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Theoretical evaluation on potential cytotoxicity of graphene quantum dots Lijun Liang, Zhe Kong, Zhengzhong Kang, Hongbo Wang, Li Zhang, and Jia-Wei Shen ACS Biomater. Sci. Eng., Just Accepted Manuscript • DOI: 10.1021/acsbiomaterials.6b00390 • Publication Date (Web): 20 Sep 2016 Downloaded from http://pubs.acs.org on September 25, 2016

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ACS Biomaterials Science & Engineering

1

Theoretical evaluation on potential cytotoxicity of graphene

2

quantum dots

3

Lijun Liang1 *, Zhe Kong2, Zhengzhong Kang3 4, Hongbo Wang5，Li Zhang6, Jia-Wei

4

Shen7,*

，

，

5

1

6

University, No. 1, 2nd Street, Jianggan District, Hangzhou, 310018, People's Republic

7

of China

8

2

9

Hangzhou, No. 1, 2nd Street, Jianggan District, Hangzhou, 310018, People’s Republic

College of Life Information Science and Instrument Engineering, Hangzhou Dianzi

College of Materials and Environmental Engineering, Hangzhou Dianzi University,

10

of China

11

3

12

People’s Republic of China

13

4

14

Royal Institute of Technology, SE-10691 Stockholm, Sweden

15

5

16

District, Hangzhou 310018, People’s Republic of China

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6

18

District, Hangzhou, 310012, People’s Republic of China

19

7

20

District, Hangzhou 310016, People’s Republic of China

Department of Chemistry, Zhejiang University, Zheda Road 38, Hangzhou, 310028,

Division of Theoretical Chemistry and Biology, School of Biotechnology, KTH

College of Automation, Hangzhou Dianzi University, No. 1, 2nd Street, Jianggan

Department of Chemistry, Zhejiang Sci-Tech University, No. 2, 2nd Street, Jianggan

School of Medicine, Hangzhou Normal University, Xuelin

21 22

Email address:

23

[email protected] (L. Liang)

24

[email protected] (J.-W. Shen)

25 1

ACS Paragon Plus Environment

street

16,

Jianggan


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Abstract

2

Owing to unique morphology, ultra-small lateral sizes and exceptional properties,

3

graphene quantum dots (GQDs) hold great potential in many applications, especially

4

in the field of electrochemical biosensors, bioimaging, drug delivery etc. Its biosafety

5

and potential cytotoxicity to human and animal cells is a growing concern in recent

6

years. In this work, the potential cytotoxicity of GQDs was evaluated by molecular

7

dynamics simulations. Our simulation demonstrates that small size GQDs could easily

8

permeate into lipid membrane in a vertical way. It is relatively difficult to permeate

9

into lipid membrane for GQDs that larger than GQD61 in ns time-scale. The thickness

10

of POPC membrane could even be affected by small size of GQDs. Free energy

11

calculations revealed that the free energy barrier of GQDs permeation through lipid

12

membrane could greatly change with the change of GQDs size. Under high GQDs

13

concentration, the GQDs molecules could rapidly aggregate in water but disaggregate

14

after entering into the membrane interior. Moreover, high concentrations of GQDs

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could induce changes in the structure properties and diffusion properties of the lipid

16

bilayer, and it may affect the cell signal transduction. However, GQDs with relative

17

small size are not large enough to mechanically damage the lipid membrane. Our

18

results suggest that the cytotoxicity of GQDs with small size is low and may be

19

appropriate for biomedical application.

20 21

Key words: Molecular dynamics simulations, graphene quantum dots, cytotoxicity,

22

lipid membrane, membrane disruption.

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2


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1. Introduction

2

Owing to unique electrical and mechanical properties, graphene has been extensively

3

studied in past few years [1-4]. Although sharing with the same single atomic layered

4

motifs as that of graphene and graphene oxide, the lateral dimension of graphene

5

quantum dots (GQDs) is less than 100 nm [5]. Originating from graphene and

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graphene oxide, GQDs inherit many unique electrical and mechanical properties of

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graphene [6-8]. More interestingly, GQDs showed numerous novel physical

8

properties different from graphene due to abundant surface functionality, the quantum

9

confinement and edge effects [9]. It makes GQDs hold great promise for potential

10

applications especially in electrochemical biosensors, bioimaging, drug delivery

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system etc [10-13]. As a promising material in biomedical field, GQDs have been

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extensively studied in recent years. Zhu et al pointed out that GQDs could serve as an

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excellent bioimaging agents [14]. Chen reported that GQDs could be used for targeted

14

cancer fluorescent imaging, tracking and monitoring of drug delivery [15].

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Remarkably, comparing with graphene and graphene oxide, GQDs exhibited huge

16

advantages in bioimaging and bio-labeling both in vivo and in vitro [16-20].

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To realize the application of GQDs in biomedical fields, the prerequisites of

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evaluation and understanding of biosafety and cytotoxicity of GQDs is necessary and

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essential. The cytotoxicity of graphene has been extensively evaluated by both

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theoretical and experimental studies in past few years [21-25]. The toxicity of

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graphene and graphene-oxide (GO) nanowalls against bacterial was firstly reported

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from Akhavan’s group [26]. They found that GO nanowalls reduced by hydrazine

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were more toxic to the bacteria than the unreduced graphene oxide nanowalls [26].

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Fan et al. pointed out that GO is incubated with FBS due to the extremely high protein

25

adsorption ability of GO [27]. Liao et al found that graphene sheets are more

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damaging to mammalian fibroblasts than the less densely packed graphene oxide. The

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toxicity of graphene depends on the exposure environment (i.e., whether or not

28

aggregation occurs) and mode of interaction with cells (i.e., suspension versus

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adherent cell types) [22]. Recent studies also reveal the anti-bacterial activity of 3



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1

graphene and GO with severe cytotoxicity to bacteria such as Escherichia coli. [28].

2

In addition, due to different oxidization extent and surface chemistry of GO sheets,

3

GO shows low cytotoxicity in some experiments but high cytotoxicity in others [29,

4

30]. These studies greatly enhanced our understanding on the cytotoxicity of graphene

5

and GO. However, it is worth noting that in most work graphene and GO were

6

modified by polyethylene glycol (PEG), proteins or other materials for cytotoxicity

7

and other biological effects evaluations [31-34]. The research of cellular uptake of

8

pristine graphene and GO is still very limited. In addition, Akhavan et al pointed out

9

that the genotoxicity of graphene in human stem cells depended on the size of

10

graphene [35], and the cytotoxicity of GO is also dose-dependent through the test of

11

GO on reproduction capability of mammals [36]. Thus, the cytotoxicity of GQDs is

12

different from that of graphene and GO, and it is necessary and crucial to evaluate the

13

cytotoxicity of pristine GQD. From Zhang’s report, the cytotoxicity of GQDs is lower

14

than that of the micrometer-sized GO, and GQDs is considered appropriate for

15

utilization in biomedical fields [37]. However, the mechanism of cell uptake of GQDs

16

at molecular level is still unclear, which greatly limit the understanding of GQDs

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biosafety and real applications of GQDs in biomedical fields. Moreover, the effect of

18

size and the concentration of GQDs on the translocation of GQDs through lipid

19

membrane are obscure.

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Molecular dynamics (MD) simulations have been widely applied to investigate

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complicated problems in the field of bio-nanotechnology in recent years [38-41]. MD

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simulation could provide atomistic details of nanoparticle translocation through lipid

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membrane, and it has been successfully used to investigate the cytotoxicity of

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graphene from other groups [42-45]. Wei et al found that graphene could disrupt the

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structures and functions of proteins through exceptionally strong molecular

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interactions [46]. The process of graphene nanosheet insertion and lipid extraction

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from Escherichia coli membrane were uncovered by Tu et al.[43]. Yan et al

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investigated the wrapping process of dendrimer-like nanoparticle and the permeation

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of two-dimensional nanomaterials by lipid membrane [47-49], and they found that the 4


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graphene sheets experienced the unique self-rotation during membrane wrapping. In

2

this study, a series of simulations were performed to investigate the translocation

3

process of GQDs through lipid membrane. The details of all simulations performed

4

were listed in Table-1.

5

2. Computational details.

6

2.1 System setup

7

For clarity and simplicity, all GQDs were constructed from pristine graphene based on

8

the reference [5], and GQDs derived from GO were not considered in this study. The

9

graphene sheet was set in the x-y plane, as described in our previous work [50, 51].

10

One carbon atom as center atom with the Cartesian coordinates (0, 0, 0). Other carbon

11

atoms with x2 + y2 < R2 were rendered as the carbon atoms in GQDs, and R is the

12

radius of GQDs. To keep the integrity of benzene rings, R varies from 0.375 nm,

13

0.618 nm, 1.08 nm, 1.65 nm to 2.05 nm. As shown in Figure 1, all the geometry of

14

GQDs in this study were circle, and the definition of GQDs were based on the number

15

of fused benzene rings. More specifically, they are GQD7 (a), GQD19 (b), GQD61

16

(c), GQD151 (d) to GQD275 (e) in Figure 1. The edges of GQDs with different size

17

were firstly saturated by hydrogen atoms. After that, the structures of all GQDs were

18

optimized by Gaussian 03 [52]. Then the optimized structures were used as the initial

19

structure of GQDs in MD simulation. In all MD simulations, 256 POPC lipids were

20

used with 128 lipids in each layer. POPC lipids membrane was equilibrated by 20 ns

21

MD simulation in NPT ensemble and the equilibrated structure were used as the

22

initial structure for the simulation of GQDs translocation. After that, GQDs were

23

placed on top of the POPC membrane with the distance of 4.5 nm (from the center of

24

mass of GQDs to the center of mass of POPC membrane in z direction). All systems

25

were then solvated in TIP3P water with water boxes size of 10.0 nm × 10.0 nm ×

26

12.0nm to eliminate any potential periodic boundary effects.

27

2.2 MD Simulation

28

Carbon atoms away from the edge were assigned neutral charge as our previous 5



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1

work, while the partial charges of hydrogen atoms and the linked carbon atoms in

2

GQDs is +0.115 e and -0.115e, as described in reference [53]. Keeping partial charges

3

fixed does not allow for electric charge rearrangement and polarization effects in the

4

MD simulations. The parameters of GQDs including harmonic bond potentials for the

5

C-H and C-C bonds, harmonic angles for the C-C-H and C-C-C angles, harmonic

6

dihedral potentials as well as Lennard-Jones (LJ) parameters were taken from the

7

reference [53]. The parameters for POPC lipid membrane was derived from

8

CHARMM27 force field [54]. The water molecules were represented by TIP3P model

9

[55], which is considered to be suitable for the CHARMM27 force field.

10

GROMACS-5.0.4 were used to perform all MD simulations with time step of 2 fs

11

[56]. All systems were experienced energy minimization and equilibration. The NpT

12

ensemble was used in the simulation with constant temperature of 310K imposed by

13

Berensdsen thermostat and 1 bar pressure controlled by a semi-isotropic

14

Parrinello-Rahman barostat. The cutoff for the non-bonded van der Waals interaction

15

was set by a switching function starting at 1.0 nm and reaching zero at 1.2 nm. The

16

long range electrostatic interaction was calculated by particle mesh Ewald (PME)

17

summation, with a cutoff of 1.2 nm for the separation of the direct and reciprocal

18

space summation. All bond lengths and all angles involving hydrogen atoms were

19

constrained using LINCS algorithm during simulation. Data were analyzed by

20

GROMACS tools and home scripts.

21

2.3 PMF calculations

22

Based on the results of unbiased MD simulations, the translocation free energy

23

profiles of GQDs with different size (GQD7, GQD19, and GQD61) through lipids

24

membrane were calculated. It was determined by using umbrella samplings method

25

with the reaction coordinate starting from -3.5 nm to 0.5 nm along the z axis [57].

26

Each window with different reaction coordination was extracted from the trajectory of

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steered molecular dynamics simulation. In each system, 100 equidistant windows with

28

width of 0.04 nm were set and a harmonic force constant of 1000 kJ·mol-1·nm-2 was

29

used to position restrain the ion in each window. 5 ns simulation was performed in 6


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each window, and last 3 ns was used for analysis. The biased distributions were

2

recombined, and the PMF were calculated by weighted histogram analysis method

3

(WHAM) [58].

4 5

Table-1. Details of all simulations performed in this study. System

Size GQD

of Concentration

Method

Number of Atoms

Simulation time (ns)

(GQD:POPC)

GQD7

GQD7

1:256

MD

129,311

100

GQD19

GQD19

1:256

MD

128,045

100

GQD61

GQD61

1:256

MD

129,266

100

GQD151

GQD151

1:256

MD

129,206

100

GQD275

GQD275

1:256

MD

129,116

100

GQD7-SMD

GQD7

1:256

SMD

129,311

20

GQD19-SMD

GQD19

1:256

SMD

128,045

20

GQD61-SMD

GQD61

1:256

SMD

129,266

20

GQD151-SMD

GQD151

1:256

SMD

129,206

20

GQD275-SMD

GQD275

1:256

SMD

129,116

20

GQD7-PMF

GQD7

1:256

NEMD

129,311

5*120

GQD19-PMF

GQD19

1:256

NEMD

128,045

5*120

GQD61-PMF

GQD61

1:256

NEMD

129,266

5*120

GQD151-PMF

GQD151

1:256

NEMD

129,206

5*100

GQD7-H1

GQD7

12:256

MD

127,862

100

GQD7-H2

GQD7

24:256

MD

127,649

100

GQD7-H3

GQD7

36:256

MD

127,412

200

GQD7-H4

GQD7

48:256

MD

127,193

100

7



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1 2

Figure 1. Graphene quantum dots with different size: (a) GQD7, (b) GQD19, (c)

3

GQD61, (d) GQD151 and (e) GQD275.

4

3. Results and discussion

5

3.1 Permeation of GQDs with different sizes

6

The absorption process of single GQDs with different size was observed in our

7

simulation. As shown in Figure 2, the distance between the center of mass of GQDs

8

and the center of mass of the POPC lipid membrane were measured. In the systems of

9

relative small size (GQD7 and GQD61), GQDs transported into the cell membrane.

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GQD7 and GQD61 entered into the POPC lipid membrane after about 2.5 ns. It is

11

worth noting that GQDs with small size tends to stay at the position of 1.0 nm away

12

from the middle of lipid membrane but not in the middle of the lipid membrane

13

GQD151 and GQD275 could also absorb in the lipid membrane after about 2.5 ns.

14

Although the absorption time onto the lipid membrane are almost same for different

15

GQDs, the entering process of GQDs with different size is distinctly different. As seen

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in Figure 2, GQD151 and GQD275 kept the adsorbed state and diffuse on top of the

17

lipid membrane until the end of 100 ns simulation. It implies that the permeation

18

processes of GQD151 and GQD275 are much longer than that of GQD7 and GQD19.

19

These results show that the size of GQDs has little effect on the absorption time of

20

GQDs onto the membrane surface, however, the size of GQDs could greatly affect the

21

permeation process of GQDs through lipid membrane. More interestingly, as

22

displayed in Figure 3a and 3b, GQD7 and GQD61 tend to vertically permeate lipid

23

membrane. In this way, they could pass through the lipid membrane with less

24

disruption of the structure of lipid membrane. Meanwhile, the hydrophobic interaction

25

between GQD7/GQD61 and hydrophobic tail of lipid could be maximized in this 8


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1

manner. To better interpret this phenomenon, the angle between the plane of GQDs

2

and lipid membrane were calculated and displayed in Figure 3e. Herein, the angle was

3

defined between the x-y plane of lipid membrane and the surface of GQDs. In the

4

initial state, angles between GQDs and lipid membrane are 0° in all systems. During

5

the simulation, GQDs with small size (GQD7 and GQD61) could permeate into the

6

POPC membrane, and the angles between GQD7/GQD61 and lipid membrane mainly

7

range from 45º to 70º. In the way of vertical translocation through the lipid membrane,

8

the benzene ring of GQDs could interact with –CH2- group in the hydrophobic tail of

9

lipid membrane much stronger. It could greatly enhance the interaction between

10

GQDs and POPC lipid membrane. However, the GQDs with big size (GQD151 and

11

GQD275) could only absorb on the lipid membrane surface, and the angles between

12

these GQDs and lipid membrane is in the range of 0º to 10º.

13 14

Figure 2. The distance between the center of mass of different GQDs and the center of

15

mass of lipid membrane as a function of simulation time. The dashed green line

16

represents the location of upper end of POPC membrane.

9



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1 2

Figure 3. GQDs with different size on the membrane after 100 ns MD simulation: (a)

3

GQD7 (b) GQD61; (c) GQD151; (d) GQD275. The GQDs are shown by VDW model

4

with VMD. N atoms (blue) and P atoms (yellow) in the membrane are also shown in

5

VDW model. (e) The angles between different GQDs and x-y plane of lipid membrane

6

as a function of simulation time.

7

3.2 Free energy calculation of translocation of GQDs

8

To further interpret the translocation mechanism of GQDs through lipid membrane,

9

the free energy profile was estimated by PMF calculations, as shown in Figure 4. In

10

all systems (GQD7, GQD19 and GQD61), the free energy of GQDs decreases from

11

the water phase into lipid membrane. It implicated that GQDs preferred to enter into

12

lipid membrane, and it could explain that GQDs tended to adsorb on the membrane.

13

In all systems, the free energy for GQDs in the center of lipid membrane is defined as

14

zero point. The free energy difference between the lowest point to the GQDs in the

15

water phase is -56.3kJ/mol, -55.2kJ/mol and -56.1kJ/mol for GQD7, GQD19 and

16

GQD61. It shows that the free energy difference for these GQDs permeating from

17

water phase into lipid membrane is almost same. The position corresponding to

18

lowest free energy in z direction is -1.10 nm and 0.98 nm for GQD7 and GQD19.

19

However, the position corresponding to lowest free energy in z direction increased to

20

1.75 nm for GQD61. It implies that the location of lowest free energy in z direction is

21

far away from the middle of lipid membrane with increase of GQDs size from

22

GQD19 to GQD61. It also showed that the location of lowest free energy for GQDs is 10


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1

not in the middle of the lipid membrane and translocation of GQDs in the middle of

2

lipid membrane is one of the transition states for GQDs permeating through

3

membrane. The free energy barrier increases from 35.1 kJ·mol-1 to 96.2 kJ·mol-1 with

4

the increase of GQDs size from GQD19 to GQD61. Based on the free energy barrier

5

in Figure 4, the translocation rate ratio of two GQDs with different size can be

6

roughly estimated by equation 1:

7

∆G ( GQD 7) −∆G ( GQD 61) k (GQD7) RT ∝e k (GQD61)

(1)

8

The reaction rate of GQD7 passing through the middle of POPC membrane was about

9

9 orders of magnitude of that of GQD61. It implicated that the size of GQDs could

10

greatly affect the translocation process. The free energy barrier for GQDs passing

11

through the lipid membrane is much higher with the increase of size of GQDs. The

12

relative small GQDs (GQD7 and GQD19) could enter into the membrane, and they

13

could stay in the lowest free energy point. For the case of GQD61, the dimeter of

14

GQD61 is 2.16 nm, and it is close to the thickness of one layer of POPC membrane.

15

Thus, it could still enter into one layer of POPC membrane in a vertical way, and it

16

can still stay in the lowest free energy point. However, the GQDs could not permeate

17

into the lipid membrane with the vertical way when the diameter of GQDs is larger

18

than 2.5 nm (GQD161). In this case, the diameter of GQDs is larger than the thickness

19

of one layer of POPC membrane. Thus, GQDs with large size need to pass through

20

the middle of lipid membrane if GQD permeate the lipid membrane with a vertical

21

manner. It is not energy favorable since the middle of lipid membrane is one transition

22

state for GQDs to pass. Especially, the free energy gap between lowest free energy

23

point to transition state is much larger with the increase of size of GQDs, as seen in

24

Figure S1 (shown in Supporting Information) for GQD151 passing though the lipid

25

membrane. This indicates that it needs more time for large GQDs entering into the

26

membrane. It is in accordance with the Alexey’s coarse-grain model simulation [59],

27

the permeation process of larger graphene is in the ms scale.

11



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1 2

Figure 4. Potential of mean force (PMF) of GQDs with different size translocating

3

through POPC membrane. (a) GQD7; (b) GQD19; (c) GQD61. Two green dashed

4

lines represent the location of two ends of POPC membrane.

5

To investigate the effect of GQDs size on the structure disrupting of membrane, the

6

thickness and diffusion coefficients of POPC membrane in x-y plane in different

7

systems were calculated. Herein, the distance between the positions of P atoms with

8

highest density was considered as the thickness of lipid bilayer. With the permeation

9

of GQD7, the thickness of POPC membrane is 3.94 nm, while it is 3.84 nm with the

10

permeation of GQD19. Especially, the structure of POPC membrane could be greatly

11

affected by the permeation of large GQDs. With bigger size of GQD151, the thickness

12

of POPC membrane is around 4.02 nm, and it is 4.13 nm in the system of GQD275. It

13

implicated that the thickness of POPC membrane could be slightly affected by the

14

permeation of GQDs. In these two systems with relative large GQDs (GQD151 and

15

GQD275), GQDs could not permeate the lipid membrane within 100 ns simulation.

16

GQD with big size is difficult to permeate the lipid bilayer, and the absorption of

17

GQD on the lipid bilayer almost has little effect on the thickness of lipid membrane.

18

However, it intrigued the asymmetrical distribution of lipid membrane, as seen in

19

Figure 5c and 5d. It showed that the permeation of GQD into lipid membrane could

20

affect the structure of POPC lipid bilayer, even the size of GQD is very small. The

21

diffusion coefficient of lipids are considered to relate to the signal transduction

22

[60-62]. As see in Figure 6, the diffusion coefficients of lipids in different systems are

23

different. The MSD of different system in parallel simulations could be found in

24

Figure S2 (see Supporting Information). However, there is no obvious decrease of

25

diffusion coefficients from the system of GQD7 to GQD151. It suggests that the size 12


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1

(from GQD7 to GQD275 in this work) of GQDs could not obviously change the

2

mobility of lipids.

3

3.3 Permeation of GQDs at high concentration

4

To better understand the cytotoxicity of GQDs, the translocation process of GQD7

5

with different concentrations were simulated and analyzed (the number of GQD7 in

6

these simulations could be found in Table-1). The initial structure of GQDs on the top

7

of POPC membrane was shown in Figure S3 (see Supporting Information). As seen in

8

Figure 7, the final structures and distribution of GQDs in the lipid membrane at

9

different concentration after 100 ns simulation were displayed. In all systems, GQDs

10

dispersed in the POPC membrane after permeation of GQDs. Most of them

11

mono-dispersed to the two sides of lipid membrane after permeation, as shown in

12

Figure 7. It is in consistence with the results of PMF calculations of the translocation

13

process of GQD7. As displayed in Figure 4a, the free energy minimums almost have b 120

120

GQD19

GQD7 100

3

Density of P atoms (kg/m )

Density of P atoms (kg/m3)

a

80 60 40 20

100 80 60 40 20 0

0

0

2

4

14 c 120

6 Z (nm)

8

10

0

12

2

4

d 120

GQD151



1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60


100 80 60 40 20 0

6 Z (nm)

8

10

12

8

10

12

GQD275

100 80 60 40 20 0

0

2

4

6 Z (nm)

8

10

12

0

2

4

6 Z (nm)

15 16

Figure 5. The density of P atoms of lipid bilayer along the z direction. The distance

17

between two highest points was considered as the thickness of lipid bilayer. (a) GQD7;

18

(b) GQD19; (C) GQD151; (d) GQD275. 13



10

MSD (nm2)

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60

1 GQD7 GQD19 GQD151 GQD275

0.1

0.01 50 1

55

60

65

70

75

Time /ns

2

Figure 6. The MSD (mean squared displacement) of lipid membrane in x-y plane as a

3

function of simulation time in different systems: GQD7 (black line), GQD19 (red

4

line), GQD151 (pink line) and GQD275 (blue line).

5

equal distance to the membrane surface and middle of lipid (z = -1.12 nm and 1.12

6

nm). Moreover, the simulation of system GQD7-H3 (36 GQD7 molecules with 256

7

POPC lipids) were extended to 200 ns. As shown in Figure 8a, multiple GQDs were

8

aggregated before the translocation of GQDs into lipid membrane during first 10 ns,

9

which is similar to the permeation of fullerene into POPC membrane [63]. After that,

10

the aggregates permeated into POPC membrane at 20 ns, as shown in Figure 8b. In

11

Figure 8c, one could found that most of GQDs were still aggregated with one big

12

cluster. It could be confirmed from the cluster analysis of GQDs, as plotted in Figure

13

9. However, different from the aggregates at 10 ns, most of GQDs in the aggregates at

14

110 ns tends to be parallel to the lipid membrane surface. It could be confirmed from

15

the analysis of angles between GQDs surface and x-y plane of lipid membrane, as

16

shown in Figure 10. From the data of Figure 10, the averaged angles are in the range

17

of 45º to 60º. Considering that the model of GQD7 is not rigid in our simulation, the

18

GQD7 tends to be parallel to the main chain of POPC molecules. At the same time,

19

with simulation extended, the size of biggest aggregate in system GQD7-H3

20

decreased from 33 at 50 ns to 10 at 200 ns. It indicates that after the permeation into 14


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1

lipid membrane, the aggregate of GQD7 tends to dissociate and disperse to the

2

position of energy minimum in both sides of POPC membrane. It could expect that all

3

GQD7 molecules would mono-dispersed in the POPC membrane with long enough

4

simulation time. These results reveal that high concentration GQD7 tends to permeate

5

into membrane by aggregates but mono-disperse in the membrane after permeation.

6

On the basis of the analysis of density of P atoms in the systems of GQD7-H3 and

7

GQD7-H4, as shown in Figure S4 (see Supporting Information), one could find that

8

the thickness of POPC membrane is about 3.84 nm in the system with 36 GQD7

9

molecules, and it is around 3.60 nm in the system with 48 GQD7 molecules. It

10

implicated that the structure of POPC membrane could be greatly affected by the

11

permeation of high concentration of GQDs. Our simulation results also demonstrated

12

that the diffusion coefficient of lipid membrane in x-y plane could also be affected by

13

high concentration of GQDs. As shown in Figure S5 (see Supporting Information), the

14

diffusion coefficient decreased from 8.2×10

15

molecules to 2.6×10 -7 cm2·s-1 in the system with 48 GQD7 molecules. It is similar to

16

the finding that the diffusion coefficient of lipid membrane could be affected by high

17

concentration membrane protein on lipid membrane [64].

-7

cm2·s-1 in the system with 36 GQD7

18 19

Figure 7. The final structures and distribution of GQDs in the lipid membrane at

20

different concentration after 100 ns simulation: (a) GQD7-H1; (b) GQD7-H2; (c)

21

GQD7-H3; (d) GQD7-H4. GQDs were represented by yellow vdW model, POPC

22

membrane were represented by line model, and the P atoms in membrane were 15



1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60

1

represented by blue vdW model. Water molecules were not shown for clarity.

2 3

Figure 8. The aggregated structures and distribution of GQD7-H3 during the

4

simulation: (a) 10 ns; (b) 20 ns; (c) 60 ns; (d) 110 ns; (e) 150ns and (f) 200 ns. GQDs

5

were represented by yellow vdW model, POPC membrane were represented by line

6

model, and the P atoms in membrane were represented by blue vdW model. Water

7

molecules were not shown for clarity.

8 9 10

Figure 9. The number of cluster and the maxsize of cluster in GQD7-H3 as a function of simulation time.

16


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75 60

Averaged angle /º

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60


45 30

GQD7-H3

15 0 0

40

80 120 Time /ns

160

200

1 2 3

Figure 10. The angle distribution of GQDs in the lipid membrane during the simulation.

4

It is worth noting that the oxygen-containing functional groups of GQDs could

5

have different behaviors from pristine GQDs. Although they could not induce any

6

damage to the cell membrane, the oxygen-containing GQDs could result in

7

aggregation on surface of the organism and lead to its inactivation due to probable

8

deoxygenation by the media [65] or connection of the microorganisms onto the

9

oxygen groups [66]. Moreover, reactive oxygen species (ROS) generation plays an

10

important role in cytotoxicity issue. However, in classical MD simulation of

11

biological system, it is very difficult to simulate the forming and breaking of chemical

12

bonds, and this is due to the limitation of molecular mechanics based methods.

13

Therefore, the effect of ROS generation is not major concern in this study. In this

14

work we mainly focus on the structure, free energy and dynamics etc. of pristine

15

GQDs translating through membrane by MD simulation, trying to understand the

16

fundamental problems of pristine GQDs translating mechanism at the molecular level.

17

Nevertheless, we would focus on the mechanism of cellular uptake of GO in future

18

work, and try to understand the effect of ROS generation by utilization of both MD

19

simulation and quantum mechanics calculation.

20

4.

21

In summary, the potential cytotoxicity of GQDs was evaluated based on the size and

Conclusions

17



1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60

1

concentration of GQDs by molecular dynamics simulations. In the 100 ns scale

2

simulation, GQDs with relative small size (GQD7, GQD19 and GQD61) could

3

permeate into the POPC membrane. The permeation of GQDs could affect the

4

thickness of POPC membrane, even the size of GQD is very small (GQD7). However,

5

the mechanical damage of POPC membrane was not observed in the permeation of

6

small size GQDs and absorption of GQDs with bigger size. The free energy

7

calculations further revealed that the free energy barrier could greatly change with the

8

change of size of GQDs. Under high concentration of GQD7 molecules, the GQDs

9

molecules rapidly aggregate in water but disaggregate after entering the membrane

10

interior. All of them tend to mono-disperse in the two side of POPC membrane but not

11

in the center of POPC membrane. High concentrations of GQDs induce changes in the

12

structure properties and diffusion properties of the lipid bilayer, and it may affect the

13

cell signal transduction. However, GQDs with small size are not large enough to

14

mechanically damage the membrane. Our results suggest that the cytotoxicity of

15

GQDs with small size is low and could be appropriate for biomedical application.

16

Acknowledgement

17

We acknowledge financial support by the National Natural Science Foundation of

18

China (Grant Nos. 21503186, 21403049, 21674032, 61602142), Zhejiang Provincial

19

Natural Science Foundation of China (Grant Nos. LY13F040006, LY14B030008,

20

LY15E030009), The Science and Technology Project of Zhejiang Province (No.

21

2014C33220).

22

Supporting Information Available

23

The potential of mean force of GQD151 translocating through POPC membrane,

24

MSD (mean squared displacement) of lipid membrane in x-y plane as a function of

25

simulation time in different systems in seven parallel simulations, the initial structure

26

of multiple GQDs on top of POPC membrane, the thickness and the MSD of lipid

27

membrane in two systems of GQD7-H3 and GQD7-H4. This material is available free

28

of charge via the internet at htpp://pubs.acs.org. 18


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1

Notes

2

The authors declare no competing ﬁnancial interest.

3

19



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1

References

2

[1] K.S.A. Novoselov, A.K. Geim, S. Morozov, D. Jiang, M. Katsnelson, I. Grigorieva,

3

S. Dubonos, A. Firsov, Two-dimensional gas of massless Dirac fermions in graphene,

4

Nature, 438 (2005) 197-200.

5

[2] A.K. Geim, K.S. Novoselov, The rise of graphene, Nat. Mater., 6 (2007) 183-191.

6

[3] X. Yang, C. Cheng, Y. Wang, L. Qiu, D. Li, Liquid-mediated dense integration of

7

graphene materials for compact capacitive energy storage, Science, 341 (2013)

8

534-537.

9

[4] K.S. Novoselov, V.I. Fal, L. Colombo, P.R. Gellert, M.G. Schwab, K. Kim, A

10

roadmap for graphene, Nature, 490 (2012) 192-200.

11

[5] M. Bacon, S.J. Bradley, T. Nann, Graphene quantum dots, Part. Part. Sys. Char.,

12

31 (2014) 415-428.

13

[6] K.A. Ritter, J.W. Lyding, The influence of edge structure on the electronic

14

properties of graphene quantum dots and nanoribbons, Nat. Mater., 8 (2009) 235-242.

15

[7] Z.Z. Zhang, K. Chang, F.M. Peeters, Tuning of energy levels and optical

16

properties of graphene quantum dots, Phys. Rev. B, 77 (2008) 235411.

17

[8] P.G. Silvestrov, K.B. Efetov, Quantum dots in graphene, Phys. Rev. Lett., 98 (2007)

18

016802.

19

[9] J. Shen, Y. Zhu, X. Yang, C. Li, Graphene quantum dots: emergent nanolights for

20

bioimaging, sensors, catalysis and photovoltaic devices, Chem. Commun., 48 (2012)

21

3686-3699.

22

[10] V. Gupta, N. Chaudhary, R. Srivastava, G.D. Sharma, R. Bhardwaj, S. Chand,

23

Luminscent graphene quantum dots for organic photovoltaic devices, J. Am. Chem.

24

Soc., 133 (2011) 9960-9963.

25

[11] L. Li, G. Wu, G. Yang, J. Peng, J. Zhao, J.-J. Zhu, Focusing on luminescent

26

graphene quantum dots: current status and future perspectives, Nanoscale, 5 (2013)

27

4015-4039.

28

[12] M.-L. Tsai, W.-C. Tu, L. Tang, T.-C. Wei, W.-R. Wei, S.P. Lau, L.-J. Chen, J.-H.

29

He, Efficiency Enhancement of Silicon Heterojunction Solar Cells via Photon

30

Management Using Graphene Quantum Dot as Downconverters, Nano Lett., (2015), 20


Page 20 of 27

Page 21 of 27

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60


1

309-313.

2

[13] X.T. Zheng, A. Ananthanarayanan, K.Q. Luo, P. Chen, Glowing graphene

3

quantum dots and carbon dots: properties, syntheses, and biological applications,

4

Small, 11 (2015) 1620-1636.

5

[14] S. Zhu, J. Zhang, C. Qiao, S. Tang, Y. Li, W. Yuan, B. Li, L. Tian, F. Liu, R. Hu,

6

Strongly green-photoluminescent graphene quantum dots for bioimaging applications,

7

Chem. Commun., 47 (2011) 6858-6860.

8

[15] M.-L. Chen, Y.-J. He, X.-W. Chen, J.-H. Wang, Quantum-dot-conjugated

9

graphene as a probe for simultaneous cancer-targeted fluorescent imaging, tracking,

10

and monitoring drug delivery, Bioconjug. Chem., 24 (2013) 387-397.

11

[16] H. Sun, L. Wu, N. Gao, J. Ren, X. Qu, Improvement of photoluminescence of

12

graphene quantum dots with a biocompatible photochemical reduction pathway and

13

its bioimaging application, ACS Appl. Mater. Interfaces, 5 (2013) 1174-1179.

14

[17] S. Zhu, J. Zhang, X. Liu, B. Li, X. Wang, S. Tang, Q. Meng, Y. Li, C. Shi, R. Hu,

15

Graphene quantum dots with controllable surface oxidation, tunable fluorescence and

16

up-conversion emission, RCS Adv., 2 (2012) 2717-2720.

17

[18] Z. Fan, Y. Li, X. Li, L. Fan, S. Zhou, D. Fang, S. Yang, Surrounding media

18

sensitive photoluminescence of boron-doped graphene quantum dots for highly

19

fluorescent dyed crystals, chemical sensing and bioimaging, Carbon, 70 (2014)

20

149-156.

21

[19] C.-L. Huang, C.-C. Huang, F.-D. Mai, C.-L. Yen, S.-H. Tzing, H.-T. Hsieh, Y.-C.

22

Ling, J.-Y. Chang, Application of paramagnetic graphene quantum dots as a platform

23

for simultaneous dual-modality bioimaging and tumor-targeted drug delivery, J. Mater.

24

Chem. B, 3 (2015) 651-664.

25

[20] L. Wang, W. Li, B. Wu, Z. Li, S. Wang, Y. Liu, D. Pan, M. Wu, Facile synthesis

26

of fluorescent graphene quantum dots from coffee grounds for bioimaging and

27

sensing, Chem. Eng. J., 300 (2016) 75-82.

28

[21] Y. Chong, C. Ge, Z. Yang, J.A. Garate, Z. Gu, J.K. Weber, J. Liu, R. Zhou,

29

Reduced Cytotoxicity of Graphene Nanosheets Mediated by Blood-Protein Coating,

30

ACS Nano, 9 (2015) 5713-5724. 21



1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60

1

[22] K.-H. Liao, Y.-S. Lin, C.W. Macosko, C.L. Haynes, Cytotoxicity of graphene

2

oxide and graphene in human erythrocytes and skin fibroblasts, ACS Appl.

3

Mater. Interfaces s, 3 (2011) 2607-2615.

4

[23] Y. Zhang, S.F. Ali, E. Dervishi, Y. Xu, Z. Li, D. Casciano, A.S. Biris,

5

Cytotoxicity effects of graphene and single-wall carbon nanotubes in neural

6

phaeochromocytoma-derived PC12 cells, ACS Nano, 4 (2010) 3181-3186.

7

[24] R. Zhou, H. Gao, Cytotoxicity of graphene: recent advances and future

8

perspective, WIREs Nanomed. Nanobi., 6 (2014) 452-474.

9

[25] O. Akhavan, E. Ghaderi, H. Emamy, F. Akhavan, Genotoxicity of graphene

10

nanoribbons in human mesenchymal stem cells, Carbon, 54 (2013) 419-431.

11

[26] E. Ghaderi, Toxicity of graphene and graphene oxide nanowalls against bacteria,

12

ACS Nano, 4 (2010) 5731-5736.

13

[27] W. Hu, C. Peng, M. Lv, X. Li, Y. Zhang, N. Chen, C. Fan, Q. Huang, Protein

14

corona-mediated mitigation of cytotoxicity of graphene oxide, ACS Nano, 5 (2011)

15

3693-3700.

16

[28] S. Liu, T.H. Zeng, M. Hofmann, E. Burcombe, J. Wei, R. Jiang, J. Kong, Y. Chen,

17

Antibacterial activity of graphite, graphite oxide, graphene oxide, and reduced

18

graphene oxide: membrane and oxidative stress, ACS Nano, 5 (2011) 6971-6980.

19

[29] M. Lv, Y. Zhang, L. Liang, M. Wei, W. Hu, X. Li, Q. Huang, Effect of graphene

20

oxide on undifferentiated and retinoic acid-differentiated SH-SY5Y cells line,

21

Nanoscale, 4 (2012) 3861-3866.

22

[30] X. Zhang, W. Hu, J. Li, L. Tao, Y. Wei, A comparative study of cellular uptake

23

and cytotoxicity of multi-walled carbon nanotubes, graphene oxide, and nanodiamond,

24

Toxicol. Res., 1 (2012) 62-68.

25

[31] C.-H. Lu, C.-L. Zhu, J. Li, J.-J. Liu, X. Chen, H.-H. Yang, Using graphene to

26

protect DNA from cleavage during cellular delivery, Chem. Commun., 46 (2010)

27

3116-3118.

28

[32] L. Zhang, Z. Lu, Q. Zhao, J. Huang, H. Shen, Z. Zhang, Enhanced chemotherapy

29

efficacy by sequential delivery of siRNA and anticancer drugs using PEI‐grafted

30

graphene oxide, Small, 7 (2011) 460-464. 22


Page 22 of 27

Page 23 of 27

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60


1

[33] G. Duan, S.-g. Kang, X. Tian, J.A. Garate, L. Zhao, C. Ge, R. Zhou, Protein

2

corona mitigates the cytotoxicity of graphene oxide by reducing its physical

3

interaction with cell membrane, Nanoscale, 7 (2015) 15214-15224.

4

[34] T. Wu, B. Zhang, Y. Liang, T. Liu, J. Bu, L. Lin, Z. Wu, H. Liu, S. Wen, S. Tan,

5

Heparin-modified graphene oxide loading anti-cancer drug and growth factor with

6

heat stability, long-term release property and lower cytotoxicity, RSC Adv., 5 (2015)

7

84334-84342.

8

[35] O. Akhavan, E. Ghaderi, A. Akhavan, Size-dependent genotoxicity of graphene

9

nanoplatelets in human stem cells, Biomaterials, 33 (2012) 8017-8025.

10

[36] E. Ghaderi, E. Hashemi, E. Akbari, Dose-dependent effects of nanoscale

11

graphene oxide on reproduction capability of mammals, Carbon, 95 (2015) 309-317.

12

[37] C. Wu, C. Wang, T. Han, X. Zhou, S. Guo, J. Zhang, Insight into the cellular

13

internalization and cytotoxicity of graphene quantum dots, Adv. Healthc. Mater., 2

14

(2013) 1613-1619.

15

[38] L. Liang, E.-Y. Chen, J.-W. Shen, Q. Wang, Molecular modelling of translocation

16

of biomolecules in carbon nanotubes: method, mechanism and application, Mol. Sim.,

17

42 (2016) 827-835.

18

[39] L. Liang, J.-W. Shen, Z. Zhang, Q. Wang, DNA sequencing by two-dimensional

19

materials: As theoretical modeling meets experiments, Biosens. Bioelectron, (2015),

20

doi: http://dx.doi.org/10.1016/j.bios.2015.12.037.

21

[40] Y. Zhang, H. Han, N. Wang, P. Zhang, Y. Fu, M. Murugesan, M. Edwards, K.

22

Jeppson, S. Volz, J. Liu, Improved Heat Spreading Performance of Functionalized

23

Graphene in Microelectronic Device Application, Adv. Func. Mater., 25 (2015)

24

4430-4435.

25

[41] L. Liang, Z. Zhang, Z. Kong, Y. Liu, J.-W. Shen, D. Li, Q. Wang, Charge-tunable

26

insertion process of carbon nanotubes into DNA nanotubes, J. Mol. Graphics Modell.,

27

66 (2016) 20-25.

28

[42] C.A. Jimenez‐Cruz, S.g. Kang, R. Zhou, Large scale molecular simulations of

29

nanotoxicity, WIREs Sys. Bio. Med., 6 (2014) 329-343.

30

[43] Y. Tu, M. Lv, P. Xiu, T. Huynh, M. Zhang, M. Castelli, Z. Liu, Q. Huang, C. Fan, 23



1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60

1

H. Fang, Destructive extraction of phospholipids from Escherichia coli membranes by

2

graphene nanosheets, Nat. Nanotechnol., 8 (2013) 594-601.

3

[44] J. Mao, R. Guo, L.-T. Yan, Simulation and analysis of cellular internalization

4

pathways and membrane perturbation for graphene nanosheets, Biomaterials, 35

5

(2014) 6069-6077.

6

[45] B. Luan, T. Huynh, L. Zhao, R. Zhou, Potential toxicity of graphene to cell

7

functions via disrupting protein–protein interactions, ACS Nano, 9 (2014) 663-669.

8

[46] L. Ou, Y. Luo, G. Wei, Atomic-level study of adsorption, conformational change,

9

and dimerization of an α-helical peptide at graphene surface, J. Phys. Chem. B, 115

10

(2011) 9813-9822.

11

[47] L. Yan, X. Yu, Charged Dendrimers on Lipid Bilayer Membranes: Insight

12

through Dissipative Particle Dynamics Simulations, Macromolecules, 16 (2009)

13

6277-6283.

14

[48] J. Mao, P. Chen, J. Liang, R. Guo, L. Yan, Receptor-Mediated Endocytosis of

15

Two-Dimensional Nanomaterials Undergoes Flat Vesiculation and Occurs by

16

Revolution and Self-Rotation, ACS Nano, 10 (2016) 1493-1502.

17

[49] R. Guo, J. Mao, L. Yan, Unique Dynamical Approach of Fully Wrapping

18

Dendrimer-like Soft Nanoparticles by Lipid Bilayer Membrane, ACS Nano, 7 (2013)

19

10646-10653.

20

[50] Z. Zhang, J. Shen, H. Wang, Q. Wang, J. Zhang, L. Liang, H. Ågren, Y. Tu,

21

Effects of graphene nanopore geometry on DNA sequencing, J. Phys. Chem. Lett., 5

22

(2014) 1602-1607.

23

[51] Y. Kang, Z. Zhang, H. Shi, J. Zhang, L. Liang, Q. Wang, H. Ågren, Y. Tu, Na+

24

and K+ ion selectivity by size-controlled biomimetic graphene nanopores, Nanoscale,

25

6 (2014) 10666-10672.

26

[52] M. Frisch, G.W. Trucks, H. Schlegel, G.E. Scuseria, M.A. Robb, J.R. Cheeseman,

27

J.A. Montgomery, T. Vreven, K.N. Kudin, J. Burant, Gaussian 03, revision C. 02, 4

28

(2004).

29

[53] D. Cohen-Tanugi, J.C. Grossman, Water desalination across nanoporous

30

graphene, Nano Lett., 12 (2012) 3602-3608. 24


Page 24 of 27

Page 25 of 27

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60


1

[54] A.D. MacKerell Jr, D. Bashford, M. Bellott, R.L. Dunbrack Jr, J.D. Evanseck,

2

M.J. Field, S. Fischer, J. Gao, H. Guo, S. Ha, All-atom empirical potential for

3

molecular modeling and dynamics studies of proteins, J. Phys. Chem. B, 102 (1998)

4

3586-3616.

5

[55] W.L. Jorgensen, J. Chandrasekhar, J.D. Madura, R.W. Impey, M.L. Klein,

6

Comparison of simple potential functions for simulating liquid water, J. Chem. Phys.,

7

79 (1983) 926-935.

8

[56] B. Hess, C. Kutzner, D. Van Der Spoel, E. Lindahl, GROMACS 4: algorithms for

9

highly efficient, load-balanced, and scalable molecular simulation, J. Chem. Theo.

10

Comput., 4 (2008) 435-447.

11

[57] G.M. Torrie, J.P. Valleau, Nonphysical sampling distributions in Monte Carlo

12

free-energy estimation: Umbrella sampling, J. Comput. Phys., 23 (1977) 187-199.

13

[58] J.M. Rosenbergl, The weighted histogram analysis method for free-energy

14

calculations on biomolecules. I. The method, J. Comput. Chem., 13 (1992)

15

1011-1021.

16

[59] A.V. Titov, P. Král, R. Pearson, Sandwiched graphene− membrane

17

superstructures, ACS Nano, 4 (2009) 229-234.

18

[60] K. Simons, D. Toomre, Lipid rafts and signal transduction, Nat. Rev. Mol. Cell

19

Bio., 1 (2000) 31-39.

20

[61] F. Mollinedo, C. Gajate, Lipid rafts as major platforms for signaling regulation in

21

cancer, Adv. Bio. Reg.n, 57 (2015) 130-146.

22

[62] J.T. Groves, J. Kuriyan, Molecular mechanisms in signal transduction at the

23

membrane, Nat. Struct. Mol. Bio., 17 (2010) 659-665.

24

[63] J. Wong-Ekkabut, S. Baoukina, W. Triampo, I.M. Tang, D.P. Tieleman, L.

25

Monticelli, Computer simulation study of fullerene translocation through lipid

26

membranes, Nat. Nanotechnol., 3 (2008) 363-368.

27

[64] J.-H. Jeon, M. Javanainen, H. Martinez-Seara, R. Metzler, I. Vattulainen, Protein

28

Crowding in Lipid Bilayers Gives Rise to Non-Gaussian Anomalous Lateral Diffusion

29

of Phospholipids and Proteins, Phys. Rev. X, 6 (2016) 021006.

30

[65] O. Akhavan, E. Ghaderi, A. Esfandiar. Wrapping bacteria by graphene 25



1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60

1

nanosheets for isolation from environment, reactivation by sonication, and

2

inactivation by near-infrared irradiation. J. Phys. Chem. B, 115(2011) 6279-6288.

3

[66] E. Hashemi, O. Akhavan, M. Shamsara, R. Rahighi, A. Esfandiar, A. R. Tayefeh.

4

Cyto and genotoxicities of graphene oxide and reduced graphene oxide sheets on

5

spermatozoa. RSC Adv., 4(2014), 27213-27223.

6

26


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The permeation process of graphene quantum dots through lipids membrane Theoretical evaluation on pote 26x8mm (300 x 300 DPI)


Theoretical Evaluation on Potential Cytotoxicity of ... - ACS Publications

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