Focus Article
Large scale molecular simulations of nanotoxicity Camilo A. Jimenez-Cruz,1 Seung-gu Kang1 and Ruhong Zhou1,2∗ The widespread use of nanomaterials in biomedical applications has been accompanied by an increasing interest in understanding their interactions with tissues, cells, and biomolecules, and in particular, on how they might affect the integrity of cell membranes and proteins. In this mini-review, we present a summary of some of the recent studies on this important subject, especially from the point of view of large scale molecular simulations. The carbon-based nanomaterials and noble metal nanoparticles are the main focus, with additional discussions on quantum dots and other nanoparticles as well. The driving forces for adsorption of fullerenes, carbon nanotubes, and graphene nanosheets onto proteins or cell membranes are found to be mainly hydrophobic interactions and the so-called 𝜋–𝜋 stacking (between aromatic rings), while for the noble metal nanoparticles the long-range electrostatic interactions play a bigger role. More interestingly, there are also growing evidences showing that nanotoxicity can have implications in de novo design of nanomedicine. For example, the endohedral metallofullerenol Gd@C82 (OH)22 is shown to inhibit tumor growth and metastasis by inhibiting enzyme MMP-9, and graphene is illustrated to disrupt bacteria cell membranes by insertion/cutting as well as destructive extraction of lipid molecules. These recent findings have provided a better understanding of nanotoxicity at the molecular level and also suggested therapeutic potential by using the cytotoxicity of nanoparticles against cancer or bacteria cells. © 2014 Wiley Periodicals, Inc. How to cite this article:
WIREs Syst Biol Med 2014. doi: 10.1002/wsbm.1271
INTRODUCTION
T
he ability to synthesize molecular structures with dimensions in the order of tens to hundreds of nanometers, displaying distinct properties from their bulk counterparts, gave birth to the vast field of nanoscience.1,2 During the past two decades,3 technical advances in the characterization and manufacture of nanomaterials (NMs) and nanoparticles (NPs) allowed them to transcend basic sciences and permeate everyday life, dramatically changing several areas of medicine and technology.
∗ Correspondence 1 Computational
to: [email protected]
Biology Center, IBM Thomas J. Watson Research Center, Yorktown Heights, NY, USA 2 Department of Chemistry, Columbia University, New York, NY, USA Conflict of interest: The authors have declared no conflicts of interest for this article.
Versatility in thermal, mechanical, and physicochemical properties made NMs useful in consumer products and manufacturing processes. Commonplace commercial products such as electronic components, sun creams, food color additives, and surface coatings, increasingly contain NPs. Moreover, the resistance of some NMs to degradation under biological conditions along with their structural robustness and area-to-volume ratio makes them attractive for diagnostic and therapeutical purposes like biosensing, cellular imaging or as drugs/gene carriers.4,5 The expanding use of NPs introduces increased incidental and medical exposure, raising relevant questions on biocompatibility and biosafety of these materials.6–9 Specifically, the same properties that make NPs attractive to consumer and medical products, could potentially make them harmful to human or environmental health.10–16 NPs can enter the body via contact with the skin, inhalation, ingestion, or
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medical injection/implants, before being distributed to various organs where they will reside or be metabolized. Therefore, understanding the interactions of NMs and NPs with cell membranes, proteins, nucleic acids, and other biologically relevant systems becomes a pressing necessity. Several means of interaction of the NPs with the body have been identified and reported. NPs can compromise the integrity of cell membranes and lead to cellular lysis.17–20 They can also interact with proteins inducing conformational changes, modulate activity or display competitive binding and sequestration.21–26 However, introduced covalent and noncovalent passivations can modulate toxicity and biological response, ultimately altering the usability and function of NPs.27–29 Of particular interest are the interactions that lead to the adsorption of proteins by the NM, and the effects in its biocompatibility. Specifically, upon exposure to NPs, proteins form dynamic molecular complexes commonly termed ‘corona’, which provides the NP of biological identity, and affects its fate, transport and nanotoxicity properties while shielding further binding of other biomolecules. Experimental studies of the interaction of NMs with biological systems have provided a wealth of important information on the details of these interactions. However, due to intrinsic limitations in the instrumental resolution, a molecular-level description of these mechanisms is beyond current experimental capacity. To this end, computer simulations can provide an excellent link between experimental results and atomistic details of the NP-biomolecule complexes. Here, we review recent efforts in the computational studies on nano–bio interactions. Current advances in hardware, software, and energy functions make the molecular dynamics (MD) simulation an amenable tool for the challenge in providing static and dynamic behaviors of NMs toward target biomolecules. After a brief summary on MD method in the next section, we present several examples on how NMs interact with proteins in various contexts. In particular, we will stress on their impact on three-dimensional (3D) structural integrity and the corresponding native function, possibly leading to cytotoxicity, with complementary observations made from in-vivo and in-vitro approaches. Synergistic studies of this kind unveil the intricate subtleties of bionanotoxicity and provide direction for further developments in the exciting area of nanomedicine, which will be discussed in the last section with recent examples of specific interaction of nanoparticles with biomolecules.
METHODS: A BRIEF INTRODUCTION TO MOLECULAR MODELING OF BIOMOLECULES/NPS INTERACTIONS A very brief introduction to the methodology used in molecular dynamics simulations follows. MD simulations can be understood as the iterative numerical integration of the classical equations of motion for a set of interacting particles. Newtonian mechanics obeys ( ) 𝜕U r1 , … , rn 𝜕 2 ri m i 2 = Fi = − 𝜕ri 𝜕t where m and r are the mass and position of the ith particle, while U is the position-dependent potential energy of the configuration. It is important to note that here, each ‘particle’ can be one or several atoms of a molecule or even a cluster of molecules (as in the case of coarse-grained and multiscale models). Typical energy functions (force fields) for MD simulations can be further divided into three main terms: U = Ubonded + Unonbonded + Usolvation where bonded interactions account for vibrations, angle oscillations, proper and improper torsion potentials, while nonbonded interactions are usually reduced to van der Waals and electrostatic interactions in the case of all-atom simulations, or to native/nonnative terms in case of coarse-grained Go-type models. Another concern when carrying MD simulations are the interactions with the solvent. Here, one can choose to either include explicitly all the (rigid or polarizable) water molecules, or to use an effective term that accounts for solvation effects. The mathematical form and parameter values of modern atomistic force fields and water models are often obtained from ab-initio quantum mechanical calculations, further refined to reproduce experimental data such as gas-phase geometries, vibrational spectra, dipole moments and free energies of vaporization, solvation, and sublimation, among many others. As an example, the Amber force field is commonly used to model peptides and proteins and can be functionally written as30,31 : 0 Ubonded =
∑
( )2 ∑ ( )2 Kr r − req + K𝜃 𝜃 − 𝜃eq
bonds
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∑
angles
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∑ i Tf > BSA, consistent with the AFM results. Further simulations on the two representative proteins BFg and BSA, with much larger simulation boxs (with up to 3.8 million atoms for BFg), and longer time scales (five 1 μs trajectories each for BFg and BSA, with a total of 10 μs) confirm these earlier findings. Simulation reveals that the protein’s innate structural properties, morphology and flexibility, play an important role in recognizing SWCNT. For
example, BFg, due to its flexibility originated from the elongated helices, displayed a large conformational change, as wrapping around SWCNT (Figure 3). This explains the strongest binding capacity to SWCNT. By contrast, BSA was not changed on SWCNT as much, again in excellent agreement with the CD spectra. More importantly, conformational feasibility endows BFg to be tightly packed on SWCNT (i.e., corona), compared to other serum proteins, thus resulting in enhanced biocompatibility. In in-vivo tests with two human cell lines, the SWCNT coated with BFg showed no noticeable cytotoxicity, while the other cases with BSA, Ig or Tf, still caused significant damages on cells (Figure 2). As discussed in previous examples, hydrophobic interaction with aliphatic and aromatic residues
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(a)
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FIGURE 3 | Interaction of serum protein BFg with SWCNT. (a) The setup of the simulation system for BFg interacting with SWCNT in water (the BFg protein is shown in ribbon view, SWCNT in light-blue wires, and water in red dots [only O atoms shown]); (b) One representative 1 μs MD trajectory showing the wrapping of the long helices of BFg around the SWCNT. Despite the significant amount of computational resources applied, the complete wrapping is still beyond the current reach (even with the state-of-the-art supercomputers).
is regarded as the driving force for carbonaceous nanoparticular interaction with proteins. Because the interaction is governed by inter-molecular nonbonding interactions (i.e., electrostatics and dispersion), the specific roles of long-range and dispersion forces have been widely studied. Thus, it is noteworthy how each term would interplay in molecular binding in both qualitative and quantitative manner. For example, Tomásio et al. carried out MD simulations of two tryptophan-rich peptide sequences in the presence of a SWCNT or graphene sheet49 using a force field modified to include multipoles up to quadrupoles and polarization effects of the NM.49 Next, they mutated the tryptophans to phenylalanines and tyrosines and examined changes to the binding structures and binding affinities compared to the wild type (WT) peptides. Their results highlight the stronger binding of tryptophan over phenylalanine and tyrosine along with peptide sequence selectivity based on curvature and defects of the carbon-based nanostructure.50 Finally, the authors stressed that if 𝜋–𝜋 stacking of the aromatic groups and the graphitic surface is expected to drive the interactions, smooth van der Waals
interactions alone compares poorly to first-principle calculations, suggesting that polarizability and multipole treatment of the electrostatics of the carbon atoms should be preferred, at the increased computational expense. While detailed treatment of polarizability is preferred,34,51 it has been shown52 that modern, fixed charge force fields provide an adequate description of the strength of 𝜋–𝜋 interactions between aromatic residues and the graphitic materials, but the structural binding patterns predicted by these force fields are less satisfactory. Second to carbon-based nanostructures, noble metal nanomaterials such as gold nanoparticles (AuNPs) and nanorods (AuNRs) are among the most used in nanotechnology and nanobiomedicine.5,53–55 Surface chemistry and structure have been identified as the relevant factors that contribute to their biological responses like protein adsorption, cellular uptake and cytotoxicity.9,56 Motivated by previous results on cell viability of different coatings,53 in a recent paper,57 Wang et al. reported a careful characterization of a hard BSA corona around AuNRs. From their experimental results, they concluded that
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FIGURE 4 | Interaction between AuNRs and BSA using MD simulation from 0 to 223 ns. (a) Crystal structures of BSA and the sulfur atoms around plane S (bottom view) and (b) those around plane S (depicted as green plane, side view). (c) Representative temporal snapshots of BSA binding to the gold surface. The unfolding secondary structures are highlighted in green. (d,e) Number of sulfur atoms in contact and contact surface area of an individual BSA on the gold surface accompanying with time. In (a–c), BSA is rendered as a cartoon representation and the three domains are colored cyan, red, and blue. The sulfur atoms are highlighted in a van der Waals representation and colored yellow. (Reprinted with permission from Ref 57. Copyright 2013 American Chemical Society)
the protein adsorption is attributed to the 12 sulfur atoms present in the protein. This Au-thiol coordination bonds (Au-S) were modeled computationally as a harmonic potential, mimicking the chemical adsorption. This schema allowed them to discuss the binding stability, relevant residues, and concomitant conformational changes of the hard corona (see Figure 4). The protein-coated nanorods (BSA-AuNRs) reduced the acute toxicity of the NPs, effectively suppressing potentially destructive effects on the cell membrane. Nanosilver ions and particles have been identified as effective antibacterial17,58 and antifungal59 agents, making the characterization of their
bioresponse and cellular viability critical. Earlier this year, Cho’s group60 presented a GPU-optimized, Go-type MD simulation approach for the study of biocorona formation. As an example, they exposed 15 coarse grained apolipoproteins to a citrate-covered, negatively charged silver NP. The protein-NP interactions are modeled by using a Debye–Hückel potential for ion-concentration dependent charge interactions. In this study, a significant decrease in the 𝛼-helical content of the protein is observed upon interaction with the AgNP. Similarly, Ding et al. combined all-atom discrete MD simulations with coarse grained (CG) methodologies to provide complementary descriptions of a citrate-coated silver NP (AgNP), interacting
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FIGURE 5 | Ubiquitin-AgNP corona formation. (a) The number of ubiquitin molecules bound to AgNP, , was computed as the function of from ten independent simulations (in different colors) of the coarse-grained molecular system. (b) The average number of ubiquitins bound to AgNP, , features a power-law approximately linear) in a log–log plot. The exponent is approximately ∼0.23. (c) The final structure from one of the simulations (corresponding to the black line with the highest in panel a). The ubiquitins are in cartoon representation. The citrates correspond to the red spheres. The large dark-green sphere denotes the AgNP, and the blue spheres on the surface of the AgNP are the positively charged atoms. One of AgNP-bound ubiquitin is unfolded on the nanoparticle surface (right). In a coarse-grained DMD simulation with a higher stoichiometry of ubiquitin to AgNP (50:1), ubiquitin (black line) competed with citrate (red) to bind AgNP by displacing initially bound citrates (d). At this high stoichiometry, multilayers of ubiquitins were found to deposit onto the surface of the AgNP (e). (Reprinted with permission from Ref 61)
with up to 50 ubiquitin protein molecules.61 The AgNP was simulated as an agglomerate of hydrophobic spheres with a small fraction of positively charged particles, resembling residual silver ions (see Figure 5). The all-atom simulations revealed competitive binding of ubiquitin and citrates to AgNP. The adsorption was thus dominated by specific electrostatic interactions between the AgNP and the eleven negatively charged groups present in the ubiquitin structure. By including the results of the all-atom simulation into a
two-bead per residue, coarse-grained, structure-based potential for the protein molecules, they were able to model the poly-ubiquitin corona formation. Ubiquitins remained folded upon binding to the AgNP, with the protein helix facing to the nanoparticle. Higher concentrations of ubiquitin molecules resulted in a multilayer corona, where the first (hard) layer was held in place by the specific electrostatic interactions described above, and a second (soft) layer, was stabilized by protein–protein interactions.
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Another major category of nanoparticles are quantum dots (QDs), which are semiconductor nanocrystals (∼2–100 nm) with unique optical and electrical properties62,63 widely applied in biomedical imaging and electronics industries. Their unique fluorescence spectrum renders them optimal fluorophores for biomedical imaging.64,65 Recent studies suggest that QD toxicity depends on multiple factors derived from both the inherent physicochemical properties of QDs and environmental.66 QD size, charge, concentration, outer coating material, and oxidative, photolytic, and mechanical stability can each or collectively determine the QD toxicity. For instance Lovri´c et al.67 found that CdTe QDs coated with mercaptopropionic acid (MPA) and cysteamine were cytotoxic to rat pheochromocytoma cell (PC12) cultures at concentrations of 10 μg/mL. Uncoated CdTe QDs were cytotoxic at 1 μg/mL. Cytotoxicity was more pronounced with smaller positively charged QDs (2.2 ± 0.1 nm) than with larger equally charged QDs (5.2 ± 0.1 nm) at equal concentrations assay. QD size was also observed to affect subcellular distribution, with smaller cationic QDs localizing to the nuclear compartment and larger cationic QDs localizing to the cytosol. The mechanisms involved in cell death were not known but were considered to be due to the presence of free Cd (QD core degradation), free radical formation, or interaction of QDs with intracellular components leading to loss of function. To our knowledge, there are no large scale MD simulations on QDs interacting with biomacromolecules (proteins or cell membranes), and current simulations are focused on the electronic properties of QDs with or without organic coating (for biocompatibility). Recent theoretical studies show that the passivating ligands can form stable coordinations on QD surface, sustaining the chemical structure of the QD. Another interesting study by Azpiroz et al.68 with density functional theory (DFT) also shows that organic passivation on CdSe QDs undergoes a weakly coupled electrostatic interaction formation rather than a weak van der Waals interaction between molecules. Very recently, Zhou and coworkers (in preparation) also used DFT to investigate the electronic structures of QD (CdSe)13 passivated by OPMe2 (CH2 )n Me ligands with different lengths and various numbers of branches (Me = methyl group, n = 0, 1–3). They found that the absorption peak in the ultraviolet–visible (UV–vis) spectra displays a clear blue-shift, on the scale of ∼100 nm, upon the binding of ligands. Once the total number of ligands bound with (CdSe)13 reached a saturated number (9 or 10), no more blue-shift occurred in the absorption peak in the UV–vis spectra. On the other hand, the
aliphatic chain length of ligands has a negligible effect on the optical properties of the QD core. Analyzes of the bonding characteristics confirm that optical transitions are dominantly governed by the central QD core rather than the organic passivation. Interestingly, the density of states (DOS) share similar characteristics as vibrational spectra, even though there is no coordination vibration mode between the ligands and the central QD. These findings provide insights to the design of safer organic passivation of QDs for biomedical applications. Overall, these recent studies show that of the vast physicochemical characteristics, functional coating and QD core stability might be the most significant factors in assessing the risk of QD toxicity in real-world exposure scenarios.
NANOMEDICINE: IMPLICATIONS FROM NANOTOXICITY Properly tamed, however, the interesting and varied properties of NMs will allow for the development of new, highly effective, specifically tailored, diagnostic and therapeutic techniques. In this section, we review some very recent examples of de novo nanomedicine design with implications learned particularly from nanotoxicity. Fullerenes, rigid carbon-cages with nanometric dimensions, allow for convenient surface functionalizations, modifying their biomedical function. There are many recent attempts for their use in both diagnostics and therapeutics.69 One interesting example, based on implications from the ‘nanotoxicity’ concept, is the biomedical application of endohedral metallofullerene derivatives in high-efficacy cancer diagnostics and therapeutics. Due to its high proton relaxivity, Gadolinium ion (Gd3+ ), chelated by organic molecules, can be employed as biologically safe, high contrasting agent for magnetic resonance imaging (MRI). By using mouse models of liver cancer, Chen et al.70 investigated the antitumoral efficacy of multihidroxylated metallofullerenol Gd@C82 (OH)22 NPs and compared it to widely used antineoplastic agents. A histological examination implies that Gd@C82 (OH)22 has no direct toxic effect on tumor cells, where only marginal amount of the exposed dose has been detected in tumor tissues. Rather, Gd@C82 (OH)22 was found to indirectly suppress the cancer growth through inhibiting metastatic pathways, or stimulating immune response. For example, a mouse model, xenografted with human pancreatic cancer cells, showed a remarkably restriction of cancer volume growth with Gd@C82 (OH)22 treatment, as accompanied with significant reduction of microvessel density (MVD).71 More
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specifically, matrix metalloproteinase-2/-9 (MMP-2/ -9) were found to be effectively reduced in both expression and activity levels with more dramatic changes in MMP-9. MMPs are well known enzymes for cancer prognosis and metastasis, as involved in angiogenesis and extracelluar matrix (ECM) degradation, thus attracting as potential target for antimetastatic agents. For molecular level understanding, atomistic molecular dynamic simulations have been performed for Gd@C82 (OH)22 and MMP-9. Compared to pure carbon materials, Gd@C82 (OH)22 rarely affects protein’s overall structure even with frequent contacts with Gd@C82 (OH)22 , where MMP-9 remained in native fold for the simulation time duration (up to 500-ns). Interestingly, Gd@C82 (OH)22 seemed to be easily clustered from the early stage of simulation, consistent with earlier experimental findings by small-angle X-ray scattering (SAXS) and AFM. This implicates much sophisticated interaction between Gd@C82 (OH)22 and a target protein. Differently from pristine carbonaceous nanomaterials, Gd@C82 (OH)22 can use hydrogen bonds via surface hydroxyl groups, as well as electrostatic interaction via induced charge by Gd3+ , while with to some extent of hydrophobicity. All together resulted in a specific interaction at the ligand specificity S1’ loop of MMP-9. Simulation also revealed that the direct blocking on the zinc-coordinated active site is less likely, as rationalized by repulsive electrostatics around the ligand binding site. The tight binding at the S1’ loop blocks MMP-9’s function as the ligand mediator, leading to its inhibition, thus nanotoxic to MMP-9. Free-energy landscapes and contact modes are shown in Figure 6. Both the suppression of MMPs expression and specific binding mode make Gd@C82 (OH)22 a potentially more effective nanodrug for cancers, such as breast cancer and pancreatic cancer, than the traditional drugs which usually target the proteolytic sites directly without selective inhibition. The current findings with a new exocite binding mode at the ligand specificity S1’ loop provide new insights and directions for future de novo design of nanomedicine for fatal diseases such as pancreatic cancer. Direct penetration and endocytosis of graphitic NMs may also affect integrity of cell membranes.19,72 Graphene nanosheets display strong cytotoxicity against both Gram-negative and Gram-positive bacterial cells. Nevertheless, the biological response of these materials is attenuated when surrounded by proteins as discussed above, making them less harmful to mammalians. Therefore, carefully used, this cytotoxic behavior can once again potentially be directed to produce novel antibiotics.
The translocation of fullerene clusters through lipid bilayers has been investigated by Wong-Ekkabut et al.19 by using a coarse-grained model, resulting in a favorable permeation of the lipid bilayer by the fullerene. Similar results have been observed for nanoflakes,73 nanotubes,74–76 and other derivatives.77,78 Aiming to advance the current understanding of the effects of graphene and graphene-oxide nanosheets in the integrity of bacterial cell membranes, Zhou and coworkers79 reported combined experimental and theoretical studies of Escherichia coli cell membranes in the presence of the aforementioned NMs. In the time-resolved TEM images, roughly three stages of cell morphology were identified during the incubation, dependent on the NM concentration. From the initial morphology (stage I), a reduction in the phospholipid density is observed without the visible presence of any cut (stage II). Finally, stage III is characterized by the total loss of cellular integrity, with possible leaking of the cytoplasm. Unbiased, atomistic simulations of membrane-NM systems in explicit solvent were carried out for both graphene and graphene-oxide, using E. coli outer and inner membranes. Serious insertion and disruption of the cell membranes were observed during the simulations. Two of the interaction modes were identified which resulted in the partial loss of integrity of the cell membrane, but differed in the molecular mechanism. The Type A mechanism, best described as a cut, supports the experimental results discussed before. The other type of insertion, Type B, displays a destructive extraction of phospholipid molecules from the membrane by the graphene. This mode of membrane disruption has not been observed before, and explains the reduction in phospholipid density obtained in the TEM images. A detailed characterization of these membrane-graphene systems showed that the lipid extraction by the graphene nanosheets is robust. The process is dominated by an exceptionally strong graphene-lipid dispersion interaction, which shadows the self-attraction between phospholipid molecules. The same mode was successfully identified with both graphene and graphene-oxides in each type of membrane (see Figure 7).
CONCLUDING REMARKS AND FUTURE PERSPECTIVES The synthesis, development and characterization of nanostructures have revolutionized modern life. Current advances in nanotechnology bring new and exciting possibilities to consumer fields such as clothing, cosmetics or electronics. More importantly, the medical application of NMs and NPs for preventive,
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Molecular simulations of nanotoxicity
0.0
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FIGURE 6 | Binding free-energy landscapes and residue-specific contacts on MMP-9, as well as representative binding modes and pathway of Gd@C82 (OH)22 on MMP-9. (a) Binding free-energy surface for fullerenol C82 (OH)22 on MMP-9 shows a nonspecific binding mode (left), and almost all surface residues of MMP-9 contribute to contact with C82 (OH)22 (right). (b) Metallofullerenol Gd@C82 (OH)22 interacts with MMP-9 along a specified binding mode (left) and contacts with only a specific set of residues near the ligand-specificity S1′ loop and SC loop (right). A residue was assigned to be in a contact when any atom in the residue was within 5.0 Å of any atom of Gd@C82 (OH)22 [or C82 (OH)22 ]. The site participation is presented by the total number of frames of each residue in contact normalized by all frames and trajectories. (c) Representative binding mode (a solid ball) showing that Gd@C82 (OH)22 binds between the S1′ ligand-specificity loop (green ribbon) and the SC loop (purple ribbon), leading to the ligand binding groove. An alternative mode with a gray ball is shown that Gd@C82 (OH)22 can bind at the back entrance of the S1′ cavity leading into the active site (ball and stick for active sites and orange ball for the catalytic Zn2+) (left). Possible binding pathway: depending on major driving forces and duration time (only the first 100 ns is shown), the binding dynamics is characterized with three different phases. Phase I: a diffusion-controlled nonspecific electrostatic interaction; phase II: a transient nonspecific hydrophobic interaction; and phase III: a specific hydrophobic and hydrogen-bonded stable binding (right). (Reprinted with permission from Ref 71. Copyright 2012 Proceedings of the National Academy of Sciences USA)
diagnostic and therapeutic purposes has the potential to allow for more effective, less invasive, personalized treatments. As nanotechnology grows as a field, its consequences in the health of people and environment require careful assessment. To this
end, concerted experimental and theoretical efforts to attain an improved understanding of the interactions of NMs with living organisms are required. Of growing interest are large-scale molecular simulations of biomolecules/NP interactions, which allow for a
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(a)
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FIGURE 7 | Graphene nanosheet insertion and lipid extraction. (a,b) Representative simulated trajectories of graphene nanosheet insertion and lipid extraction in the outer membrane (pure POPE) and inner membrane (3:1 mixed POPE–POPG) of E. coli (the snapshot times are shown in the top left corners).Water is shown in violet and the phospholipids in tan lines with hydrophilic charged atoms as colored spheres (hydrogen, white; oxygen, red; nitrogen, dark blue; carbon, cyan; phosphorus, orange). The graphene sheet is shown as a yellow-bonded sheet with a large sphere marked at one corner as the restrained atom in simulations. Extracted phospholipids are shown as larger spheres. (Reprinted with permission from Ref 79. Copyright 2012 Nature Nanotechnology)
detailed atomistic understanding of the often seemingly contradictory findings from the complex in-vitro and in-vivo experiments. Here, we provide a discussion of recent results on computer simulations regarding nanotoxicity by exploring the effects on biomolecules upon exposure to different NPs. While hydrophobicity plays a driving role in the interactions of proteins with carbon-based NMs, long-range electrostatic attraction/repulsion dominates the interaction with noble metals. Dependence of the effect on thermodynamic
and kinetic properties on the sequence and native structure of the protein, and on the curvature, rigidity, residual charge and functionalization of the NMs are further described. Computational results modeling NMs interacting with one or several protein domains are reviewed, with emphasis on the dynamic nature of the protein binding. Characterizations of the driving forces of protein binding to NPs are fundamental to the understanding of the so-called ‘corona’, which in turn modulates the biocompatibility and fate of the NP. Cytotoxicity studies of NMs can also
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be used for de novo design in nanomedicine applications. In this review, the use of endohedral metallofullerenol Gd@C82 (OH)22 as an antitumor agent, and graphene/graphene-oxide as an effective antibacterial are taken as examples of such applications. In both cases, MD simulations allowed for a detailed description of the binding mechanisms by which the selective targeting takes place. Computational
modeling presents itself as a powerful tool to obtain detailed descriptions of the inter-molecular interactions at an atomistic level. With increasing computational power and continuous theoretical efforts to improve and extend the accuracy of current energy functions, the future of computer-aided design of de novo nanomedicine applications is a promising area of research.
REFERENCES 1. Kroto HW, Heath JR, O’Brien SC, Curl RF, Smalley RE. C60: Buckmisnterfulleren. Nature 1985, 318:162–163. 2. Iijima S. Helical microtubules of graphitic carbon. Nature 1991, 354:56–58. 3. Rao CNR, Sood AK, Subrahmanyam KS, Govindaraj A. Graphene: the new two-dimensional nanomaterial. Angew Chem Int Ed Engl Jan. 2009, 48:7752–7777. 4. Noon WH, Kong Y, Ma J. Molecular dynamics analysis of a buckyball-antibody complex. Proc Natl Acad Sci U S A 2002, 99(Suppl 2):6466–6470. 5. Jain PK, El-Sayed IH, El-Sayed MA. Au nanoparticles target cancer. Nano Today 2007, 2:18–29. 6. Smart SK, Cassady AI, Lu GQ, Martin DJ. The biocompatibility of carbon nanotubes. Carbon 2006, 44:1034–1047. 7. Dietz K-J, Herth S. Plant nanotoxicology. Trends Plant Sci 2011, 16:582–589. 8. Mahmoudi M, Azadmanesh K, Shokrgozar MA, Journeay WS, Laurent S. Effect of nanoparticles on the cell life cycle. Chem Rev 2011, 111:3407–3432. 9. Aillon KL, Xie Y, El-Gendy N, Berkland CJ, Forrest ML. Effects of nanomaterial physicochemical properties on in vivo toxicity. Adv Drug Deliv Rev 2009, 61:457–466. 10. Brayner R. The toxicological impact of nanoparticles. Nano Today 2008, 3:48–55. 11. Fischer HC, Chan WCW. Nanotoxicity: the growing need for in vivo study. Curr Opin Biotechnol 2007, 18:565–571.
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28. Partha R, Conyers JL. Biomedical applications of functionalized fullerene-based nanomaterials. Int J Nanomedicine 2009, 4:261–275. 29. Bitar A, Ahmad NM, Fessi H, Elaissari A. Silica-based nanoparticles for biomedical applications. Drug Discov Today 2012, 17:1147–1154. 30. Cornell WD, Cieplak P, Bayly CI, Gould IR, Merz KM, Ferguson DM, Spellmeyer DC, Fox T, Caldwell JW, Kollman PA. A second generation force field for the simulation of proteins, nucleic acids, and organic molecules. J Am Chem Soc 1995, 117:5179–5197. 31. Duan Y, Wu C, Chowdhury S, Lee MC, Xiong G, Zhang W, Yang R, Cieplak P, Luo R, Lee T, et al. A point-charge force field for molecular mechanics simulations of proteins based on condensed-phase quantum mechanical calculations. J Comput Chem 2003, 24:1999–2012.
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48. Ge C, Du J, Zhao L, Wang L, Liu Y, Li D, Yang Y, Zhou R, Zhao Y, Chai Z, et al. Binding of blood proteins to carbon nanotubes reduces cytotoxicity. Proc Natl Acad Sci U S A 2011, 108:16968–16973. 49. Tomásio S, Walsh T. Modeling the binding affinity of peptides for graphitic surfaces. Influences of aromatic content and interfacial shape. J Phys Chem C 2009, 113:8778–8785. 50. Walsh TR, Tomasio SM. Investigation of the influence of surface defects on peptide adsorption onto carbon nanotubes. Mol Biosyst 2010, 6:1707–1718. 51. Xie Y, Kong Y, Gao H, Soh AK. Molecular dynamics simulation of polarizable carbon nanotubes. Comput Mater Sci 2007, 40:460–465. 52. Yang Z, Wang Z, Tian X, Xiu P, Zhou R. Amino acid analogues bind to carbon nanotube via 𝜋-𝜋 interactions: comparison of molecular mechanical and quantum mechanical calculations. J Chem Phys 2012, 136:025103. 53. Wang L, Jiang X, Ji Y, Bai R, Zhao Y, Wu X, Chen C. Surface chemistry of gold nanorods: origin of cell membrane damage and cytotoxicity. Nanoscale 2013, 5:8384–8391. 54. Dykman L, Khlebtsov N. Gold nanoparticles in biomedical applications: recent advances and perspectives. Chem Soc Rev 2012, 41:2256–2282. 55. Tiwari P, Vig K, Dennis V, Singh S. Functionalized gold nanoparticles and their biomedical applications. Nanomaterials 2011, 1:31–63.
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58. Ahamed M, Alsalhi MS, Siddiqui MKJ. Silver nanoparticle applications and human health. Clin Chim Acta 2010, 411:1841–1848. 59. Kim K-J, Sung WS, Suh BK, Moon S-K, Choi J-S, Kim JG, Lee DG. Antifungal activity and mode of action of silver nano-particles on Candida albicans. Biometals 2009, 22:235–242. 60. Li R, Chen R, Chen P, Wen Y, Ke PC, Cho SS. Computational and experimental characterizations of silver nanoparticle-apolipoprotein biocorona. J Phys Chem B 2013, 117:13451–13456. 61. Ding F, Radic S, Chen R, Chen P, Geitner NK, Brown JM, Ke PC. Direct observation of a single nanoparticle-ubiquitin corona formation. Nanoscale 2013, 5:9162–9169. 62. Bruchez M, Moronne M, Gin P, Weiss S, Alivisatos AP. Semiconductor nanocrystals as fluorescent biological labels. Science 1998, 281:2013–2016. 63. Dabbousi BO, Mikulec FV, Heine JR, Mattoussi H, Ober R, Jensen KF, Bawendi MG. (CdSe)ZnS core–shell quantum dots: synthesis and characterization of a size series of highly luminescent nanocrystallites. J Phys Chem B 1997, 101:9463–9475. 64. Alivisatos P. The use of nanocrystals in biological detection. Nat Biotechnol 2004, 22:47–52. 65. Chan WCW, Maxwell DJ, Gao X, Bailey RE, Han M, Nie S. Luminescent quantum dots for multiplexed biological detection and imaging. Curr Opin Biotechnol 2002, 13:40–46. 66. Hardman R. A toxicologic review of quantum dots: toxicity depends on physicochemical and environmental factors. Environ Health Perspect 2006, 114:165–172. 67. Lovri´c J, Bazzi HS, Cuie Y, Fortin GRA, Winnik FM, Maysinger D. Differences in subcellular distribution and toxicity of green and red emitting CdTe quantum dots. J Mol Med (Berl) 2005, 83:377–385. 68. Azpiroz JM, Matxain JM, Infante I, Lopez X, Ugalde JM. A DFT/TDDFT study on the optoelectronic
70. Chen C, Xing G, Wang J, Zhao Y, Li B, Tang J, Jia G, Wang T, Sun J, Xing L, et al. Multihydroxylated [Gd@C82 (OH)22 ]n nanoparticles: antineoplastic activity of high efficiency and low toxicity. Nano Lett Oct. 2005, 5:2050–2057. 71. Kang S, Zhou G, Yang P, Liu Y, Sun B, Huynh T, Meng H, Zhao L, Xing G, Chen C, et al. Molecular mechanism of pancreatic tumor metastasis inhibition by Gd@ C82 (OH)22 and its implication for de novo design of nanomedicine. Proc Natl Acad Sci U S A 2012, 109:15431–15436. 72. Pogodin S, Baulin V. Can a carbon nanotube pierce through a phospholipid bilayer? ACS Nano 2010, 4:5293–5300. 73. Titov AV, Král P, Pearson R. Sandwiched graphenemembrane superstructures. ACS Nano 2010, 4: 229–234. 74. Shi X, Kong Y, Gao H. Coarse grained molecular dynamics and theoretical studies of carbon nanotubes entering cell membrane. Acta Mech Sin 2008, 24:161–169. 75. Wallace EJ, Sansom MSP. Carbon nanotube/detergent interactions via coarse-grained molecular dynamics. Nano Lett 2007, 7:1923–1928. 76. Baoukina S, Monticelli L, Tieleman DP. Interaction of pristine and functionalized carbon nanotubes with lipid membranes. J Phys Chem B 2013, 117: 12113–12123. 77. D’Rozario RSG, Wee CL, Wallace EJ, Sansom MSP. The interaction of C60 and its derivatives with a lipid bilayer via molecular dynamics simulations. Nanotechnology 2009, 20:115102. 78. Izvekov S, Violi A, Voth GA. Systematic coarse-graining of nanoparticle interactions in molecular dynamics simulation. J Phys Chem B 2005, 109:17019–17024. 79. Tu Y, Lv M, Xiu P, Huynh T, Zhang M, Castelli M, Liu Z, Huang Q, Fan C, Fang H, et al. Destructive extraction of phospholipids from Escherichia coli membranes by graphene nanosheets. Nat Nanotechnol 2013, 8:594–601.
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Large scale molecular simulations of nanotoxicity Camilo A. Jimenez-Cruz,1 Seung-gu Kang1 and Ruhong Zhou1,2∗ The widespread use of nanomaterials in biomedical applications has been accompanied by an increasing interest in understanding their interactions with tissues, cells, and biomolecules, and in particular, on how they might affect the integrity of cell membranes and proteins. In this mini-review, we present a summary of some of the recent studies on this important subject, especially from the point of view of large scale molecular simulations. The carbon-based nanomaterials and noble metal nanoparticles are the main focus, with additional discussions on quantum dots and other nanoparticles as well. The driving forces for adsorption of fullerenes, carbon nanotubes, and graphene nanosheets onto proteins or cell membranes are found to be mainly hydrophobic interactions and the so-called 𝜋–𝜋 stacking (between aromatic rings), while for the noble metal nanoparticles the long-range electrostatic interactions play a bigger role. More interestingly, there are also growing evidences showing that nanotoxicity can have implications in de novo design of nanomedicine. For example, the endohedral metallofullerenol Gd@C82 (OH)22 is shown to inhibit tumor growth and metastasis by inhibiting enzyme MMP-9, and graphene is illustrated to disrupt bacteria cell membranes by insertion/cutting as well as destructive extraction of lipid molecules. These recent findings have provided a better understanding of nanotoxicity at the molecular level and also suggested therapeutic potential by using the cytotoxicity of nanoparticles against cancer or bacteria cells. © 2014 Wiley Periodicals, Inc. How to cite this article:
WIREs Syst Biol Med 2014. doi: 10.1002/wsbm.1271
INTRODUCTION
T
he ability to synthesize molecular structures with dimensions in the order of tens to hundreds of nanometers, displaying distinct properties from their bulk counterparts, gave birth to the vast field of nanoscience.1,2 During the past two decades,3 technical advances in the characterization and manufacture of nanomaterials (NMs) and nanoparticles (NPs) allowed them to transcend basic sciences and permeate everyday life, dramatically changing several areas of medicine and technology.
∗ Correspondence 1 Computational
to: [email protected]
Biology Center, IBM Thomas J. Watson Research Center, Yorktown Heights, NY, USA 2 Department of Chemistry, Columbia University, New York, NY, USA Conflict of interest: The authors have declared no conflicts of interest for this article.
Versatility in thermal, mechanical, and physicochemical properties made NMs useful in consumer products and manufacturing processes. Commonplace commercial products such as electronic components, sun creams, food color additives, and surface coatings, increasingly contain NPs. Moreover, the resistance of some NMs to degradation under biological conditions along with their structural robustness and area-to-volume ratio makes them attractive for diagnostic and therapeutical purposes like biosensing, cellular imaging or as drugs/gene carriers.4,5 The expanding use of NPs introduces increased incidental and medical exposure, raising relevant questions on biocompatibility and biosafety of these materials.6–9 Specifically, the same properties that make NPs attractive to consumer and medical products, could potentially make them harmful to human or environmental health.10–16 NPs can enter the body via contact with the skin, inhalation, ingestion, or
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medical injection/implants, before being distributed to various organs where they will reside or be metabolized. Therefore, understanding the interactions of NMs and NPs with cell membranes, proteins, nucleic acids, and other biologically relevant systems becomes a pressing necessity. Several means of interaction of the NPs with the body have been identified and reported. NPs can compromise the integrity of cell membranes and lead to cellular lysis.17–20 They can also interact with proteins inducing conformational changes, modulate activity or display competitive binding and sequestration.21–26 However, introduced covalent and noncovalent passivations can modulate toxicity and biological response, ultimately altering the usability and function of NPs.27–29 Of particular interest are the interactions that lead to the adsorption of proteins by the NM, and the effects in its biocompatibility. Specifically, upon exposure to NPs, proteins form dynamic molecular complexes commonly termed ‘corona’, which provides the NP of biological identity, and affects its fate, transport and nanotoxicity properties while shielding further binding of other biomolecules. Experimental studies of the interaction of NMs with biological systems have provided a wealth of important information on the details of these interactions. However, due to intrinsic limitations in the instrumental resolution, a molecular-level description of these mechanisms is beyond current experimental capacity. To this end, computer simulations can provide an excellent link between experimental results and atomistic details of the NP-biomolecule complexes. Here, we review recent efforts in the computational studies on nano–bio interactions. Current advances in hardware, software, and energy functions make the molecular dynamics (MD) simulation an amenable tool for the challenge in providing static and dynamic behaviors of NMs toward target biomolecules. After a brief summary on MD method in the next section, we present several examples on how NMs interact with proteins in various contexts. In particular, we will stress on their impact on three-dimensional (3D) structural integrity and the corresponding native function, possibly leading to cytotoxicity, with complementary observations made from in-vivo and in-vitro approaches. Synergistic studies of this kind unveil the intricate subtleties of bionanotoxicity and provide direction for further developments in the exciting area of nanomedicine, which will be discussed in the last section with recent examples of specific interaction of nanoparticles with biomolecules.
METHODS: A BRIEF INTRODUCTION TO MOLECULAR MODELING OF BIOMOLECULES/NPS INTERACTIONS A very brief introduction to the methodology used in molecular dynamics simulations follows. MD simulations can be understood as the iterative numerical integration of the classical equations of motion for a set of interacting particles. Newtonian mechanics obeys ( ) 𝜕U r1 , … , rn 𝜕 2 ri m i 2 = Fi = − 𝜕ri 𝜕t where m and r are the mass and position of the ith particle, while U is the position-dependent potential energy of the configuration. It is important to note that here, each ‘particle’ can be one or several atoms of a molecule or even a cluster of molecules (as in the case of coarse-grained and multiscale models). Typical energy functions (force fields) for MD simulations can be further divided into three main terms: U = Ubonded + Unonbonded + Usolvation where bonded interactions account for vibrations, angle oscillations, proper and improper torsion potentials, while nonbonded interactions are usually reduced to van der Waals and electrostatic interactions in the case of all-atom simulations, or to native/nonnative terms in case of coarse-grained Go-type models. Another concern when carrying MD simulations are the interactions with the solvent. Here, one can choose to either include explicitly all the (rigid or polarizable) water molecules, or to use an effective term that accounts for solvation effects. The mathematical form and parameter values of modern atomistic force fields and water models are often obtained from ab-initio quantum mechanical calculations, further refined to reproduce experimental data such as gas-phase geometries, vibrational spectra, dipole moments and free energies of vaporization, solvation, and sublimation, among many others. As an example, the Amber force field is commonly used to model peptides and proteins and can be functionally written as30,31 : 0 Ubonded =
∑
( )2 ∑ ( )2 Kr r − req + K𝜃 𝜃 − 𝜃eq
bonds
+
∑
angles
K𝜙 (1 + cos (n 𝜙 − 𝛿))
dihedrals
Unonbonded =
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∑ i Tf > BSA, consistent with the AFM results. Further simulations on the two representative proteins BFg and BSA, with much larger simulation boxs (with up to 3.8 million atoms for BFg), and longer time scales (five 1 μs trajectories each for BFg and BSA, with a total of 10 μs) confirm these earlier findings. Simulation reveals that the protein’s innate structural properties, morphology and flexibility, play an important role in recognizing SWCNT. For
example, BFg, due to its flexibility originated from the elongated helices, displayed a large conformational change, as wrapping around SWCNT (Figure 3). This explains the strongest binding capacity to SWCNT. By contrast, BSA was not changed on SWCNT as much, again in excellent agreement with the CD spectra. More importantly, conformational feasibility endows BFg to be tightly packed on SWCNT (i.e., corona), compared to other serum proteins, thus resulting in enhanced biocompatibility. In in-vivo tests with two human cell lines, the SWCNT coated with BFg showed no noticeable cytotoxicity, while the other cases with BSA, Ig or Tf, still caused significant damages on cells (Figure 2). As discussed in previous examples, hydrophobic interaction with aliphatic and aromatic residues
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(a)
(b)
t = 100 ns
t = 300 ns
t = 600 ns
t = 1000 ns
FIGURE 3 | Interaction of serum protein BFg with SWCNT. (a) The setup of the simulation system for BFg interacting with SWCNT in water (the BFg protein is shown in ribbon view, SWCNT in light-blue wires, and water in red dots [only O atoms shown]); (b) One representative 1 μs MD trajectory showing the wrapping of the long helices of BFg around the SWCNT. Despite the significant amount of computational resources applied, the complete wrapping is still beyond the current reach (even with the state-of-the-art supercomputers).
is regarded as the driving force for carbonaceous nanoparticular interaction with proteins. Because the interaction is governed by inter-molecular nonbonding interactions (i.e., electrostatics and dispersion), the specific roles of long-range and dispersion forces have been widely studied. Thus, it is noteworthy how each term would interplay in molecular binding in both qualitative and quantitative manner. For example, Tomásio et al. carried out MD simulations of two tryptophan-rich peptide sequences in the presence of a SWCNT or graphene sheet49 using a force field modified to include multipoles up to quadrupoles and polarization effects of the NM.49 Next, they mutated the tryptophans to phenylalanines and tyrosines and examined changes to the binding structures and binding affinities compared to the wild type (WT) peptides. Their results highlight the stronger binding of tryptophan over phenylalanine and tyrosine along with peptide sequence selectivity based on curvature and defects of the carbon-based nanostructure.50 Finally, the authors stressed that if 𝜋–𝜋 stacking of the aromatic groups and the graphitic surface is expected to drive the interactions, smooth van der Waals
interactions alone compares poorly to first-principle calculations, suggesting that polarizability and multipole treatment of the electrostatics of the carbon atoms should be preferred, at the increased computational expense. While detailed treatment of polarizability is preferred,34,51 it has been shown52 that modern, fixed charge force fields provide an adequate description of the strength of 𝜋–𝜋 interactions between aromatic residues and the graphitic materials, but the structural binding patterns predicted by these force fields are less satisfactory. Second to carbon-based nanostructures, noble metal nanomaterials such as gold nanoparticles (AuNPs) and nanorods (AuNRs) are among the most used in nanotechnology and nanobiomedicine.5,53–55 Surface chemistry and structure have been identified as the relevant factors that contribute to their biological responses like protein adsorption, cellular uptake and cytotoxicity.9,56 Motivated by previous results on cell viability of different coatings,53 in a recent paper,57 Wang et al. reported a careful characterization of a hard BSA corona around AuNRs. From their experimental results, they concluded that
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Molecular simulations of nanotoxicity
(a)
(b)
Domain II
Domain II Domain I
Domain III Plane S
Domain III
Domain I 0 ns
12 ns
50.7 ns
90 ns
117 ns
223 ns
Contact S atom number
(d)
(e)
12
8
4
0 0
50
100 150 Time (ns)
200
Contact surface area (nm2)
(c)
15
10
5
0 0
50
100 150 Time (ns)
200
FIGURE 4 | Interaction between AuNRs and BSA using MD simulation from 0 to 223 ns. (a) Crystal structures of BSA and the sulfur atoms around plane S (bottom view) and (b) those around plane S (depicted as green plane, side view). (c) Representative temporal snapshots of BSA binding to the gold surface. The unfolding secondary structures are highlighted in green. (d,e) Number of sulfur atoms in contact and contact surface area of an individual BSA on the gold surface accompanying with time. In (a–c), BSA is rendered as a cartoon representation and the three domains are colored cyan, red, and blue. The sulfur atoms are highlighted in a van der Waals representation and colored yellow. (Reprinted with permission from Ref 57. Copyright 2013 American Chemical Society)
the protein adsorption is attributed to the 12 sulfur atoms present in the protein. This Au-thiol coordination bonds (Au-S) were modeled computationally as a harmonic potential, mimicking the chemical adsorption. This schema allowed them to discuss the binding stability, relevant residues, and concomitant conformational changes of the hard corona (see Figure 4). The protein-coated nanorods (BSA-AuNRs) reduced the acute toxicity of the NPs, effectively suppressing potentially destructive effects on the cell membrane. Nanosilver ions and particles have been identified as effective antibacterial17,58 and antifungal59 agents, making the characterization of their
bioresponse and cellular viability critical. Earlier this year, Cho’s group60 presented a GPU-optimized, Go-type MD simulation approach for the study of biocorona formation. As an example, they exposed 15 coarse grained apolipoproteins to a citrate-covered, negatively charged silver NP. The protein-NP interactions are modeled by using a Debye–Hückel potential for ion-concentration dependent charge interactions. In this study, a significant decrease in the 𝛼-helical content of the protein is observed upon interaction with the AgNP. Similarly, Ding et al. combined all-atom discrete MD simulations with coarse grained (CG) methodologies to provide complementary descriptions of a citrate-coated silver NP (AgNP), interacting
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20
16
(b) 32
N bound
(a) 25
15
10
5
0
Power-law Stretched exponential ~0.21
8
4
2
0
0.5
1
1
1
Time, × 106 t.u.
10
100
1000
Time, × 102 t.u.
10000
(c)
Number of NP-bound molecules
(d)
(e)
80
Ubiquitin Citric Acid
60
40
20
0 0
0.5
1
Time, × 106 t.u.
FIGURE 5 | Ubiquitin-AgNP corona formation. (a) The number of ubiquitin molecules bound to AgNP, , was computed as the function of from ten independent simulations (in different colors) of the coarse-grained molecular system. (b) The average number of ubiquitins bound to AgNP, , features a power-law approximately linear) in a log–log plot. The exponent is approximately ∼0.23. (c) The final structure from one of the simulations (corresponding to the black line with the highest in panel a). The ubiquitins are in cartoon representation. The citrates correspond to the red spheres. The large dark-green sphere denotes the AgNP, and the blue spheres on the surface of the AgNP are the positively charged atoms. One of AgNP-bound ubiquitin is unfolded on the nanoparticle surface (right). In a coarse-grained DMD simulation with a higher stoichiometry of ubiquitin to AgNP (50:1), ubiquitin (black line) competed with citrate (red) to bind AgNP by displacing initially bound citrates (d). At this high stoichiometry, multilayers of ubiquitins were found to deposit onto the surface of the AgNP (e). (Reprinted with permission from Ref 61)
with up to 50 ubiquitin protein molecules.61 The AgNP was simulated as an agglomerate of hydrophobic spheres with a small fraction of positively charged particles, resembling residual silver ions (see Figure 5). The all-atom simulations revealed competitive binding of ubiquitin and citrates to AgNP. The adsorption was thus dominated by specific electrostatic interactions between the AgNP and the eleven negatively charged groups present in the ubiquitin structure. By including the results of the all-atom simulation into a
two-bead per residue, coarse-grained, structure-based potential for the protein molecules, they were able to model the poly-ubiquitin corona formation. Ubiquitins remained folded upon binding to the AgNP, with the protein helix facing to the nanoparticle. Higher concentrations of ubiquitin molecules resulted in a multilayer corona, where the first (hard) layer was held in place by the specific electrostatic interactions described above, and a second (soft) layer, was stabilized by protein–protein interactions.
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Another major category of nanoparticles are quantum dots (QDs), which are semiconductor nanocrystals (∼2–100 nm) with unique optical and electrical properties62,63 widely applied in biomedical imaging and electronics industries. Their unique fluorescence spectrum renders them optimal fluorophores for biomedical imaging.64,65 Recent studies suggest that QD toxicity depends on multiple factors derived from both the inherent physicochemical properties of QDs and environmental.66 QD size, charge, concentration, outer coating material, and oxidative, photolytic, and mechanical stability can each or collectively determine the QD toxicity. For instance Lovri´c et al.67 found that CdTe QDs coated with mercaptopropionic acid (MPA) and cysteamine were cytotoxic to rat pheochromocytoma cell (PC12) cultures at concentrations of 10 μg/mL. Uncoated CdTe QDs were cytotoxic at 1 μg/mL. Cytotoxicity was more pronounced with smaller positively charged QDs (2.2 ± 0.1 nm) than with larger equally charged QDs (5.2 ± 0.1 nm) at equal concentrations assay. QD size was also observed to affect subcellular distribution, with smaller cationic QDs localizing to the nuclear compartment and larger cationic QDs localizing to the cytosol. The mechanisms involved in cell death were not known but were considered to be due to the presence of free Cd (QD core degradation), free radical formation, or interaction of QDs with intracellular components leading to loss of function. To our knowledge, there are no large scale MD simulations on QDs interacting with biomacromolecules (proteins or cell membranes), and current simulations are focused on the electronic properties of QDs with or without organic coating (for biocompatibility). Recent theoretical studies show that the passivating ligands can form stable coordinations on QD surface, sustaining the chemical structure of the QD. Another interesting study by Azpiroz et al.68 with density functional theory (DFT) also shows that organic passivation on CdSe QDs undergoes a weakly coupled electrostatic interaction formation rather than a weak van der Waals interaction between molecules. Very recently, Zhou and coworkers (in preparation) also used DFT to investigate the electronic structures of QD (CdSe)13 passivated by OPMe2 (CH2 )n Me ligands with different lengths and various numbers of branches (Me = methyl group, n = 0, 1–3). They found that the absorption peak in the ultraviolet–visible (UV–vis) spectra displays a clear blue-shift, on the scale of ∼100 nm, upon the binding of ligands. Once the total number of ligands bound with (CdSe)13 reached a saturated number (9 or 10), no more blue-shift occurred in the absorption peak in the UV–vis spectra. On the other hand, the
aliphatic chain length of ligands has a negligible effect on the optical properties of the QD core. Analyzes of the bonding characteristics confirm that optical transitions are dominantly governed by the central QD core rather than the organic passivation. Interestingly, the density of states (DOS) share similar characteristics as vibrational spectra, even though there is no coordination vibration mode between the ligands and the central QD. These findings provide insights to the design of safer organic passivation of QDs for biomedical applications. Overall, these recent studies show that of the vast physicochemical characteristics, functional coating and QD core stability might be the most significant factors in assessing the risk of QD toxicity in real-world exposure scenarios.
NANOMEDICINE: IMPLICATIONS FROM NANOTOXICITY Properly tamed, however, the interesting and varied properties of NMs will allow for the development of new, highly effective, specifically tailored, diagnostic and therapeutic techniques. In this section, we review some very recent examples of de novo nanomedicine design with implications learned particularly from nanotoxicity. Fullerenes, rigid carbon-cages with nanometric dimensions, allow for convenient surface functionalizations, modifying their biomedical function. There are many recent attempts for their use in both diagnostics and therapeutics.69 One interesting example, based on implications from the ‘nanotoxicity’ concept, is the biomedical application of endohedral metallofullerene derivatives in high-efficacy cancer diagnostics and therapeutics. Due to its high proton relaxivity, Gadolinium ion (Gd3+ ), chelated by organic molecules, can be employed as biologically safe, high contrasting agent for magnetic resonance imaging (MRI). By using mouse models of liver cancer, Chen et al.70 investigated the antitumoral efficacy of multihidroxylated metallofullerenol Gd@C82 (OH)22 NPs and compared it to widely used antineoplastic agents. A histological examination implies that Gd@C82 (OH)22 has no direct toxic effect on tumor cells, where only marginal amount of the exposed dose has been detected in tumor tissues. Rather, Gd@C82 (OH)22 was found to indirectly suppress the cancer growth through inhibiting metastatic pathways, or stimulating immune response. For example, a mouse model, xenografted with human pancreatic cancer cells, showed a remarkably restriction of cancer volume growth with Gd@C82 (OH)22 treatment, as accompanied with significant reduction of microvessel density (MVD).71 More
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specifically, matrix metalloproteinase-2/-9 (MMP-2/ -9) were found to be effectively reduced in both expression and activity levels with more dramatic changes in MMP-9. MMPs are well known enzymes for cancer prognosis and metastasis, as involved in angiogenesis and extracelluar matrix (ECM) degradation, thus attracting as potential target for antimetastatic agents. For molecular level understanding, atomistic molecular dynamic simulations have been performed for Gd@C82 (OH)22 and MMP-9. Compared to pure carbon materials, Gd@C82 (OH)22 rarely affects protein’s overall structure even with frequent contacts with Gd@C82 (OH)22 , where MMP-9 remained in native fold for the simulation time duration (up to 500-ns). Interestingly, Gd@C82 (OH)22 seemed to be easily clustered from the early stage of simulation, consistent with earlier experimental findings by small-angle X-ray scattering (SAXS) and AFM. This implicates much sophisticated interaction between Gd@C82 (OH)22 and a target protein. Differently from pristine carbonaceous nanomaterials, Gd@C82 (OH)22 can use hydrogen bonds via surface hydroxyl groups, as well as electrostatic interaction via induced charge by Gd3+ , while with to some extent of hydrophobicity. All together resulted in a specific interaction at the ligand specificity S1’ loop of MMP-9. Simulation also revealed that the direct blocking on the zinc-coordinated active site is less likely, as rationalized by repulsive electrostatics around the ligand binding site. The tight binding at the S1’ loop blocks MMP-9’s function as the ligand mediator, leading to its inhibition, thus nanotoxic to MMP-9. Free-energy landscapes and contact modes are shown in Figure 6. Both the suppression of MMPs expression and specific binding mode make Gd@C82 (OH)22 a potentially more effective nanodrug for cancers, such as breast cancer and pancreatic cancer, than the traditional drugs which usually target the proteolytic sites directly without selective inhibition. The current findings with a new exocite binding mode at the ligand specificity S1’ loop provide new insights and directions for future de novo design of nanomedicine for fatal diseases such as pancreatic cancer. Direct penetration and endocytosis of graphitic NMs may also affect integrity of cell membranes.19,72 Graphene nanosheets display strong cytotoxicity against both Gram-negative and Gram-positive bacterial cells. Nevertheless, the biological response of these materials is attenuated when surrounded by proteins as discussed above, making them less harmful to mammalians. Therefore, carefully used, this cytotoxic behavior can once again potentially be directed to produce novel antibiotics.
The translocation of fullerene clusters through lipid bilayers has been investigated by Wong-Ekkabut et al.19 by using a coarse-grained model, resulting in a favorable permeation of the lipid bilayer by the fullerene. Similar results have been observed for nanoflakes,73 nanotubes,74–76 and other derivatives.77,78 Aiming to advance the current understanding of the effects of graphene and graphene-oxide nanosheets in the integrity of bacterial cell membranes, Zhou and coworkers79 reported combined experimental and theoretical studies of Escherichia coli cell membranes in the presence of the aforementioned NMs. In the time-resolved TEM images, roughly three stages of cell morphology were identified during the incubation, dependent on the NM concentration. From the initial morphology (stage I), a reduction in the phospholipid density is observed without the visible presence of any cut (stage II). Finally, stage III is characterized by the total loss of cellular integrity, with possible leaking of the cytoplasm. Unbiased, atomistic simulations of membrane-NM systems in explicit solvent were carried out for both graphene and graphene-oxide, using E. coli outer and inner membranes. Serious insertion and disruption of the cell membranes were observed during the simulations. Two of the interaction modes were identified which resulted in the partial loss of integrity of the cell membrane, but differed in the molecular mechanism. The Type A mechanism, best described as a cut, supports the experimental results discussed before. The other type of insertion, Type B, displays a destructive extraction of phospholipid molecules from the membrane by the graphene. This mode of membrane disruption has not been observed before, and explains the reduction in phospholipid density obtained in the TEM images. A detailed characterization of these membrane-graphene systems showed that the lipid extraction by the graphene nanosheets is robust. The process is dominated by an exceptionally strong graphene-lipid dispersion interaction, which shadows the self-attraction between phospholipid molecules. The same mode was successfully identified with both graphene and graphene-oxides in each type of membrane (see Figure 7).
CONCLUDING REMARKS AND FUTURE PERSPECTIVES The synthesis, development and characterization of nanostructures have revolutionized modern life. Current advances in nanotechnology bring new and exciting possibilities to consumer fields such as clothing, cosmetics or electronics. More importantly, the medical application of NMs and NPs for preventive,
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Molecular simulations of nanotoxicity
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FIGURE 6 | Binding free-energy landscapes and residue-specific contacts on MMP-9, as well as representative binding modes and pathway of Gd@C82 (OH)22 on MMP-9. (a) Binding free-energy surface for fullerenol C82 (OH)22 on MMP-9 shows a nonspecific binding mode (left), and almost all surface residues of MMP-9 contribute to contact with C82 (OH)22 (right). (b) Metallofullerenol Gd@C82 (OH)22 interacts with MMP-9 along a specified binding mode (left) and contacts with only a specific set of residues near the ligand-specificity S1′ loop and SC loop (right). A residue was assigned to be in a contact when any atom in the residue was within 5.0 Å of any atom of Gd@C82 (OH)22 [or C82 (OH)22 ]. The site participation is presented by the total number of frames of each residue in contact normalized by all frames and trajectories. (c) Representative binding mode (a solid ball) showing that Gd@C82 (OH)22 binds between the S1′ ligand-specificity loop (green ribbon) and the SC loop (purple ribbon), leading to the ligand binding groove. An alternative mode with a gray ball is shown that Gd@C82 (OH)22 can bind at the back entrance of the S1′ cavity leading into the active site (ball and stick for active sites and orange ball for the catalytic Zn2+) (left). Possible binding pathway: depending on major driving forces and duration time (only the first 100 ns is shown), the binding dynamics is characterized with three different phases. Phase I: a diffusion-controlled nonspecific electrostatic interaction; phase II: a transient nonspecific hydrophobic interaction; and phase III: a specific hydrophobic and hydrogen-bonded stable binding (right). (Reprinted with permission from Ref 71. Copyright 2012 Proceedings of the National Academy of Sciences USA)
diagnostic and therapeutic purposes has the potential to allow for more effective, less invasive, personalized treatments. As nanotechnology grows as a field, its consequences in the health of people and environment require careful assessment. To this
end, concerted experimental and theoretical efforts to attain an improved understanding of the interactions of NMs with living organisms are required. Of growing interest are large-scale molecular simulations of biomolecules/NP interactions, which allow for a
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FIGURE 7 | Graphene nanosheet insertion and lipid extraction. (a,b) Representative simulated trajectories of graphene nanosheet insertion and lipid extraction in the outer membrane (pure POPE) and inner membrane (3:1 mixed POPE–POPG) of E. coli (the snapshot times are shown in the top left corners).Water is shown in violet and the phospholipids in tan lines with hydrophilic charged atoms as colored spheres (hydrogen, white; oxygen, red; nitrogen, dark blue; carbon, cyan; phosphorus, orange). The graphene sheet is shown as a yellow-bonded sheet with a large sphere marked at one corner as the restrained atom in simulations. Extracted phospholipids are shown as larger spheres. (Reprinted with permission from Ref 79. Copyright 2012 Nature Nanotechnology)
detailed atomistic understanding of the often seemingly contradictory findings from the complex in-vitro and in-vivo experiments. Here, we provide a discussion of recent results on computer simulations regarding nanotoxicity by exploring the effects on biomolecules upon exposure to different NPs. While hydrophobicity plays a driving role in the interactions of proteins with carbon-based NMs, long-range electrostatic attraction/repulsion dominates the interaction with noble metals. Dependence of the effect on thermodynamic
and kinetic properties on the sequence and native structure of the protein, and on the curvature, rigidity, residual charge and functionalization of the NMs are further described. Computational results modeling NMs interacting with one or several protein domains are reviewed, with emphasis on the dynamic nature of the protein binding. Characterizations of the driving forces of protein binding to NPs are fundamental to the understanding of the so-called ‘corona’, which in turn modulates the biocompatibility and fate of the NP. Cytotoxicity studies of NMs can also
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Molecular simulations of nanotoxicity
be used for de novo design in nanomedicine applications. In this review, the use of endohedral metallofullerenol Gd@C82 (OH)22 as an antitumor agent, and graphene/graphene-oxide as an effective antibacterial are taken as examples of such applications. In both cases, MD simulations allowed for a detailed description of the binding mechanisms by which the selective targeting takes place. Computational
modeling presents itself as a powerful tool to obtain detailed descriptions of the inter-molecular interactions at an atomistic level. With increasing computational power and continuous theoretical efforts to improve and extend the accuracy of current energy functions, the future of computer-aided design of de novo nanomedicine applications is a promising area of research.
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