adsorption with phenol model

8/7/2019 adsorption with phenol model

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Interaction of Hydrated Amino Acids with Metal Surfaces: A Multiscale Modeling

Description

Pim Schravendijk, Luca M. Ghiringhelli, Luigi Delle Site, and Nico F.A. van der Vegt*

Max-Planck-Institute for Polymer Research, Ackermannweg 10, D 55128 Mainz, Germany

ReceiVed: August 28, 2006; In Final Form: NoVember 7, 2006

We present a multiscale modeling procedure that offers the opportunity to study hydrated biomolecules atmetal surfaces. First principle DFT calculations and classical atomistic simulations are used interactively inorder to account for both quantum and statistical aspects of molecular conformations at the surface. Wepresent models for water, benzene, phenol, alanine, and phenylalanine at the (111) surface of nickel. Thesemodels are subsequently used in classical atomistic simulations to study physical-chemical aspects of aminoacids at a Ni(111)/water interface. Application of this method to a larger set of molecular building blocksopens a computational route for molecular engineering of bio/inorganic hybrid systems.

I. Introduction

A chemical realistic modeling procedure for describing

peptide interactions with metal surfaces is of considerablescientific interest because of a complex interplay of quantum(electronic) and classical (solute and solvent) degrees of freedomthat, so far, is only little understood. The ability to model theseinteractions in chemically realistic environments can supportrational procedures for the on-demand design of peptides thatspecifically bind to inorganic surfaces (aptamers). This will helpthe design of bio/inorganic composite materials, for example,combining optimal solute binding specificity in proteins (e.g.,antibodies) with the optimal signaling properties of inorganicmaterials.1

Surface binding up- and down-modulating amino acids insynthetic polypeptides have been identified recently by system-atic experimental studies.2,3 These studies have only recentlybecome possible because of technical developments such ascombinatorial peptide engineering4,5 and have opened the wayto predict, on an empirical basis, overall protein adhesion byconsidering adhesive properties of the constituting amino acids.

In a previous paper, we outlined a procedure6 suitable todescribe interactions of solvated molecules (i.e., benzene) withtransition- and noble-metal surfaces. In the current paper, weextend upon that work by reporting the interaction of solvatedamino acids with a Ni(111) surface. For this purpose, we usean iterative procedure that converges once the surface interac-tions described by the resulting classical model are consistentwith those obtained from first principle quantum calculations,in terms of both conformational and energetic aspects. We applythis procedure to benzene, phenol, alanine, and phenylalanine.We, moreover, describe the modeling of water at the metalsurface and combine that with the modeling of organic solutesto study amino acid surface interactions under aqueous condi-tions at 300 K.

In view of the chemical complexity of biological molecules,as well as the system sizes that can be treated by first principleelectronic structure calculations, it would be desirable to developmodels describing surface interactions of isolated amino acidresidues on a case-to-case basis. This means that we aim to

develop molecular building blocks whose surface interactionsare carefully parameterized and that can be combined to form

any peptide sequence or larger biomolecule. This approach issimilar in spirit to the development of biomolecular force fieldsin which reproducing condensed-phase properties of smallmolecules (e.g., resembling the amino acid residues) plays akey role. In contrast to biomolecular force fields, however, nogeneral atomistic parameterization can be performed for themetal surface atoms because the delocalized electrons in themetal surface will be perturbed differently by different solutes,which can only be described with quantum mechanical calcula-tions. The resulting parameter sets describing the interaction ofspecific solutes with the metal surface are therefore transferableonly for chemical groups with the same chemical composition.They can be used to describe any peptide sequence, andinteractions with the solvent can be incorporated using existing

force fields. Obviously, this choice necessarily introducesapproximations, which should be examined carefully wheneverpossible. We assume that the water-surface as well as allbuilding block-surface interactions add in a pairwise additivefashion. This assumption should be reasonable as long as thechemically active groups on the biomolecule have relativelyisolated electronic properties (e.g., a polar group in an alkanechain). Then, one may combine any number of those groups ina macromolecule, as long as the surface-adsorbed groups willbe separated far enough to not disturb the electron distributionof the neighboring solute-metal interactions.

Although the benzene and phenol models discussed in thispaper can be used as building blocks in peptide sequences, theneutral amino acid models discussed in this work do not describe

all features present in peptide bonds. In forthcoming work, wewill therefore report the surface interaction of the peptide bond(based on a study ofn-methyl acetamide), which may then becombined with further models for the amino acid side chainanalogs to construct oligopeptides.

This paper is organized as follows. After a description ofcomputational details in Section II, we introduce in Section IIIthe modeling procedure and the newly developed models andparameter sets for phenol, alanine, phenylalanine, and tyrosineinteracting with the (111) surface of nickel. The modelingof water-surface interactions, described previously* Corresponding author. E-mail: [email protected].

2631J. Phys. Chem. C2007, 111, 2631-2642

10.1021/jp065568u CCC: $37.00 2007 American Chemical SocietyPublished on Web 01/23/2007


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in ref 6, will be discussed more extensively in Section III aswell. In Section IV, the newly developed models are used tostudy surface interactions under aqueous conditions at 300 K.

II. Computational Details

We extend the molecular building block approach that hasalready been applied successfully to organic-inorganic inter-faces.6,7 In this approach, macromolecule-surface interactionsare described at two levels. First, building-block molecules arechosen that describe the recurring parts in the macromolecule.The small sizes of these building blocks allow for quantummechanical calculations of the building block-surface interac-tions, and the resulting data are used for parameterizing atomisticsolute-surface interaction potentials. On the other, classicalatomistic, level, this quantum-based parameterization describesbuilding block-surface interactions, not only for the singlebuilding blocks but also for the structures such as (oligo)peptidesor proteins that can be constructed from combinations of thesebuilding blocks. In the current paper, we will model phenol andthe neutral forms of alanine, phenylalanine, and tyrosine andstudy their interaction with a Ni(111) surface from the bulk-hydrated state. The Ni(111) metal surface has been chosenbecause its properties for similar systems have been well-testedin previous works.6-11 Extension to other surfaces (e.g., Pt, Pd,Au) is straightforward as will be discussed later on. As an idealmodel surface, any oxidation effects at the surface will beignored.

Quantum calculations of the amino acids alanine and phe-nylalanine have been performed using the DFT based finite-electronic temperature method of Alavi et al.12 (FEMD), asimplemented in the CPMD code.13 Note that amino acids underphysiological conditions exist mainly (>99%) in the zwitterionicstate, whereas in the case of peptides the end groups arecondensed to form peptide bonds and are therefore of differentchemical nature. The neutral amino acids used here are thereforenot the exact representations of any of those two states but are

still of interest because the neutral state (as well as thezwitterionic state) has been shown to bind to a Ni(111) surface. 14

The zwitterionic state of amino acids as well as the peptideform are currently under investigation14 and will be a matter offollowing publications.

We used the Gromacs 3.3 code,15 adapted to allow for anycombination of atom-wall potentials divided over the molecule,thus enabling a convenient parameterization of both configu-rational and energetic data from quantum calculations. Surfacesare introduced by atom-wall potentials acting in the z directionat both z borders of the box, and an empty space larger thanany cutoff length is added to remove periodicity in the zdirection. With surfaces at two sides of the box, the systemactually represents a slit; however, the distance between the

surfaces was chosen large enough (>7 nm) to enable a layer ofbulk-like water in the center, several nanometers thick. The boxsize in the x and y direction extended 3.4 nm. Pressure couplingand particle mesh Ewald (PME) treatment of electrostaticinteractions is performed by (semi) two-dimensional schemesthat are part of the Gromacs 3.3 package. Pressure couplingwas chosen to be coupled only to the box size in the x and ydirections, parallel to the surface, and the box size in the zdirection was set constant. The PME Fourier spacing was setat 0.12 nm, the real space and neighbor search cutoff was set at0.9 nm, and van der Waals interactions were cut off at 1.4 nm.Simulations were carried out in a system similar to the onedescribed in the previous paper,6 consisting of 3000 watermolecules at constant pressure and temperature (NPT) (1 atm

with a coupling constant of 1.0 ps) (300 K with a couplingconstant of 0.5 ps), using Nose-Hoover temperature coupling16

and Parrinello-Rahman pressure coupling,17 but additionallyintroducing the molecules described above and using the LINCSbond constraint algorithm.18 All simulations were performedwith a molecular dynamics (MD) integration time step of 2 fs.The solute-surface and solvent-surface potentials were treatedindependent from the solute-solute, solute-solvent, and solvent-solvent intermolecular potentials, which were described by the

GROMOS 43a1 force field.19

As a solvent, the SPC/E watermodel was used.20 The natural question arising at this point isif the use of a specific force field for water causes modeldependencies. We address this question below and show thatthis is not the case. In addition to MD simulations, severalLangevin dynamics (LD) simulations (300 K) were performedin which the solute interacts with the metal surface in a vacuumenvironment. The LD algorithm, available in Gromacs,15 wasused with a friction coefficient of 1 ps-1.

Solute-surface potentials of mean force (PMFs) in waterwere obtained via constraint-biased simulation with forceaveraging21 as provided by the Gromacs package. The constraintdirection was chosen perpendicular to the surface (i.e., in the zdirection). For each PMF, 142 defined constraint distances from

the surface were chosen, in steps of 0.01 nm from 0.19 to 1.00nm, in 0.02 nm steps from 1.00 to 2.00 nm and in 0.10 nmsteps for distances up to 3.00 nm. In all cases, this proved tobe far enough to reach a bulk-hydrated state of the solutemolecule as indicated by a constant value for the PMF at thesedistances. Several constraint sites on the solute molecule weretested, each separately, to see if any site dependence for thePMF was present. The following interaction sites were chosenas constraint sites: for benzene and phenol the geometric centerof the ring; for alanine the carbonylic oxygen and the aminenitrogen; for phenylalanine the ring center, carbonylic oxygen,and amine nitrogen. It is important that only the z componentof the constraint site was kept fixed. The constraint site couldmove in the xy directions, and the remaining parts of the

molecule could move in any direction as long as this movementwould not displace the z distance of the constraint site.The starting conformations at the 142 distances mentioned

above were generated by first solvating the molecule in themiddle of the slit, following by pulling it to an adsorbed state(at z ) 0.19 nm). From there, the z distance was increased in asequence of short pull runs. Once all starting points weregenerated, 3 ns production runs were performed for each zdistance, enabling the calculation of distance-dependent meanforces and mean surface interaction energies.

III. Modeling Surface Interactions

The quantum-atomistic multiscale modeling of amino acid-metal surface interactions is more complex than our previous

multiscale modeling of benzene/polycarbonate adsorption.6,7This is largely due to the low symmetry and relatively largenumber of interaction sites in amino acids and the fact that theseinteraction sites are located too close to each other to be treatedseparately. The low symmetry greatly affects the number ofquantum calculations necessary: even though alanine has feweratoms than benzene, there are many more nonequivalentgeometries in which it can be oriented relative to the surface.A complete quantum analysis of all of these configurations atall possible metal lattice sites would require a very expensivecomputational effort. An extension to our multiscale modelingprocedure6 is introduced here to cope with the before-mentionedproblems in a way that actually enhances the procedures generalvalidity and applicability.

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We start by emphasizing that the attention lies on findingglobal minimum energy conformations because these will bethe dominant contribution to the sampling statistics during longruns at the atomistic level. Calculations on intermediate, higher-energy states would provide only superfluous information atthe current modeling precision. Therefore, the starting point ofthe modeling will be a selection of initial configurations thataims to give us the extreme binding conformations. Theelectronic properties of the metal binding of these configurations

were calculated by a series of quantum density functionalcalculations (geometry optimization),14 considering per config-uration the various possible positions on the metal lattice. Thedata retrieved from these calculations give us the parameters(interaction sites, interaction strength, optimal distances), basedon which an atomistic model is constructed. However, becausewe are studying a molecule with a large number of orientationaldegrees of freedom, the molecule is likely to get trapped in localenergy minima during quantum calculations. We resolve thisby performing LD simulations of the molecule in vacuum,applying the modeled molecule-surface interaction parameters,and sample its configuration space at a range of distancesperpendicular to the surface, using the constraint method andsites described in the Methodology section. This set of LD runs

can sample a larger amount of the phase space than could bedone by quantum optimization runs, in a fraction of the time.If the LD simulation generates (only) the surface adsorbingconformations that were already found in our initial quantumcalculations, then our classical model is considered complete.If, however, the LD simulation generates adsorbing (low-energy)conformations that were not previously found in quantumcalculations, then these conformations are used as startingconfigurations in a new series of quantum-based structureoptimizations. If this second set of quantum calculations resultsin interaction properties that differ from the LD simulations,then the atomistic modeling is adjusted. Then, the procedure isrepeated (LD run with new modeling, quantum calculation ofLD output), until consistency between the quantum and the

atomistic level is reached and all LD lowest energy conforma-tions correspond to stable conformations from quantum calcula-tions with respect to the energy and the configuration.

The newly modeled molecules are introduced below, startingwith phenol which forms a logical extension to the benzenemodeling we performed previously.6 First, however, we describethe modeling of water at the metal surface.

a. Water. A large amount of research of either experimentalor theoretical nature exists in the field of water-surfaceinteraction.22-27,29 Recent development in this field focuses onthe aqueous-solid interface.23,25,26 The basics of the atomisticmodeling of the interaction of water with metal surfaces appliedby us has been introduced in a previous publication.6 However,

in that paper we considered only one classical water model.Here we briefly repeat how the water-surface interaction ismodeled and then proceed further by showing that the relevantinterfacial hydration properties are independent of the classicalwater model chosen to describe water-water interactions. Tomodel the water-surface interaction, we make use of quantumdensity functional calculations of small clusters of watermolecules at the metal surface. The water cluster configurationsare chosen by considering all relevant ways for liquid water tointeract with the surface. Examples are shown schematically inFigure 1.

We base our modeling idea on a well accepted and by nowproven statistical property of liquid water; that is, it is locallyand instantaneously tetrahedral. This allows us to imagine that

locally at the surface water conformations must consist of fullor half tetrahedra (see Figure 1) because of the confinement ofthe surface. Among possible arrangements like the ones shownin Figure 1, some are not allowed according to data availablefrom quantum calculations, that is, those conformation display-ing hydrogen down-like structures. Thus, we have a firstscreening of possible conformations that must be reproducedby our modeling. The second important point is that, becausewe want to explore many relevant configurations, we should

carry out many calculations. Because first principles quantumcalculations are computationally very demanding, a solution tothis problem comes from the observation that the tetrahedralstructure of Figure 1 can be described well by differentcombinations of substructures consisting of monomers, dimers,and trimers in different conformations. As shown in Figure 1,we then make what we would call the first layer approxima-tion; that is, only the molecule close to the surface and thosedirectly bonded to that participate to the adsorption strength.

The adsorption energy per water molecule in these allowedconfigurations is calculated using a quantum-based approach10

(see the caption of Figure 1 for the calculation used), and theresults are used to parameterize a water-surface potential forclassical simulations. We have chosen an attractive 10-4

potential to describe the interaction between water oxygen andthe surface (see eq 1). No interaction was applied between waterhydrogen and the surface. See Table 1 for exact values of theparameters used.

In the classical simulation of the metal-water interface, water-water interactions are accounted for by a classical force field,and therefore the energy of water-water hydrogen bondingshould not be included in the water-surface interaction energy.

b. Phenol. Phenol on Ni(111) has a maximum surface

interaction energy that is nearly 15% lower than the maximumsurface interaction energy of benzene (see Table 2 and ref 14).Its conformation at the surface has the hydroxyl oxygen liftedby 0.5 , which has some effect on the adjacent carbon atomas well. The hydroxyl hydrogen is pointing down, ending up atthe same height as the phenylic carbon atoms (see Figure 2).As a basis of the atomistic modeling of phenol, the benzenemodel described previously6 is chosen. To describe the interac-tion with the surface, Morse potentials are used on the aromaticring carbon atoms (Cr) (eq 2), and repulsive 10-4 potentials(eq 3) are used on the ring hydrogens (Hr). The modelparameters are given in Table 1, and atom types are defined inFigure 3. For all attractive atom types, a zcutoffof 1.4 nm wasused.

Besides the substitution of one of the hydrogens of benzeneby a hydroxyl group, only a reduction of interaction energy wasneeded, which was done by scaling down all carbon-wallinteractions. The carbon next to the hydroxyl group (Cp) wasscaled down to half the interaction energy of the other carbonatoms (Cr). A weak 10-4 repulsion was put on the hydroxyl

UAttr.10-4 ) {210-4 [25(

z)10

- (

z)4

] z e zcutoff0 z > zcutoff

(1)

UAttr.Morse ) {M(1 - e-a(z-))2 - M z e zcutoff

0 z > zcutoff(2)

URep.10-4 ) {210-4 [25(

z)10

- (

z)4

+35] z e

0 z > (3)

Hydrated Amino Acids at Metal Surfaces J. Phys. Chem. C, Vol. 111, No. 6, 20072633


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oxygen (Op), and an attractive 10-4 potential (see eq 1) wasput on the hydroxyl hydrogen (Hp).

Configurations of phenol with the OH group pointing awayfrom the surface did not need to be taken into account in QMcalculations, because the perturbing effect of the OH group willbe lower the further it is away from the surface, and anyinteraction would be similar to the inclination dependence ofbenzene modeled previously.6 The configuration where phenolis pointing with the OH group toward the Ni(111) surface iscurrently being investigated by quantum calculations.30 Pre-liminary calculations indicated a weak interaction, not signifi-

cantly higher than the error margin of the quantum calculation.Currently, no special modeling considerations were made toaccount for this orientation in the classical atomistic simulation.

c. Neutral Alanine. Four distinct conformations were usedin the initial quantum calculations of neutral alanine, asdescribed in ref 14. As an example, the strongest bindingconformation (0.57 eV) is shown in Figure 4; the energies andoptimal surface interaction site distances of all configurationsare shown in Table 3. The amine nitrogen (N) and the carbonyloxygen atom (Oc) interact attractively with the surface. Surfaceattraction at these sites is of the order of one to two hydrogenbond strengths and is modeled with attractive 10-4 potentials.An interesting feature is the influence of the methyl side-group.Even when not directly interacting with the surface, the methyl-surface distance is found to influence the total surface interactionenergy. The origin of this influence might be an effect of theposition of the methyl group on the surface orientation of theinteracting amine and carboxyl groups. A simple scheme tomodel this effect is chosen that includes a weak repulsive Morsepotential (eq 4) on the center of mass of the CR and C atoms:this additional interaction site is referred to as CR (see Figure3).

The optimal distances (values) for these potentials areretrieved from the binding conformations in our quantumcalculations. Binding energies for the potentials ( values) arechosen such that the sum of all site-surface potentials (eqs 1-4)acting on the molecule will reproduce the total surface interac-tion energy as found in the quantum calculation, for everyconformation tested. Atoms with no specific surface interactionwere given a simple repulsive 10-4 potential (with parametersbased on the repulsive hydrogens in the benzene ring, see groupR in Table 1) to prevent the occurrence of unphysical conforma-tions corresponding to a penetration of the surface. The firstiteration of this modeling resulted in minimal energy LDconformations that had both the amino nitrogen and the carbonyl

oxygen interaction sites at optimum distance. The total surfaceinteraction energy corresponded to the sum of both interactions.Consecutive quantum calculations of the LD output structuresshowed that this was actually a nonbonding conformation.Consequently, a modification of our initial modeling was neededto ensure that only one interaction site at a time will be able tobind with optimal interaction energy. This was reached byintroducing a repulsive site (Cv) at a position between the aminonitrogen and the carbonylic oxygen (at 40% of the bond lengthbetween carbonylic carbon and CR, see Figure 3), leading to aseesaw-like mechanism that allows for the binding of either theamino or the carbonyl group. LD runs with this modeling leadto optimal configurations close to the previous quantumcalculations, see Figure 5, and this modeling was therefore

TABLE 1: Overview of Molecule-Ni(111) Surface ForceField Parametersa

interaction potential type (kJ/mol) (nm)

WaterOw-Ni attractive 10-4 6.40 0.24Hw-Ni no interaction

BenzeneCr-Ni Morse (a ) 35 nm-1) 17.5 0.20Hr-Ni repulsive 10-4 4.27 0.20

PhenolCr-Ni Morse (a ) 35 nm-1) 15.8 0.20Hr-Ni repulsive 10-4 4.27 0.20Cp-Ni Morse (a ) 35 nm-1) 7.96 0.20Op-Ni repulsive 10-4 1.00 0.25Hp-Ni attractive 10-4 0.70 0.22

AlanineOc-Ni attractive 10-4 8.90 0.23N-Ni attractive 10-4 15.0 0.22CR-Ni rep. Morse (a ) 6.0 nm-1) 4.0 0.58Cv-Ni repulsive 10-4 10.0 0.38R-Ni repulsive 10-4 4.27 0.20

PhenylalanineOc-Ni attractive 10-4 8.90 0.23N-Ni attractive 10-4 15.0 0.22Hv-Ni repulsive 10-4 4.27 0.17

Cv-Ni repulsive 10-4 10.0 0.38Cr-Ni Morse (a ) 35 nm-1) 17.5 0.20Hr-Ni repulsive 10-4 4.27 0.20R-Ni repulsive 10-4 4.27 0.20

TyrosineOc-Ni attractive 10-4 8.90 0.23N-Ni attractive 10-4 15.0 0.22Hv-Ni repulsive 10-4 4.27 0.17Cv-Ni repulsive 10-4 10.0 0.38Cr-Ni Morse (a ) 35 nm-1) 15.8 0.20Hr-Ni repulsive 10-4 4.27 0.20Cp-Ni Morse (a ) 35 nm-1) 7.96 0.20Op-Ni repulsive 10-4 1.00 0.25Hp-Ni attractive 10-4 0.70 0.22R-Ni repulsive 10-4 4.27 0.20

a

See Figure 3 for the corresponding structures and eqs 1-4 for thepotential functions used. The R covers all interactions with no specificinteraction with the surface (unlabeled atoms in Figure 3).

TABLE 2: Ab Initio Interaction Energies and OptimalDistances Used to Model Interaction Potentials for ClassicalMDa

interaction Eads (eV) dopt (nm)

water-Ni 10 -0.25 0.24benzene-Ni 44 -1.05 0.20phenol-Ni 44 -0.9 0.20 (ring)Ala-Ni -0.57 0.22 (N)Phe-Ni 9 -1.1 0.20 (ring)

a For neutral phenylalanine (Phe-Ni), only the optimal configurationwas evaluated. For neutral alanine (Ala-Ni), the energy of the

configuration with highest surface interaction is given. A completeoverview is given in Table 3.

TABLE 3: Properties of Four Neutral AlanineConformations That Were Evaluated in QuantumMechanical DFT Calculations14 a

conformation Eads (eV) N (nm) Oc (nm) C (nm)

NdownCHup -0.57 0.22 0.32 0.46NdownCHdown -0.32 0.24 0.33 0.36OdownCHup -0.37 0.46 0.25 0.57OdownCHdown nonbonding 0.39 0.22 0.27

a Shown are the adsorption energy on the Ni(111) surface, and the

atom-to-top Ni layer distance of three of the atoms whose surfacedistance most strongly affects the surface interaction.

URep.Morse ) {M(1 - e-a(z-))2 z e

0 z > (4)



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chosen for our MD production runs. The final potentialparameters are given in Table 1. Note that even though only

one point (minimized energy) per conformation is used, the factthat we use four different conformations for our potential fit,with various groups interacting simultaneously for everyconformation, makes this more elaborate than a one-pointparameterization.

d. Neutral Phenylalanine and Tyrosine. Phenylalanine andtyrosine can, in a building block manner, be constructed bycombining characteristics of the alanine and the benzene/phenolmodeling. The repulsive CR site, present in the alanine model,is discarded in the phenylalanine and tyrosine models. Instead,because repulsive aliphatic hydrogen atoms proved to be anecessary modeling element to prevent unphysical conforma-tions with C hydrogens penetrating the surface, these wereintroduced (one on the CR, two on the C) as virtual sites in the

phenylalanine and tyrosine molecules. We use the terminologyvirtual sites because, apart from experiencing surface repul-sion, they do not interact with the solvent; in the GROMOS43a1 force field19 hydrogens connected to aliphatic CR and Ccarbons are absorbed into united-atom potentials centered onthe carbon positions. Interactions of other atoms (e.g., solvent)with these aliphatic CHn groups are described using these unitedatom potentials. Virtual sites in Gromacs are built from thecoordinates of 2, 3, or 4 vicinal atoms; any forces on the virtualsites are spread out over these atoms at the end of each timestep. The bond lengths and angular potentials of our virtual siteswere taken from the all-atom OPLS force field.31 In the resultinglowest energy conformation obtained from LD simulations invacuum, we found that both the amine nitrogen and the phenyl

ring could reach their positions of maximum interaction energy,

leading to a total surface interaction energy of 1.59 eV (153kJ/mol). This is different from the initial quantum calculationcorresponding to a 1.1 eV binding conformation, where theamino and carboxyl group were chosen to point away from thesurface, to minimize surface contact of the aliphatic Chydrogens.9 Following our iterative multiscale modeling pro-cedure, we tested the lowest energy LD conformation in aconsecutive quantum calculation,32 and it was found to be astable conformation (1.5 eV). One aliphatic C hydrogen is closeto the surface but can apparently find a stable position withina hollow site of the surface. Because the optimal LD interactionenergy and conformation were close enough to the optimalconformation found by quantum calculations (within the 0.1eV error of the quantum calculation), no further optimization

of the modeling was needed. The final combination of atom-surface potentials is given in Table 1 and Figure 3.

IV. Analysis of Surface Interactions at the Ni(111)/H2O

Interface

For benzene and phenol, an in-depth analysis can be madeof the surface interaction mechanism under aqueous conditions.Because of the high symmetry of the benzene ring, its center-of-mass-surface distance was recently shown to provide alogical choice for an order parameter along which a free energycan be calculated,6 by which the surface interaction can becharacterized. When extending this analysis to a phenol

molecule, we meet with a slightly broken symmetry; we can,however, still take the distance between the geometric centerof the ring and the surface as the order parameter of interest.For benzene and phenol, their planar structures enable us tostudy not only the PMFs z-dependency but also the surfaceinclination dependence described by the angle between thenormal of the aromatic ring and the normal of the surface, at agiven z distance, as will be discussed in more detail below.

Following the surface interaction mechanisms of hydratedamino acids is more problematic, not only because of their lowsymmetry as compared to a benzene ring but also because oftheir many degrees of conformational freedom and variousinteraction sites. Therefore, several order parameters can be

Figure 1. Graphical aid showing the rationale behind the ab initio modeling. The water configurations studied by quantum mechanic DFT calculationswere chosen by considering all relevant ways for liquid water to interact with the surface. Some examples are schematically shown here. In whitecircles are tetrahedral water substructures at the water-metal surface. I: A possible tetrahedral water structure with two waters interacting with themetal surface. II: A trimer, the upper part of a tetrahedral water structure, with one water interacting with the metal surface and two additionalhydrogen-bonded water molecules. III: One of many configurations that could be discarded immediately because no electronic hydrogen-metalinteraction exists. The isolated water tetrahedron is an unstable structure, both in vacuo and metal-bound. Therefore, ab initio calculations are donewith smaller subunits. In DFT calculations, we can represent the tetrahedral structures shown in I and II by using all relevant water structuresconsisting of (A) a water monomer, representing one of the symmetry axes of the tetrahedron; (B) a water dimer, representing the metal-boundwater with its first hydrogen-bonded neighbor (the tetrahedral center); (C) a water trimer, directly representing structure II. The bare water -metalinteraction energies (excluding contributions of H2O-H2O hydrogen bonding) are calculated as Eads ) E(surf+Nmol) - Esurf- ENmol, where Nmol refersto the number of water molecules in the system, E(surf+Nmol) denotes the QM energy of the combined surface + water substructure, Esurfis the QMenergy of the isolated metal surface, and ENmol is the energy of the isolated water substructure. Eads/NH2O equals 0.25 ( 0.05 eV on Ni(111) independentofNH2O ()1, 2, or 3). The complete overview of all conformations evaluated in quantum density functional calculations is given in ref 10.

Figure 2. Minimal energy phenol configuration when adsorbed onNi(111), as found by quantum calculations.44 The green area representsthe location of the nickel surface.



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chosen to follow the process, all of which will, however, beinterdependent: at any surface distance of a given interactionsite, all other interaction sites will contribute to the PMF at thatparticular distance. Here, we limit ourselves to presenting onlythe free-energy difference between the bulk-hydrated state anda selection of local free-energy minimum states with strongsurface interaction, and a description of the interaction energiesinvolved. To better understand hydration effects, we will alsocompare the explicit solvent MD simulations with the LD

simulations of the amino acids in vacuum that were performedduring our modeling procedure. The total combination of relativesurface interaction energies for the various amino acids, andcomparisons of surface interactions under solvated and non-solvated conditions, can give essential information to help usunderstand the main chemical factors that determine metal-surface interaction of amino acids, and in this way facilitatethe design of surface-binding peptides.

a. Water. We used our water modeling procedure to describethe Ni(111)/water interface using different classical water mo-dels. Applied were the three-site SPC,33 SPC/E,20 and TIP3P34

models, the four-site TIP4P34 model, and the five-site TIP5P35

model. For each water model, we studied the water structure atthe surface and the benzene-surface and phenol-surface

potential of mean force based on simulations of one solutemolecule in water.

Figure 6 compares the water structure near Au(111) and Ni-(111) for the SPC, SPC/E, TIP3P, TIP4P, and TIP5P watermodels. The water-metal interaction for Ni(111) amounts to0.25 eV per molecule (24.1 kJ/mol) at an optimal distance of2.4 ; for Au(111) it amounts to 0.10 eV per molecule (9.7kJ/mol) with an optimal distance of 3.1 .6 Clearly, the structureof the metal-water interface is independent of the classical

water model. On Ni(111) the hydrogen density profile (dashedline) is highly structured (four peaks for z < 7) becausehydrogen atoms belonging to water molecules in the firstadsorbed water layer either correspond to OH bonds aligningthe surface or OH bonds hydrogen bonded to water moleculesin the second adsorbed layer. Hydrogens belonging to watermolecules in the second water layer, in turn, donate hydrogenbonds to waters in the first layer and water molecules in thebulk. On Au(111) the hydrogen density profile is less structured,indicating reduced hydrogen bonding between the water layers.

The peak height for the first water layer on Au(111) is in theorder of 1/3rd of the peak height of the first water layer onNi(111). The relative peak heights are, however, not a good

measure of the relative water densities at the Au(111) and Ni-(111) surfaces because these peaks are narrower than themolecular diameter of water and the density varies rapidly overthis range. Therefore, we integrated the water (oxygen) densityprofile over the first peak up to the first minimum. The resultingnumber, expressed in units area per molecule, is given in Table4 for all five water models. For Au(111), surface areas in therange of 9.3-9.9 2 per molecule were found, for Ni(111) therange was 8.4-8.9 2 per molecule. Note that these values arein the same order as those reported by Shelley et al.28,29 foratomistic simulations of a water slab near Hg surfaces.

Different water models produce only small differences in thestructure of the metal-water interface, independent of the

Figure 3. Atom types used in Table 1. Shown are (A) benzene, (B)phenol, (C) water, (D) neutral alanine, (E) neutral phenylalanine, and(F) neutral tyrosine.Atom names refer to the atom names in Table 1.The sites CR and CV are specially introduced for the currentmodeling: CR is a virtual site located exactly at the center of mass of

CR and C. CV is a repulsive virtual site needed to prevent simultaneousadsorption of the N and carbonyl O (OC) atoms within the samemolecule, as explained in the text. Note that the water hydrogens (HW)do not have any interaction with the surface. Unlabeled atoms havethe general repulsion R.

Figure 4. One of the four alanine-Ni(111) conformations as men-tioned in Table 3. Main characteristics of this conformation (Ndown-

CHup) are the binding of the amine nitrogen to a top site of the Ni(111)surface and the methyl group pointing away from the surface. Thecarbonylic oxygen is pointing slightly toward the surface. A detailedquantum analysis of this and other structures is given in ref 14.



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strength of the water-metal interaction. This is an additionalconfirmation that the current modeling procedure allows for a

separated treatment of molecule-surface interactions whileusing existing force fields for molecule-molecule interactions.b. Benzene and Phenol. Both the benzene and phenol

z-dependent PMFs (Figure 7) are qualitatively very similar. Thestructural details of the PMFs follow the water oxygen densityfluctuations (Figure 6). Upon approaching z ) 0.5 nm (corre-sponding to the position of the second oxygen peak in the waterdensity profile) from larger distances, the benzene (phenol)molecule starts to expel water from the second water adsorptionlayer and the PMF increases rapidly. The PMF profiles correlatewith the water density data from Figure 6, where we can finda first water layer around z ) 0.2-0.3 nm, a second water layeraround z ) 0.5-0.6 nm, and a very weak third water layeringaround z ) 0.8-0.9 nm.

Our previous paper6 showed that the potential of mean force(PMF) for displacing a benzene molecule from bulk water tothe surface not only gives an idea about adsorption/desorptionenergies and intermediate energy barriers but also helps inunderstanding the critical steps in the adsorption/desorptionmechanism.6 Any crucial effect that the choice of classical watermodel would have on the adsorption mechanism would thereforebe visible in the corresponding PMF. Figure 7 shows that withall classical water models similar features are observed in thePMF, with a major free-energy barrier (50-60 kJ/mol high)starting almost directly at the surface (0.20 nm), ranging untilca. 0.60 nm. Additional oscillations occur further away fromthe surface up to ca. 1.50 nm. Differences are found in thebarrier heights and the free-energy difference between the bulk-

hydrated (z > 3 nm) and surface-adsorbed state (z ) 0.2 nm).

These variations are partly accounted for by the error marginsof these PMF calculations (between 4 and 8 kJ/mol at z ) 0.2nm), see the caption of Figure 7, and small differences in waterdensities at the surface. In addition, differences in bulk waterdensity and solvation free energies for the various watermodels36-40 are likely to be a source for variations in theresulting free-energy difference.

In Figure 8A, two-dimensional PMFs for benzene and phenolare shown as a function ofz and the inclination angle betweenthe aromatic ring- and surface normals. A selection of snapshotsof benzene at various surface distances is provided in Figure8B. In this two-dimensional free-energy landscape, a moredetailed picture is shown, and we can subdivide several zoneswhen approaching the surface (z ) 0.2 nm) from the bulk

Figure 5. Justification of the atomistic modeling. Solid line: averagealanine-surface interaction energy (A and B) and site-surface distance(C and D) obtained from a series of 3 ns constrained LD simulations

in vacuum at 300 K. Dots: optimal energy (A and B) and optimaldistance (C and D) of zero-temperature quantum calculation optimizedstructures. In part C, the distance of the carbonylic O is shown (verticalaxis) for a given constrained distance of the amino N (horizontal axis).In part D, the distance of the amino N (right vertical axis) is shownfor a given constrained distance of the carbonylic O (horizontal axis).The LD data (solid lines) were obtained by constraining either the Nor the O surface distance in the simulation. The distances andcorresponding energies from the quantum calculations are all retrievedfrom the three binding configurations found in quantum calculations.14

The atomistic model samples the quantum-based conformation andenergy either correctly or within a 0.05 nm distance. Parameters forthis modeling are mentioned in Table 1.

Figure 6. Normalized water densities near Au(111) (top graph) andNi(111) (bottom graph) for SPC,33 SPC/E,20 TIP3P,34 TIP4P,34 andTIP5P35 water models. Solid line, water oxygen; dashed line, waterhydrogen.

TABLE 4: Surface Area Per Water Molecule in the FirstAdsorbed Layera

area per molecule (2)

water model Au(111) Ni(111)

SPC 9.52 8.64SPC/E 9.27 8.37TIP3P 9.46 8.61TIP4P 9.61 8.79TIP5P 9.86 8.92

a Determined by the integral of the oxygen density peak adjacent tothe surface in Figure 6.



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solvent (z > 1 nm). In the bulk zone going from z > 1 nmtoward z ) 0.8 nm the sampling is distributed evenly over allangles. For benzene (less clear for phenol), it can be seen thataround 0.7 nm (in between the third and second water layer;see Figure 6) already some structural effects occur and theparallel conformation (cos() ) 1) is slightly less sampled. Atdistances corresponding to the second adsorbed water layer(between z ) 0.6 nm and z ) 0.5 nm), benzene (phenol)

preferentially orients parallel to the metal surface. (The distribu-tion of cos() is narrowed down to 0.9-1.0 in Figure 8A. Asnapshot of the parallel conformation can be seen in Figure 8B,at 0.49 nm.) This orientation is favored energetically becauseof O-Hhydrogen bonding involving water molecules inthe first and second surface hydration layers. This type of weakwater-aromatic hydrogen bonding is described properly by theforce field.41 In Figure 7, this causes a shoulder to appear inthe PMF at z ) 0.58 nm. A rather flat landscape with respectto the orientational degree of freedom is found in the regionbetween the second and first adsorbed water layer (0.4 nm < z< 0.5 nm). Although, toward z ) 0.4 nm, perpendicularorientations are favored in comparison to parallel ones. In Figure7 a shoulder is observed at these distances, and in Figure 8B

one can see how a perpendicular orientation minimizes thedisplacement of water molecules in both the first and secondadsorbed water layer. As the geometrical center of the ringapproaches the first adsorbed water layer, the benzene (phenol)ring normal gets significantly tilted with respect to the surfacenormal and finally lines up with the surface normal, driven byan energetic stabilization of 1 eV due to the overlap of benzene-(phenol)--orbitals with free electrons in the surface. In thisprocess, first layer water molecules are expelled from thehydrophilic nickel surface, even before significant benzene-(phenol)-surface binding interaction is present. It is clear thatif the benzene would adsorb in a solvent-free environment thenthe z-dependent angular distribution plot would show a randomorientation for distances of ca. 0.5 nm and higher and would

show a similar profile to the solvated state only for shortdistances below 0.3 nm, because here the orientation of the ringis governed by the presence of the surface.

c. Amino Acids. To study surface-interacting amino acidconformations, we performed several MD runs in which a singleinteraction site-surface distance (order parameter) was con-strained and a PMF as a function of that order parameter was

obtained by integrating the mean force starting from z ) 3 nmdownward toward the surface. Using this method, the distanceswith minimal free energy were determined. Snapshots ofcorresponding conformations as well as the average surfaceinteraction energies and relative free energies (as compared toz ) 3 nm) at these distances are shown in Figure 9.

Comparison of the free energies in Figure 9A and B as wellas Figure 9C and D shows that, independent of whether thedistance constraint is applied on the oxygen or nitrogen, similarvalues are obtained, which is an indication that sufficientsampling has been reached. The snapshots show that the aminoacid conformations in Figure 9A and B as well as those in Figure9C and D are similar. The free energies for phenylalanine

viewed from the amino N (Figure 9C) and carbonylic O (Figure9D) order parameters are lower than those for the alanine cases(about 9 kJ/mol). This is rather counterintuitive because thebulky phenyl ring did not contribute to the surface interactionenergy for these order parameters, but intuitively its excludedvolume can be assummed to displace more water moleculesfrom the surface as compared to the methyl group from alanine.An explanation could be that in the case of alanine the smallmethyl group is able to come close to the surface, thereby weak-ening the interaction of amine and carbonyl groups. In contrast,the bulky phenyl group of phenylalanine will not be able topass the hydration layers present at the surface and thereforewill not hinder binding of the amine and carbonyl groups.

Figure 7. Solute-surface PMFs for displacing the geometrical center of the phenyl ring of (A) benzene and (B) phenol perpendicular to a Ni(111)

surface, in liquid water (300 K) described with five classical water models. PMFs were obtained by integrating the average constraint force on thesolute center of mass along 140 discrete points between 0.20 and 3.00 nm from the metal surface. At each point, 3 ns MD runs were performedto sample the mean constraint force. Solid black line, SPC water;33 dashed black line, SPC/E water;20 dashed/dotted black line, TIP3P water;34 solidgray line, TIP4P water;34 dashed gray line, TIP5P water.35 The errors of these PMF calculations ranged between 4 and 8 kJ/mol at z ) 0.2 nm andwere estimated by calculating the block average error estimate45 for every constraint distance and taking the square root of the integral of the squareof all error estimates from bulk (z ) 3.0 nm) toward the closest distance to the surface that was sampled (z ) 0.20 nm).



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The large positive free energy obtained by displacing thephenyl ring in phenylalanine toward the surface (Figure 9E)requires additional comments. This conformation has the lowesttotal solute-surface interaction energy of all structures thatinteract with the surface as found here, but it is the leastfavorable from a free-energy point of view (Gads ) +32.6kJ/mol ( 19.8 kJ/mol). A reasoning to explain this observationwould be to consider the fact that transferring the ring to thesurface requires a displacement of more water molecules thanin all other cases. In Figure 10 the surface interaction energiesfor water, phenylalanine, and the interaction sites of phenyla-

lanine are plotted against the distance of the constrained centerof mass of the phenyl ring. The contribution of water-surfaceinteractions decreases over the distance from bulk to optimaladsorption with 140 kJ/mol, about 70 kJ/mol more than for theother cases, where, instead of the phenyl ring, the O or Ninteracts with the surface (data not shown). One can get animpression of this solvent effect when comparing the pheny-lalanine surface interaction in explicit solvent MD (Figure 9E)with LD runs in vacuum (Figure 9F), where we find free-energyminima at the same distance (geometrical center of the ring at0.22 nm), similar interaction energies (-134.7 kJ/mol in explicit

Figure 8. (A) Two-dimensional PMFs for benzene (left) and phenol (right) at Ni(111) in SPC/E water (300 K). The solute (center-of-mass)-to-surface distance is plotted vertically; the cosine of the angle between the solute- and surface normal vectors is plotted horizontally. The 2D-PMF,G(z,) ) G(z) + G(|z), was calculated from constrained MD. By applying a constraint to keep the z coordinate fixed, G(z) (see Figure 7) isobtained by integrating the mean constraint force along the z coordinate. G(|z) ) -kBTlnP(|z) is obtained from the conditional distribution

function, P(|z), sampled in the constrained MD runs. (B) Benzene snapshots at a selection of center-of-mass constraint distances from the surface.



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solvent MD and -137.8 kJ/mol in vacuum LD), but largelydifferent free-energy values (+32.6 kJ/mol in explicit solventMD vs -93.3 kJ/mol in vacuum LD). By looking at the water-and solute-surface interaction energies in Figure 10, it isapparent that a competition between water and nitrogen playsan important role: every increase in solute-surface interactionenergy is accompanied by a decrease of water-surface interac-tion energy and vice versa.

The similarities in the surface interaction mechanism ofphenol and benzene (Figure 8) indicate that the tyrosine-surfaceinteraction is similar to that of phenylalanine.

V. Discussion

Four factors are necessary at the level of computer simulationsin order to get a complete theoretical picture of peptideadsorption on metal surfaces. First, any molecule-metal surfaceinteraction has to be parameterized by ab initio methods andcannot be represented by generalized force fields. An efficient

procedure to obtain a correct parameterization is presented inthe current paper. The second factor is related to the competitiveadsorption energies of the solvent and solute, where one shouldtake into account the number of solvent molecules that have to

be displaced by the solute.6,42

It has become apparent, however,that taking these competitive effects alone does not provide thefull description of the system because it does not properlyaccount for solute hydration and the surface hydrophilicity.6 Thisis the third factor that should be taken into account: dependingon surface hydrophilicity, dense adsorbed water layers may existclose to the surface. Intrusion of these mutually hydrogen-bonded layers causes energy barriers for surface approach.Therefore, explicit solvent (atomistic) simulations are necessary,using timescales long enough to allow for solvent rearrange-ments. The fourth factor concerns the geometry and orientation.Especially for longer molecules like polypeptides, many con-formations exist next to the surface and a correct sampling has

Figure 9. Snapshots of amino acids and parts of the surrounding water at the free-energy minimum distance of the constraint sites chosen, determinedby the PMF method as explained in the text. Eint denotes the sum of interaction energies for all solute interaction sites with the surface. G denotesthe free energy of surface interaction, taken by the difference in the PMF between the distance given here, and the bulk state at z ) 3.0 nm. Errorbars are calculated by calculating the block average error estimate45 for every constraint distance and taking the square root of the integral of thesquare of all error estimates from bulk (z ) 3.0 nm) toward the constraint distance mentioned here. G for a given molecule is found to beindependent of the interaction site (within the error of the calculation) and dramatically dependent on the presence of water. (A) Alanine, aminoN constrained at z ) 0.24 nm. (B) Alanine, carbonyl O constrained at z ) 0.28 nm. (C) Phenylalanine, amino N constrained at z ) 0.24 nm. (D)Phenylalanine, carbonyl O constrained at z ) 0.29 nm. (E) Phenylalanine, geometrical center-of-ring constrained at 0.22 nm. (F) As in part E, buttaken from an LD simulation in vacuum.



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to be performed to find all possible surface-interacting confor-mations. Because of solvent effects, the outcome may well benon-trivial.

The multiscale simulation approach presented here combinesconfigurational and chemical information needed for engineeringsurface-interacting peptides. The simulations provide insight intothe mechanisms of surface interactions in hydrated systems and

can therefore directly be applied to support and explainobservations in experimental studies. Obviously, the approachdescribed here requires extension. QM calculations of thepeptide group (CONH) and water interacting with Pt surfacesare currently being performed by us. Future work on additionalmodeling of amino acid residue interactions with this surfacewill result in a molecular construction set opening the way tothe modeling of a variety of peptide-surface systems.

Amino acid interactions with the nickel surface modeled inthis work are transferable to surfaces of different chemicalcomposition. The surface interaction energies of benzene andwater can be arranged as a series with comparable energies forNi, Pt, Pd, and Rh.6 Therefore, the mechanisms described herefor nickel are likely to be similar on Pt, Pd, and Rh. Several

preliminary generalizations comparing our modeling withexperimental results can then be made. First of all, oursimulations explain why phenylalanine, for which QM densityfunctional calculations indicate that it binds strongly to a metalsurface (i.e., Ni, Pt, Pd), is not found within the strong metalbinders experimentally.2,3 The strongest binding configurationof a phenylalanine molecule in vacuo (the interacting aromaticring oriented parallel to the surface) is shown by us tocorrespond with a highly unfavorable free energy when in thepresence of solvent (Figure 9). Although in our simulationsphenylalanine is a better binder than alanine (contrary toexperimental findings on oligo-peptides2,3), one has to take intoaccount that our simulations concerned only single amino acids.We will at a later point extend our simulations to peptide chains,

for which different behavior may be observed. Tyrosine isexperimentally found to be a relatively strong binder amongthe uncharged amino acids.2,3 Because our simulations showedthat the planar-ring conformation is unlikely to bind to ahydrated surface, the most reasonable explanation will be aninteraction of the phenol hydroxyl group in tyrosine with themetal surface. Because of electronic polarization effects, surfacedefects might contribute to this interaction. Here it should alsobe kept in mind that in most experiments polycrystalline noble

metals are used;2,3

hence, interactions with alternative crystalplanes and surface defects require attention in future calculations.We note that QM density functional calculations of theadsorption of water10 and benzene43 on metal surface defectshave already been performed recently.

VI. Conclusions

We have reported an iterative multiscale modeling procedurethat uses (1) quantum density functional calculations to obtainsurface interaction energies and optimum distances and (2)classical atomistic simulations to overcome energy barriers andguide localizing global conformational minima. After consis-tency between the atomistic modeling and the quantum calcula-tions has been reached, the fast sampling obtainable withatomistic simulation is used to investigate interactions ofbiological molecules with metal surfaces in water. Followingthis approach, it is now possible to take into account electrondelocalization effects in interactions of amino acids with metalsurfaces and to fully consider the water-solute and water-surface interactions present in hydrated systems. These advance-ments are essential in approaching a realistic modeling ofexperimental peptide-surface systems. As an application, themultiscale modeling of hydrated phenol, alanine, phenylalanine,and tyrosine has been performed, and various adsorptionproperties (adsorption energy, free energy, structural informa-tion) have been obtained.

Several general conclusions concerning the chemistry of

surface adsorption can be drawn from the current study. Mostimportantly, it is found that quantum-based binding energiesalone do not suffice to understand the thermodynamic aspectsof protein-surface interactions. Instead, one should account forthe competing effects of solvent and other adsorbing groups,potentially introducing free-energy barriers for surface approach.Additionally, our multiscale modeling can be applied to studygeometrical effects in detail, as exemplified with our analysisof benzene and phenol, which can help in understanding thekinetics of adsorption processes.

Acknowledgment. We thank Kurt Kremer, Matej Praprot-nik, and Hatice Duran for helpful discussions.

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Figure 10. Dependence of surface interaction energies (averaged over3 ns simulations) of phenylalanine and water on the phenyl-ring-surfacez-distance at 300 K. Shown are the overall solute-surface interactionenergy (blue line), the water-surface interaction energy (green line,right vertical axis), phenyl ring-surface interaction energy (red line),and amino nitrogen-surface interaction energy (brown line). Water isexpelled as the ring approaches the surface, causing a loss in water-surface interaction energy of approximately 140 kJ/mol over the distancefrom 0.75 to 0.20 nm. Over the same distance the solute gains amaximum of 130 kJ/mol at 0.22 nm (see snapshot E in Figure 9). Thestatistical noise in the water-surface and solute-surface interactionenergy profiles is correlated due to a competition of water and aminonitrogen binding. The large statistical uncertainty (19.8 kJ/mol) in thefree-energy calculation (snapshot E in figure Figure 9) is due to the

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