Modeling Interactions between Multicomponent Vesicles and

Jul 19, 2016 - We examine factors that influence the orientation of the nanopins in the host vesicle. We report the length of the hydrophilic segment ...
0 downloads 0 Views 9MB Size
Modeling Interactions between Multicomponent Vesicles and Antimicrobial Peptide-Inspired Nanoparticles Xiaolei Chu, Fikret Aydin, and Meenakshi Dutt* Department of Chemical and Biochemical Engineering, Rutgers, The State University of New Jersey, Piscataway, New Jersey 08854, United States S Supporting Information *

ABSTRACT: We examine the interaction between peptideinspired nanoparticles, or nanopins, and multicomponent vesicles using the dissipative particle dynamics simulation technique. We study the role of nanopin architecture and cholesterol concentration on the binding of the nanopins to the lipid bilayer, their insertion, and postembedding selforganization. We find the insertion to be triggered by enthalpically unfavorable interactions between the hydrophilic solvent and the lipophilic components of the nanopins. The nanopins are observed to form aggregates in solution, insert into the bilayer, and disassemble into the individual nanopins following the insertion process. We examine factors that influence the orientation of the nanopins in the host vesicle. We report the length of the hydrophilic segment of the nanopins to regulate their orientation within the clusters before the embedding process and in the bilayer, after the postinsertion disassembly of the aggregates. The orientation angle distribution for a given nanopin architecture is found to be driven by energy minimization. In addition, higher concentration of cholesterol is observed to constrain the orientation of the nanopins. We also report thermal fluctuations to induce transverse diffusion of nanopins with specific architectures. The incidence of transverse diffusion is observed to decrease with the concentration of cholesterol. Our results can provide guidelines for designing peptideinspired nanoparticles or macromolecules that can interface with living cells to serve as sensors for applications in medicine, sustainability, and energy. KEYWORDS: dissipative particle dynamics, antimicrobial peptides, lipid vesicle, spontaneous insertion, nanopins, transverse diffusion

T

binding, insertion, and self-organization of the nanopins in a lipid bilayer can accelerate the development of bionanomaterials for functional integration with cells. AMP-mimetic nanopins can be synthesized14−19 such that they encompass a rigid hydrophobic aromatic chain or an αhelix, with a chain-like hydrophilic functional group.11−13 Amphiphilic amino-acid-based polymers with a stiff backbone, such as arylamide oligomers, have a greater antibacterial activity than typical AMPs such as maganin.16,18 Nonpeptidic polymeric molecules such as phenylene ethynylenes and polyguanidinium oxanorbornene have been reported to form oligomers that adopt amphiphilic and rigid structures in the membrane environment and exhibit strong antimicrobial activity.15,17 However, experimental techniques are limited in

he interaction between peptides, proteins, or macromolecules with cell membranes underlie many natural and synthetic processes such as the signal transduction,1 blood coagulation cascade,2 drug delivery,3 sensing,4 and disruption of microbial membranes.5 The amphiphilic nature of these molecules enables them to interact with the membrane and potentially induce changes significant enough to affect the internal organization and behavior of cells. These molecules include amphiphilic antineoplastic agents such as vinblastine and chlorpromazine, which destabilize the membrane at high concentrations through drug−membrane interactions,6,7 or antimicrobial peptides (AMPs), which in critical concentrations can self-organize into transmembrane pores or channels in bacterial membranes.8−10 AMPs can inspire the design of pin-like nanoparticles, or nanopins, with precisely controlled spatial organization within the cell membrane for applications involving cellular sensing or stimulation to promote proliferation and growth. An extensive understanding of the basic processes inducing the interfacial © 2016 American Chemical Society

Received: December 24, 2015 Accepted: July 19, 2016 Published: July 19, 2016 7351

DOI: 10.1021/acsnano.5b08133 ACS Nano 2016, 10, 7351−7361

Article

www.acsnano.org

Article

ACS Nano

Figure 1. Image of CG model for (a) DPPC, (b) DMPC, (c) cholesterol, (d) ternary vesicle encompassing 767 DPPC, 767 DMPC, and 767 cholesterol molecules (33% of cholesterol), (e) nanopin H0, (f) nanopin H1, (g) nanopin H3, and (h) nanopin H4.

disassembly following insertion into the bilayer, the dependence of the orientation angle distribution of the nanopins in the bilayer on their architecture, and the spontaneous transverse diffusion, or flip-flop, of nanopins of specific architectures. Investigations of the organization of multiple large inclusions in the bilayer have demonstrated their aggregation due to depletion-induced attraction.46,76−83 The inclusions are found to adopt orientations that minimize hydrophobic mismatch and disruption to the bilayer.46,76−84 In addition, the insertion of large inclusions into a bilayer has been observed to induce flipflop of the lipids. However, the impromptu transverse diffusion of the inclusions has not been reported in the literature. Our results can potentially guide the design of nanoscale sensors or probes to interface with living cells for applications in medicine, energy, or sustainability.

their ability to identify the key factors (for example, dimensions of the nanopins,23,24,46 hydrophobic mismatch,23,24 electrostatic interactions,22 membrane composition,11 temperature and pH20,21) and nanoscale mechanistic processes underlying the interaction among the cell and the peptide-inspired particles. This lack of resolution can be addressed by suitable computational techniques to resolve the underlying mechanisms and factors and further guide and refine the design of the mimics for specific functions. The dynamical and structural properties of membranes can be resolved by using all-atom simulations, but the investigation is restricted to small length and time scales.25−31 These methods are not conducive for investigating events taking place on the mesoscopic scale, such as rupture and fusion of membranes, phase segregation in mixed membrane bilayers, and the dynamics and structure of membranes interacting with nanoparticles.32 Dynamical processes occurring at extensive time and length scales can be investigated using coarse-grained (CG) models, which are used in conjunction with Monte Carlo,33−35 molecular dynamics (MD),31,36,37 Brownian dynamics, or dissipative particle dynamics (DPD)46,53−56 simulation methods 38,39 or mean field theoretical approaches.40−45 Multiple computational techniques46−52 have been adopted to examine the interactions between nanoparticles and biomembranes. Our computational approach is DPD,46,53−56 which is based on the MD simulation technique and is capable of resolving both the continuum and molecular scales while capturing the hydrodynamics of the system. The previous studies have used the DPD technique to study chains flowing in microfluidic devices,57,58 block copolymers,59,60 polymers in a melt or in dilute solution,61 high concentration colloidal suspensions,62 the aggregate structure and self-assembly dynamics, and domain formation and phase change in lipidic complexes.63,64 Interactions between nanoparticles and lipid bilayers have also been studied by the DPD method, such as, the embedding of amphipathic nanotubes spontaneously into a phospholipid bilayer and their spatial organization, 65,66 displacement of the nanoparticles across vesicle67 and membrane68,69 bilayers, and different mechanisms for a nanoparticle to penetrate into a vesicle spontaneously.70 We examine the interaction of peptide-inspired nanopins with model lipid bilayers. These nanostructures can be simplified representations of membrane-active peptides and nanoparticles, including tilted peptides, AMPs, surfactant-like peptides, amphiphilic drugs, and rod−coil macromolecules.8−18,71−75 The key findings of this investigation include the aggregation of the nanopins in solution and their

RESULTS AND DISCUSSION We model a three-component vesicle composed of two distinct amphiphilic phospholipids, 1,2-dipalmitoyl-sn-glycero-3-phosphocholine (DPPC) and 1,2-dimyristoyl-sn-glycero-3-phosphocholine (DMPC), and cholesterol, as respectively illustrated as bead-spring models in Figure 1a, b, and c. Specific soft repulsive interaction parameters are implemented according to an established approach to model the variations in the hydrophobicity of the head and tail groups and the dissimilarity in the architectures of various lipid species.46,85−87 To effectively model the rigidity of the cholesterol molecule, additional bonds linking the diagonal pairs of beads are introduced within the ring moiety of the cholesterol molecule, as shown in Figure 1c. These additional bonds are responsible for the difference in the stiffness between phospholipid and cholesterol molecules. As a consequence, the concentration of cholesterol will impact the fluidity of the bilayer by decreasing the average area per lipid.86 We draw inspiration from linear α-helical AMPs and model the amphiphilic macromolecules by pin-like structures, or nanopins. Experimental counterparts of the nanopins include synthetic AMP mimetics such as arylamide oligomers, poly(phenyleneethynylene), polynorbornene, and rod−coil molecules.8−18 Our study is focused on the capture of short nanopins by the bilayer, their insertion, orientation, and spatial organization in the vesicle bilayer, and their possible translocation across the bilayer.46,65,66,88,89 These results could potentially help to provide insight into experimental phenomena on the interactions between synthetic amphiphilic macromolecules or AMPs with cell membranes.20−24,88,89 We model the nanopin with a stiff hydrophobic rod encompassing six beads. The spacing between the center of mass of a pair of 7352

DOI: 10.1021/acsnano.5b08133 ACS Nano 2016, 10, 7351−7361

Article

ACS Nano consecutive beads is set to be 0.5 rc. A hydrophilic segment with controllable length and flexibility is attached to one end of the hydrophobic rod, allowing us to tune the effective hydrophobicity of the nanopins. We consider three nanopin architectures: H0 is a pure hydrophobic rigid rod with six beads; H1 has the same architecture as H0 but with a hydrophilic bead at one of the ends of the hydrophobic rigid rod; and H3 and H4 are composed of a single tether encompassing respectively three and four hydrophilic beads that are attached to the hydrophilic bead in H1, as shown in Figures 1e−h. Further details of the models and the computational methodology are provided in the methodology section. We begin with a preassembled ternary vesicle encompassing a 1:1:1 mixture of DPPC, DMPC, and cholesterol, as shown in Figure 1d, in a simulation cell of size 50 rc × 50 rc × 50 rc. The simulation box is periodic along all three dimensions, with a total of 2301 phospholipids and cholesterol molecules. The total number of particles in the system (including the solvent particles) is 375 000. The phospholipid and cholesterol molecules are spatially arranged in the vesicle bilayer such that the hydrophilic head moieties are exposed to the aqueous solution on both sides of the bilayer, and the hydrophobic tails lie in the core of the bilayer. We run the simulation for a time span of 30 000 τ and monitor the system total energy along with the pair, bond, and angle energies of the bilayer components to check for equilibration, as shown in Supporting Information (SI) Figure SI1a−d. We introduce 10 H4 nanopins into the system and ensure that they are initially out of range of interacting with other nanopins and with the vesicle surface. The total number of particles in the simulation cell is maintained constant by substituting each solvent bead with a nanopin bead. The simulation is run until all the nanopins have been inserted into the vesicle bilayer. The system is allowed to equilibrate for an interval of 30 000 τ before the next 10 H4 nanopins are introduced into the simulation box using an identical protocol. We repeat this process until 80 nanopins are inserted in the vesicle bilayer. We examine the mechanisms underlying the interaction between the nanopins and the vesicle, the orientation and organization of the nanopins in the bilayer, and the role of cholesterol. We adopt the same protocol for the other nanopin architectures (H0, H1, H3). For the characterizations of each system, we use particle trajectories from four independent simulations with the same initial positions but distinct initial velocities and random seeds. Aggregation of Nanopins in Solution. At earlier times, the enthalpic penalty of unfavorable interactions between the hydrophobic segments of the nanopins and the hydrophilic solvent promotes the self-assembly of the former, as shown in Figure 2a. The size of the aggregates can range from two to four nanopins, for a set of 10 nanopins introduced into the simulation box. The nanopin aggregates adopt bundle-like structures where the rigid hydrophobic rods are aligned parallel or antiparallel to one another, as shown in Figure 3. The H3 and H4 nanopins adopt orientations that favor the free energy minimization of the system by maximizing the tether conformational and solvent configurational entropy. Hence, the hydrophilic tethers of the H4 nanopins assembled in a bundle will orient themselves away from each other. We would like to note that the self-assembly of acetylated AMPs in aqueous medium prior to the incipience of peptide−membrane interactions has been reported in experiments.90−92 The AMP aggregates have been posited to be responsible for increased

Figure 2. (a−d) Snapshot of the system encompassing 10 H4 nanopins and 2301 lipid and cholesterol molecules, with three nanopins at times (a) 150 τ, (b) 160 τ, (c) 225 τ, and (d) 340 τ. (e) Time evolution of the interaction count between tail groups of inserted nanopins and membrane components and between head groups of inserted nanopins and membrane components.

cell-selective toxicity, in comparison to their monomer counterparts.90,93 Spontaneous Capture, Insertion, and Spatial Organization of Nanopins in a Vesicle. Following the aggregation of nanopins in the solution, we observe their spontaneous binding to the vesicle bilayer, as shown in Figure 2b. It is shown that the binding events are triggered by interactions between the bilayer and the lipophilic components of the nanopins. This observation gives rise to a hypothesis that the unfavorable enthalpic interactions between the hydrophilic solvent and the lipophilic components of the nanopins initiate the spontaneous capture of the nanopins by the vesicle bilayer and drive their subsequent embedding into the bilayer. To test this hypothesis, we investigate the insertion of a single H4 nanopin aggregate by examining the temporal evolution of the number of hydrophilic−hydrophilic (head−head) and hydrophobic−hydrophobic (tail−tail) interactions between the vesicle bilayer and the nanopins. Two beads are interacting if the distance between their centers of mass is within the interaction range threshold. The initial capture of a nanopin aggregate by the bilayer is characterized by the sudden rise in the number of interactions between the nanopin and the hydrophobic components of the bilayer compared to that for the hydrophilic components (see Figure 2e), thereby supporting the hypothesis. The slower rise 7353

DOI: 10.1021/acsnano.5b08133 ACS Nano 2016, 10, 7351−7361

Article

ACS Nano

Figure 3. Cluster configurations for nanopins of architecture (a) H0, (b) H1, (c) H3, and (d) H4.

Figure 4. Snapshot of a system with 10 H4 nanopins and 2301 lipid and cholesterol molecules at times (a) 1000 τ, (b) 5000 τ, (c) 15 000 τ, and (d) 25 000 τ. Not all nanopins are shown for clarity.

procedure for H0, H1, and H3 nanopin architectures. We observe similar trends in the preinsertion assembly of the nanopins, their capture, rapid insertion into the bilayer, and disassembly. These results further corroborate that the nanopin−bilayer association is predominantly driven by interactions between the hydrophobic components.88,94−97 In earlier studies, nanoparticles and model transmembrane proteins with dimensions much larger than the bilayer amphiphilic molecules were observed to aggregate in the bilayer.46,65,66 This observation is attributed to depletioninduced attraction. In this study, the disparity in the dimensions of the nanopins and the bilayer amphiphilic molecular species is not significant. The H1 nanopin is 4 rc in length, whereas the bilayer width spans 5 rc. The conformational entropy of the

in the number of interactions between the hydrophobic components (over a time interval of 115 τ) indicates the disassembly of the nanopin aggregate accompanied by the diffusion of the individual nanopins in the vesicle bilayer, as shown in Figure 2c and d. The interaction count between the hydrophobic components increases until the aggregate completely disassembles and all the nanopins are no longer having any significant interactions with each other. We surmise the disassembly of the nanopins to be driven by the increasing conformational entropy of the hydrophilic chains and the orientational entropy of the nanopins in favor of minimizing the free energy. The role of the overall hydrophobicity of the nanopins on the insertion dynamics can be understood by repeating the 7354

DOI: 10.1021/acsnano.5b08133 ACS Nano 2016, 10, 7351−7361

Article

ACS Nano amphiphilic molecules endows a slightly higher effective dimension than that corresponding to the nanopin. Hence, the presence of the nanopins in the bilayer has minimal impact on the conformational entropy of the neighboring amphiphilic molecules. Therefore, the disassembly of the nanopin aggregate minimizes the free energy by increasing both the translational and orientational entropy of the nanopins. We examine the insertion process for 10 H4 nanopins (see Figure 4) through the number of interactions between nanopins, and the nanopins and bilayer components, as a function of time, and this is shown in Figure 5a. The aggregation of the nanopins in solution, prior to their insertion into the vesicle bilayer, is captured by the high nanopin− nanopin interaction count.

encounters during diffusion in the bilayer due to higher nanopin concentrations. We report similar trends for nanopin architectures H3, H1, and H0, as shown in Figure 5b, but note differences in the nanopin−bilayer interaction counts after all 10 nanopins are inserted into the vesicle. We find nanopin architecture H0 to have the highest interaction count with components of the vesicle bilayer, with a negligible difference in the corresponding results for nanopins H1, H3, and H4. The observed difference can be explained by the orientation of the nanopins in the vesicle bilayer, as discussed in the next section. Orientation of Nanopins in a Vesicle Bilayer. We observe differences in the orientation of the nanopins inserted in the bilayer to be affected by their hydrophobicity and architecture, as summarized in Figure 6. Our results show

Figure 5. (a) Temporal evolution of the number of interactions between the nanopin H4 and membrane components and other nanopins; (b) temporal evolution of the number of interactions between the bilayer and nanopins H0, H1, H3, and H4, respectively.

Figure 6. Images of a ternary vesicle with different numbers and types of nanopins. Not all nanopins are shown due to the crosssectional view of the vesicles.

architectures H4, H3, and H1 to adopt orientations that are roughly parallel to the bilayer normal with the hydrophilic component in proximity to the aqueous region. However, the H0 nanopin is observed to adopt a range of orientations including perpendicular to the bilayer normal. To better understand such discrepancies in the orientations, we examine the angle distributions of the nanopins in the bilayer, as shown in Figure 7. We measure the angle between two vectors: the first vector connects the centers of mass of the vesicle and the rigid part of nanopin, and the other vector connects the centers of the two beads located at the extremities of the rigid part of a nanopin. As the vesicle is approximately spherical, the angle measured can be effectively regarded as the tilt angle away from the normal of the local bilayer surface. The angle measurements demonstrate nanopin architectures H1, H3, and H4 to adopt orientations that deviate slightly (35.55° for H1, 36.29° for H3, and 37.22° for H4 at 33% cholesterol concentration) from the bilayer normal. Experimental studies on short amphiphilic

The initial interaction between the nanopins and the vesicle bilayer is demonstrated by the rise in the number of nanopin− bilayer interactions at 7600τ. The stepwise increase in the nanopin−bilayer interactions indicates the insertion of the aggregates into the bilayer. The nanopin−nanopin interaction becomes negligible when all the inserted aggregates have disassembled into the individual nanopins, as demonstrated by the very low values of the nanopin−nanopin interaction count. The same observations hold for a larger number of inserted nanopins, as shown in Figure SI2. There are a small number of interactions between the 80 H4 nanopins after their complete insertion into the bilayer and disassembly of the aggregates. We would like to note that the number of interactions between the nanopins is very low in comparison to those between the nanopins and bilayer components (approximately 2%). We surmise that the interactions between the 80 H4 nanopins arise due to their long hydrophilic hairs as well as frequent 7355

DOI: 10.1021/acsnano.5b08133 ACS Nano 2016, 10, 7351−7361

Article

ACS Nano

Figure 7. Orientation angle and energy distribution for different nanopin architectures and concentrations of cholesterol. For the nanopins H3 and H4, a tilt angle larger than 90 deg is not observed at cholesterol concentrations of 33% and 50%; thus energy could not be sampled in the range beyond 90 deg.

the three nanopin architectures is driven by minimization of the pair interaction energies. Earlier studies have shown the orientation of transmembrane proteins to be regulated by the hydrophobic mismatch.24,100−103 For transmembrane proteins with a negative hydrophobic mismatch, the proteins with hydrophilic ends exposed to the aqueous environment have tilt angles close to 0°, while those entirely embedded in the hydrophobic region tend to have random orientations.103 Transverse Diffusion of Nanopins across the Monolayers. We find that at a cholesterol concentration of 33%, 5.7% of the H1 nanopins reside in the inner monolayer of the vesicle such that their hydrophilic head groups are exposed to the solvent. Since all the nanopins are inserted into the outer monolayer of the vesicle from solution, the inverted orientation could result either from the direct penetration of the nanopins into the inner monolayer of the vesicle during the insertion process or from the transverse diffusion of nanopins from the outer to inner monolayer. We observe a spontaneous flip-flop event for a nanopin of architecture H1, as shown in Figure 8, to occur after residing in the outer monolayer of the vesicle for a

transmembrane helices reported a tilting angle of 26° using solid-state NMR and an angle of 32° using the IR dichroism method.98,99 In comparison, the angle distribution for nanopin architecture H0 is observed to be a symmetric Gaussian distribution over the range of 0 to 180 deg with an average angle of 89.93°. We surmise that the purely hydrophobic architecture favors an orientation that is normal to the bilayer surface to minimize unfavorable contact with the solvent particles and maximize favorable contacts with the hydrocarbon groups in the bilayer. This hypothesis is supported by the large number of interactions of nanopin architecture H0 with the hydrophobic components of the vesicle, as shown in Figure 5b. We determine whether the angle distribution for the different types of nanopins is a result of the energy minimization of the system. We compute the pair interaction energy of the nanopins with other components of the system for each particular angle that is associated with the angle distribution plot and draw correspondence with the angle distribution. We observe the trends in the pair interaction energy to complement the angle distribution. Hence, the angle distribution for 7356

DOI: 10.1021/acsnano.5b08133 ACS Nano 2016, 10, 7351−7361

Article

ACS Nano

Figure 8. Images of a single H1 nanopin undergoing transverse diffusion at times (a−d) 585 τ, 625 τ, 660 τ, and 710 τ, respectively.

relatively long time interval (∼15 000 τ). Other cases of H1 nanopins undergoing transverse diffusion are provided in Figure SI3, which shows temporal evolution of the nanopins’ tilt angle during the intermonolayer translocation process. We calculate the rate of transverse diffusion defined as the number of translocated nanopins averaged over the simulation time. Combining the results from simulations using four random seeds, we obtain the transverse diffusion rate of H1 nanopins in bilayers with 33% cholesterol to be (1.9 ± 0.4) × 10−5 τ−1. To understand the occurrence of transverse diffusion for nanopin architecture H1, we note that the orientation of nanopins with hydrophilic moieties is governed by three factors. The first factor is the hydrophobic mismatch between the hydrophobic region of the bilayer and the hydrophobic part of the nanopin, the second factor is the dimensions of hydrophilic part of the nanopin, and the third factor is the rigidity of the vesicle bilayer, which will be discussed in the next section. The hydrophobic mismatch tends to drive the nanopins to orientate themselves parallel to the local bilayer surface, as shown for nanopin architecture H0, which is purely hydrophobic. Since the three nanopin architectures possess identical hydrophobic lengths, we believe that the hydrophobic mismatch effect is the same for all three architectures. For nanopin architectures H3 and H4, the unfavorable enthalpic interaction between the hydrophilic hair of the nanopin and the hydrophobic region of the vesicle prohibits the complete immersion of the nanopins into the core of the bilayer. For nanopin architecture H1, the unfavorable enthalpic penalty is not as significant due to the short length of the hydrophilic part. Hence, nanopin architecture H1 is able to adopt a metastable state, which could result in intermonolayer diffusion or return to its original state. This hypothesis is supported by the temporal evolution of the number of interactions during the transverse diffusion of nanopin H1, as shown in Figure 9. The change of orientation (see Figure 8a,b) from parallel to perpendicular (to the bilayer normal) is characterized by the increase in the interaction count between the hydrophilic bead of H1 and the tail beads of the bilayer components, as well as a decrease in the number of interactions

Figure 9. Temporal evolution of the number of interactions between various components of the nanopins and the membrane during transverse diffusion of a single H1 nanopin.

between the hydrophilic groups of the nanopin and the bilayer components. The perpendicular orientation is maintained for 50 τ before the nanopin assumes an antiparallel orientation to the outward bilayer normal and resumes diffusing in the inner monolayer. We surmise that the transverse diffusion of the nanopins is activated by thermal fluctuations, which result in a transient increase in the nanopin−cholesterol interaction count and an intermonolayer difference in the population of cholesterol molecules interacting with the nanopin, as shown in Figures SI4 and SI5. The fluctuation-induced changes cause the nanopin orientation to deviate from the bilayer normal and adopt a parallel configuration to the bilayer plane, while simultaneously submerging into the hydrophobic core of the vesicle. The enthalpically unfavorable parallel configuration of the nanopin drives it to adopt an orientation that is opposite the outward bilayer normal. This phenomenon of transverse diffusion is also found in both computational and experimental studies for 7357

DOI: 10.1021/acsnano.5b08133 ACS Nano 2016, 10, 7351−7361

Article

ACS Nano

Table 1. Orientation Angles and Transverse Diffusion Rates for Nanopin Architectures H4, H3, H1, and H0 in Vesicles with 10%, 33%, and 50% Concentrations of Cholesterol H4

average angle (deg) standard deviation (deg) transverse diffusion rate (10−5 × τ−1)

H3

H1

H0

10% chol

33% chol

50% chol

10% chol

33% chol

50% chol

10% chol

33% chol

50% chol

10% chol

38.9

37.2

30.9

33.45

31.9

22.61

36.4

35.5

29.2

90.4

89.9

90.0

18.3

17.2

14.8

17.8

15.5

11.98

17.3

17.1

14.7

28.3

31.1

26.7

3.8 ± 0.5

1.9 ± 0.4

1.6 ± 0.3

1.7 ± 0.5

not observed

not observed

3.3 ± 0.3

not observed

not observed

33% chol

50% chol

vesicle. We investigated the role of the nanopin architecture and cholesterol concentration. The capture of the nanopin by the vesicle bilayer was observed to be activated by the unfavorable enthalpy between the lipophilic components of the nanopins and the aqueous medium. Some of the nanopins were observed to aggregate in solution prior to the capture by the bilayer, followed by their insertion and disassembly in the bilayer. The architectures of the nanopins were observed to influence their relative orientation within the aggregates prior to capture by the bilayer and their orientation in the bilayer postinsertion. Purely hydrophobic nanopins tend to arrange themselves in the lipophilic core of the vesicle, with their length perpendicular to the surface normal vector of the vesicle. Nanopins with hydrophilic components adopt orientations that expose the hydrophilic groups to the aqueous environment and embed the hydrophobic components in the hydrophobic core of the vesicle bilayer. Nanopins with a short hydrophilic group are able to overcome the energy barrier for a wide range of concentrations of cholesterol to accomplish translocation from the outer to the inner monolayer of the vesicle. Nanopins with a longer hydrophilic functional group are observed to participate in transverse diffusion across the monolayers for low concentrations of cholesterol. In addition, we observe the occurrence of transverse diffusion across the bilayer to decrease with cholesterol concentration. For higher concentrations of cholesterol in the vesicle bilayer, the nanopins in the bilayer are constrained to adopt a narrow range of angles due to increased rigidity of the bilayer. One of the most important aspects in designing nanopins or synthetic peptides is predicting their behavior upon adsorption onto the host cells. Depending upon the application, it would be desirable to locate the synthetic peptides in a particular region of the bilayer, such as the inner or outer monolayer or in the hydrophobic core of the vesicle. For example, an earlier study demonstrates the antimicrobial activity of human AMP LL-37 against Escherichia coli cells to result from the translocation of the peptides from the outer membrane to the periplasmic space.106 As demonstrated by our findings, the ratio of the hydrophobic to hydrophilic segments of a peptide and the length of the flexible hydrophilic segment determine their targeted region in the bilayer as well as the frequency of translocation events. Thus, the architectural features of the nanopins (various degrees of hydrophobicity and lengths of hydrophilic segment) contribute to their optimal design for specific applications. From the perspective of chemical synthesis, corresponding architectures of synthetic peptides can be obtained by using suitable combinations of hydrophobic and hydrophilic amino acids. Our results can potentially help in guiding the design of nanoscale sensors or probes to interface

alamethicin, a peptide with a short but strong polar head group and a relatively long hydrophobic region.104,105 Role of Cholesterol. In order to obtain insight into the impact of the concentration of cholesterol on the interaction, capture, and embedding of the nanopins into the vesicle and their spatial organization, we repeated the studies using vesicles with two relative concentrations of cholesterol: (50% cholesterol, 25% DPPC, and 25% DMPC) and (10% cholesterol, 45% DPPC, and 45% DMPC). The angle distribution measurements (see Figure 7) demonstrate nanopins H1, H3, and H4 to adopt smaller tilt angles, in response to the increasing concentration of cholesterol in the vesicle. In addition, we note that the orientation angle of the nanopins lies within a narrower range for higher concentrations of cholesterol. The angle distribution of the H0 nanopins does not significantly change with respect to corresponding results for vesicles with various concentrations of cholesterol. We expect the higher relative concentration of cholesterol in the bilayer to increase its mechanical rigidity and thereby constrain the nanopins to adopt orientation that are more closely aligned with the bilayer normal. Table 1 provides the average orientation angle for each nanopin architecture and bilayers with different relative concentrations of cholesterol. Another consequence of the higher mechanical rigidity of the bilayer is the reduced mobility of the bilayer components and the nanopins. Hence, increasing the bilayer rigidity is also expected to influence the intermonolayer transverse diffusion of the nanopins. We observe the translocation occurrence rate for H1 to decrease from (3.8 ± 0.5) × 10−5 τ−1 to (1.6 ± 0.3) × 10−5 τ−1 as the cholesterol mole fraction increases from 10% to 50%. We also observe the transverse diffusion of H4 and H3 nanopins for cholesterol concentrations of 10%, with rates of (1.7 ± 0.5) × 10−5 τ−1 and (3.3 ± 0.3) × 10−5 τ−1, respectively. Our results show that a flexible bilayer can overcome the enthalpic penalty between the hydrophobic region of the vesicle and the hydrophilic hairs of the nanopins to enable a few flipflop events induced via thermal fluctuations. These calculations are based on sampling of equilibrated configurations of 80 inserted nanopins of all types spanning a time interval of 240 000 τ and four random seeds. We would like to note that the measurement of the number of interactions between the nanopins and the various bilayer components, as shown in Figure SI6, indicates that there is no preferential interaction of the nanopins with the cholesterol molecules in comparison to the phospholipid molecules.

CONCLUSIONS To conclude, we have investigated the interactions of peptideinspired nanoparticles, or nanopins, with a multicomponent 7358

DOI: 10.1021/acsnano.5b08133 ACS Nano 2016, 10, 7351−7361

Article

ACS Nano

(7) Schrier, S. L.; Zachowski, A.; Devaux, P. F. Mechanisms of Amphipath-Induced Stomatocytosis in Human Erythrocytes. Blood 1992, 79, 782−786. (8) Shai, Y. Mechanism of the Binding, Insertion and Destabilization of Phospholipid Bilayer Membranes by Alpha-Helical Antimicrobial and Cell Non-Selective Membrane-Lytic Peptides. Biochim. Biophys. Acta, Biomembr. 1999, 1462, 55−70. (9) Oren, Z.; Shai, Y. Mode of Action of Linear Amphipathic AlphaHelical Antimicrobial Peptides. Biopolymers 1998, 47, 451−463. (10) Bechinger, B. The Structure, Dynamics and Orientation of Antimicrobial Peptides in Membranes by Multidimensional Solid-State NMR Spectroscopy. Biochim. Biophys. Acta, Biomembr. 1999, 1462, 157−183. (11) Sakai, N.; Mareda, J.; Matile, S. Rigid-Rod Molecules in Biomembrane Models: From Hydrogen-Bonded Chains to Synthetic Multifunctional Pores. Acc. Chem. Res. 2005, 38, 79−87. (12) Cornelissen, J.; Fischer, M.; Sommerdijk, N.; Nolte, R. J. M. Helical Superstructures from Charged Poly(styrene)-Poly(isocyanodipeptide) Block Copolymers. Science 1998, 280, 1427− 1430. (13) Bhosale, S.; Sisson, A. L.; Sakai, N.; Matile, S. Synthetic Functional π-Stack Architecture in Lipid Bilayers. Org. Biomol. Chem. 2006, 4, 3031−3039. (14) Findlay, B.; Zhanel, G. G.; Schweizer, F. Cationic Amphiphiles, a New Generation of Antimicrobials Inspired by the Natural Antimicrobial Peptide Scaffold. Antimicrob. Agents Chemother. 2010, 54, 4049−4058. (15) Ishitsuka, Y.; Arnt, L.; Majewski, J.; Frey, S. L.; Ratajczak, M.; Kjaer, K.; Tew, G. N.; Lee, K. Y. C. Amphiphilic Poly(phenyleneethynylene)s Can Mimic Antimicrobial Peptide Membrane Disordering Effect by Membrane Insertion. J. Am. Chem. Soc. 2006, 128, 13123−13129. (16) Tew, G. N.; Liu, D. H.; Chen, B.; Doerksen, R. J.; Kaplan, J.; Carroll, P. J.; Klein, M. L.; DeGrado, W. F. De novo Design of Biomimetic Antimicrobial Polymers. Proc. Natl. Acad. Sci. U. S. A. 2002, 99, 5110−5114. (17) Gabriel, G. J.; Madkour, A. E.; Dabkowski, J. M.; Nelson, C. F.; Nusslein, K.; Tew, G. N. Synthetic Mimic of Antimicrobial Peptide with Nonmembrane-Disrupting Antibacterial Properties. Biomacromolecules 2008, 9, 2980−2983. (18) Tang, H.; Doerksen, R. J.; Jones, T. V.; Klein, M. L.; Tew, G. N. Biomimetic Facially Amphiphilic Antibacterial Oligomers with Conformationally Stiff Backbones. Chem. Biol. 2006, 13, 427−435. (19) Oh, N. M.; Oh, K. T.; Youn, Y. S.; Lee, E. S. Artificial Nano-Pin as a Temporal Molecular Glue for the Targeting of Acidic Tumor Cells. Polym. Adv. Technol. 2014, 25, 842−850. (20) Radzishevsky, I. S.; Rotem, S.; Bourdetsky, D.; Navon-Venezia, S.; Carmeli, Y.; Mor, A. Improved Antimicrobial Peptides Based on Acyl-Lysine Oligomers. Nat. Biotechnol. 2007, 25, 657−659. (21) Graham, D. Y.; Shiotani, A. New Concepts of Resistance in the Treatment of Helicobacter Pylori Infections. Nat. Clin. Pract. Gastroenterol. Hepatol. 2008, 5, 321−331. (22) Leszczynska, K.; Namiot, A.; Fein, D. E.; Wen, Q.; Namiot, Z.; Savage, P. B.; Diamond, S.; Janmey, P. A.; Bucki, R. Bactericidal Activities of the Cationic Steroid CSA-13 and the Cathelicidin Peptide LL-37 against Helicobacter Pylori in Simulated Gastric Juice. BMC Microbiol. 2009, 9, 187. (23) Benjamini, A.; Smit, B. Robust Driving Forces for Transmembrane Helix Packing. Biophys. J. 2012, 103, 1227−1235. (24) Ren, J. H.; Lew, S.; Wang, J. Y.; London, E. Control of the Transmembrane Orientation and Interhelical Interactions within Membranes by Hydrophobic Helix Length. Biochemistry 1999, 38, 5905−5912. (25) Farago, O. ″Water-Free″ Computer Model for Fluid Bilayer Membranes. J. Chem. Phys. 2003, 119, 596−605. (26) Brannigan, G.; Brown, F. L. H. Solvent-Free Simulations of Fluid Membrane Bilayers. J. Chem. Phys. 2004, 120, 1059−1071.

with living cells for applications in medicine, energy, or sustainability.

METHODS The details of the DPD simulation technique46,53,86 along with the pair and nonpair interactions87 used in this study can be obtained from our previous investigation. Physical correspondence of the model was obtained by an established approach.85

ASSOCIATED CONTENT

S Supporting Information *

The Supporting Information is available free of charge on the ACS Publications website at DOI: 10.1021/acsnano.5b08133. Figure of time evolution of the energy associated with pairwise interactions, bonds, and angles of the bilayer components; figure of interaction count between nanopin H4 and bilayer components and other nanopins for different numbers of nanopins inserted into the bilayer; figure of time evolution of interaction count between different bilayer components with nanopins that undergo transverse diffusion and no transverse diffusion; figure of time evolution of interaction count between cholesterol molecules in different monolayers with nanopins that undergo transverse diffusion; figure of time evolution of the tilt angle of nanopins that undergo transverse diffusion and no transverse diffusion; figure of interaction count between nanopin H1 and all bilayer components as the number of inserted nanopins increases (PDF) Movie of insertion of a cluster of nanopin architecture H4 into the vesicle bilayer (AVI)

AUTHOR INFORMATION Corresponding Author

*E-mail: [email protected]. Notes

The authors declare no competing financial interest.

ACKNOWLEDGMENTS Portions of the work presented in this study used highperformance computational resources provided by the Rutgers Discovery Informatics Institute (rdi2.rutgers.edu) and the Texas Advanced Computing Center through XSEDE allocation TG-DMR1400125. REFERENCES (1) Cohen, G. B.; Ren, R.; Baltimore, D. Modular Binding Domains in Signal Transduction Proteins. Cell 1995, 80, 237−248. (2) Dahlbäck, B.; Villoutreix, B. O. The Anticoagulant Protein-C Pathway. FEBS Lett. 2005, 579, 3310−3316. (3) Shi, N. Q.; Qi, X. R.; Xiang, B.; Zhang, Y. A Survey on “Trojan Horse” Peptides: Opportunities, Issues and Controlled Entry to “Troy. J. Controlled Release 2014, 194, 53−70. (4) Bader, M. W.; Sanowar, S.; Daley, M. E.; Schneider, A. R.; Cho, U.; Xu, W.; Klevit, R. E.; Le Moual, H.; Miller, S. I. Recognition of Antimicrobial Peptides by a Bacterial Sensor Kinase. Cell 2005, 122, 461−472. (5) Sato, H.; Feix, J. B. Peptide−Membrane Interactions and Mechanisms of Membrane Destruction by Amphipathic α-Helical Antimicrobial Peptides. Biochim. Biophys. Acta, Biomembr. 2006, 1758, 1245−1256. (6) Rosso, J.; Zachowski, A.; Devaux, P. F. Influence of Chlorpromazine on the Transverse Mobility of Phospholipids in the Human-Erythrocyte Membrane-Relation to Shape Changes. Biochim. Biophys. Acta, Biomembr. 1988, 942, 271−279. 7359

DOI: 10.1021/acsnano.5b08133 ACS Nano 2016, 10, 7351−7361

Article

ACS Nano

(49) Wong-Ekkabut, J.; Baoukina, S.; Triampo, W.; Tang, I. M.; Tieleman, D. P.; Monticelli, L. Computer Simulation Study of Fullerene Translocation through Lipid Membranes. Nat. Nanotechnol. 2008, 3, 363−368. (50) Li, Y.; Chen, X.; Gu, N. Computational Investigation of Interaction Between Nanoparticles and Membranes: Hydrophobic/ Hydrophilic Effect. J. Phys. Chem. B 2008, 112, 16647−16653. (51) Huang, C. J.; Zhang, Y.; Yuan, H. Y.; Gao, H. J.; Zhang, S. L. Role of Nanoparticle Geometry in Endocytosis: Laying Down to Stand Up. Nano Lett. 2013, 13, 4546−4550. (52) Shi, X. H.; von Dem Bussche, A.; Hurt, R. H.; Kane, A. B.; Gao, H. J. Cell Entry of One-Dimensional Nanomaterials Occurs by Tip Recognition and Rotation. Nat. Nanotechnol. 2011, 6, 714−719. (53) Groot, R. D.; Warren, P. B. Dissipative Particle Dynamics: Bridging the Gap Between Atomistic and Mesoscopic Simulation. J. Chem. Phys. 1997, 107, 4423−4435. (54) Smith, K. A.; Jasnow, D.; Balazs, A. C. Designing Synthetic Vesicles that Engulf Nanoscopic Particles. J. Chem. Phys. 2007, 127, 084703. (55) Illya, G.; Lipowsky, R.; Shillcock, J. C. Two-Component Membrane Material Properties and Domain Formation from Dissipative Particle Dynamics. J. Chem. Phys. 2006, 125, 114710. (56) Laradji, M.; Sunil Kumar, P. B. Dynamics of Domain Growth in Self-Assembled Fluid Vesicles. Phys. Rev. Lett. 2004, 93, 198105. (57) Fan, X. J.; Phan-Thien, N.; Chen, S.; Wu, X. H.; Ng, T. Y. Simulating Flow of DNA Suspension Using Dissipative Particle Dynamics. Phys. Fluids 2006, 18, 063102. (58) Chen, S.; Phan-Thien, N.; Fan, X. J.; Khoo, B. C. Dissipative Particle Dynamics Simulation of Polymer Drops in a Periodic Shear Flow. J. Non-Newtonian Fluid Mech. 2004, 118, 65−81. (59) Chou, S. H.; Tsao, H. K.; Sheng, Y. J. Morphologies of Multicompartment Micelles Formed by Triblock Copolymers. J. Chem. Phys. 2006, 125, 104903−194903. (60) Ortiz, V.; Nielsen, S. O.; Discher, D. E.; Klein, M. L.; Lipowsky, R.; Shillcock, J. Dissipative Particle Dynamics Simulations of Polymersomes. J. Phys. Chem. B 2005, 109, 17708−17714. (61) Spenley, N. A. Scaling Laws for Polymers in Dissipative Particle Dynamics. Europhys. Lett. 2000, 49, 534−540. (62) Boek, E. S.; Coveney, P. V.; Lekkerkerker, H. N. W.; vanderSchoot, P. Simulating the Rheology of Dense Colloidal Suspensions Using Dissipative Particle Dynamics. Phys. Rev. E: Stat. Phys., Plasmas, Fluids, Relat. Interdiscip. Top. 1997, 55, 3124−3133. (63) Kranenburg, M.; Venturoli, M.; Smit, B. Phase Behavior and Induced Interdigitation in Bilayers Studied with Dissipative Particle Dynamics. J. Phys. Chem. B 2003, 107, 11491−11501. (64) Yamamoto, S.; Maruyama, Y.; Hyodo, S. Dissipative Particle Dynamics Study of Spontaneous Vesicle Formation of Amphiphilic Molecules. J. Chem. Phys. 2002, 116, 5842−5849. (65) Dutt, M.; Kuksenok, O.; Little, S. R.; Balazs, A. C. Forming Transmembrane Channels Using End-Functionalized Nanotubes. Nanoscale 2011, 3, 240−250. (66) Dutt, M.; Nayhouse, M. J.; Kuksenok, O.; Little, S. R.; Balazs, A. C. Interactions of End-functionalized Nanotubes with Lipid Vesicles: Spontaneous Insertion and Nanotube Self-Organization. Curr. Nanosci. 2011, 7, 699−715. (67) Arai, N.; Yasuoka, K.; Zeng, X. C. A Vesicle Cell under Collision with a Janus or Homogeneous Nanoparticle: Translocation Dynamics and Late-Stage Morphology. Nanoscale 2013, 5, 9089−9100. (68) Yang, K.; Ma, Y. Q. Computer Simulation of the Translocation of Nanoparticles with Different Shapes across a Lipid Bilayer. Nat. Nanotechnol. 2010, 5, 579−583. (69) Ding, H. M.; Tian, W. D.; Ma, Y. Q. Designing Nanoparticle Translocation through Membranes by Computer Simulations. ACS Nano 2012, 6, 1230−1238. (70) Chen, X. M.; Tian, F. L.; Zhang, X. R.; Wang, W. C. Internalization Pathways of Nanoparticles and Their Interaction with a Vesicle. Soft Matter 2013, 9, 7592−7600. (71) Lins, L.; Charloteaux, B.; Thomas, A.; Brasseur, R. Computational Study of Lipid-Destabilizing Protein Fragments: Towards a

(27) Shillcock, J. C. Spontaneous Vesicle Self-Assembly: A Mesoscopic View of Membrane Dynamics. Langmuir 2012, 28, 541−547. (28) Tieleman, D. P.; Leontiadou, H.; Mark, A. E.; Marrink, S. J. Simulation of Pore Formation in Lipid Bilayers by Mechanical Stress and Electric Fields. J. Am. Chem. Soc. 2003, 125, 6382−6383. (29) Damodaran, K. V.; Merz, K. M. A Comparison of DMPC-Based and DLPE-Based Lipid Bilayers. Biophys. J. 1994, 66, 1076−1087. (30) Moore, P. B.; Lopez, C. F.; Klein, M. L. Dynamical Properties of a Hydrated Lipid Bilayer from a Multinanosecond Molecular Dynamics Simulation. Biophys. J. 2001, 81, 2484−2494. (31) Essmann, U.; Perera, L.; Berkowitz, M. L. The Origin of the Hydration Interaction of Lipid Bilayers from MD Simulation of Dipalmitoylphosphatidylcholine Membranes in Gel and LiquidCrystalline Phases. Langmuir 1995, 11, 4519−4531. (32) Cooke, I. R.; Deserno, M. Solvent-Free Model for SelfAssembling Fluid Bilayer Membranes: Stabilization of the Fluid Phase Based on Broad Attractive Tail Potentials. J. Chem. Phys. 2005, 123, 224710. (33) West, B.; Schmid, F. Fluctuations and Elastic Properties of Lipid Membranes in the Gel L-Beta ′ State: a Coarse-Grained Monte Carlo Study. Soft Matter 2010, 6, 1275−1280. (34) Farago, O. Mode Excitation Monte Carlo Simulations of Mesoscopically Large Membranes. J. Chem. Phys. 2008, 128, 184105. (35) Farago, O. Fluctuation-Induced Attraction Between Adhesion Sites of Supported Membranes. Phys. Rev. E 2010, 81, 050902. (36) Stevens, M. J.; Hoh, J. H.; Woolf, T. B. Insights into the Molecular Mechanism of Membrane Fusion from Simulation: Evidence for the Association of Splayed Tails. Phys. Rev. Lett. 2003, 91, 188102. (37) Marrink, S. J.; Risselada, H. J.; Yefimov, S.; Tieleman, D. P.; de Vries, A. H. The MARTINI Force Field: Coarse Grained Model for Biomolecular Simulations. J. Phys. Chem. B 2007, 111, 7812−7824. (38) Noguchi, H.; Takasu, M. Self-Assembly of Amphiphiles into Vesicles: A Brownian Dynamics Simulation. Phys. Rev. E: Stat. Phys., Plasmas, Fluids, Relat. Interdiscip. Top. 2001, 64, 041913. (39) Noguchi, H. Fusion and Toroidal Formation of Vesicles by Mechanical Forces: A Brownian Dynamics Simulation. J. Chem. Phys. 2002, 117, 8130−8137. (40) Katsov, K.; Muller, M.; Schick, M. Field Theoretic Study of Bilayer Membrane Fusion. I. Hemifusion Mechanism. Biophys. J. 2004, 87, 3277−3290. (41) May, S.; Kozlovsky, Y.; Ben-Shaul, A.; Kozlov, M. M. Tilt Modulus of a Lipid Monolayer. Eur. Phys. J. E: Soft Matter Biol. Phys. 2004, 14, 299−308. (42) Lee, W. B.; Mezzenga, R.; Fredrickson, G. H. Self-Consistent Field Theory for Lipid-Based Liquid Crystals: Hydrogen Bonding Effect. J. Chem. Phys. 2008, 128, 074504. (43) Ginzburg, V. V.; Balijepailli, S. Modeling the Thermodynamics of the Interaction of Nanoparticles with Cell Membranes. Nano Lett. 2007, 7, 3716−3722. (44) Ayton, G.; Voth, G. A. Bridging Microscopic and Mesoscopic Simulations of Lipid Bilayers. Biophys. J. 2002, 83, 3357−3370. (45) Wang, Z. J.; Deserno, M. A Systematically Coarse-Grained Solvent-Free Model for Quantitative Phospholipid Bilayer Simulations. J. Phys. Chem. B 2010, 114, 11207−11220. (46) Dutt, M.; Kuksenok, O.; Nayhouse, M. J.; Little, S. R.; Balazs, A. C. Modeling the Self-Assembly of Lipids and Nanotubes in Solution: Forming Vesicles and Bicelles with Transmembrane Nanotube Channels. ACS Nano 2011, 5, 4769−4782. (47) Ge, Z. P.; Li, Q.; Wang, Y. Free Energy Calculation of Nanodiamond-Membrane Association-The Effect of Shape and Surface Functionalization. J. Chem. Theory Comput. 2014, 10, 2751− 2758. (48) Van Lehn, R. C.; Ricci, M.; Silva, P. H. J.; Andreozzi, P.; Reguera, J.; Voitchovsky, K.; Stellacci, F.; Alexander-Katz, A. Lipid Tail Protrusions Mediate the Insertion of Nanoparticles into Model Cell Membranes. Nat. Commun. 2014, 510.1038/ncomms5482. 7360

DOI: 10.1021/acsnano.5b08133 ACS Nano 2016, 10, 7351−7361

Article

ACS Nano Comprehensive View of Tilted Peptides. Proteins: Struct., Funct., Genet. 2001, 44, 435−447. (72) El Kirat, K.; Lins, L.; Brasseur, R.; Dufrene, Y. F. Fusogenic Tilted Peptides Induce Nanoscale Holes in Supported Phosphatidylcholine Bilayers. Langmuir 2005, 21, 3116−3121. (73) Colherinhas, G.; Fileti, E. Molecular Dynamics Study of Surfactant-Like Peptide Based Nanostructures. J. Phys. Chem. B 2014, 118, 12215−12222. (74) Walther, F. J.; Waring, A. J.; Hernandez-Juviel, J. M.; Ruchala, P.; Wang, Z. D.; Notter, R. H.; Gordon, L. M. Surfactant Protein C Peptides with Salt-Bridges (″ion-locks″) Promote High Surfactant Activities by Mimicking the Alpha-Helix and Membrane Topography of the Native Protein. PeerJ 2014, 2, e485. (75) Schreier, S.; Malheiros, S. V. P.; de Paula, E. Surface Active Drugs: Self-Association and Interaction with Membranes and Surfactants. Physicochemical and Biological Aspects. Biochim. Biophys. Acta, Biomembr. 2000, 1508, 210−234. (76) Sintes, T.; Baumgartner, A. Protein Mediated Attraction in Membranes Induced by Lipid Fluctuations. Biophys. J. 1997, 73, 2251−2259. (77) de Meyer, F.J.-M.; Venturoli, M.; Smit, B. Molecular Simulation of Lipid-Mediated ProteinProtein Interactions. Biophys. J. 2008, 95, 1851−1865. (78) de Meyer, F.J.-M.; Rodgers, J. M.; Willems, T. F.; Smit, B. Molecular Simulation of the Effect of Cholesterol on Lipid Mediated Protein-Protein Interactions. Biophys. J. 2010, 99, 3629−3638. (79) Yiannourakou, M.; Marsella, L.; de Meyer, F.; Smit, B. Towards an Understanding of Membrane-Mediated Protein-Protein Interactions. Faraday Discuss. 2010, 144, 359−367. (80) Philips, R.; Ursell, T.; Wiggins, P.; Sens, P. Emerging Roles for Lipids in Shaping Membrane-Protein Function. Nature 2009, 469, 379−385. (81) Gil, T.; Ipsen, J. H.; Mouritsen, O. G.; Sabra, M. C.; Sperotto, M. M.; Zuckermann, M. J. Theoretical Analysis of Protein Organization in Lipid Membranes. Biochim. Biophys. Acta, Rev. Biomembr. 1998, 1376, 245−266. (82) Weikl, T. R. Fluctuation-Induced Aggregation of Rigid Membrane Inclusions. Europhys. Lett. 2001, 54, 547−553. (83) Dan, N.; Pincus, P.; Safran, S. A. Membrane-Induced Interactions Between Inclusions. Langmuir 1993, 9, 2768−2771. (84) Venturoli, M.; Smit, B.; Sperotto, M. M. Simulation Studies of Protein-Induced Bilayer Deformations, and Lipid-Induced Protein Tilting, on a Mesoscopic Model for Lipid Bilayers with Embedded Proteins. Biophys. J. 2005, 88, 1778−1798. (85) Aydin, F.; Ludford, P.; Dutt, M. Phase Segregation in BioInspired Multi-Component Vesicles Encompassing Double Tail Phospholipid Species. Soft Matter 2014, 10, 6096−6108. (86) Koufos, E.; Muralidharan, B.; Dutt, M. Design of Nanostructured Hybrid Inorganic-Biological Materials via Self-Assembly. AIMS Mater. Sci. 2014, 1, 103. (87) Sebastiano, M.; Chu, X.; Aydin, F.; Chong, L.; Dutt, M. Interactions of Bio-Inspired Membranes with Peptides and PeptideMimetic Nanoparticles. AIMS Mater. Sci. 2015, 2, 303. (88) Matsuzaki, K.; Murase, O.; Fujii, N.; Miyajima, K. Translocation of a Channel-Forming Antimicrobial Peptide, Magainin-2, across Lipid Bilayers by Forming a Pore. Biochemistry 1995, 34, 6521−6526. (89) Binder, H.; Lindblom, G. Charge-Dependent Translocation of the Trojan Peptide Penetratin across Lipid Membranes. Biophys. J. 2003, 85, 982−995. (90) Oren, Z.; Shai, Y. Cyclization of a Cytolytic Amphipathic AlphaHelical Peptide and Its Diastereomer: Effect on Structure, Interaction with Model Membranes, and Biological Function. Biochemistry 2000, 39, 6103−6114. (91) Kustanovich, I.; Shalev, D. E.; Mikhlin, M.; Gaidukov, L.; Mor, A. Structural Requirements for Potent versus Selective Cytotoxicity for Antimicrobial Dermaseptin S4 Derivatives. J. Biol. Chem. 2002, 277, 16941−16951. (92) Stella, L.; Mazzuca, C.; Venanzi, M.; Palleschi, A.; Didone, M.; Formaggio, F.; Toniolo, C.; Pispisa, B. Aggregation and Water-

Membrane Partition as Major Determinants of the Activity of the Antibiotic Peptide Trichogin GA IV. Biophys. J. 2004, 86, 936−945. (93) Radzishevsky, I. S.; Rotem, S.; Zaknoon, F.; Gaidukov, L.; Dagan, A.; Mor, A. Effects of Acyl versus Aminoacyl Conjugation on the Properties of Antimicrobial Peptides. Antimicrob. Agents Chemother. 2005, 49, 2412−2420. (94) Blondelle, S. E.; Houghten, R. A. Design of Model Amphipathic Peptides Having Potent Antimicrobial Activities. Biochemistry 1992, 31, 12688−12694. (95) Wieprecht, T.; Dathe, M.; Epand, R. M.; Beyermann, M.; Krause, E.; Maloy, W. L.; MacDonald, D. L.; Bienert, M. Influence of the Angle Subtended by the Positively Charged Helix Face on the Membrane Activity of Amphipathic, Antibacterial Peptides. Biochemistry 1997, 36, 12869−12880. (96) Wieprecht, T.; Dathe, M.; Krause, E.; Beyermann, M.; Maloy, W. L.; MacDonald, D. L.; Bienert, M. Modulation of Membrane Activity of Amphipathic, Antibacterial Peptides by Slight Modifications of the Hydrophobic Moment. FEBS Lett. 1997, 417, 135−140. (97) Tytler, E. M.; Segrest, J. P.; Epand, R. M.; Nie, S. Q.; Epand, R. F.; Mishra, V. K.; Venkatachalapathi, Y. V.; Anantharamaiah, G. M. Reciprocal Effects of Apolipoprotein and Analogs on MembranesCross-Sectional Molecular Shapes of Amphipathic-Alpha Helixes Control Membrane Stability. J. Biol. Chem. 1993, 268, 22112−22118. (98) Marassi, F. M.; Opella, S. J. Simultaneous Assignment and Structure Determination of a Membrane Protein from NMR Orientational Restraints. Protein Sci. 2003, 12, 403−411. (99) Kukol, A.; Arkin, I. T. Vpu Transmembrane Peptide Structure Obtained by Site-Specific Fourier Transform Infrared Dichroism and Global Molecular Dynamics Searching. Biophys. J. 1999, 77, 1594− 1601. (100) Park, S. H.; Opella, S. J. Tilt Angle of a Trans-Membrane Helix Is Determined by Hydrophobic Mismatch. J. Mol. Biol. 2005, 350, 310−318. (101) Killian, J. A. Hydrophobic Mismatch Between Proteins and Lipids in Membranes. Biochim. Biophys. Acta, Rev. Biomembr. 1998, 1376, 401−416. (102) Bond, P. J.; Sansom, M. S. P. Insertion and Assembly of Membrane Proteins via Simulation. J. Am. Chem. Soc. 2006, 128, 2697−2704. (103) Kandasamy, S. K.; Larson, R. G. Molecular Dynamics Simulations of Model Trans-Membrane Peptides in Lipid Bilayers: A Systematic Investigation of Hydrophobic Mismatch. Biophys. J. 2006, 90, 2326−2343. (104) Jayasinghe, S.; Barranger-Mathys, M.; Ellena, J. F.; Franklin, C.; Cafiso, D. S. Structural Features that Modulate the Transmembrane Migration of a Hydrophobic Peptide in Lipid Vesicles. Biophys. J. 1998, 74, 3023−3030. (105) Kessel, A.; Schulten, K.; Ben-Tal, N. Calculations Suggest a Pathway for the Transverse Diffusion of a Hydrophobic Peptide across a Lipid Bilayer. Biophys. J. 2000, 79, 2322−2330. (106) Sochacki, K. A.; Barns, K. J.; Bucki, R.; Weisshaar, J. C. RealTime Attack on Single Escherichia Coli Cells by the Human Antimicrobial Peptide LL-37. Proc. Natl. Acad. Sci. U. S. A. 2011, 108, 77−81.

7361

DOI: 10.1021/acsnano.5b08133 ACS Nano 2016, 10, 7351−7361