104 6.1 Introduction
Nanomaterials with their unique electrical, optical and magnetic properties have attracted great attention in physics, chemistry and biology1. The unique nanoparticle properties resulted in a breakthrough of nanomaterial utilization in biosensing2, imaging3 and therapeutic applications4. But the rapid fabrication and utilization of nanomaterials in the scientific field and in consumer products such as sun cream and clothing demand a comprehensive understanding of the potential risk for the environment and biological systems including the human body. Upon entering a biological medium nanoparticles (NP) may interact with surrounding biomacromolecules, such as proteins. The adsorption of protein at the NP surface is promoted by several factors such as hydrogen or chemical bonds due to functional groups, solvation forces, electrostatic interaction, van der Waals interaction, etc. leading to nanoparticle-protein bioconjugate hybrid structures. The protein binding can be classified into two categories: long-term binding and quick, reversible binding with a fast exchange rate175. Those NP structures with protein layers, also called protein corona, show a different biological reactivity compared to the bare NPs. The new ‘biological identity’ of NPs has a strong impact on its cellular uptake process176,177. It was shown that NP uptake is possible, even across the blood-brain barrier based on the specific interaction of adsorbed protein with the receptors in cells178. This characteristic of the nanoparticle-protein complexes could pave the way for the application of these materials in biomedicine especially for drug delivery or compound transportation to the brain. At the same time NPs arose great concerns about nanosafety due to possible NP-induced perturbance in protein interactions, cellular signal transduction and DNA transcription179. As many of the studies showed the importance of NP-protein hybrid structures for the toxicity of the nanomaterials on a molecular and cellular level, it is a crucial task to fully characterize the hybrid-structures after protein adsorption to gain information about the size, shape, binding constants, binding ratios and protein structure on the NP surface. This fundamental information will help the design and evaluation process of nanotoxicology experiments. The most common methods in biochemistry for detection of protein-nanoparticle interaction and hybrid structures are UV-Vis180 and fluorescence (PL)181 spectroscopy, dynamic light scattering (DLS)182, isothermal titration calorimetry (ITC)183 and surface plasmon resonance (SPR)184 spectroscopy. But the high extinction coefficient as well as the associated spectral interference of gold nanoparticles and the low NP concentration that is in the nanomolar range are critical features, which make the application of spectroscopic techniques and DLS difficult. The time needed for immobilization of the components on the chip hinders the daily use of the SPR method. The requirement of exothermal or endothermal binding reaction is essential for the ITC technique. AUC (analytical
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ultracentrifugation) shows none of these deficits and is already used for the characterization of proteins, inorganic nanoparticles and their bioconjugate hybrid structures. But with increasing sample complexity, especially in the case of hybrid structures, which include not only a size, but also a density distribution, it is necessary to use additional, independent methods to evaluate the density distribution. For this approach AUC should be combined with other techniques such as electron microscopy (EM), DLS and flow field fractionation (FFF). The latter method could provide the diffusion coefficient distribution, which depends on size and shape. The latest example of a combined analysis is the combination of AUC and DLS in size and shape analysis for macromolecules185, and the combination of AUC and FFF in analysis of the organic-inorganic hybrid colloid ferritin186. As the light scattering measurements are not fractionation based, only average diffusion coefficients with a strong shift to smaller values can be obtained. Therefore, it is desirable to combine the AUC data with a fractionation method such as FFF. As a consequence, the AUC and the asymmetrical flow field flow fractionation (AF4) techniques are combined in this chapter. In the first part, Hcp1_Q54C and Hcp1_cys3 are investigated in the sedimentation velocity mode of AUC showing the assembly and disassembly structures of the Hcp1 hexamer. In the second part, the same sedimentation velocity experiment yields the sedimentation coefficient of Au-NPs and their Hcp1 hybrid structures. The AF4 is used for the evaluation of the diffusion coefficient distribution, which in combination with the sedimentation coefficient distribution provides the protein binding ratio. The determination of the amount of constituents in the protein corona, including the density of the hybrid structure, is shape independent. This important advantage makes this method suitable for the characterization of complex structures such as core-shell nanoparticles, and polymer-protein-, NP-protein hybrid structures. The findings about hybrid compositions are important for further in vitro-, cellular uptake- and nanotoxicology studies.
In this chapter the results arise from the collaboration with Marius Schmid in Cölfen’s group and therefore, are a part of his submitted doctoral thesis. The AF4 data and the simulation data originate from Marius Schmid’s work.
6.2 Experimental 6.2.1 Chemicals
Sodium dodecyl sulfate 98% [SDS], Sodium citrate dihydrate 99%, Gold(III) chloride trihydrate ACS [HAuCl4·3H2O], Tannic acid ACS, TrizBase 99.9% and Tris(2-carboxyethyl)phosphine hydrochloride 98% [TCEP] were purchased from Sigma Aldrich and NaCl was purchased from
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VWR. All chemicals were used without any purification steps. For the sample preparation with Hcp1 as well as the buffer and salt solution Milli-Q water was used. All plastic tools in the protein sample preparation, all buffer solution as well as the NaCl solution were cleaned in the autoclave before use.
6.2.2 Protein preparation
The storage buffer (50 mM Tris, 500 mM NaCl, 150 mM Imidazol, 10% Glycerol) of the proteins was removed with PD Spin Trap G-25 from GE Healthcare. The proteins were dispersed in water or 100 mM NaCl solution. TCEP as a reduction agent to avoid the formation of the disulfide bonds between the cysteines in Hcp1_cys3 and Hcp1_Q54C structures was added to the protein solution leading to a final concentration of 2 mM TCEP in the solution.
6.2.3 Sample preparation
For the AUC experiments of Hcp1_Q54C and Hcp1_cys3, the proteins were dispersed in water or in 0.1 wt% SDS solution.
For the Au-NP-Hcp1 samples the 10.7 nm Au-NPs from chapter 3, synthesized by the protocol of Slot et al.115, were used. The synthesis details were given in the experimental part of this chapter. The gold nanoparticles were directly used after the synthesis. The NP concentration was determined by measuring the absorbance of the plasmonic peak at 520 nm using the extinction coefficient of Au-NPs with the size of 10 nm119. The protein concentration was calculated by measuring the absorbance at 280 nm with a NanoDrop®ND-1000 from PEQLAB and using an extinction coefficient of 24200 M-1cm-1. The bioconjugates were prepared by adding the Hcp1_cys3 or Hcp1_Q54C protein solution (2-11 equivalents based on Au-NP concentration) to Au-NP solution and fast mixing. The mixtures were shaken for 24 h.
6.2.4 Analytical methods
The absorption spectra were recorded on a UV-Vis Cary 50 Probe from Varian. The fluorescence spectra were recorded on a Luminescence spectrometer LS50 B from Perkin Elmer. For all AUC measurements a Beckmann Optima XL-I analytical centrifuge (Beckman, Spinco division, Palo Alto, CA) with scanning absorption optics (280-800 nm, accuracy ± 2 nm) with an AN 60 Ti Rotor was used at 25 °C. Titanium 2 channel cells with an optical path length of 12 mm were used.
Radial absorbance data were collected at 60000 rpm and at wavelengths of 280 nm for Hcp1 protein samples, and at 3000 rpm and at wavelengths of 525 nm for Au-NP-Hcp1 samples with scans taken at 1 min intervals in continuous scanning mode with radial increments of 0.05 cm.
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The sedimentation velocity data with diffusion correction c(s) for the protein samples (density of 1.371 calculated by Sednterp software) and without diffusion correction ls-g*(s) were evaluated with Sedfit 14.1 by Schuck187. The AF4 measurements were performed on a self-built AF4 system consisting of AF4 controller/valve construction (Separation Sytem Eclipse DUALTEC, Wyatt), a channel oven from Shimadzu, a pump system (series Infnity 1260, Agilent), a UV/Vis detector DAD SL (series Infnity 1100), Agilent, MALLS detector (Dawn 8+, Wyatt), RI detector (series Infinity 1260, Agilent) and an auto sampler (series Infnity 1260, Agilent). The detector data was baseline corrected using the software ASTRA version 6.0.3.16 (Wyatt). The MALLS detector was calibrated by measurements with toluene. The channel calibration was accomplished with either BSA, Apoferritin or polystyrene standards. The evolution solution was 50 mM Tris buffer with a pH of 7.6 at 20 °C for Au-NP-Hcp1 samples.
6.3 Results and Discussion
The Hcp1 protein is a homohexameric toroid protein structure with an inner diameter of 4 nm and outer diameter of 9 nm, which was successfully modified with each six cysteines at the upper and lower rim (Hcp1_cys3) and six cysteines inside of the cavity (Hcp1_Q54C) by Schreiber et al.94, 95. The characterization of the Hcp1 hexamer structure is the first target of the AUC investigation. The sedimentation velocity mode is used and the data are recorded with the absorption optics at a wavelength of 280 nm. Schreiber investigated the hexamer structure of Hcp1_Q54C with fluorescence spectroscopy due to the tryptophan residues, which are located at the interface between the Hcp1 monomers. As a result, the tryptophan moieties are exposed to the solvent environment in case of disassembly of the hexamer into single Hcp1 monomers leading to a red-shift of the maximum in the fluorescence spectrum. The monomer formation occurs in a 0.1 wt% SDS solution and reveals a molecular mass of around 18500 Da for the Hcp1 monomer. The results of the AUC investigations for Hcp1_Q54C samples in water and 0.1 wt%
SDS solutions are shown in Fig. 52 A. The sedimentation coefficient distribution c(s) exhibits main species at 3.81 and 2.08 Svedberg (S) representing the Hcp1_Q54C hexamer and monomer structure. The Hcp1_Q54C sample in water even reveals four Hcp1_Q54C oligomers between 5.5 and 13 S, as well as small disassembly structure at 2.25 S. The sedimentation coefficient distributions of Fig. 52 A are converted into the molecular mass distribution in Fig. 52 B. And the determined masses of 113000 and 19000 Dalton (Da) match the published results of 112000 Da93 for the Hcp1_Q54C hexamer and 18500 Da for the monomer94 quite well.
Additionally, small amount of Hcp1_Q54C oligomer (two hexamers) at 200000 Da and
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disassembly structure at 48000 Da can be detected in the molecular mass distribution of Hcp1_Q54C sample in water. In comparison to the c(M) in Fig. 52 B with one oligomer species at 200 000 Da, the c(S) distribution in Fig. 52 A reveals large oligomers between 7 S and 13 S, which could be the ‘ghost peak’ due to diffusion correction of s, as mentioned in the fundamental part. As a reference experiment, the Hcp1_Q54C sample in water is investigated with fluorescence spectroscopy at an excitation wavelength of 280 nm. The fluorescence emission spectrum in Fig. 52 C shows a maximum at 338 nm which is typical for a stable Hcp1 hexamer structure94. The fluorescence result again supports the assigned hexamer species at 3.81 S in the c(s) distribution in Fig. 52 A. As Hcp1_cys3 also has the same hexamer structure as the Hcp1_Q54C mutant, similar s-value and emission maximum should be detected. The AUC result points out a hexamer species at 3.78 S matching the above obtained sedimentation coefficient of Hcp1_Q54C, as shown in Fig. 53 A. A maximum at 338 nm can also be observed in the fluorescence emission spectrum of Fig. 53 B. The molecular weight distribution in Fig. 53 C, based on the c(s) distribution in Fig. 53 A, shows a main species with a mass of around 110000 Da matching the mass of 112000 Da for Hcp1_Q54C. As a summary, the Hcp1_Q54C structure characterization with AUC provides hydrodynamic information such as the s-values and molecular masses of the hexamer and monomer species. The Hcp1_cys3 structure has due to the similar hexameric structure the same sedimentation coefficient and emission maximum as the Hcp1_Q54C structure. Both Hcp1 structures, Hcp1_Q54C and Hcp1_cys3, exhibit the same characteristics in the AUC and fluorescence investigations, which further prove that the hexamer structure withstands the cysteine modification.
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Fig. 52: A) Sedimentation coefficient distributions c(s) of Hcp1_Q54C in water and 0.1 wt% SDS solution detected at an absorption wavelength of 280 nm. B) Calculation of the molecular weight in Da based on the c(s) distribution in A). C) Fluorescence emission spectrum of Hcp1_Q54C sample in water recorded at an excitation wavelength of 280 nm.
A
C
B
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Fig. 53: A) Sedimentation coefficient distribution c(s) of Hcp1_cys3 in water detected at an absorption wavelength of 280 nm. B) Fluorescence emission spectrum of Hcp1_cys3 sample in water recorded at an excitation wavelength of 280 nm. C) Calculation of the molecular weight in Da based on the c(s) distribution in A).
Both Hcp1 structures are very promising in order to create functionalized bio-materials due to the incorporation of nanomaterials like CdSe-QDs in the protein cavity. Furthermore, the tendency of Hcp1_cys3 to trigger the self-assembly of nanoparticles into elongated structures provides the possibility of the application of biomolecules in a template-free alignment of nanoparticles188. The thiol functionality at different positions of the Hcp1 structure (Hcp1_cys3, Hcp1_Q54C) constitutes binding sites for nanoparticles. Due to the high affinity of gold to thiol groups, gold nanoparticles (Au-NPs) of around 10.7 nm in diameter are chosen for the study with AUC and AF4. For both proteins a pronounced shift of the sedimentation coefficients s of the Au-NPs after the protein addition can be observed in Fig. 54 A-B. The shift is caused by a decrease in density for the Au-Hcp1 structure in comparison to the pure Au-NPs, which indicates the attachment of Hcp1_cys3 and Hcp1_Q54C to the nanoparticle surface. For Hcp1_cys3 with the easily approachable cysteine groups outside of the cavity Au-NPs should
A B
C
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bind most effectively. In the sample with Au-NPs and 11 equivalents (eq.) Hcp1_cys3 protein a maximum at 850 Svedberg (S) and a shoulder around 1200 S in the sedimentation coefficient s distribution can be identified. With decreasing protein concentration the maximum shifts to a higher s-value of 1210 S (red curve in Fig. 54 A), which overlaps with the shoulder in the sedimentation coefficient distribution of Au-NP solution with 11 eq. Hcp1_cys3. To investigate the flexibility of the homohexameric structure, the Hcp1_Q54C mutant with solvent accessible cysteine groups inside of the cavity is used. The s value of Au-NPs shifts from 1560 to 1210 S verifies the protein adsorption on the nanoparticle surface. This finding indicates a flexible toroid structure of Hcp1_Q54C, which allows a nanoparticle penetration to a certain degree.
After the single AUC experiment free protein in solution should be detected, since excess of protein is used. But, unfortunately the high dilution in the mixture makes the detection of free protein impossible, since we could determine that the used protein concentration in the mixtures will show an absorbance less than 0.02 at 280 nm. In summary, the binding of Hcp1_cys3 and Hcp1_Q54C to Au-NPs can be detected with AUC, but additional information is needed to identify the hybrid structure. Therefore, AF4 is measured to get supplementary information on the bioconjugate hybrid structures. According to the theories developed by Wahlund et al., the diffusion coefficient D distribution can be extracted from AF4 fractograms107. This way the diffusion coefficient distributions were obtained for all three samples. While the Au-NPs sample with 11 eq. Hcp1_cys3 shows a bimodal distribution consisting of a species with diffusion coefficients of 1.7E-7 cm²/min and 2.2E-7 cm²/min, as shown in Fig. 55 A, the samples with 2 eq. protein both show a broad distribution of monomeric species with a diffusion coefficient of 1.8E-7 cm²/min in Fig. 55 B. These results are consistent with the AUC results exhibiting a bimodal distribution at high protein concentration (Fig. 54 A, green curve) and a unimodal distribution at stoichiometric concentration (Fig. 54 A-B, red curve).
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Fig. 54: The sedimentation coefficient distribution ls g*(s) of Au-Hcp1 mixtures. Au-NPs solution (black) with 2 eq. (red), 11 eq. (green) Hcp1_cys3 in (A) and 2 eq. (red) Hcp1_Q54C in (B). The detection wavelength is 520 nm.
Fig. 55: A) Diffusion coefficient (D) distribution of Au-NP mixture with 11 eq. Hcp1_cys3 with two different Au-Hcp1_cys3 species in the AF4 fractogram. B) Diffusion coefficient (D) distribution of Au-NP mixtures with 2 eq. Hcp1_cys3 (red) and 2 eq. Hcp1_Q54C (blue).
For the quantification of species 1 and 2 in the Au-Hcp1_cys3 (11 eq.) mixture we plotted Gauss curves in the s-distribution (green curve in Fig. 54 A) and D-distribution (Fig. 55 A) and compared the area of each curve, as shown in Fig. 56 A-B. From the s-distribution a ratio of 1:1.2 and from the D distribution a ratio of 1:0.9 are determined for species 1 and 2. In the first approximation a ratio of 1:1 can be identified for both species. This result confirmed the quantitative consistency of the AUC and AF4 data.
A B
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Fig. 56: Gauss-Plotting in the s- (A) and D-distribution (B) of Au-NPs sample with 11 eq.
Hcp1_cys3. The red curve is the plotted Gauss distribution for species 1 and green for species 2.
The blue curve is the cumulative Gauss distribution.
Since the new Au-NP structures contain Hcp1, the composition of these hybrid structures is of great interest. Therefore, the density of the hybrid structure should be calculated and the amount of Hcp1 in the NP-protein bioconjugate can, thus, be determined. Up to now, there is unfortunately no method to investigate the density of mixtures. Therefore, a new methodology for the Au-NP mixtures is developed. The first approximation is the spherical shape of the hybrid structure. Anyhow, it is not working with samples that show a change in shape as a result of the addition of large molecules such as proteins. To overcome this limitation, a known shape of the hybrid structure can be assumed. Another possibility is the combination of information gained by AUC and AF4. As the sedimentation coefficient s is dependent on shape, size and composition of the particle, the combination with the diffusion coefficient D, which depends on size and shape, can render information on the composition. The composition is simulated by using a selection procedure for the smallest RMS between the frictional coefficients f that were calculated from the sedimentation coefficient s compared with the one calculated from the diffusion coefficient D.
The basis of the simulation approach for the determination of the composition of the Au-NP-Hcp1 hybrid structures is represented in the following equations. The molecular weight 𝑀part of the particle can be calculated accordingly:
𝑀𝑝𝑎𝑟𝑡= 𝜌𝑝𝑎𝑟𝑡𝑉𝑝𝑎𝑟𝑡𝑁𝐴 (31) 𝜌part – density of Au-NP, 𝑉part – volume of Au-NP, 𝑁𝐴 – Avogadro constant.
With the knowledge of the molecular weight of the NP 𝑀𝑝𝑎𝑟𝑡, the molecular weight of the hybrid structure 𝑀ℎ𝑦𝑏𝑟𝑖𝑑 can be calculated:
A B
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𝑀hybrid= 𝑥𝑀prot+ 𝑀part (32)
𝑥 – number of adsorbed protein on Au-NP, 𝑀prot – molecular weight of Hcp1 protein.
The volume of the hybrid structure 𝑉ℎ𝑦𝑏𝑟𝑖𝑑 can be calculated as sum of the volumes of the nanoparticle 𝑉𝑝𝑎𝑟𝑡 and the adsorbed proteins 𝑉𝑝𝑟𝑜𝑡.
𝑉𝑝𝑟𝑜𝑡 = 𝑀𝑝𝑟𝑜𝑡
𝜌𝑝𝑎𝑟𝑡𝑁𝐴 (33)
𝑉ℎ𝑦𝑏𝑟𝑖𝑑 = 𝑉𝑝𝑎𝑟𝑡+ 𝑥𝑉𝑝𝑟𝑜𝑡 (34)
With the knowledge of the volume of the hybrid structure, the density of the hybrid structure 𝜌ℎ𝑦𝑏𝑟𝑖𝑑 can be calculated. Therefore, the volume ratios of the NP and protein, 𝜙𝑝𝑎𝑟𝑡 and 𝜙𝑝𝑟𝑜𝑡 are calculated and then the weighted average of the density 𝜌ℎ𝑦𝑏𝑟𝑖𝑑 is calculated.
With the knowledge of the volume of the hybrid structure, the density of the hybrid structure 𝜌ℎ𝑦𝑏𝑟𝑖𝑑 can be calculated. Therefore, the volume ratios of the NP and protein, 𝜙𝑝𝑎𝑟𝑡 and 𝜙𝑝𝑟𝑜𝑡 are calculated and then the weighted average of the density 𝜌ℎ𝑦𝑏𝑟𝑖𝑑 is calculated.