational changes in the M4-MA helix lead to these overall structural changes in the intracellular region of the receptor (Figure S8). The initial outward bending of the M4 helix is induced by the local helix bending around residue D434 which in turn breaks the salt bridge D434-R251 within one subunit and creates a new one, but now between D434 of one subunit and R306 of the neighboring subunit. The other characteristic changes occur in the bending of the MA helix around residues D417 and E418, leading to a break of the inter-subunit salt bridge D417K415 followed by a 20 torsion of the MA helix in this region and the formation of a new inter-subunit salt bridge D417-R416. These conformational changes occur within all subunits. All these structural transitions induce substantial volume changes within different internal pore regions of the receptor: 4 Structure 24, 1–10, May 3, 2016

Molecular Switching of the Hydrophobic Gates L260 and V264 Open the Ion Channel Ion-channel gating typically takes place on a submillisecond timescale (Thompson et al., 2010). The extremely large simulation system of the 5-HT3R, containing 186,500 atoms, cannot be accessed by classical MD over milliseconds in a conventional supercomputer (Dror et al., 2012). Instead, we used an advanced free-energy sampling method, namely well-tempered metadynamics, to explore the activation process of the 5-HT3R. Metadynamics relies on a history-dependent potential acting on a selected number of collective variables (CVs) and can substantially speed up sampling and reconstruct the free-energy surface associated with the CVs (Barducci et al., 2010). To choose the correct CVs for efficient sampling of the gating process, we first compared the crystal structure of 5-HT3R in the closed-channel state (Hassaine et al., 2014) (PDB: 4PIR) with the bacterial pLGIC homolog GLIC in the open-channel state (Nury et al., 2011) (PDB: 3P50). Two obvious differences were found in the structure of these two channel proteins. (1) in the open-channel structure of GLIC in the M2 helix of each subunit, two hydrophobic residues I233 and A237 adopt different rotamer structures compared with the hydrophobic residues L260 and V264 at corresponding positions in the 5-HT3R (Figure 6). (2) Such differences influence water and ion penetration in the channel: in GLIC the I233 residues form a pentagon with a cross-sectional area of 100 A˚2 (Figure 6A), while in the 5-HT3R the L260 residues span a cross-sectional area of only 40 A˚2. Similarly, the A237 residues of GLIC span a regular pentagon of 118 A˚2, but the corresponding plane of the V264 residues in the 5-HT3R is only 57 A˚2. Therefore, we used side-chain dihedral angles c1 of L260 and V264 in subunit D as two CVs for well-tempered metadynamics simulations. Our simulations revealed two characteristic conformations for both L260 and V264, a closed state I and an open state II (Figure 6B). The free energies are 17 kJ/mol for state I and 33 kJ/mol for state II. In the closed state I, a hydrophobic constriction is formed by adjacent pentameric rings of residues L260, V264, and I268, respectively (Figures 7A and S8A). In the open-channel state II (Figures 7B and S8A), a continuous water channel is formed, crossing the hydrophobic constriction site (Movie S4). Residues interaction network analysis on M2 channel gating (residues 250–271) show that in the closed state I, all five submits interacted with each other almost identically (Figure S8B, left panel). However, in the active receptor state II, chains A, B, and C are much closer to each other than to subunits D and E which, on the other hand, are located closely together (Figure S8, right panel). Consequently, much larger space is created for movement of water molecules (Movie S4). DISCUSSION Here, we compare our findings on the activation process of the 5-HT3R with published data on pLGICs.

Please cite this article in press as: Yuan et al., A Gating Mechanism of the Serotonin 5-HT3 Receptor, Structure (2016), http://dx.doi.org/10.1016/ j.str.2016.03.019

Figure 4. Domain Movements and Hydrophobic Pore of 5-HT3R (A) The normal modes of collective motions were calculated by principal component analysis. The sizes of the red arrows scale with the amplitudes of the particular motions. (B) Global twisting of the receptor’s extracellular domain by D4 = 5 –7 , indicated by overlaying the structure of the 5-HT3R before (bold) and after (faint) binding of serotonin. (C) Tilting of the b sandwich by Dq = 5 –7 .

(1) Ligand binding. Although the general architecture of the ligand-binding region of pLGICs has been established as a result of numerous functional, structural, and modeling studies (Auerbach, 2014; Changeux, 2014; Corringer et al., 2012; Hassaine et al., 2014; Lummis, 2012; Smart and Paoletti, 2012; Thompson et al., 2010), our MD simulations on the 5-HT3R delivered the first atomic detailed description of how an activating neurotransmitter binds to a synaptic pLGIC based on relevant structural data (Hassaine et al., 2014; Kesters et al., 2013). (2) Conformational changes in the EC region coupling to the TM channel. MD simulations have proposed various models on allosteric transitions in non-mammalian LGICs (Changeux, 2014). For the GluCl receptor from C. elegans, MD simulations indicated that channel activation is induced by opposite movements of the extracellular versus the TM domains coupled via b1-b2 and M2-M3 loops (Calimet et al., 2013). Despite the similarities with the 5-HT3R, the two cases are not directly comparable. The GluCl channel could only be stabilized in an open state when ivermectin, a synthetic small-molecule allosteric modulator, was bound at the interfaces between TM helices of adjacent subunits, potentiating neurotransmitter binding (Althoff et al., 2014). We, however, have elucidated the direct activation of a neuronal pLGIC by its neurotransmitter, which is the actual relevant process in fast neuronal signal transmission. (3) Opening the TM channel. A hallmark of pLGICs is the hydrophobic constriction zone in the center of the TM channel which, according to the hydrophobic gating concept, is dehydrated in the closed-channel state and becomes hydrated when the pore diameter enlarges beyond a threshold size, allowing water molecules and finally hydrated ions to pass (Aryal et al., 2015; Beckstein and Sansom, 2006; Zhu and Hummer, 2012). Until now an atomistic description of the gating process was missing, which we here delivered for the 5-HT3R. We found that an increase in the pore diameter in the hydrophobic gate is necessary but not sufficient for hydration and opening of the gate. Our MD simulations revealed that in addition to increasing the pore

diameter, the hydrophobic amino acids have to perform a conformational switch to drastically reduce steric hindrance and thus open the hydrophobic gate for water passage. This might represent a general mechanism for activating pLGICs. (4) Conformational changes in the intracellular receptor domain. Vertebrate pLGICs possess an intracellular domain of 70–150 residues important for receptor trafficking, clustering at the synapse, and gating (Bouzat et al., 1994; Thompson et al., 2010; Zuber and Unwin, 2013). In the 5-HT3R this domain is a major determinant of channel conductance (Kelley et al., 2003; Peters et al., 2010). Here the MA helices extend from the M4 helices to the intracellular side, creating a closed vestibule (Hassaine et al., 2014) Potential lateral portals between MA helices are blocked by the post-M3 loop leaving only a 3.3-A˚ narrow tunnel. Further, the MA helical bundle tightens into a 17-A˚-long, 4.2-A˚ narrow hydrophobic pore. Therefore, the crystal structure of the non-activated 5-HT3R does not offer an exit pathway for the ions. Our MD simulations show that the structural changes in the TM region during channel opening couple to the intracellular MA helices, and induce an opening of lateral portals at the intracellular vestibule and the formation of a hydrophobic pore at the end of the helical bundle. This is the first mechanistic description of how ions are released from neuronal pLGICs into the intracellular space. In summary, we combined classical all-atom, long-timescale MD simulations with advanced free-energy sampling methods to explore the activation process of the 5-HT3R integrated in a lipid bilayer (Figure 8). Our MD simulation study is comparable with a stopped-flow experiment: We found that the fast binding of serotonin to the ‘‘aromatic ligand-binding cage’’ exerts a structural stress to the receptor, which relaxes by multiple structural transitions toward its activated state. First, the ligand-binding cage undergoes structural relaxation processes to form optimal interactions with and integration of the ligand, which finally induces distinct conformational transitions of W156 sequentially in all subunits. The structural reorganization of the ligand-binding cage then triggers overall movements in each of the extracellular subunits, first a 6 tilting followed by 6 twisting, which appears as a blooming/opening of the entire EC domain expanding substantially the EC inner pore volume. The EC Structure 24, 1–10, May 3, 2016 5

Please cite this article in press as: Yuan et al., A Gating Mechanism of the Serotonin 5-HT3 Receptor, Structure (2016), http://dx.doi.org/10.1016/ j.str.2016.03.019

Figure 5. Flexibility Pattern of a 5-HT3R Subunit and Cross-Sectional View of the 5-HT3R Channel before and after Activation by Serotonin (A) Flexibility of particular regions in a single submit are indicated by wider radius of tube and a red color. The most flexible regions are colored red, medium flexible regions white, and least flexible regions green. (B) Cross section of 5-HT3R in the non-activated state before binding of serotonin (left panel) and after binding of serotonin reaching an open, water-filled channel state (right panel). Hydrophobic regions are colored in orange and hydrophilic regions in cyan. (C) Pore diameter along z axis of the 5-HT3R for the non-activated receptor state (two independent MD simulations, black and cyan) and for the open, water-filled channel state (two independent MD simulations, red and blue).

rigid-body motions couple via the b1-b2 and the Cys loops to the M2-M3 loops of the TM region. In consequence, the receptor’s TM core first expands by an overall outward movement of the TM helices. The expansion enables the side chains of V264 and L260 of the hydrophobic gate to change conformation to an open-channel structure, allowing water molecules to translocate from the extracellular to the intracellular vestibule. Later, beyond our present simulation time, the receptor might enlarge the water channel further for the passage of Na+ ions across the constriction site. The structural changes in the TM region finally couple to the MA helix, inducing a drastic expansion of the intracellular vestibule together with opening of side portals for the exit of the cations leaving the TM ion channel and the entrance of anions. As our simulations are based on the first high-resolution structure of a mammalian pLGIC and describe the activation process of the whole receptor from the extracellular to the intracellular region, they are of direct relevance for understanding synaptic signal transmission. EXPERIMENTAL PROCEDURES Missing Loop Filling and Refinements Since the small M2-M3 loop in each subunit was not resolved in the X-ray structure of the 5-HT3R (Hassaine et al., 2014), the loop refinement protocol in Modeller (Eswar et al., 2007) V9.10 was used to complete and refine this structural region. A total of 5,000 loops were generated, and the conformation with the lowest Discrete Optimized Protein Energy score was chosen for constructing the starting receptor structure. Protein Structure Preparation All protein models were prepared in the Schro¨dinger software suite using the OPLS_2005 force field. Five nanobodies were removed from the crystal structure (Hassaine et al., 2014) (PDB: 4PIR). Hydrogen atoms were added to the repaired crystal structure of the 5-HT3R to reflect the physiological pH (7.0) using the PROPKA (Sondergaard et al., 2011) tool in Protein Preparation tool in Maestro to obtain the optimized hydrogen-bond network. For this, the constrained energy minimizations were performed on the full-atomic models, with an atom allowed motion of 0.4 A˚ excluding hydrogens, which were free to move. Ligand Structure Preparation The structure of the neurotransmitter serotonin was obtained from the PubChem (Wang et al., 2012) online database. The LigPrep module in the Schro¨dinger 2014 software suite was introduced for geometric optimization

6 Structure 24, 1–10, May 3, 2016

using the OPLS_2005 force field. The ionization state of serotonin was calculated with the Epik (Greenwood et al., 2010) tool employing Hammett and Taft methods together with ionization and tautomerization tools. Protein-Ligand Docking The docking was performed using Glide (Friesner et al., 2004). Based on the crystal structure of the 5-HT3R (Hassaine et al., 2014), serotonin was docked into the aromatic cage between two subunits close to W156 in a pose as found in the crystal structure of a serotonin-binding protein in complex with serotonin (5HTBP; PDB: 2YMD) (Kesters et al., 2013). Cubic boxes centered on the ligand mass center with a radius of 8 A˚ for all ligands defined the docking binding regions. Flexible ligand docking was executed for all structures. Twenty poses per ligand out of 20,000 were included in the post-docking energy minimization. We also placed the initial docking pose at random pose and obtained a similar binding mode as reported previously (Kesters et al., 2013) (Figure S1). Since the top three were found to be identical with each other, the best scored pose for the ligand, which was similar to that of previous work (Kesters et al., 2013), was chosen as the initial structure for MD simulations. 3D Multiple Sequence Alignment The 3D multiple sequence alignment was done in Strap (Gille et al., 2014), a JAVA-based tool. It first predicts the secondary structure of each sequence and aligns them with the specified PDB file afterward. The conservation of each motif was then submitted to WebLogo for visualization (Crooks et al., 2004). Molecular Dynamics Simulations We modeled the protein, POPC lipids, water molecules, and ions using the newest CHARMM 36 force field (Klauda et al., 2010), and the ligand using the CHARMM CGenFF small-molecule force field (Vanommeslaeghe et al., 2012). The membrane system was built by the g_membed (Wolf et al., 2010) tool in Gromacs (Pronk et al., 2013) V4.6.5 with the receptor crystal structure pre-aligned in the OPM (Orientations of Proteins in Membranes) database (Lomize et al., 2011). Pre-equilibrated 234 POPC lipids coupled with 40,760 TIP3P water molecules and 0.15 M NaCl in a box 100 A˚ 3 100 A˚ 3 180 A˚ were used for building the protein/membrane system. This resulted in 186,500 atoms in the simulating system in all. The ligand geometry was submitted to the Gaussian 09 program (Frisch et al., 2009) for optimization at Hartree-Fock 6-31G* level when generating force-field parameters. The system was gradually heated from 0 K to 310 K followed by 1 ns initial equilibration at a constant volume and temperature set to 310 K. Next, an additional 30-ns constrained equilibration was performed at a constant pressure and temperature (310 K, 1 bar) while the force constant on each atom was trapped off gradually from 10 kcal/mol to 0 kcal/mol. All covalent bond lengths to hydrogen atoms were constrained with M-SHAKE. van der Waals and short-range electrostatic interactions were cut off at 10 A˚. Long-range electrostatic interactions were computed by the Particle Mesh Ewald summation scheme. 2 3 700-ns MD simulations were produced for both the apo form of 5-HT3R and

Please cite this article in press as: Yuan et al., A Gating Mechanism of the Serotonin 5-HT3 Receptor, Structure (2016), http://dx.doi.org/10.1016/ j.str.2016.03.019

Figure 6. The Hydrophobic Channel Gate (A) Comparison of dimensions of the hydrophobic constriction sites (channel gates) of the 5-HT3R in the closed-channel state (PDB: 4PIR) with that of GLIC in the open-channel state (PDB: 3P50). Shown are cross sections of the M2 TM helix of 5-HT3R (green) and GLIC (gray). Five L260 and five V264 residues (green) in the 5-HT3R were superimposed with corresponding residues I233 and A237 (purple) in GLIC. (B) Left: free-energy changes in the conformational space of the dihedral angles c1 of L260 and V264 show two distinct minima of the closed (I) and open (II) channel state in the 5-HT3R. Right: distinct side-chain conformations of V264 (top) and L260 (bottom) of the 5-HT3R in the closed-channel state before binding of serotonin (I, green) and in the activated channel state after binding of serotonin (II, cyan).

agonist-bound receptor. The MD simulation results were analyzed in Gromacs (Pronk et al., 2013) and VMD (Humphrey et al., 1996). Bio3D (Grant et al., 2006) was introduced for principal component analysis, and the pore diameters were calculated by HOLE (Smart et al., 1996). Potential Cl ions entrance pathways were predicated by Caver3.0 (Chovancova et al., 2012) command line version. All MD simulations were conducted in Gromacs V4.6.5. Helix Bending The bending of a helix with respect to the channel axis was calculated in Bendix (Dahl et al., 2012) with default settings. Helices were categorized as linear, curved, or kinked according to their shapes (Bansal et al., 2000). In the first step we created helical axes for each helix, based on a previously developed algorithm. Axes are calculated only for helices of a minimal length of four residues, and a local helix axis was calculated for a residue, based on its backbone atomic coordinates and those of the three residues succeeding it in the sequence. Accordingly, axes are not calculated for the three carboxy-terminal residues of the helix. Using these axes the helix is assigned to a linear, curved, or kinked geometry, according to bending angle criteria between successive axes (Dalton et al., 2003; Lee et al., 2007). Hydrophobicity Surface Calculation The hydrophobicity surface of 5-HT3R was calculated by UCSF Chimera: to each amino acid residue a hydrophobicity value is assigned according to the hydrophobicity scale of Kyte and Doolittle (1982). Pore Diameter and Volume Calculation The HOLE (Smart et al., 1996) version 2.0 method was used for pore diameter calculation. The program requires the coordinates of the ion channel of interest in Brookhaven PDB format. An initial point p, which lies anywhere within the

Figure 7. The hydrophobic Gate of the 5-HT3 Receptor (A) The cross section perpendicular to the membrane plane through the A and D subunits of the pentameric 5-HT3R (left panel) in the crystal structure in the absence of serotonin reveals a closed hydrophobic gate formed by the L260 and V264 residues (cross sections at L260 and V264 planes, right panel) preventing the entrance of water molecules. Amino acid residues of the M2 helices along the channel are indicated. (B) A continuous water channel was formed by opening the gate of 5-HT3R by movements of side chains of L260 and V264.

central channel, is also needed. In addition, the user specifies a vector v in the direction of the channel axis (referred to as the channel direction vector). The program reads atoms from the pdb file and sets up a van der Waals radius for each (various sets are available). The maximum radius R(p) of a sphere centered at a point p without overlap with the van der Waals surface of any atom is calculated by RðpÞ = minNi =atom 11 ½jxi  pj  vdWi ;

(Equation 1)

where xi is the position of atom number i, vdWi its van der Waals radius, and Natom the total number of atoms. The radius R(p) can be regarded as an objective function of the point p. By using Monte Carlo simulated annealing, adjusting p, the radius of the sphere can be maximized within the pore of the channel. In all cases p is kept on a plane normal to the channel direction vector v (Smart et al., 1996). The grid filling the pore space generated by HOLE (Smart et al., 1996) was imported into UCSF Chimera for volume calculation. The ‘‘Measure Volume and Area’’ tool reports the total surface area and enclosed volume of a surface model. These tools compute surface area and enclosed volume from surface triangles rather than analytically (Goddard et al., 2007; Pettersen et al., 2004). Metadynamics Simulations Free-energy profiles of the systems were calculated using well-tempered metadynamics in Gromacs (Pronk et al., 2013) V4.6.5 with Plumed (Bonomi et al., 2009) V1.3 algorithm. Metadynamics adds a history-dependent potential V(s,t) to accelerate sampling of the specific CVs s(s1,s2,.,sm) (Barducci et al., 2008). V(s,t) is usually constructed as the sum of multiple Gaussians centered along the trajectory of the CVs (Equation 2).

Structure 24, 1–10, May 3, 2016 7

Please cite this article in press as: Yuan et al., A Gating Mechanism of the Serotonin 5-HT3 Receptor, Structure (2016), http://dx.doi.org/10.1016/ j.str.2016.03.019

Correlation Network Analysis Correlated atomic fluctuations of a particular receptor state were characterized as reported elsewhere (Grant et al., 2006; Scarabelli and Grant, 2014; Skjaerven et al., 2014) using Bio3DT. The network nodes represent residues, which are connected through edges weighted by their constituent atomic correlation values. Community analysis and node centrality with Bio3D and suboptimal path calculation with the WISP software (Van Wart et al., 2014) were performed on each network to characterize network properties and identify residues involved in the dynamic coupling of distal sites. The parameters for the suboptimal path analysis included input source and sink nodes, as well as the total number of paths to be calculated. The latter parameter was set to 500 paths, which was found to yield converged results in all cases (Scarabelli and Grant, 2014). SUPPLEMENTAL INFORMATION Supplemental Information includes eight figures and four movies and can be found with this article online at http://dx.doi.org/10.1016/j.str.2016.03.019. AUTHOR CONTRIBUTIONS S.Y. and H.V. initialized the project. S.Y., S.F., and H.V. designed the experiments. S.Y. performed the MD simulations and analyzed the results. S.Y., S.F., and H.V. wrote the manuscript. ACKNOWLEDGMENTS The major part of computing was done at the Shanghai Supercomputer Center. S.F. was funded by the National Center of Science, Poland (grant 2011/03/ B/NZ1/03204). H.V. was supported by the Swiss National Science Foundation (grant 31003A-133141), the European Community (Project SynSignal, grant FP7-KBBE-2013-613879), and internal funds of the EPFL.

Figure 8. Gating Mechanism of 5-HT3R Side-view cross section through the 5-HT3R shows two protein subunits with two bound serotonin molecules near the W156 residues in the aromatic ligandbinding cages. The global quaternary motions (black arrows) induce (1) the entrance of water molecules (red) into the central transmembrane channel across the hydrophobic gate represented by V264 and L260, (2) a deeper penetration of sodium ions (blue), and (3) the entrance of chloride ions (green) into the intracellular vestibule via lateral portals.

Vðs; tÞ =

n X j=1

Gðs; tj Þ =

n X j=1

wj

m Y

exp 

i=1

½si ðtÞ  si ðtj Þ2 2s2

! (Equation 2)

Periodically, during the simulation another Gaussian potential, whose location is dictated by the current values of the CVs, is added to V(s, t) (Li et al., 2013). In our simulations, the dihedral angles of W156, c1 and c2, were assigned as the CVs s1 and s2, while the width of Gaussians, s, was set to 5 . Similarly, in the second metadynamisc simulation, the c1 angle of L260 and V264 were used as CVs. The time interval, t, was 0.09 ps. Well-tempered metadynamics involves adjusting the height, wj, in a manner that depended on V(s, t) where the initial height of Gaussians w was 0.03 kcal/mol, the simulation temperature was 310 K, and the sampling temperature DT was 298 K. The convergence of our simulations was judged by the free-energy difference between states X and A at 10-ns intervals. Once the resulting data remained stable over time, the simulation was considered as converged. Each metadynamics simulation lasted 120 ns, and the results were analyzed upon convergence. Residues Interaction Network Analysis StructureViz (Morris et al., 2007) and RINalyzer (Doncheva et al., 2012), plugin modules in Cytoscape (Shannon et al., 2003) were used for residues interaction network (RIN) analysis (Scarabelli and Grant, 2014). These modules allow the interactive analysis of a RIN in a 2D representation of amino acid residues as nodes and their non-covalent interactions as edges together with the corresponding 3D protein structures in UCSF Chimera (Pettersen et al., 2004).

8 Structure 24, 1–10, May 3, 2016

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