Ligand-induced structural changes of thiolate-capped gold

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Ligand-induced structural changes of thiolate-capped gold nanoclusters observed with resistive-pulse nanopore sensing Bobby D. Cox, Patrick Woodworth, Peter D Wilkerson, Massimo F Bertino, and Joseph E. Reiner J. Am. Chem. Soc., Just Accepted Manuscript • DOI: 10.1021/jacs.8b12535 • Publication Date (Web): 16 Feb 2019 Downloaded from http://pubs.acs.org on February 16, 2019

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Ligand-induced structural changes of thiolate-capped gold nanoclusters observed with resistive-pulse nanopore sensing Bobby D. Cox, Patrick H. Woodworth, Peter D. Wilkerson, Massimo F. Bertino and Joseph E. Reiner* Department of Physics, Virginia Commonwealth University, Richmond, VA USA 23284

Supporting Information Placeholder ABSTRACT: Nanopore-based resistive pulse sensing with biological nanopores has traditionally been applied to biopolymer analysis, but more recently, interest has grown in applying the technique to characterizing water-soluble metallic clusters. This paper reports on the use of alpha hemolysin (αHL) for detecting a variety of thiolate-capped gold nanoclusters. The ligands studied here are p-mercaptobenzoic acid (p-MBA), tiopronin (TP) and thiolated PEG7 (S-PEG7). Individual clusters trapped in the cisside of an αHL pore for extended periods (>10 sec) exhibit fluctuations between numerous substates. We compare these current steps between the three different ligands and find that they scale with the mass of the corresponding ligand, which suggests that nanopore sensing could be used to characterize intraparticle surface modifications.

Monolayer protected clusters (MPC) exhibit a number of important chemical and physical properties that give them the potential to be useful for many applications including medical imaging and diagnosis,1 biochemical detection,2 as well as virus targeting and immunosensors.3-5 Of particular interest are thiolate protected gold clusters because of their ease of synthesis and wellcharacterized ground state structures through x-ray diffraction and DFT calculations.6-8 Despite these clusters being stable over extended periods of time,9,10 little is known about the kinetic changes and reordering of ligands on the cluster surface, and although speculation has been made regarding ligand motion (i.e. lateral ligand place exchange11) the lack of analytical techniques for probing ligand dynamics makes it difficult to characterize this behavior. Some have reported on the dynamic nature of the p-MBA and m-MBA surfaces,12 but in most cases, studies of lateral ligand place exchange have resulted in strikingly different timescales and conclusions.13-20 New techniques are required to better understand the kinetic activity of ligands on protected metallic nanoclusters. We demonstrate here that resistive-pulse nanopore sensing21,22 is a useful tool for measuring ligand-induced kinetics. A few reports have described the use of nanopores for characterizing metallic nanoparticles23,24 and nanoclusters.25,26 These experiments utilized alpha hemolysin (αHL) pores, which have been demonstrated to detect small intramolecular fluctuations with latch-zone analysis.27 Here we present αHL-based nanopore analysis of ligand-induced kinetics of individual thiolate-capped nanoparticles. We compare the fluctuations associated with particles protected with pmercaptobenzoic acid (p-MBA), tiopronin (TP) and thiolated PEG7 (S-PEG7). We find that all three gold nanoclusters can be trapped

in the sensing region of a single αHL channel for extended periods and this enables the observation of discrete changes in the current that we associate with ligand rearrangement on the particle. Similar multi-step transitions have been reported previously for sulfonatecapped ligands (MPSA),28 but those fluctuations were assumed to result from Brownian diffusion or binding between the nanocluster ligands and residues in the constriction region of the αHL pore.24,29 Here we show that the multi-state fluctuations seen for our particles most likely result from intraparticle rearrangements. We report several experiments and develop a simplified geometric model to support this claim. This work motivates the growing use of nanopore-based resistive pulse sensing as a tool for nanocluster characterization. Specifically, by trapping individual clusters in the pore for extended periods, we can characterize core-ligand dynamics in real time, which is currently not possible with other single-particle characterization techniques. Nanoparticles entering the pore remain trapped for extended periods and this enables clear visualization of the particle kinetics. Figure 1 shows a schematic illustration of the experimental procedure along with a sample current trace of the nanopore response to a single cluster capture. Complete details of the methodology are in the Supporting Information.

Figure 1. Schematic illustration of the experimental setup (not to scale) and a typical current trace for a single p-MBA capped cluster in the αHL pore. (A) A micropipette tip filled with a preformed sample of Aux(SR)y nanoparticles is positioned near the

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cis-side of an αHL pore. The p-MBA and TP particles are negatively charged in the solution conditions studied while the SPEG7 is positively charged30 (see Supporting Information) so the appropriate transmembrane voltage polarity drives particles into the pore. (B) Time trace for a typical p-MBA capture experiment. A constant transmembrane voltage (|V| = 70 mV throughout this work) is held fixed while an ejection pressure is applied at the entry of the cluster-filled micropipette tip (15  2) hPa. A sudden decrease in the current indicates the entry of a single nanoparticle and the applied pressure is quickly reduced to zero. (C) Particles spend ca. 10-30 seconds trapped in the pore and yield noisy fluctuations. Data shown here was taken in 3M KCl at pH 8.0. Figure 2A shows a typical current trace from a p-MBA capped nanoparticle entering the cis-side of the pore. The unfiltered current appears noisy, and steps are barely resolvable. Applying a 100-Hz low-pass filter reduces the noise to reveal discrete states like those shown in Fig. 2B (red trace in Fig. 2A). The all-points histogram of the filtered current in Fig. 2C shows that the current trace yields well-resolved discrete current states. A Gaussian mixture model fits these current states and the peak positions are plotted with the state number in Fig. 2D. This apparent linear dependence between peak number and peak current over a ca. 20 pA range shows that the states are spaced by (2.29  0.04) pA (1 S.D.), which corresponds to a relative change in the overall current of 1%/step. A histogram of the current steps between states (Fig. 2E) shows that current transitions occur between adjacent states and next-nearest adjacent states (i.e. i =  1,  2 states). This suggests that the current steps result from ligand rearrangement on the particle surface because ligand changes that occur along the pore axis will affect the ionic current less than ligand changes perpendicular to the pore axis. A more detailed discussion of this along with current step distributions for p-MBA at pH 7 and SPEG7 are in the Supporting Information. We also describe our geometric model in the Supporting Information that assumes the cluster geometry dominates the current behavior. This model is used to extract cluster and ligand sizes (d = 1.9 nm and  = 0.6 nm), which are in quantitative agreement with previous measurements31,32 and suggest that the current step transitions result from cluster shape changes on the order of the ligand sizes.

Figure 2. Low frequency filtering indicates clear transitions in the nanoparticle-induced current. (A) Typical current trace (black) and corresponding filtered signal (red) resulting from the cis-side capture of a single p-MBA gold nanoparticle exhibits noisy fluctuations. (B) Zooming in on the 100-Hz low-pass filtered signal illuminates clear transitions between several substates (dashed lines). Note that the current scales differ between (A) and (B). (C) An all-points histogram of the filtered current (black line) fitted with a Gaussian mixture model (red line) yields (D) a peak

position distribution that is linear over the 9 sub-states in (B) and (C). The slope of the least-squares fit line (solid black) is (2.29  0.04) pA. (E) The current step distribution shows the current transitions occur in both the positive and negative direction and the step sizes are described with a 4-component Gaussian mixture model (red line) with peak positions (-4.63  0.14) pA, (-2.50  0.08) pA, (2.36  0.09) pA, and (4.67  0.12) pA. This shows that current transitions between states are limited to adjacent states or next-nearest adjacent states (i.e. i = 1 state or 2 states). All data was taken with a 70 mV transmembrane potential in 3M KCl at pH 8.0. To further explore the possibility that the discrete current steps in Fig. 2B result from ligand-based structural changes of the nanocluster we performed experiments with TP and S-PEG7 capped particles. Tiopronin and p-MBA have similar mass (MWTP = 163.2 Da, MWpMBA = 154.2 Da) while the S-PEG7 is about 2.5× heavier than either TP or p-MBA (MWSPEG-7 = 386.5 Da). All three ligands form stable gold nanoclusters,31 which makes them ideal for testing the role ligands play in the observed current steps. Figures 3A and 3C show TP particles exhibit similar steps in the current to those seen for p-MBA capped particles, but the kinetics are about 10-fold slower than the fluctuations associated with pMBA particles. In two of the three cases (p-MBA, S-PEG7) we observe kinetics consistent with previously observed timescales from NMR measurements of Au140(S(CH2)5CH3)53 (i.e. ligand exchange between sites at rates of 100 to 400 s-1)17

Figure 3. p-MBA, Tiopronin and S-PEG7 capped particles yield different current traces, which suggests the ligands play a major role in the nanoparticle-induced current-step fluctuations. (A) Typical p-MBA capped particle current traces exhibit long periods of stability followed by shorter discrete steps. The slight upward shift in the blockade current ca. 2 sec. after particle capture results from shutting off the particle injection spray. (B) The corresponding step-time distribution (time between steps in either direction) follows a double exponential function with fast and short characteristic times (fast-p-MBA = (0.08  0.01) s, slow-p-MBA = (0.35  0.08) s). (C) Similar behavior is observed with tiopronin-capped nanoclusters, but the time scale has increased over 10-fold indicating the ligand configuration is more stable for tiopronin-

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Journal of the American Chemical Society capped particles. (D) This is quantified with a step-time distribution with fast-TP = (2.5  0.5) s and slow-TP = (32  18) s. (E) Rapid fluctuations between numerous states for S-PEG7 capped particles show current steps distinct from TP and p-MBA particles. The S-PEG7 clusters bind potassium cations30, which makes them cationic and explains the polarity reversal with respect to the capture voltages utilized for p-MBA and TP capture (see Supporting Information). (F) Step time distribution for the S-PEG7 particles shows fluctuations with single exponential dependence and mean switch time  = (15.5  0.9) ms. (G) Current substates extracted from all points histograms (see Figs. 2B-D) for the three ligands studied herein (p-MBA = black, TP = red, S-PEG7 = blue) show quantitative agreement between ligand mass and current step size. Linear fits to the data yield slopes of mp-MBA = (2.35  0.02) pA, mTP = (2.59  0.04) pA and mS-PEG7 = (6.49  0.10) pA, which are consistent with the corresponding mass of each ligand. Data taken with a 70 mV transmembrane potential in 3M KCl at pH 8 To rule out the possibility that the current steps result from binding between the ligands and nanopore residues we studied SPEG7-capped particles. S-PEG7 behave like polycations in the high ionic strength solutions used here30 (See Supporting Information) and they have no functional group that can interact with the pore walls so the discrete current steps shown in Fig. 3E indicate that the current steps reported herein most likely result from ligand rearrangement on the cluster surface. This is further supported by the data in Fig. 3G, which compares the current steps for the three ligands. Interestingly, the ratio of the current steps agrees with the ratio of the corresponding ligand masses (iS-PEG7/ip-MBA = 2.8  0.1, MS-PEG-7/Mp-MBA = 2.5 and iTP/ip-MBA = 1.1  0.1, MTP/Mp-MBA = 1.06). Moreover this agreement improves if we subtract the sulfur mass from each ligand ((MS-PEG-7 – MS)/(Mp-MBA – MS) = 2.9 and (MTP – MS)/(Mp-MBA – MS) = 1.1). One explanation for this improvement is that the sulfur atom is in plane with the gold surface, so only the non-sulfur portion of the ligand has any effect on the current steps. Figure 3 shows that the size of the current steps scales with the mass of the ligands, and the type of ligand influences the kinetics of the current step fluctuations. Both observations support our assertion that ligand modification and rearrangement on the cluster surface yield discrete current steps. Previous reports showed similar current steps for mercaptopropane sulfonate (MPSA)-capped gold nanoparticles. These fluctuations were attributed to both Brownian motion28 and binding with various residues in the pore.23,24,28 Given the reported timescales for our observed current steps (~100 ms) it seems unlikely that the steps reported herein result from random Brownian motion (i.e. Stokes-Einstein relation and the diffusion equation for a 2 nm particle in water suggest that the particle completely diffuses throughout the cis-side vestibule of the pore cavity (~30 nm3) in ca. 2 ns). Earlier reports with MPSA ligands that argue for binding with various residues in the pore24 are consistent with the observed current fluctuations reported therein because current fluctuations occurred between two states rather than the ten or so seen in this work. It is also worth noting that our results were obtained in pH conditions (pH 6 – pH 8) that stabilize the salt bridges between the glutamic acid and lysine residues in the constriction region of the αHL pore, and this reduces the likelihood of interactions between ligands and pore. This is in contrast to the aforementioned efforts that studied ligand-residue interactions.29 In that work, extreme pH conditions (pH ~ 2) or mutated versions of the pore were used to enhance the binding between ligands and pore. This report illustrates the usefulness of nanopore sensing for characterizing water-soluble metallic clusters. Nanopore sensing has shown promise with metallic cluster analysis and these results show the possibility of studying individual clusters over extended periods of time, which opens the door to further analysis of ligand-

core dynamics and also suggests a novel approach to small molecule sensing with metallic clusters operating in conjunction with nanopore sensors.

ASSOCIATED CONTENT Supporting Information The Supporting Information is available free of charge on the ACS Publications website. Methodology, geometric model of the cluster-induced current steps, pH studies of the cluster-induced kinetics, polarity dependence of PEG-capped particle blockades, nanopore-based comparison between polydisperse and monodisperse cluster mixtures. (PDF)

AUTHOR INFORMATION Corresponding Author *[email protected]

Notes The authors declare no competing financial interests.

ACKNOWLEDGMENT JER acknowledges support from VCU Seed funding. All authors acknowledge Charlotte Ann Olmsted for assistance with the TOC graphic.

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