Comparative Study of Materials-Binding Peptide Interactions with Gold

Aug 29, 2014 - Department of Chemistry, University of Miami, 1301 Memorial Drive, Coral Gables, Florida 33146, United States. §. Department of Chemic...
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Comparative Study of Materials-Binding Peptide Interactions with Gold and Silver Surfaces and Nanostructures: A Thermodynamic Basis for Biological Selectivity of Inorganic Materials J. Pablo Palafox-Hernandez,†,# Zhenghua Tang,‡,# Zak E. Hughes,†,# Yue Li,§ Mark T. Swihart,§ Paras N. Prasad,∥,⊥ Tiffany R. Walsh,*,† and Marc R. Knecht*,‡ †

Institute for Frontier Materials, Deakin University, Geelong, Victoria 3216, Australia Department of Chemistry, University of Miami, 1301 Memorial Drive, Coral Gables, Florida 33146, United States § Department of Chemical and Biological Engineering and ∥Department of Chemistry and Institute for Laser Photonics and Biophotonics, University at Buffalo (SUNY), Buffalo, New York 14260, United States, and ⊥ Department of Chemistry, Korea University, Seoul 151-747, Korea ‡

S Supporting Information *

ABSTRACT: Controllable 3D assembly of multicomponent inorganic nanomaterials by precisely positioning two or more types of nanoparticles to modulate their interactions and achieve multifunctionality remains a major challenge. The diverse chemical and structural features of biomolecules can generate the compositionally specific organic/inorganic interactions needed to create such assemblies. Toward this aim, we studied the materials-specific binding of peptides selected based upon affinity for Ag (AgBP1 and AgBP2) and Au (AuBP1 and AuBP2) surfaces, combining experimental binding measurements, advanced molecular simulation, and nanomaterial synthesis. This reveals, for the first time, different modes of binding on the chemically similar Au and Ag surfaces. Molecular simulations showed flatter configurations on Au and a greater variety of 3D adsorbed conformations on Ag, reflecting primarily enthalpically driven binding on Au and entropically driven binding on Ag. This may arise from differences in the interfacial solvent structure. On Au, direct interaction of peptide residues with the metal surface is dominant, while on Ag, solvent-mediated interactions are more important. Experimentally, AgBP1 is found to be selective for Ag over Au, while the other sequences have strong and comparable affinities for both surfaces, despite differences in binding modes. Finally, we show for the first time the impact of these differences on peptide mediated synthesis of nanoparticles, leading to significant variation in particle morphology, size, and aggregation state. Because the degree of contact with the metal surface affects the peptide’s ability to cap the nanoparticles and thereby control growth and aggregation, the peptides with the least direct contact (AgBP1 and AgBP2 on Ag) produced relatively polydispersed and aggregated nanoparticles. Overall, we show that thermodynamically different binding modes at metallic interfaces can enable selective binding on very similar inorganic surfaces and can provide control over nanoparticle nucleation and growth. This supports the promise of bionanocombinatoric approaches that rely upon materials recognition.

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INTRODUCTION

In contrast to the complex synthetic protocols required for making artificial multicomponent nanostructures, nature exploits biomolecular recognition and self-assembly to achieve high degrees of spatial localization of inorganic materials that persist over lengths greater than the micron scale.8−10 In biological systems, proteins and peptides are used to achieve this organization and control, based upon their ability to bind, recognize, and organize nanostructured inorganic materials.

Controllable nanomaterial assembly to achieve precise positioning of nanoparticles of multiple materials represents a significant challenge.1,2 Known avenues to achieve such assemblies rely upon complex, multistep synthetic protocols. Such multifunctional multicomponent assemblies are of great interest because they may open up access to new properties for advanced applications in plasmonics, optics, catalysis, biosensing, and related fields.3−7 New and direct approaches are needed to overcome the synthetic challenges in creating these assemblies. © XXXX American Chemical Society

Received: April 28, 2014 Revised: August 20, 2014

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Table 1. Comparison of Peptides Isolated for Affinity to Au and Ag peptide

sequence

pIa

material

AuBP1

WAGAKRLVLRRE

11.7

AuBP2

WALRRSIRRQSY

12.0

AgBP1

TGIFKSARAMRN

12.0

AgBP2

EQLGVRKELRGV

8.9

Au Ag Au Ag Au Ag Au Ag

ΔGb,c (kJ/mol) −37.6 −35.3 −36.4 −36.7 −31.6 −35.9 −35.3 −36.2

± ± ± ± ± ± ± ±

0.9 0.8 0.3 0.8 0.2 1.0 1.2 1.0

anchor assignmentb

entropy assignmentb

strong weak medium weak medium weak medium weak

medium high medium medium high high medium high

a

Calculated using http://web.expasy.org/compute_pi/. bTop values represent Au binding; bottom values represent Ag binding. cValues for Au from ACS Nano, 2013, 7, 9632−964.

affinities of peptides initially selected for Ag-binding.16,21 Similarly, the Au-binding peptides’ affinities on Ag were very close to their binding affinities on Au, suggesting that bioselection techniques alone may not deliver the desired level of compositional specificity. On the other hand, one peptide initially selected for Ag-binding showed significant selectivity for Ag over Au. Its binding affinity on Au was substantially lower than that of the other peptides studied. This degree of selectivity for Au could be exploited for applications that rely upon such specific recognition. These results indicate that additional studies beyond peptide isolation are required to achieve both material binding affinity and selectivity from a single sequence.

This ability is rooted in their chemical and structural diversity. The fact that these biomolecules exert fine control over the nucleation, growth, and organization of inorganic materials in biological systems suggests that they can provide similar functions for synthetic inorganic nanomaterials. This approach, termed bionanocombinatorics,11−14 is an exciting new route to producing multifunctional multicomponent nanoparticle assemblies with diverse properties and applications. A significant challenge in developing peptide-based strategies for bionanocombinatorics is understanding the relationship between peptide sequence and corresponding materials-binding affinity and selectivity.11,15,16 At present, the de novo design of a peptide sequence with predictable binding affinities for multiple materials is not possible; however, bioselection techniques such as phage- and cell-surface display have been used to isolate peptide sequences with affinity for particular inorganic materials.17,18 Unfortunately, such techniques do not necessarily select against peptides with high degrees of affinity for multiple different inorganic materials. To this end, a deeper understanding of the origins of material selectivity, i.e. identification of peptide sequences that bind preferentially to one specific surface and not to other similar interfaces, is required to advance peptide-based bionanocombinatorics. A few researchers have previously sought to identify, probe, and characterize selective binding of peptides.11 Fang et al. used subtractive phage display methods to isolate peptides that selectively precipitated TiO2 and not SiO2.19 This was hypothesized to reflect selective oxide binding. Hnilova et al. reported selectivity of Au and SiO2 binding peptides.15 There, two disparate materials, one metal and one oxide, were used, with substantially different structures, surface charges, and related properties. Additionally, Tamerler and co-workers have studied Au and Ag selectivity using peptides displayed on a cell surface;16 however, no absolute binding affinities of the individual sequences were reported. In this contribution, we have examined and quantified the selectivity and cross-material affinity of Au and Ag binding peptides for Au and Ag metallic interfaces, including 2D flat surfaces and 3D nanoparticles dispersed in water. These two metals possess similar structures (face centered cubic - fcc) and dielectric properties. Thus, they present a significant challenge to achieving material selective peptide binding. Here, a close partnership between experimental thermodynamic characterization, advanced molecular simulation, and nanoparticle synthesis and characterization demonstrates that selectivity (large differences in binding affinity between peptides) was achieved only on the Au surface and not on the Ag surface. Peptides initially selected for Au-binding20 had binding affinities on Ag that were almost identical to the binding

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RESULTS AND DISCUSSION Peptide Library. In the context of Au and Ag binding by peptides, four sequences were selected for analysis. Two of the peptides, AuBP1 and AuBP2, were originally isolated by Sarikaya and co-workers for binding to the Au surface,20 while the AgBP1 and AgBP2 peptides were selected for affinity for Ag by Tamerler and colleagues.16 The sequence, pI, and Gibbs free energy (ΔG) of binding on Au and Ag for all of the peptides studied are presented in Table 1. We measured and compared the ΔG values of these four peptides on the Au and Ag surfaces, to quantify the basis of their affinity and determine if materialsselective binding is possible. Experimental Surface Binding Analysis. Quantitative binding affinity measurements were made using quartz crystal microbalance (QCM) analysis.11,22 These results are directly comparable to previous results performed under the identical conditions for Au surfaces.11 For this study, Au QCM sensors were used; however, a thin Ag layer was sputter coated onto the sensor. Based upon the coating rate, the Ag thickness was at least 10 nm, thus fully coating the sensor surface. Tappingmode AFM imaging was used to characterize the local surface roughness of uncoated (Au) and coated (Ag) sensors (Supporting Information, Figure S1) to rule out the possibility that differences in binding arise from differences in surface roughness. Both films were quite smooth, with RMS roughness increasing slightly from 1.1 to 2.4 nm after Ag coating. Scanning electron microscopy (SEM) with backscattered electron detection, in which contrast is based upon atomic number, showed that the Ag film uniformly coated the Au (Supporting Information, Figure S2). Elemental mapping in the SEM using energy-dispersive X-ray analysis (EDS) also showed uniform coverage of Ag (Supporting Information, Figure S3). Next, the binding analysis was completed using standard methods (see the Experimental section), from which the association (k a ) and dissociation (k d ) constants were B

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mol for the AuBP2 sequence. In contrast, for binding on the Au surface, the ΔG values ranged from −31.6 ± 0.2 kJ/mol to −37.6 ± 0.9 kJ/mol for the AgBP1 and AuBP1 peptides, respectively. Thus, selective binding of one peptide relative to another on the Ag surface is not expected; however, on the Au surface, substantial selectivity of AuBP1 binding over AgBP1 binding is likely. The measured binding equilibrium constants allow prediction of the surface coverages for competitive binding of the two peptides on the same surface, for which the Langmuir adsorption isotherms can be written as

determined. The equilibrium constant (Keq) and Gibbs free energy of binding (ΔG) were then calculated from ka and kd. Figure 1 displays the QCM binding analysis of the four selected peptides on the Ag surface. The change in resonance

θAuBP1 =

θAgBP1 =

KAuBP1[AuBP1] 1 + KAuBP1[AuBP1] + KAgBP1[AgBP1]

(1)

KAgBP1[AgBP1] 1 + KAuBP1[AuBP1] + KAgBP1[AgBP1]

(2)

For equal concentrations of the two peptides, the ratio of the fractional coverages (θAuBP1/θ AgBP1) is simply equal to the ratio of the equilibrium constants (KAuBP1/KAgBP1), which in this case is ∼11.2. Thus, substantial selectivity of the Au surface for AuBP1 over AgBP1 is possible. Figure 2a presents the fractional

Figure 1. QCM binding data for the (a) AuBP1, (b) AuBP2, (c) AgBP1, and (d) AgBP2 binding to Ag. Data is inverted for more intuitive presentation.

frequency of the QCM directly corresponds to the mass of peptide adsorbed at the metal surface. Note that the data is inverted for intuitive presentation. Figure 1a specifically displays the analysis for the AuBP1 peptide. These measurements used a flow cell in which the mass of peptide bound to the sensor increased as a function of time, as the concentration in the inlet stream was held constant. Eventually the system reached a steady state at which the surface coverage was in equilibrium with the inlet peptide concentration. The results are consistent with the formation of a monolayer of peptide at the metal interface;11 the lack of measurable energy dissipation strongly suggests the formation of a rigid biomolecular interface. Multilayer adsorption would produce a highly flexible deposit, resulting in significant energy dissipation. As the mass of the peptide in solution increased, the rate of absorption and amount of peptide adsorbed onto the metal surface also increased, saturating at slightly higher levels based upon shifts in the equilibrium coverage. Langmuir fitting of the binding data at the selected peptide concentrations provides kobs values, which were plotted as a function of the peptide concentration (Supporting Information, Figure S4). From this linear plot, the ka and kd constants were directly determined based upon the slope and y-intercept values of the best fit line, respectively. The Keq and ΔG values were then computed from ka and kd, as previously demonstrated.11 Note that peptide desorption from the metallic surface does occur upon flowing of water over the peptide-saturated interface (Supporting Information, Figure S5); however, a significant fraction of the biomolecules remain bound. Table 1 presents the ΔG values for the four peptides studied here. Both the newly determined values for binding to Ag and previously determined values for binding to Au11 are presented for comparison. Note that identical conditions were used for all measurements, so the values are directly comparable. Interestingly, all four sequences demonstrated equivalent binding affinities for the Ag surface, with values ranging from −35.3 ± 0.8 kJ/mol for the AuBP1 peptide to −36.7 ± 0.8 kJ/

Figure 2. Surface coverages of AuBP1 and AgBP1 vs solution-phase peptide concentrations, as predicted from the Langmuir isotherm for competitive binding (eqs 1 and 2) on (a) Au and (b) Ag using the measured Keq values for each peptide.

coverages of these two peptides predicted by Langmuir competitive binding (eqs 1 and 2), as a function of the peptide concentrations in solution. The dashed lines correspond to equal solution-phase concentrations of the two peptides. As shown in Figure 2b, the coverage of AgBP1 on the Ag surface is just slightly higher than that of AuBP1, for equal solution concentrations of the two peptides. Together, these results display the degree of selectivity possible on Au in contrast to the minimal selectivity that can be achieved on Ag. Advanced Molecular Dynamics (MD) Simulations of Interfacial Adsorption. Initially, an analysis of the contact between all 20 amino acids at both the Ag and Au aqueous interfaces was conducted to identify if materials selectivity between the two metals could be identified at the amino acid level. We used well-tempered metadynamics23 MD simulations (see Methods), to calculate the free energy of adsorption, as summarized in Figure 3. Most amino acids showed a diminished binding affinity on Ag compared with binding at the Au interface. Only four exceptions to this were found: Asp, Glu, Ser, and Lys. Interestingly, most of these residues have oxygen-rich side chains. It is possible that this difference in adsorption strength could be due to the slightly greater structuring of water at the Ag/solvent interface compared with C

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direct surface contact (Supporting Information, Figure S9). These data indicate that the adsorbed configurations for Ser on Ag and Au are very similar, as can be seen in example snapshots of the adsorbed state, shown in Figure 4. In contrast, the

Figure 3. Calculated adsorption free energies at the aqueous metal interface for all 20 naturally occurring amino acids. These energies correspond to the overall minimum in the free energy vs distance above the surface (Supporting Information, Figures S6−S8), which corresponds to a “direct contact” adsorbed state for all except Leu, Phe, and Trp adsorbed on Ag, for which the overall minimum on Ag is a “solvent mediated” state.

the Au/solvent interface.24 As summarized in our previous reports,11,24,25 the structuring of liquid water at both the Ag and Au interfaces is similar. The water molecules closest to both metallic interfaces show an increased propensity to act as hydrogen bond donors compared to water molecules in the bulk, with the average orientation of water in the first interfacial layer such that the hydrogen atoms are directed away from the surface. The density profiles of water as a function of distance from the surface show a greater degree of structuring for Ag relative to Au. On the basis of these findings and since none of the four peptide sequences considered in this study are rich in these residues, it might naively be expected that none of these sequences would show strong anchor points at the aqueous Ag interface. Overall, the calculations suggest that Cys, Arg, Tyr, and Trp have the strongest binding affinity on Au, whereas the amino acids Arg, Met, Cys, Asp, and Ser are among the strongest binders on Ag. Note that the features of the adsorption profile (free energy as a function of distance from the surface, shown in Figures S6−S8 of the Supporting Information) were typically quite different for Ag vs Au; many of the Ag profiles exhibited a pronounced free energy barrier between the “direct contact” adsorbed state and a second, “solvent mediated,” (often, but not exclusively, more weakly bound than the “direct contact” state) adsorbed state (via adsorption on to the first water layer at the interface), a feature not typically present in the corresponding Au profiles. Such solvent-mediated adsorption states are not without precedent; they been identified from previous simulations of peptide adsorption at the aqueous titania interface.26 The possible structural basis for these differences in binding affinity at the Au and Ag aqueous interfaces was then investigated further. Three exemplar amino acids were chosen, based on differences in the Ag and Au binding free energies (Figure 3): Trp, Tyr, and Ser. Both Trp and Tyr have aromatic side chains and show a large reduction in binding free energy on Ag compared with Au. In contrast, Ser shows very similar binding free energies at the two aqueous metallic interfaces. The relative proportion of the adsorbed configurations were thus calculated where either the side chain or the backbone is in

Figure 4. Representative snapshots of two exemplar amino acids, serine at top and tryptophan at bottom, from the metadynamics simulations, adsorbed at the Au (left) and Ag (right) aqueous interfaces. Water is not shown for clarity.

contact plots for Trp and Tyr, shown in the Supporting Information, Figure S9, reveal significant differences in the relative proportion of ring-surface contact states vs backbonesurface contact states when adsorbed on Ag compared with Au. Both Trp and Tyr feature a dramatic reduction in ring-surface contact on Ag. This difference in conformational preference at the Ag interface can also be seen in the orientational preferences of the side-chain ring (with respect to the surface) as shown in the Supporting Information, Figure S10. These data indicate that the aromatic rings prefer to lie flat on Au but with a reduced and spatially less persistent preference when adsorbed at the Ag interface. These differences are anticipated to substantially impact on the modes of peptide binding at Ag interfaces compared with Au. Taken together, all of the data from the metadynamics simulations indicate that peptide adsorption on Ag(111) may feature a mix of direct-contact and solvent-mediated adsorption, while adsorption at the Au surface is chiefly mediated via direct contact. Therefore, Arg residues (common to all four sequences) in the peptides should yield a reasonable degree of binding on both Ag and Au surfaces, taking into account the caveats mentioned herein. In particular, our previous work strongly suggests that peptide adsorption preferences cannot be inferred from amino acid adsorption preferences alone.11 The strong interplay between peptide sequence, 3D peptide conformation(s), and peptide adsorption determines the binding affinity. The affinity of a peptide is not a mere additive sum of amino acid binding preferences. Our previous studies have indicated that some residues (as present in the peptide) can show a variability in the degree of surface contact, even when the calculated free energy of binding of the corresponding amino D

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acid is found to be moderately high.11 This suggests that the local environment in the peptide sequence can modulate the binding affinity of the residue compared to the corresponding amino acid alone. In order to determine the binding preferences of the peptides, Replica Exchange with Solute Tempering (REST) MD simulations were carried out for each of the eight interfaces; in each system we modeled a single peptide chain (one of AuBP1, AuBP2, AgBP1, or AgBP2) in the presence of the aqueous metal interface for either the Ag(111) or Au(111) surface. Application of REST, in partnership with a clustering analysis of reference replica trajectories (see Methods) allowed for the estimation of the Boltzmann-weighted ensemble of adsorbed conformations and thus establish molecular-level connections between the binding behavior of a peptide and its conformational preferences. Initially, an analysis of the contact between the peptide and the metal surface in each case was carried out for the REST simulations. Figure 5 shows the degree of residue-surface

when adsorbed on Ag, all four sequences showed fewer contact points overall. Also, the degree of contact in the contact distributions for Ag was broadly reduced compared to Au for all four peptide sequences. Very few instances showed a similar or greater degree of residue contact on Ag vs Au (AuBP1: V8; AuBP2: L3, R8, R9, Q10). Furthermore, the distributions of surface contact for Ag also differed compared with Au, most notably for AgBP1, which shows a grouping of contact points at one end of the peptide chain. In contrast, the contact points for the Au-binding sequences, while showing differences in magnitude between the two interfaces, exhibited a very similar distribution of contact points along the chain for Ag vs Au. It was also of interest to note a common set of residues that did not make substantive contact with either interface (AuBP2: S6; AgBP1: T1, G2; AgBP2: E1, V12). The relatively diminished surface contact for AgBP2 adsorbed on Ag is due to the fact that this adsorbed peptide features a mix of both direct and solvent-mediated surface adsorption, as further indicated by the average height of each C(α) atom above the metal surface (see Figure S11, Supporting Information). This was not a clear feature of the other three sequences adsorbed on Ag. No such solvent-mediated binding was noted for any of the sequences adsorbed on Au. On the basis of our previous work,11 and the present calculations of amino acid binding strengths at the aqueous Au and Ag interfaces, we have estimated the scale of the enthalpic contribution to the experimentally determined ΔG values for each sequence by assigning a score to each contact residue in the sequence. These scores are then summed to provide a total enthalpic score for each peptide, which is then used to classify the binding enthalpy contribution as Weak, Medium, or Strong for each sequence (see Supporting Information “Contact Residue Scoring” for details). These assignments are summarized in Table 1 and indicate a clear difference in the enthalpic character of the peptide binding to Au relative to Ag. The conformational entropy of the adsorbed peptide was also estimated.11 This metric accounts for the number of distinct binding conformations observed when the peptide is adsorbed at the interface. As outlined previously,11 one way in which a peptide can support a strong binding affinity is by featuring a large number of distinct classes of adsorbed conformations, with each distinct class of conformation relating to a different basin on the underlying potential energy landscape of the interfacial system. As we have emphasized previously,11 the conformational entropy does not comprise the entire change in system entropy, the genuine calculation of which would be computationally intractable. Clustering over the peptide backbone atoms has then been used to identify these distinct classes of adsorbed conformation (see Methods). The number of configurations belonging to each cluster provides a population for each cluster. To a first approximation, the distribution of cluster populations for each system indicates the relative fraction of different adsorbed conformations that would be expected in the corresponding experimental system (neglecting multichain adsorption effects). The total number of clusters as well as the percentage populations of the top 10 clusters for each of the eight systems are given in Table S2 of the Supporting Information, showing that adsorption on Ag always yielded a greater number of clusters for each sequence, compared with Au. The conformational entropy of the system was then evaluated using the definition of the discrete entropy shown below:

Figure 5. Degree of residue-surface contact, indicated by colored circles, for all four sequences adsorbed on Au (left) and Ag (right) aqueous interfaces.

contact for the four different peptides on the two metallic surfaces. The degree of contact is defined as the fraction of the reference replica trajectory that each residue spends within a predetermined separation from the surface. Full details of the percentage values of surface contact for each residue and for each of the eight simulations are given in Table S1 of the Supporting Information. These data show that all four peptides featured a greater degree of contact overall with the Au surface compared with the Ag surface. This finding is consistent with the calculated adsorption free energies (see Figure 3) of all 20 amino acids at both the Ag and Au aqueous interfaces. On the Au surface, all four peptide sequences featured a strong degree of residue-surface contact, somewhat uniformly distributed along the length of the chain (Figure 5). In contrast, E

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n

Sconf = −∑ pi ln(pi )

(3)

i=1

Here n is the total number of clusters for a given peptide/metal system, and pi is the fraction of the configurations from the reference replica trajectory that belong to the ith cluster. These data, given in Table 2, show that each of the sequences Table 2. Conformational Entropy Values Calculated Using Cluster Populations for Each of the Four Peptide Sequences Adsorbed at the Aqueous Au and Ag Interfaces peptide

Au(111)

Ag(111)

AuBP1 AuBP2 AgBP1 AgBP2

2.56 2.71 3.11 2.77

3.09 2.84 3.73 3.34

adsorbed on Ag supports a greater conformational entropy compared with Au. This broadly tallies with the number and distribution of residues that have contact with the Au and Ag surfaces. By aligning these discrete entropy values with the definition of high, medium, and low conformational entropy published previously,11 it was determined that Sconf > 3 corresponds to the previous definition of high conformational entropy. Therefore, these data in Table 2 show that the conformational entropy tended to be higher for adsorption onto Ag, indicating that all sequences supported a greater number of distinct adsorbed conformations at the aqueous Ag interface compared with Au. Taken together, the residuesurface contact data, in partnership with conformational entropy analysis, suggest that the mode of adsorption for these peptides is very different for these two aqueous metal interfaces, with adsorption on Au appearing to be more enthalpically driven, while conformational entropy influences appeared more dominant for the Ag surface.11 This implies that the peptides adsorb onto Au in somewhat flatter configurations, while on Ag the peptide chains feature a greater variety of 3D adsorbed conformations. Representative snapshots from the REST MD simulations, given in Figure 6, illustrate this contrast in the adsorption for Au- and Ag-binding, in this instance for the AgBP2 peptide. Fabrication of Peptide-Capped Ag and Au Nanoparticles. Once the binding of the peptides had been studied through experiment and simulation, their use in the fabrication of both Au and Ag nanoparticles was investigated. For this, the peptide (0.1 mM) was combined with 2.0 equiv of Au3+ or Ag+ for 15 min while stirring,27 and then 2 equiv of NaBH4 was added to reduce the metal ions and generate peptide-capped nanoparticles. Using this approach, all of the four sequences were able to generate stable nanomaterials of both compositions without precipitation for at least 15 days. Once the eight different materials were generated, they were characterized by UV−vis spectroscopy and TEM imaging. Figure 7a presents the UV−vis spectra of the Ag nanostructures. Here, a localized surface plasmon resonance (LSPR) absorbance band was observed near 400 nm, consistent with the formation of small Ag nanoparticles. The strongest absorbance was observed from the particles capped with the AuBP2 peptide. Interestingly, the particles prepared using the AuBP1 peptide presented a weaker plasmon band; however, the absorbance displayed a large shoulder and a significant degree of scattering at higher wavelengths. Finally, the materials

Figure 6. Typical structures of the adsorbed conformations for AgBP2 on Au (top) and Ag (bottom).

Figure 7. Extinction spectra of stable colloidal dispersions of nanoparticles of (a) Ag and (b) Au prepared using each of the four peptides.

generated using AgBP2 demonstrated a relatively weak and broad plasmon absorbance, suggestive of a polydisperse or partially aggregated population of nanoparticles. Figure 7b presents the UV−vis absorbance of the Au materials fabricated using the same four peptides. For these, nearly identical absorbance spectra were observed, with a narrower plasmon band whose intensity varied only slightly between the different materials. Further characterization of the particles via TEM imaging was conducted to more directly study the particle morphology and size. Figure 8 shows TEM images of the peptide-capped Ag (top) and Au (bottom) nanoparticles. While nearly spherical particles were observed for all four of the Au materials, various morphologies and sizes were noted for the Ag structures. A mixture of spherical and aggregated Ag particles was obtained when the AuBP1 peptide was employed as the capping agent. For the spherical structures, a particle size of 8.0 ± 1.8 nm was noted. When the same peptide was used to generate Au particles, spherical materials with a diameter of 4.2 ± 1.1 nm were observed. When the AuBP2 peptide was used as the surface capping agent, spherical particles of 5.3 ± 1.1 and 3.8 ± 0.9 nm were prepared using Ag and Au, respectively. Spherical particles were also observed for both particle compositions using the AgBP1 peptide; however, the Ag F

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peptide-mediated nucleation and growth of Ag nanoparticles is governed by more than the overall peptide-metal binding affinity; more subtle biorecognition events may play a very important, and yet unclear, role. In particular, there appears to be a clear connection between the small number and degree of anchor points for the peptides adsorbed on Ag compared with Au anchor points (Figure 5) and the diminished control over particle size and morphology for the Ag materials. Note that the nanoparticle size and morphology do not correlate with the binding free energy (ΔG) to the metallic surface, thus the total binding strength does not appear to control the inorganic material structure. In all cases, the average Ag particle size is larger than the corresponding Au particle size. While all four peptides on the Au surface show a comparable and strong degree of residue contact, the corresponding degree of contact on the Ag surfaces, based upon the number of anchor points, is not only weaker but also fewer in number. The worst performers for Ag nanoparticle growth, AgBP1 and AgBP2, also support the fewest contacts. As such, the degree of contact with the metal surface may affect the ability of the peptide to effectively cap the nanoparticle as it grows, such that the weakest interfacial contact (as seen for AgBP1 and AgBP2 on Ag nanoparticles) confers the weakest control over nanoparticle size and morphology. Note that for Au binding, a significantly larger number of anchor points are present for all of the peptides as compared to binding to Ag. The number and strength of contacts between the peptide and surface is related to the enthalpic component of binding, which is only one factor in the overall binding affinity. Thus, peptide-mediated nanoparticle growth may be more closely related to the enthalpic component of binding than to the overall binding affinity. Furthermore, the degree of interfacial water structuring may also play a key role in determining the number and degree of anchor points. However, additional evidence is required to confirm this hypothesis. This is consistent with our previous observations for peptide-mediated synthesis of Au nanoparticles.27 Taken together, advanced molecular simulations, in partnership with experimental characterization, could provide vital clues regarding the viability of a given sequence to perform well as a nanoparticle nucleation and growth agent. At present, the a priori prediction of materials binding peptides, their compositional selectivity, and their effectiveness in mediating nanoparticle nucleation and growth remains out of reach. However, along with the experimental and molecular simulation approaches presented here, machine learning techniques are being applied by us to classify peptides based upon binding affinity for particular materials.28 It is our goal that ultimately a combination of these complementary approaches can be expected to provide predictive capabilities.

Figure 8. TEM images of Ag (top) and Au (bottom) nanoparticles prepared using each peptide, as labeled. Note that the images in the bottom row (for Au) are at higher magnification than the top row (for Ag) due to the differences in particle size.

nanoparticles were more polydisperse and larger. More quantitatively, the AgBP1-capped Ag materials had a mean diameter of 13.1 ± 4.5 nm, while the Au materials were 3.2 ± 0.7 nm in diameter. Finally, when the AgBP2 peptide was employed, significantly aggregated particles were noted for the Ag materials, such that no size distribution could be determined. For this, a mixture of morphologies was noted, mostly demonstrating a linearly aggregated ribbon-like morphology. When the same sequence was used to generate Au materials, spherical particles were observed with an average diameter of 3.6 ± 1.0 nm. Note that when both Au and Ag nanoparticles are prepared in the absence of peptide, larger and more irregular structures were observed (Supporting Information, Figure S13), confirming the effects of the peptides in controlling particle morphology. Additionally, when NaBH4 was substituted with ascorbic acid as the reductant for the generation of nanoparticles using the AuBP2 peptide, large, polycrystalline, porous materials were observed (Supporting Information, Figure S14). This indicates that specific reagents and conditions are required to generate high quality nanomaterials using biomimetic approaches. Comparing the results in Figures 7 and 8 reveals a correlation between the size and morphology of the nanoparticles and their optical properties. For the peptide-capped Ag structures, the most aggregated materials were observed when using the AgBP2 sequence. These structures gave rise to a relatively weak and very broad absorbance feature, reflecting the polydispersity and aggregation state of the materials. This trend was also observed for the AuBP1-capped Ag structures, which were also somewhat aggregated, resulting in diminished absorptivity with a significant degree of scattering at higher wavelengths. Finally, Ag particles prepared with the AuBP2 and AgBP1 sequences were spherical and nonagglomerated, which gave rise to the strongest and sharpest plasmon absorptions. For Au, spherical materials of comparable size were observed for all four systems, thus generating the similar plasmonic absorptions observed. Although all four peptides had very similar binding affinities on the bulk Ag surface, their effectiveness in modulating the nucleation and growth of Ag nanoparticles varied substantially, from AuBP2, which produced small, spherical, discrete nanoparticles, to AgBP2, which produced larger, nonspherical, and aggregated metallic structures. All four peptides are classified as entropic binders on Ag, with simulations revealing that they have relatively few surface contacts and a relatively high number of surface-bound conformations compared with peptides adsorbed on Au. Together, this suggests that the

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CONCLUSIONS In summary, we have investigated the materials-specific binding capabilities of peptides isolated based upon affinity for Ag (AgBP1 and AgBP2) and Au (AuBP1 and AuBP2) surfaces via an integrated program of experimental binding measurements, advanced molecular simulation, and nanomaterial synthesis. Binding measurements showed that compositional selectivity was possible on Au; however, minimal selectivity was possible for these peptides binding on Ag. This is quite important for the design of peptide sequences that control the growth and assembly of nanostructures, which may require high degrees of material specificity. Corresponding molecular simulations of G

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standard method reported previously.27 HAuCl4 and AgNO3 were selected as the metal precursors for Ag and Au nanoparticle syntheses, respectively. In a typical preparation, 500 μL of a 1 mM aqueous peptide solution and 10 μL of 0.1 M aqueous solution of metal precursor were first mixed for at least 15 min in a vial, followed by addition of 30 μL of an ice cold, freshly prepared NaBH4 (0.1 M) aqueous solution. Upon the addition of reducing agent, the color of the Au solution instantly turned from pale yellow to wine red, while the color of the Ag solution gradually changed from colorless to bright yellow, with exception of the AgBP2-capped Ag nanoparticles, which formed a gray dispersion. The products were left undisturbed for at least 1 h at room temperature before characterization. Characterization. TEM analysis was completed using a JEOL JEM-2010 microscope operating at an acceleration voltage of 200 kV. The TEM specimens were prepared by pipetting 30 μL of the nanoparticle dispersion onto carbon-coated Cu grids (Ted Pella) and allowing them to dry at ambient condition. UV−vis optical absorbance analysis of the Ag and Au nanoparticles were conducted using a Shimadzu 3600 UV−vis-NIR scanning spectrophotometer employing a 1 cm path length quartz cuvette. General Details of the Molecular Dynamics Simulations. For all MD simulations summarized below, we used GROMACS version 4.5.5.29 For the Au interface, our recently developed polarizable GolPCHARMM25 force-field was used here in partnership with the CHARMM22*30,31 and the modified TIP3P32,33 force-fields. Likewise for Ag, we used the polarizable AgP-CHARMM force-field24 with the CHARMM22*30,31 and the modified TIP3P32,33 force-fields. The metal atoms were held fixed in position throughout the simulations. For all metadynamics simulations, a time step of 1 fs was used with the LJ nonbonded interactions switched off between 10.0 and 11.0 Å, and a cutoff of 13.0 Å used for the PME summation. Metadynamics Molecular Dynamics Simulations. We performed 40 well-tempered metadynamics23 simulations, covering adsorption of the 20 naturally occurring amino acids at both the aqueous Au(111) and Ag(111) interfaces. The PLUMED34 plugin was used to apply the metadynamics approach to the amino acid adsorption simulations. The amino acids were capped by acetyl and N-methyl groups at the N- and C-termini, respectively. The L-chiral forms of the amino acids were modeled according to their protonation state at pH 7 with either a Na+ or Cl− used as a counterion to ensure overall charge neutrality where necessary. All of these simulations were carried out in the canonical (NVT) ensemble at a temperature of 300 K using the simulation details as already specified. The bias was applied to the position of the center of mass of each amino acid along the z axis (i.e., the direction perpendicular to the metal surface). Gaussians of 1 Å width were deposited every 1 ps for 100 ns, and the initial Gaussian height was set to 0.084 kJ mol−1. A well-tempered metadynamics bias factor of 10 was used. The zero point of the free energy was calculated as the average free energy at a distance greater than 15 Å from the surfaces. The uncertainty was determined from the difference between the final free energy and the average free energy over the last 5 ns of simulation. REST MD Simulations. A total of 8 REST35,36 simulations were carried out, two for each of the four peptide sequences listed in Table 1: one when adsorbed on Au and one when adsorbed on Ag. All REST simulations reported here have modeled a single peptide chain (one each from our set of four sequences) in its zwitterionic form, adsorbed onto either the planar Au(111) or Ag(111) surface, under aqueous conditions. Counterions (Na+ or Cl−) were used to ensure overall charge neutrality where necessary. The REST simulations were implemented according to our recent development, testing, and validation study, in the NVT (Canonical) ensemble, with an effective temperature range spanning 300−433 K. A total of 16 replicas were used to span this effective temperature window. All REST simulations were carried out for 15 × 106 MD steps, except for the AgBP1/ Ag(111) system, which required 20 × 106 MD steps. This duration of REST simulation yields conformational sampling that is projected to be equivalent to μs trajectories of conventional MD. Additional details, including evidence of sampling equilibration, can be found in the Supporting Information (Additional Computational Details).

both amino acids and peptides on the two metal surfaces indicated very different modes of binding, with mainly enthalpically driven binding on Au and entropically driven binding on Ag. Finally, these peptides were used to synthesize Au and Ag nanoparticles in buffer-free solutions, where significant differences in particle morphology and size were observed as a function of peptide sequence and metal composition. No significant trends between binding affinity and particle structure were evident, but clear connections between the number and strength of residue contacts and the ability to control nanoparticle growth were noted. In particular, if few and weak residue-surface contacts are predominant, we suggest that this confers poor capping of the nanoparticle during growth. Taken together, our results indicate that material-specific binding could be achieved using the peptide library studied based upon the differences in Au affinities but not based upon differences in affinity for Ag. This indicates that bioselection techniques can, in principle, provide the level of material binding specificity anticipated; however, independent confirmation of the expected selectivity is necessary. This provides clear guidance for the development of protocols for bionanocombinatorics and other applications that rely upon materials selective recognition.

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EXPERIMENTAL AND COMPUTATIONAL METHODS

Chemicals. All FMOC-protected amino acids, Wang resins, and peptide synthesis reagents (piperidine (>99.0%), N,N′-diisopropylethylamine (DIPEA, >99.0%), O-(benzotriazol-1-yl)-N,N,N′,N′-tetramethyluronium hexafluorophosphate (HBTU, >98.0%), and Nhydroxybenzotriazole monohydrate (HOBt hydrate, >98.0%)) were purchased from Advanced ChemTech. Trifluoroacetic acid (TFA, 99.5%) and tri-isopropyl silane (TIS, 98.0%) were purchased from Alfa Aesar. Ammonium hydroxide (20.0%) and hydrogen peroxide (30.0%) were purchased from VWR. Solvents, including acetonitrile, methanol, and N,N-dimethylformamide for peptide synthesis and purification were purchased from VWR. All the chemicals were used as received without further purification. Nanopure water (18.2 MΩ.cm; Millipore, Bedford, MA) was employed for all experiments. Peptide Synthesis. All the peptides were prepared using a TETRAS model synthesizer (Creosalus) employing standard FMOC protocols. After the synthesis, the peptide was cleaved, purified by HPLC, and subjected to MALDI-MS for confirmation, as in our previous report.11 QCM Analysis. QCM measurements were performed using a QSense E4 instrument (Biolin Scientific), as previously reported.11 The Au crystal sensors were purchased, while Ag sensors were prepared by sputter deposition of metallic Ag onto the Au surface. The Au sensors were first immersed in 1:1:5 (v/v/v) 30% H2O2/ammonium hydroxide/water solution for 5 min and then rinsed with copious amounts of water, while Ag sensors were cleaned with 2% SDS solution for 30 min and then rinsed with water. All QCM sensors were dried with N2 and then subjected to UV/ozone cleaning prior to use. Once inserted into the instrument, sensor integrity was determined by measuring the frequency change when continuously flowing with water for ∼5 min. Afterward, the peptide solution was flowed over the metal surface, and the frequency change and dissipation energy were recorded. Data analysis to quantify binding kinetics and thermodynamics was carried out as previously described in detail.11,22 Briefly, an exponential fit for each peptide concentration provides a first-order adsorption rate constant (kobs) for each concentration. Plotting kobs vs peptide concentration provides the adsorption and desorption rate constants (ka and kd) as the slope and intercept, respectively. The ratio ka/kd gives the equilibrium constant, from which the Gibbs free energy of adsorption can be computed directly. Synthesis of Ag and Au Nanoparticles. Ag and Au nanoparticles capped with different peptides were synthesized following the H

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REST MD Clustering Analysis. Detailed analysis was carried out on the constant-ensemble run at an effective temperature of 300 K (herein referred to as the reference trajectory). We classified the Boltzmann-weighted ensemble from our reference trajectories into groups of like structures, on the basis of similarity of their backbone structures, via the Daura clustering algorithm37 with a root meansquared deviation (RMSD) cutoff between backbone atoms of 2 Å. We performed our clustering analysis over the entire 15 ns trajectory in each case (except for the AgBP1/Ag(111) system, where we used the entire 20 ns trajectory). The population of a given cluster was calculated as the percentage fraction of the number of frames that were assigned membership of that cluster divided by the total number of frames in the trajectory. REST MD Contact Residue Analysis. We define a contact residue as one that maintains persistent contact with the surface. To quantify persistent contact, first, for each reference trajectory, we calculated the distance between the topmost layer of either the Au or Ag surface and each residue in the sequence. On the basis of these data, distance cutoffs were established to identify a range of separations where each particular residue was in immediate contact with the metallic surface. We then calculated the fraction of frames in the reference trajectory for which each residue was found within the contact range of surfaceresidue separation. We then defined a residue to be a contact residue if that residue was found to bind persistently to the surface. Our definition of persistent contact was satisfied if the given residue was found within contact range for 60% or more of the entire reference trajectory.

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ASSOCIATED CONTENT

S Supporting Information *

Additional experimental and computational analyses discussed in the text. This material is available free of charge via the Internet at http://pubs.acs.org.

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AUTHOR INFORMATION

Corresponding Authors

*E-mail: tiff[email protected] (T.R.W.). *E-mail: [email protected] (M.R.K.). Author Contributions #

These authors contributed equally.

Notes

The authors declare no competing financial interest.

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ACKNOWLEDGMENTS This material is based upon work supported by the Air Office of Scientific Research, grant number FA9550-12-1-0226. We gratefully acknowledge the Victorian Life Sciences Computation Initiative (VLSCI) for allocation of computational resources, and T.R.W. thanks veski for an Innovation Fellowship.

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REFERENCES

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