Carbon Nanotube Based Artificial Water Channel Protein - American

Feb 26, 2009 - 100049, China, Department of Physics, Duke UniVersity, Durham, North Carolina. 27708, and ... UniVersity of Georgia, Athens, Georgia 30...
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Carbon Nanotube Based Artificial Water Channel Protein: Membrane Perturbation and Water Transportation

2009 Vol. 9, No. 4 1386-1394

Bo Liu,†,‡ Xiaoyi Li,*,†,‡ Baolei Li,§ Bingqian Xu,| and Yuliang Zhao*,†,‡ College of Chemistry and Chemical Engineering, Graduate UniVersity of Chinese Academy of Sciences, Beijing 100049, China, Key Laboratory for Biomedical Effects of Nanomaterials & Nanosafety, Institute of High Energy Physics, Chinese Academy of Sciences & National Center for Nanoscience and Technology of China, Beijing 100049, China, Department of Physics, Duke UniVersity, Durham, North Carolina 27708, and Faculty of Engineering & Nanoscale Science and Engineering Center, UniVersity of Georgia, Athens, Georgia 30602 Received October 6, 2008; Revised Manuscript Received January 20, 2009

ABSTRACT We functionalized double-walled carbon nanotubes (DWCNTs) as artificial water channel proteins. For the first time, molecular dynamics simulations show that the bilayer structure of DWCNTs is advantageous for carbon nanotube based transmembrane channels. The shielding of the amphiphilic outer layer could guarantee biocompatibility of the synthetic channel and protect the inner tube (functional part) from disturbance of the membrane environment. This novel design could promote more sophisticated nanobiodevices which could function in a bioenvironment with high biocompatibility.

Due to their structural uniqueness and facile manipulation, carbon nanotubes (CNTs) are promising building blocks for microscopic devices in nanoscale, which falls into subcellular dimensions. Advances in nanotechnology and molecular biology have extended CNTs for biomedical and biotechnological applications, such as nanoscale biosensors and molecule transporters.1-5 Progress in relating fields has shown promise that through sophisticated modification CNTs could be synthesized as functional units in biological systems, even as elements to assemble an artificial cell.6 Molecular Dynamics simulations were used by previous studies7-10 to investigate transport property of pristine singlewalled carbon nanotubes (SWCNTs) and more elaborate models based on SWCNTs as nanoscale channels have been proposed. For example, by modifying the tube wall to be hydrophilic, SWCNTs are modeled as transporters of water-methanol mixture11 and anion-doped SWCNTs can be modeled as ion channels.12 MD studies also anticipate that the gating of SWCNT channels could be tuned by * Corresponding authors, [email protected] and [email protected]. † College of Chemistry and Chemical Engineering, Graduate University of Chinese Academy of Sciences. ‡ Key Laboratory for Biomedical Effects of Nanomaterials & Nanosafety, Institute of High Energy Physics, Chinese Academy of Sciences & National Center for Nanoscience and Technology of China. § Department of Physics, Duke University. | Faculty of Engineering & Nanoscale Science and Engineering Center, University of Georgia. 10.1021/nl8030339 CCC: $40.75 Published on Web 02/26/2009

 2009 American Chemical Society

applying mechanical force13 or posing charge.14 Early explorations on transport property and possible applications of nanochannels imply that it is possible to design CNTbased nanobiodevices15 which have controllable functions such that they could work as transmembrane channels for substitutes of certain channel proteins. To explore the application of CNT-based nanodevices as transmembrane channels, model membranes are needed. One widely used method13-15 to simulate the biomembrane system is to embed the CNT as a channel into graphite sheet(s) as the membrane on which atoms are deleted at desired sites to accommodate the tube. Another widely used model membrane is the CNT-bundle system in which packed CNTs serve as both channels and the membrane to separate the solvate, which is a method that can be applied both theoretically16,17 and experimentally.18 Recently, Coarse Grain Molecular Dynamics (CGMD) studies19,20 demonstrated that by functionalizing the nanotube with hydrophilic termini, the tube could be spontaneously inserted into lipid bilayer and maintain the gesture as a transmembrane channel. Because of the readiness to covalently modify CNTs with hydrophilic moieties,21,22 the theoretical studies19,20,23 provide an idea to design applicable synthetic transmembrane devices. Moreover, due to the availability of model lipid bilayers,24 the studies predict a possible method to investigate the transport

property of CNT-based transmembrane channels experimentally. Two problems should be considered in the design of synthetic transmembrane channels: first, biocompatibility of the model needs to be measured because some synthetic tubes may associate with cellular toxicity;25,26 second, the working performance of the synthetic nanobiodevice in comparison to its protein counterpart should be examined. To our knowledge, pioneer designs of synthetic transmembrane channels exclusively consider SWCNTs. However, to develop more diverse applications of CNT-based nanobiodevices, we should explore designs beyond monolayer channels. Primitive SWCNTs are charge neutral and hydrophobic; they are compatible with the hydrophobic region of biomembranes. By contrast, functionalized SWCNTs could be polar or hydrophilic, thus hydrophobic mismatch between the synthetic channel and lipid bilayer would impair the property of the biomembrane and direct interactions between the two would interfere with the function of the nanobiodevice. For example, the conductivity of nanochannels was reported to depend greatly on the environment outside the channel,27 which suggests that it is significant to design a proper shielding structure for synthetic transmembrane channels in order to prevent interference from complex biological systems, as well as to guarantee controllable functions. To achieve the dual goals of designing synthetic transmembrane channels, we propose novel models based on double-walled carbon nanotubes (DWCNTs) for the prototype of functional biomimetic channels. On one hand, the inner tube could be modified to secure certain biological functions; on the other hand, the outer tube as the shield could be grafted with hydrophilic ends to minimize hydrophobic mismatch with biomembranes and thus could guarantee biocompatibility. Additionally, the model may be tested and extended experimentally in synthetic lipid bilayers. In this study, we designed transmembrane water channels with modified DWCNTs and embedded the models into dimiristoylphosphatidylcholine (DMPC) membranes to mimic Aquaporins (AQPs),28,29 which are a family of membrane channel proteins that are of physiological significance,30 to facilitate water permeation but exclude proton leakage.31,32 It has been proposed that the charged NPA region of AQPs dominate the bipolar orientation of water molecules inside the channel, which may be the determinant of proton selectivity.33-35 We used MD simulations to investigate the interplay between CNTs and biomembranes. The performance of the CNT-based models as artificial water channel proteins in the biological environment was examined. In addition, to exploring proton exclusion of the CNT-based models, we proposed a method to measure the stability of the water molecule’s orientation in a water wire. Model Design. As shown in Figure 1, the artificial water channel protein was designed by modified DWCNTs with armchair type (11, 11) CNT as the outer tube and armchair type (6, 6) CNT as the inner tube such that the interlayer distance is 3.4 Å, which is the typical value for DWCNTs.36 Two equivalent charges are assigned to a pair of next-nearestneighboring carbon atoms roughly at the middle of the inner Nano Lett., Vol. 9, No. 4, 2009

Figure 1. An overview of the simulation system and the CNTbased models: (a) simulation box; (b) DWCNT model; (c) SWCNT model. In (b) and (c), charged carbon atoms are rendered by red balls.

tube. The two charged carbon atoms lay along the long axis of the nanotube with a distance about 2.4 Å, which corresponds to the distance between the two partial charge groups in the NPA region of AQP proteins.37 Both ends of the outer tube are grafted with alternating hydroxyl and hydrogen termini. All CNT tubes, excluding hydrophilic ends, are equal in length of about 33 Å. For comparison, different magnitudes of charge were assigned to the two selected atoms. We refer those CNT-based models (or corresponding simulation systems) with partial charge of +0.1e, +0.25e, and +0.5e per atom as model D1, D2, and D3, respectively. In addition, one SWCNT model of armchair type (6, 6) was built and we refer this model (or corresponding simulation system) as model S. The tube in model S was grafted with the same hydrophilic groups in the similar fashion as compared to DWCNT models and the charged sites on the SWCNT were assigned +0.25e each, as shown in Figure 1c. Simulation Details. MD simulations were performed by NAMD38 of version 2.6 with CHARMM27 force field39,40 and TIP3P water model.41 Carbon atoms of CNTs were assumed to be type CA and the force field and partial charges on the hydrophilic ends of CNTs were modeled as that of corresponding groups in tyrosine in CHARMM27 force field. In this way, the partial charge distributions on these CNTbased models also agree with results calculated by the PEOE algorithm.42 All simulation systems were neutralized by adding sodium and chloride ions, and the salt concentration was kept at 0.15 M (physiological condition). Simulations were performed at constant temperature, 310 K, and pressure, 1 atm, with periodic boundary conditions and the PME method43 for full electrostatics. Bonds to all hydrogen atoms 1387

were kept rigid, and multiple time steps were used with 2, 2, and 4 fs as the inner, middle, and outer time step, respectively. After the embedding of CNTs into biomembranes (see below), all systems were first equilibrated in the NpT ensemble for 2 ns with the CNT restrained and then for another 3 ns without any restrains. The structure of pre-equilibrated DMPC was downloaded from the Web site of Klauda’s group.44 More water molecules were added into this patch to reach a fully hydrated state: 54 waters/lipid. Then, the membrane system was subjected to 10 ns equilibration at 310 K, 1 atm. Because the simulation systems in our study were CNT-DMPC complexes, we did not apply extra restrains to the membrane,45 and a separate simulation of the fully hydrated DMPC model was performed for a production run of 10 ns yielding an area/lipid value of 53.9 ( 0.54 Å2. Tcl boundary forces in NAMD38 were implemented to generate a cylindrical vacuum in the DMPC membrane. We applied radial forces to falling-in atoms to gradually increase the size of the hole at the center of the membrane with the NpT MD run. The DMPC membranes with proper size of hole were created consecutively for SWCNT and then for DWCNT. When the hole reached the proper size for the CNT, the corresponding CNT was inserted into the membrane. Then simulation systems were followed by equilibrations and then MD simulations of 10 ns, except for models D1 and D3, which underwent MD runs of 30 ns. Data were collected every 1 ps. The Interplay between the CNT-Based Model and DMPC. Nanobiodevices as artificial proteins should be biocompatible to operate in biological conditions. To examine the extent to which synthetic transmembrane channels influence biomembranes, we investigated the interplay between the CNT-based water channels and the lipid bilayer. To be concise, this paper only presents results of model D2 and model S when comparing the DWCNT model with the SWCNT model in interplaying with DMPC. We have confirmed that all DWCNT models behave the same in perturbing DMPC, which indicates that shielded charged atoms have little influence on the lipid bilayer. Diffusion behavior of lipids and CNTs in the membrane was studied and the Einstein equation (1)46,47 was used to calculate the lateral tracer diffusion coefficient. DT ) lim tf∞

1 〈|r(t) - r(0)|2〉 2dt

(1)

where r(t) is the center of mass (COM) position of the particle at time t and d is the number of dimensional degrees of freedom. To calculate the lateral diffusion coefficients, d ) 2 and r(0) and r(t) should be projected onto the membrane plane to yield the lateral mean square displacement (LMSD) of the particle. Figure 2a shows the average LMSD plots for the COM of DMPC in different models. All curves tend to satisfy the Einstein equation (1) in the long run. Linear fits in the time range of 2-3 ns yield lateral tracer diffusion coefficients of (13.44 ( 0.15) × 10-8 cm2/s, (7.78 ( 0.04) × 10-8 cm2/s, 1388

Figure 2. The in-plane (membrane plane) average LMSD for the COM of the lipids and the synthetic channels. (a) Lipids diffusion in CNT-DMPC complexes compared with in pure DMPC. (b) SWCNT and DWCNT diffusion in membranes.

and (3.10 ( 0.04) × 10-8 cm2/s for pure DMPC and DMPC in model S and model D2, respectively. Note that our result of DT value for pure DMPC agrees quite well with reported values, in both computational studies (12 × 10-8 cm2/s,48 13 × 10-8 cm2/s 49) and experimental studies (12.5 × 10-8 cm2/s,50 15.2 × 10-8 cm2/s 51). Lateral diffusion is one of the essential processes for biological membranes to function, such as in cell signaling and trafficking.52,53 In a homogeneous environment, the mean square displacement (MSD) of a particle is supposed to be linear to time. However, synthetic transmembrane channels as obstacles may hinder the diffusion of lipids such that result in the so-called anomalous diffusion of membranes.47 To be biocompatible, CNT-based transmembrane channels should not interfere significantly the diffusion behavior of DMPC. We define the obstacle concentration in our study as the fraction of lattice points occupied by CNTs in the membrane plane.54 Then the obstacle concentrations yield roughly 0.1 and 0.05 for model D2 and model S, respectively. The simulation results show that, under the current obstacle concentrations, DMPC monomers behave with normal diffusion. Thus in general, CNT-based channels pose little disturbance to lipids’ diffusion pattern. It is evident that the presence of CNTs leads to smaller DT for DMPC and DWCNT exerts a little higher influence on diffusion behavior of DMPC, compared with SWCNT. This difference can be attributed to relative large volume and mass of DWCNT which would lead to inertia of molecules in the vicinity of the tube. However, the decline in diffusion coefficient of DMPC is moderate and the diffusion behavior of lipids remains normal. This suggests that the fluidity of the Nano Lett., Vol. 9, No. 4, 2009

biological membrane is not impaired by the synthetic transmembrane channels and the CNT-based models are likely to be compatible with biomembranes. The average LMSD profiles for the COM of CNTs were also determined. As shown in Figure 2b, LMSD plots of SWCNT yield a two-range curve of power law time dependence, which means that SWCNT has non-Brownian diffusion aspect in the lipid bilayer. This phenomenon may be attributed to the small volume of SWCNTs which renders the tube to be easily confined in neighboring lipids such that the diffusion of the tube may associate with the cage effect.55 By contrast, the DWCNT model seems to abide by the Einstein equation (1) in the long run. Again, a linear fit in the time range of 2-3 ns yields a lateral tracer diffusion coefficient of (1.98 ( 0.02) × 10-8 cm2/s for the DWCNT model. The larger diffusion coefficients of CNT-based water channels compared with that of AQP proteins56 suggest that the synthetic transmembrane channels which are smaller in volume (compared with AQP tetramer) and more regular in shape could diffuse more freely in biomembranes. DMPC monomers are packing around the freely diffusible CNTs and oxygen atoms on the phosphate group or the ester linkages of DMPC can form hydrogen bonds with hydroxyl groups on two ends of the CNT. Although this behavior exerts no impact on the orientation of DMPC headgroups, DMPC monomers which contact CNTs hardly detach themselves from the tube and run into bulk DMPC; they diffuse with CNTs during the simulations. We define interfacial DMPC as those lipids that have contact with the tube, and correspondingly, the region which is occupied by these monomers are referred as interfacial region. By contrast, noninterfacial DMPCs are those lipids that are in the bulk DMPC region, and the corresponding region is referred as the noninterfacial region. Simulations show that interfacial DMPCs are not as active as noninterfacial ones. The muffled motion of interfacial lipids could be demonstrated by the small value of their average root mean square deviations (RMSDs) which were calculated by the RMSD trajectory tool in VMD57 with a frame span of 1 ps and the average structure as the reference. The calculations yield average RMSDs for single lipid of 1.62 Å for pure DMPC, 1.33 Å for interfacial DMPC in model D2, 1.66 Å for noninterfacial DMPC in model D2, 1.21 Å for interfacial DMPC in model S, and 1.52 Å for noninterfacial DMPC in model S. The results demonstrate that interfacial lipids have appreciably smaller average RMSDs compared with those of noninterfacial ones, which means that interfacial lipids suffer from loss of molecular flexibility. Visual inspection also confirmed that the difference is evident. A similar phenomenon was reported by MD simulations of a pristine nanotube embedded in the DMPC bilayer,26 where loss in displacement of lipids is found in the vicinity of the nanotube. Our result also agrees with previous CGMD studies20 which indicates that interfacial lipids firmly pack around immerged nanotube and suffer from loss of area per lipid. In addition, our simulations reveal a less appreciable difference between noninterfacial DMPC and pure DMPC lipids in molecular flexibility, especially for Nano Lett., Vol. 9, No. 4, 2009

DWCNT-based models. This means that although lipids which are close to CNTs suffer from loss of flexibility, the influence of CNTs on bulk DMPC is reduced. Hydrophobic mismatch could induce embedded synthetic channels to tilt for better accommodation in the membrane.58 Figure 3a shows the tilt-angle distribution for the embedded channels, which indicates that compared with SWCNT, DWCNT tilts in a small range of angles and are more likely to parallel the membrane normal. This result agrees with published CGMD studies58 and is attributed to the fact that the tilting of wide tubes would induce local membrane to deform thus associate with large energy penalties.58 In addition, because of the relative large contact surface of DWCNT exposed to DMPC, more lipids can exert forces onto the CNT simultaneously. These forces tend to offset each other due to the lateral isotropic property in the biomembrane environment. The collective effect will result in a less perturbation to the CNT. Thus, wide tubes tend to be stable in the lipid bilayer. The tilting of CNTs and the orientation of interfacial DMPC tails were correlated. As shown in Figure 3c, there is a strong coupling between the tilting of SWCNT and the orientation of nearby lipid tails. Arrows demonstrate the similar pattern in both curves of CNT tilt angle and interfacial DMPC tail vector orientation angle for model S. By contrast, in the case of DWCNT, this kind of coupling is less appreciable, as shown in Figure 3d. We observed that this difference can be attributed partly to lipids wrapping around CNTs. As shown in Figure 3b, although both CNTs have introduced rigid walls toward interfacial DMPC monomers, the DWCNT could give more degrees of freedom to lipids because its outer wall is less curved such that fewer energy penalties are associated with the wrapping. Another reason for the less correlated motion between CNT tilting and lipids tail orientation in the DWCNT model is that the influence of nearby lipids to the tube is reduced, which is supported by the inert tilting behavior of DWCNT. The coupling between CNT tilting and the lipid tail orientation shows that DWCNT models could induce less correlated motion and thus pose less perturbation to local DMPC than the SWCNT model. Although evident coupling between the tilting of CNTs and the orientation of interfacial DMPC tails was observed, less appreciable is the correlation between the tilting of CNTs and the orientation of noninterfacial DMPC tails. Distinguishing interfacial DMPC from noninterfacial DMPC may be of significance for studying the interplay between synthetic transmembrane channels and the biomembrane because interfacial DMPCs which diffuse with the tube could shield it from bulk DMPC environment and thus may help channel models achieve biocompatibility, though interfacial DMPCs feature different dynamic properties compared with bulk DMPC due to the perturbation of the synthetic transmembrane channel. Water Dynamics inside the Synthetic Channels. As artificial water channel proteins to mimic AQPs, synthetic transmembrane water channels should be able to transport water molecules and exclude proton leakage. The perfor1389

Figure 3. CNT tilting coupled with lipid tail orientation. (a) CNT tilt-angle distribution; histograms, and their Gaussian fit curves. (b) Wrapping behavior of lipids around CNTs. (c) The coupling between CNT tilting and lipid tail orientation in model S. (d) The coupling between CNT tilting and lipid tail orientation in model D2. CNT tilt angle is defined as the angle between the long axis of the CNT and the biomembrane normal. Lipid tail orientation is defined as the angle between Sn1 and Sn2 vectors59 of the lipid and the biomembrane normal.

Table 1. Comparison of Observed Water Permeation Events for Models in This Paper and with That of AQP,34 MD Simulation Results for 10 ns Time Scale S

D1a

D2

D3a

AQPb

no. of water visitors 165 207 161 149 51.2 no. of permeation events 7 16.7 4 3.7 4 a 30 ns results were divided by 3. b The results in ref34 are for four pore regions in AQP1 protein (tetramer) in the 10 ns simulation; thus to compare the conductivity of single channels, the results are divided by 4.

mance of the designs as substitutes for water channel proteins was investigated. The interaction between hydrophilic ends of synthetic transmembrane channels and polar groups of the biomembrane helps to prevent lipids from blocking the entrances of the nanotube;20 channel pores keep open during simulations. Water molecules can readily fill in the tube and diffuse along the channel. As summarized in Table 1, for a 10 ns time scale, model D1 has the highest observed water conductivity. In models D2 and D3, observed full permeation rates of water are comparable to that of AQP.34 As shown in Figure 4, the neighboring water molecules are hopping concertedly inside the channel and the water wire frequently jumps in a pulselike motion, which could result in a displacement of about 10 Å in a time period around 100 ps for the neighboring water molecules. The concerted motion of water molecules in confinement60 could lead to a burst in water 1390

Figure 4. Water molecules permeate through transmembrane channels. Left panels: Traced permeation trajectories of water molecules in model D1 (upper panel) and D3(lower panel). Right panels: Zoomed-in concerted motion of the water molecules for the pulselike motion of the water wire. For clarity, only one trajectory is shown in left panels and seven trajectories of neighboring water molecules are shown in right panels.

flow through shorter pristine carbon nanotubes9 and can be described by the continuous-time random-walk (CTRW) model.61 The free energy profiles in Figure 5a can provide knowledge about the dynamics of water inside the channel. Nano Lett., Vol. 9, No. 4, 2009

Figure 5. Water dynamics inside the synthetic channels. (a) Free energy profiles for water through the channels, z is the position coordinate along the tube and F(z) ) -KBT ln[F(z)/F0], where F(z) is the water density along the channel pore. (b) Water dipole states and dominant configuration of the water wire. (c) Average profile of θ in 10 ns simulations; insets: Average profiles of θ in consecutive time intervals of 10 ns for models D1 and D3. (d) Probability distribution of θ in 10 ns simulations; insets: Probability distributions of θ sampled in consecutive time intervals of 10 ns for model D1 and model D3. In (c) and (d), colors of curves for the main figures are referenced in (a); θ is the angle between the water dipole and the channel’s long axis. Models D1, D2, and D3 refer to DWCNT models with two charged sites of +0.1e, +0.25e, and +0.5e per each atom on the inner tube, respectively. Model S refers to the SWCNT model with +0.25e partial charge on the two selected sites. (See Model Design.)

First, the free energy barriers for water transportation are within 1.2 KBT. Because of the low transportation barriers, water molecules can readily permeate through CNT channels under physiological conditions. Second, the free energy barriers for water to enter the tubes are close in value for different models. This result could explain in part the close in value of water visiting numbers. Third, a larger magnitude of charge assigned to the sites on the tube will result in a higher free energy barrier for water transportation. Interestingly, the energy barrier for water transportation in model D2 is appreciably higher than that in model S, though both models were assigned by the same magnitude of charge on two selected sites. To verify whether or not water dynamics inside the tube are different in the two models, first we determined onedimensional diffusion coefficient of water (Dwat) inside the channel from average MSD of the water molecules. Calculation yields Dwat of 0.0304 ( 0.0010 Å2/ps for model D2 and 0.0564 ( 0.0005 Å2/ps for model S. Then according to the CTRW model,61 hopping rates61 (k) of water chains inside the tube can be determined from Dwat. Calculation yields k of 0.009 ps-1 for model D2 and 0.0167 ps-1 for model S. Finally, following the method provided in the Appendix of ref 17, permeation rates of water molecules through the channel can be predicted from hopping rates as 0.64 ns-1 for model D2 and 1.19 ns-1 for model S. Note that predicted Nano Lett., Vol. 9, No. 4, 2009

permeation rates of water molecules agree with observed permeation events in our simulations. These results show that water transportation in model S is indeed faster than that in model D2. The difference between water dynamics in model D2 and model S should be attributed to “shielding effect” of the outer CNT. We monitored the profile of conformational energies (bond, angle, and dihedral energy are included) for the inner tube in model D2 and the tube in model S (hydrophilic ends are excluded). Interestingly, conformational energy of the tube is higher in model S than in model D2 throughout the simulations (the difference is 138.1 ( 40.9 kcal/mol). Because the tube in model S interacts directly with neighboring lipids, forces exerted by firmly packing lipids would result in local deformation of tube and thus could lead to a higher conformational energy. By contrast, in model D2, the inner functional channel is protected by an outer layer from the pressure of lipids such that the channel would be less deformed by the environment and thus result in a lower conformational energy. The faster movement of water molecules in the channel of model S may be explained by the direct interaction between the tube and local lipids: random forces exerted by neighboring lipids to the channel may result in random deformation of the tube and thus inside water molecules would be subjected to alternating lateral pressure. This would increase the fluctuation of water 1391

molecules inside the channel and thus helps water molecules overcome transportation energy barriers. The difference in water conductivity between shielded and naked channels provides an example to show that the function of CNT-based transmembrane channels would be affected by local lipid environment if we fail to protect the tube with a biocompatible outer layer. Despite of the ability to transport water molecules, synthetic water channels as artificial AQP proteins should also exclude proton leakage. For AQPs, the origin of proton exclusion is of great interest in the scientific community.62 Despite what dominates the proton exclusion for the protein, proton conduction along a water wire can be viewed as two complementary steps:63 (1) the propagation of proton and (2) the reorientation of the water chain to prepare for propagation of the next proton. An MD study64 based on empirical force field which explicitly treats protons shows that the reorientation step is the limiting step for proton transportation along a water wire. This conclusion is also supported by ab initio MD simulation results65 of proton conduction along CNT channels. These findings indicate that water reorientation may pose effective barriers for fast transport of protons through the water wire. Thus, in one water wire, a stable configuration of water molecule orientations which seldom flip will contribute to a low conductivity of protons through the line. As shown in Figure 5c, owing to strong electrostatic interactions between water and charged carbon atoms, water wires inside CNT channels, as the water wire in AQP channels, preferably adopt bipolar orientations. Although the magnitude of charge on each selected carbon atom in model D3 is two times as large as that in model D2, there seems little difference between model D3 and model D2 in average water dipole orientation profiles. In addition, Figure 5d shows that, in the two models, probability distributions of water gestures are nearly identical. However, these two models are different in maintaining the gesture of individual water molecules inside the channel. Below, we propose a method to quantify the stability of water gestures in one water wire such that we can show the difference between these two models in excluding proton leakage. As is shown in Figure 5b, when we examine the gesture of an individual water molecule in one water wire by investigating the angle θ between the water dipole and the long axis of the channel (i.e., the direction which this water wire propagates), we divide an angle range of 180° into three consecutive intervals: [0°, 60°), [60°, 120°), and [120°, 180°]. We further define water dipole state 1 when θ of this molecule falls into [0°, 60°), 0 when [60°, 120°), and -1 when [120°, 180°]. Thus, each water molecule has three possible dipole states:1, 0, or -1. To quantify the stability of the individual water gesture in different models, we examined how well the configuration of water dipole states in the water wire preserves. Because on average there are 12 water molecules fully confined by the synthetic water channels during the simulations, the water wire inside the tube should have 312 different possible configurations. For models D2 and D3, if we always investigate the 12 water 1392

molecules’ dipole states along the channel, there would be one configuration that predominates (whose probability of occurrence exceeds 50%): -1 -1 -1 -1 -1 -1 0 1 1 1 1 1. First, we define this combination of water dipole states as dominant configuration of the water wire. Then we define deviant configuration of the water wire as any combination of water dipole states which is not consistent with the dominant configuration. Correspondingly, the water dipole of each individual water molecule could be in dominant state if it renders the wire to be in dominant configuration or deviant state if it renders the wire to be in deviant configDom uration. We use Pwire to denote the probability of the water wire to be in dominant configuration. Finally, If we assume an equal probability of each water molecule in the water wire to be in deviant state and use PDev wat to denote this variable Dom Dev Dom Dev (Pwat + Pwat ) 1,Pwire + Pwire ) 1), the following equation Dev 12 Dom holds: (1 - Pwat ) ) Pwire . Dom Under the above assumptions, model D2 yields Pwire,D2 ) Dev Dom 0.532, thus Pwat,D2 ) 0.051; model D3 yields Pwire,D3 ) 0.878, Dev Dev thus Pwat,D3 ) 0.011. Note that the magnitude of Pwat must 9,13,17 be proportional to the average flipping rate of water molecules in that water wire. It is obvious that model D3 Dev has a smaller Pwat , which means that water orientations in the water wire of model D3 are much more stable compared with that in model D2. Thus, considering the readiness to reorient water molecules, model D3 should be more efficient to exclude proton conduction. For model D1, essentially no single configuration of water dipole states has a probability of occurrence above 0.5; water dipole states are frequently disturbed inside the tube. As shown in the insets of parts c and d of Figure 5 for model D1, when the average profile of water orientations along the channel and the probability distribution of θ are obtained for consecutive time intervals of 10 ns, the curves show stepwise shifts. By contrast, the corresponding curves for model D3 are quite converged. This phenomenon implies that the combination of water dipole states inside the tube in model D1 is volatile; there must be collective shifts of water dipole states in the water wire during the 30 ns simulation. Visual inspection further confirmed this phenomenon; there are cases in which all waters in the wire occupy the same state: -1 during first 10 ns and many other combinations of water dipole states occur during the 30 ns simulation. Thus, water orientations in model D1 are free to adjust such that this model should be least efficient in excluding proton leakage. Owing to the limit of classical MD models, proton conduction is not explicitly treated in our study. Although the limiting step of proton transfer along a water wire is the water reorientation step, it does not follow that the limiting step for the dynamic process of proton transportation across the biomembrane must be the reorientation step. It may be possible that the entering step is more rate limiting for proton transfer across the biomembrane. Further study is needed to address this problem. Conclusion Remarks. For the first time, we proposed DWCNT-based nanodevices as artificial transmembrane channel proteins. We functionalized the DWCNTs with Nano Lett., Vol. 9, No. 4, 2009

hydrophilic ends on the outer tube and charged sites on the inner tube and then embedded them into biomembranes to explore their biological functions as AQP water channel proteins. The novel design shows good biocompatibility and the ability to function as AQPs. We distinguished interfacial lipids from noninterfacial lipids, which may be of significance in studying the interplay between embedded synthetic nanodevices and the biomembrane. To explore proton exclusion of synthetic water channels, we established a method to quantify the stability of water molecules’ orientation in a water wire. The method may be extended to study dynamics of nanoscale chained molecules other than water wires. As biological membranes are hydrophilic at two outer surfaces but hydrophobic at the inner region, the modified outer tube renders the synthetic transmembrane channel hydrophilic at two ends but hydrophobic at the body; thus the model could be biocompatible in biomembranes. On the other hand, a modified inner tube gives the channel model special functions that primitive CNTs do not bear such that the model could do biological services similar to what its protein counterpart does. Synthetic transmembrane channels based on DWCNTs would be preferred when the functional inner tube needs to be isolated from biomembrane environment; shielding the functional part of the nanobiodevice with an amphiphilic outer tube would help to minimize the hydrophobic mismatch between the synthetic channel and the biological membrane and protect the functional inner tube from disturbance of neighboring lipids. Biocompatible nanobiodevices would introduce less negative influence to relevant biological processes and thus potentially be of low nanotoxicity. Moreover, embedding CNT-based nanochannels into a model lipid bilayer is applicable experimentally; thus novel transmembrane models could be tested and extended in experiments. Accordingly, the present model acts as a prototype to promote the design of more sophisticated nanobiodevices. Acknowledgment. This work was supported by MOST 973 program (2006CB705600), NSFC (10525524), the Knowledge Innovation Program of Chinese Academy of Sciences, and the Scientific Research Foundation for the Returned Overseas Chinese Scholars, the Ministry of Education. The authors thank F. Q. Zhu and J. Y. Li for helpful discussion and Luis Gracia for providing rmsd trajectory tools. References (1) Martin, C. R.; Kohli, P. Nat. ReV. Drug DiscoVery 2003, 2 (1), 29– 37. (2) Bianco, A.; Kostarelos, K.; Prato, M. Curr. Opin. Chem. Biol. 2005, 9 (6), 674–679. (3) Ke, P. C.; Qiao, R. J. Phys.: Condens. Matter 2007, 19 (37), 25. (4) Yang, W. R.; Thordarson, P.; Gooding, J. J.; Ringer, S. P.; Braet, F. Nanotechnology 2007, 18, 12. (5) Endo, M.; Strano, M. S.; Ajayan, P. M. Potential applications of carbon nanotubes. In Carbon Nanotubes; Springer-Verlag: Berlin, 2008; Vol. 111, pp 13-61. (6) Pohorille, A.; Deamer, D. Trends Biotechnol. 2002, 20 (3), 123–128. (7) Zhang, F. J. Chem. Phys. 1999, 111 (19), 9082–9085. (8) Mao, Z. G.; Sinnott, S. B. J. Phys. Chem. B 2000, 104 (19), 4618– 4624. Nano Lett., Vol. 9, No. 4, 2009

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