Biomolecular Adsorption at Aqueous Silver Interfaces: First

Jeffrey Comer , Ran Chen , Horacio Poblete , Ariela Vergara-Jaque , and Jim E. ... J. Pablo Palafox-Hernandez , Zhenghua Tang , Zak E. Hughes , Yue Li...
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Biomolecular Adsorption at Aqueous Silver Interfaces: FirstPrinciples Calculations, Polarizable Force-Field Simulations, and Comparisons with Gold Zak E. Hughes,*,† Louise B. Wright,‡ and Tiffany R. Walsh† †

Institute for Frontier Materials, Deakin University, Geelong, Victoria 3216, Australia Department of Chemistry and Centre for Scientific Computing, University of Warwick, Coventry CV4 7AL, U.K.

‡

S Supporting Information *

ABSTRACT: The molecular simulation of biomolecules adsorbed at noble metal interfaces can assist in the development of bionanotechnology applications. In line with advances in polarizable force fields for adsorption at aqueous gold interfaces, there is scope for developing a similar force field for silver. One way to accomplish this is via the generation of in vacuo adsorption energies calculated using first-principles approaches for a wide range of different but biologically relevant small molecules, including water. Here, we present such first-principles data for a comprehensive range of bioorganic molecules obtained from plane-wave density functional theory calculations using the vdW-DF functional. As reported previously for the gold force field, GolP-CHARMM (Wright, L. B.; Rodger, P. M.; Corni, S.; Walsh, T. R. GolP-CHARMM: firstprinciples based force-fields for the interaction of proteins with Au(111) and Au(100). J. Chem. Theory Comput. 2013, 9, 1616− 1630), we have used these data to construct a a new force field, AgP-CHARMM, suitable for the simulation of biomolecules at the aqueous Ag(111) and Ag(100) interfaces. This force field is derived to be consistent with GolP-CHARMM such that adsorption on Ag and Au can be compared on an equal footing. Our force fields are used to evaluate the water overlayer stability on both silver and gold, finding good agreement with known behaviors. We also calculate and compare the structuring (spatial and orientational) of liquid water adsorbed at both silver and gold. Finally, we report the adsorption free energy of a range of amino acids at both the Au(111) and Ag(111) aqueous interfaces, calculated using metadynamics. Stronger adsorption on gold was noted in most cases, with the exception being the carboxylate group present in aspartic acid. Our findings also indicate differences in the binding free energy profile between silver and gold for some amino acids, notably for His and Arg. Our analysis suggests that the relatively stronger structuring of the first water layer on silver, relative to gold, could give rise to these differences.

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appropriately. Force fields developed to describe the interactions of biomolecules at inorganic interfaces have recently appeared; one example is the CHARMM-METAL FF.31 This force field is based purely on Lennard-Jones (LJ) parameters for various transition metals, combined using standard mixing rules. However, the use of unmodified, surface-atom-centered LJ parameters guarantees that the most favorable mode of adsorption is in the hollow sites of the metal surface by definition;32 the more widely accepted view is that nonmetal adsorption onto noble metal surfaces proceeds via atop site adsorption,33 extending to more complex molecular adsorbates (e.g., Hush and co-workers34). Moreover, the simplicity of this model neglects the contribution of the polarizability of the metal atoms to the dynamic forces. One other recent FF, GolP,35 is focused purely on describing gold interfaces; this FF

INTRODUCTION The interactions between biomolecules and noble metal surfaces are an area of increasing interest, with potential applications in areas such as materials synthesis,1−3 biosensing,4 nanomedicine,5 and the separation of chiral molecules.6,7 However, a deeper understanding of the interactions on the atomic scale is needed to enable full exploitation of these interfaces in such applications. By combining the results of molecular simulations with experimental data, a more complete picture will emerge, detailing how biomolecules interact with metal surfaces/nanoparticles and why some molecular species show surface-binding selectivity over different crystallographic orientations of the same material8−12 and/or different material compositions.8,13−19 A number of studies have used molecular simulation to investigate the interactions of biomolecules with gold8,20−30 surfaces under aqueous conditions. However, some of the force fields (FFs) used in the past to model these interfaces have not been designed to capture biomolecule/noble metal interactions © 2013 American Chemical Society

Received: July 25, 2013 Revised: September 22, 2013 Published: September 30, 2013 13217

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captures polarization via the rigid-rod dipole approach36 and uses virtual interaction sites to ensure atop-site adsorption.33,34,37−44 This FF was recently revised and adapted; GolP-CHARMM37 provides a description of the Au(111) and (100) interfaces, in partnership with the CHARMM22*45 biomolecular FF (and other recent members of the CHARMM suite). In contrast to gold,8,20−30 there have been relatively few simulation studies of the interaction of biomolecules with aqueous silver interfaces. Molecular mechanics (MM) has been used in conjunction with surface-enhanced Raman scattering (SERS) experiments to investigate the interaction of a few biomolecules with Ag interfaces (e.g., Aliaga et al.46). The adsorption of phenylalanine on the (111) surfaces of various metals, including silver, has been studied using densityfunctional theory (DFT).47 The CHARMM-METAL FF31 contains parameters for describing biomolecule−silver interactions, but to our knowledge, the use of this FF has not yet been reported for the simulation of biomolecules adsorbed on silver under aqueous conditions. A molecular dynamics (MD) simulation study of tetraphenylporphyrins on silver under dry conditions48 described the silver surface with the GolP model but used the CHARMM-METAL parameters as initial values of the LJ nonbonding interactions, which were then refined. However, the details of this adjustment process and the revised parameters themselves are not reported in this study. In terms of past simulations of the aqueous silver interface, the Ag(111)−water interface has been modeled with the water−surface interaction described through the use of an external field.49 The behavior of a water overlayer on Ag(111) has also been studied using DFT50−53 as well as modeled classically by Siepmann and Sprik54 using MD simulations, where the classical model included an electrostatic induction treatment for the silver atoms as well as two- and three-body terms describing the water−metal interaction. It has been hypothesized that water overlayers on some transition-metal surfaces can support a 2D crystalline structure, though some recent studies have questioned this hypothesis; see, for example, the review by Hodgson and Haq.53 This behavior is thought to occur on Pt(111), where the distance between Pt atoms, ∼2.7 Å, is very close to the oxygen−oxygen spacing in bulk ice (∼2.7 Å). In contrast, no such 2D lattice has been observed on either Ag or Au.52,53 The stability of water overlayers on various transition-metal surfaces at 300 K has been studied using both FF-based54 and Car−Parrinello MD55 (CPMD) simulations. The CPMD study indicated that a hexagonal water overlayer structure was unstable on both Ag(111) and Au(111) but stable on Pt(111) (although in their approach the simulation time was limited to ∼10 ps).55 In contrast, the FF-based simulation of a hexagonal water overlayer structure on Pt(111) and Ag(111) by Siepmann and Sprik54 indicated that the crystalline water overlayer was stable on both Pt(111) and Ag(111), although in this case the time scale of their simulations was less than 100 ps. Whether the differences seen in these two studies was an outcome of the different potential energy surfaces on which these simulations were conducted or was a consequence of the relatively modest time scales simulated is an open question. Thus, given the limitations in interpreting the conflicting findings arising from both of these studies, there is scope for further investigation using a FF that is specifically tailored to capturing silver−water interactions. In addition, the simulation of an overlayer can act as another valuable check of the veracity of the description of

the interaction of water with the Ag(111) and Au(111) surfaces. In addition to the scarcity of molecular simulation data available for any adsorbates at the aqueous silver interface, there is also only a limited number of previous studies that have used static first-principles DFT calculations to predict the binding geometry and energies of adsorbates on the silver surface in vacuo. Much of this focus has been on the adsorption of water38,39,41,43,44,56 and benzene,57−60 where the Ag(111) surface has been the sole focus of these calculations. One of the limitations of the earlier DFT studies was the use of generalized gradient approximation (GGA) functionals and their known drawbacks, not capturing long-ranged (London) dispersion, and also not describing short-ranged to midranged dispersion interactions reliably. In recent years, however, several new exchange-correlation functionals, some involving nonlocal correlation contributions, have been developed to address some of these limitations.61,62 Although the field is still not fully mature, this new generation of functionals is proving useful for the reliable study of the interaction of small molecules with metal surfaces.41,43,44,58,59,63 Therefore, a comprehensive overview of adsorption on silver, on both the Ag(111) and Ag(100) surfaces, would bridge substantial gaps in our knowledge base for these systems. There are several aims of the work presented herein: (1) to gain an overview of small-molecule adsorption on Ag(111) and Ag(100) in vacuo using first-principles calculations with the nonlocal vdW functional revPBE-vdW-DF61,64 and to compare these data with those similarly obtained for the Au(111) and Au(100) surfaces; (2) to use these data to expand the GolPCHARMM FF to enable a description of the aqueous peptide− silver interface, thus giving a firm basis for evaluating crossmaterial adsorption comparisons; (3) to apply our new FF, denoted AgP-CHARMM herein, in investigating the structure and stability of both bulk water and water overlayers on both Ag(111) and Au(111); and (4) to evaluate and compare the free energy of adsorption for a range of representative amino acids at the aqueous interface for both Ag(111) and Au(111) using our FFs in partnership with meta-dynamics simulations.65

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METHODS

DFT Calculations. All DFT calculations were performed using the Quantum Espresso code (version 5.0.1),66 with the revPBE-vdW-DF exchange-correlation functional61,64 and ultrasoft pseudopotentials67 generated from scalar relativistic calculations. For the Ag(111) surface, a silver slab constructed from a p(4 × 3) supercell, four layers deep, was used (a total of 48 Ag atoms) for most DFT calculations, with larger supercells of p(4 × 4) and p(6 × 3) being used for some of the larger adsorbates. For the Ag(100) surface cells, p(3 × 3) and p(4 × 4), both five layers deep, were used; see Table S1 in the Supporting Information for more details. In the case of some adsorbates, it was deemed unnecessary to calculate their adsorption energy on both surfaces. For instance, we were motivated to calculate the adsorption energies of propene and phenylamine on Ag(111) because there were experimental data available for comparison. However, neither molecule is an optimal analogue for amino acid fragments, so in light of the computational expense, we did not determine the corresponding adsorption energy of these molecules on Ag(100). The cutoffs for the plane-wave kinetic energies and electron densities were 25 and 200 Ry, respectively. The Gaussian smearing method, with a width of 0.05 Ry, was used for Brillouin zone integration. Initially, each molecule was positioned close to the silver surface and the geometry was optimized, where none of the metal layers were held fixed during the relaxations. (During the relaxations, the interlayer spacing of the metal slab did increase slightly, but the in-plane 13218

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structure of the surface was retained, i.e., it was not distorted, and the slab remained flat.) The initial configurations used were taken from the minimum-energy configurations of the same molecules on the Au(111)/(100) surfaces37 (with the metal atom coordinates modified to allow for the slightly larger lattice constant of Ag). For those molecules that were not optimized on the Au surfaces, the initial configurations used were identified either from previous studies or by using the configuration of a similar molecule, previously established, as a starting point. For all molecules not originally optimized on Au and for some of the larger adsorbates, multiple initial configurations were optimized. All systems had periodic boundary conditions applied in all three dimensions and were constructed such that along the z axis a distance of ∼10 Å separated the molecule from the periodic image of the slab surface. A k-point mesh of 4 × 4 × 1 was used, and a 0.026 eV/Å force convergence criterion was applied. After each geometry had been optimized, the adsorption energy of the system was then determined from a single-point calculation using a vacuum layer of 25 Å and a k-point mesh of 6 × 8 × 1 or 8 × 8 × 1 for the Ag(111) and Ag(100) surfaces, respectively. The adsorption energy, Eads, was calculated from Eads = EAg‐mol − EAg − Emol

in the surface layer. For the Ag/Au(111) surface, two virtual interaction sites are present at (a/2(2)1/2, a/2(6)1/2, 0) and (−a/ 2(2)1/2, −a/2(6)1/2, 0), whereas for the Au/Ag(100) surface there is only a single virtual interaction site at (a/2(2)1/2, a/2(2)1/2, 0), where a is the lattice constant. In the case of the bulk metal atoms, the LJ parameters are associated with the metal atoms. During the fitting procedure, the LJ parameters of the virtual interaction sites and bulk atoms were altered to reproduce the energies and molecule−surface separations. The adsorption energies for AgP-CHARMM were calculated using GROMACS version 4.5.5.68 The LJ interactions were switched off between 9.0 and 10.0 Å whereas the electrostatic interactions were evaluated using the particle mesh Ewald (PME) method69 with a realspace cutoff of 11.0 Å. The calculations were performed in the canonical (NVT) ensemble, with the temperature maintained via the Nosé-Hoover thermostat70 with a coupling constant of 0.2 ps. A Ag(111)/(100) surface comprising a 6(3)1/2 × 10/10 × 10 supercell (constructed using the same lattice parameter of 4.165 Å as for the DFT calculations) that was five layers thick was placed in a periodic box of 30.6 × 29.5 × 70.0/29.5 × 29.5 × 70.0 Å (giving a vacuum layer between surfaces of ∼60 Å). For the unadsorbed systems, the minimum-energy configuration was determined by placing the molecule in the center of the interslab vacuum layer and minimizing the energy. For the adsorbed systems, simulated annealing MD simulations were performed. The temperature of the silver surface was maintained at 300 K, but the temperature of the molecule was reduced over 30−40 ps to 1 K. At least five configurations were generated for each molecule. After the minimum-energy configurations had been obtained, both the adsorbed and unabsorbed configurations were simulated for 50 ps at 300 K with all particles except for the silver dipoles fixed in position. The final interaction energy was calculated as the difference between the bond, angle, dihedral, and vdW interactions plus half of the difference between the electrostatic interactions of the adsorbed and unabsorbed systems, as detailed in previous studies.36,37 FF-Based MD Simulations. For all MD simulations reported in this section, the actual silver atoms in the metal slab were held fixed in space while the dipole particles were free to rotate (according to their restraint potential and also the temperature of the thermostat). Recently reported tests indicate that there is very little difference between the binding free energies obtained using a rigid substrate compared to those calculated using a slab where all atoms can move.71 The MD simulations of water (both bulk liquid and the overlayer) and amino acids with the aqueous silver interface were performed using GROMACS version 4.5.5.68 The PLUMED plugin72 was used to apply the meta-dynamics approach to the amino acid adsorption simulations. The CHARMM-modified version of the TIP3P73,74 water model was used for the water molecules and the CHARMM22* FF parameters used for the amino acids.45,75 For all 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. For the water overlayer, a 8(3)1/2 × 8(3)1/2R30° cell was used because this cell shape allows the water molecules to form a 2D crystalline structure, unlike the primitive cell structure. The water molecules were arranged in the H-down formation such that half the water molecules lay above a silver atom with their dipoles approximately parallel to the surface, and the other half lay above a silver atom with one hydrogen directed downward (as shown in Figure S1, Supporting Information). The water molecules were initially placed such that the oxygens of the nearly parallel water molecules were 2.9 Å above the metal surface, and the oxygens of the H-down water molecules were 3.3 Å above the Ag/Au atoms. This setup was determined to be the lowest-energy structure in static DFT calculations of water overlayers on Ag(111).50 A 25 ps simulation, at a temperature of 100 K, was then performed where the oxygen atom positions were held fixed, allowing the water molecules to rotate and maximize the chances of a crystalline structure forming. After this, three independent production runs of 5 ns were performed for each metal, with the temperature of the metal slab and water atoms

(1)

where EAg‑mol is the energy of the system with the molecule adsorbed to the silver surface, EAg is the energy energy of the silver surface, and Emol is the energy of the molecule in vacuum. Both the clean surface and the adsorbate molecules were relaxed beforehand. During the single-point calculations, the forces were checked to ensure that the threshold was not exceeded. In Vacuo Force Field: Parameter Fitting and Calculations. Full details of the AgP-CHARMM FF are given in Supporting Information section “Development of AgP-CHARMM force field”. Justification and details of the changes made to the rigid-rod dipole parameters are also provided in this section of the Supporting Information. In summary, AgP-CHARMM follows the same form as GolP35 and GolP-CHARMM,37 employing virtual interaction sites on the Ag surface and capturing polarization via the rigid-rod dipole approach36 with all Ag−Ag LJ interactions set to zero. For all MD simulations and calculations reported in this section, the actual silver atoms in the metal slab were held fixed in space while the rigid-rod dipole particles were free to rotate according to their thermostat temperature. When GolP-CHARMM FF37 was developed, the adsorption energies (and geometries) of small organic molecules on the Au(111) and Au(100) surfaces, in vacuo, were determined using plane-wave DFT calculations.37 A comparison of the energies obtained from the calculations and experimental adsorption energies showed that, in general, DFT calculations using the revPBE-vdW-DF functional61,64 agreed well with experimental data; as a result, the FF was parametrized using both first principles and experimental data. Unfortunately, in the case of silver, there are few available experimental adsorption energies of small organic molecules on silver surfaces, and thus the parametrization of our FF, AgP-CHARMM, was done using the data obtained from our DFT calculations, comparing to experiment where possible. For both Ag surfaces, our total set of molecules was subdivided into two subsets: a fitting set and a validation set. The parameters for the force field were fit against the energies and geometries of the molecules in the fitting set, and the force field results for the validation set were then tested against the results of the DFT calculations. Each set of molecules contained species featuring different functional groups: alkanes, alkenes, and oxygen-, nitrogen- and sulfur-containing species. In addition, where possible, the types of molecules were further identified (i.e., hydroxyl oxygen and carbonyl oxygen), and an example of each was placed in both sets. Table S1 in the Supporting Information provides the details of the assignment to each set. To ensure the correct adsorption of species (i.e., atop Ag atoms), the GolP/AgP model35 distinguishes between metal atoms that are in the surface layer from those in the bulk. The metal atoms in the surface layers do not interact through any LJ forces (i.e., ε = 0). Instead, LJ parameters are assigned to virtual interaction sites present 13219

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Table 1. Adsorption Energies (E/kJ mol−1) and Separation Distances (d/Å) Calculated in Vacuo Using vdW-DF for Various Molecules on the Ag(111) and Ag(100) Surfaces

independently thermostatted at 300 K via the Nosé-Hoover thermostat. The interaction of both the Ag(111) and Ag(100) surfaces with bulk water was modeled with surfaces of 40.81 × 44.18 Å2 and 44.18 × 44.18 Å2 used for Ag(111) and Ag(100), respectively. In both systems, the silver slabs were 5 layers thick and there was a total of 2045 water molecules present. The cell dimension along the z axis was adjusted such that the bulk density of the water was equal to that obtained from a box of modified TIP3P water simulated at the same temperature and ambient pressure. Once the correct density had been obtained, a production run, of 10 ns duration, was performed in the canonical (NVT) ensemble with the Nosé-Hoover thermostat used to maintain the temperature at 300 K. The adsorption free energy of six different amino acids (Ala, Arg, Asp, His, Lys, and Met) at the aqueous Ag(111) and Au(111) interfaces was also calculated. The amino acids were capped by acetyl and N-methyl groups at the N- and C-termini, respectively. The Lchiral 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. Simulations of histidine on Au(111) using the GolP-CHARMM FF indicated that the difference in the adsorption of the protonated and unprotonated forms of histidine was not sufficient to alter the rank ordering of the binding energies (Arg > His > Asp ≈ Lys > Ala).37 Thus, only the unprotonated form of histidine was considered here. Each system contained 2040 water molecules, with a 8(3)1/2 × 15 supercell used for the metal slab, which was five atomic layers thick. The free energy of adsorption of the amino acids to the metal surfaces was calculated using the well-tempered meta-dynamics approach.65 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 meta-dynamics 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.

Ag(111)

■

Ag(100)

molecule

Eads

d

Eads

d

methane ethane butane hexane cyclohexane ethene propene but-2-ene 1,3-butadiene benzene toluene water methanol methanoic acid methanal acetone methanamide ethanamide phenol ammonia methanamine ethanamine phenylamine imidazole indole methanethiol ethanethiol dimethyl sulfide diethyl sulfide

−14.6 −23.0 −40.5 −54.4 −45.6 −25.6 −32.3 −39.9 −41.5 −47.5 n/a −19.3 −31.3 −29.7 −24.3 −38.8 −35.6 −38.6 −56.9 −35.6 −48.3 −50.8 −59.3 −54.0 n/a −38.5 n/a n/a −64.2

3.83 3.91 3.89 3.88 3.74 3.38 4.04 3.62 3.43 3.38

−15.4 −23.1 −40.1 −55.0 n/a −27.7 n/a −41.6 −45.1 −49.9 −59.6 −21.3 −31.8 −30.6 n/a −38.7 −35.6 n/a −57.0 −38.8 −52.5 n/a n/a −57.9 −73.4 −43.2 −46.5 −57.6 −68.1

3.66 3.79 3.81 3.84

2.99 2.87 2.99 3.05 3.09 2.85 2.83 3.15 2.65 2.65 2.86 3.03 2.63 3.05

3.07

3.26 3.65 3.19 3.43 3.39 2.79 2.78 2.97 2.83 2.65 3.38 2.65 2.65

2.62 3.37 −51.8 2.96 2.90 3.03

reports of DFT calculations for this system; the results are summarized in Table 2.38,39,41,43,44,56 To ensure that the minimum-energy configuration was identified, two alternate starting configurations were also minimized: one with the oxygen atom above a hollow site and one configuration on an atop site but with the water dipole moment perpendicular to the metal surface. We chose to use the adsorption of a single water molecule in the parametrization of this force field. This was done to maintain consistency with our parametrization procedure for the aqueous gold−peptide interface, GolPCHARMM,37 because we seek to make cross-material comparisons of peptide binding on silver and gold on an equal footing. This parametrization procedure for water in GolP-CHARMM was shown previously to yield excellent agreement with first-principles simulations76 (using the vdWDF functional) for the interface between Au(111) and liquid water. Our results for water show general agreement with the previous theoretical results on Ag(111). However, we note that our comparison in Table 2 does not highlight the differing surface coverages inherent in a number of these previous studies. The adsorption predicted with revPBE-vdW-DF is stronger than for those calculated using the PW91 and PBE functionals41,56 but weaker than the binding predicted from BLYP, PBE+D3, and optB88-vdW functionals.38,43,44 Both the present work and previous studies identified the optimum binding configuration to have the water molecule

RESULTS AND DISCUSSION DFT Calculations. The optimized adsorption energies and separation distances for the various molecules adsorbed on the Ag(111) and Ag(100) surfaces, calculated using PW-DFT, are summarized in Table 1. For hydrocarbon species, the separation distance was defined as the difference along the z axis between the top layer of metal atoms and the average position of the carbon atoms. For those species containing a heteroatom, the separation distance was defined as the distance between the top layer of metal atoms and the heteroatom. The minimum-energy configurations of the molecules on the two silver surfaces are shown in Figures S2−S6 in the Supporting Information. Unfortunately, unlike in the case of gold37 there is little experimental evidence available on silver for comparison against the results of our DFT calculations. Therefore, in addition to making such comparisons where possible, the results of our DFT calculations have also been compared against previous theoretical studies38,39,41,43,44,56−60 of molecular adsorption on Ag(111) and also with the adsorption energies determined for molecules adsorbing to the Au(111) surface using the revPBEvdW-DF functional.37 This will allow trends to be identified in Ag/Au binding that can then be captured in the parametrization of the AgP-CHARMM FF. Water. Although there are no experimental values for the adsorption energy of water on Ag(111), there are numerous 13220

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Table 2. Summary of DFT Calculations of Water Adsorption on the Ag(111) Surface study

functional

Eads/kJ mol−1a

dOsurf/Åb

dOAg/Åc

α/degd

rOH/Åe

θHOH/degf

δO/Åg

this work Izvekov38 Sanchez39 Michaelides56 Carrasco41 Tereshchuk43

revPBE-vdW-DF BLYP PBE PW91 PBE PBE PBE+D3 PBE revPBE-vdW-DF optPBE-vdW-DF optB88-vdW-DF revPBE-vdW-DF

−19.3 −51.1 −19.3 −17.4 −14.5 −13.3 −30.3 −13.2 −18.5 −25.8 −27.1 −18.3

2.99

2.97 2.15 2.70 2.78 2.67 2.69 2.68 2.79 3.04 2.75 2.70 3.06

20.0 29.4 −17.0 9.0 0.5 4.5 0.4

0.98 0.98

105.0 105.5

0.05

0.97 0.98

105.0 104.2

0.29

22.0

0.98

Carrasco44

Gold37

3.03

0.27 0.56

104.6

0.07

Eads is the adsorption energy. bdOsurf is the distance between the oxygen atom of the water molecules and the Ag(111) surface plane. cα is the angle between the dipole of the water molecule and the in-plane surface parallel. drOH is the bond length of the water molecule. eθHOH is the bond angle of the water molecule. fdOAg is the distance between the oxygen and the closest Ag atom. gδO is the lateral distance (in the xy plane) between the oxygen and the top site. a

using the revPBE-vdW-DF functional compare well against the experimental adsorption energies, especially in the case of gold.80 Previous theoretical values of the adsorption energy vary considerably depending on the functional used.57−60 In particular, as previously discussed, the GGA functionals significantly underestimate the adsorption energies. Overall, most functionals show benzene adsorbing more strongly to Au(111) than to Ag(111), in agreement with the experimental evidence, with only the PBE+vdWsurf and PBE functionals and the MP2 method (with a cluster model), showing the opposite trend. Our calculated adsorption geometry on Ag(111) was found to agree closely with those obtained from some of the more recent previous studies; an in-depth discussion is given in the Supporting Information, “Benzene Geometry” section. Comparison with Experiment. Aside from benzene, there are a small number of other molecules for which experimental adsorption data on Ag(111) are available. Table 3 summarizes

situated on an atop site. The widest variation between the results is in the case of the tilt angle, α, with values ranging from ∼0 to 30°. The revPBE-vdW-DF functional gives an angle toward the upper end of this range, but this may be dependent on the surface coverage and thus the unit cell size.63 As the surface coverage was decreased, the tilt angle increased from close to zero up to 21.8°. In the present study, a p(4 × 3) supercell was used, Michaelides and Carrasco used p(2 × 2) supercells, Da Silva used p(3 × 3), and Izekov p(4 × 4). Thus, it seems probable that surface coverage affects the tilt angle,63 in addition to the different functionals used in these studies. There is some minor variation in the lateral displacement of the oxygen atom with respect to the Ag atop site; the revPBE-vdWDF functional places the oxygen almost directly atop the Ag atom, and the PW91 and semiempirical PBE+D3 functionals have the oxygen slightly displaced to one side. In comparison to gold, we found that a single water molecule adsorbed more strongly to Ag(111) than to Au(111). In general, most DFT results show Ag(111) to be the more favorable surface. As found for the Ag(111) surface, the most favorable binding site on the Ag(100) was atop an Ag atom, followed by the bridge site and then the hollow site, with interaction energies of −21.3, −18.7, and −15.9 kJ mol−1, respectively. Table S2 in the Supporting Information summarizes the adsorption energy at each site. A previous study of water adsorption on a Ag(100) cluster, performed at the configuration interaction (CI) level using an ab initio embedding approach, calculated adsorption energies of 50.2−32.2 kJ mol−1,77 which are somewhat larger than the values calculated in this study. However, both studies found the hollow site to be the most unfavorable adsorption site. As has been noted for the gold surfaces,37 water was found to bind more strongly to the Ag(100) surface than to the Ag(111) surface. Benzene. The other molecule whose adsorption onto Ag(111) has been studied in some detail is benzene. Here, in addition to the results of a number of previous theoretical studies,57−60 experimental data are also available for comparison.78,79 Table S3 (Supporting Information) compares the adsorption energy calculated in the present study against literature values. In addition to our calculation of Eads for benzene on Ag(111), we also calculated Eads for benzene on Au(111). The adsorption energies calculated for benzene on both Ag(111) (−47.5 kJ mol−1) and Au(111) (−56.9 kJ mol−1)

Table 3. Comparison of Experimental and Theoretical Adsorption Energies of Molecules on the Ag(111) Surface, Determined in Vacuo (Experimental Results at Submonolayer Coverages) Eads/kJ mol−1 molecule

experimental

acetone methanal phenol phenylamine ethene propene 1,3-butadiene

−37.687 −25.1,88 −29.089 −67.579 −73.390 −36.7 to −39.681,91,92 −42.593 −54.494

previous DFT −31.842

−6.840 −3.340

present DFT −38.8 −24.3 −54.3 −59.3 −25.6 −32.3 −41.5

the experimentally determined adsorption energies of molecules on Ag(111) available in the literature, along with the results of past theoretical studies40,42 and our own calculations. For methanal and acetone, there is very good agreement between the experimental energies and our calculated values. In contrast, the binding energy reported by Reckien et al., obtained using the semiempirical DFT-D functional, overestimates the binding energy;42 as in the case of benzene and water, although the adsorption energies obtained using this functional are greater than those obtained using the vdW-DF 13221

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indicating that binding to (111) and (100) is approximately equally likely, rather than showing a distinct preference for the (111) facet over (100). For convenience, in Table S4, we collate our set of calculated adsorption data that is common between silver and gold.37 For both silver and gold, the difference in the adsorption energies of alkane and oxygencontaining species to the two surfaces is ≤1.2 kJ mol−1 for all species except water. In the case of the nitrogen- and sulfurcontaining species (and to a lesser extent the alkenes), the preference for the (100) surface is more marked. In addition, the facet selectivity of (100) over (111) for the nitrogen- and sulfur-containing species is much greater for gold than it is for silver. For methanamine, imidazole, methanethiol, and diethylsulfide, the values of EAg(100) − EAg(111) are −4.2, −3.9, −4.7, and −3.9 kJ mol−1, respectively. In contrast, for the same molecules on gold the respective facet-selective energy differences are −6.5, −13.1, −6.3, and −8.7 kJ mol−1. Fitting of Force-Field Parameters. As with the parameter fitting for the Au(111) surface,37 the generic Ag−Ag interaction parameters were the first ones fitted using the alkane adsorption data. Once satisfactory Ag−Ag parameters had been thus determined, a series of bespoke Ag−X parameters were developed for various atom types to capture the weak chemisorption of these species. The full details of the development, fitting, and validation of the AgP-CHARMM FF are given in the SI in section “Development of AgPCHARMM Force-field”. The final values of the FF parameters for Ag(111) and Ag(100), along with the corresponding parameters for gold, determined previously,37 are summarized in Tables 4 and 5.

functional, the lowest-energy configurations were very similar. In the case of phenol and phenylamine, our DFT results indicate a rank ordering in binding energies, PhNH2 > PhOH > benzene, reflecting the trend in the experimental binding energies of these aromatic species, and the agreement between the absolute experimental energies and our calculated values is not as good as seen for methanal and acetone. Experimental binding energies are available for aliphatic alkene molecules adsorbed on Ag(111), determined using temperature-programmed desorption, TPD. For ethene and 1,3-butadiene, from our calculations the revPBE-vdW-DF functional yields adsorption energies in better agreement with the corresponding experimental values than those previously reported by Bocquet et al.,40 who used the PW91 functional, known to be problematic in reliably capturing weak, nonbonded interactions. However, our calculated adsorption energies are still significantly lower than those determined experimentally. It should be noted, however, that the value for the experimental adsorption energy of ethene on Ag(111) has been taken from the work of Jalkanen and Zerbetto81 and was estimated from the peak desorption temperature using the Redhead analysis. The reported experimental adsorption values for ethene and 1,3-butadiene on Au(111) are −27.0/−27.7 and −46.2 kJ mol−1, respectively.80 Thus, the experimental results indicate that aliphatic alkenes bind more strongly to Ag than to Au. In contrast, our PW-DFT calculations for ethene with the revPBE-vdW-DF functional give Au(111) as the preferred surface, although this difference is small enough (∼2 kJ mol−1) to consider these two adsorption energies to be effectively equivalent. Nevertheless, for the Ag(111) surface, again our vdW-DF calculations recover the experimental trend in the binding energies: 1,3-butadiene > propene > ethene. Although these findings for the aliphatic alkenes suggest that there is still scope for improvement for the vdW-DF-type functionals, our results here should not adversely affect the quality of the fit of our AgP-CHARMM silver−peptide force field due to the lack of unsaturated aliphatic carbons in any of the naturally occurring peptide residues. Material and Facet Binding Selectivity in Vacuo. There are no experimental adsorption energies for alkanes to Ag(111). However, Morikawa et al. calculated the adsorption energies of n-butane on Ag(111) and Au(111) using the GGA functional,82 and whereas the resultant binding energies are low, ∼−8 kJ mol−1, this study did identify Au(111) as the more favorable binding surface. For our calculations using the revPBE-vdW-DF functional, Au(111) remained the more favorable surface over Ag(111). Overall, we found that most molecules adsorbed more strongly to Au(111) than to Ag(111), with the exceptions being the oxygen-containing species: water, methanol, methanamide, and methanoic acid. In general, the difference in adsorption energies for Au(111) and Ag(111) for the alkanes, alkenes, and oxygen-containing species (∼2 to 3 kJ mol−1) was less than for the nitrogen- and sulfur-containing species (except for imidazole, where the energy difference is only 0.1 kJ mol−1). For those species containing oxygen, nitrogen, or sulfur, it was found that adsorption was mediated by the heteroatom binding atop an Ag atom. Except for butane, acetone and methanamide, every molecule was found to bind more strongly to the Ag(100) surface than to the Ag(111) surface; similar behavior was observed for Au(111) and Au(100)37 (Table 1). Even for these three cases, the energy difference is marginal (0.4 kJ mol−1 or less),

Table 4. LJ Interaction Parameters for the Ag(111) Surface, with Corresponding Parameters for Au(111)37 Shown for Comparison (M = Metal) Au37

Ag M−M M−M (C sp2)a M−N (N-terminal/lysine) M−N (imidazole) M−O (water/hydroxyl) M−O (amide) M−O (carbonyl) M−H (polar/thiol) M−S a

ε/kJ mol−1

σ/Å

ε/kJ mol−1

σ/Å

0.41 0.60 2.60 1.80 0.90 1.00 0.50 0.20 2.40

3.85 3.28 2.85 2.90 3.10 3.10 3.15 2.73 2.90

0.48 1.30 0.90 1.60 0.70 0.70 0.70 0.28 3.20

3.80 3.20 2.90 2.85 3.10 3.10 3.10 2.70 2.85

Applies only to the virtual sites not the bulk metal atoms.

For the Ag(111) surface, bespoke interaction parameters were needed for hydroxyl oxygens, amide oxygens, carbonyl oxygens, polar and thiol hydrogens, sulfur, and two types of nitrogen atoms (the unprotonated histidine residue and the unprotonated nitrogen in lysine or at the N-terminus of amino acids). In addition, a refined set of Ag−Ag parameters were required between the virtual interaction sites and sp2 carbons.37 For the Ag(100) surface, the types of bespoke interaction parameters were the same as those needed for Au(100) in the GolP-CHARMM force field: sp2 aromatic carbons, the N in aromatic groups, the N of aliphatic amines (when unprotonated), the O of water and aliphatic alcohols, the O of amide and carbonyl groups, the H of polar and thiol groups, the S of thiols, and the S of sulfides. 13222

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that extends to ∼6 Å from the surface; a visual inspection of the trajectories also confirms the existence of some water molecules in a second “layer” rather than in direct contact with the metal surface. Liquid Water Interface. Figure 1a,b shows the water density profile for the oxygen and hydrogen atoms of the water molecules in bulk liquid water at both the Ag(111) and Ag(100) interfaces. Simulations of the Ag(111) and Ag(100) slabs in bulk liquid water produced results that appear to be very similar to those for the corresponding Au surfaces, but with the height of the first peak being slightly greater37 in the interfacial density profile. The Ag(111) profile compared favorably with previous classical simulation results.49 The density profiles at the Ag(100) interface are shifted slightly closer to the silver surface compared to Ag(111), consistent with the fact that the interaction of a single water molecule with Ag(100) is relatively stronger compared with Ag(111) (Table 1). The water dipole moment profiles (Figure 1c) indicate that the water molecules in the first layer above both silver surfaces are, on average, oriented such that the hydrogen atoms are directed away from the surface. Similar to what was noted for the gold surfaces, the first peak in this orientational profile is sharper for Ag(100) than for Ag(111). The hydrogen bonding of the water molecules is shown as a function of the distance from the silver surfaces in Figure S9 (Supporting Information). As was the case for the aqueous gold interface, the water molecules closest to the silver surface show an increased propensity to act as hydrogen bond donors compared to water molecules in the bulk.37 Overall, the differences between the Ag(111)/(100)−water and Au(111)/ (100)−water interfaces appear to be quite minor, with the 1.0/ 0.4 kJ mol−1 differences in the adsorption energies of individual water molecules in vacuo on Ag versus Au, respectively, not yielding a large net effect on this relative behavior of bulk water at the interface. However, these relatively slight differences could play an important role in conferring facet and materials binding selectivities of certain biomolecules under aqueous conditions.83 Amino Acid Adsorption Free Energies. The free energies of adsorption of the amino acids at the aqueous Ag(111) and Au(111) interfaces calculated in this study are shown in Figure 2. Figure S10 shows the position of the center of mass of the amino acid as a function of time for two representative systems. It is clear that the energy landscape was fully sampled in this collective variable. As might be expected, the combination of the stronger binding of water and the weaker binding most other functional groups to Ag(111) relative to the Au(111) surface (in vacuo) suggests that the amino acids should show stronger binding on the Au(111) surface; however, as noted in the previous section, the presence of liquid water may modulate these preferences. The relative ordering of the calculated amino acid adsorption free energies, ΔGads, at the aqueous Au(111) interface is Arg > Met > HisA > Asp ≈ Ala ≈ Lys, which broadly agrees with the ordering of potential energy differences (ΔPEads) of adsorption (calculated under aqueous conditions), Arg > HisA > Lys ≈ Asp > Ala, previously calculated using a “compartmentalized” approach.37 We also note in the case of Au(111) that the genuine binding free energy ΔGads did not always agree, in terms of absolute value, with ΔPEads. In particular, the differences between ΔGads and ΔPEads for both arginine and alanine are substantial; taking error bars into account, the maximal differences between these two measures of binding are 18 and 15 kJ mol−1 for Arg and

Table 5. LJ Interaction Parameters for the Ag(100) Surface, with corresponding parameters for Au(100)37 Shown for Comparison (M = Metal) Au37

Ag M−M M−M (C sp2 aromatic)a M−N (terminal/lys) M−N (imidazole) M−O (water/aliphatic alcohol) MO (amide/carbonyl) M−H (polar and thiol) M−S (thiol) M−S (sulfide) a

ε/kJ mol−1

σ/Å

ε/kJ mol−1

σ/Å

2.00 2.50 4.80 3.60 1.65 1.00 0.60 4.60 5.30

3.90 3.40 3.10 3.12 3.26 3.25 2.70 3.10 3.20

2.10 2.80 5.40 4.40 1.50 0.60 0.60 5.20 6.90

4.00 3.40 3.00 3.00 3.275 3.35 2.725 3.00 3.00

Applies only to the virtual sites, not the bulk metal atoms.

The generic Ag−Ag parameters for the Ag(111) surface were only slightly modified from the existing Au−Au parameters, but most of the bespoke terms were altered more substantially. The σ value for many of the interactions was increased slightly, consistent with the slightly longer lattice parameter of Ag compared to that of Au. The geometries of most adsorbates were a good match to those determined from the DFT calculations. However, as found for the GolP-CHARMM FF, whereas the final geometry of the water molecule placed the water oxygen atop an Ag atom, the water dipole was oriented perpendicular to the metal surface rather than tilted at an angle. The root-mean-squared deviations (rmsd) of the adsorption energies of the fitting set of molecules determined from the DFT calculations and obtained using the force field were 3.0 and 0.6 kJ mol−1 for Ag(111) and Ag(100), respectively. The corresponding rmsd values for the validation set were 3.9 and 2.4 kJ mol−1 for Ag(111) and Ag(100), respectively. Water Overlayers. As previously discussed, on the basis of experimental observations, a crystalline water overlayer on either Au(111) or Ag(111) is expected to be unstable,52,53 with water molecules in the overlayer free to move at room temperature.55 Simulations of a water overlayer on both Ag(111) and Au(111) at 300 K using the AgP-CHARMM and GolP-CHARMM FFs, respectively, reproduced this behavior. The initial crystalline structure quickly broke up, as shown in Figure S7 (Supporting Information). In agreement with the first-principles simulations of Schnur and Groß,55 by 10 ps the original hexagonal structure is almost completely lost in our simulations. After 1 ns of simulation, the water molecules are spread in a disordered fashion on the surface on the metal (Figure S7, Supporting Information). The surface-oxygen distribution function of the water molecules on the Ag(111) surface was calculated over the first 10 ps of the trajectory (to align with the simulation and analysis time scale reported by Schnur and Groß), as shown in Figure S8a in the Supporting Information. Our data closely resemble those reported by Schnur and Groß for their first-principles simulations of the same system and over the same time period (distribution functions averaged over longer time periods changed only slightly).55 The average density profiles of the overlayer water molecules on both Ag(111) and Au(111), calculated over the last 4 ns of our 5 ns production runs, are similar for these overlayer systems, with the peak of the Au profile being somewhat wider and positioned slightly closer to the surface (Figure S8b, Supporting Information). The density profiles also show a tail 13223

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Figure 2. Free energy of adsorption for the six amino acids simulated.

entropic contribution of a molecule can play a critical role in the prediction of the binding affinity, and the free energy of adsorption has been shown here to differ markedly from the potential energy of adsorption. We therefore suggest caution in interpreting binding results based on differences that do not incorporate a quantitative description of the change in entropy. Our free-energy data obtained using GolP-CHARMM broadly indicate that these binding affinities are weaker than those calculated using the original GolP force field,24 whereas with GolP-CHARMM the relative binding strength for arginine has increased and that of lysine has decreased. The adsorption enthalpies of the 20 amino acids in their zwitterionic forms (i.e., not the capped forms as in previous studies or the present work) at the aqueous Au(111) interface have also been determined using the CHARMM-METAL FF, employing the compartmentalized approach (where the protonation state of histidine was not stated).22 This study reported the order of ΔHads as Arg > Met > Lys ≈ His > Asp > Ala. Clearly, this comparison between the ΔHads of the zwitterionic amino acids and the ΔGads of capped amino acids should be treated with caution. However, both studies do agree that arginine and methionine bind more strongly than alanine and aspartic acid. The differences in ΔGads between gold and silver, indicated in Figure 2, show that binding to gold was clearly favored over binding to silver in the majority of cases, with the most pronounced differences seen for Arg, HisA, and Met adsorption. The only two examples to contradict this trend were Lys, where the binding was roughly isoenergetic, and Asp, where there was a slight preference for silver over gold. These differences in material binding selectivity in aqueous solution show some striking differences and also some close similarities, with binding energy trends (albeit not binding free energy trends) calculated in vacuo. For example, the in vacuo binding energies of imidazole on silver and gold are almost identical, yet HisA showed one of the largest differences in ΔGads between gold and silver in aqueous solution. We note, however, that some of this difference could be ascribed to the differences between imidazole and the imidazole-containing amino acid. However, our DFT calculations predicted that the adsorption of oxygen-containing species, such as methanoic acid, is preferred on silver relative to gold in vacuo. This preference appears to be consistent with our predicted ΔGads preference (albeit slight) for the adsorption of the oxygen-rich Asp molecule, under aqueous conditions, onto silver over gold. The same is true in the comparison of in vacuo binding energies of

Figure 1. Density profiles of the (a) the oxygens and (b) the hydrogens of water molecules and (c) the water dipole moment profile of the Ag(111) and Ag(100) slabs solvated in bulk liquid water.

Ala, respectively. The most striking difference in this sense is seen for alanine. The ΔPEads value was found to be positive37 (i.e., it was unfavorable for the molecule to adsorb to the surface) whereas ΔGads was −8.7 ± 1.0 kJ mol−1. Thus, the 13224

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Figure 3. Adsorption free-energy profiles of (a) alanine, (b) arginine, (c) aspartate, (d) histidine, (e) lysine, and (f) methionine on the Ag(111) and Au(111) surfaces.

sulfur-containing molecules versus the binding of Met in solution. In terms of comparison with experiment, as with our DFT results, there are fewer data points to compare against in the case of amino acid adsorption onto aqueous silver interfaces such that we can make direct comparisons. In presenting the following discussion, we recognize that the experimental data

described herein offer only very indirect means of comparison; this basis of comparison can be fraught with challenges in terms of appropriate interpretation. In particular, when comparing amino acid adsorption (as calculated here) with peptide adsorption, it is necessary to take into account that peptide adsorption is not necessarily an additive sum of the binding affinity of each individual residue in the sequence. This is 13225

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binding to the Ag(111) and Ag(100) surfaces, evaluated using plane-wave DFT calculations in vacuo, using the vdW-DF functional. Our in vacuo DFT results indicate that most of the molecules considered showed an energetic preference for the (100) surface over the (111) and that most of these molecules also favored adsorption onto gold over silver, with the exception of oxygen-rich species, including water. We then used these data to develop, fit, and validate a polarizable force field to describe the interface between peptides and the aqueous silver interface, AgP-CHARMM. AgP-CHARMM was derived to be consistent with the existing GolP-CHARMM force field for peptide−gold interfaces to enable cross-material adsorption comparisons on an equal footing. We then applied AgP-CHARMM in the investigation of the structure and stability of water overlayers on both the Ag(111) and Au(111) surfaces, with our results yielding excellent agreement with known behaviors reported previously. We also predicted the structure of the interface between bulk liquid water and the Ag(111) and Ag(100) surfaces. Finally, we used AgPCHARMM to calculate the free energy of adsorption for a range of six representative amino acids for both Ag(111) and Au(111). These simulations revealed not only that, in general, the binding of the amino acids to silver was weaker than seen for gold, but also that the form of the adsorption free-energy profiles differed between the two materials, particularly for Arg and His. Our free-energy profiles suggest that, with the exception of Met, the expulsion of water molecules in the first solvation layer at the Ag(111) interface presented a free-energy barrier that the adsorbate must overcome to make close contact with the silver surface. The presence of this barrier was not seen in a majority of the gold adsorption cases. The results of our DFT calculations show that, relative to gold, water binds more strongly to silver. Although this led to only minor structural differences in the Ag−water interface compared to the Au− water interface, this difference appeared to play a significant role in the adsorption of amino acids to the Ag(111) and Au(111) surfaces.

because of the interplay between the peptide sequence, conformation, and binding propensity.84 In their screening study of silver-particle-binding peptide identification, Naik et al.1 noted that their sequences were rich in hydroxyl-containing residues. Furthermore, the nonsilver-binding sequence reported by Currie et al.85 was rich in histidine residues. Both of these observations are broadly consistent with our findings primarily a preference for oxygen-rich specieswhereas His shows the weakest binding in our set of adsorbates. A recent experimental study of silver-binding peptide selection reported that Asp, Cys, Lys, Gln, and Tyr were overexpressed whereas His, Gly, Trp, and Val were underexpressed in the sequences of strong-binding peptides.86 Although such over/underexpression of peptide residues does not necessarily correlate with the binding strength of the individual amino acids, this finding is consistent with the relatively stronger adsorption calculated for Lys and Asp as well as the weaker binding of His. Alongside the difference in the absolute free energies of amino acids to silver and gold, the shape of the free-energy profiles (shown in Figure 3) also warrants further comment. Although the position of the primary minimum was typically the same for both metals, ∼4.5 Å, most of the profiles for Au(111) were not only deeper but also broader than those calculated for Ag(111). From the free-energy profiles of the amino acids at the Au(111) interface determined from the GolP FF, Hoefling et al.23 proposed a three-stage pathway consisting of diffusion (where the interaction between the amino acid and the gold surface is negligible), association (where the free-energy profile becomes negative), and binding (where the gradient of the profile increases again). The freeenergy profile of the six amino acids simulated on the Au(111) surface seems to be consistent with this mechanism, although GolP-CHARMM appears to show a more distinctive separation between the binding and association stages, with a small maximum in the profiles suggesting where the ingress of the amino acid may lead to the expulsion of the strongly bound water molecules in the first solvation layer at the metal interface. In the case of Ag(111), the fact that the water molecules are bound more tightly to the surface means that this local maximum, ranging up to ∼6 Å, is typically greater for silver than for gold, with one exception in the case of Asp (where the barrier was similar for both silver and gold). These findings suggest that whereas it is thermodynamically favorable for the amino acids to adsorb onto the Ag(111) surface there is likely to be a kinetic barrier to adsorption, compared to adsorption on Au(111). Snapshots of some representative configurations of the amino acids adsorbed on the Ag(111) and Au(111) surfaces are shown in Figure S11. The configurations agree with those found in the compartmentalized study of the amino acids on the Au(111) surface using the GolP-CHARMM FF.37 For instance, it was found that Arg prefers to adsorb to both Au(111) and Ag(111) with the guanidinium group parallel to the metal surface, whereas HisA is seen to adsorb on both surfaces with the imidazole ring perpendicular to the surface, via the unprotonated nitrogen.

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ASSOCIATED CONTENT

S Supporting Information *

GROMACS input and parameter files can be obtained from the authors upon request. Details of the minimum-energy configurations of benzene on Ag(111) and Au(111), the development of the AgP-CHARMM FF, and the plane-wave DFT calculations for the different molecules. Adsorption energies at different sites on the Ag(100) surface. Comparison of previous theoretical values for the adsorption of benzene to the Au(111) and Ag(111) surfaces. Plane-wave DFT minimumenergy configurations of the molecules on the Ag(111) and Ag(100) surfaces. Adsorption energy and separation distances, from both the DFT and FF calculations, of the test and validation sets of molecules to the Ag(111) and Ag(100) surfaces. Snapshots taken at various times from the simulation of the water overlayer on the Ag(111) and Au(111) surfaces. Distribution function and density profiles of the same simulations. Hydrogen bond profiles of the liquid water interface on Ag(111) and Ag(100). Plots of the collective variable of the meta-dynamics runs as a function of time for the free energy. Snapshots of representative configurations of the amino acids on the Ag(111) and Au(111) surfaces. This material is available free of charge via the Internet at http:// pubs.acs.org/.

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CONCLUSIONS The interaction of amino acids/peptides with noble metal surfaces is an area of growing interest, yet in contrast to gold, there are relatively fewer studies, either experimental or theoretical, reported for silver interfaces. In this work, we presented a comprehensive overview of organic molecule 13226

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subtractive bacteriophage display approach. J. Mater. Chem. 2008, 18, 3871−3875. (15) Goede, K.; Busch, P.; Grundmann, M. Binding specificity of a peptide on semiconductor surfaces. Nano Lett. 2004, 4, 2115−2120. (16) Willett, R. L.; Baldwin, K. L.; West, K. W.; Pfeiffer, L. N. Differential adhesion of amino acids to inorganic surfaces. Proc. Natl. Acad. Sci. U.S.A. 2005, 102, 7817. (17) Hayashi, T.; Sano, K. I.; Shiba, K.; Iwahori, K.; Yamashita, I.; Hara, M. Critical amino acid residues for the specific binding of the Tirecognizing recombinant ferritin with oxide surfaces of titanium and silicon. Langmuir 2009, 25, 10901−10906. (18) Schneider, J.; Colombi Ciacchi, L. Specific material recognition by small peptides mediated by the interfacial solvent structure. J. Am. Chem. Soc. 2012, 134, 2407−2413. (19) Hnilova, M.; So, C. R.; Oren, E. E.; Wilson, B. R.; Kacar, T.; Tamerler, C.; Sarikaya, M. Peptide-directed co-assembly of nanoprobes on multimaterial patterned solid surfaces. Soft Matter 2012, 8, 4327−4334. (20) Braun, R.; Sarikaya, M.; Schulten, K. Genetically engineered gold-binding polypeptides: structure prediction and molecular dynamics. J. Biomater. Sci., Polym. Ed. 2002, 13, 747−757. (21) Verde, A. V.; Acres, J. M.; Maranas, J. K. Investigating the specificity of peptide adsorption on gold using molecular dynamics simulations. Biomacromolecules 2009, 10, 2118−2128. (22) Feng, J.; Pandey, R. B.; Berry, R. J.; Farmer, B. L.; Naik, R. R.; Heinz, H. Adsorption mechanism of single amino acid and surfactant molecules to Au111 surfaces in aqueous solution: design rules for metal-binding molecules. Soft Matter 2011, 7, 2113−2120. (23) Hoefling, M.; Iori, F.; Corni, S.; Gottschalk, K.-E. The conformations of amino acids on a gold(111) surface. Chem. Phys. Chem 2010, 11, 1763−1767. (24) Hoefling, M.; Iori, F.; Corni, S.; Gottschalk, K.-E. Interaction of amino acids with the Au(111) surface: adsorption free energies from molecular dynamics simulations. Langmuir 2010, 26, 8347−8351. (25) Di Felice, R.; Corni, S. Simulation of peptide-surface recognition. J. Phys. Chem. Lett. 2011, 2, 1510−1519. (26) Feng, J.; Slocik, J. M.; Sarikaya, M.; Naik, R. R.; Farmer, B. L.; Heinz, H. Influence of the shape of nanostructured metal surfaces on adsorption of single peptide molecules in aqueous solution. Small 2012, 8, 1049−1059. (27) Hoefling, M.; Monti, S.; Corni, S.; Gottschalk, K.-E. Interaction of β-sheet folds with a gold surface. PLoS One 2011, 6, e20925. (28) Makarucha, A. J.; Todorova, N.; Yarovsky, I. Nanomaterials in biological environment: a review of computer modelling studies. Eur. Biophys. J. 2011, 40, 103−115. (29) Vila Verde, A.; Beltramo, P. J.; Maranas, J. K. Adsorption of homopolypeptides on gold investigated using atomistic molecular dynamics. Langmuir 2011, 27, 5918−5926. (30) Yu, J.; Becker, M. L.; Carri, G. A. The influence of amino acid sequence and functionality on the binding process of peptides onto gold surfaces. Langmuir 2012, 28, 1408−1417. (31) Heinz, H.; Vaia, R. A.; Farmer, B. L.; Naik, R. R. Accurate simulation of surfaces and interfaces of face-centered cubic metals using 12−6 and 9−6 Lennard-Jones potentials. J. Phys. Chem. C 2008, 112, 17281−17290. (32) Cerda, J. R.; de Andres, P. L.; Flores, F.; Perez, R. Transport of physisorbed Xe atoms on Ni(110) using a scanning tunneling microscope: a theoretical approach. Phys. Rev. B 1992, 45, 8721−8729. (33) Chen, D. L.; Al-Saidi, W. A.; Johnson, J. K. Noble gases on metal surfaces: insights on adsorption site preference. Phys. Rev. B 2011, 84, 241405. (34) Bilic, A.; Reimers, J. R.; Hush, N. S. Adsorption of pyridine on the gold(111) surface: implications for “alligator clips” for molecular wires. J. Phys. Chem. B 2002, 106, 6740−6747. (35) Iori, F.; Di Felice, R.; Molinari, E.; Corni, S. GolP: an atomistic force-field to describe the interaction of proteins with Au(111) surfaces in water. J. Comput. Chem. 2009, 30, 1465−1476.

AUTHOR INFORMATION

Corresponding Author

*E-mail: [email protected]. Notes

The authors declare no competing financial interest.

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ACKNOWLEDGMENTS We thank the Victoria Partnership for Advanced Computing (VPAC) and the National Computing Infrastructure (NCI) for the provision of computational resources. This work was partially supported by the Engineering and Physical Sciences Research Council (grant number EP/I001514/1). This Programme Grant funds the Materials Interface with Biology (MIB) consortium. L.B.W. thanks the EPSRC for DTA studentship funding. This work was also partially supported by the Air Force Office of Scientific Research (grant no. FA9550-12-1-0226). Z.E.H. and T.R.W. thank veski for research funding, and T.R.W. thanks veski for an Innovation Fellowship.

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