1

Supporting information:

Hemoglobin-driven Iron-directed Assembly of Gold Nanoparticles.

Jacquelyn G. Egan, Christopher Drossis, Iraklii I. Ebralidze, Holly M. Fruehwald, Nadia O. Laschuk, Jade Poisson, Hendrick W. de Haan and Olena V. Zenkina*

Faculty of Science, University of Ontario Institute of Technology, 2000 Simcoe Street North, Oshawa, ON, Canada, L1H 7K4.

Table of Contents

Detailed kinetics and calculation of rate constants of the formation dimers and trimers from primary single gold nanoparticles.

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Figure S1. The number of single particles and dimers at short times 2

Figure S2. The number of single particles, dimers and trimers at longer times 3

Instrumentation and experimental results. 3Figure S3 UV-Vis of acetonitrile solutions of L with different concentrations of Fe2+. 4Figure S4 Jobs plot for L and Fe2+ indicating the formation of a 3:1 (L:M) complex formation in solution.

4

Scheme S1 Proposed structure for the metal–complex formation upon reaction of L with Fe2+ ions in the solution.

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Additional characterization data for Fe2+-L3 metal complex. 5Figure S5 Determination of the extinction coefficient ε524 for complex Fe2+-L3. 6Figure S6 Comparison of ATR-FTIR spectra of L and Fe2+-L3 6 Additional characterization for L-Au NPs. 7Figure S7 Aqueous solutions of L-AuNPs with different concentrations of Fe3+. 7Figure S8 Aqueous solutions of L-AuNPs with different concentrations of Fe2+. 7Figure S9 Comparison of ATR-FTIR spectra of L and L-AuNP. 8Coarse grained molecular dynamics simulation details. 8Multi-level simulation details. 10Video descriptions 12Figure S10 1H NMR of L 13Figure S11 13C{1H} NMR of L 14References 14

Electronic Supplementary Material (ESI) for RSC Advances.This journal is © The Royal Society of Chemistry 2018

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Detailed kinetics and calculation of rate constants of the formation dimers, and trimers from primary single gold nanoparticles.

Rate Constants

As discussed in the main text, for the first 1.87 ms simulations the kinetics are dominated by single nanoparticles joining into dimers as

𝑆 + 𝑆 𝑘1→𝐷

with rate of change of number of single particles and dimers defined by

‒ �̇�𝑆 = 2𝑘1𝑁2𝑆

�̇�𝐷 = 𝑘1𝑁2𝑆

These equations can be solved to give

𝑁𝑆(𝑡) =𝑆0

1 + 2𝑘1𝑆0𝑡

𝑁𝐷(𝑡) = 𝐷0 +𝑘1𝑆2

0𝑡

1 + 2𝑘1𝑆0𝑡

The data for the number of single particles and dimers at short times is shown below along with these lines using the rate constants presented in the main text. Over the time range plotted, the number of single particles decreases quite linearly and the number of dimers increases in a similar manner.

Figure S1. The number of single particles (NS) and dimers (ND) at short times.

From t=1.87 ms to 7.17 ms the reactions are dominated by

𝑆 + 𝑆 𝑘1→𝐷

𝑆 + 𝐷 𝑘2→𝑇

with the corresponding rate equations:

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‒ �̇�𝑆 = 2𝑘1𝑁2𝑆 + 𝑘2𝑁𝑆𝑁𝐷

�̇�𝐷 = 𝑘1𝑁2𝑆 ‒ 𝑘2𝑁𝑆𝑁𝐷

�̇�𝑇 = 𝑘2𝑁𝑆𝑁𝐷

This is a complicated set of coupled equations to solve analytically. However, numerical integration using simple forward Euler methods yields good results. The predictions for the number of single particles, dimers, and trimers solved by numerical integration and using the rate constants presented in the main text are plotted below. Note that while the decay in the number of single particles is still approximately linear, the non-linear nature of the number of dimers – where the saturation is starting to appear – and the number of trimers – which increases faster than linear – is apparent and captured by the kinetic predictions. For the number of single particles and dimers, the kinetic estimates deviate from the data to a greater extent near the end of the time period as tetramers begin to form.

Figure S2. The number of single particles (NS), dimers (ND) and trimers (NT) at longer times

Instrumentation and experimental results.

Nuclear magnetic resonance spectra were measured using a Bruker Avance™III HD NanoBay 400 MHz spectrophotometer. UV-Visible spectra were measured using a Varian Cary 50 Bio UV-Visible Spectrophotometer. All IR measurements were recorded on a Bruker ALPHA Platinum ATR.

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300 400 500 600 7000.0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

L 3.910-7M 7.810-7M 1.210-6M 1.910-6M 2.710-6M 3.910-6M 5.110-6M 6.210-6M 7.410-6M 8.610-6M 1.010-5M 1.210-5M 1.410-5M 1.610-5M 2.010-5M 2.810-5M 4.010-5M 5.910-5M

Abso

rban

ce (a

u)

Wavelength (nm)

450 500 550 6000.00

0.04

0.08

Figure S3. UV-Vis of acetonitrile solutions of L with different concentrations of Fe2+. Concentration of L was 6.0·10-5 M.

0.0 0.1 0.2 0.3 0.4 0.50.00

0.01

0.02

0.03

0.04

0.05

0.06

0.07

0.08

Abso

rban

ce a

t 524

nm

(au)

Mole fraction Iron (II)

Figure S4. Jobs plot for L and Fe2+ indicating the formation of a 3:1 (L:M) complex formation in solution.

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NN

NN

NO

SS

NN

NN N

OSS

NN

N N

N

O

SS

Fe

NN

N N

N

O

SS

L

Fe2+-L3

2+2 ClO4

Fe(ClO4)2

Scheme S1. Proposed structure for the metal–complex formation upon reaction of L with Fe2+ ions in the solution.

Additional characterization for Fe2+-L3 Metal complex

UV-Vis: λmax(AcN)/nm 220infl (ε/ M-1 cm-1 2295) 276infl (2294) 320sh (9441) 480infl (17291) 524infl (19425).

MS-ESI: m/z: calc. for M2+-H (M2+: C78H69Fe15O3S62+) 754.66, found 754.63.

Extinction coefficient was calculated for Fe2+L3 complex at 524 nm to be ε524=7.67×103 M-1 s-1

.

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0.000 0.001 0.002 0.003 0.004 0.005

0.005

0.010

0.015

0.020

0.025

0.030

0.035

0.040

0.045Ab

sorb

ance

at

524

nm (a

u)

Concentration of Fe2+L3 (mM)

Adj. R-Square 0.99831Intercept 0.00268Slope 7.67342

=7.673103 M-1 s-1

Figure S5. Determination of the extinction coefficient ε524 for complex Fe2+-L3.

ATR-FTIR spectra. The ATR-IR measurements of iron complex Fe2+-L3 was measured and compared with IR spectrum of the initial ligand (Figure S6).

4000 3500 3000 2500 2000 1500 1000 50020

40

60

80

100

Tran

smitt

ance

(%)

Wavenumber (cm-1)

L Fe2+-L complex

Figure S6. Comparison of ATR-FTIR spectra of L and Fe2+-L3.

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IR spectrum of L.

IR: 3405 cm-1 (heterocyclic N), 2927 cm-1 (CH2), 2849 cm-1 (CH2), 1710 cm-1 (C=O), 1566 cm-1 (C=C-C), 1433 cm-1 (CH2), 1209 cm-1 (C-N), 1019 cm-1 (aromatic C-H), 821cm-1(C-H), 740 cm-

1 (CH2 rocking).

IR spectrum of Fe2+L3 metal complex

IR: 3367 cm-1 (heterocyclic N), 1632 cm-1 (amide), 1430 cm-1 (CH2), 1289 cm-1 (C-N), 1240 cm-

1 (C-N), 1036 cm-1 (aromatic C-H).

Additional characterization for L-AUNPs.

Figure S7. Aqueous solutions of L-AuNPs with different concentrations of Fe3+.

Figure S8. Aqueous solutions of L-AuNPs with different concentrations of Fe2+.

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ATR-FTIR characterization for L-AUNPs.

The ATR-IR measurements of L-AuNPs show intense vibrational peaks from organic molecule L adsorbed on gold nanoparticles that confirm surface functionalization (See Figure S9). Carboxylate vibrational peaks from citrate groups were weak but clearly detectable on the IR spectrum at 1574 cm-1 and 1406 cm-1.

4000 3500 3000 2500 2000 1500 1000 500

40

60

80

100

Tran

smitt

ance

(%)

Wavenumber (cm-1)

L L-AuNP

Figure S9. Comparison of ATR-FTIR spectra of L and L-AuNP.

IR spectrum of L-AuNP.

IR: 3330 cm-1 (heterocyclic N), 2930 cm-1 (CH2), 2855 cm-1 (CH2), 1715 cm-1 (C=O), 1584 cm-1 (C=C-C), 1574 cm-1 ( COO-), 1406 cm-1 ( COO-), 1392 cm-1 (C-N), 1303 cm-1 (C-N), 1203 cm-1 (C-N), 1219 cm-1 (C-N), 1074 cm-1 (aromatic C-H), 870 cm-1, 795 cm-1 (C-H), 732 cm-1 (CH2 rocking).

Coarse grained molecular dynamics simulation details.

Molecular dynamics (MD) simulations were done using the Hoomd Blue coarse-grained MD simulation package. Simulations of two types were performed as detailed below.

Simulation of L-Au NPs in presence of Fe

The ligands were modelled as single particles with a diameter of 1 σ, thus giving the system a scale of 1 σ=1.3 nm which is the diameter of one ligand. The ligands were rigidly bonded to the gold

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nanoparticles. The gold nanoparticles in this scale have a diameter of 6.154 σ. The mass of the ligands was set to 1 μ giving the system a mass scale of 1 μ=2.99·10-25 kg (the mass of one ligand). A Brownian thermostat was used with the temperature set to room temperature: 1 kT=4.14·10-21 J. The simulation box was set to have a volume of 5.288·10-15 L, such that putting 100 gold nanoparticles in the simulation box yielded the experimental concentration of 3.14·10-8 M. Using the scales set above, a time scale can then be calculated

𝜏 = 𝜇𝜎2

𝑘𝑇= 1.105 ∙ 10 ‒ 11𝑠.

Simulations were run with a time step of dt=0.1 τ for 1.89·109 dt corresponding to 0.002088 s of experimental time.

Rather than model the iron explicitly, the presence of iron was modelled implicitly by giving the ligands two possible states; iron bonded, or not iron bonded. A shifted Lennard-Jones potential with a short ranged attractive portion was implemented between them the iron bonded and non-iron bonded ligands to mimic the sharing of an iron ion between multiple ligands. The shifted Lennard-Jones (LJ) is given by

𝑉𝑆𝐿𝐽(𝑟) = 4𝜀[( 1𝑟 ‒ ∆)12 ‒ ( 1

𝑟 ‒ ∆)6],𝑟 < (𝑟𝑐𝑢𝑡 + ∆)

= 0, 𝑟 ≥ (𝑟𝑐𝑢𝑡 + ∆)

where where di and dj are the particle radii. Based on the activation energy of Δ =

(𝑑𝑖 + 𝑑𝑗)2

‒ 1

an Fe-phen bond (where phen is a phenanthroline moiety of L) of Ea=25.98 kJ/mol,[S1] the Lennard-Jones was given ε=10.42 kT. A shifted LJ without an attractive portion was imposed between all other particles types to prevent particles from overlapping (excluded volume). The initial ratio of iron bonded ligands to non-iron bonded was calculated using the rate constants of iron and phen in aqueous solution, k+=2.95·106 M-1s-1 and k-=5.6 s-1.[S1] Every 1,000,000 time steps the probabilities of ligands changing states were calculated and ligands were given a random chance to switch based on these rate constants.

Sixty ligands were rigidly bonded around the surface of each gold NP. This number was chosen to represent the corona of ligands visible in the cryo-TEM images by covering the surface of the gold NP as evenly as possible without taking too long to distribute the positions randomly. In HOOMOD Blue, the properties of rigid bodies need to be explicitly set as properties of the central particle. For the radial and translational friction, the particles were approximated as spheres with a radius of Rgold+2Rphen,

𝛾 = 6𝜋𝜂(𝑅𝑔𝑜𝑙𝑑 + 2𝑅𝑝ℎ𝑒𝑛)

𝛾𝑟𝑜𝑡 = 8𝜋𝜂(𝑅𝑔𝑜𝑙𝑑 + 2𝑅𝑝ℎ𝑒𝑛)3

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where η is the dynamic viscosity of water, which in simulation units is giving by η=10.69 μσ-1τ-1. The moment of inertia was also set as that of a sphere with radius Rgold+2Rphen and a mass of mgold+60mphen:

𝐼 =25

(𝑀𝑔𝑜𝑙𝑑 + 60𝑀𝑝ℎ𝑒𝑛)(𝑅𝑔𝑜𝑙𝑑 + 2𝑅𝑝ℎ𝑒𝑛)2(1 0 00 1 00 0 1).

An approximation for the rate of reaction for a diffusion controlled reaction is[S1]

𝑘 = 𝐴 = 16𝜋𝑅𝐷𝑁0 ∙ 1000 = 7.47 ∙ 109𝑀 ‒ 1𝑠 ‒ 1.

This is a second order reaction and the collision frequency was found using

𝑟𝑎𝑡𝑒 = 𝑘[𝑔𝑜𝑙𝑑𝑁𝑃]2𝑁0𝑉 = 23453 𝑠,

which corresponds to approximately 48 collisions with this volume and simulation time. Averaging over the simulation time, there is one collision every 8.2·107 time steps. When two clusters collide, the nanoparticles rearrange into a new structure - we refer to this as equilibration. This equilibration occurs on a much smaller time scale than the diffusion leading to collision thus suggesting that the two scales can be separated. This leads to the multiscale approach that is discussed in the next section.

Simulation of L-Au NPs in presence of hemoglobin

Simulations were also performed in which hemoglobin mediated aggregation instead of iron. The hemoglobin used in the experiments was amorphous and glue-like, so an appropriate amorphous model was used. Hemoglobin was modelled as four particles, each representing a heme site, harmonically bonded to each other. Each particle making up hemoglobin was given a diameter of 1.923 σ, one half the diameter of hemoglobin. A shifted Lennard-Jones with an attractive well was imposed between the heme particles and ligand particles to model a ligand on gold nanoparticle providing a coordination site for an iron ion bond to a heme group. The same well-depth as the sharing of an iron ion between ligands was used. Note that these simulations model a system in which there are no free ions and thus all ligands were set to be in a non-iron bonded state. Due to the harmonic bonds used to join heme particles together, the time step had to be reduced to 0.05 τ.

A weak attractive force with a well depth of 1 ε was implemented between heme particles to simulate the aggregation of hemoglobin in solution[S2].

Multi-level simulation details.

Model

From the previous model a number of conclusions can be reached:

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i) the system is very dilute and the majority of simulation time is spent just simulating clusters diffusing around without interacting,

ii) once two clusters collide and the Au NP equilibrate in the new clusters, their positions within the clusters do not change appreciably,

ii) the time scales of clusters equilibrating and collisions occurring can be separated,

and iv) two connected Au NPs have an average of 3 to 4 ligands in contact.

These observations allow the development of a much more efficient multi-scale simulation approach. First, the approximate isotropy of the Au NPs can also be taken advantage of by treating the ligand coated Au NPs as a single particle, thus greatly reducing the number of particles in the simulation. More importantly, the simulation can be separated into the two time scales: diffusion and collision. During diffusion, the details of the arrangement of the Au NP in the cluster are unimportant and thus the entire cluster can be treated as a single, rigid object. While the clusters are not necessarily isotropic, the approximate radius of a cluster can estimated as the distance from the center of mass of the cluster to the NP that is furthest away. The translational and rotational friction of the rigid body cluster were given by

𝛾 = 6𝜋𝜂𝑅𝑚𝑎𝑥

𝛾𝑟𝑜𝑡 = 8𝜋𝜂𝑅𝑚𝑎𝑥3

where η is the dynamic viscosity of water, in simulation units, η=22.67 μσ-1τ-1. The moment of inertia was set as

𝐼 =25

𝑛𝑀(𝑅𝑚𝑎𝑥)2(1 0 00 1 00 0 1)

where n is the number of particles in the cluster.

The scale for these simulations was set so 1 σ=10.6 nm, the diameter of one gold nanoparticle including its corona of ligands. The thermal energy, kT, again was set equal to kBT$ corresponding to room temperature, and the mass scale was set to the mass of one gold NP with ligands given by 4.66·10-21 kg.

The time scale was found to be τ=1.12·10-8 s.

Simulations were performed at the experimental concentration of 3.14·10-8 M.

Simulation method

In these multi-scale simulations, two different simulation “instances” were used within the HOOMD Blue package: the diffusion scale, and the collision scale. The diffusion scale simulation instance was initialized once at the beginning, and the collision scale was initialized whenever a collision occurred. In the diffusion scale, particles were initialized in random positions while

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ensuring that there were no overlapping particles. Each particle was rigidly bonded to a “ghost” particle, located in the exact same position. These “ghost” particles serve as the centers of each cluster, with every particle in the cluster rigidly bound to it. They also hold all of the information of the cluster: the radius, the moment of inertia, the radial and translational friction. At the diffusion scale, only the center particle is integrated over.

A Brownian integrator was used on all of the center particles such that diffused through the box. Every 20 time steps, the distances between all cluster cores were checked to see if any two clusters were within a distance of two times the maximum possible jump distance between checks. If the two clusters were within that distance, the time between checks would decrease to every 5 time steps, and the distances between the particles in each nearby cluster would be checked to see if they were close enough to begin a collision.

If any two particles within the nearby clusters came within 1 σ of each other, the collision scale simulation instance was initialized with the particles from the colliding clusters. In this simulation instance the particles were no longer rigidly bonded. Instead, an attractive SLJ corresponding to three ligand interactions was set between gold nanoparticles. The energy of the system was monitored during collisions. If the system reached a local minimum energy and was stable at that energy, the collision was considered successful, and the positions of the particles were saved. If the clusters never came together or reached a minimum energy, then fell out of that minimum, then the collision was considered to be a failure. Once the collision was finished, the simulation would return to the diffusion scale. If the collision was a failure, then the diffusion scale would continue with no changes. Otherwise, the positions of the particles in the final frame of the collision scale simulation would be saved and in the diffusion scale all the particles involved in the collision would be rigidly bound to one of the two 'ghost' particles in the saved configuration (the other ghost particle would then have nothing bonded to it). This models two clusters colliding, equilibrating, and then being combined into a single rigid cluster.

Using this approach, simulations of 1000 gold nanoparticles were performed for the equivalent of 5 seconds of experiment time. These trajectories are over two orders of magnitude longer than the non-multi-scale approach and allowed for the generation of much larger clusters and the exploration of the aggregation process over a much longer time scale.

Video descriptions.

Short videos were generated from different points in the three types of simulations.

Single scale simulations with iron as the template

Videos were produced from the non-multiscale simulations with iron at four times the experimental concentrations. The gold NPs are represented in gold, non-iron bonded ligands are represented in cyan, and iron bonded ligands in blue. Gold_beginning.mpg is taken from near the

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beginning where clusters of gold NPs can be seen beginning to form. Gold_end.mpg was taken from the end of the same simulation. Here the final clusters can be seen. The cluster sizes range from 1 to around 20.

Single scale simulations with hemoglobin as the template

These video were generated from the simulations with hemoglobin at four times the experimental concentrations. Gold_Hemo_beginning.mpg shows the simulation near the beginning where the hemoglobin can be seen very quickly finding gold NPs to attach to, and some small clusters forming. Gold_Hemo_end.mpg was taken from the end of the same simulation. Here the final clusters of gold and hemoglobin can be seen, and a collision between two clusters actually occurs.

Multi-scale simulations with iron as the template

Three videos were generated from the multi-level simulation. Multiscale_1beginning.mpg corresponds to the beginning where collisions happen quite frequently, so the simulation has to pause for each collision. However, because the collisions are between monomers or small clusters, they happen quite quickly. Multiscale_2middle.mpg was taken from the middle of the same simulation. The clusters are larger now and the density of clusters is lower, so collisions are less frequent, but also take slightly longer to occur. Multiscale_3end.mpg was taken near the end of the simulation. Clusters are near their final size and collisions are much less frequent.

10

23N24

28

252726

172216

15

192018

N21

N12

13

N14

29

31 N30

32

33 34

O11

9

87

65

43

S2

S1

NL_II62-63_PHEN-COL.010.ESP

9.0 8.5 8.0 7.5 7.0 6.5 6.0 5.5 5.0 4.5 4.0 3.5 3.0 2.5 2.0 1.5 1.0 0.5Chemical Shift (ppm)

2.092.072.021.761.781.891.041.032.151.081.021.001.000.982.08

CHLOROFORM Acetone

3120

2534

27

18 33

3

32

19,26

8

5

9

7

6

4

9.35 9.

329.

31 8.92 8.91

8.87 8.85 8.

50 8.48

8.01

7.99

7.88 7.

867.

84 7.82

7.55 7.

547.

52

3.52

3.49

3.48

3.47

3.44

1.95 1.92

1.71 1.

681.

61 1.58

1.36 1.

331.

261.

150.

90 0.88

0.86

Figure S10. 1H NMR of the L.

14

10

23N24

28

252726

172216

15

192018

N21

N12

13

N14

29

31 N30

32

33 34

O11

9

87

65

43

S2

S1

NL_II62-63_PHEN-COL.012.esp

160 152 144 136 128 120 112 104 96 88 80 72 64 56 48 40 32 24 16 8Chemical Shift (ppm)

CHLOROFORM

23

13 16

22

29

2832

31

7

34

10

1517

20

8

19,26

5

2718

33

6

9

3,4

24.9

025

.58

29.6

930.9

233

.87

49.2

2118.

24

123.

2012

3.39

125.

7312

9.48

132.

15134.

7513

7.33

144.

2214

5.63

149.

1815

0.06

150.

58

156.

76

162.

44

Figure S11. 13C{1H} NMR of the L.

References

[S1] R. K. Adhikamsetty, N. R. Gollapalli, S. B. Jonnalagadda, Int. J. Chem. Kinet. 2008, 40, 515-523

[S2] E. Y. Chi, S. Krishnan, T. W. Randolf, J. F. Carpenter, Pharm. Res. 2003, 20, 1325-1336