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Large scale molecular simulations of nanotoxicity

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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 [email protected](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. WIREs Syst Biol Med 2014, 6:265–279. doi: 10.1002/wsbm.1271 This article is categorized under: Biological Mechanisms > Chemical Biology Analytical and Computational Methods > Computational Methods
Interactions between BFG, Ig, Tf, BSA, and SWCNTs. AFM images of proteins after incubation with SWCNTs for 10 min (a) and 5 h (b). Molecular modeling illustrations for proteins (in beads representation) binding to SWCNTs after incubation for 10 min (c) and 5 h (d). (e) Locations of the most preferred binding sites on proteins for SWCNTs. Residues highlighted in van der Waals representation corresponding to tyrosine colored in red and phenylalanine colored in green. Other parts of protein are presented in transparent pink with new cartoon drawing method. (f) The detailed orientations of aromatic rings of tyrosine and phenylalanine residues interacted to six‐member rings of SWCNTs colored in silver. The tyrosine residues are rendered as licorice representation and colored in red, and phenylalanine residues are in green. (g) The far‐UV CD spectra of proteins after incubation with SWCNTs and the insets are near‐UV CD spectra of proteins incubated with SWCNTs. (Reprinted with permission from Ref . Copyright 2011 Proceedings of the National Academy of Sciences USA)
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Characterization of a representative trajectory of HP35 adsorption onto the graphene sheet. (a) Snapshots of the trajectory at various times. Aromatic residues are shown in blue. (b) Geometrical descriptors of the HP35 adsorption. Top: contact surface area, middle: root mean square deviation to the HP35 native structure, bottom: distance between the graphene and the aromatic residues. (Reprinted with permission from Ref . Copyright 2011 American Chemical Society)
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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 . Copyright 2012 Nature Nanotechnology)
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Binding free‐energy landscapes and residue‐specific contacts on MMP‐9, as well as representative binding modes and pathway of [email protected]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 [email protected]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 [email protected]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 [email protected]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 [email protected]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 . Copyright 2012 Proceedings of the National Academy of Sciences USA)
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Ubiquitin‐AgNP corona formation. (a) The number of ubiquitin molecules bound to AgNP, <Nbound>, 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, <Nbound>, 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 <Nbound> 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 )
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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 . Copyright 2013 American Chemical Society)
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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).
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