Computational Study of Binding of Î¼-Conotoxin GIIIA to Bacterial

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Computational Study of Binding of µ-Conotoxin GIIIA to Bacterial Sodium Channels NaVAb and NaVRh Dharmeshkumar Patel, Somayeh Mahdavi, and Serdar Kuyucak Biochemistry, Just Accepted Manuscript • DOI: 10.1021/acs.biochem.5b01324 • Publication Date (Web): 09 Mar 2016 Downloaded from http://pubs.acs.org on March 17, 2016

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Computational Study of Binding of µ-Conotoxin GIIIA to Bacterial Sodium Channels NaV Ab and NaV Rh

Dharmeshkumar Patel, Somayeh Mahdavi and Serdar Kuyucak* School of Physics, University of Sydney, Sydney, New South Wales 2006, Australia.

AUTHOR INFORMATION Corresponding Author *School of Physics, University of Sydney, New South Wales 2006, Australia. Telephone: +61 2 9036 5306. E-mail: [email protected]

ABBREVIATIONS GIIIA, µ-conotoxin GIIIA; MD, molecular dynamics; NaV , Voltage-gated sodium; PMF, potential of mean force; RMSD, root mean square deviation; SF, selectivity filter.

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ABSTRACT: Structures of several voltage-gated sodium (NaV ) channels from bacteria have been solved recently but the same feat may not be achieved for the mammalian counterparts in near future. Thus, at present, computational studies of the mammalian NaV channels have to performed using homology models based on the bacterial crystal structures. A successful homology model for the mammalian NaV 1.4 channel was recently constructed using the extensive mutation data on binding of µ-conotoxin GIIIA to NaV 1.4, which was further validated through studies of binding of other µ-conotoxins and ion permeation. Understanding the similarities and differences between the bacterial and mammalian NaV channels is an important issue, and the NaV 1.4–GIIIA system provides a good opportunity for such a comparison. To this end, we study the binding of GIIIA to the bacterial channels NaV Ab and NaV Rh. The complex structures are obtained using docking and molecular dynamics simulations, and the dissociation of GIIIA is studied through umbrella sampling simulations. The results are compared to those obtained from the NaV 1.4–GIIIA system, and the differences in the binding modes arising from the changes in the selectivity filters are highlighted.


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Voltage-gated sodium (NaV ) channels are arguably the most important ion channels in life forms. 1 The recent determination of the crystal structures of several NaV channels from bacteria 2–6 was, therefore, greeted with great enthusiasm as they are expected to provide a molecularlevel understanding of how the NaV channels function, and thereby facilitate development of new therapeutics for diseases caused by dysfunctional NaV channels. 7,8 Already many molecular dynamics (MD) simulations of the bacterial NaV channels have been performed to study binding sites and permeation mechanism of Na+ ions, 9–21 gating motions, 22–24 and ligand binding to the outer vestibule, 25,26 the inner cavity, 27–29 and the voltage sensor. 30,31 The tetrameric symmetry, which facilitated solution of the bacterial NaV structures, is lost in the mammalian NaV channels. Thus, it is quite unlikely that crystal structures for the mammalian NaV channels will be determined in near future. This raises the important question of whether the insights gained from the studies of the bacterial NaV channels can be transferred to the mammalian counterparts. Unlike in potassium channels, there are significant differences between the structures of the bacterial and the mammalian NaV channels. For example, the key selectivity filter (SF) motif is not conserved between prokaryotes and eukaryotes (EEEE in the former and DEKA in the latter), suggesting that different selectivity and permeation mechanisms are in operation. Similarly, there are substantial differences between the extracellular loops of the bacterial and the mammalian NaV channels. Thus, understanding the functional consequences of the structural differences between the bacterial and mammalian NaV channels is a significant issue at present, and to address it, well-validated homology models of the mammalian NaV channels are required. So far, there have been a few attempts to construct homology models for the mammalian NaV 1.4 channel 32–35 based on the bacterial NaV Ab structure. 2,3 In all cases, the mutagenesis data from binding of µ-conotoxins to NaV 1.4 were used to constrain and validate the homology models. The most extensive mutagenesis data are available for the binding of µ-conotoxin GIIIA to NaV 1.4. The NaV 1.4 homology model constructed using these data 35 was able to describe all the GIIIA contact residues identified in experiments (K8, K11, R13, K16, and R19), 36–39 as well as accounting for their relative strenghths from the persistence of the conACS Paragon Plus Environment

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tact distances. The binding free energy of GIIIA, determined from the potential of mean force (PMF) calculations (−9.9 kcal/mol), was also in good agreement with the experimental value (−10.5 kcal/mol). 40 The NaV 1.4 homology model was further validated through studies of binding of other µ-conotoxins (PIIIA, KIIIA, and BuIIIB), 41 again providing a consistent description of the available mutation data. 42–45 Finally, we used this NaV 1.4 model in MD simulations to study the permeation mechanism of Na+ ions and Na+ /K+ selectivity, 46 and obtained a satisfactory description of the physiological data, including the effect of mutations of the DEKA and EEDD residues. 47–50 Thus the present NaV 1.4 model provides a reliable platform for a comparative study of the bacterial and mammalian NaV channels. Here, we study binding of µ-conotoxin GIIIA (GIIIA) to the bacterial NaV channels NaV Ab 2,3 and NaV Rh. 4 The latter is included in this study because the EEEE motif is two residues down from the conserved W residues in all bacterial NaV channels except in NaV Rh where it is one residue up from W, which is closer to the positioning of the EEDD ring in the mammalian NaV channels (see Fig. 1). Thus NaV Rh may provide a better model for the mammalian NaV channels. Complex models of GIIIA with NaV Ab and NaV Rh are constructed using docking and MD simulations, and the binding modes are compared to that of NaV 1.4–GIIIA. 35 Unbinding of GIIIA from NaV Ab and NaV Rh is studied from umbrella sampling MD simulations and PMF calculations, and the results are again contrasted with those from NaV 1.4. We find that the GIIIA mainly interacts with the SF residues in NaV Ab and NaV Rh while it does not reach the SF in NaV 1.4 and interacts most strongly with the residuesin the vestibule. METHODS Building of NaV Rh and NaV Ab Channel Models. The model systems are prepared using the VMD software. 51 The crystal structures of the pore domains of NaV Ab (PDB ID: 4EKW) 3 and NaV Rh (PDB ID: 4DXW) 4 are embedded in a lipid bilayer composed of 216 POPE molecules for both channels. The systems are solvated and ionized with 0.1 M NaCl solution. In both models there is a Na+ ion in the SF as observed in the crystal structures. We have equilibrated each system in two stages, following the protocols developed in our lab. 52,53 ACS Paragon Plus Environment

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First the protein is fixed and the system is equilibrated with pressure coupling until the correct water and lipid densities are obtained. At that point, the x and y dimensions of the simulation box are fixed and pressure coupling is applied only in the z direction normal to the lipid bilayer ˚ 3 for NaV Ab and 105 × 81 × 98 A ˚ 3 for NaV Rh). In the second (the box sizes are 95 × 96 × 97 A stage, the restraints on the protein atoms are relaxed gradually by first reducing those on the ˚ to 0 in 3 ns, followed by relaxation of the backbone side chain atoms from k = 30 kcal/mol/A atoms in a similar manner. In each case, the resulting system is simulated for 50 ns to check the stability of the model. The RMSD of the backbone atoms of NaV Rh and NaV Ab have formed a plateau after the first few ns, which remained stable throughout the 50 ns of MD simulations, confirming the stability of the models. Docking of GIIIA to NaV Ab and NaV Rh. The conotoxin GIIIA is a 22-residue peptide with the sequence RDCCTOOKKC KDRQCKOQRC CA and has an amidated C-terminal. It has three Arg, four Lys and two Asp residues with a net charge of +6e. The NMR structure of GIIIA is shown in Figure 1 (PDB ID: 1TCJ). 54 The side chains of the basic residues that are involved in the binding of GIIIA to NaV channels are explicitly indicated in Figure 1. GIIIA has three disulphide bonds between C3–C15, C4–C20 and C10–C21, which help to stabilize its structure at the binding interface. The ensemble of NMR structures indicate that the loop between the hinges at C3 and C10 remains flexible. But this does not interfere with the binding of GIIIA to NaV channels because the loop points away from the binding interface. The initial docking of GIIIA to the the crystal structures of NaV Ab and NaV Rh is done using the program HADDOCK. 55,56 We have previously used HADDOCK for docking toxin peptides to potassium 57–60 and sodium 35,41 channels, and obtained quite good poses that required minimal refinement via MD simulations. Ten NMR conformers of GIIIA are available in PDB, and all are used in ensemble docking to increase the sampling of the side chain orientations. HADDOCK works best when experimental data are invoked as restraints. Unfortunately, there are no mutation data for the complex structures considered here. We have, therefore, performed blind docking first to identify potential restraints without introducing a bias. From clustering analysis of the blind docking results, strongly interacting pairs are identified, which are then ACS Paragon Plus Environment

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used as restraints in further docking calculations. In each case, the best complex is selected according to the energy score, the number of contacts made, and the blocking of the channel by GIIIA. In the second stage, the complex structure obtained from docking is refined in MD simulations. In each case, the docking pose is aligned with the corresponding channel model in the membrane, and the toxin coordinates are transferred to the channel model. The channel-toxin complexes are then equilibrated using the same protocol as in the relaxation of the channel protein in the membrane. The equilibrated system is run for a further 50 ns to check the stability of the complex system. For both NaV Ab–GIIIA and NaV Rh–GIIIA, the complex structure is found to be well-equilibrated. In particular, all the channel–toxin contacts are preserved throughout the simulations, and the the Na+ ion initially placed in the SF has remained in there. Therefore, the trajectory data generated during these MD simulations are used in characterizing the binding mode of each complex. MD simulations and PMF calculations. MD simulations are performed using the NAMD code (version, 2.7) 61 with the CHARMM36 force field. 62 An NpT ensemble is used with periodic boundary conditions. Pressure and temperature of the simulation system are kept at 1 atm and 300 K, respectively, using Langevin coupling with damping coefficients of 5 ps−1 . The ˚ while short-range Lennard-Jones interactions are switched off within a distance of 10–13.5 A the long-range electrostatic interactions are calculated without truncation using the particlemesh Ewald algorithm. A time step of 2 fs is used in integration of the equations of motion. The trajectory data is saved at 1 ps intervals for analysis while the reaction coordinate is saved at every time step in umbrella sampling simulations. The PMFs for the dissociation of GIIIA from NaV Ab and NaV Rh are constructed using umbrella sampling MD simulations. The method has been described in detail previously, 63 and applied to binding of several potassium 57–60 and sodium 35,41 channel toxins. Therefore, only a brief description is given here. The reaction coordinate is taken as the distance between the center of masses of the channel protein and the toxin. In previous PMF calculations for toxins, ˚ pulling was found to be sufficient for the toxins to reach the bulk region. Therefore, 15 A ACS Paragon Plus Environment

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˚ intervals using steered initially 30 umbrella windows are created along the channel axis at 0.5 A ˚ 2 and pulling speed v = 5 A/ns. ˚ MD with a force constant of k = 30 kcal/mol/A After each pulling step, the toxin is equilibrated at the window position for 0.3 ns with the same force constant before proceeding with the next window. After all the windows are generated, umbrella ˚ 2, sampling simulations are performed for all windows in parallel using k = 30 kcal/mol/A which is found to be optimal for peptides of this size. 63 When the overlap of distributions of the reaction coordinates between the neighboring windows drops below 5%, the PMF suffers from errors due to numerical instabilities. To avoid such errors, extra windows are included between the windows 3-4, 10-11 and 11-12 in NaV Rh–GIIIA and between the windows 4-5 in NaV Ab–GIIIA. The PMF is constructed using the weighted histogram analysis method, 64 which unbiases the data collected for each umbrella window and combines the individual PMFs optimally. The convergence of the PMF is checked from block data analysis of the data, and the umbrella sampling simulations are continued until the convergence is obtained. RESULTS AND DISCUSSION Binding Modes of the NaV Rh–GIIIA and NaV Ab–GIIIA complexes. Blind docking with HADDOCK has revealed that the K11 side chain of GIIIA makes contacts with the residues in the SF of NaV Ab while this role is taken over by the R13 side chain in the case of NaV Rh. These interactions are used as restraints in further docking calculations with HADDOCK, and a dominant pose is found from clustering analysis in each case. As a further test of the uniqueness of the predicted poses, the restraints are switched between NaV Ab and NaV Rh, that is, the R13 side chain is used as a restraint for the NaV Ab SF and the K11 side chain for the NaV Rh SF. In the former case, NaV Ab–GIIIA poses that preserved the E177–R13 contacts are found from docking calculations but these are quickly broken in subsequent MD simulations, indicating that the complex structure is not stable. In the latter case, even HADDOCK failed to find NaV Rh– GIIIA poses with S180–K11 contacts, so such a complex structure is not feasible either. The dominant poses predicted by HADDOCK are then refined in MD simulations as described in Methods. The Na+ ion initially placed in the SF is observed to remain in the SF ACS Paragon Plus Environment

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throughout the MD simulations. We have also checked the possibility that a second Na+ ion may be present in the SF while GIIIA is bound. However, when a second Na+ ion is placed in the SF, it is observed to go down to the cavity within few ns of MD simulations. This is consistent with the finding that tetrodotoxin binds to NaV Ab with much higher affinity when there is only one Na+ ion in the SF compared to two Na+ ions. 26 The snapshots of the NaV Rh–GIIIA and NaV Ab–GIIIA complexes obtained from the MD simulations are shown in Figure 3. For comparison, the NaV 1.4–GIIIA complex from an earlier study 35 is also included at the bottom of the figure. A more quantitative description of the binding modes is provided in Table 1, where the average N–O distances between the interacting residues are shown. The average distances are determined from the 50 ns MD simulations of the NaV Ab–GIIIA and NaV Rh–GIIIA complex structures. Again the corresponding distances for the NaV 1.4–GIIIA complex are included in table for comparison. We first consider the NaV Ab–GIIIA complex. It is seen from Figure 3 and Table 1 that the binding mode is dominated by the K11 residue in GIIIA, whose side chain makes contacts with the E177 side chains in the SF from domains II, III, and IV. A complementary view of the complex structure from the bottom of the channel (Figure 4) shows that the K11 side chain occupies a central location in the SF and can single-handedly block the pore. This is reminiscent of the “pore blocking lysine” theme commonly found in toxin blockers of potassium channels. A second contact is provided by the K16 side chain in GIIIA, which makes a bridge between the side chains of E158(III) and E189(IV). Finally the R13 side chain makes a relatively weak contact with the side chain of E189(III) as indicated by the larger contact distance and its fluctuations. We note that the contact distances of pairs in equilibrium do not provide a good indication of the strength of the interaction. A much better measure is given by the persistence of the contact distances while the toxin is pulled away from the binding pocket in umbrella sampling simulations, which will be discussed in the next subsection. Although the same basic residues are involved in the binding of GIIIA to NaV 1.4 (Figure 4 and Table 1), its binding mode is quite different from that of NaV Ab–GIIIA. First, no GIIIA residues make contacts with the SF residues in NaV 1.4. The toxin remains in the vestibule of ACS Paragon Plus Environment

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NaV 1.4, and blocking of the pore is only possible because of the extensive network of contacts made by the side chains of K11, R13, and K16 with the EEDD ring of residues. This can be seen more clearly from the bottom view in Figure 4. Secondly, as indicated by the mutagenesis data 36–38 and MD simulations, 35 R13 plays a dominant role in the binding of GIIIA to NaV 1.4, followed by K16 and K11 in importance. In addition, K8 and R19 make contacts with the peripheral residues on the P2 helix. This is very different from the ranking of the interactions in the NaV Ab–GIIIA complex; K11 clearly plays a dominant role while K16 and R13 are involved in peripheral interactions, with R13 making the weakest contact. MD simulations mentioned above indicate that the SF in NaV Ab with four glutamate side chains is too narrow to accommodate the R13 side chain, which explains the reversal of the roles between R13 and K11 when going from NaV 1.4 to NaV Ab. We next consider the NaV Rh–GIIIA complex. As seen in Figure 1, the EEEE locus in the SF of NaV Ab is replaced by SSSS in NaV Rh and the EEEE ring is shifted three residues up to the beginning of the P2 helix. Because this position of the EEEE ring is similar to the EEDD ring in NaV 1.4, NaV Rh may provide a better model for the mammalian NaV channels. Inspection of Figure 3 and Table 1 shows that the R13 side chain inserts into the SF, making contacts with the side chains of S180(III) and S181(I). Clearly, replacement of the glutamate side chains in NaV Ab with the smaller serine side chains in NaV Rh has created sufficient space in the SF to allow the entry of the bulkier R13 side chain. As can be seen from Figure 4, the R13 side chain occupies a central position in the SF and can block the pore. In addition, the K11 and K16 side chains make contacts with the E183 side chains in domains IV and III, respectively. While the ordering of the interactions in the NaV Rh–GIIIA complex is similar to that in NaV 1.4–GIIIA, the binding modes are qualitatively different. In particular, the R13 side chain cannot insert to the narrow SF in NaV 1.4 and make contacts with the residues there. In that regard, the binding modes of NaV Rh–GIIIA and NaV Ab–GIIIA have more in common, namely, the side chain of a basic residue inserts into the SF and blocks the pore. Umbrella Sampling Simulations and PMF Calculations. Further information on channel– toxin complexes can be gained by performing umbrella sampling simulations for the dissociaACS Paragon Plus Environment

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tion of the toxin from the channel and constructing the corresponding PMF. To this end, we have calculated the PMFs for the dissociation of GIIIA from NaV Ab and NaV Rh using the protocols described in Methods. An issue sometimes encountered in umbrella sampling simulations of flexible ligands is that the ligand is permanently distorted by the applied harmonic forces. 65 To check against such a possibility, the average RMSD of GIIIA is calculated in each umbrella window and plotted as a function of the reaction coordinate (Figure 5). It is seen that while GIIIA is distorted in some windows during the dissociation process, this is not permanent and the RMSD returns to nearbulk values once GIIIA reaches the bulk region. This is also verified visually by aligning a GIIIA structure from the last window with the NMR structure. A second issue often neglected in PMF calculations is showing that the PMF has actually converged, and the equilibration data is excluded from the production data. This can be achieved most easily by performing block data analysis of the full data set. A monotonic change in PMFs usually indicates that the system is still equilibrating while fluctuation of PMFs around a base line signals convergence. Here the individual PMFs are constructed from 2 ns blocks of data, which are slided in 1 ns steps over the available set. The block data analysis results for the NaV Ab–GIIIA and NaV Rh–GIIIA PMFs are shown in Figure 6. In NaV Ab, the PMFs monotonically decrease in the first 4 ns and then fluctuate around a base line. Therefore, the first 4 ns is excluded and the final PMF is constructed from 4–8 ns of data. We note that arbitrarily excluding the first 1 ns for equilibration would have resulted in a much deeper PMF. In NaV Rh, the system equilibrates faster (2 ns) but there are substantial fluctuations of the block PMFs around the base line. The different behavior of the PMFs could be due to the stronger chargecharge interactions of E177–K11 in NaV Ab–GIIIA, which provide a solid anchoring point for GIIIA and suppress fluctuations while the relatively weaker charge-dipole interactions of R13 with S180 and S181 in NaV Rh–GIIIA allow more fluctuations of GIIIA in the softer binding pocket. The PMFs for dissociation of GIIIA from NaV Ab and NaV Rh are compared to that from NaV 1.4 in Figure 7. The behavior of the PMFs for the sodium channel toxins are qualitatively ACS Paragon Plus Environment

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different from those of potassium channels, where the number and quality of channel–toxin contacts determine the well depth of the PMF. 57–60 Here there is an additional factor, namely, how deeply the toxin inserts into the pore. Because the systems are different, the reaction coordinates used in the PMFs do not provide a good measure of this quantity. A better measure is the distance of the toxin center of mass from the SF locus, defined as the center of the Cα ˚ atoms of SSSS in NaV Rh, EEEE in NaV Ab, and DEKA in NaV 1.4, which are 11, 13, and 18 A, respectively. Thus the deeper insertion of GIIIA in NaV Rh compared to NaV Ab compensates for the less number of contacts (Table 1), resulting in similar well depths in the PMFs (Figure 7). The NaV 1.4–GIIIA PMF provides an even more dramatic example of this effect. Despite having many more high quality contacts compared to the bacterial channels (Table 1), the NaV 1.4– GIIIA PMF is only slightly deeper because the toxin is further away from the SF. This example highlights the importance of the topology of the ligand in the design of high-affinity blockers of the mammalian NaV channels—in order to improve the affinity of a blocker, it is essential to enable its deeper insertion into the pore. How the contact distances change during dissociation provide complementary information on the relative strength of the pair interactions. Persistence of a contact as the toxin is pulled away indicates a strong coupling while a quick break up of a pair indicates a weak coupling. Using the contact distance analysis, we can also understand the specific features of the PMFs. In Figure 8, we show how the contact distances for NaV Ab–GIIIA listed in Table 1 change with the pulling distance of GIIIA. The binding mode of NaV Ab–GIIIA is quite similar to those of potassium channels, and this is also reflected in the PMF. The initial sharp rise in the PMF is ˚ where the side chains stretch due to the persistence of the E177–K11 contact up to z = 32 A, to keep contact. After that, all the contacts are broken, and the the PMF gradually rises due to the screened Coulomb interaction between the channel and toxin charges. The weakness of the E189–R13 contact, alluded to earlier, can be clearly seen here from its break up after only ˚ pulling. It can also be seen from Figure 8 that the glitch in the NaV Ab–GIIIA PMF around 1A ˚ is due to the fluctuations of the K16 contacts in these windows. z = 30 A We next discuss evolution of the contact distances in NaV Rh–GIIIA (Figure 9). The exACS Paragon Plus Environment

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change of glutamates in the SF of NaV Ab with serines in NaV Rh has resulted in a softer binding pocket as discussed in the convergence of the PMFs. The steep rise in the NaV Rh–GIIIA PMF ˚ is traced to R13 displacing K11 and making contact with E183(IV). The R13 after z = 29 A ˚ This is followed by an intermediate region where the and K16 contacts persist up to z = 32 A. ˚ they are completely broken, which corresponds to the contacts become loose. After z = 34 A, shoulder region in the PMF associated with the long-range Coulomb interactions. CONCLUSIONS Here we have presented a comparative study of ligand binding to the pore domain of the bacterial and mammalian NaV channels. For this purpose, we have studied the binding of µ-conotoxin GIIIA to the bacterial NaV Ab and NaV Rh channels and compared the binding modes with the mammalian NaV 1.4 channel. The main difference between the binding modes of bacterial and mammalian NaV channels is the position of the toxin. In the bacterial NaV channels, GIIIA gets closer to the SF and the side chain of a basic residue inserts into the SF, blocking the pore. This occurs even in the case of NaV Rh, where the EEEE ring is moved three residues up from its location in the SF filter of all other bacterial channels. In contrast, GIIIA ˚ further away from the SF remains firmly in the vestibule of NaV 1.4, with its position at least 5 A compared to the bacterial NaV channels. Blocking of the pore in this wider part of the channel requires an extensive network of contacts between the basic residues of GIIIA and the EEDD ring in the vestibule. Indeed, some µ-conotoxins (e.g., KIIIA) fail to block the unitary current in single NaV 1.4 channels, 42,44,66 because they don’t have sufficient contacts with the channel residues. Comparison of the PMFs and variation of contact distances during dissociation reveal further differences in binding mechanism and energetics between the bacterial and mammalian NaV channels. Another important finding of this study is the role played by the deeper insertion of the toxin in enhancing the binding affinity. Blockers of the mammalian sodium channels have rather smaller affinity compared to those of potassium channels. The main difference between the two types of channels is the presence of a pore inserting lysine in the latter, which makes a significant ACS Paragon Plus Environment

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contribution to the binding free energy. In the bacterial NaV channels a similar theme operates, which results in a GIIIA affinity close to NaV 1.4 despite the smaller number of contacts GIIIA makes with NaV Ab and NaV Rh compared to NaV 1.4. This observation suggests that improving the affinity of the mammalian sodium channel blockers may be facilitated through topological considerations, which should enable a deeper insertion of the ligand into pore. In this study, we have focused on the consequences of the differences between the SFs of the bacterial NaV Ab and NaV Rh, and the mammalian NaV 1.4 channels using the binding of GIIIA as an example. Because the SF sequences are conserved within both groups (except NaV Rh), we expect the main finding of this study to apply to other NaV 1 channel–µ-conotoxin systems. Of course, there are wide variations in the binding affinity and selectivity of various µ-conotoxins to the NaV 1 isoforms, which arise from the differences in the vestibule and the extracellular linker regions of the channels. Understanding these features requires construction of validated homology models for all NaV 1 isoforms and computational study of binding of µ-conotoxins to the NaV 1 models. We hope to pursue these problems in future studies. ACKNOWLEDGMENTS Calculations were performed using the HPC facilities at the National Computational Infrastructure (Canberra) and the Victorian Life Sciences Computation Initiative (Melbourne).


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(36) Sato, K., Ishida, Y., Wakamatsu, K., Kato, R., Honda, H., Ohizumi, Y., Nakamura, H., Ohya, M., Lancelin, J. M., Kohda, D., Inagaki, F. (1991) Active site of µ-conotoxin GIIIA, a peptide blocker of muscle sodium channels. J. Biol. Chem. 266, 16989–16991. (37) Chang, N. S., French, R. J., Lipkind, G. M., Fozzard, H. A., Dudley Jr., S. (1998) Predominant interactions between µ-conotoxin Arg-13 and the skeletal muscle Na+ channel localized by mutant cycle analysis. Biochemistry 37, 4407–4419. (38) Xue, T., Ennis, I. L., Sato, K., French, R. J., and Li, R. A. (2003) Novel interactions identified between µ-conotoxin and the Na+ channel domain I P-loop: implications for toxinpore binding geometry. Biophys. J. 85, 2299–2310. (39) Choudhary, G., Aliste, M. P., Tieleman, D. P., French, R. J., Dudley Jr., S. C. (2007) Docking of µ-conotoxin GIIIA in the sodium channel outer vestibule. Channels 1, 344–352. (40) Wilson, M. J., Yoshikami, D., Azam, L., Gajewiak, J., Olivera, B. M., Bulaj, G., Zhang, M. M. (2011) µ-Conotoxins that differentially block sodium channels NaV 1.1 through NaV 1.8 identify those responsible for action potentials in sciatic nerve. Proc. Natl. Acad. Sci. U.S.A. 108, 10302–10307. (41) Mahdavi, S., and Kuyucak, S. (2014) Systematic study of binding of µ-conotoxins to the sodium channel NaV 1.4. Toxins 6, 3454–3470. (42) Zhang, M. M., Green, B. R., Catlin, P., Fiedler, B., Azam, L., Chadwick, A., Terlau, H., McArthur, J. R., French, R. J., Gulyas, J., et al. (2007) Structure/function characterization of µ-conotoxin KIIIA, an analgesic, nearly irreversible blocker of mammalian neuronal sodium channels. J. Biol. Chem. 282, 30699–30706. (43) Zhang, M. M.; Han, T. S., Olivera, B. M., Bulaj, G., Yoshikami, D. (2010) µ-conotoxin KIIIA derivatives with divergent affinities versus efficacies in blocking voltage-gated sodium channels. Biochemistry 49, 4804–4812.


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Table 1: Strongly interacting residues in the NaV Ab–GIIIA, NaV Rh–GIIIA, and NaV 1.4– GIIIA complexesa GIIIA K8-Nz K11-Nz K11-Nz K11-Nz R13-N2 R13-N1 R13-N2 K16-Nz K16-Nz R19-N2

NaV Ab – E177-Oǫ (II) E177-Oǫ (III) E177-Oǫ (IV) – E189-Oǫ (III) – E189-Oǫ (IV) E158-Oǫ (III) –

MD Avg. – 2.67±0.4 2.89±0.2 2.76±0.3 – 3.52±0.5 – 2.89±0.2 2.71±0.1 –

NaV Rh – E183-Oǫ (IV) – – S180-Oγ (III) S181-Oγ (I) – E183-Oǫ (III) – –

MD Avg. – 2.83±0.3 – – 2.98±0.3 2.88±0.1 – 2.68±0.2 – –

NaV 1.4 D1248-O1 D1241-O1 D1532-O1 – E403-O1 E758-O2 D1532-O2 E758-O1 D1241-O2 D762-O2

MD Avg. 4.0±1.2 2.7±0.4 2.6±0.1 – 2.7±0.4 2.8±0.3 2.7±0.2 2.7±0.1 2.7±0.1 2.7±0.4

The average N–O distances obtained from MD simulations are given as MD Avg. (in units of ˚ A). The domains in bacterial NaV channels are shown in parenthesis. The N and O atoms in side chains are indicated with subscripts. a


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FIGURE LEGENDS Figure 1. Sequence alignment for the NaV Rh, NaV Ab, and NaV 1.4 channels. The signature residues in the selectivity filter (SF) are highlighted in yellow. Note the exchanged positions of E177 and S180 in NaV Ab with S180 and E183 in NaV Rh. Figure 2. NMR structure of GIIIA. The side chains of the basic residues involved in the binding of GIIIA to the NaV channels are indicated with blue sticks. Three disulfide bonds (C3–C15, C4–C20, and C10–C21) and the C3–K9 hydrogen bond are shown with yellow sticks and a dashed line, respectively. Figure 3. Snapshots of the NaV Ab, NaV Rh, and NaV 1.4 channels in complex with GIIIA. All the contact pairs involved in the binding are explicitly indicated. To give a clear picture of the binding mode, we provide two cross sections of the complex showing the monomers DI and DIII (left panel), and DII and DIV (right panel) separately. Figure 4. Bottom view of the NaV –GIIIA complexes showing the GIIIA residues involved in the blocking of the channel. Three basic residues (K11, R13, and K16) are required to block the mammalian NaV 1.4 whereas a single residue (K11 for NaV Ab and R13 for NaV Rh) is sufficient to block the bacterial NaV channels. Figure 5. Average RMSD of the backbone atoms of GIIIA for each umbrella window. Because of the flexibility of the loop between C3 and K10, only the residues 11–22 are included in the RMSD calculations. The bulk RMSD value of GIIIA is indicated by the dashed line. Figure 6. Convergence of the NaV Ab–GIIIA and NaV Rh–GIIIA PMFs from block data analysis. PMFs obtained from 2 ns blocks of data are slided over the available data in 1 ns steps. Figure 7. PMFs for the dissociation of GIIIA from NaV Ab, NaV Rh, and NaV 1.4. Note that the position of the binding site relative to the SF varies more than that indicated by the reaction coordinate due to the differences in the systems (see the text for discussion). ACS Paragon Plus Environment

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Figure 8. Change of the average pair distances with the pulling distance of GIIIA from NaV Ab. All the contact pairs listed in Table 1 are shown. The average distances are obtained from the trajectory data used in the construction of the final PMF in Fig. 7. Error bars are not ˚ for most points. included to avoid cluttering but are about 1 A Figure 9. Same as Fig. 8 but for the NaV Rh–GIIIA complex.


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Figure 1:


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Figure 2:


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Figure 3:


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Figure 5:


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Figure 7:


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Figure 9:


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Figure 10: For Table of Contents Use Only. Computational Study of Binding of µ-Conotoxin GIIIA to Bacterial Sodium Channels NaV Ab and NaV Rh Dharmeshkumar Patel, Somayeh Mahdavi, and Serdar Kuyucak


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Computational Study of Binding of Î¼-Conotoxin GIIIA to Bacterial

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