Interaction of Graphene and its Oxide with Lipid Membrane: A

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Interaction of Graphene and its Oxide with Lipid Membrane: A Molecular Dynamic Simulation Study Junlang Chen, Guoquan Zhou, Liang Chen, Yu Wang, Xiaogang Wang, and Songwei Zeng J. Phys. Chem. C, Just Accepted Manuscript • DOI: 10.1021/acs.jpcc.5b10635 • Publication Date (Web): 09 Mar 2016 Downloaded from http://pubs.acs.org on March 12, 2016

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The Journal of Physical Chemistry

Interaction of Graphene and its Oxide with Lipid Membrane: A Molecular Dynamic Simulation Study Junlang Chen*, †, Guoquan Zhou†, §, Liang Chen†, §, Yu Wang†, Xiaogang Wang†, Songwei Zeng‡ † School of Sciences, Zhejiang A & F University, Lin'an 311300, China ‡ School of Information and Industry, Zhejiang A & F University, Lin'an 311300, China § Key Laboratory of Chemical Utilization of Forestry Biomass of Zhejiang Province, Zhejiang A & F University, Lin’an, 311300, China

ABSTRACT Graphene nanosheet has exhibited increasing prospect in various biomedical applications because of its extraordinary properties. Meanwhile, recent experiments have shown that graphene has antibacterial activity or cytotoxicity and can cause cell membrane damage. Therefore, it is necessary to understand the interactions between graphene and cell membrane to avoid its adverse effects. Here, we use molecular dynamics simulation to explore these interactions. The results show that pristine graphene (PG) can readily penetrate into the bilayer and has no effect on the integrity of membrane. When graphene oxide (GO) is embedded in the membrane, several lipids are pulled out of the membrane to the surface of GO, resulting in the pore formation and water molecules flowing into the membrane. The difference between PG and GO in the membrane originates from GO's oxygen-contained groups, which enhance the adsorption of the lipids on GO surface. However, the main interactions between GO and membrane are still determined by the strong dispersion interactions between its hydrophobic domains and the lipid tails of the bilayer. Therefore, the toxicity of coated GO can be weakened, since its hydrophobic domains are screened by polymers. The findings may offer new perspective for better designing GO based nano-carrier or antibiotics and other biomedical applications. Keyword: pristine graphene; graphene oxide; lipid bilayer; molecular dynamics simulation;

INTRODUCTION Two-dimensional pristine graphene (PG) and graphene oxide (GO) with remarkable properties present growing potential in various biomedical applications1-4, such as biosensors5-8, enzyme immobilization9-11 and drug delivery12-14. Prior to these applications, understanding their cytotoxicity and the interactions between graphene nanosheet and cell membrane is fundamentally essential. Many experiments have

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shown that graphene-based nanoparticles can enter cells either through direct penetration or via endocytosis with little toxicity or without inducing cell membrane damage13, 15-17. For example, polyethyleneimine (PEI) modified GO and then non-covalently bound with plasmid DNA can enter HeLa cells for intracellular transfection. Cellular toxicity tests of PEI-GO complex show low toxicity. Besides, no membrane rupture is observed in cell uptake of PEGylated GO13. GO has great prospect as an ideal nano-carrier for drug delivery. On the other hand, graphene can cause cell membrane damage or degradation and further cell death18-21. Through measuring the efflux of hemoglobin from suspended red blood cells, which indicated that cell membrane was broken, Liao et al found that GO had strong hemolytic activity, whereas coated GO with chitosan nearly eliminated hemolytic activity21. Omid Akhavan and Elham Ghaderi speculated that the membrane damage was caused by direct contact of bacteria with the extremely sharp edges of graphene, similarly through assessing the RNA outflow of two kinds of bacteria (Gram-negative Escherichia coli and Gram-positive Staphylococcus aureus)18. Moreover, their experiments evidenced that reduced GO was more toxic than GO. However, experiments conducted by Liu et al showed opposite results that GO possessed stronger antibacterial activity against the same bacteria (Escherichia coli) than reduced GO19. Because of the conflicting findings of the above experimental studies, computer simulations are applied to investigate the interactions between graphene and lipid membrane22-24. For example, molecular dynamics (MD) simulations performed by Tu et al demonstrated that graphene could extract large amounts of phospholipids from the cell membrane due to the strong dispersion interactions between graphene and lipid tails22. Titov et al revealed that graphene could be adsorbed and lay flat in the center of membrane, using coarse-grained (CG) MD simulations. The graphene-membrane hybrid was highly stable and just like a sandwich23. Using CG model and dissipative particle dynamics (DPD) simulations, Guo et al studied the role of size and edge of graphene nanosheet in its translocation across the lipid bilayer. They discovered the permeation of small graphene into bilayer center through insertion and rotation driven by transbilayer lateral pressure24. However, different from Tu's results, no lipid extraction or cell membrane rupture happened in the above two CG simulations. Obviously, there is still no consensus in the interactions between graphene and cell membrane, and whether graphene will cause cell membrane damage is still under debate. Furthermore, the current simulation studies are focused mainly on PG, while in fact GO is adopted in biological experiments instead of PG for better water-solubility. Here, we performed MD simulations to investigate the behavior of both PG and GO in and out of the membrane. We observed that PG could quickly 'jump' into the membrane and stay therein with its basal plane parallel to the lipid tails. When GO penetrated into the bilayer, membrane rupture occurred, and several lipids


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were pulled out of the membrane to the surface of GO, resulting in the pore formation and water molecules flowing into the membrane. This lipid extraction and water pore formation may provide novel molecular mechanisms of GO's cytotoxicity and antibacterial activity.

METHODS Molecular dynamics (MD) simulations were executed on the hydrated bilayers with graphene nanosheets. Three kinds of PG and GO were constructed, as shown in Figure 1. One was much smaller than the bilayer thickness, one was close to the bilayer thickness and the other was greater than the bilayer thickness. GO was built based on a molecular model of C10O1(OH)1(COOH)0.5 (i.e., 2 epoxy, 2 hydroxyl groups on both sides of the graphene basal plane, and 1 carboxyl group on the edge, per 20 carbon atoms), which was widely used in MD simulations25-27. The fully hydrated bilayers were developed by Tieleman and Berendsen28. Two kinds of lipid bilayers were employed. One was composed of 128 DPPC lipids for small PG or GO and the other was composed of 256 lipids for middle and large sheets.

Figure 1. Atomic structures of PG and GO with three kinds of sizes. (a, d) Small size: 2.1×2.1 nm2, (b, e) middle size: 3.1×4.1 nm2, (c, f) large size: 3.7×5.4 nm2. Color scheme: cyan (C), red (O), white (H). The force field parameters for DPPC lipids and graphene were taken from Berger group29. The bonded parameters of carbon atoms in the basal plane of PG and GO were taken from Patra group30, 31, in which the C-C bond lengths of 1.42 Å, C-C-C bending angles of 120°, and C-C-C-C planar dihedral angles, were maintained by harmonic potentials with spring constants of 1.350×105 kJ mol-1 nm-2, 223.2 kJ mol-1 rad-2 and 13.18 kJ mol-1, respectively. The carbon atoms in graphene were treated as uncharged Lennard-Jones balls with a cross section of σcc = 0.34nm and a depth of the



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potential well of εcc = 0.36 kJ/mol32. The parameters of hydroxyl, carboxyl and epoxy groups were taken from the GROMOS53a6 force field for serine, glutamic acid and dialkyl ether, which were compatible with those of DPPC lipids. Water was represented by the simple point charge (SPC) model33. All simulations were performed under the isothermal-isobaric ensemble (NPT) using Gromacs package 4.5.534, 35. The particle-mesh Ewald (PME) method was applied to calculate the long-range electrostatic interactions36, 37, whereas the vdW interactions were treated with a smooth cutoff of 1.0 nm. The temperature was kept stable at 323K using the V-rescale thermostat38 and the pressure was controlled semi-isotropically at 1 bar by a Berendsen barostat39. Bond lengths within graphene and membrane were constrained by LINCS algorithm40, which allowed an integration time of 2 fs. Periodic boundary conditions were used in all directions.

RESULTS AND DISCUSSION A. Simulation Results: Pristine Graphene In the three independent simulations, PG finally penetrated into the membrane. Taking the smallest PG as an example to illustrate its translocation, we employed the COM distance (denoted as d) and the tilt angle θ (θ=0° or 90° mean that PG is parallel or perpendicular to the membrane, respectively.) between PG and membrane to monitor this process, which could be divided roughly into four stages (see Figure 2b). Initially, PG was positioned vertically out of the membrane with d=3nm. During the first 10 ns, PG was fluttering randomly in the aqueous solution. Meanwhile, θ fluctuated dramatically and could reach every possible value from 0° to 90°. In the following 29 ns, PG was lying flat on the interface between membrane and water (see snapshot at t=21ns). However, from t=39 ns to 40 ns, PG rapidly "jumped" into the bilayer. Then, PG stayed therein in the rest of simulation time. We repeated the simulation with a different initial configuration, and the fast insertion was still observed (see Supporting Information, Figure S1). After entering the membrane, PG could only remain vertical, and θ was close to 90° with slight fluctuations.


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Figure 2. A representative trajectory of PG in small size entering the lipid bilayer. (a) Snapshots at critical time points. Carbon (cyan), oxygen (red), nitrogen (blue) and phosphorous (tan) atoms in head groups are shown as spheres, while the acyl chains are represented as dynamic-bonds. PG is depicted as a cyan-bonded sheet. Water molecules are shown in violet. (b, c) Time evolutions of the COM distance (black), the tilt angle (red) and the interaction energy between PG and membrane. The fast insertion of PG into the lipid bilayer was driven by the hydrophobic and strong dispersion interactions between PG and lipid tails. We therefore calculated the interaction energy of PG and the bilayer. Here, the interaction energy was defined as the vdW interaction between PG and membrane. The energy curve presented the same trend as that of COM distance (see Figure 2b, c). The energy difference of PG in and out of the bilayer reached about 600 kJ/mol. It was clear that the huge fall of the energy made PG fast adsorbed into the membrane. Once PG entered the membrane, all the three parameters were kept stable except for negligible fluctuations, indicating that the structure of graphene-membrane hybrid was highly stable.

Figure 3. The most probable orientation of PG in membrane. Histograms of the COM distance (a) and the tilt angle (c) between PG and lipid bilayer after entering the membrane, (b) and (d) represent their corresponding interaction energies. To better understand the orientation of PG in the membrane, we found out the most probable tilt angle and COM distance between PG and lipid bilayer. Figure 3 showed the histograms of d and θ after PG entered the bilayer and their corresponding interaction energies. We observed that the most probable value of d was not 0 but 0.6 nm (see Figure 3a). Since there is a gap between the upper and lower leaflet of the bilayer, PG at the center of membrane was not the most energetically favorable. While d=0.6nm, the graphene was wrapped by the lipid tails at maximum extent, which was consistent with their interaction energy (Figure 3b). We then counted the frequencies of the tilt angle every 1 degree and calculated the corresponding interaction energy, as shown in Figure 3c, d. The most probable value of θ was near 86°, implying that the



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PG prefers locating vertically in the membrane.

Figure 4. The trajectories of PG located vertically (a) or lay flat (b) in the center of membrane. (c) The tilt angle and (d) the interaction energy between PG and membrane in the above two systems. To confirm this most probable orientation of PG in the membrane, we performed two additional simulations with PG located vertically or lay flat in the membrane center, as shown in Figure 4a, b. In system a, it was found that θ was close to 90° through the whole simulation. However, in system b, PG rotated gradually with the increasing θ from 0° to 90° in the first 20ns (see Figure 4c). In the remaining time, PG located vertically the same as system a, indicating that a vertically oriented PG in the membrane was highly stable. This stable structure was different from Titov's simulations. Titov revealed that graphene-membrane structure was very stable and just like a sandwich with PG lying flat in the center of the bilayer23. However, it was clear that PG lying flat was not energetically favorable. As illustrated in Figure 5d, in the first 20 ns, the interaction energy between PG and DPPC bilayer in system b was about 100kJ/mol higher than that of system b. After PG rotated to the vertical state, the interaction energies of both systems were in good agreement with each other. Figure 5 showed the insertion of bigger PG in the membrane. We observed that larger PG could still readily enter the bilayer. After the insertion, the bilayer was still kept intact but was deformed slightly (see Figure 5b), which was well consistent with the other two simulations23, 24.


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Figure 5. The insertions of larger PG in the lipid bilayer. (a, b) Initial and final structures of middle PG. (c, d) Initial and final structures of large PG. On the other hand, PG with larger size could not rotate freely when it initially lay flat in the center of membrane, since the system was subjected to local minima. We therefore put the PG artificially with different tilted angles, and then calculated their interaction energies, as shown in Figure 6. The results that PG located vertically in the membrane center was highly stable and energetically favorable were in line with those of the smallest PG (see Figure 3d).

Figure 6. (a) Snapshots at each tilt angle. (b) The corresponding interaction energies between lipid bilayer and PG (middle size) with the increasing tilt angle from 0° to 85°. B. Simulation Results: Graphene Oxide

Figure 7. Trajectory of GO diffusing onto the lipid bilayer. (a) Initial configuration. (b) Final snapshot. (c) Time evolution of COM distance between GO and membrane. The red dashed line denotes the averaged z-coordinate of phosphorus atoms of the upper leaflet, which represent the center of the interface between water and membrane. Different from the PG's fast insertion, GO always remained at the interface between the head groups and water during the simulation. Figure 7b showed the final snapshot of the 1000 ns simulation. We observed that half of the GO inserted the bilayer while another half was in the water. The difficulty for GO to enter the membrane was mainly due to the fact that oxygen-contained groups render GO much hydrophilic, thusly favoring its location on the hydrophilic interface rather than in the hydrophobic



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inner bilayer.

Figure 8. Snapshots of two separate systems. (a-d) GO lay flat initially at the membrane center; (e-h) GO was positioned vertically at the membrane center. (b, f) are the final configurations of 200ns simulations. (c, g) are the side views of water distributions. (d, h) are the top views of DPPC bilayers, where black circles specify the water pores. However, GO can still enter the membrane by cell's endocytosis. We constructed two separate systems, in which GO was embedded in the bilayer vertically and horizontally. Similar to the PG in the membrane, GO became more and more difficult to rotate in the membrane with the increasing size. The results of the smallest GO were presented in Figure 8 (the results of GO in middle and large size were given in Supporting Information, Figure S2 and S3). Interestingly, it was found that in both systems, GO rotated to the tilted state and several lipids were pulled out of the membrane to the surface of GO, resulting in the pore formation and water molecules flowing into the membrane. In the first 25 ns, the tilt angle decreased from 90° to 45° in the first system and increased from 0° to 45°in the second system, as shown in Figure 9a. Meanwhile, water pore was formed (see Figure 8d, h) and about 60 or 15 water molecules entered the membrane in each system (see Figure 9b).

Figure 9. Time evolutions of (a) the tilt angle and (b) the number of water molecules within 1nm from the bilayer center, (c) the probability density of the tilt angle. Figure 9c showed the frequencies of the tilt angle. The most probable tilt angles were about 25°and 30°, implying that GO was slanting in the membrane (see Figure


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8b, f). The difference between PG and GO in the membrane was determined by the oxygen-contained groups on GO surface. The structure of GO located horizontally was not stable because of the electrostatic interaction between the oxygen-contained groups on GO and the head groups of DPPC lipids. Radial distribution functions (RDF) were calculated to illustrate this interaction. In Figure 10a (black line), the first and second peaks of RDF were near 0.2 and 0.34 nm, meaning a hydrogen bond formed between the phosphate groups of DPPC and GO. The following peaks represented the interactions between oxygen atoms on GO and the whole phosphate groups, since the phosphate group was composed of four negatively charged oxygen atoms and one positively charged phosphorus atoms. These peaks implied that oxygen-contained groups on GO strongly interacted with the head groups of DPPC lipids. While the peak of RDF for acyl chains around GO was at ~0.5 nm (Figure 10a, red line), close to the collision radius between carbon atoms on GO and DPPC lipids. These interactions between GO and DPPC lipids could overcome the self-attraction among the lipids themselves, and thus several lipids were pulled out of the membrane to the GO surface. Figure 11 showed one such lipid. The interaction energy presented the same trend as the COM distance between GO and the lipid. Though the electrostatic energy was much weaker (about 150kJ/mol less) than the vdW interaction, it played the key role in the extraction, compared with PG in the membrane. Figure 11c highlighted the structure of DPPC on GO surface. The lipid lay flat on GO surface to maximize their interactions.

Figure 10. (a) Radial distribution functions for GO with respect to phosphate groups and lipid tails of DPPC. (b) Radial distribution functions for GO with respect to water molecules.

Figure 11. The interactions between one DPPC lipid and GO. (a) COM distance, (b) vdW and Coulomb energies and (c) the structure highlights one lipid adsorbed on GO surface. After the extraction, water pore was formed and many water molecules flew into the membrane. Figure 10b showed the RDF of water molecules around GO. The first



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(at ~0.18nm) and second peaks (at ~0.35 nm) corresponded to the hydrogen bonds, indicating that water in the membrane prefer to surround the GO because of the electrostatic attraction. After these peaks, the RDF rose gradually with the increasing distance, since more and more water molecules were included out of the membrane.

CONCLUSION In summary, using computational simulations, we have investigated the interaction of pristine and functionalized graphene with a DPPC bilayer. We demonstrated that PG could diffuse easily into the bilayer with its basal plane parallel to lipid tails, due to hydrophobic interaction between graphene and lipid tails. However, GO could not enter the membrane in simulation time scale but prefer to stay at the water-membrane interface, since the oxygen-contained groups render GO much hydrophilic. When GO was placed in the membrane, membrane rupture occurred and several lipids were pulled out of the membrane to the surface of GO, resulting in the pore formation and water molecules flowing into the membrane. Through energy analysis, we found that GO's cytotoxicity mainly originated from the strong dispersion interactions between the hydrophobic domains of GO and the lipid tails of membrane. That can explain why coated GO can effectively reduce its toxicity and improve its biocompatibility, because GO is covered by PEI or PEG, which screen its hydrophobic domains. Just as a double-edged sword, GO, on the one hand, owns cytotoxicity and strong antibacterial activity. On the other hand, its toxicity should be circumvented when served as a nano-vehicle to deliver drug. Further study should be concentrated on which factors determine the pore size and whether water pore is irreversible or reversible, since irreversible water pore can cause cell death. AUTHOR INFORMATION Corresponding Author * Email: [email protected]

ACKNOWLEDGEMENT This work was partially supported by Zhejiang Provincial Natural Science Foundation of China (Grant No. LY16A040014) and the National Natural Science Foundation of China (Grant No. 11574272, 61575178). The authors acknowledge Shanghai Supercomputer Center and Shanghai University HPC ZQ3000&4000 for computational resources. ASSOCIATED CONTENT Supporting Information. Another trajectory of PG entering the lipid bilayer with different initial structure.

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(31) Patra, N.; Song, Y.; Kral, P. Self-Assembly of Graphene Nanostructures on Nanotubes. ACS NANO 2011, 5,1798-1804. (32) Hummer, G.; Rasaiah, J. C.; Noworyta, J. P. Water Conduction through the Hydrophobic Channel of a Carbon Nanotube. Nature 2001, 414,188-190. (33) Jorgensen, W. L.; Chandrasekhar, J.; Madura, J. D.; Impey, R. W.; Klein, M. L. Comparison of Simple Potential Functions for Simulating Liquid Water. J. Chem. Phys. 1983, 79,926-935. (34) Berendsen, H. J. C.; Spoel, D. v. d.; Drunen, R. v. GROMACS: A Message-passing Parallel Molecular Dynamics Implementation. Comput. Phys. Commun. 1995, 91,43-56. (35) Hess, B.; Kutzner, C.; van der Spoel, D.; Lindahl, E. GROMACS 4: Algorithms for Highly Efficient, Load-Balanced, and Scalable Molecular Simulation. J. Chem. Theory Comput. 2008, 4,435-447. (36) Darden, T.; York, D.; Pedersen, L. Particle mesh Ewald: An Nlog(N) Method for Ewald Sums in Large Systems. J. Chem. Phys 1993, 98,10089-10092. (37) Essmann, U.; Perera, L.; Berkowitz, M. L.; Darden, T.; Lee, H.. Pedersen, L. G. A Smooth Particle Mesh Ewald Method. J. Chem. Phys. 1995, 103,8577-8593. (38) Bussi, G.; Donadio, D.; Parrinello, M. Canonical Sampling Through Velocity Rescaling. J. Chem. Phys. 2007, 126,014101. (39) Berendsen, H. J. C.; Postma, J. P. M.; van Gunsteren, W. F.; DiNola, A.; Haak, J. R. Molecular Dynamics with Coupling to an External Bath. J. Chem. Phys. 1984, 81,3684-3690. (40) Hess, B.; Bekker, H.; Berendsen, H. J. C.; Fraaije, J. G. E. M. LINCS: A Linear Constraint Solver for Molecular Simulations J. Comput. Chem. 1997, 18,1463-1472.



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Interaction of Graphene and its Oxide with Lipid Membrane: A

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