Affinity Scale for the Interaction of Amino Acids with Silica Surfaces

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J. Phys. Chem. C 2009, 113, 5741–5750

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Affinity Scale for the Interaction of Amino Acids with Silica Surfaces Albert Rimola,†,‡ Mariona Sodupe,*,‡ and Piero Ugliengo*,† Dipartamento di Chimica IFM, NIS Centre of Excellence and INSTM (Materials and Technology National Consortium), UdR torino, UniVersita` di Torino, Via P. Giuria 7, 10125 Torino, Italy, and Departament de Química, UniVersitat Auto`noma de Barcelona, Bellaterra 08193, Spain ReceiVed: December 18, 2008

A systematic computational study of the adsorption of 15 different amino acids (AA) (Gly, Ala, Met, Phe, Ser, Thr, Cys, Tyr, Asn, Gln, Asp, Glu, His, Lys, and Arg) on a hydroxylated silica surface has been addressed by ab initio ONIOM2(B3LYP/6-311++G(d,p):MNDO) method within a cluster approach. A model cluster cut out from the (001) surface of an all-silica edingtonite terminated by silanol groups (2.2 OH nm-2) was used to simulate the surface. The adsorption process is mainly dictated by the hydrogen-bond (H-bond) interactions between both the COOH moiety and the side-chain functionalities of the considered AA and the terminal silanol groups of the surfaces. The computed adsorption energies were corrected for basis set superposition error and the role of dispersive interactions, not accounted for by the B3LYP functional, were estimated in a posterior fashion showing to be substantial for the adsorption free energies. Large AA and rich in hydrophilic functionalities in the lateral chain exhibit the most favorable adsorption energies because of the complementary role between dispersive interactions and H-bonds of medium strength with the silica surface. On the basis of the computed adsorption energies, an affinity scale of the considered AA for hydroxylated silica surface is established, which indicates that the nonpolar (Gly, Ala, Met, Phe) and the basic ones (His, Lys, Arg) are the least and the most prone to be adsorbed on the silica surface, respectively. Finally, assessments of the reliability of the structures obtained were performed by comparing the computed adsorption energies with experimental data related to the hydrophilic/hydrophobic character of the AA. Introduction The nature of the interaction of biomolecules with inorganic materials is a subject of extraordinary relevance due to its direct implication in promising fields such as bionanotechnology, where the disciplines of chemistry, material science, and medicine merge.1-3 For instance, there is convincing evidence that some inorganic materials can be loaded with molecules with pharmaceutical properties and then released inside a living body in a controlled way, a fact of paramount importance in the development of drug delivery systems.4 In the context of bone tissue engineering, the key steps by which biological structures adhere to an implant (usually an inorganic bioglass) are essentially mediated by processes occurring at their surfaces,4,5 which become relevant in the design of biocompatible materials. Moreover, in the field of biosensors, the surface/biomolecule junction is crucial for their functionality,6,7 and in proteomics, strategies involving nanocrystals and nanoparticles have recently been adopted to provide information on low-abundant peptides.8-12 The interaction of biomolecules with inorganic material precursor salts plays also a fundamental role in biomineralization processes. Particularly, hydroxyapatite grows spontaneously outside the Hench 45S5 Bioglass (45% SiO2/24.5% Na2O/24.5% CaO/6% P2O5), when the latter is in contact with biological body fluids,5 whereas amino acids, peptides, and proteins are involved in most stages of the generation of biosilica structures,13-15 from transport, nucleation, and growth to structure stabilization. Related to the aforementioned lines, several experimental works * To whom correspondence should be addressed. E-mail: mariona. [email protected] (M.S.), [email protected] (P.U.). † Universita` di Torino. ‡ Universitat Auto`noma de Barcelona.

using typical spectroscopic methods have analyzed the adsorption of proteins both on hydroxyapatite and silica surfaces.16-23 However, only a few theoretical studies have focused on the protein/surface modeling, and mainly by means of semiempirical24 or force-field25,26 methods, because ab initio studies on these systems are not yet feasible because of their enormous complexity. Nevertheless, by studying very simplified models useful information from ab initio calculations can be obtained. In that respect, several works concerning the adsorption of single amino acids onto mineral oxides have recently appeared; that is, alanine on the TiO2 (110) rutile surface,27 lysine on quartz28,29 and on amorphous silica30 models, glycine on different silica surface models,31-36 and alanine on edingtonite silica surfaces.37 All of these works, however, only involve the interaction of a reduced number of amino acids (glycine, alanine, and lysine) with different hydroxylated surfaces, and, thus, the behavior of other natural amino acids interacting with silica-based materials remains still unknown. The contact between inorganic surfaces and relevant biochemical molecules is also of interest in the field of prebiotic chemistry.38 Indeed, it has been suggested that minerals might have been crucial in concentrating, selecting, and organizing prebiotic organic molecules to convert them into the essential macromolecules for life.39-41 This is an early suggestion from Desmond Bernal,39 who advocated the special role of mineral clays as promoters for the condensation of monomer building blocks because they provide adsorption sites that may concentrate, protect, and activate basic biomolecules (e.g., amino acids) for their polymerization. Additionally, the detection of some spectral frequency bands probably belonging to glycine42 in the interstellar medium (mainly composed of dust of silica grains and water ice) has strengthened the thesis that amino acids might

10.1021/jp811193f CCC: $40.75  2009 American Chemical Society Published on Web 03/17/2009

5742 J. Phys. Chem. C, Vol. 113, No. 14, 2009 have been originated in the cold interstellar medium and then seeded the Earth where they became ready for polymerization. In fact, several simulation experiments highlight that interactions between raw biomolecules and the interstellar material not only served to protect biomolecules from the collision of asteroidal bodies with Earth but also that such impacts could have supplied the energy necessary to activate them for further polymerization.43 Direct experimental measurements of such events are difficult to reproduce, so that the use of quantum chemical methods may greatly contribute to provide useful information. Because of that, biopolymerization processes activated by mineral surfaces33,44-49 as well as reactions occurring in the interstellar medium50-61 have recently been addressed from a theoretical point of view. Amino acids, like other biomolecules, exhibit high melting points and low vapor pressures, which, in combination with their thermal instability, makes it difficult to obtain them in gas phase because they decompose to some extent before or upon sublimation. For years, glycine has been the only exception to that (it indeed can sublimate before decomposing) so that the experimental study of the free-water features of glycine/solid interfaces has been possible to date. Such studies, in combination with theoretical calculations, have provided a fruitful interplay between theory and experiment; for example accurate infrared spectral studies carried out by chemical vapor deposition of glycine on silica surfaces32,62,63 can be properly described and rationalized at the ab initio level.31,32 Notwithstanding, novel techniques for obtaining neutral biomolecules in the gas phase have recently been developed,64-72 among which the fast thermal heating technique71,72 is of particular interest for adsorption phenomena processes. This technique consists of a rapid heating of the solid sample followed by fast condensation of the vapor, so that molecules do not have enough time to decompose. To date, the gas-phase infrared spectra of different natural amino acids have been successfully registered by means of this technique, showing that all of them are found in its neutral form with no decomposing traces.71,72 The purpose of the present work is to perform a systematic quantum chemical study to analyze the interaction of several amino acids (with different biochemical properties) on a silica surface. The simulation of the amino acid interaction onto silica surfaces in the absence of water solvent is particularly interesting because it represents the best approximation to understand the intrinsic amino acid-silica contact features as well as to provide an intrinsic ladder of silica surface affinities for different amino acids. The results of this work will allow knowing which lateral chains are more prone to efficiently interact with the silica functionalities and, hence, to determine which amino acids would be responsible for the protein/solid surface contact. In the following, we provide some possible answers for a silica model exhibiting an OH density of 2.2 OH nm-2 and some clues about the differences occurring with silica models either poorer or richer in surface hydroxyl groups. Methods The interaction of different amino acids with a hydroxylated silica surface has been explored by means of ab initio cluster calculations. A large cluster model cut out from the (001) surface of an all-silica edingtonite was used to simulate a silanolcontaining silica surface. Part a of Figure 1 shows the internal crystalline edingtonite structure, whereas the cluster adopted, derived from this bulk framework, is shown in parts b and c of Figure 1 (lateral and top views, respectively). The present model was proposed in the past73,74 to mimic the features of an

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Figure 1. Cluster model of the silica surface used in this work. Section (a): Bulk of the edingtonite periodic structure from which the cluster model has cut out along the (001) plane. Sections (b) and (c): Lateral and top views, respectively, of the silica surface terminated by isolated hydroxyl groups. The regions treated at the high and low level of theory within the ONIOM2 cluster approach are shown as balls and tubes, respectively. Atoms labeled by asterisks define the regions considered to compute the counterpoise corrections (text). Section (d): High-level zone of the cluster. Hereafter, for the sake of clarity, only this zone will be represented in the figures.

amorphous silica outgassed at around 400 °C, its SiOH density being 2 OH nm-2, which lies between a fully hydroxylated amorphous silica (4.5 OH nm-2) and one containing isolated silanols (1 OH nm-2). Because this cluster is too large, all structures were optimized using the ONIOM275-77 two-layer strategy combining the B3LYP/6-311++G(d,p) level of theory78,79 for the high-level zone with the MNDO Hamiltonian80 for the low-level real system (atoms depicted in balls and sticks, respectively, in parts b and c of Figure 1). For consistency, amino acids are always included in the high-level zone and then treated at the B3LYP/6-311++G(d,p) level. Along the work, and for the sake of clarity, those figures depicting the optimized amino acid/silica systems will only show the high level zone (part d of Figure 1). Geometry optimizations and frequency calculations have been performed using Gaussian 03.81 Thermodynamic corrections have been obtained assuming an ideal gas, unscaled harmonic vibrational frequencies, and the rigid rotor approximation by standard statistical methods.82 Amino acid adsorption energies were corrected for basis set superposition error (BSSE) using the counterpoise correction method.83 Limited only to the BSSE evaluation, the lower part of the cluster model (from the oxygen atoms labeled by asterisks in part b of Figure 1 downward) was replaced by hydrogen

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atoms and the BSSE evaluated by single-point B3LYP/6311++G(d,p) energy calculations following the counterpoise recipe. This procedure has been checked by performing some counterpoise calculations considering the complete cluster model, showing that the BSSE values differ by less than 0.1 kcal mol-1 and, thus, we expect computed BSSE values for the small cluster to be reasonably accurate. The amino acids’ adsorption is expected to take place through H-bonds; however, because the nature of the silica bulk is essentially hydrophobic, dispersive forces will be likely relevant in the binding mechanism, especially for large amino acids. Because the adopted methodology did not take into account the dispersion originated by the fluctuating instantaneous dipoles, a rather simple strategy was adopted, to at least partially take them into account. In particular, the DFT+D method recently proposed by Grimme,84 which has been proved to be very effective for a number of cases where dispersive interactions are relevant,84,85 was adopted. The dispersive correction to the BSSE-corrected adsorption free energies ∆Gc298 (hereafter referred as ∆Gc298 + D) was computed, in a posteriori fashion, by using the Grimme routine as encoded in the MOLDRAW program,86 to compute the D term and add it to the B3LYP ∆Gc298 to give the final ∆Gc298 + D. Results and Discussion There are 20 different natural R-amino acids (AA) constituting proteins, all of them based on a HOOC-CHR-NH2 structure. The HOOC-CH-NH2 moiety, equal for all, is the backbone chain, characterized by possessing both a carboxyl and an amino group. In contrast, the lateral chain R differentiates one AA from the others. In the present work, a total of 15 R-AA (Gly, Ala, Met, Phe, Ser, Thr, Cys, Tyr, Asp, Glu, Asn, Gln, His, Lys, and Arg) covering a broad range of different AA chemical features, have been chosen. Figure 2 reports the B3LYP/6311++G(d,p)-optimized structures of the most stable conformer for each AA. All of these AA can be divided as nonpolars and polars, and among the latter ones in neutral, basic, acidic, and amidic (Figure 2). Some gas-phase AA structures are described in the literature (Gly,87-89 Ala,68,89 Phe,90-92 Ser,70,89,93 Cys89,93 Thr,94 Tyr,90,92,95 Glu,96 His,92,97 and Arg98); however, for the remaining ones (Met, Asp, Asn, Gln, and Lys), a conformational analysis has been required to locate the most stable conformation. As one can observe, glycine (Cs symmetry) and alanine display a bifurcated H-bond between the NH2 group and the carbonyl oxygen. The other AA exhibit a OH · · · NH2 H-bond between the hydroxyl and amino groups of the backbone, whereas the lateral chain establishes, as far as possible, H-bonds involving the backbone amino protons and the basic sites of the side chain. His and Arg are two intriguing cases because these side chains boast two tautomeric forms. For His, the N-H group of the imidazole ring can be given in the Nδ or Nε, the former case being slightly more stable.97 For Arg, the side chain or may be either (CH2)3-NH-C(dNH)NH2 (CH2)3-NdC(NH2)2, the latter tautomer being more stable.98 Results are organized as follows. First, the optimized structures of the AA/silica surface systems (S-AA) will be shown and the most interesting structural features discussed. It is worth noting that, among all the located minima adducts, only the most stable ones for each system will be presented. Information related to all of the remaining structures (geometries and energies) is available in the Supporting Information. Second, the computed adsorption energies will be presented and discussed. Finally, correlations between these adsorption energies with some physicochemical AA properties will be reported.

Figure 2. Most stable isomers of the amino acids tested to be adsorbed on the silica surface model. Geometries optimized at B3LYP/6311++G(d,p). Classification based on the features of the side chain.

Structural Features of the S-AA Systems. Glycine. Let us first start with the adsorption of Gly, the simplest amino acid, on the edingtonite (001) surface. As mentioned, Gly has been the subject of many experimental works because it readily sublimates, a fact that allows us to study glycine-surface systems in the absence of water. Consequently, some IRspectroscopic studies as well as theoretical works were performed to understand the nature of the glycine-surface interaction.31-33,62,63 In this sense, the CdO Gly stretching mode is usually used as fingerprint because it provides a very intense band in the spectrum. In particular, works concerning the interaction of Gly with amorphous silica in absence of water concluded that Gly should be adsorbed in its neutral form, engaging at least two H-bonds between the SiOH groups and the CdO Gly group, this interaction inducing a significant ν(CdO) bathochromic shift well-supported by experiments.31,32 Furthermore, periodic ab initio calculations for Gly interacting with the (001) surface of edingtonite revealed that the closest structure resembling this situation corresponds to S-Glyp (Figure 3), where, in addition to the SiOH · · · OdC H-bonds, the OH and NH2 protons of glycine are involved in H-bonds with the oxygen atoms of the silanol groups.31 Considering that the S-Glyp was optimized in a periodic approach within symmetry constraints, it is interesting to see how it compares with that computed for the present case, without imposing symmetry, namely S-Glyc (Figure 3). At first glance, both systems show a similar overall structure, including the same sequence of H-bonds. An important difference between the

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Figure 3. B3LYP/6-311G(d,p)-optimized structure for the glycine interacting with a 2D periodic slab model (S-Glyp, from ref 31), and ONIOM2[B3LYP/6-311++G(d,p):MNDO]-optimized geometry of glycine interacting with the present silica surface cluster model (S-Glyc). For the sake of clarity, in S-Glyp only the upper part of the slab is shown, whereas in S-Glyc only the high-level zone is represented. Bond lengths in angstroms.

periodic and the cluster systems is that in the former case a symmetry plane defined by the SiOH moiety was imposed, which led to a nonessential imaginary frequency associated to a rocking out-of-plane mode of the whole Gly. In contrast, for the present cluster model no symmetry restrictions were imposed so that Gly leans toward the surface. Because of that, the H-bond distances differ significantly with respect to S-Glyp; namely, in the cluster model the SiOH · · · OdC H-bond distances are larger, whereas the other two are shorter. Irrespective of that, it is important to remark that both treatments provide very similar ν(CdO) values (1746 and 1743 cm-1 for the periodic and cluster calculations, respectively) which, moreover, are in excellent agreement with the experimental32 data, if the proper scaling factor (0.975) is employed.34 In addition, both methods give similar adsorption energies (-13.2 and -11.6 kcal mol-1, for S-Glyp and S-Glyc respectively, after correcting for BSSE). Thus, despite some differences in the H-bond lengths, the two approaches are in very good agreement with two important observables such as the adsorption energy and the CdO frequency. In a recent work, Nonella et al.,37 by means of periodic ab initio molecular dynamic simulations, found that the adsorption of alanine onto silica surfaces occurs via a SiOH · · · NH2 H-bond involving one free interstrand silanol group of the surface. We have tried to reproduce this adsorption mode using our silica surface model but all attempts collapsed to a geometry without this H-bond feature. In addition, the resulting structure, which lies 1.3 kcal mol-1 above S-Glyc (S-Glyc available in the Supporting Information), only exhibits one SiOH · · · OdC H-bond, giving rise to significant discrepancies with the experiment.32 Differences between Nonella’s work and the present work probably arise from the different surface models employed. That is, despite the fact that both surfaces are hydroxylated to maximize all H-bond patterns, Nonella’s model37 is significantly richer in surface silanol groups (around 4-5 OH nm-2) than our model (moderately hydroxylated with 2.2 OH nm-2), a fact that probably favors the formation of the interstrand SiOH · · · NH2 H-bond. From the present results, two important conclusions can be established: i) the contact between Gly and the surface occurs via H-bonds between both COOH and NH2 and the surface SiOH groups, and ii) in S-Glyc two silanol groups are involved in the interaction, whereas the other two remain unperturbed. According to these results, the strategy adopted to deduce the initial geometries of the different S-AA systems was to keep

Figure 4. ONIOM2[B3LYP/6-311++G(d,p):MNDO]-optimized geometry of nonpolar AA interacting with the present silica surface cluster model. For the sake of clarity, only the high-level zones are shown. Bond lengths in angstroms.

the interaction of the COOH/NH2 moiety, common to all AA as for Gly, while engaging the AA side chain with the available silanol groups of the surface. Nonpolar AA. With respect to the AA with an essentially hydrophobic character, it is reasonable to assume that no significant changes will occur compared to Gly because the lateral chains cannot establish H-bonds with the free silanol groups. This is indeed the case for Ala, its interaction with the silica surface being only given through the NH2 and COOH groups (S-Ala of Figure 4). This situation can be probably extended to other nonpolar amino acids such as valine, leucine, and isoleucine (R ) CH-(CH3)2, CH2-CH-(CH3)2 and CH(CH3)-CH2-CH3, respectively). However, Met and Phe (R ) (CH2)2-S-CH3 and CH2-C6H5, respectively) are particularly interesting because their side chains present electron donor groups. Figure 4 also shows the computed structures for the most stable Met- and Phe-silica adducts (S-Met and S-Phe, respectively). In S-Met, two silanol groups are pointing directly toward the lone pair electrons of the S atom, thus establishing two H-bonds of moderate strength (around 2 Å). It is worth noting that the interactions provided by the backbone chain have not been significantly perturbed with respect to S-Glyc, the most important changes being a slight increase of the NH2 · · · SiOH H-bond length and the decrease of one of the SiOH · · · OdC interactions. Similar variations are observed in the S-Phe structure. In this case, however, the H-bond involving the side chain takes place between the proton of the SiOH groups and the negative electrostatic potential at the center of the aromatic ring; that is, an OH · · · π H-bond is established. The distance from the H of the silanol groups to the center of the ring (around 3.5 Å, Figure 4) reveals that these H-bonds are rather weak. Polar/Neutral AA. The AA belonging to the polar/neutral class (Ser, Thr, Cys, and Tyr) share a common feature: the side chains boast a proton bonded to an electronegative atom. This is a special situation because the lateral chains can interact via

Interaction of Amino Acids with Silica Surfaces

Figure 5. ONIOM2[B3LYP/6-311++G(d,p):MNDO]-optimized geometry of polar/neutral AA interacting with the present silica surface cluster model. For the sake of clarity, only the high-level zones are shown. Bond lengths in angstroms.

H-bonding with the surface both as proton acceptor and proton donor. The starting structures of these S-AA systems were built according to this (Figure 5); however, after optimizing only S-Cys and S-Tyr show stable structures in which the side chains act simultaneously as proton acceptor and proton donor with the silanols of the surface. For S-Ser and S-Thr, the OH lateral groups act only as H-bond acceptors with one SiOH. This is probably due to the fact that the size of the side chain of Ser and Thr (R ) CH2-OH and CH(-OH)-CH3, respectively) are not large enough to properly interact with the two silanol groups. In contrast, the large S atomic radius in Cys (R ) CH2-SH) and the size of the phenol ring in Tyr (R ) CH2-C6H4-OH) enable simultaneous H-bond interactions with the two SiOH to take place. Polar/Acidic AA. These AA are characterized by having a carboxylic group in the side chain (Figure 2). Because the interaction of Gly with the silica surface takes place mainly through the backbone carboxyl group (Figure 3), the interaction of the side chains with the other silanol groups is expected to occur in a similar fashion; that is through two SiOH · · · OdC H-bonds plus another one between OH and one SiOH. S-Asp, the most stable adduct of the Asp interacting with the silica surface, shows such a situation (part a of Figure 6), although the NH2 · · · SiOH H-bond of the backbone chain has been lost because NH2 interacts with the OH group of the side chain. In contrast, for S-Glu, the backbone interaction is similar to S-Glyc but now the COOH side-chain group acts simultaneously as acceptor and donor H-bonding toward the same SiOH. Differences between S-Asp and S-Glu may be due to the size of the side chain: glutamic acid has a large lateral group, so a similar interaction to that of S-Asp would imply a greater structural deformation because the Glu lateral chain should be fitted in a very constrained region. Polar/Amidic AA. This class of AA present a similar behavior to that observed for the acidic ones because the CONH2 amide group is similar to COOH, in terms of H-bonding; that is the

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Figure 6. ONIOM2[B3LYP/6-311++G(d,p):MNDO]-optimized geometry of polar/acidic (a) and polar/amidic (b) AA interacting with the present silica surface cluster model. For the sake of clarity, only the high-level zones are shown. Bond lengths in angstroms.

CO can act as a proton acceptor and NH2 as proton donor. S-Asn, the most stable structure for the Asn case (part b of Figure 6), involves three H-bonds between the lateral chain and the silanol groups (in a very similar fashion to S-Asp). Gln exhibits a longer side chain so that a different structure was located (S-Gln of part b of Figure 6), which in turn is different from S-Glu. That is, in S-Gln, the CO acts as H-bond acceptor interacting with one SiOH, whereas the NH2 amide interacts as proton donor with the other silanol, thus maximizing the sidechain H-bond interactions (1.77 and 1.97 Å, part b of Figure 6). We have also computed the analogous structures of S-Asp and S-Gln with glutamine and asparigine respectively, but they were found to lie higher in energy than the respective groundstate adducts (S-Asn and S-Gln structures in the Supporting Information). Polar/Basic AA. Because these amino acids have a basic group in their lateral chains, it is reasonable to assume an H-bond acceptor character when interacting with the SiOH groups of the surface. This is indeed the case for the adsorption of His and Lys (S-His and S-Lys of Figure 7, respectively), where the main interaction is given between the proton of one silanol group and the N atoms of the imidazole ring (for His) and of the amine group (for Lys). In contrast, the arginine-silica surface interaction shows a more complex H-bond pattern (S-Arg of Figure 7) due to the presence of both acceptor and donor H-bond groups. As aforementioned, His and Arg can be found in two tautomeric forms. Accordingly, we have computed different His- and Arg-silica surface structures accounting for all tautomers (geometries being available in the Supporting Information). For both cases, their adsorption onto silica surface takes place in its most stable gas-phase tautomer (Figure 2); that is, His has the Nδ protonated and Arg presents the (CH2)3-NdC(NH2)2 side-chain form. The adsorption of His in the Nδ-H form allows for the typical H-bonds of the backbone chain, contrarily to the adsorption of its Nε-H form, where

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Figure 7. ONIOM2[B3LYP/6-311++G(d,p):MNDO]-optimized geometry of polar/basic AA interacting with the present silica surface cluster model. For the sake of clarity, only the high-level zones are shown. Bond lengths in angstroms.

two of these H-bonds are lost (S-His in the Supporting Information). On the other hand, Arg adsorbed in its most stable gas-phase tautomer leads to the formation of strong H-bonds with the surface (1.7-2.0 Å), in detriment of the weak interactions given by the other tautomer upon adsorption (Hbond lengths around 2.1-2.9 Å, S-Arg in the Supporting Information). Adsorption Energies of the S-AA Systems. Table 1 summarizes the computed adsorption energies (electronic, including the ZPE correction, enthalpies, and Gibbs free energies) corrected for BSSE and including the contribution of dispersion for the lowest-energy structures of S-AA. These results show that the adsorption energy increases as a function of the number of polar groups in the AA side chain as a consequence of stronger H-bonds with the surface. This is reflected by the ∆Eint values in Table 1, which shows that acidic, amidic, and basic amino acids are those that exhibit larger adsorption energies. Whereas ∆Hc298 are all negative values, the c are all positive (unfavorable process), and if computed ∆G298 full credit to this data is given, one could conclude that no one amino acid will adsorb on this kind of silica surface in gasc arise from the phase conditions. The positive values of ∆G298 entropic term of the adsorption, ∆S, which is large and negative. c c is a delicate balance between ∆H298 < 0 and Thus, ∆G298 c c > 0, for which ∆G298 is, although small, positive for -T∆S298 all cases. However, as aforementioned, the dispersive interactions expected to be relevant especially for large AA are not taken into account by B3LYP and because of that the Grimme c values post-DFT correction (D) has been added to the ∆G298 (for further details see the Methods section). As one can see from the column labeled as D of Table 1, the role of the dispersive forces on the adsorption energy is substantial, this term spanning the -6.0 to -13 kcal mol-1 range. Therefore, when this correction is added, the final free adsorption energies c + D) become all negative (favorable process) showing (∆G298 that AA adsorption on hydroxylated silica does indeed occur.

Figure 8. Comparison of the adsorption energies with AA properties. Section (a): Plot of ∆Gc298 + D (in kcal mol-1) vs the experimental vapor-aqueous free energy of transfer of the amino acid side-chain analogues (hydration potential, in kcal mol-1, text for further details) obtained by Wolfenden et al. (from ref 100). Section (b): Plot of ∆Gc298 + D (in kcal mol-1) vs the hydropathy index of amino acids (text for further details) obtained by Kyte et al. (from ref 104).

It is worth noting that for five different cases (Gly, Ser, Glu, Gln, and Lys) the AA adsorption was also computed by means of a B3LYP/6-311G(d,p) periodic treatment using the CRYSTAL06 code,99 envisaging the present cluster as a unit cell of a bidimensional silica slab. Results show that the computed ∆Ecint are in reasonable agreement with those obtained here by cluster calculations, exhibiting for all cases a systematic larger adsorption energy by about 2-4 kcal mol-1, probably due to the different approximations and basis set adopted for the periodic calculations (Cartesian coordinates and energy values reported in the Supporting Information). These results show that longrange effects, although not being very large, appear to be stabilizing, thereby favoring the adsorption process. Overall, relative silica affinities for the different AA seem to be modulated by the H-bond interactions occurring at the surface (particularly those from the side chain), whereas dispersive interactions (more favorable in AA with large hydrocarbon chains) mainly contribute to the adsorption process by greatly c + D values, lowering the adsorption energy. From the ∆G298 the following sequence for the relative silica surface affinities for amino acids can be established: nonpolar < polar/neutral < polar/amidic ∼ polar/acidic < polar/basic. In this sense, it is worth mentioning that, among the amino acids tested, Ala and Arg exhibit the lowest and largest silica surface affinity, respectively.

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TABLE 1: Adsorption Energies of the Most Stable S-AA Systems: Non-corrected and BSSE-Corrected Electronic Adsorption c c Energies (∆Eint and ∆Eint , respectively), ZPE-corrected Adsorption Energies (∆U0c ), Adsorption Enthalpies (∆H298 ), Entropic a c c Term to the Adsorption Processes (-T∆S298) at T ) 298 K and Adsorption Free Energies (∆G298) S-AA

∆Eint

BSSE

c ∆Eint

∆Uc0

∆Hc298

-T∆Sc298

∆Gc298

D

∆Gc298 + D

ν(CdO)b

S-Gly S-Ala S-Phe S-Met S-Ser S-Thr S-Cys S-Tyr S-Asp S-Glu S-Asn S-Gln S-His S-Lys S-Arg

-13.8 -13.2 -14.0 -17.3 -14.8 -15.5 -13.5 -19.6 -20.0 -22.2 -19.2 -21.5 -19.2 -21.3 -21.8

2.2 2.1 3.7 3.9 3.5 3.4 3.6 3.8 3.6 3.9 3.5 4.1 3.8 3.7 4.5

-11.6 -11.1 -10.3 -13.4 -11.3 -12.1 -10.0 -15.8 -16.4 -18.3 -15.7 -17.4 -15.4 -17.6 -17.3

-9.2 -8.8 -8.0 -10.4 -8.7 -9.8 -7.3 -12.3 -13.1 -15.0 -12.6 -14.1 -12.7 -14.7 -14.2

-9.7 -9.2 -8.5 -11.3 -9.3 -10.3 -7.9 -13.3 -14.3 -15.9 -13.7 -15.0 -13.4 -15.4 -15.1

14.1 13.7 15.6 17.8 15.3 15.6 16.4 18.5 18.1 17.7 17.3 18.4 16.7 17.1 17.9

4.4 4.5 7.1 6.5 6.0 5.3 8.5 5.2 3.8 1.8 3.6 3.4 3.3 1.7 2.8

-7.5 -6.3 -10.9 -10.9 -10.9 -10.1 -11.1 -12.2 -11.5 -10.4 -11.1 -11.4 -11.6 -10.6 -12.6

-3.1 -1.8 -3.8 -4.4 -4.9 -4.8 -2.6 -7.0 -7.7 -8.6 -7.5 -8.0 -8.3 -8.9 -9.8

1699 1691 1696 1698 1698 1698 1697 1700 1703 1693 1709 1691 1701 1688 1691

a Contribution of the dispersive forces to the adsorption processes (D) and final adsorption energies (∆Gc298 + D). All data in kcal mol-1. The B3LYP-scaled stretching frequencies of the CdO backbone bond (ν(CdO)) are also included, in cm-1. b Scaling factor of 0.975 obtained from ref 34.

AA Adsorption Energies vs AA Features. It is interesting c to compare the computed ∆G298 + D with some AA physicochemical properties reported in the literature. Because our model of the silica surface is hydroxylated, we have compared, as a first test, AA adsorption energies with the experimental water affinity of the AA side chains (RH) obtained by Wolfenden et al.100 These affinities were determined by obtaining the free energy of transfer of the RH side-chain analogues from water solution to the vapor phase (RHsolution f RHvapor) from measures of the partial pressure of RH in dilute aqueous solution.100-102 According to this procedure, Wolfenden et al. established a ladder of “hydration potentials”, which correspond to the affinities of the amino acids side chains for solvent water: the more negative the hydration potential is, the higher is the affinity to water. The fact that these water affinities were obtained for the RH amino acid side-chain molecules is of particular interest because, in that way, inconsistencies given by the presence of zwitterion and neutral forms of amino acids in water solution and in the gas phase, respectively, are avoided. The correlation c + D and the experimental between the computed ∆G298 hydration potentials is shown in part a of Figure 8 (the original data from Wolfenden et al. is available in the Supporting Information). As observed, the correlation is reasonably good (correlation coefficient r2 ) 0.93), with the exception of Arg, for which the water affinity obtained by Wolfenden et al. is dramatically larger than that predicted by our calculations. The large disagreement can be rationalized if one accounts for the possible tautomeric form of the (CH2)3-NdC(NH2)2 lateral chain. One of the resonant forms of this tautomer is zwitterioniclike ((CH2)3-N--C(NH2)2+), exhibiting simultaneously a negative and a positive charge. Because water can efficiently stabilize charges, this tautomer can be largely stabilized in aqueous solution, thereby strongly favoring the transfer from vapor phase to the solution. Consequently, the interaction of the Arg side chain with water is expected to be larger than on the silica surface. Nevertheless, it is worth remarking that both the c + D values hydration potential and the computed ∆G298 determine Arg as the AA with the largest affinity for water and for hydroxylated silica surfaces, as well. Accordingly, because the RH/water systems are H-bonded complexes, this correlation seems to suggest a similarity between liquid water and silica surfaces, this latter behaving like a solid solvent. Remarkably, c + D also compare very well with the solvation free ∆G298

energies of neutral amino acids in water obtained by Chang et al.103 by means of Monte Carlo (MC) simulations using the OPLS-AA force field, which was parametrized by taking the same experimental values provided by Wolfenden100 (Supporting Information). As mentioned, both H-bonding and dispersive interactions are responsible for the gas-phase AA adsorption onto silica surface. According to that, it is then interesting to compare the c + D with experimental properties accounting computed ∆G298 for both hydrophilic/hydrophobic character of the AA, such as defined by the hydropathy index (HI). This index represents the hydrophilicity/hydrophobicity of a given amino acid and is usually used in bioinformatics to evaluate the hydrophilic/ hydrophobic properties of a protein along its amino acid sequence, the so-called hydropathy character. HI is coded as a number, whose sign and magnitude is proportional to the hydrophilic (HI < 0) or hydrophobic (HI > 0) character of the AA. Kyte et al.104 obtained a set of HI for the amino acids by measuring free energies of transfer of the side chains of the amino acids between various phases with the purpose of identifying the hydrophilic/hydrophobic character of protein c + surface-exposed regions. The Kyte-based HI versus ∆G298 D plot is represented in part b of Figure 8 (numerical data available in the Supporting Information) and shows a good correlation (r2 ) 0.92). AA considered as polar by the HI have large and negative ∆Gc298 + D because they are able to establish H-bonds with the surface, contrarily to the amino acids categorized as nonpolar, which exhibit negative but small ∆Gc298 + D values. However, some outliers are observed. For instance, c the ∆G298 + D adsorption energy of Met is larger compared to what should be expected by the Kyte-based HI. This can be understood considering that Met (Figure 4), which is categorized as nonpolar, establishes two H-bonds between the silanol groups of the surface, and, because these SiOH groups are slightly more acidic than water, the Met adsorption is more favorable than its water solvation. It is worth remarking that the correlation c + D, the values appears to be worse if, instead of using ∆G298 c 2 of ∆G298 versus HI are reported (r ) 0.92 and 0.86, respectively). This means that both H-bonds and dispersive interactions are likewise important to obtain a proper affinity scale of the amino acids for a hydroxylated silica surface. This is the case, for instance, of Arg, which becomes the amino acid with the largest adsorption energy only after including dispersive forces.

5748 J. Phys. Chem. C, Vol. 113, No. 14, 2009

Figure 9. Cartoons of the Connolly surfaces105 of amorphous silica models106 envisaging 1.5 (left) and 4.5 (right) OH nm-2 interacting with Phenylalanine (left) and Lysine (right). The AA have been manually docked to the surfaces without resorting to any kind of quantum mechanical calculations.

Finally, one critical point that may render the present results rather specific is the adoption of a silica surface model with a silanol density of 2.2 OH nm-2, which is lower than that exhibited by an amorphous silica sample outgassed at 373 K (4.5 OH nm-2). Figure 9 shows on the left/right Connolly surfaces105 of models of amorphous silica envisaging 1.5/4.5 OH nm-2 respectively, derived from a periodic ab initio B3LYP calculations.106 The blue/red zones represent regions of positive/ negative electrostatic potential, which are due to the surface OH groups. Gray patches are due to siloxane bridges and represent regions in which electrostatic fields are almost negligible. Phe has been manually docked (no QM calculations have been run) toward the 1.5 OH nm-2 surface so that H-bonds between the COOH group and the surface silanols similar to those computed for the edingtonite cluster model are established. The phenyl group, on the contrary, mainly interacts via London dispersive interactions with the hydrophobic patches. On the right, the case of Lys is shown: here, because of the hydrophilic character of the surface, the AA will fit in by matching its own electrostatic features with those of the surface. For this case, interestingly, despite an almost double OH density of the surface compared to the edingtonite cluster, there is no need to break the H-bonds occurring between the surface silanols to properly accommodate the Lys AA. In conclusion, we believe that the features predicted for the edingtonite model with 2.2 OH nm-2 will not be dramatically different from those resulting for a more hydroxylated (and realistic) silica surface, whereas a silica pretreated at very high T will favor the adsorption of hydrophobic AA compared to the hydrophilic ones because of the favorable dispersive interactions with the hydrophobic surface patches. Conclusions The interaction of 15 amino acids with different chemical properties (nonpolar, polar/neutral, polar/acidic, polar/amidic, and polar/basic) on a model of amorphous silica represented by the hydroxylated (001) face of edingtonite under gas-phase conditions has been ab initio studied using a cluster approach with the ONIOM2(B3LYP/6-311++G(d,p):MNDO) level of theory. The adopted silica model exhibits 2 OH nm-2 and mimics a real material outgassed at 400 °C. AA dockings toward the silica surface model have been performed by maximizing the H-bond interactions between the four isolated surface SiOH silanol groups and the different AA functional groups contained at the backbone and side chains. The most stable Gly-silica surface adduct exhibits four H-bond interactions: two of them involve the CdO group and

Rimola et al. two silanols, another one originates from the glycine OH and the fourth (and weakest) involves the amino group of glycine as proton donor. The other AA tend to establish the maximum number of H-bonds between the side chain and the available SiOH groups for a proper adsorption, which give rise to a sort of S-AA complexes. The computed adsorption energies reveal that amino acids with side chains functionalized by polar groups and that contain large aliphatic groups exhibit the most favorable interaction energies with the surface, a fact that suggests that binding via H-bond and dispersion are the main driving forces for the gasphase adsorption of amino acids onto the hydroxylated silica surface. From the results obtained, the following scale on the affinity of amino acids toward a moderately hydroxylated silica surface holds: nonpolar ∼ polar/neutral < polar/amidic ∼ polar/ acidic < polar/basic. In particular, the largest and negative ∆Gc298 + D computed adsorption energy belongs to Arg. In general, both H-bond interactions between the side-chain groups and the surface hydroxyl groups as well as the dispersive interactions between the side-chain hydrophobic moieties and the siloxane groups contribute to determine the relative silica surface affinities of the amino acids Finally, it has been found that the computed adsorption energies are reasonably well correlated with, on one hand, the water affinities of the neutral amino acid side-chain analogues obtained by thermodynamic measurements and, on the other hand, with the hydropathy index, a number obtained experimentally that reflects the hydrophobic/hydrophilic character of natural amino acids. The trends obtained in the present work for the edingtonite model with an OH density of 2.2 OH nm-2 are expected to hold also for more hydroxylated silica surfaces. However, silica surfaces pretreated at very high T, and resulting in lower OH densities, are expected to favor the adsorption of hydrophobic AA compared to the hydrophilic ones because of the favorable dispersive interactions with the hydrophobic surface patches. Acknowledgment. Financial support from MCYT and DURSI, through the CTQ2005-08797-C02-02/BQU and SGR200500244 projects, and the use of the Catalonia Supercomputer Centre (CESCA) are gratefully acknowledged. AR is indebted to Ramo´n Areces Foundation for a postdoctoral fellowship at the University of Torino. MS and PU kindly acknowledge BSCMN for generous allowance of computing time through the “BCV-2008-2-0013: Simulation of peptide folding induced by inorganic materials” project. Supporting Information Available: Absolute energies and Cartesian coordinates of optimized geometries for isolated and adsorbed amino acids. This material is available free of charge via the Internet at http://pubs.acs.org. References and Notes (1) Proteins at Interfaces. Physicochemical and Biochemical Studies; Brash, J. L., Horbett, T. A., Eds.; American Chemical Society: Washington D.C., 1987. (2) Gray, J. J. Curr. Opin. Struct. Biol. 2004, 14, 110. (3) Proteins at Interfaces II: Fundamentals and Applications; Horbett, T. A., Brash, J. L.; Eds.; American Chemical Society: Washington D.C., 1995. (4) Vallet-Regí, M. Chem.sEur. J. 2006, 12, 5934. (5) Hench, L. L.; Splinter, R. J.; Allen, W. C.; Greenlee, T. K. Biomed. Mater. Symp. 1971, 117. (6) Pividori, M. I.; Alegret, S. Top. Curr. Chem. 2005, 260, 1. (7) Sen, T.; Sebastianelli, A.; Bruce, I. J. J. Am. Chem. Soc. 2006, 128, 7130. (8) Castellana, E. T.; Russell, D. H. Nano Lett. 2007, 7, 3023.

Interaction of Amino Acids with Silica Surfaces (9) McLean, J. A.; Stumpo, K. A.; Russell, D. H. J. Am. Chem. Soc. 2005, 127, 5304. (10) Wagner, M. S.; Horbett, T. A.; Castner, D. G. Langmuir 2003, 19, 1708. (11) Xia, N.; May, C. J.; McArthur, S. L.; Castner, D. G. Langmuir 2002, 18, 4090. (12) Zhang, Y.; Wang, X.; Shan, W.; Wu, B.; Fan, H.; Yu, X.; Tang, Y.; Yang, P. Angew. Chem., Int. Ed. 2005, 44, 615. (13) Patwardhan, S. V.; Patwardhan, G.; Perry, C. C. J. Mater. Chem. 2007, 17, 2875. (14) Belton, D.; Paine, G.; Patwardhan, S. V.; Perry, C. C. J. Mater. Chem. 2004, 14, 2231. (15) Patwardhan, S. V.; Clarson, S. J.; Perry, C. C. Chem. Commun. 2005, 1113. (16) Giacomelli, C. E.; Bremer, M. G. E. G.; Norde, W. J. Colloid Interface Sci. 1999, 220, 13. (17) Giacomelli, C. E.; Norde, W. J. Colloid Interface Sci. 2001, 233, 234. (18) Gibson, J. M.; Popham, J. M.; Raghunathan, V.; Stayton, P. S.; Drobny, G. P. J. Am. Chem. Soc. 2006, 128, 5364. (19) Goobes, R.; Goobes, G.; Shaw, W. J.; Drobny, G. P.; Campbell, C. T.; Stayton, P. S. Biochemistry 2007, 46, 4725. (20) Long, J. R.; Shaw, W. J.; Stayton, P. S.; Drobny, G. P. Biochemistry 2001, 40, 15451. (21) Shaw, W. J.; Long, J. R.; Dindot, J. L.; Campbell, A. A.; Stayton, P. S.; Drobny, G. P. J. Am. Chem. Soc. 2000, 122, 1709. (22) Tarasevich, Y. I.; Monakhova, L. I. Colloid J. 2002, 64, 482. (23) Churchill, H.; Teng, H.; Hazen, R. M. Am. Mineral. 2004, 89, 1048. (24) Latour, R. A., Jr.; Hench, L. L. Biomaterials 2002, 23, 4633. (25) Raffaini, G.; Ganazzoli, F. Langmuir 2004, 20, 3371. (26) Raut, V. P.; Agashe, M. A.; Stuart, S. J.; Latour, R. A. Langmuir 2005, 21, 1629. (27) Carravetta, V.; Monti, S. J. Phys. Chem. B 2006, 110, 6160. (28) Gambino, G. L.; Grassi, A.; Marletta, G. J. Phys. Chem. B 2006, 110, 4836. (29) Gambino, G. L.; Lombardo, G. M.; Grassi, A.; Marletta, G. J. Phys. Chem. B 2004, 108, 2600. (30) Stievano, L.; Yu Piao, L.; Lopes, I.; Meng, M.; Costa, D.; Lambert, J.-F. Eur. J. Mineral. 2007, 19. (31) Rimola, A.; Sodupe, M.; Tosoni, S.; Civalleri, B.; Ugliengo, P. Langmuir 2006, 22, 6593. (32) Lomenech, C.; Bery, G.; Costa, D.; Stievano, L.; Lambert, J. F. ChemPhysChem 2005, 6, 1061. (33) Rimola, A.; Tosoni, S.; Sodupe, M.; Ugliengo, P. ChemPhysChem 2006, 7, 157. (34) Rimola, A.; Civalleri, B.; Ugliengo, P. Langmuir 2008, in press. (35) Costa, D.; Tougerti, A.; Tielens, F.; Gervais, C.; Stievano, L.; Lambert, J. F. Phys. Chem. Chem. Phys. 2008, 10, 6360. (36) Costa, D.; Lomenech, C.; Meng, M.; Stievano, L.; Lambert, J.-F. J. Mol. Struct.: THEOCHEM 2007, 806, 253–259. (37) Nonella, M.; Seeger, S. ChemPhysChem 2008, 9, 414. (38) Lambert, J.-F. Orig. Life EVol. Biosph. 2008, 38, 211. (39) Bernal, J. D. The Physical Basis of Life; Routledge and Kegan Paul ed.: London, 1951. (40) Hazen, R. M. Am. Mineral. 2006, 91, 1715. (41) Orgel, L. E. Orig. Life EVol. Biosph. 1998, 28, 227. (42) Kuan, Y.-J.; Charnley, S. B.; Huang, H.-C.; Tseng, W.-L.; Kisiel, Z. Astrophys. J. 2003, 593, 848. (43) Blank, J. G.; Miller, G. H.; Ahrens, M. J.; Winans, R. E. Orig. Life EVol. Biosph. 2001, 31, 15. (44) Boehme, C.; Marx, D. J. Am. Chem. Soc. 2003, 125, 13362. (45) Nair, N. N.; Schreiner, E.; Marx, D. J. Am. Chem. Soc. 2006, 128, 13815. (46) Pollet, R.; Boehme, C.; Marx, D. Orig. Life EVol. Biosph. 2006, 36, 363. (47) Schreiner, E.; Nair, N. N.; Marx, D. J. Am. Chem. Soc. 2008, 130, 2768. (48) Rimola, A.; Tosoni, S.; Sodupe, M.; Ugliengo, P. Chem. Phys. Lett. 2005, 408, 295. (49) Rimola, A.; Sodupe, M.; Ugliengo, P. J. Am. Chem. Soc. 2007, 129, 8333. (50) Arnaud, R.; Adamo, C.; Cossi, M.; Milet, A.; Valle´e, Y.; Barone, V. J. Am. Chem. Soc. 2000, 122, 324. (51) Walch, S. P.; Bauschlicher, C. W. J.; Ricca, A.; Bakes, E. L. O. Chem. Phys. Lett. 2001, 333, 6. (52) Basiuk, V. A. J. Phys. Chem. A 2001, 105, 4252. (53) Woon, D. E. Int. J. Quantum Chem. 2002, 88, 226. (54) Largo, A.; Redondo, P.; Barrientos, C. Int. J. Quantum Chem. 2004, 98, 355. (55) Maeda, S.; Ohno, K. Chem. Phys. Lett. 2004, 398, 240. (56) Mendoza, C.; Ruette, F.; Martorell, G.; Rodríguez, L. S. Astrophys. J. Lett. 2004, 601, L59.

J. Phys. Chem. C, Vol. 113, No. 14, 2009 5749 (57) Holtom, P. D.; Bennett, C. J.; Osamura, Y.; Mason, N. J.; Kaiser, R. I. Astrophys. J. 2005, 626, 940. (58) Feldmann, M. T.; Widicus, S. L.; Blake, G. A.; Kent, D. R.; Goddard, W. A. J. Chem. Phys. 2005, 123, 034304. (59) Maeda, S.; Ohno, K. Astrophys. J. 2006, 640, 823. (60) Koch, D. M.; Toubin, C.; Xu, S.; Peslherbe, G. H.; Hynes, J. T. J. Phys. Chem. C 2007, 111, 15026. (61) Koch, D. M.; Toubin, C.; Peslherbe, G. H.; Hynes, J. T. J. Phys. Chem. C 2008, 112, 2972. (62) Basiuk, V. A.; Gromovoy, T. Y.; Golovaty, V. G.; Glukhoy, A. M. Orig. Life EVol. Biosph. 1990-1991, 20, 483. (63) Groenewegen, J. A.; Sachtler, W. M. H. J. Catal. 1974, 33, 176. (64) Chapo, C. J.; Paul, J. B.; Provencal, R. A.; Roth, K.; Saykally, R. J. J. Am. Chem. Soc. 1998, 120, 12956. (65) Snoek, L. C.; Kroemer, R. T.; Hockridge, M. R.; Simons, J. P. Phys. Chem. Chem. Phys. 2001, 3, 1819. (66) Bakker, J. M.; Aleese, L. M.; Meijer, G.; Helden, G. v. Phys. ReV. Lett. 2003, 91, 203003. (67) Lesarri, A.; Mata, S.; Cocinero, E. J.; Blanco, S.; Lo´pez, J. C.; Alonso, J. L. Angew. Chem., Int. Ed. 2002, 41, 4673. (68) Blanco, S.; Lesarri, A.; Lo´pez, J. C.; Alonso, J. L. J. Am. Chem. Soc. 2004, 126, 11675. (69) Lesarri, A.; Sa´nchez, R.; Cocinero, E. J.; Lo´pez, J. C.; Alonso, J. L. J. Am. Chem. Soc. 2005, 127, 12952. (70) Blanco, S.; Sanz, M. E.; Lo´pez, J. C.; Alonso, J. L. Proc. Natl. Acad. Sci. U.S.A. 2007, 104, 20183. (71) Linder, R.; Nispel, M.; Ha¨ber, T.; Kleinermanns, K. Chem. Phys. Lett. 2005, 409, 260. (72) Linder, R.; Seefeld, K.; Vavra, A.; Kleinermanns, K. Chem. Phys. Lett. 2008, 453, 1. (73) Civalleri, B.; Casassa, S.; Garrone, E.; Pisani, C.; Ugliengo, P. J. Phys. Chem. B 1999, 103, 2165. (74) Civalleri, B.; Ugliengo, P. J. Phys. Chem. B 2000, 104, 9491. (75) Dapprich, S.; Koma´romi, I.; Byun, K. S.; Morokuma, K.; Frisch, M. J. J. Mol. Struct.: THEOCHEM 1999, 461-462, 1. (76) Maseras, F.; Morokuma, K. J. Comput. Chem. 1995, 16, 1170. (77) Vreven, T.; Morokuma, K. J. Comput. Chem. 2000, 21, 1419. (78) Becke, A. D. J. Chem. Phys. 1993, 98, 5648. (79) Lee, C.; Yang, W.; Parr, R. G. Phys. ReV. B 1988, 37, 785. (80) Dewar, M. J. S.; Thiel, W. J. Am. Chem. Soc. 1977, 99, 4899. (81) Frisch, M. J. Trucks, G. W.; Schlegel, H. B.; Scuseria, G. E.; Robb, M. A.; Cheeseman, J. R.; Montgomery, J., J. A.; Vreven, T.; Kudin, K. N.; Burant, J. C.; Millam, J. M.; Iyengar, S. S.; Tomasi, J.; Barone, V.; Mennucci, B.; Cossi, M.; Scalmani, G.; Rega, N.; Petersson, G. A.; Nakatsuji, H.; Hada, M.; Ehara, M.; Toyota, K.; Fukuda, R.; Hasegawa, J.; Ishida, M.; Nakajima, T.; Honda, Y.; Kitao, O.; Nakai, H.; Klene, M.; Li, X.; Knox, J. E.; Hratchian, H. P.; Cross, J. B.; Bakken, V.; Adamo, C.; Jaramillo, J.; Gomperts, R.; Stratmann, R. E.; Yazyev, O.; Austin, A. J.; Cammi, R.; Pomelli, C.; Ochterski, J. W.; Ayala, P. Y.; Morokuma, K.; Voth, G. A.; Salvador, P.; Dannenberg, J. J.; Zakrzewski, V. G.; Dapprich, S.; Daniels, A. D.; Strain, M. C.; Farkas, O.; Malick, D. K.; Rabuck, A. D.; Raghavachari, K.; Foresman, J. B.; Ortiz, J. V.; Cui, Q.; Baboul, A. G.; Clifford, S.; Cioslowski, J.; Stefanov, B. B.; Liu, G.; Liashenko, A.; Piskorz, P.; Komaromi, I.; Martin, R. L.; Fox, D. J.; Keith, T.; Al-Laham, M. A.; Peng, C. Y.; Nanayakkara, A.; Challacombe, M.; Gill, P. M. W.; Johnson, B.; Chen, W.; Wong, M. W.; Gonzalez, C.; Pople, J. A. Gaussian 03, Revision C.02; Gaussian, Inc.: Wallingford, CT, 2004. (82) McQuarrie, D. Statistical Mechanics; Harper and Row: New York, 1986. (83) Boys, S. F.; Bernardi, F. Mol. Phys. 1970, 19, 553. (84) Grimme, S. J. Comput. Chem. 2006, 27, 1787. (85) Antony, J.; Grimme, S. Phys. Chem. Chem. Phys. 2006, 8, 5287. (86) Ugliengo, P. http://www.moldraw.unito.it:MOLDRAW; H1 (32bit) ed. Torino, 2005. (87) Jensen, J. H.; Gordon, M. S. J. Am. Chem. Soc. 1991, 113, 7917. (88) Godfrey, P. D.; Brown, R. D. J. Am. Chem. Soc. 1995, 117, 2019. (89) Simon, S.; Gil, A.; Sodupe, M.; Bertra´n, J. J. Mol. Struct.: THEOCHEM 2005, 727, 191. (90) Ruan, C.; Rodgers, M. T. J. Am. Chem. Soc. 2004, 126, 14600. (91) Huang, Z.; Yu, W.; Lin, Z. J. Mol. Struct.: THEOCHEM 2006, 758, 195. (92) Rimola, A.; Rodríguez-Santiago, L.; Sodupe, M. J. Phys. Chem. B 2006, 110, 24189. (93) Noguera, M.; Rodríguez-Santiago, L.; Sodupe, M.; Bertran, J. J. Mol. Struct.: THEOCHEM 2001, 537, 307. (94) Zhang, M.; Lin, Z. J. Mol. Struct.: THEOCHEM 2006, 760, 159. (95) Zhang, M.; Huang, Z.; Lin, Z. J. Chem. Phys. 2005, 122, 134313. (96) Sun, W.; Kinsel, G. R.; Marynick, D. S. J. Phys. Chem. A 1999, 103, 4113. (97) Kovacevic, B.; Rozman, M.; Klasinc, L.; Srzic, D.; Maksic, Z. B.; Ya´n˜ez, M. J. Phys. Chem. A 2005, 109, 8329. (98) Ling, S.; Yu, W.; Huang, Z.; Lin, Z.; Haran˜czyk, M.; Gutowski, M. J. Phys. Chem. A 2006, 110, 12282.

5750 J. Phys. Chem. C, Vol. 113, No. 14, 2009 (99) Dovesi, R.; Saunders, V. R.; Roetti, C.; Orlando, R.; ZicovichWilson, C. M.; Pascale, F.; Civalleri, B.; Doll, K.; Harrison, N. M.; Bush, I. J.; D’Arco, P.; Llunell, M. CRYSTAL2006 User’s Manual; http:// www.crystal.unito.it; University of Torino: Torino, 2006. (100) Wolfenden, R.; Andersson, L.; Cullis, P. M.; Southgate, C. C. B. Biochemistry 1981, 20, 849. (101) Wolfenden, R. J. Am. Chem. Soc. 1976, 98, 1987. (102) Wolfenden, R. Biochemistry 1978, 17, 201.

Rimola et al. (103) Chang, J.; Lenhoff, A. M.; Sandler, S. I. J. Phys. Chem. B 2007, 111, 2098. (104) Kyte, J.; Doolittle, R. F. J. Mol. Biol. 1982, 157, 105. (105) Connolly, M. L. J. Mol. Graphics 1993, 11, 139. (106) Ugliengo, P.; Sodupe, M.; Musso, F.; Bush, I. J.; Orlando, R.; Dovesi, R. AdV. Mater. 2008, 20, 1.

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