Modeling of the Functionalized Gold Nanoparticles Aggregation in the

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C: Plasmonics; Optical, Magnetic, and Hybrid Materials

Modeling of the Functionalized Gold Nanoparticles Aggregation in the Presence of Dopamine, A Joint MD/QM study Mohammad Khavani, Mohammad Izadyar, and Mohammad Reza Housaindokht J. Phys. Chem. C, Just Accepted Manuscript • DOI: 10.1021/acs.jpcc.8b06600 • Publication Date (Web): 24 Oct 2018 Downloaded from http://pubs.acs.org on October 31, 2018

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The Journal of Physical Chemistry

Modeling of the Functionalized Gold Nanoparticles Aggregation in the Presence of Dopamine, A Joint MD/QM study Mohammad Khavani, Mohammad Izadyar*, Mohammad Reza Housaindokht Computational Chemistry Research Lab., Department of Chemistry, Faculty of Science, Ferdowsi University of Mashhad, Mashhad, Iran [email protected] Tel: +985138805533 Fax: +985138796416

Abstract Investigation of gold nanoparticles (AuNPs) aggregation at the atomic level is a great challenge from the experimental view, while theoretical methods facilitate our understanding of the AuNPs aggregation process. In this article, by applying full atomistic molecular dynamic (MD) simulations, the aggregation process and the stability of the functionalized AuNPs by 4-amino-3-hydrazino-5mercapto-1,2,4-triazole (AT) was investigated. Moreover, the ability of AT-AuNPs for selective detection of dopamine was analyzed. Theoretical results showed that AT groups on the surface of AuNPs increase the resistance of nanoparticles against aggregation. Also, AT-AuNPs are only accumulated in the presence of dopamine in a mixture of different analytes. In this process, dopamine acts as a molecular 1 ACS Paragon Plus Environment

The Journal of Physical Chemistry 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60

bridge between the AT-AuNPs, which facilitates the nanoparticle aggregation through the hydrogen bond interactions. Quantum chemistry calculations confirmed that the structural feature of dopamine is a more effective factor on the AT-AuNPs aggregation than hydrogen bonding. Finally, a comparison between the theoretical results and experimental data showed the capability of theoretical approach in modeling and designing new efficient sensors based on the functionalized AuNPs.

Introduction Gold nanoparticles (AuNPs) are interesting colorimetric probes, which detect a wide range of analytes by monitoring the color changes 1,2. A change in the color of AuNPs in the presence of the target, due to their aggregation, makes them as a simple, fast and useful sensor, without any complexity. Because of these properties, pure and functionalized AuNPs are applied for detection of analytes in different fields such as food safety 3, ion sensing 4,5 and diagnosis of diseases 6-8. In addition to AuNPs, other nanomaterials 9,10 such as iron oxide nanocomposite 11, fiber nanocomposites

12

and SiC nanocomposites

13

have considerable ability for

colorimetric sensing of different compounds. For example, Guo et al. investigated the sensing ability of iron oxide nanocomposite

11

for detection of glucose. Their results indicate that this

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nanomaterial is very sensitive to glucose, but the mechanism of sensing is different from the reported mechanism for AuNPs. The structural and surface properties of nanomaterials have a key role on their sensing ability for detection of different analytes

14-16

. For example, Yi and coworkers reported an electrochemical sensor

for p-hydroxyaniline composed of graphene, AuNPs and carbon nanosphere 17. The obtained results indicate that graphene and carbon nanosphere improve the AuNPs properties for selective detection of p-hydroxyaniline. Moreover, surface properties of the nanomaterial depends on different parameters such as solvent

18

, ionic

strength 19,20, functional groups 21 and temperature 22. Chen and coworkers investigated the ability of the DNA functionalized AuNPs in sensing of bisphenol A 23. On the basis of their results, AuNPs aggregate in the presence of bisphenol A, because of a competitive binding of bisphenol A and aptamer. They claimed that this aptasensor can detect 0.1 ng.ml-1 (limit of detection, LOD) of bisphenol A, which can be observed by naked eye due to color changes. Hupp et al. reported some modified AuNPs with 11-mercaptoundecanoic acid as a colorimetric sensor for detection of small concentration of Pb2+, Hg2+ and Cd2+ 24. This functionalized AuNPs aggregate in the presence of Pb2+. Also, polythymine functionalized AuNPs were used for detection of Hg2+ as an optical sensor by Willner group 25. The selectivity of this sensor towards Hg2+ was investigated 3 ACS Paragon Plus Environment


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in the presence of Ca2+, Cd2+, Co2+, Cu2+ and other divalent metal ions. Nandi and et al. by employing the functionalized AuNPs by lysine and dithiothreitol detected mercury ions, with a LOD of 27 and 58 pM, respectively 26. Chen et al. designed functionalized AuNP with glutathione as an optical probe for detection of Cu2+ ion 27. This optical sensor selectively detects Cu2+ in the presence of Cd2+, Fe2+, Co2+, Ni2+, and Zn2+, with 10 nM LOD. Wu and coworkers employed tween 80 surfactants for simplification of the AuNPs functionalization process 28. By using tween 80 as a protective agent, AuNPs are functionalized during 2-3 hours. Zhang group, by employing a theoretical method, evaluated the penetration mechanism of the lipid membranes by the functionalized AuNPs 29. The obtained results indicated that surface charges of AuNPs have an important role in the interactions of AuNPs and lipid bilayer. Computational modeling of the functionalized gold nanoparticles is of interest for describing and predicting their properties and possible behaviors, which are important from the theoretical viewpoint. In this procedure, a comparison of the predicted properties with the experimental one

30

is valuable to have a

knowledge of the reliability of the theoretical method for simulation of the real systems. our aim here is the investigation of the aggregation process of the functionalized AuNPs by 4-amino-3-hydrazino-5-mercapto-1,2,4-triazole (AT)


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groups in the presence of dopamine, by employing molecular dynamic (MD) simulations and density functional theory (DFT) calculations. Dopamine is an important neurotransmitter in the nervous system 31, whose value in the brain has an important effect on the usual movement techniques

such

as

electrochemistry 32,

high-performance

31

. Some liquid

chromatography 33 and spectroscopic approaches 34 were used for quantitative determination of dopamine. Colorimetric detection of dopamine by AT functionalized gold nanoparticles (AT-AuNPs) is a simple, useful and low-cost method. In this context, Feng and co-workers by employing the colorimetric method investigated the sensing ability of the functionalized AuNPs by AT groups for detection of dopamine in the presence of different analytes

31

. Their results

indicate that AT-AuNPs acts as a selective and sensitive sensor for dopamine. These functionalized AuNPs aggregate in the presence of dopamine, while other analytes such as catechol, ferulic acid, gallic acid, histamine, histidine and phenylalanine cannot aggregate these functionalized AuNPs. Therefore, to understand the aggregation mechanism of AuNPs in the presence of dopamine, a molecular approach is of great importance, which is the purpose of this research.

Theoretical methods



MD simulations details Theoretical methods such as DFT calculations and MD simulations have a remarkable ability to investigate physical properties of nanostructures at atomistic scale 35,36. By employing full atomistic MD simulations, the aggregation process of pure AuNPs, functionalized AuNPs and sensing ability of these nanostructures were investigated completely, in which AuNPs are composed of 249 gold atoms with a diameter of 2.0 nm. Functionalized AuNPs have 26 AT groups that make it possible to cover the whole surface. To simulate the aggregation process of AuNPs at 50 ns, five pure AuNPs were distributed randomly in a cubic box of water, at a distance of 15 Å from each other. Also, in order to investigate the effects of AT groups on the stability of AuNPs, 50 ns MD simulations were performed on five AT-AuNPs based on a report, in which it was claimed that AT-AuNPs can be used as a sensor for selective detection of dopamine 31. For this purpose, dynamical behavior of five AT-AuNPs in the presence of different analytes (Table 1) was investigated. In this procedure, 100 units of each analyte were distributed randomly around the ATAuNPs, at a distance of 10 Å from the AT-AuNPs. MD simulations were performed by using Amber 12.0 software 37. General amber force fields (GAFF) 38 was employed for AT and different analytes.


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Structural parameters of the AT group and analytes were obtained by geometrical optimization at M06-2X/6-311++G(d,p) level 39,40. Atomic charges were calculated by CHelpG method at the same level. Force field parameters, reported by Heinz and et. al 41, were used for Au nanoparticle simulation and Au-S bond (S atom of the AT group) parameters were obtained from the Groenhof report 42. To investigate the dynamical behavior of pure and functionalized AuNPs, reported parameters by Heinz et al. can be useful, but these parameters have limitations such as the neglect of electronic structure effects and the restriction of noncovalent interactions with metals. At the first step of MD simulations, energy minimization in an implicit solvent environment has been performed to reduce the unfavorable short contact for all systems. Then, each system was immersed in a box of water by using TIP3P solvent model (number of water molecules for each system was reported in Table S1), in which solvent molecules were distributed in a distance of 15 Å from the solutes 43. 100000 steps of energy minimization were performed on the systems. Then, temperature was increased from 0 to 308.15 K (according to experimental conditions) by employing 2000 ps of NVT MD simulations within a restraining force constant of 2.5 kcal.mol-1 for solute structures. After this step, all structures were equilibrated in an NPT ensemble (308.15 K and 1 bar) during 2000 ps, without any positional restraints. Finally, 50 ns MD simulations were 7 ACS Paragon Plus Environment


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performed as the product step in an NPT ensemble within a 2 fs time step on the obtained structures from the equilibration step. Long-range electrostatic interactions were calculated by applying the Particle Mesh Ewald (PME) 44 with 8 Å direct cut-off. A time step of 2 fs was used throughout with SHAKE constraints on all bonds involving hydrogen atoms 45. Temperature

and

pressure

in

NPT

ensemble

were

controlled,

using Langevin thermostat 46 with a collision frequency of 2 ps-1 and relaxation time of 1 ps for temperature and pressure, respectively. Table 1. The names and colors of twenty analytes, which have been analyzed by employing MD simulations. Number 1 2 3 4 5 6 7 8 9 10

Analyte Dopamine Histidine Tyrosine Tryptophan Phenylalanine Lysine Catechol Glutamic acid Fructose Glutathione

Color Red Green Blue Yellow Brown Gray Violet Cyan Magenta Orange

Number 11 12 13 14 15 16 17 18 19 20

Analyte Trihydroxybenzoic acid Gallic acid Ferulic acid Uric acid Urea Histamine Serotonin Epinephrine Isopernaline Norepinephrine

Color Indigo Maroon turquoise Dark green Black dots Red dots Green dots Blue dots Yellow dots Orange dots

DFT calculation details Quantum mechanics (QM) calculations at M06-2X/6-311++G(d,p) 39,40 level of theory were applied in water to investigate the possible interactions of AT and different analytes. Charge transfer, electrostatic and donor-acceptor interactions between AT and different analytes were analyzed by applying the natural bond


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orbital (NBO) method 47. The effect of water, as a solvent on the complexes of the AT-analyte was considered, using the conductor-like polarizable continuum model (CPCM) 48. All quantum chemistry calculations were performed by using Gaussian 09 computational package 49. Finally, in order to investigate the nature of the interactions between AT and different analytes, the analyses of the quantum theory of atoms in molecules (QTAIM) 50, electron localization function (ELF), 51 localized orbital locator (LOL), 52 2D and 3D non-covalent interaction (NCI) plots 53 were applied by using Multi-WFN 3.4 54

Results and discussion Dynamical properties of AT-AuNP Dynamical behavior of the AT groups on the surface of the AuNP was investigated by MD simulations. The obtained structure of a functionalized AuNP by AT groups, Figure 1-A, indicates that AT groups are arranged in a parallel position relative to the AuNP surface. Figure 1-B shows the considerable number of hydrogen bond (H-bond) formation between the ATs on the surface of AuNP. Also, the analysis of the first peak of the radial distribution function (RDF) of the HN….H bond (Figure 1-C) confirmed H-bond formation between AT groups. 9 ACS Paragon Plus Environment


Figure 1-D shows a uniform distribution of AT groups on the AuNP surface, in which AT groups fully covered the nanoparticle surface. Moreover, on the basis of Figure 1-E, the solubility of AT-AuNP increases in water due to the presence of ATs on the surface of the nanoparticle in comparison with pure AuNP. Finally, the stabilization of AT-AuNPs was confirmed by the contribution of 26 molecules of AT and 240 water molecules. Further discussion on the stability is the topic of the next section.

Figure 1. The obtained structure of the AT-AuNP after 50 ns MD simulations (A) theoretical number of the NH….H bonds (B), RDF plot (C), theoretical distance between the AT groups (D) and the calculated number of water molecules around the pure AuNP (black) and AT-AuNP (red) (E) during the simulation time.

The role of AT groups on the AuNPs aggregation


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The

stability

of

gold

nanoparticles

can

be

explained by

their

inherent resistance against the aggregation process. The aggregation of AuNPs changes the color of the system, which acts as an indicator to detect different analytes. On the basis of the experimental results, 31 3-month stability without any aggregation was confirmed for AT-AuNPs. Therefore, the investigation of the AT effects on the stability and AuNPs aggregation is of importance to be examined through the MD simulation methods. Figure 2 shows the obtained structures of AuNPs and AT-AuNPs after 50 ns MD

simulations

in

water, indicating

that

AT

groups

decrease

nanoparticle aggregation. Root mean square deviation (RMSD) plot (Figure 2-C) indicates that after 18 ns, AuNPs are fully aggregated, which is in contrast to ATAuNPs behavior. On the basis of Figure 2-D, a compaction in the radius of gyration (Rg= 2 nm) is confirmed in the case of pure AuNPs, showing their aggregation. A comparison of the theoretical distances of the AuNPs and ATAuNPs mass centers is depicted in Figure 2-E, in which the effective role of AT groups on the stability and dispersion of gold nanoparticles is observed. Electrostatic repulsion in water stabilizes the dispersed AT-AuNPs, while the presence of van der Waals (vdW) interactions in the case of pure AuNPs accelerates their aggregation. In manifestation of this behavior, the role of water molecules in H-bond formation with AT groups on AuNPs is vital. An increase in 11 ACS Paragon Plus Environment


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the number of water molecules around the AT-AuNPs (Figure 2-F) and a reduction in the solvent accessible surface area (SASA), in the case of pure AuNPs (21%), are the reasonable evidences for this claim.

Figure 2. The obtained structures of AuNPs (A) and AT-AuNPs (B) after 50 ns MD simulations and the plots of RMSD (C), Rg (D), the distance of gold nanoparticles (E) and the calculated number of water molecules around the pure AuNP and AT-AuNP (F), during the simulation time.

Further investigation on the aggregation process of AuNPs was completed by the potential energy analysis (Figure 3) in which, an elastoplastic mechanism was confirmed, in agreement with Dutta work 55. On the basis of different analyses, surface

disappearance,

compression

and

plastic

deformation

of

gold

nanoparticles determine the initial fast energy decrease, subsequent release the


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stress and stabilization, while the potential energy diagram of the AT-AuNPs shows a different behavior.

Figure 3. Potential energy diagrams of the AuNPs and AT-AuNPs during the simulation time.

The mechanism of dopamine detection by AT-AuNPs The mechanism of dopamine detection by AT-AuNPs is shown in Figure 4. According to this figure, linked dopamine molecules to AT receptors act as a bridge between the AT-AuNPs leading to aggregation of gold nanoparticles through the H-bond interactions. To investigate the selectivity of AT-AuNPs towards dopamine, the aggregation process of gold nanoparticles was considered in the presence of twenty different analytes (Table 1) through MD simulations.



Figure 4. The mechanism of AT-AuNPs aggregation for detection of dopamine.

The obtained structures of AT-AuNPs in the presence of different analytes are shown in Figure 5 after 50 ns MD simulations. According to this figure, a complete aggregation of AT-AuNPs is only observed in the presence of dopamine, while other analytes such as fructose and phenylalanine do not aggregate AT-AuNPs and some analytes such as glutathione, histamine and ferulic acid show partially aggregation. On the other hand, on the basis of UV-vis analysis of experimental studies, AT-AuNPs show a significant red-shift in the presence of dopamine, while the changes in UV-vis spectra are negligible in the presence of glutathione, histamine and ferulic acid, which is in good agreement with the MD results. There are one amine and two hydroxyl groups in the dopamine structure, which form three H-bonds (OH….N and N….HN) with the AT groups on the ATAuNPs.


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The structures of epinephrine, norepinephrine, and isopernaline as the neurotransmitters are similar to dopamine, which can induce the aggregation of the AT-AuNPs,

while

a

lower

sensitivity is obtained

for

epinephrine,

norepinephrine and isopernaline in comparison with dopamine. The comparison of the structures shows that dopamine has one primary amine group, while epinephrine and isopernaline have one secondary amine group of –NH-CH3- and – NH-CH2-(CH3)2-, respectively. Although the structure of norepinephrine is very similar to dopamine, fully AT-AuNPs aggregation is only observed in the presence of dopamine. In other words, it can be concluded that the functionalized gold nanoparticles are structural selective sensors. Dopamine is detected by AT-AuNPs, due to its particular dopamine structure, primary amine and hydroxyl groups have an effective role in the aggregation of AT-AuNPs. Dopamine is a molecular bridge, which reduces the interparticle distance and derives the AT-AuNPs aggregation through the Hbond formation with AT groups. These behaviors make the nanoparticles selective sensor.



Figure 5. The obtained structures of the AT-AuNPs in the presence of different analytes after 50 ns MD simulations (for clearly water molecules were omitted and the analytes numbers are according to Table 1.)


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Figure 6-A shows the calculated RMSD values of AT-AuNPs in the presence of different analytes. Obtained results in the previous section revealed that AT-AuNPs are stable against aggregation in comparison with pure AuNPs. In Figure 6-A for a better comparison, the RMSD values of the pure AuNPs are also provided. This figure clearly shows that in the presence of dopamine, ATAuNPs behave similarly to the pure AuNPs. In other words, in the presence of dopamine, functionalized gold nanoparticles lose their stability and accumulate as pure nanoparticles. The RMSD plots of other analytes reveal that their presence in the solution does not have a significant effect on the aggregation and dynamical behavior of the AT-AuNPs.

Figure 6. The calculated RMSD (A) and Rg (B) plots of the AT-AuNPs in the presence of different analytes (for a better comparison RMSD of pure AuNPs, also provided).



Rg values (Figure 6-B) of AT-AuNPs reveal that the studied nanoparticles in the presence of dopamine have the most compact structure in comparison with the calculated Rg values of other analytes (Rg values of AT-AuNPs in the presence of different analytes in the equilibration step are reported in Figure S1). It’s well worth mentioning that RMSD and Rg plots also show the AT-AuNPs sensitivity towards dopamine, because the time of aggregation decreases in comparison with pure AuNPs (15 and 18 ns, respectively). On the basis of RMSD and Rg values of AT-AuNPs in the presence of different analytes (Figure 6), the aggregation and compaction of dopamine-(AT-AuNPs) were confirmed, respectively. In the case of other analytes, the stability of AT-AuNPs was unchanged. The average number of H-bond interactions between the studied analytes and AT-AuNPs were calculated and illustrated in Figure 7-A. Although catechol, because of two hydroxyl groups, has the maximum number of H-bond interaction with AT groups of AuNPs in comparison with other analytes, it cannot form a bridge with AT receptors because of improper structure. Therefore, this analyte is not able to aggregate AT-AuNPs. Another important parameter, which can be considered in evaluation of the sensitivity of AT-AuNPs, is the affinity of AT-AuNPs towards different analytes as shown in Figure 7-B. Although norepinephrine, fructose, isopernaline and gallic acid show a lower distance with AT-AuNPs in comparison with dopamine, during 18 ACS Paragon Plus Environment

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the simulation time, it cannot aggregate AT-AuNPs. This behavior makes it hard to be sensed by AT-AuNPs, while dopamine is selectively detected.

Figure 7. The calculated average number of H-bonds (A) and distance (B) between different analytes and AT-AuNPs (numbers are according to Table 1).

Further RDF analyses of different NH….N and NH….O bonds of the analytes and AT-AuNPs are provided in Figure S2. These RDFs reveal that dynamical behavior of different analytes in the presence of AT-AuNPs are similar to each other. Finally, MD simulation results indicate that AT groups increase the stability of the AuNPs against aggregation, through an improvement in the repulsive interactions between AuNPs. On the basis of experimental data, a change in color is only observed in the presence of dopamine, which is according to MD simulation results of the AuNPs aggregation. On the basis of experimental results, after dopamine, norepinephrine has a considerable effect on the aggregation of the functionalized AuNPs. To remove the concern about the initial conditions of AT-AuNPs, which can be important on the 19 ACS Paragon Plus Environment


aggregation process, 50 ns MD simulations were repeated for two times in the presence of dopamine and norepinephrine with different initial positions of ATAuNPs. The obtained results indicate that the aggregation process of AT-AuNPs in the presence of dopamine is more than norepinephrine (Figure S3), which confirmed that the aggregation process of AT-AuNPs is independent of the initial position of the nanoparticles. Also, the obtained results from MD simulations are in good agreement with the experimental data, but some discrepancies between these results can be seen, which may be related to the difference of the nanoparticle model with the real system in the experiments. For example, experimental studies do not provide any aggregation results for AT-AuNPs in the presence of glutathione, histamine and ferulic acid, while MD simulations show partially aggregation. Because the selected size of the simulated nanoparticles are smaller than actual size, where a larger surface curvature reduces ligand packing density, which is the source of some discrepancies between the theoretical and experimental results. To describe quantitatively interaction strength between the analytes and ATAuNPs and their role in nanoparticle aggregation, quantum chemistry analyses were applied and discussed in the next section. Non-covalent interaction analysis


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On

the

basis

of

MD

nanoparticle aggregation through

simulation H-bond

results,

dopamine

formation between

induces

AT-AuNPs.

Therefore, molecular analysis of these interactions is important. For this purpose, the structures of AT-analyte-AT complexes were optimized in water (Figure S4) by employing DFT method at M06-2X/6-311++G(d,p) level of theory. According to Table 2, the calculated binding energies (∆Ebin) of the AT2analyte complexes in the case of catechol and dopamine are the highest values. Therefore, it may be concluded that catechol is the predominate analyte in comparison with dopamine from the selectivity viewpoint. But, in evaluation of the selectivity, a proper structure for bridge formation is of a greater importance in comparison with binding energy. Because of an improper structure, catechol cannot form [AT-analyte-AT] bridge; therefore dopamine makes the most stable complex in comparison with other analytes. NCI analysis can be used in recognition of the attractive and repulsive interactions based on electron density and the sign of the second derivative in perpendicular direction of the bond (λ2). NCI index provides a 2D plot of the reduced density gradient, RDG, versus the electron density, ρ. The reduced density gradient is described by equation 1. RDG=1/2(3π2)1/3 {|∇ρ|/ρ4/3}


(1)


where λ2 can be either positive or negative, depending on the type of interaction. Negative λ2 (λ20). Finally, van der Waals interactions are specified by a negligible λ2 (λ2≈0). Figure 8-A shows 2D NCI plot of dopamine-AT2 complex (2D NCI plot of other analytes are represented in Figure S5). Considering NCI plots, three definite regions, specified by blue, green and red ovals, indicate H-bond, vdW and repulsion interactions, respectively. Table 2. The calculated ∆Ebin (kcal.mol-1) of different analyte-AT2 complexes in water -∆Ebin/H-bond Analyte -∆Ebin/H-bond Analyte Dopamine 4.85 Trihydroxybenzoic acid 4.67 Histidine 3.76 Gallic acid 4.16 Tyrosine 4.22 Ferulic acid 4.62 Tryptophan 4.70 Uric acid 3.22 Phenylalanine 4.38 Urea 3.04 Lysine 2.61 Histamine 3.79 Catechol 10.90 Serotonin 4.67 Glutamic acid 3.17 Epinephrine 4.74 Fructose 4.14 Isopernaline 3.01 Glutathione 2.86 Norepinephrine 3.64


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Figure 8. 2D (A) and 3D (B) NCI plots of the dopamine-AT2 complex.

Negative character of λ2, Figure 8-A, shows H-bond formation between dopamine and AT molecules. On the basis of the sign(λ2)ρ values in 2D NCI plots, the strength of H-bonds between AT and tyrosine, catechol, fructose, gallic acid, epinephrine, isopernaline and norepinephrine is similar to dopamine-AT H-bond, while a bridge shape is only observed in the optimized structure of the dopamine complex, which controls the aggregation process. To have a better insight into the interactions, 3D NCI plots are also represented in Figure 8-B. In this plot, each interaction is represented by color filled isosurfaces such as blue, red and green isosurfaces, which reveal H-bond, repulsive and vdW interactions, respectively. According to Figure 8-B, dopamine through the hydroxyl group interacts stronger with AT in comparison with amine side. 3D NCI plots of other analytes (Figure S6) indicate that for some complexes 23 ACS Paragon Plus Environment


such as urea, histamine, lysine and catechol vdW interactions affect the complex stability more than H-bond. Since the strength of a vdW interaction is less than that of H-bond, these analytes cannot aggregate AuNPs in comparison with dopamine. NCI analysis indicates that structural features and H-bond strength are the origin of the AT-dopamine-AT bridge-shaped structure, hydrogen bond interaction and the main factors of the AT-AuNPs aggregation, which are predominate in the case of dopamine in comparison with other analytes. Topological parameters and electronic interaction energy To evaluate the nature of analyte-AT group interactions, topological parameters of the H-bonds during the nanoparticles aggregation were calculated by employing QTAIM analysis and reported in Table S2. QTAIM-based analysis of electron density at different characteristic points, particularly at the bond critical points (BCP), is a powerful tool to investigate different chemical or physical phenomena. The magnitude of the electron density function estimated at the BCP can reflect the strength of a given bond. Laplacian (L(r)) represents the curvature of the electron density in three-dimensional space at the BCP of the atomic interaction. Generally, negative character of Laplacian indicates that electron density is locally concentrated, similar to the shared electron (covalent) interactions, while a positive Laplacian means that electron density is locally


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depleted, such as closed-shell (electrostatic) interactions.The ratio of the kineticenergy density (G) and potential-energy density (V) at the BCPs can be used to indicate the regions corresponding to covalent or noncovalent interactions. If the ratio of -G/V is less than 0.5, the interaction is a characteristic of the shared covalent interaction. When the ratio of -G/V is between 0.5 and 1, the interaction in a chemical system is partly covalent. If the ratio of -G/V is greater than 1, the interactions are typically noncovalent. The nature of interactions can be determined by applying the ratio of the kinetic energy density (G) to the potential energy density (V). The calculated –G/V values, Table S2, indicate that most of the interactions between the analytes and AT groups have a non-covalent character. Moreover, theoretical values of LOL and ELF, Table S2, confirm H-bond formation between the analytes and AT groups. As an example, a graphical representation of ELF and LOL parameters of the NH….N and OH….N bonds in dopamine complex is depicted in Figure 9.



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Figure 9. The ELF and LOL plots of the NH….N and OH….N interactions in the dopamine complex.

All orbital details are mathematically chosen to include the highest possible percentage of the electron density, and the most accurate possible picture of the wave function (ψ) is provided by NBO method. A useful aspect of this method is related to information about the interactions in both real occupied and virtual unoccupied orbitals that facilitate the analysis of intermolecular solvent−solute and receptor-analyte interactions. To evaluate donor−acceptor interactions, the secondorder perturbation theory analysis of the Fock matrix was carried out. The interactions result in a loss of occupancy from the localized NBOs of the idealized Lewis structure into empty non-Lewis orbitals. In this analysis, the stabilization energy, E(2), related to the delocalization trend of electrons from donor to acceptor orbitals was calculated via perturbation theory. If the stabilization energy between a donor bonding orbital and an acceptor orbital is large, there is a strong interaction. For each donor orbital (i) and acceptor orbital (j), E(2) is associated with i → j delocalization, is given by equation 2: E(2)= ∆Ei,j= qi{F2(i,j)/(Ej-Ei)}

(2)

where qi is ith donor orbital occupancy; Ei and Ej are diagonal elements, and (i,j) are off-diagonal elements associated with NBO Fock matrix.


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There are two types of H-bond interactions, NH….N and OH….N of the complexes, in which OH….N interaction is stronger than NH….N bond, based on NCI and electron density analyses. To determine charge transfer effects on the complex formation, electronic energy or stabilization energy E(2) values between the lone pair (LP) electrons of oxygen and nitrogen as the donors with the antibonding orbitals of N‒H and O‒H (σ*N‒H and σ*O‒H) as the acceptors were calculated. According to E(2) values in Table 3, OH….N interactions are stronger than NH….N ones. For example, the calculated average interaction energy of OH….N and NH….N bonds in dopamine complex are 20.27 and 8.03 kcal.mol-1, respectively. In other words, OH….N interaction is more effective than NH….N on the stability of the AT-analyte complexes and AuNPs aggregation. Overall, DFT calculations and MD simulation results reveal that in spite of similar interactions of the analytes and AT-AuNPs, all of them are not able to aggregate AT-AuNP. Because, hydrogen bond formation and structural features of the analyte are important parameters in aggregation mechanism. In the case of dopamine, the structural characters are the most important factor in AT-AuNPs aggregation.


The Journal of Physical Chemistry 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47

Page 28 of 39

Table 3. The electronic energy (kcal.mol-1) between the LP electrons of nitrogen and oxygen with σ*N‒H and σ*O‒H for different analyte complexes (for atom numbering see Figure S3). Analyte

D-A

E(2)

Dopamine

LPN3→σ*N48-H50 LPN25→σ*O1-H21 LPN27→σ*O2-H22

8.03 19.10 21.44

Analyte

D-A

Histidi ne

LPN3→σ*N18-H32 LPN3→σ*O15-H34 LPN3→σ*N17-H29

2.35 52.27 Tyrosin 19.10 e

E(2)

Analyte

Lysine

LPN29→σ*O1-H24

46.58

Glutath ione

LPO3→σ*N63-H64 LPN42→σ*O4-H36 LPN56→σ*O6-H37

2.35 52.15 51.29

LPN3→σ*N19-H15 LPN21→σ*N4-H13 LPN28→σ*N4-H13 LPN33→σ*N7-H16 LPN29→σ*O2-H25 LPN31→σ*O3-H26 LPN43→σ*O1-H21 LPN45→σ*N4-H19

5.46 4.75 Urea 13.44 22.62 21.67 28.40 soperna 18.48 line 2.65

Phenylalanine LPO2→σ*N49-H50 LPN26→σ*N3-H19

5.24 4.54

LPO3→σ*N36-H38 LPN29→σ*O2-H21 LPN41→σ*O6-H24 LPN50→σ*O3-H22

2.31 26.05 11.02 14.18

Ferulic acid

LPO1→σ*N36-H38 LPN29→σ*O2-H23 LPN3→σ*O3-H24

1.12 8.74 37.30

Serotonin

LPN3→σ*N37-H38 LPN51→σ*N2-H17 LPN28→σ*O1-H25

10.06 9.17 18.99

Fructose

Uric acid

Epinep hrine

Catech ol

Trihydr oxyben zoic acid

D-A

E(2)

LPO3→σ*N32-H35 LPN41→σ*O2-H24

3.72 24.68

LPO2→σ*N40-H41 LPO1→σ*N36-H39 LPN17→σ*O1-H13 LP19→σ*O2-H14 LPN21→σ*O1-H15 LPN23→σ*O2-H16 LPN35→σ*O4-H16

3.44 2.56 23.07 35.51 19.38 35.16 35.15

D-A

E(2)

LPN32→σ*O1-H27 LPN46→σ*N3-H20

49.66 16.50

Glutam ic acid

LPN22→σ*O1-H18 LPN36→σ*O2-H19

28.65 36.64

Gallic acid

LPN21→σ*O2-H16 LPN23→σ*O1-H15 LPN35→σ*O3-H17

21.14 29.54 19.98

LPN3→σ*N20-H22 LPN13→σ*N3-H8 LPN34→σ*N2-H5

1.86 Histam 7.54 ine 10.74

LPN3→σ*N24-H27 LPN2→σ*N38-H40 LPN34→σ*N1-H13

1.68 5.69 2.46


21.86 25.24 25.72


23.33 27.74 22.82


Analyte

Trypto phan

Norepi nephrin e

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Conclusion Full atomistic MD simulations reveal that pure AuNPs are not stable, while functionalized AuNPs by AT groups have considerable stability against aggregation. AT-AuNPs are sensitive to dopamine, which can selectively detect it in the presence of other analytes. AT-AuNPs aggregate in the presence of dopamine, because of H-bond formation with AT groups. On the basis of obtained results, structural features of dopamine and H-bond interactions are the main factors in AT-AuNPs aggregation, in which the structural properties of dopamine are more important. DFT calculations reveal that OH….N bonds have a main role in the stability of the AT complexes with different analytes in comparison with the NH….N interactions. Finally, a good agreement between the obtained results from MD simulations and DFT calculations with the experimental data reveals that theoretical approach has an interesting application in modeling of the sensors based on the functionalized gold nanoparticles. Supporting Information MD simulation details (Table S1), topological parameters (Table S2), the calculated Rg values of AT-AuNPs in the presence of different analytes in the equilibration step (Figure S1), RDF plots of different H-bonds between the analytes and AT-AuNPs (Figure S2), The obtained structures after 50 ns MD



simulations for AT-AuNPs in the presence of dopamine and norepinephrine with different initial positions (Figure S3), optimized structures of different analyte complexes (Figure S4), 2D NCI plots of AT complexes with different analytes (Figure S5) and 3D NCI plots of AT complexes with different analytes (Figure S6).

Acknowledgments Research Council of the Ferdowsi University of Mashhad is acknowledged for financial support (Grant No. 3/44401). We hereby acknowledge that part of this computation was performed at the HPC center of Ferdowsi University of Mashhad. Also, we declare that there is not any conflict of interest.

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