pubs.acs.org/Langmuir © 2010 American Chemical Society
Biotemplating of Metallic Nanoparticle Arrays Through Site-Specific Electrostatic Adsorption on Streptavidin Crystals Matthew M. Shindel, Daniel R. Mumm,* and Szu-Wen Wang* Department of Chemical Engineering and Material Science, University of California, Irvine, California 92697-2575 Received February 19, 2010. Revised Manuscript Received April 13, 2010 The protein streptavidin exhibits unique properties advantageous for “bottom-up” nanofabrication applications. It self-assembles into various 2-D crystalline lattices onto which nanoparticles can be attached through both electrostatic and ligand-receptor mechanisms. We examine the electrostatic adsorption of gold nanoparticles onto nonclose-packed streptavidin crystals and show that site-specific attachment preferentially occurs in between protein molecules. The resulting nanoparticle arrangement consequently displays a long-range structural coherence with the underlying protein lattice, although with a reduced degree of order relative to that of the biological template. Monte Carlo simulations reveal that this remittent ordering is due to (1) the random offset between the nanoparticles and their respective adsorption sites and (2) nonspecific binding to the surface of the protein molecules. Overall, our results indicate that streptavidin crystals are capable of templating ordered nanoparticle arrays.
Introduction The ability to fabricate architectures with designer functionalities at increasingly minute length scales is of chief concern in the fields of nanotechnology and nanoscience. This enterprise requires assembly processes with the capacity to specifically tailor both the spatial arrangement and material composition of a system’s constituent building blocks. Although such endeavors may appear daunting, nature has already mastered the art of nanofabrication. The biological realm teems with intricate nanoscale “devices” that exhibit a high degree of functionality for applications such as information storage (e.g., double-helical DNA1), energy conversion (e.g., photosynthetic protein complexes2), and microenvironment regulation (e.g., aquaporins3 and lipid membranes1). Furthermore, these biomaterial systems are constructed en masse, with relatively little energy input via self-assembly.4 Recent work has demonstrated that self-assembling biological materials can be employed as fabrication platforms for inorganic nanostructures.5-11 Two-dimensional protein crystals are particularly suited to function as templates for ordered nanoparticle arrays. The predefined organization and orientation of *Corresponding authors. E-mail:
[email protected] (D.R.M.), wangsw@ uci.edu (S.-W.W.). Phone: 949-824-3858 (D.R.M.), 949-824-2383 (S.-W.W.). Fax: 949-824-2541. (1) Alberts, B.; Johnson, A.; Lewis, J.; Raff, M.; Roberts, K.; Walters, P. Molecular Biology of the Cell, 4th ed.; Garland Science: New York, 2002. (2) McDermott, G.; Prince, S. M.; Freer, A. A.; Hawthornthwaite-Lawless, A. M.; Papiz, M. Z.; Cogdell, R. J.; Isaacs, N. W. Nature 1995, 374, 517–521. (3) Agre, P.; Bonhivers, M.; Borgnia, M. J. J. Biol. Chem. 1998, 273, 14659– 14662. (4) Vincent, J. F. V. Mater. Today 2002, 5, 28–41. (5) Allred, D. B.; Sarikaya, M.; Baneyx, F.; Schwartz, D. T. Nano Lett. 2005, 5, 609–613. (6) Behrens, S. S. J. Mater. Chem. 2008, 18, 3788–3798. (7) Gazit, E. FEBS J. 2007, 274, 317–322. (8) Lagziel-Simis, S.; Cohen-Hadar, N.; Moscovich-Dagan, H.; Wine, Y.; Freeman, A. Curr. Opin. Biotechnol. 2006, 17, 569–573. (9) Pum, D.; Neubauer, A.; Gyorvary, E.; Sara, M.; Sleytr, U. B. Nanotechnology 2000, 11, 100–107. (10) Seeman, N. C.; Belcher, A. M. Proc. Natl. Acad. Sci. U.S.A. 2002, 99, 6451– 6455. (11) Sotiropoulou, S.; Sierra-Sastre, Y.; Mark, S. S.; Batt, C. A. Chem. Mater. 2008, 20, 821–834.
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the protein subunits can give rise to a pattern of discretely located adsorption sites with geometrical arrangement and long-range translational order that mimic the underlying crystal. Twodimensional crystals of various S-layer and chaperonin proteins, formed at solid-liquid interfaces, have previously been used to assemble ordered arrays of metallic and semiconducting nanoparticles through electrostatic and chemical adsorption.12-15 However, previous studies with these protein crystal systems have not explored the possibility of sequentially exploiting different adsorption modes to generate combinatorial arrays that have distinct nanoparticle species adsorbed at precise locations on the template. Such work may unveil new avenues for the bottom-up fabrication of designer nanomaterials with novel functionalities depending on the combination and sizes of the nanoparticles used, the overall geometry of the 2-D particle ensemble, and the distance between the individual nanoscale constituents. The protein streptavidin is a promising candidate for this type of application because it possesses multiple nanoparticle binding modalities.16 Streptavidin’s unique structural and physicochemical properties enable it to serve as a functionally versatile material in the fields of both biotechnology and nanotechnology. The macromolecule is composed of four identical subunits, each of which contains a binding site for the vitamin biotin (KA ∼ 1015 M-1).17-19 The subunits are situated so that pairs of binding sites are displayed on opposite molecular faces. The protein’s symmetric quaternary structure and its affinity for biotinylated ligands have already been (12) Bergkvist, M.; Mark, S. S.; Yang, X.; Angert, E. R.; Batt, C. A. J. Phys. Chem. B 2004, 108, 8241–8248. (13) Hall, S. R.; Shenton, W.; Engelhardt, H.; Mann, S. ChemPhysChem 2001, 2, 184–186. (14) Mark, S. S.; Bergkvist, M.; Yang, X.; Teixeira, L. M.; Bhatnagar, P.; Angert, E. R.; Batt, C. A. Langmuir 2006, 22, 3763–3774. (15) McMillan, R. A.; Paavola, C. D.; Howard, J.; Chan, S. L.; Zaluzec, N. J.; Trent, J. D. Nat. Mater. 2002, 1, 247–252. (16) Shindel, M. M.; Mohraz, A.; Mumm, D. R.; Wang, S. Langmuir 2009, 25, 1038–1046. (17) Bayer, E. A.; Ben-Hur, H.; Wilchek, M. Methods Enzymol. 1990, 184, 80–89. (18) Chaiet, L.; Wolf, F. J. Arch. Biochem. Biophys. 1964, 106, 1–5. (19) Green, M. N. Methods Enzymol. 1990, 184, 51–67.
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exploited in biosensing, bioseparations, and clinical diagnostic applications.20-23 The technological functionality of streptavidin can potentially be extended to nanofabrication processes, similar to those of S-layer and chaperonin proteins, through its ability to selfassemble into 2-D crystals on biotinylated lipid films at both the air-water and solid-liquid interfaces.24-26 In contrast to the other aformentioned protein crystal systems, streptavidin crystallization has a polymorphic nature that has been well documented; a variety of crystalline lattices with varying geometries and degrees of symmetry can be generated using this single biomolecule.27 The space group of the protein lattice and its corresponding microscopic domain morphology can be controlled by manipulating parameters such as the pH, ionic strength, interfacial biotin ligand concentration, and particular amino acid residues present at interprotein contact sites within the resulting crystal.27-33 This hierarchical polymorphism may enable streptavidin crystals to act as a modular nanofabrication tool, capable of templating ordered nanoparticle arrays with a variety of distinct geometries and interparticle spacings. Additionally, the non-close-packed structure and degree of symmetry of certain streptavidin crystal lattices (including the one detailed here) may be amenable to templating applications with anisotropic particles such as quantum rods, whose diameters span only several nanometers but whose axial dimensions can extend up to hundreds of nanometers.34,35 It should also be noted that at the air-water interface, streptavidin crystal domains have characteristic dimensions that range from tens to hundreds of micrometers and crystals grown directly on solid substrates can achieve confluency over macroscopic length scales.25,27 To the best of our knowledge, the typical domain sizes in protein systems previously used in templating applications are on the order of several micrometers or smaller.12,15 The ability to template microscopic or even macroscopic nanoparticle arrays could prove useful for device fabrication. Streptavidin will also crystallize at nonplanar interfaces such as on the surfaces of lipid vesicles and tubules.36-38 This particular facet of the streptavidin crystal system may permit the construction of quasi-3-D inorganic nanostructures. Finally, the multiple adsorption modes inherent in the streptavidin crystal system is unique compared to most other 2-D crystallizable protein systems; in addition to electrostatics, a ligand-receptor interaction (20) Chilkoti, A.; Schwartz, B. L.; Smith, R. D.; Long, C. J.; Stayton, P. S. Nat. Biotechnol. 1995, 13, 1198–1204. (21) Schetters, H. Biomol. Eng. 1999, 16, 73–78. (22) Schmidt, T. G.; Skerra, A. J. Chromatogr., A 1994, 676, 337–345. (23) Skerra, A.; Schmidt, T. G. M. Biomol. Eng. 1999, 16, 79–86. (24) Blankenburg, R.; Meller, P.; Ringsdorf, H.; Salesse, C. Biochemistry 1989, 28, 8214–8221. (25) Calvert, T. L.; Leckband, D. Langmuir 1997, 13, 6737–6745. (26) Darst, S.; Ahlers, M.; Meller, P.; Kubalek, E.; Blankenburg, R.; Ribi, H.; Ringsdorf, H.; Kornberg, R. Biophys. J. 1991, 59, 387–396. (27) Wang, S.; Robertson, C. R.; Gast, A. P. Langmuir 1999, 15, 1541–1548. (28) Edwards, T. C.; Koppenol, S.; Frey, W.; Schief, W. R; Vogel, V.; Stenkamp, R. E.; Stayton, P. S. Langmuir 1998, 14, 4683–4687. (29) Lou, C.; Wang, Z.; Wang, S. Langmuir 2007, 23, 9752–9759. (30) Lou, C.; Shindel, M.; Graham, L.; Wang, S. Langmuir 2008, 24, 8111–8118. (31) Ratanabanangkoon, P.; Gast, A. P. Langmuir 2002, 19, 1794–1801. (32) Wang, S.; Robertson, C. R.; Gast, A. P. J. Phys. Chem. B 1999, 103, 7751–7761. (33) Yatcilla, M. T.; Robertson, C. R.; Gast, A. P. Langmuir 1998, 14, 497–503. (34) Manna, L.; Scher, E. C.; Alivisatos, A. P. J. Am. Chem. Soc. 2000, 122, 12700–12706. (35) Peng, X.; Manna, L.; Yang, W.; Wickham, J.; Scher, E.; Kadavanich, A.; Alivisatos, A. P. Nature 2000, 404, 59–61. (36) Dang, T. X.; Farah, S. J.; Gast, A.; Robertson, C.; Carragher, B.; Egelman, E.; Wilson-Kubalek, E. M. J. Struct. Biol. 2005, 150, 90–99. (37) Ratanabanangkoon, P.; Gropper, M.; Merkel, R.; Sackmann, E.; Gast, A. P. Langmuir 2002, 18, 4270–4276. (38) Wilson-Kubalek, E. M.; Brown, R. E.; Celia, H.; Milligan, R. A. Proc. Natl. Acad. Sci. U.S.A. 1998, 95, 8040–8045.
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(biotin-binding) can be exploited to induce nanoparticle adsorption.16 The thiol functional group of cysteine residues has previously been used to covalently bind certain types of nanoparticles to crystalline chaperonin films, resulting in the formation of ordered nanoparticle arrays.15 However, this adsorption pathway is applicable only to materials capable of forming covalent bonds with sulfur atoms. In contrast, biotinylated linkers can be synthesized (often from commercial precursors) with customizable end-groups, lengths, and rigidity. This may allow streptavidin monolayers to bind a broad range of nanomaterials as well as tune the distance between the adsorbed particles and the protein template. Exploiting both the physical (electrostatic) and receptor-ligand adsorption modes on streptavidin crystals may enable the templating of ordered, multicomponent nanoparticle arrays that would have application in fields such as nanoelectronics, sensing, and catalysis.11,39 All of these attributes suggest that the streptavidin crystal system possesses a unique utility for nanoscale “directed-assembly” applications compared to other 2-D crystallizable proteins. We previously demonstrated that metallic nanoparticles can adsorb on streptavidin crystals through two independent mechanisms: (1) electrostatic and (2) biotin-binding. We also showed that particle coverage could be tuned, both during and after deposition, by altering parameters such as pH and particle surface chemistry.16 However, when we used particles that were large compared to the lattice dimensions of the protein crystal, particle ordering was not observed. In the investigation described here, we probe the electrostatic adsorption of particles whose nominal diameter is comparable to the size of the streptavidin lattice.
Materials and Methods Protein Crystallization. Streptavidin crystals were generated in accordance with previously established methods described in the literature.27 Briefly, miniature Langmuir troughs (∼1.5 mL) composed of Delrin were filled with an aqueous subphase of 500 mM NaCl and 5 mM phosphate buffer at a pH of 8.5. A lipid solution (1.5 μL, 0.25 mg/mL) containing 5 mol % 1,2-dipalmitoyl-sn-glycero-3-phosphoethanolamine-N-(cap biotinyl) (biotinX DHPE, Molecular Probes) and 95 mol % 1,2-ditridecanoyl-snglycero-3-phosphocholine (DTPC, Avanti) dissolved in a 10/90% (v/v) methanol/chloroform mixture was then spread on the surface of the subphase. After the solvent was allowed to evaporate for 15 min, an equimolar solution of avidin (Sigma) labeled with fluoresceinisothiocyanate (FITC, Sigma), and unlabeled streptavidin (Prozyme) was injected beneath the lipid film to a final concentration of 70-80 nM. The fluorescently labeled avidin was used to provide contrast for the crystalline streptavidin domains. Crystallization was allowed to proceed for 30 min before examining the resulting protein film with an epifluorescence microscope (Olympus). Streptavidin is known to self-assemble into square C222 crystals at the pH used here.33 Nanoparticle Adsorption. Streptavidin crystals grown at the air-water interface were transferred to carbon-coated transmission electron microscopy (TEM) grids (Ted Pella) via the LangmuirSch€afer technique. Samples were then briefly rinsed with water, and the excess solution was wicked away with filter paper. A 5 μL droplet of a 5-nm citrate-stabilized colloidal gold solution (Ted Pella), buffered with sodium phosphate (1 mM) at pH 7, was then placed on the grid (Figure 1) for 5 min. The final particle concentration was ∼70 nM. Samples were then rinsed with water and stained with uranyl acetate (1% w/v) for 2 min. The temperature was held constant at 25 C throughout the entire procedure. This experiment was repeated a total of three times. (39) Shipway, A. N.; Katz, E.; Willner, I. ChemPhysChem 2000, 1, 18–52.
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Article where
Figure 1. Illustration depicting the experimental protocol for adsorbing gold nanoparticles on 2-D streptavidin crystals. (1) Crystals grown at the air-water interface are transferred to a carbon-coated TEM grid by the Langmuir-Sch€ afer technique. (2) After the sample is briefly washed with water, a droplet of the 5 nm nanoparticle sol (pH 7) is then placed on the adsorbed crystal domains for 5 min.
Nanoparticle Zeta Potential. The zeta potential of the gold nanoparticles was measured by electrophoretic light scattering on a Zetasizer Nano ZS (Malvern) under solution conditions identical to those of the particle adsorption experiments (25 C, 1 mM sodium phosphate buffer, pH 7). The resulting measured value, -72.2 ( 2.0 mV, was used to approximate the nanoparticle electrostatic surface potential in theoretical calculations and simulation runs. Transmission Electron Microscopy (TEM). Samples were imaged with a Phillips CM-20 transmission electron microscope at a working voltage of 80 kV. Images were obtained over a magnification range of 20 000-38 000. Monte Carlo Simulation and Effective Diameter Calculation. Monte Carlo simulations were employed to model the adsorption of gold nanoparticles on the streptavidin crystal. To compare with our experimental results, we designed separate simulation algorithms to emulate either site selectivity in the adsorption process or random sequential adsorption40 (RSA). All code was written in the MATLAB language. Site-Specific Adsorption. To simulate site-specific adsorption, we generated a registry containing the x-y coordinates of adsorption-site centers. On the basis of our experimental results, we assumed that adsorption takes place within the negative space of the crystal lattice. These points on the simulated substrate therefore act as virtual analogs to the centers of the negative spaces in the C222 streptavidin crystal and were arranged with a commensurate pitch and geometry (5.8 nm 5.8 nm, square lattice).26 The substrate was then divided into a series of square cells, each of which was centered on a point listed in the registry. The dimension of these cells was equivalent to their center-tocenter spacing (5.8 nm). The spatial dependency of adsorption observed in the real system was incorporated into the simulation by superimposing an adsorption probability map onto the substrate. This map was generated by modeling the probability of adsorption for an isolated particle (one adsorbing in a local area devoid of other adsorbed particles) at a given point within a cell as either a continuous/normal or discrete/uniform distribution (Figure 2a,b). In the continuous case, we assume that the probability of adsorption has a value of unity at the center of each cell and decays in a radially symmetric manner. Therefore, we model the adsorption probability within each cell as a Gaussian distribution centered at the cell’s midpoint using the following equations: Pðx, yÞ ¼ exp -
½ðx - xi Þ2 þ ðy - yi Þ2 =a2 σ2
(40) Feder, J. J. Theor. Biol. 1980, 87, 237–254.
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! ð1Þ
a a a a xi - , y i e ðx, yÞ e xi þ , yi þ 2 2 2 2
ð2Þ
Here, xi and yi are the coordinates of the cell’s center, a is the distance between adjacent cell centers (5.8 nm), and σ is the standard deviation of the distribution, which dictates the spread of the adsorption probability throughout each cell. The standard deviation was treated as an adjustable parameter enabling us to tune the nominal particle offset systematically for comparison with experimental data. Smaller values of σ increase the likelihood that a particle will adsorb at or near the center of a given cell. As σ is increased, the relative probability of adsorption at any point within a cell becomes more uniform, approaching a completely random adsorption profile (P(x, y) = 1 for all points (x, y) in the cell) as σ f ¥. We generated arrays with this type of probability map using σ = 0.05, 0.1, 0.2, 0.3, and 0.6 to compare with experimental data. Simulations where adsorption was discretely confined to a specific area around the center of each cell were also performed to examine the case in which particle adsorption is absolutely restricted to the streptavidin crystal negative space. Prior examinations of the C222 streptavidin crystal have revealed that the areas in between the protein molecules assume a geometry that closely resembles an ellipse (Figure 2c).26,41 For a unit cell containing one protein molecule, the major and minor axes of each ellipse form a 45 angle with the two crystal lattice vectors. Using the dimensions of the streptavidin tetramer reported by Weber et al.42 and the C222 crystal,27 we estimate the lengths of these axes to be 4.0 and 2.6 nm, respectively. Thus, for these simulations, the adsorption probability map was constructed by generating an elliptical adsorption site around the center of each cell at 45 relative to the horizontal. All points lying on or within the boundary of the ellipse were given an adsorption probability of unity (P(x, y) = 1), and all other points in the cell were given a value of zero. Creating the probability map in this way strictly relegates adsorption to the crystal’s negative space but allows for a uniform probability of particle placement anywhere within that space. The likelihood of a charged particle adsorbing at a certain location on a substrate is affected by its attraction to the substrate as well as the amount of electrostatic repulsion it experiences from previously adsorbed particles. The latter can be accurately calculated as long as the electrostatic surface potential or surface charge density of the particles is known. In contrast, it is considerably more difficult to take the particle-substrate interaction into account in our experimental system. The surface potential of the protein and protein crystal are highly nonuniform. This precludes our ability to include the particle-substrate interaction directly in our simulation. However, one method of indirectly accounting for this interaction is to rescale the bulk interparticle electrostatic pair potential (wij) by a constant factor to an effective value that quantitatively describes the interparticle interaction in the presence of the interface:43,44 1 weff ij wij λ
ð3Þ
Semmler and co-workers found that λ = 2.8 accurately accounted for the particle-substrate interaction in their experimental system (charged latex colloids adsorbing on mica).45 As a first approximation, we used this value of λ for our simulations. We (41) Frey, W.; Brink, J.; Schief, W. R., Jr; Chiu, W.; Vogel, V. Biophys. J. 1998, 74, 2674–2679. (42) Weber, P.; Ohlendorf, D.; Wendoloski, J.; Salemme, F. Science 1989, 243, 85–88. (43) Adamczyk, Z.; Zembala, M.; Siwek, B.; Warszynski, P. J. Colloid Interface Sci. 1990, 140, 123–137. (44) Weronski, P. Colloids Surf., A 2007, 294, 254–266. (45) Semmler, M.; Mann, E. K.; Ricka, J.; Borkovec, M. Langmuir 1998, 14, 5127–5132.
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Figure 2. Adsorption probability maps used in site-specific Monte Carlo simulations. (a) Continuous/normal probability map with σ = 0.3 and (b) discrete/uniform probability map. The gray intensity scale corresponds to the adsorption probability and is applicable to both maps in a and b. (c) Projection map of the C222 streptavidin crystal from ref 26 showing the elliptical geometry of the crystal’s negative space (reprinted with permission from Elsevier, copyright 1991).
also assumed that the total potential experienced by an adsorbing particle could be found from a pairwise summation of its interactions with all other particles already bound to the surface43,46,47 wi, net ¼
1X wij ðrij Þ λ j6¼i
ð4Þ
where wij is the electrostatic repulsion between the adsorbing particle i and the jth particle on the substrate and rij is the distance separating the particle pair. This repulsion can be thought of as an activation barrier to adsorption. The probability that an incoming particle surmounts this energy threshold is given by a Boltzmann factor (eq 5), the value of which depends on the net potential from eq 4 and the particle’s intrinsic thermal energy kT (where k is the Boltzmann constant and T is the absolute temperature). φðwi, net Þ ¼ exp
- wi, net kT
ð5Þ
Because the gold nanoparticles were deposited on streptavidin crystals in the presence of nonsymmetric electrolytes and because the dimensionless product of the Debye parameter (κ = 1.2 108 m-1) and the nominal particle radius in our experimental system was less than 5, we used Oshima’s linear superposition approximation to calculate the electrostatic pair potentials.48 The particle electrostatic surface potential was assumed to be constant and approximated by the zeta potential measured for the gold nanoparticles. After the adsorption probability map was generated, the simulation algorithm proceeded by sequentially placing particles on the substrate by first selecting an x-y location at random and then checking for physical overlap with surrounding particles. If the placement of a particle at that location resulted in overlap, then the site was immediately rejected and a new one was chosen. If that particular location was free from overlap, then a test particle was placed at the site and the effective total interparticle potential was then tabulated. This calculation included only adsorbed particles whose distance from the test particle was less than or equal to the effective hard-sphere particle diameter that was determined by numerically solving the equation wðrÞ ¼ λkT
ð6Þ
where w(r) is Oshima’s expression for the interparticle electrostatic potential as a function of distance.45,48 Using eq 6, the
(46) Gray, J. J.; Bonnecaze, R. T. J. Chem. Phys. 2001, 114, 1366–1381. (47) Oberholzer, M. R.; Stankovich, J. M.; Carnie, S. L.; Chan, D. Y. C.; Lenhoff, A. M. J. Colloid Interface Sci. 1997, 194, 138–153. (48) Ohshima, H.; Healy, T. W.; White, L. R. J. Colloid Interface Sci. 1982, 90, 17–26.
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effective diameter for the 5 nm gold particles in a 1 mM phosphate buffer at pH 7 was found to be 10.5 nm. Considering both the adsorption probability (defined by the particular point chosen on the substrate and the type of probability map employed) and the interparticle interaction, the likelihood of the test particle remaining at the chosen point (x, y) was then given by the product of the adsorption probability at that location (P) and the Boltzmann factor (j) given in eq 5. Fðx, yÞ ¼ Pðx, yÞφðwi, net Þ
ð7Þ
Particle adsorption was treated as an irreversible process. To mitigate edge effects, particle placement was subject to periodic boundary conditions.40 The diameter of the simulated particles was normally distributed around a nominal value of 5 nm with a standard deviation of 0.3 nm. These values were determined from TEM images of the gold nanoparticles. A given simulation run proceeded until a surface coverage of 6%, commensurate with the experimentally observed coverage, was reached. The nominal offset between particles and the adsorption-site centers was calculated for each site-specific simulation run by averaging the distances between each particle placed on the substrate and the nearest cell center. Images of simulated arrays were used in subsequent pair-correlation function analysis. Random Sequential Adsorption. Monte Carlo simulation was also employed to generate arrays on unpatterned surfaces. This algorithm was the same as in the site-specific case, except that P(x, y) is fixed at a value of unity for all points on the substrate. Each of these simulation runs was carried out until the particle surface coverage reached 10%, a value consistent with the nanoparticle coverage observed experimentally on noncrystalline regions of the protein monolayer. Image Analysis. Particle surface coverage and line profiles of TEM images were obtained using the ImageJ (Scion) software package. Particle coverages are reported in terms of nominal area fraction with uncertainty values corresponding to the standard deviation of the measurement. ImageJ was also used to generate fast Fourier transforms (FFT) for both TEM images and binary particle masks. Particle masks were generated from TEM images by thresholding a range of grayscale intensities in the image and then screening for particles on the basis of size and circularity. All pixels associated with features identified as particles are reassigned a grayscale intensity of 0 (black), and all other pixels are given a value of 255 (white). This produces an image of the nanoparticles with the background crystal removed, inhibiting the protein lattice from contributing to the diffraction pattern resulting from the FFT. Radial pair correlation function (pcf or g(r)) analysis was employed to examine the global distribution of interparticle spacing in both experimentally obtained and simulated particle arrays. The coordinates of particles within a given image were Langmuir 2010, 26(13), 11103–11112
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identified using a protocol originally developed by Crocker and Grier.49 A pcf for the particle array was then calculated using this data. The particle identification procedure and pcf calculation were implemented using software written in Interactive Data Language (IDL, ITT). The routines were provided by Dr. Eric R. Weeks (Emory University, Atlanta, GA) and are publicly available at http://www.physics.emory.edu/∼weeks/idl/ download.html. Molecular Modeling. The pdb file 1SWD50 was used to map the electrostatic surface potential of the streptavidin molecule. This particular file contains the structure of a streptavidin molecule with two filled biotin binding sites on the same molecular face, similar to the protein’s arrangement after it has been crystallized on a biotinylated lipid film. The structure contained in the pdb file was prepared for continuum electrostatics calculations using PDB2PQR software.51,52 PROPKA was used to determine the side-chain ionization states at the desired pH (7 or 8).53 The protein surface potential was then mapped using the APBS plugin in the PyMOL software package.54,55
Results and Discussion Streptavidin Crystallization Produces C222 Crystal Domains. Two-dimensional streptavidin crystals were grown on biotinylated lipid films spread at the air-water interface.27 Crystallization was performed in a basic (pH 8.5) environment to favor the growth of square C222 crystals over other crystalline polymorphs.27,33 Crystals were grown in the presence of fluorescently tagged avidin (a noncrystallizable biotin-binding protein56). This facilitates imaging of the crystals that appear as dark X- and H-shaped domains embedded within a fluid, fluorescent avidin film (Figure 3a). Transmission electron microscopy of negatively stained streptavidin domains confirms that the protein assembled into crystalline lattices with C222 space-group symmetry (Figure 3b). The crystal lattice parameters, determined through FFT calculations, were found to be a = b = 5.8 ( 0.3 nm and γ = 89.8 ( 1.1, consistent with previously published results.26,27 During the crystal-staining process, the stain selectively accumulates in the areas in between protein molecules. This, by definition, is a negative stain. In the remainder of the article, we refer to the area in between protein molecules as negative sites or the crystal’s negative space. This describes locations within the crystal only and is not meant to have any implication toward electric charge. Electrostatic Nanoparticle Adsorption on Streptavidin Crystals Exhibits Site Selectivity. Five-nanometer citratestabilized gold nanoparticles were adsorbed on protein monolayers containing both crystalline streptavidin domains (Figure 3c) and regions of disordered protein molecules (Figure 3d). FFT analysis of the crystalline regions containing gold nanoparticles revealed that the underlying protein array exhibits the same lattice parameters as the nascent crystal (Figure 3c, inset), indicating that particle adsorption does not have any deleterious effects on the protein lattice structure. We performed adsorption experiments at (49) Crocker, J. C.; Grier, D. G. J. Colloid Interface Sci. 1996, 179, 298–310. (50) Stenkamp, R. E.; Trong, I. L.; Klumb, L.; Stayton, P. S.; Freitag, S. Protein Sci. 1997, 6, 1157–1166. (51) Dolinsky, T. J.; Nielsen, J. E.; McCammon, J. A.; Baker, N. A. Nucleic Acids Res. 2004, 32, W665–667. (52) Dolinsky, T. J.; Czodrowski, P.; Li, H.; Nielsen, J. E.; Jensen, J. H.; Klebe, G.; Baker, N. A. Nucleic Acids Res. 2007, 35, W522–525. (53) Li, H.; Robertson, A. D.; Jensen, J. H. Proteins: Struct., Funct., Bioinf. 2005, 61, 704–721. (54) Baker, N. A.; Sept, D.; Joseph, S.; Holst, M. J.; McCammon, J. A. Proc. Natl. Acad. Sci. U.S.A. 2001, 98, 10037–10041. (55) DeLano, W. L. DeLano Scientific: Palo Alto, CA, 2002. (56) Ku, A. C.; Darst, S. A.; Kornberg, R. D.; Robertson, C. R.; Gast, A. P. Langmuir 1992, 8, 2357–2360.
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Figure 3. (a) Fluorescence micrograph of X- and H-shaped streptavidin crystal domains (dark) within a fluorescent avidin matrix. (b) TEM imaging showing streptavidin molecules arranged in a square C222 lattice. The sample was negatively stained with uranyl acetate, causing protein molecules to appear bright and stained regions to appear dark. (Inset) Lattice parameters were obtained from FFT. (c) TEM image of a negatively stained sample of 5 nm gold nanoparticles adsorbed on a streptavidin crystal. (Inset) FFT produces the C222 diffraction pattern. (d) TEM image of a negatively stained sample of 5 nm gold nanoparticles adsorbed on a noncrystalline protein monolayer.
pH values below 7 and found the particle coverage to be inversely related to pH (Supporting Information). This is consistent with our previous findings for the adsorption of 20 nm gold particles on C222 streptavidin crystals16 and suggests that the electrostatic interaction between particles and the protein layer is the driving force for adsorption. Through local line profile analyses (Figure 4a,b), we examined the location of adsorbed particles relative to the protein molecules in the crystalline lattice. Figure 4b shows that the distance between the centers of adsorbed nanoparticles and adjacent negative sites along axes coherent with the crystal lattice vector is consistent with the characteristic dimension of the underlying protein crystal. This suggests that particles have a tendency to adsorb preferentially in or over the negative spaces within the crystal. If this trend is persistent over longer length scales, then the global particle arrangement should exhibit some degree of long-range periodicity. To test this hypothesis, we performed FFT analysis on binary masks of the adsorbed nanoparticles. These masks have the background crystal removed, so the resulting diffraction pattern is strictly produced by the particles in the original image. Our data shows that the molecular arrangement of the protein film has a pronounced effect on the structure of the particle array. FFTs acquired from masks of particles residing on streptavidin crystals (Figure 4c,d) produced square diffraction patterns with a = b = 5.8 ( 0.1 nm and γ = 89.9 ( 0.7, consistent with the underlying crystal. The larger measure of uncertainty associated with the protein crystal lattice parameters (i.e., 0.3 nm for a and b and 1.1 for γ) likely stems from those values being obtained from a larger sample set. Slight differences in conditions including ambient temperature, the quality of the staining, and the TEM grid DOI: 10.1021/la1007507
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Figure 4. (a) Electron micrograph showing the tendency of nanoparticles to adsorb in the negative spaces of the streptavidin crystal. (b) Plot of the intensity profile for the red line drawn in image a. (c, d) Binary particle masks and associated diffraction patterns obtained from FFT for particles adsorbed on a crystalline region. (e, f) Particle mask and the diffraction pattern for nanoparticles adsorbed on a noncrystalline region.
support film may have increased the spread of the extracted parameters for the protein lattice. In contrast to the crystalline regions, the diffraction patterns from masks of particles adsorbed on noncrystalline regions (Figure 4e,f) do not contain any high-intensity spots, indicating that the particle spacing is aperiodic when the underlying protein molecules are disordered. Any degree of long-range organization in the nanoparticle array must therefore stem from the protein template rather than an independent, particle self-assembly process. This corroborates our hypothesis that particle adsorption on the crystalline regions occurs in a site-specific fashion, with deposition generally confined to the areas in between protein molecules. The nanoparticles’ preferential adsorption in the crystal’s negative space could be a consequence of a localized energy minimum arising from (1) selective electrostatic attraction to these sites or, if the nanoparticles are generally attracted to the protein crystal, (2) maximization of the number of local nanoparticle-protein contacts. The gold particles are negatively charged because of the passivating citrate adlayer, and deposition was carried out at pH 7, a value above the protein’s isoelectric point (which occurs between pH 5 to 6 according to the manufacturer). Therefore, at pH 7 each protein molecule possesses a net negative charge, which, if distributed in an even fashion over the whole of each molecule, would likely serve to inhibit adsorp11108 DOI: 10.1021/la1007507
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tion anywhere on the crystalline lattice. This suggests that despite its net negative electrostatic character, the protein lattice contains localized areas to which the nanoparticles are selectively attracted. Furthermore, adsorption experiments carried out within a range of pH 5 to 6 produced larger nanoparticle surface coverages (Supporting Information) but showed no evidence of particle ordering. Thus, when the net charge on the crystalline lattice is nearly neutral or slightly positive, the particles do not appear to have a significant preference for the areas in between biomolecules. Coupled together, these observations suggest that the siteselective nature of nanoparticle adsorption (at pH 7) can be explained by the particles experiencing a local electrostatic repulsion from the top facet of the surrounding protein molecules and a concomitant electrostatic attraction to the crystal’s negative space. It is also possible that additional short-ranged interactions, such as hydrogen bonding, may contribute to the nanoparticles’ adherence to these sites. The crystalline and noncrystalline regions of the protein monolayer exhibited a pronounced difference in the number of adsorbed nanoparticles. The particle surface coverage on regions exhibiting a crystalline protein arrangement was 5.6 ( 0.1%. The coverage on noncrystalline regions was significantly higher, reaching a value of 9.4 ( 0.4%. This may suggest that the structure of the protein film affects the kinetics of the adsorption process. This could be explained by a higher density of viable adsorption sites in the disordered protein regions. In a crystalline domain, the space in between protein molecules accounts for ∼30% of the projected surface area (using the molecular dimensions published by Weber42 and assuming that the projected 2-D area of the protein molecule can be roughly modeled as a rectangle). The 2-D jamming limit for unoriented, rectangular, and elliptical particles with a finite aspect ratio is 40% of the total surface. It is therefore possible that, in our system, the area fraction associated with negative space is larger in the noncrystalline regions, resulting in higher nanoparticle coverage over a given period of time. However, the disordered protein regions likely contain a significant population of avidin, which has a more basic isoelectric point (pI ∼ 10) than streptavidin.59 The additional positive charge provided by these molecules may have also contributed to the disparity in surface coverage. Nanoparticle Arrays Exhibit Long-Range Structural Coherence with Underlying Streptavidin Crystals. To examine the relative particle spacing with respect to the underlying protein template, we calculated dimensionless radial pair correlation functions (pcf or g(r)) for both the nanoparticle array and protein crystal. The pcf provides a statistical mapping of the probability associated with finding features separated by a given distance.60 The shape of the spectrum and peak locations are dependent on the geometrical arrangement of constituent features (e.g., atoms, molecules, particles, etc.) and the degree of order in the system. The pcf for a perfectly crystalline lattice exhibits sharp, deltafunction-like peaks. In real systems, however, peaks are broadened and attenuated by feature spacings being distributed around nominal values, imaging artifacts, and measurement error typically associated with determining feature locations. This is illustrated in Figure 5a, where g(r) curves for an experimentally produced streptavidin crystal and an ideal square lattice with a (57) (58) 5218. (59) (60) 1999.
Vigil, R. D.; Ziff, R. M. J. Chem. Phys. 1989, 91, 2599–2602. Viot, P.; Tarjus, G.; Ricci, S. M.; Talbot, J. J. Chem. Phys. 1992, 97, 5212– Green, N. M. Adv. Protein Chem. 1975, 29, 85-133. Allen, S. M.; Thomas, E. L. The Structure of Materials; Wiley: New York,
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Figure 5. (a) g(r) for the streptavidin crystal and an ideal square lattice. The peak intensities for the ideal g(r) have been scaled down to facilitate plotting. (b) g(r) for 5 nm gold particles adsorbed on streptavidin crystals overlaid on the g(r) for the underlying streptavidin crystal. (c) g(r) data for nanoparticles adsorbed on crystalline and noncrystalline regions of the protein monolayer.
pitch of 5.8 nm have been plotted. The two pcf’s exhibit excellent agreement in peak location, although the peaks in the protein crystal spectra are much broader and their intensities approach unity more rapidly than those in the ideal case. The g(r) data shown in Figure 5b reveals a direct correlation between nanoparticle spacing and the structure of the underlying template, where the peaks in the two plots are centered at the same relative radial distances. The location of the first prominent peak in the nanoparticle pcf is commensurate with the position of the third peak in the crystal g(r), which is actually a convolution of the signals produced by the crystal’s third and fourth nearest neighbors (2a and (5)1/2a, where a is the crystal lattice parameter). With the exception of the shoulder at 8.2 nm, this indicates that the dominant nearest-neighbor spacing in the nanoparticle array is 11.6 nm (2a). The particles’ apparent preference for this nearestneighbor spacing can be explained in terms of interparticle electrostatic repulsion. Taking the ionic double-layer into account, we determined the effective particle diameter under our experimental conditions to be 10.5 nm. Adsorption events resulting in spacings greater than this value should be relatively unfettered by interparticle repulsion, and the crystal’s third nearest neighbor is the first spacing on the template that exceeds this cutoff. At separations smaller than the effective diameter, the electrostatic repulsion between particles begins to compete with the particles’ attraction to the template. This is demonstrated by the low-intensity shoulder at 8.2 nm (the crystal’s second nearest neighbor) and the absence of a peak at 5.8 nm (the crystal’s first nearest neighbor) in the nanoparticle g(r). The minimal electrolyte Langmuir 2010, 26(13), 11103–11112
concentration used in our experiments produces a Debye screening length of κ-1 = 8.3 nm, which exceeds the particle radius. Consequently, the gold adsorbates energetically occlude a greater areal fraction of the substrate than their actual projected area. The fact that the peak at 8.2 nm is underdeveloped suggests that whereas this spacing is attainable by the nanoparticles, concurrent occupancy at sites situated at this distance is kinetically hindered by an energetic activation barrier precipitated by electrostatic repulsion. The lack of a peak at 5.8 nm indicates that particle adsorption at adjacent negative sites (along crystal lattice vectors) is thermodynamically unfavored at the ionic strength used here. Although the arrangement of nanoparticles does appear to conform to the structure of the underlying protein crystal, the g(r) data also displays a reduced degree of long-range order in the nanoparticle array relative to that in the template. Previous work with S-layer and chaperonin crystals produced nanoparticle arrays exhibiting a higher degree of long-range order.12,14,15 However, the crystal structures in these systems were all hexagonally close-packed, possibly producing electrostatic adsorption sites that were more pointlike. Prior investigations have demonstrated that the amount of relative offset in a templated particle array can be attenuated as the ratio of particle size to the size of the adsorption site is increased, as long as the spacing between sites is sufficiently large compared to the particle diameter.61-63 (61) Aizenberg, J. Phys. Rev. Lett. 2000, 84, 2997. (62) Hur, J.; Won, Y. Langmuir 2008, 24, 5382–5392. (63) Lee, I.; Zheng, H.; Rubner, M.; Hammond, P. Adv. Mater. 2002, 14, 572–577.
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This is due to an enhancement in the degree to which the electrostatic force exerted on an approaching particle is focused toward the center of an adsorption site. Additionally, the degree of offset in neighboring particles can be correlated at sufficiently small distances because of the interparticle electrostatic interaction.62 The remittent quality of ordering in our system may in part be due to the similar sizes between adsorption sites and nanoparticles. The positive electrical potential responsible for the nanoparticles’ selective attraction to these sites is likely distributed over the area encompassed by each site. This would diminish the electrostatic focusing effect, allowing for an increased degree of displacement between the centers of adsorbed particles and adsorption sites. Furthermore, because the particle effective diameter is close in value to the particle nearest-neighbor spacing, this effect may be exacerbated by interparticle repulsion, which could transmit the offset in one particle to its surrounding neighbors. Particle adsorption on the noncrystalline areas of the protein film produces arrays with a markedly different structure from those generated by the crystalline regions (Figure 5c). Transitioning from an ordered to a disordered protein film causes the particles’ first-nearest-neighbor spacing to shift from 11.6 to 10.6 nm. The latter value is consistent with the calculated, effective nanoparticle diameter of 10.5 nm. Additionally, unlike the g(r) obtained from particles on streptavidin crystals, the data obtained from the noncrystalline regions does not exhibit any structural features prior to the first peak. Taken together, these findings suggest that when the protein monolayer is disordered, the particles’ nearest-neighbor spacing is dictated by interparticle electrostatics, as opposed to the underlying film structure. Finally, the presence of three initial peaks followed by a rapid convergence to unity demonstrates that the particle arrangement on the noncrystalline regions is endowed with some short-range structure but lacks the degree of long-range ordering exhibited by arrays assembled on the protein crystal. These structural characteristics may be due to the arrangement of the underlying biomolecules or may have evolved as a consequence of particle packing as the surface coverage approaches its jamming limit,43,46,47 which we estimate to be 12.5% (Supporting Information). Monte Carlo Simulations Yield Nanoparticle Arrays Consistent with Arrays Templated by Streptavidin Crystals. The TEM images, FFT spectra, and g(r) data all indicate that the nanoparticle arrangement on the protein crystal conforms to the organization of the protein molecules as a result of the particles preferentially adsorbing in the crystal’s negative space. However, the adsorbed nanoparticle array has a reduced degree of longrange translational order relative to that of the underlying biological template. Monte Carlo simulations were employed to help elucidate the origins of this structural disparity. We surmised two potential factors that could be contributing to the degradation of long-range order in the particle array: (1) the average amount of relative offset between the particles on the template and the centers of their respective adsorption sites and (2) nonspecific particle adsorption outside of the negative spaces (i.e., on top of the protein molecules). To examine the relative contribution of these two effects, we performed site-specific adsorption simulations using two distinct adsorption probability maps described previously. Figure 6a shows the g(r) calculated from site-specific simulation results, using continuous probability maps, for different values of σ. As expected, increasing the value of σ produces particle arrays with less correlated interparticle spacings, resulting in broadening of the peaks in the pcf and a more rapid approach 11110 DOI: 10.1021/la1007507
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to unity. The g(r) spectra for all values of σ as well as the spectra produced using a discrete probability map exhibit a negligible amount of signal at 5.8 nm and a minor shoulder at 8.2 nm, similar to our experimental data (Figure 6a,c). Because electrostatics was the only interaction taken into account in the simulations, this data further supports our hypothesis that interparticle repulsion inhibits spacings shorter than the protein crystal’s third nearest neighbor. Overall, the peak locations in the pcf’s obtained from both types of site-specific simulation generally match our experimental results. This structural similarity indicated that our site-specific Monte Carlo simulations could be used to indirectly examine the nominal offset and occurrence of nonspecific adsorption in the gold nanoparticle arrays templated by streptavidin crystals. Monte Carlo Simulations Show That the Degree of Ordering is Affected by Particle Offset and Nonspecific Adsorption. The pcf for σ = 0.3 closely resembles our experimental data for nanoparticles adsorbed on streptavidin crystals in both peak location and intensity (Figure 6b). The short-range differences in the two data sets are likely due to an overestimation of the interparticle repulsion stemming from our inability to accurately account for the effect of the substrate on the interparticle interaction. The energy scaling factor used in the simulation (λ = 2.8) tends to overestimate interparticle repulsion at the interface in systems with small particles at low ionic strengths.45,64 The assumption of pairwise additivity applied to the particle pair potentials may have also contributed to the differences in the data, as recent work has demonstrated that at low ionic strength this assumption can lead to an overestimation of the repulsion felt by an individual particle within a many-body ensemble.65 However, at distances greater than ∼12 nm, the simulation result for σ = 0.3 is in excellent agreement with the experimental data. The nominal particle offset for this value of σ is 1.5 nm. The g(r) data in Figure 6b therefore suggests that the average nanoparticle offset should be near 1.5 nm in the experimental system. Simulations using a discrete adsorption probability map mimic a situation where nanoparticle adsorption is restricted to the areas in between biomolecules in the crystalline template. The pcf produced by this type of simulation is shown in Figure 6c along with the experimental nanoparticle g(r). The differences between the two spectra at short distances can again be attributed to an overestimation of interparticle repulsion. The nominal particle offset for the discrete simulation is 1.1 nm, which is smaller than the value of 1.5 nm obtained through simulation with a continuous probability map. Consequently, the peaks in the simulated g(r) are sharper and more intense than in the experimental case. This indicates that even if particles are capable of adsorbing anywhere within the crystal’s negative sites the resulting amount of offset does not sufficiently account for the deviation between the order exhibited by the nanoparticle array and the underlying template. Therefore, in addition to particle offset, some degree of nonspecific adsorption outside of the protein crystal’s negative space also contributes to the observed degradation of long-range order in the nanoparticle array. In addition to site-specific adsorption simulations, we performed simulations on unpatterned substrates using the standard random sequential adsorption model (RSA).40,43 The g(r) derived from RSA simulations is plotted in Figure 6d, together with the experimental result obtained from nanoparticle arrays formed on noncrystalline protein monolayers. The former is consistent with (64) Semmler, M.; Ricka, J.; Borkovec, M. Colloids Surf., A 2000, 165, 79–93. (65) Merrill, J. W.; Sainis, S. K.; Dufresne, E. R. Phys. Rev. Lett. 2009, 103, 138301–138304.
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Figure 6. (a) g(r) data obtained from arrays generated by simulation using a continuous adsorption probability map with various values of σ.
(b) The result for σ = 0.3 is plotted along with g(r) obtained from nanoparticles adsorbed on streptavidin crystals. (c) Nanoparticle g(r) in plot b shown with the results acquired from simulations using a discrete adsorption probability map. (d) g(r) for nanoparticles adsorbed in noncrystalline protein regions is plotted with the g(r) obtained from arrays generated by RSA simulation.
a structured fluid43,47 and does not exhibit the degree of shortrange structure found in the experimental particle arrays. This data suggests that, like the crystalline regions, the probability of particle adsorption is not uniform over the protein monolayer and therefore the underlying protein arrangement directly influences the structure of the resulting nanoparticle array. The nanoparticle g(r) in the noncrystalline regions appears to be similar to that generated by particle adsorption on a substrate displaying randomly arrayed adsorption sites.66-68 As on the crystalline protein film, the particles could largely be inhibited from adsorbing on top of individual protein molecules and are instead attracted to areas in between biomolecules. In this way, the disordered protein configuration can give rise to an array of randomly arranged particle adsorption sites. His87 and Lys121 Are Potential Sources of Nonspecific Nanoparticle Adsorption. Areas of positive and negative charge on a protein’s surface are produced by basic (histidine, arginine, and lysine) and acidic (aspartic acid and glutamic acid) amino acid residues, respectively. Nanoparticle adsorption outside of the crystal’s negative space could therefore be due to the presence of basic amino acid residues on the surface of the streptavidin molecule that does not border the crystal’s negative space. An examination of the structure (66) Adamczyk, Z.; Weronski, P.; Musial, E. J. Colloid Interface Sci. 2002, 248, 67–75. (67) Adamczyk, Z.; Jaszczolt, K.; Michna, A.; Siwek, B.; Szyk-Warszynska, L.; Zembala, M. Adv. Colloid Interface Sci. 2005, 118, 25–42. (68) Adamczyk, Z.; Weronski, P.; Musial, E. J. Chem. Phys. 2002, 116, 4665– 4672.
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of the streptavidin molecule reveals pairs of histidines (His87) and lysines (Lys121) on the molecular facet parallel to the crystal plane (Figure 7a). Nanoparticle deposition was carried out at pH 7, which is below and close to the pKa’s of the ε-amino group of lysine (10.5) and the imidazole side-chain in histidine (6.5), respectively.69 These values are subject to change in a folded protein molecule because of factors such as variations in the local dielectric constant, the hydration state, and hydrogen bonding.70 The PROPKA53 software, used to determine the ionization state of amino acid side-chains for mapping the protein’s electrostatic surface potential, predicts pKa values of 6.8 and 7.0 for the two His87 residues and 10.2 and 9.5 for the Lys121 side-chains. Thus, all of these residues could potentially exhibit a positive charge on a given molecule within the crystal under our experimental conditions, thereby facilitating nonspecific (i.e., outside of the crystal’s negative space) nanoparticle adsorption. This hypothesis is supported by the map of the protein’s electrostatic surface potential at pH 7 (Figure 7b). The localized areas of positive potential on the top face of the protein correspond to the locations of these histidine and lysine residues. The electrostatic potential map calculated at pH 8 (Figure 7c) shows a reduction in the size of areas exhibiting a positive potential and growth in the negatively charged regions on this molecular face. Thus, nonspecific adsorption may be curtailed at higher pH values, leading to an increase in the translational order of the nanoparticle array. (69) Stryer, L. Biochemistry, 3rd ed.; Freeman & Co.: New York, 1988. (70) Petsko, G. A.; Ringe, D. Protein Structure and Function; New Science Press: London, 2004.
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Figure 7. (a) Molecular structure of the streptavidin molecule highlighting the Lys121 (blue) and His87 (purple) residues. Electrostatic surface potential map for the streptavidin molecule at (b) pH 7 and (c) pH 8. The molecular face displayed in each image is the facet that is parallel to the crystal plane and exposed to the nanoparticle solution. Potential values are normalized by kT/e, where k is Boltzmann’s constant, T is the absolute temperature, and e is the fundamental charge. All images were generated in PyMOL.
Conclusions We have demonstrated that the electrostatic adsorption of 5 nm gold nanoparticles on C222 streptavidin crystals occurs in a site-specific fashion. The negatively charged metal particles preferentially adhere to areas in between protein molecules, causing the resulting nanoparticle array to assume the 2-D structure of the underlying crystal. However, long-range ordering is degraded relative to the underlying template because of both particle offset from the centers of the adsorption sites and nonspecific adsorption outside of the preferred adsorption sites. Mapping of the protein’s electrostatic surface potential suggests that the degree of ordering in arrays templated by C222 streptavidin crystals can potentially be improved by controlling the solution pH during particle deposition or the mutagenesis of surface amino acid residues to inhibit binding to the protein itself and promote adsorption in the crystal’s negative space. Overall, the work presented here indicates that the streptavidin crystal system is amenable to templating ordered nanoparticle arrays through electrostatic interactions. Additionally, the feasibility
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of using streptavidin crystals to template arrays through biotinbinding should be explored; in contrast to the electrostatic adsorption mechanism, biotin-binding would likely result in the selective placement of nanoparticles on top of the protein molecules. Sequential exploitation of these dual adsorption mechanisms inherent in the streptavidin crystal system may enable the fabrication of ordered multicomponent nanoparticle arrays. Acknowledgment. We thank Dr. Ali Mohraz and Bharath Rajaram for helpful discussions related to pair correlation function calculations. TEM was performed at the Materials Characterization Center at the University of California, Irvine. This work was funded by the National Science Foundation (ECCS-0609195 and ECCS-0709481). Supporting Information Available: Nanoparticle coverage data and a description of nanoparticle jamming limit calculations. This material is available free of charge via the Internet at http://pubs.acs.org.
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