Nanoparticle-Based Receptors Mimic Protein-Ligand Recognition

Article abstract of DOI:10.1016/j.chempr.2017.05.016

The self-assembly of a monolayer of ligands on the surface of noble-metal nanoparticles dictates the fundamental nanoparticle's behavior and its functionality. In this combined computational-experimental study, we analyze the structure, organization, and dynamics of functionalized coating thiols in monolayer-protected gold nanoparticles (AuNPs). We explain how functionalized coating thiols self-organize through a delicate and somehow counterintuitive balance of interactions within the monolayer itself and with the solvent. We further describe how the nature and plasticity of these interactions modulate nanoparticle-based chemosensing. Importantly, we found that self-organization of coating thiols can induce the formation of binding pockets in AuNPs. These transient cavities can accommodate small molecules, mimicking protein-ligand recognition, which could explain the selectivity and sensitivity observed for different organic analytes in NMR chemosensing experiments. Thus, our findings advocate for the rational design of tailored coating groups to form specific recognition binding sites on monolayer-protected AuNPs.

Full text of DOI:10.1016/j.chempr.2017.05.016

Article
Nanoparticle-Based Receptors Mimic Protein-
Ligand Recognition
Laura Riccardi, Luca Gabrielli,
Xiaohuan Sun, Federico De
Biasi, Federico Rastrelli, Fabrizio
Mancin, Marco De Vivo
fabrizio.mancin@unipd.it (F.M.)
marco.devivo@iit.it (M.D.V.)
HIGHLIGHTS
Synthesis and molecular
simulations of AuNPs for
chemosensing
A rationale for the molecular
recognition ability of
functionalized AuNPs
Functionalized coating ligands
form transient protein-like
binding pockets
Toward the computational
nanodesign of intelligent
nanoreceptors for chemosensing
Functionalized gold nanoparticles (AuNPs) can perform different tasks, which
depend on the coating ligands that cover the metal core. By combining NMR
experiments and molecular-dynamics simulations, De Vivo and colleagues reveal
how different ligands can self-organize to modulate molecular recognition ability
in AuNPs. Results show how the composition, organization, and plasticity of
coating ligands affect the selectivity and sensitivity observed for different organic
analytes in NMR chemosensing experiments. These findings offer a unique
perspective for the rational design of intelligent nanodevices.
Riccardi et al., Chem 3, 92–109
July 13, 2017 ª 2017 The Author(s). Published
by Elsevier Inc.
http://dx.doi.org/10.1016/j.chempr.2017.05.016

Article
Nanoparticle-Based Receptors Mimic
Protein-Ligand Recognition
Laura Riccardi,1 Luca Gabrielli,2 Xiaohuan Sun,2 Federico De Biasi,2 Federico Rastrelli,2
1,3,4,
Fabrizio Mancin,2, and Marco De Vivo
*
*
SUMMARY
The Bigger Picture
The functionalization of
The self-assembly of a monolayer of ligands on the surface of noble-metal nano-
particles dictates the fundamental nanoparticle’s behavior and its functionality.
In this combined computational-experimental study, we analyze the structure,
organization, and dynamics of functionalized coating thiols in monolayer-pro-
tected gold nanoparticles (AuNPs). We explain how functionalized coating
thiols self-organize through a delicate and somehow counterintuitive balance
of interactions within the monolayer itself and with the solvent. We further
describe how the nature and plasticity of these interactions modulate nano-
particle-based chemosensing. Importantly, we found that self-organization of
coating thiols can induce the formation of binding pockets in AuNPs. These tran-
sient cavities can accommodate small molecules, mimicking protein-ligand
recognition, which could explain the selectivity and sensitivity observed for
different organic analytes in NMR chemosensing experiments. Thus, our find-
ings advocate for the rational design of tailored coating groups to form specific
recognition binding sites on monolayer-protected AuNPs.
monolayer-protected
nanoparticles is at the frontier of
nanotechnology, such that
innovative applications are
emerging in fields such as
nanomedicine, chemosensing,
and even catalysis. Importantly,
the nanoparticle’s functionality is
mainly defined by the nature of
the ligands forming the coating
monolayer. Here, we show how
the self-organization of
functionalized coating ligands in
monolayer-protected gold
nanoparticles (AuNPs) affects
their solubility and molecular
recognition abilities. We found
that coating ligands form
INTRODUCTION
The self-assembly of a monolayer of ligands on the surface of noble-metal nanopar-
ticles provides a unique pathway to the realization of ordered and complex molec-
ular structures.1–4 Within the monolayer, the ligands are aligned, oriented, and kept
in close proximity with a particular configuration of the functional groups that are
present, which can act cooperatively to perform specific tasks.5–12 This has been ex-
ploited to endow monolayer-protected nanoparticles with the ability to interact with
molecular and macromolecular entities, to detect and signal relevant targets, and
to catalyze reactions.13–16 The range of potential applications of such monolayer-
protected nanoparticles is therefore huge and spans from materials science and
electronics to bioimaging, nanomedicine, and even catalysis.17,18 In all such
applications, molecular recognition is the key point. In a similar way to proteins,
the large and organized chemical structure of the monolayer might result in the
formation of binding sites in the nanoparticles, which can thereby selectively interact
with other entities.19 Thus, the ultimate goal is now the rational design of nano-
particle-based receptors with programmed selectivity and affinity.20 To accomplish
this, however, several crucial steps must still be taken, in particular, steps toward a
deeper understanding of the process of molecular recognition within the nanopar-
ticle-coating monolayer and of the parameters that control it.
transient, protein-like binding
pockets in functionalized AuNPs.
Thus, we reveal that nanoparticle-
based chemosensing operates
through a recognition process
that is similar to that for protein-
ligand complex formation. These
findings could now herald the
arrival of the computational
nanodesign of intelligent
nanodevices with recognition
abilities toward small molecules
such as drugs, metabolites, illegal
drugs, and small molecular
markers for cancer.
In this regard, some of us recently proposed nuclear magnetic resonance (NMR) che-
mosensing as a protocol that exploits the molecular recognition ability of mono-
layer-protected gold nanoparticles (AuNPs, about 2 nm core diameter) in order to
92
Chem 3, 92–109, July 13, 2017 ª 2017 The Author(s). Published by Elsevier Inc.
This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).

Scheme 1. The NMR Chemosensing Machinery for Analyte Detection
Upper: the monolayer-protected nanoparticles (ligands 1 and 2) and analytes (3–6) investigated in
early studies.21
Lower: nanoparticle-coating thiols (1 and 7–10) used in this study and the solubility of
representative samples in CHCl3 and water.
detect target analytes.21–24 Typically, these analytes are small organic molecules
such as salicylate. This molecular recognition ability for small organic molecules
has potential implications for the detection of metabolites, illegal drugs, and mole-
cular markers for cancer, among others. The NMR experiment (nuclear Overhauser
effect [NOE] pump)25 exploits a diffusion filter to dephase the magnetization from
all the small molecular species in the sample, followed by an NOE step in which
the residual magnetization of the nanoparticles is selectively transferred to the inter-
acting molecules. From an analytical point of view, this experiment allows the NMR
spectrum of the target molecule to be isolated even in a complex mixture, revealing
its presence and identity. More generally, however, such an experiment also pro-
vides detailed information on the molecular recognition ability of the nanoparticles.
1Laboratory of Molecular Modeling & Drug
Discovery, Istituto Italiano di Tecnologia, Via
Morego 30, 16163 Genova, Italy
Indeed, binding selectivity revealed by earlier experiments was somewhat unex-
pected. 1-AuNPs, reported in Scheme 1, detect salicylate (2-hydroxybenzoate, 3;
Scheme 1) in water with a remarkable selectivity with respect to many other aromatic
anions of a similar structure, including 3- and 4-hydroxybenzoate (4 and 5; Scheme 1)
and 4-methylbenzenesulfonate (6; Scheme 1).21 Within this group, salicylate is
the molecule with the highest octanol/water partition coefficient (Pow) and the
most retained in reversed-phase chromatography. Selective recognition could
hence simply arise from the hydrophobic interaction with the inner part of the
nanoparticle-coating monolayer. Interestingly, when we elongated the alkyl portion
of 1 by four carbon atoms, we obtained 2-AuNPs (Scheme 1), which could detect
2Dipartimento di Scienze Chimiche, Universita` di
Padova, Via Marzolo 1, 35131 Padova, Italy
3IAS-5/INM-9 Computational Biomedicine
Forschungszentrum Julich,
¨
Wilhelm-Johnen-Straße, 52428 Julich, Germany
¨
4Lead Contact
*Correspondence:
fabrizio.mancin@unipd.it (F.M.),
marco.devivo@iit.it (M.D.V.)
http://dx.doi.org/10.1016/j.chempr.2017.05.016
Chem 3, 92–109, July 13, 2017
93

4-methylbenzenesulfonate in addition to salicylate. Nevertheless, 2-AuNPs could not
detect the less hydrophilic 3- and 4-hydroxybenzoate.21 This result demonstrated
that other factors were at play in determining the monolayer’s binding selectivity.
In this work, we report a detailed investigation of the recognition abilities of 1-AuNPs
and a series of previously unreported monolayer-protected nanoparticles featuring
different coating monolayers and selectivity for analytes. NMR-based experiments
combined with extensive molecular dynamics (MD) simulations indicate that
molecular recognition at the monolayer of these selected AuNPs requires the pre-
formation of specific, although transient, protein-like binding pockets in the nano-
particle-coating monolayer. We found that the presence of these pockets is, in
turn, the result of a delicate and somehow counterintuitive balance of interactions
within the monolayer itself and with the solvent. Together, these findings represent
the basis for the rational design of tailored coating groups that can form selective
recognition sites on monolayer-protected AuNPs, which are thus able to operate
with programmed recognition ability.26
RESULTS AND DISCUSSION
The chemical structure of the original ligand 1 (Scheme 1) can be divided into four
parts: (1) the thiol (gray), which ensures the grafting to the monolayer surface; (2)
the hydrophobic alkyl chain (blue); (3) an amide group (green), originally inserted
for synthetic accessibility;27 and (4) a hydrophilic oligo(ethylene glycol) (OEG)
moiety (red), which ensures water solubility. At first glance, the alkyl part of thiol 1
should be the portion providing relevant interactions with salicylate. In the nano-
particle’s coating monolayer, this alkyl part forms a hydrophobic pseudo-phase
that can accommodate hydrophobic substrates in a manner similar to that of surfac-
tant aggregates. In addition, the amide group can form additional H bonds (HBs)
with the substrate. To better understand these points, we designed a series of
analogs where the two relevant parts of the ligand were systematically modified.
In all ligands 7–10, the amide group was removed. In 7, it was substituted with an
ether group (CH2O). In this way, the removal of the amide did not substantially affect
the size of the alkyl portion, and the ligand maintained the same length as 1. In
ligands 8–10, on the other hand, removal of the amide groups was accompanied
by an increase in length of the alkyl and/or the OEG portion.
Organization of the Nanoparticle’s Coating Monolayer
Ligands 7–10 and the corresponding AuNPs (1.5–1.7 nm gold core diameter) were
prepared by standard protocols (Supplemental Information, Sections S1–S3 and
S6). Notwithstanding the similar structure of the coating thiols, nanoparticles re-
vealed different solubility properties (Scheme 1 and Supplemental Information,
Section S4.1), intended here as the ability to form stable mixtures with the solvent
that do not settle upon prolonged storage. Indeed, 1-AuNP was very soluble in
water (up to 300 mM or 15 mg/mL) and in organic solvents with high and moderate
polarity (from methanol to chloroform).21,28 7- and 8-AuNPs were also soluble in
water and organic solvents, but saturation concentration in water was lower (about
60 mM or 3 mg/mL) than that of 1-AuNP. Finally, 9- and 10-AuNPs, which contain
shorter OEG chains, were soluble only in organic solvents. On the basis of such re-
sults, we decided to further investigate the dispersion state of nanoparticles 1-, 7-,
and 8-AuNPs in water by transmission electron microscopy (TEM), dynamic light
scattering (DLS), and NMR. TEM micrographs obtained by depositing nanoparticle
water solutions on the grids showed nanoparticles homogeneously distributed over
the substrate, without apparent aggregation (Supplemental Information, Section
94
Chem 3, 92–109, July 13, 2017

Figure 1. NMR Spectra of 1-, 7-, and 8-AuNP in D2O and CDCl3
Signals from the OEG portion of the coating thiols are highlighted in blue, and signals from the alkyl
portions are highlighted in red. Symbols are as follows: *, residual water signal; and ꢀ, impurities.
S4.2). DLS measurements in water (at 15 mM nanoparticle concentration) revealed
the presence in each case of a unimodal nanoparticle distribution with average
hydrodynamic diameters of 8, 16, and 21 nm for 1-, 7-, and 8-AuNPs, respectively
(Supplemental Information, Section S4.3). Similar results were obtained by diffu-
sion-ordered spectroscopy (DOSY)-NMR experiments, which again detected the
presence of a unimodal nanoparticle distribution with average hydrodynamic diam-
eters of 5, 15, and 18 nm, respectively (Supplemental Information, Section S4.4).
Such results indicated that 1-AuNPs are present in water solution mainly as isolated
nanoparticles, whereas 7- and 8-AuNPs form small aggregates. However, both DLS
and DOSY-NMR hardly discriminate entities of similar size and consequently cannot
provide precise information on the amount of aggregates present in the solutions
with respect to isolated nanoparticles. Furthermore, if isolated and aggregated
nanoparticles were involved in multiple exchange equilibria (a likely possibility),
the interpretation of their apparent diffusivities would be even more complicated.
Deeper insight into this point came from 1H NMR investigations (Figure 1).
As expected, because of nanoparticle grafting, all the 1H signals arising from the
coating thiols were relatively broad and the hyperfine structure was not resolved.29
However, a striking difference becomes evident when comparing the spectra re-
corded in deuterated water for 1-, 7-, and 8-AuNPs (Figure 1). The signals arising
from the OEG portion, between 3.3 and 3.8 ppm, and in particular from the termi-
nal methoxy residue at 3.35 ppm, have similar linewidths for 1-AuNP (13.3 Hz) and
7-AuNP (16.6 Hz) but are significantly broader for 8-AuNP (24.5 Hz). Linewidths of
signals arising from terminal groups have been shown to strongly depend on nano-
particle size.30 Indeed, neglecting magnetic field inhomogeneities, NMR signal
broadening depends on the transverse relaxation time T2. This is determined
mainly by the dipolar interactions between the spins in the monolayer, as modu-
lated by the nanoparticle tumbling rate and by the internal mobility of the ligands
within the monolayer. Because the dynamics of the OEG portions of the coating
ligands should be independent from the ligand packing in the monolayer, broad-
ening of 8-AuNP signals indicates a slower tumbling rate (or chain rigidification)
as expected in the case of aggregates. We hence concluded that 7-AuNPs are
dispersed in water mainly as isolated nanoparticles that coexist with a small fraction
of aggregates, whereas in the case of 8-AuNP, the population of aggregates is
more pronounced.
Chem 3, 92–109, July 13, 2017
95

Table 1. 1H T1 and T2 Relaxation Times for 1-, 7-, and 8-AuNPs in CDCl3 and D2O
Solvent
Signals
Min–Max T2 (s)a
Min–Max T1 (s)a
7-AuNP
7-AuNP
8-AuNP
1-AuNP
8-AuNP
0.667–0.69
0.847–1.64
2.83
1-AuNP
CDCl3
Alkylb
OEGc
0.504–0.694
1.12–1.40
2.61
0.429–0.537
0.77–1.37
2.52
0.711–0.766
0.626–0.744
1.67
0.660–0.989
1.17–1.59
2.84
1.04–1.30
0.923–0.986
1.81
d
CH3
Alkylb
OEGc
0.006**–0.016*
0.061–0.065
0.101
0.024*–0.05*
0.031*–0.038*
0.07**
0.07*–0.11*
0.222–0.279
0.52
0.67–0.7*
0.648–0.681
0.912
0.6**–0.7**
0.64–0.64
0.70
0.596–0.62
0.638–0.638
1.13
D2O
d
CH3
Errors are within 10% unless indicated: *errors within 25%, **errors within 50%. In cases where the signal decay was found to be multi-exponential, only the largest
relaxation times are reported.
aFor each spectral region, the largest and the smallest relaxation times are reported.
bSignals in the 1.2–2.3 ppm range.
cSignals in the 3.4–3.8 ppm range.
dBroad signal at 3.3 ppm; relaxation parameters for the terminal methyl group are reported separately because they are affected by internal rotation.
Values for 1H relaxation times T1 and T2 are reported in Table 1. As expected, T2
values increase with the distance from the Au core because protons farther from
the core are less packed and consequently have larger degrees of freedom with
respect to those closer to the nanoparticle core.5,6,29,30 In CDCl3, T2 values for the
signals of the different regions (alkyl, OEG, CH3; Table 1) are similar for all AuNPs,
confirming that all nanoparticles have a similar mobility of the coating ligands,
similar sizes, and are present as isolated entities. In water, the behavior is different;
all spins of 1-AuNP relax more slowly than those of 7- and 8-AuNPs.
In the case of 8-AuNP, such an effect could be ascribed to aggregation that reduces
both the nanoparticle tumbling rate and the mobility of the chains (because of their
interlocking). However, when 1- and 7-AuNP are compared, the decrease in the T2
values for the latter is much more relevant for the signals arising from the alkyl chains
(ꢁ10-fold decrease) with respect to the signals from the outer OEG chains (ꢁ4-fold
decrease). This suggests that, besides a possible reduction of the tumbling rate,
faster relaxation of the alkyl signals in 7-AuNP could also arise from a decrease of
their internal mobility. In other words, when in water, the nanoparticles coated
with ligands devoid of the amide unit, such as 7-AuNPs, feature substantially more
rigid alkyl chains than those of 1-AuNP, which contain the amide moiety.
Subsequently, we investigated the ability of water-soluble 1-, 7-, and 8-AuNPs to
detect organic molecules with the NMR chemosensing protocol (Figure 2 and Sup-
plemental Information, Section S5). The NOE-pumping sequence was used to
analyze samples containing the nanoparticles (15 mM) and salicylate at increasing
concentrations (1–10 mM) in carbonate buffered D2O solution at pD = 10.0.
1-AuNP confirmed their reported ability to detect salicylate in water.21 Analyte sig-
nals appeared in the NOE-pumping spectra at substrate concentrations as low as
2 mM and increased in both intensity and signal-to-noise ratio with increasing con-
centration. On the other hand, and surprisingly enough, analyte signals were never
detected with a signal-to-noise ratio above 3 (which we usually set as a detection
limit) in the experiments performed with 7- and 8-AuNPs, even in the presence of
high (10 mM) concentrations of the substrate. All the nanoparticles have the same
size and a very similar structure of the coating thiols, which leads to similar longitu-
dinal relaxation rates (T1) of protons within the same moieties (Table 1). Thus, the
different signal enhancements observed in NOE-pumping experiments (Figure 2)
should rather arise from different cross-relaxation rates, which depend on the affinity
96
Chem 3, 92–109, July 13, 2017

Figure 2. NMR Spectra
(A) 1H NMR subspectrum of 5 mM sodium salicylate (3) in D2O.
(B) NOE-pumping subspectrum of the same sample in the presence of 8-AuNP.
(C) NOE-pumping subspectrum of the same sample in the presence of 7-AuNP.
(D) NOE-pumping subspectrum of the same sample in the presence of 1-AuNP.
Conditions: [AuNP] = 15 mM, carbonate buffer 20 mM, pD = 10, 298 K. See also Supplemental
Information Section S5 and Figures S33–S35.
of the analytes for the substrate. This indicates that 7- and 8-AuNPs have an affinity
for salicylate that is sensibly lower than that of 1-AuNP and confirms that partition
into the hydrophobic pseudo-phase formed by the alkyl portions of the nanopar-
ticles coating thiols cannot explain the observed substrate recognition.
Morphology and Dynamics
On the basis of this evidence, we performed a series of extended MD simulations of
all the AuNPs immersed in either explicit water or chloroform. Nanoparticles sam-
ples prepared by standard solution methods are usually composed of clusters of
different sizes. In our case, the average Au core diameter (1.7 nm) is close to that
of the Au144(SR)60 cluster31–34 (ꢁ1.6 nm), and the contribution to the observed
NOE follows the size distribution (Supplemental Information, Section S7).35 Indeed,
it has already been shown that Au144(SR)60 is the main component in nanoparticle
batches with average diameter around 2 nm.35 In addition, we have shown that
computational simulations performed with Au144(SR)60 nicely predict the properties
of nanoparticle samples with similar size distribution.36 We hence considered the
Au144(SR)60 structure as a single nanoparticle model to analyze the dynamic proper-
ties and interactions of the different coating ligands in explicit solvents through
classic MD simulations of >200 ns for each AuNP/solvent system (ꢁ3.2 ms in total).
Chem 3, 92–109, July 13, 2017
97

Figure 3. Comparison of Experimental and Calculated T1 Values of 13C Nuclei along the Chains of
1-AuNPs
Left: 1-AuNPs dissolved in water.
Right: 1-AuNPs dissolved in chloroform.
T = 298 K. 13C Larmor frequency = 125.75 MHz. See also Supplemental Information Section S11.6
and Figure S74.
To validate our model systems, we first used the MD simulations to compute the T1
relaxation times for 13C nuclei of 1-AuNPs, the only sample soluble enough to allow
us to measure the T1 values for 13C both in deuterated water and CDCl3. In 13C re-
laxometry experiments, the relaxation of each 13C nucleus depends essentially on
the fluctuations of the dipole-dipole interactions between the carbon and the
attached protons (neglecting chemical shift anisotropy mechanisms).37 In this
case, relaxation times were calculated on the basis of the MD simulations via the
Lipari-Szabo approach (Supplemental Information, Section S11).38,39 A comparison
between the experimental and the MD-calculated T1 values is reported in Figure 3,
which highlights the very good match between the two datasets. The different relax-
ivities of the carbon nuclei in the two solvents, which in turn depend on the different
mobility of the coating chains in the monolayer, are well captured by both our MD
simulations and NMR, further supporting the choice of the Au144(SR)60 model for
the computational studies.
Turning our attention to the picture emerging from the MD simulations, we noticed
first that all the monolayer-protected AuNPs were more compact in water, as re-
flected by a decrease of ꢁ10% in their radius of gyration compared with that in chlo-
roform (Figure 4A). In chloroform, the relative distribution of the three moments of
inertia and the eccentricity, which are shape descriptors, indicate that all the AuNPs
adopted a spherical shape (Figure 4B). On the contrary, monolayer conformational
preferences in water were quite different. 1- and 7-AuNP appeared relatively spher-
ical in this solvent too, but we observed progressively broader relative distributions
of moments of inertia, causing an increase in the eccentricity values, for 8-, 9-, and in
particular 10-AuNPs, suggesting a less spherical shape (Figure 4B). In 10-AuNP,
sphericity deviation can be clearly ascribed to the collapse of the coating molecules
into bundles, featuring aligned alkyl chains located at opposite poles of the nano-
particle (Figure 4D).40 For 7-, 8-, and 9-AuNPs, on the other hand, alkyl chain bundles
were not clearly detected, but simulations again indicated a similar level of confor-
mational rigidity for the alkyl portion of these nanoparticles, in line with the broad
NMR signals and short transverse relaxation times observed for 7-AuNP in the
NMR experiments (Figure 1 and Table 1). In chloroform, all AuNPs were more
98
Chem 3, 92–109, July 13, 2017

Figure 4. Shape and Solvation of AuNPs
(A and B) Probability distributions of (A) radius of gyration (Rg) and (B) moments of inertia (I, shown as box-and-whisker plots) of 1-, 7-, 8-, 9-, and 10-
AuNPs in water and chloroform. The average nanoparticle eccentricity e = (1 À Imin/Iavg), where e = 0 for a sphere and 1 for a prolate spheroid, is also
reported (SD = 0.2 except for * and #, where it is 0.3 and 0.4, respectively).
(C) Distribution of the solvent molecules and the gold and sulfur atoms (in the inset in yellow and orange, respectively) from the center of mass of the
Au144 core.
(D) Representative snapshots of the AuNPs and solvent molecules within 1 nm of the gold atoms (wires connect carbon atoms C4–C7 closer than 0.8 nm).
See also Supplemental Information Section S11.2 and Figure S70.
flexible than in water, and the OEG of 7- and 8-AuNPs was less constrained than that
of 1-AuNP, in agreement with the NMR experiments discussed above.
The elongated spheroid shape of 8-, 9-, and 10-AuNPs in water resulted in exposure
of the ligand’s alkyl chains to the solvent. This is particularly evident in 10-AuNPs.
Here, the bundling of the coating ligands created a structure with a wide, ring-
shaped, equatorial region where alkyl chains were poorly shielded from water by
few OEG chains. Indeed, the water molecules closest to the gold core were almost
exclusively found in this ring-shaped region (Figure 4D).
Chem 3, 92–109, July 13, 2017
99

Figure 5. HB Interactions
Average number of ligand-ligand and ligand-water HBs during MD simulations and the
decomposition of this number for the different coating thiol atoms (ordered top to bottom from the
outer to the inner part of the coating ligand). For NH/O, NH is present in 1-AuNP and O is present in
7- and 8-AuNPs.
The number of ligand-water HBs is reported in Figure 5 and provides interesting
information. 1-AuNP formed the larger number of HBs with the solvent (ꢁ150),
followed by 7- and 8-AuNPs (ꢁ86–88) and finally by 9- and 10-AuNPs (ꢁ76–79).
Interestingly, the trends of the computed eccentricity values in water and of the num-
ber of HBs with the solvent well correlate with the observed nanoparticle solubility
and aggregation tendency. That is, nanoparticles showing a more spherical shape,
with a consequent better shielding of the inner alkyl chains and a better solvation
in our simulations, correspond to those present in solution as individual entities. Still,
subtle differences found for quite similar nanoparticles, such as 7- and 8-AuNP, indi-
cate that a full explanation of nanoparticle stability in solution will require additional
investigations.
The substantially greater number of HBs made by 1-AuNP is due to the amide group,
particularly its carbonyl oxygen, which was responsible for ꢁ50% (ꢁ60% considering
the NH) of the interactions with water. The OEG portion accounts for the remaining
40% of HBs, with the number of interactions decreasing from the terminal and more
exposed oxygen to the inner ones. The same trend was observed in 7-, 8-, 9-, and
10-AuNPs. Here, however, the total number of HBs formed by the three outer
oxygens was larger than in 1-AuNPs (ꢁ73–79 versus ꢁ61).
The additional oxygen atom in 7- and 8-AuNPs, compared with 9- and 10-AuNPs,
formed only a few (ꢁ13–15) HBs with water. This is because the extra HB acceptor
site in 7- and 8-AuNPs is buried inside the monolayer and thus poorly exposed
to the solvent. In this view, it is striking that the insertion of the carbonyl group in
100 Chem 3, 92–109, July 13, 2017

1-AuNP increased the number of HBs from ꢁ76–79 to ꢁ150. This implies that the
amide groups not only provide an additional site of interaction with the solvent,
but also substantially affect the conformation of the ligands, which in turn modulate
solvation of the monolayer.
Another relevant feature revealed by HB analysis is the interligand interactions in
1-AuNP. In this case, the amide NH group formed 22 G 4 HBs with acceptors on
neighboring ligands. This is quite close to the average number of interactions estab-
lished with water molecules (19 G 4). Interestingly, half of the interligand HBs
formed by NH are with neighboring carbonyl oxygens, and the remaining ones
are formed with the OEG, decreasing from the outer to the inner oxygen (Figure 5).
Importantly, these results revealed that: (1) the number of interligand HBs is rela-
tively small (the theoretical maximum being about 60); (2) the formation of HBs be-
tween the amide groups and the OEG oxygens is favored by the significant bending
of the coating ligands. On the other hand, we found ꢁ90 HBs with intercalated wa-
ter, which are thus favored compared with interligand HBs. Most likely, HBs with
intercalated waters helped maintain the amide groups of 1-AuNP to be spaced
and distributed homogeneously, preventing the massive bundling observed with
the other AuNPs. Instead, the bending of some ligands of 1-AuNP, which favors
rigidification (Figure 3) through interligand HBs with the amide group (Figure 5), ex-
plains the smaller average number of interactions that OEG oxygens establish with
water, with respect to those formed by 7-, 8-, 9-, and 10-AuNPs.
In chloroform, where HBs with the solvent are not possible, the number of interligand
HBs in 1-AuNP almost doubled to 43 G 3 (Figure 5) in comparison with those in
water. At the same time, and in contrast to water, OEG headgroups were rarely
involved in these interactions, preferring extended conformations toward the
solvent. The interligand HB network generated highly structured HB chains (Fig-
ure 6). Such interactions modified the preferred monolayer conformation in these
nanoparticles, from the homogeneously distributed structure observed for the
simulations in water to a more inhomogeneous structure characterized by the pres-
ence of large ‘‘canyons’’ filled by solvent molecules (Figures 4C, 4D, and S70).
Together, these data show that the compactness, shape, and surface arrangement
of AuNPs’ coating ligands are strongly affected by both the ligand structure and
the solvent. In chloroform, ligands prefer a disordered conformation that results in
an overall spherical nanoparticle shape. The formation of interligand HBs induces
clustering of the ligands in small bundles separated by deep ‘‘canyons,’’ only for
1-AuNP. Such a behavior is in full agreement with the early observation by Rotello
et al.41,42 who detected relatively strong interligand HBs in nanoparticles coated
with amide-bearing thiols in organic solvents. On the basis of chemical oxidation ex-
periments, such interligand interactions were supposed to generate ligand
bundling, and the resulting ‘‘canyons’’ to cause the lower resistance of the gold
core to decomposition.41,42
In water, it is the dispersive-hydrophobic interaction between the alkyl chains that
induces the aggregation of the ligands to form bundles.5,6 It appears that such struc-
tures could favor aggregation and even prevent the nanoparticle’s dissolution in
water, depending on the length of the OEG chains.
The effect of the amide groups in 1-AuNP in water is therefore somehow intriguingly
counterintuitive. Amide groups do not reinforce the bundling, as in chloroform,
where they provide an additional interligand interaction.7 Instead, amide groups
Chem 3, 92–109, July 13, 2017 101

Figure 6. Structure of the Monolayer
(A) Amide group atoms closer than 0.4 nm are connected by wires, and solvent molecules within 1 nm of the Au144 core are shown. The Au144 core is
shown as a gold surface, and sulfur atoms are shown as orange spheres.
(B) Example of the HB network for 1-AuNP in water.
(C) Identification of pockets on one snapshot of 1-AuNP in water.
(D) Superposition of the docking pose of salicylate in 1-AuNP and in the LysR-type transcription factor (PDB: 2Y7K).
(E) Characterization of pockets for 1-AuNP in water. Magenta spheres indicate the ‘‘deep cavity’’ pockets (left), and yellow spheres indicate the ‘‘OEG
sinking’’ pockets (right). Gray, green, and cyan surfaces identify the alkyl, amide, and OEG regions, respectively.
(F) Time evolution and lifetime of a stable pocket formed in 1-AuNP in water. Blue color for the open pocket and black for the closed pocket.
See also Supplemental Information Sections S11.3–S11.4 and S11.7–S11.8 and Figures S71–72, S75, and S76.
favor the intercalation of water molecules, which act as spacers between the ligands,
inducing a more disordered and spherical monolayer conformation in water.
Molecular Basis for Recognition
Importantly, for 1-, 7-, and 8-AuNPs, the simulations in water revealed the formation
of transient pockets in the monolayer structure (Figure 6). The pockets showed
different sizes and shapes. The observed average number was ꢁ5 G 2 for each
AuNP, and an average volume of ꢁ0.15 G 0.04 nm3, which is enough to
102 Chem 3, 92–109, July 13, 2017

accommodate a few waters but also small organic molecules such as salicylate
(Supplemental Information, Section S11.7). The lifetime of the pockets ranged
from hundreds of picoseconds to tens of nanoseconds, revealing their transient
nature. Whereas some pockets were very flexible, i.e., they had a high frequency
of transitions between short-lived open and closed conformations, other pockets
were more structured and able to remain open for most of the simulation time
(see Figure 6F; this pocket was open for >90% of the time). Closer inspection re-
vealed that such pockets could be classified into two different topological classes.
The first class, found in all the nanoparticles, was essentially characterized by the
sinking of the OEG layer, and only fragments of such moieties composed the pocket
walls. The second class, present only in 1-AuNP, was deeper and penetrated down
into the alkyl layer (Figure S76). Notably, in this case, water molecules were often
found to form bridges either between distant parts of the same coating ligand
(i.e., amide and OEG) or between distinct ligands, creating a complex HB network
topology (see Figures 6B and S72).
The occurrence of the latter transient cavities only on the surface of 1-AuNP helps
rationalize the recognition ability of these nanosystems in NMR chemosensing
experiments. To further investigate 1-AuNP’s interaction with the bound analytes,
we reversed the NOE-pumping approach by transferring magnetization from the
analyte to the monolayer with a selective 1D-NOESY pulse scheme.43 From a qual-
itative viewpoint, the larger the number of close contacts between the monolayer
and the analyte, the stronger the nanoparticles’ NOE signals in this experiment.
The 1D-NOESY spectrum in Figure 7 clearly shows that NOE enhancements on
the alkyl portion of the coating monolayer are larger than those on the oxymethylene
moieties. This suggests that the salicylate molecule arranges in such a way as to
place its protons in close proximity to the inner alkyl portion of the monolayer. How-
ever, its position in the monolayer is not deep enough to prevent contacts with the
OEG portion, as demonstrated by the weak NOE signals observed.
This result was compared with molecular docking calculations of salicylate to the four
pockets detected on the monolayer of a representative equilibrated MD snapshot
(Figure 6). In particular, pockets 1 and 2 were of the ‘‘deep cavity’’ type, and pockets
3 and 4 were of the ‘‘OEG sinking’’ type. Interestingly, we found that the interaction
network of the salicylate’s docking pose into pockets 1 and 2 gave rise to a large
number of contacts with the alkyl portion of the coating ligand (Figure 7C), in agree-
ment with the results of the selective NOESY experiment. On the other hand, dock-
ing in pockets 3 and 4 resulted in predominant contacts with the OEG groups
(Figure S78). There is thus a good qualitative agreement between the NMR data
and the docking of salicylate to the ‘‘deep cavity’’ pockets that are present in
1-AuNPs. In addition, the predicted docking pose into pocket 2 well matched that
of salicylate co-crystallized with LysR-type transcription factors (Figure 6D).44 This
corroborates the idea that AuNPs can form protein-like pockets on the surface, as
here in 1-AuNP, and suggests that cavities with the proper topology are essential
for the specific interaction with selected analytes. Nicely enough, Lucarini et al.45
have reported that AuNPs coated with thiol 1 undergo a decrease of affinity for hy-
drophobic organic molecules as their size increases. Indeed, formation of binding
pockets in the monolayer would be more difficult in large nanoparticles, as the
decreased curvature should induce a stronger packing of the thiols, which in turn
could hamper pockets opening.
Accommodation of the substrate inside pockets in the nanoparticle-coating mono-
layer is somewhat expected. The different efficiency of 1- and 7-AuNPs in the
Chem 3, 92–109, July 13, 2017 103

Figure 7. Interactions between Analyte and AuNP
(A) 1H reference spectrum of 20 mM sodium salicylate and 10 mM 1-AuNP in D2O carbonate buffer
(pD = 10).
(B) 1D NOESY spectrum of the same sample obtained with selective excitation of the salicylate
resonances (6–9 ppm) and 300-ms mixing time. Signals of the nanoparticle highlight a negative
NOE regime (slow tumbling).
1
(C) Number of total 1H- H contacts, color coded in intervals of 1 A, between docked salicylate and
˚
1H-bearing atoms of 1-AuNP (exchangeable NH omitted). The bars have been sorted according to
the chemical shifts of the parent atoms.
See also Supplemental Information Section S12 and Figure S78.
NOE-pumping experiments, however, indicates that pocket opening might be a
prerequisite for an efficient recognition. To clarify these relevant points and define
the molecular basis for the AuNP- and analyte-recognition process, we ran two
1-ms-long MD simulations of 1-AuNP, with either salicylate or 4-hydroxybenzoate
(3 or 5 in Scheme 1, respectively) in solution. The results showed that salicylate,
which is selectively detected by 1-AuNP in chemosensing experiments (Figure 8A),
spent ꢁ48% of the simulation time bound to the monolayer (i.e., conformations in
which the distance between protons of the analyte and of the monolayer was less
than 0.4 nm). In addition, residence time related to each recognition event was quite
long, lasting tens of nanoseconds (Figure 8B). In NOE-pumping experiments, a
sizable magnetization transfer between the spins of the monolayer and those of
the analyte is detected only when the residence time of the analyte in the monolayer
104 Chem 3, 92–109, July 13, 2017

Figure 8. 1-AuNP-Analyte Interactions
(A) NOE-pumping spectra resulting from a solution of 15 mM 1-AuNP with either 5 mM salicylate
(left) or 5 mM 4-hydroxybenzoate (right). Mixing time is 1.2 s. The emergence of a signal only for
salicylate is well rationalized by the MD results outlined in (B) (see text for details).
(B) During MD simulations, 1-AuNP-analyte binding events were defined when the minimum
intermolecular proton-proton distance was less than 0.4 nm. The binding events were sorted by
their binding residence time (x coordinate) and plotted against the minimum distance between the
analyte and the Au144 core (depth of penetration in the monolayer, y coordinate). The rotational
correlation time (tc) of 1-AuNP is also reported as a visual guideline.
(C) Distribution of the analytes in the monolayer taken from MD snapshots every 25 ns. Purple
analytes lie at max 1 nm from the gold core, and green analytes lie between 1 and 1.5 nm.
(D) Binding event of salicylate to 1-AuNP.
See also Supplemental Information Section S11.9, Figure S77, and Movie S1.
is comparable, or longer, than the rotational correlation time (tc) of the nanopar-
ticle.24 Inspection of Figure 8B, where the binding events are sorted according to
their binding residence time, reveals that many binding events of salicylate visibly
exceed tc of the nanoparticle. Moreover, in agreement with NOE experiments,
MD simulations indicated that salicylate was mostly sinking into the inner part of
Chem 3, 92–109, July 13, 2017 105

the monolayer, with the aromatic ring pointing toward the gold core and the carbox-
ylate group remaining exposed to the water solution (Figure S77). We also detected
the formation of HBs between the substrate’s carboxylate and hydroxyl groups and
the monolayer amides, which were formed during 6% of the total simulation time.
On the other hand, 4-hydroxybenzoate, which is not experimentally detected by
NOE-pumping experiments with 1-AuNP (Figure 8A), was in the bound state only
for 27% of the overall simulation time. Moreover, the observed binding events of
4-hydroxybenzoate were on a short timescale (a few nanoseconds), thus rarely
above tc. Indeed, our MD simulations show that this analyte was mostly floating
on the outer OEG surface compared with salicylate. Orientation of 4-hydroxyben-
zoate inside the monolayer was similar to that observed for salicylate, with
the carboxylate group pointing toward the water phase. In this configuration, the
4-hydroxy group is located deep inside the hydrophobic portion of the cavity.
Such unfavorable conditions most likely justify the shorter permanence of the
substrate into the cavities.
Noticeably, the time of residence into the cavities is not only correlated with magne-
tization transfer but also with binding affinity, because it directly depends on the rate
of dissociation (koff) of the analytes from the monolayer. Hence, a longer residence
time suggests a higher thermodynamic affinity of the substrate for the monolayer.
Ultimately, these simulations allowed us to decipher the binding mechanism of
salicylate to 1-AuNP, which occurred through four main steps (Figure 8D and Movie
S1): (I) formation of transient pockets in the monolayer; (II) binding of salicylate into
one of these transient pockets; (III) conformational changes of the pocket in
response to analyte binding; (IV) sinking of the analyte into the inner shell of the
monolayer. Such a sequence confirms our early hypothesis that the opening of the
pocket is the essential prerequisite for ligand binding. Consequently, as found
here, the recognition ability of nanoparticles is crucially related to their ability to
form pockets with the proper structure and lifetime. Fascinatingly, the series of
events listed able also suggests an interplay between conformational selection
and induced fit,46 well mimicking the recognition and binding process for protein-
ligand complex formation.
Conclusions
Overall, this combined computational-experimental study shows that functionalized
coating ligands can self-organize through a delicate and somehow counterintuitive
balance of interactions within the monolayer itself and with the solvent. We demon-
strate that these complex interactions can also favor the formation of transient, pro-
tein-like binding pockets in monolayer-protected AuNPs, which has so far only been
hypothesized.19,47 These findings imply that nanoparticle-based recognition oper-
ates through a process that is similar to that for protein-ligand complex formation.
The investigation method designed here, which conjugates detailed NMR studies
and atomistic MD simulations, could find application in any nanoparticle-based
recognition event. The results reported here also raise new questions that merit
further investigation, e.g., which structural and physiochemical parameters predom-
inantly control monolayer organization and pocket formation, similar to that estab-
lished for binding pockets in proteins. This might help the rational design of thiols
capable of favoring the formation of selective pockets and receptors in the nanopar-
ticle-coating monolayer. Addressing such stimulating problems could herald the
arrival of the computational nanodesign of new functionalized nanoreceptors with
programmed recognition ability.
106 Chem 3, 92–109, July 13, 2017

EXPERIMENTAL PROCEDURES
Nanoparticle Synthesis
Thiols 7–10 were synthesized by standard procedures (Supplemental Information,
Sections S1 and S2). AuNPs were prepared by chemical reduction of HAuCl4
according to a two-phase, two-step protocol (Supplemental Information, Section
S3).48 In brief, tetracloroaurate was extracted in toluene with tetraoctylammonium
bromide as the phase-transfer agent. The nanoparticles were formed by reduction
with NaBH4 in the presence of dioctylamine as stabilizer. In situ exchange of the
amine with thiols provided the nanoparticles. AuNPs were characterized by TEM,
thermogravimetric analysis, UV-visible spectroscopy, and NMR. The average
diameter of the gold cores was 1.5–1.7 nm (s = 0.2 nm). The average nanoparticle
formula, calculated over all the AuNPs, was Au137(SR)58, suggesting that the samples
mainly comprised Au144(SR)60 clusters (ꢁ1.6 nm; see Negishi et al.35).
NMR Experiments
NMR experiments were performed on a Bruker AVIII 500 equipped with a 5 mm
z-gradient BBI probe. 1H relaxation times were measured at 500.13 MHz Larmor
frequency with standard CPMG (T2) and inversion recovery (T1) pulse sequences.
NOE-pumping experiments were performed with the pulse sequence detailed in
Perrone et al.21 and Salvia et al.22 13C T1 relaxation times were obtained at 125.75
MHz Larmor frequency by inversion recovery under proton decoupling conditions37
on a Bruker AVANCE DRX 500 equipped with a 5 mm z-gradient TCI 1H-13C/15N/2H
cryoprobe.
MD Simulations
The three-dimensional structure of the simulated AuNPs is based on the Au144(SR)60
model of Lopez-Acevedo et al.,31 which is the main component in our experimental
nanoparticle batches. Although this model does not account for size dispersion, it is
responsible for most of the effect observed in our experiments (see Results and Sup-
plemental Information, Section S7). We have already shown that simulations per-
formed with this model nicely reproduce the behavior of nanoparticles samples
with an average diameter of 1.6–1.8 nm.36 The coating groups, as well as salicylate
and 4-hydroxybenzoate (3 and 5 in Scheme 1), were parametrized with GAFF49 and
the atomic charges were derived by the RESP fitting procedure50 by a force-field to-
pology database-building approach as developed in RedServer.51 The van der
Waals parameters for the gold atoms were taken from Heinz et al.52 The AuNPs
were first minimized via the steepest descent algorithm in a vacuum. Then, the simu-
lation box was built to ensure a minimum distance between the AuNPs and the box
edges of at least 1 nm and filled up with either water (TIP3P model) or chloroform
molecules. For 1-AuNP, further simulations were carried out with explicit analytes
at a concentration of 35 mM. A second minimization was applied to relax the solvent
molecules around the solute. The system was then thermalized and pressurized in
different steps. After this initial phase, the system was ready for productive MD sim-
ulations. Production runs were carried out in the NPT (p = 1 bar, T = 300 K) statistical
ensemble. All bonds were constrained with LINCS, allowing a time-step set of 2 fs.
Periodic boundary conditions were applied to the systems in all directions. The PME
method was used to evaluate long-range electrostatic interactions, and a cutoff of
˚
10 A was used to account for the van der Waals interactions (full protocol in Supple-
mental Information, Sections S9 and S10). All MD simulations were performed with
GROMACS-4.6.53 Coordinates of the systems were collected every 2 ps, and the
analysis was performed every 100 ps. All analyses reported refer to the simulations
after 25 ns of equilibration. The simulation time was >200 ns for each AuNP-solvent
Chem 3, 92–109, July 13, 2017 107

system (ꢁ3.2 ms in total) and 1 ms for each of the two 1-AuNP-analyte systems (20 ms
of a single analyte-AuNP interaction in total).
SUPPLEMENTAL INFORMATION
Supplemental Information includes Supplemental Experimental Procedures, 78 fig-
ures, 2 tables, 5 schemes, and 1 movie and can be found with this article online at
http://dx.doi.org/10.1016/j.chempr.2017.05.016.
AUTHOR CONTRIBUTIONS
Experimental work was conducted by L.G., X.S., F.D.B., F.R., and F.M. Computa-
tional work was conducted by L.R. and M.D.V. All authors analyzed the results.
L.R., F.R., F.M., and M.D.V. co-wrote the manuscript.
ACKNOWLEDGMENTS
Financial support was provided by the Italian Association for Cancer Research
through investigator grant no. 18883 to M.D.V., by the European Research Council
through Starting Grant ‘‘MOSAIC’’ (no. 259014) to F.M., and by the University of Pa-
dova through the Progetto Strategico di Ateneo NAMECA. X.S. thanks the China
Scholarship Council for a PhD fellowship. Access to the Centro di Risonanze Mag-
netiche (CERM) infrastructure (Florence) provided by Mario Piccioli and technical
support from Fabio Calogiuri are gratefully acknowledged.
Received: December 7, 2016
Revised: March 20, 2017
Accepted: May 24, 2017
Published: July 13, 2017
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