Article pubs.acs.org/JPCB
Cite This: J. Phys. Chem. B 2018, 122, 3491−3499
Selectivity of Glycine for Facets on Gold Nanoparticles Qing Shao and Carol K. Hall*
Downloaded via NORTHERN KENTUCKY UNIV on June 27, 2018 at 12:10:34 (UTC). See https://pubs.acs.org/sharingguidelines for options on how to legitimately share published articles.
Department of Chemical and Biomolecular Engineering, North Carolina State University, Raleigh, North Carolina 27695, United States ABSTRACT: The performance of nanoparticles in medical applications depends on their interactions with various molecules. Despite extensive research on this subject, it remains unclear where on an inhomogeneous nanoparticle molecules prefer to adsorb. Here we investigate the selectivity of glycine molecules for facets on five bare gold nanoparticles with diameters from 1.0 to 5.0 nm. Well-tempered metadynamics simulations are conducted to calculate the adsorption free-energy landscapes of a glycine molecule on various locations for the five gold nanoparticles in explicit water. We also calculate the glycine molecule’s adsorption free energies on the five gold nanoparticles in vacuum and on three flat gold surfaces as a reference. The simulation results show that glycine molecules prefer to adsorb on the (110) facet for the 1.0 and 2.0 nm nanoparticles, the edges for the 3.0 nm nanoparticle, and the (111) facet for the 4.0 and 5.0 nm nanoparticles in water. The effect of water solvent on the selectivity is investigated through comparing the adsorption free-energy landscapes for glycine molecules on the nanoparticles in water and in vacuum. The area of the facet plays a key role in determining the selectivity of glycine molecules for the different facets, especially the shift of the selectivity as the nanoparticle diameter changes. Our simulations suggest that nanoparticle size and shape can be engineered to control the preferred adsorption location of molecules.
1. INTRODUCTION Gold nanoparticles (AuNPs) play an important role in the healthcare sector because their small size, high surface-area-tovolume ratio, and novel surface properties can be utilized in drug delivery,1−3 disease diagnosis,4,5 and in-body imaging.6,7 The performance of AuNPs in these applications depends on their interactions with various molecules.8 For example, the protein layers adsorbed on AuNPs influence their aggregation9,10 and uptake in cells.11,12 The objective of this work is to strengthen our understanding of the preferred adsorption of molecules on AuNPs and reveal what features of NPs influence this preference. The AuNP surfaces are intrinsically heterogeneous: They possess regions with various facets, the types, numbers, and areas of which depend on the NP size and shape. A large number of studies13,14 based on both molecular simulations and experiment have been conducted to investigate the adsorption of various molecules3,13,15−24 on gold materials.21−23 Some simulations-based studies have reported that on flat Au surfaces, proteins and peptides prefer to adsorb on the (111) surfaces over others. For example, Heinz et al.25 used atomistic molecular dynamics simulations to investigate the interactions of five types of peptides with Au(111) and Au(100) surfaces in aqueous solution and found that the peptides bind to (111) surfaces more strongly than to (100) surfaces. Wright et al.26 investigated the interactions of gold binding peptide AuBP1 (WAGAKRLVLRRE) on Au(111) and Au(100) surfaces using replica-exchange metadynamics simulation. They also found that the peptide adsorbs on (111) surfaces more strongly than on (100) surfaces. They hypothesized that the surface © 2017 American Chemical Society
preference of the peptide is related to the different structures of water molecules near the two surfaces. The selectivity of molecules for facets on AuNPs should be more complex than that for flat Au surfaces. Most of the research appearing in the literature mainly focuses on the difference in strengths between molecular adsorption on NPs and on flat surfaces. For example, Jiang et al.27 found that the dissociation constant between receptors and NPs decreases as the NP diameter increases. Lacerda et al.28 also found that AuNP-protein binding constants of albumin, fibrinogen, γglobulin, histone, and insulin increase as the NP diameter increases. If a flat surface can be viewed as a NP with an infinite diameter, these experiments suggest that the adsorption of molecules on NP should be weaker than that on flat surfaces. A recent simulation confirmed this hypothesis. Feng et al.16 investigated the adsorption of four types of peptides on (111) surfaces, stepped surfaces, and a 2 nm cuboctahedral NP using molecular dynamics simulations. They found that the adsorption of peptides on NPs is much weaker than that on flat surfaces. These studies indeed provide useful insights into molecular adsorption on AuNPs, especially in comparison with that on flat surfaces. Two critical questions about the selectivity of molecules for facets on AuNPs remain unanswered. First, will molecules select the same facet on a AuNP as they do on a flat surface? Special Issue: Benjamin Widom Festschrift Received: October 28, 2017 Revised: December 4, 2017 Published: December 4, 2017 3491
DOI: 10.1021/acs.jpcb.7b10677 J. Phys. Chem. B 2018, 122, 3491−3499
Article
The Journal of Physical Chemistry B
Figure 1. Five AuNPs used in this work. (from the left to the right: 1.0, 2.0, 3.0, 4.0, and 5.0 nm).
The adsorption location of molecules on AuNPs could influence their conformation and orientation and the ultimate function of AuNPs. Unlike on flat surfaces, the facets on AuNP surface have finite areas and are surrounded by other facets. The adsorption strength of molecules on a given region may change with the area of that region and the types of regions nearby. Such changes might alter the type of facets that molecules prefer to adsorb to. Second, what drives the selectivity of molecules for facets on AuNPs? The simulation study of Feng et al.16 suggested that features of NPs such as curvature influence the adsorption of peptides, while another simulation study29 suggested that the water molecules adjacent to proteins and substances drive the adsorption process. We investigate the selectivity of glycine molecules for facets on AuNPs, focusing on answering the questions posed above. The adsorption of molecules on substances is a complex process,30 and the outcome can be affected by many factors such as the chemico-physical features of molecules,28,31 the substance’s roughness32,33 and chemical heterogeneity,32,34 and the surrounding solution’s pH value and ionic strength.35 To answer the two questions, we investigate the adsorption freeenergy landscapes of glycine molecules on a AuNP. Here we consider bare AuNPs, as opposed to ligand-coated nanoparticles, so that we can focus in on how AuNP size affects the selectivity of glycine molecules for different facets. (Although it would be interesting to investigate the influence of various chemical ligands on the surface properties and function of gold nanomaterials, as others have done,36−39 this is beyond the scope of this paper.) Well-tempered metadynamics simulations are conducted to investigate the adsorption free-energy landscape of a capped glycine molecule on five AuNPs with diameters ranging from 1.0 to 5.0 nm in explicit water. We also calculate adsorption free energies for glycine molecules on the five AuNPs in vacuum and on the flat Au (100), (110), and (111) surfaces in explicit water solvent as a reference. The rest of the paper is organized as follows: Section 2 presents the molecular modeling and simulation methods, Section 3 presents the results and discussion, and Section 4 present a conclusion.
Figure 2. Facets on a 4.0 nm AuNP. The Au atoms are represented in the VDW model. The atoms on the surface are shown: the (111) facet, red; the (110) facet, blue; and the (100) facet, green. The atoms not on the outside surface are in yellow. The (111), (110), and (100) facets are labeled.
parameters developed by the Heinz group42,43 were used for gold atoms because they well describe the interfacial properties of gold materials. The water molecules were described using the SPC model44 as recommended for the GROMOS force field. The nonbonded interactions between atoms are described as a combination of van der Waals (VDW) and electrostatic interaction potentials. The VDW interaction potential is described using the Lennard-Jones 12−6 formula with a 1.0 nm cutoff, and the electrostatic interaction potential is described using the partial Mesh Ewald Sum.45 The bond, angle, and dihedral interaction potential are described as in the GROMOS54a7 force field.41 The simulation box for AuNP-glycine in explicit water solvent was created in three steps. First, a AuNP was placed in the center of a cubic box with a length of 3.5, 4.5, 6.5, or 7.0 nm; then, a glycine molecule was placed randomly in the box, and finally the rest of the box was filled with water molecules at 1.0 g/cm3. The simulation box for AuNP-glycine in vacuum was created with the same procedure as for those in water, except that water molecules were not added. The simulation box for a flat Au surface with a glycine molecule was created in four steps. First, a Au surface was placed at the bottom of a rectangular box; a glycine molecule was placed 0.4 nm above the Au surface, the rest of the box was filled with water molecules at 1.0 g/cm3, and a 2.0 nm vacuum was added above the water box. Figure 3 shows the initial configuration of a glycine molecule with a 4.0 nm AuNP and a flat Au (111) surface in explicit water. Table 1 lists the details of the eight simulation systems. 2.2. Simulation Method. An energy minimization and a molecular dynamics (MD) simulation are conducted to obtain a configuration for well-tempered metadynamics (WTMeTAD).46 For systems including a AuNP, a 10 ns isothermal−isobaric ensemble (NPT, T = 300 K, P = 1 bar) atomistic MD simulation with a 1 fs step was conducted after an energy minimization process using the steep algorithm. For
2. MOLECULAR MODELING AND SIMULATION METHODS 2.1. Molecular Model. The glycine molecule is built by capping the N and C terminals of a glycine with an acetyl group and an N-methyl amide group. The configurations of the five gold nanoparticles (AuNPs) were generated using the Nanoparticle Builder Module of OpenMD.40 Figure 1 shows the five AuNPs used in this work. As shown in Figure 1, the five AuNPs have the same shape, with (111), (110), and (100) facets and edges among these facets. Figure 2 shows the distribution of facets on a 4.0 nm AuNP. The glycine molecule is described using the GROMOS54a7 force field.41 The force-field 3492
DOI: 10.1021/acs.jpcb.7b10677 J. Phys. Chem. B 2018, 122, 3491−3499
Article
The Journal of Physical Chemistry B
Figure 3. Initial configuration of simulation systems. (a) System having a 4.0 nm AuNP and a glycine molecule in explicit water solvent. (b) System having a flat Au(111) surface and a glycine molecule in explicit water solvent. Au atoms (yellow) are represented in the VDW model, and atoms of peptide backbone are represented in the CPK model (C: cyan, O: red, N: blue, and H: white). The water molecules in panel a are in the line model and those in panel b are in the CPK model.
MeTAD ensures that the initial configuration should not affect the results significantly. The WT-MeTAD uses a Gaussian potential that decreases as the simulation progresses to ensure the smooth and convergence of the result. Canonical (NVT, T = 300 K) ensemble WT-MeTAD simulations were conducted to calculate the adsorption freeenergy landscapes for a glycine molecule on the five AuNPs. The simulations use the azimuthal (θ) and polar angles (ϕ) of the glycine Cα atom with respect to the AuNP center of mass (COM) as CVs. Equation 1 shows the expressions used to
Table 1. Details of Simulation Systems for AuNP-Glycine in Explicit Water Solvent number of water molecules
initial hill height (kJ/mol)
3.0 4.5 6.0 6.5 7.5
835 2794 6489 7671 11278
1.5 1.2 1.0 1.5 1.0
× 7.5 × 2.5 × 7.5 × 3.2 × 7.5 × 2.2
1043 1009 689
1.0 1.0 1.0
initial box size (nm × nm × nm) AuNP 1.0 nm 3.0 2.0 nm 4.5 3.0 nm 6.0 4.0 nm 6.5 5.0 nm 7.5 Au flat surface (111) 2.9 (110) 3.3 (100) 2.3
× × × × ×
3.0 4.5 6.0 6.5 7.5
× × × × ×
sigma 0.05, 0.05, 0.05, 0.05, 0.03,
0.05 0.05 0.05 0.05 0.03
0.05 0.05 0.05
bias factor 8 12 12 14 14 8 8 8
systems including a flat Au surface, a 10 ns canonical ensemble (NVT, T = 300 K) atomistic MD simulation with a 1 fs step was conducted after an energy minimization. Periodic boundary conditions are applied in all three directions. The Berendsen algorithm47 was used to control the system temperature and pressure because it can let the system reach the desired temperature and pressure quickly. The Au atoms were frozen in their positions, and all of the bonds and angles for the molecules in the system were free during the minimization and the MD simulation. The glycine molecule spontaneously adsorbs on the AuNP in the 10 ns MD simulation, and the density of water molecules reaches ∼1.0 g/cm3 in regions distant from the AuNP and Au surfaces. The energy minimization and MD simulations were conducted using Gromacs-5.1.2.48 Well-tempered metadynamics (WT-MeTAD)46 is an alternative to metadynamics (MeTAD). The basic idea of WTMeTAD is similar to that of MeTAD: The location of the system in the ensemble space is determined by the calculation of some predefined collective variables (CVs), and a positive Gaussian potential is added to the system energy to discourage it from coming back to the previous point. Eventually when enough Gaussians sum up, the system explores every point of the energy landscape evenly. At this point the energy landscape can be recovered as the opposite of the sum of all the Gaussians or through a reweighting process. The algorithm of WT-
Figure 4. Schematic showing the definition of the azimuthal angle ϕ and polar angle θ.
calculate the two angles and Figure 4 shows a schematic that illustrates their definition. θ = cos−1 ϕ = tan−1
(z Cα − zCOM) r yC − yCOM α
xCα − xCOM
(1)
where xCα, yCα, zCα, xCOM, yCOM, and zCOM are the coordinates of the Cα atom and the NP COM and r is the distance between the Cα atom and the NP COM. The combination of θ and ϕ can directly pinpoint the location of the glycine molecule on AuNP surfaces. Although the distance between the glycine 3493
DOI: 10.1021/acs.jpcb.7b10677 J. Phys. Chem. B 2018, 122, 3491−3499
Article
The Journal of Physical Chemistry B
Figure 5. Adsorption free-energy landscapes of a capped glycine molecule on AuNPs in terms of polar (ϕ) and azimuthal (θ) angles of the Cα atom and the AuNP center of mass in water. (a) 1.0, (b) 2.0, (c) 3.0, (d) 4.0, and (e) 5.0 nm AuNPs. The black lines in Figure 2a−e delineate the boundaries between individual facets. The facet types for the regions are also labeled. (f) Difference between the adsorption free energies on the (111) and (110) facets for a glycine molecule on the five AuNPs.
between the glycine Cα atom and the surface is longer than 0.5 nm. Because of the symmetry of the AuNP surface, the value of θ is constrained in the range of (π, 2π). Table 1 lists the values of the initial heights of the Gaussian hills and σ and bias factors for the simulation systems. The Gaussian hill was updated every 0.1 or 0.2 ps. The temperature was controlled around 300 K by the V-rescale algorithm49 during the WTMeTAD simulations. The Au atoms were frozen in their positions, the bonds involving hydrogen atoms were constrained to their equilibrated length using the LINCS algorithm,50 and the other bonds and angles were free to move during the simulations. Canonical (NVT, T = 300 K) ensemble WT-MeTAD simulations were conducted to calculate the binding free energy
molecule and the AuNP was considered as a possible CV, it was not chosen because the focus was more on how the adsorption free energy of glycine molecules changes with their adsorbed location on a AuNP surface than on the glycine molecule’s overall adsorption free energy. To ensure that the glycine molecules keep being adsorbed on AuNPs during the WTMeTAD simulations, the distance between the glycine Cα atom and the AuNP surface was constrained to be
