Stacking and Branching in Self-Aggregation of Caffeine in Aqueous

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Stacking and Branching in Self-Aggregation of Caffeine in Aqueous Solution: From the Supramolecular to Atomic Scale Clustering Letizia Tavagnacco, Yuri Gerelli, Attilio Cesàro, and John W. Brady J. Phys. Chem. B, Just Accepted Manuscript • DOI: 10.1021/acs.jpcb.6b06980 • Publication Date (Web): 31 Aug 2016 Downloaded from http://pubs.acs.org on September 5, 2016

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The Journal of Physical Chemistry

Stacking and Branching in Self-Aggregation of Caffeine in Aqueous Solution: From the Supramolecular to Atomic Scale Clustering

Letizia Tavagnacco†,‡, Yuri Gerelli*,#, Attilio Cesàro§,‡, and John W. Brady*,†

†Department of Food Science, Stocking Hall, Cornell University, Ithaca, New York, 14853, USA ‡

Elettra-Sincrotrone Trieste S.C.p.A., Strada Statale 14 Km 163.5, Area Science Park, I-34149 Trieste, Italy #Institut Laue-Langevin 71, avenue des Martyrs 38000 Grenoble, France §

Department of Chemical and Pharmaceutical Sciences University of Trieste Via Giorgieri 1, I-34127 Trieste, Italy

* Authors to whom correspondence should be addressed: +1 (607) 255-2897; [email protected]; +33 (0)476 20 7068; [email protected]

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Abstract The dynamical and structural properties of caffeine solutions at the solubility limit have been investigated as a function of temperature by means of MD simulations, static- and dynamic light scattering and small angle neutron scattering experiments. A clear picture unambiguously supported by both experiment and simulation emerges: caffeine self-aggregation promotes the formation of two distinct types of clusters; linear aggregates of stacked molecules, formed by 2 to 14 caffeine molecules depending on the thermodynamic conditions; and disordered branched aggregates with a size in the range of 1000-3000 Å. While the first type of association is well known to occur under room temperature conditions for both caffeine and other purine systems, such as nucleotides, the presence of the supramolecular aggregates has not been reported previously. MD simulations indicate that branched structures are formed by caffeine molecules in a T-shaped arrangement. An increase of the solubility limit (higher temperature but also higher concentration) broadens the distribution of cluster sizes, promoting the formation of stacked aggregates composed by a larger number of caffeine molecules. Surprisingly, the effect on the branched aggregates is rather limited. Their internal structure and size do not change considerably in the range of solubility limits investigated. This study provides a basis for further investigation of the temperature evolution of caffeine supramolecular structures, as well as for the complexes that can be formed by the addition of other molecules such as simple sugars.


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Introduction As a purine, the aqueous behavior of caffeine is of general interest as it can serve as a model for the aqueous behavior of the purine bases in DNA and RNA.1 In addition, caffeine, although a minor component of coffee and tea, is nevertheless considered to be the most significant active ingredient in these beverages. As a moderately hydrophobic molecule (Figure 1), the solubility of caffeine in water at room temperature is relatively limited. However, its solubility increases significantly as the temperature is raised, such that at 80ºC the solubility limit is slightly greater than 1.0 molal (moles/kg of water, abbreviated hereafter as m). It is well known that in water-purine systems, as caffeine and nucleotides for example, vertical stacking of planar bases dominates over hydrogen bonding interaction.2 Stacking is not the favored mode of association in vacuum and is therefore promoted by hydrophobic interactions.3 Needless to emphasize, such stacking is important not only in caffeine solutions, but is also the principal mechanism driving purine base pair stacking in nucleic acids. The characterization of the stacks resulting from mononucleotide self-association has been done by experimental and computational techniques. For example, self-diffusion measurements have shown that, for samples below their solubility limit, stacks with an aggregation number up to 10-20 can be formed easily.4 More interesting is the case of guanosine monophosphate (GMP), where a more complex self-aggregation process can take place. In this case it has been demonstrated that vertical stacks can interact with each other, promoting the formation of supramolecular structures composed of up to 72 GMP molecules.2 Recent work has demonstrated that at ambient temperatures the strong tendency of caffeine molecules to aggregate in aqueous solution is characterized by extended face-to-face stacks.5-7 Previous experimental thermodynamic and spectroscopic measurements,8-10 such as of the osmotic coefficient and chemical shift concentration dependences, have consistently shown that caffeine molecules aggregate extensively at room temperature, with an association constant of about 9-10 M-1. It has been suggested that the geometry of these stacks follows that of the caffeine hydrate crystal; that is, with an antiparallel alignment geometry, confirmed in more recent Raman studies.7 These associations pair up the mostly non-polar faces of the purines, removing them from exposure to water, while leaving the hydrogen bonding carbonyl oxygen atoms free to make hydrogen bonds to solvent around the periphery of the stack. This association is primarily enthalpy driven, since the extended hydrophobic face of 3 ACS Paragon Plus Environment


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caffeine structures adjacent water in such a way that there is a tendency for water molecules over the most nonpolar part, the C4-C5 bond (see Figure 1), to point a proton directly at the surface, sacrificing a hydrogen bond to another water. The face-to-face stacking frees these structured solvent molecules, which then regain their lost hydrogen bond, with its associated enthalpy change. Furthermore, the importance of the contribution of dipole interactions to the energy of stacking has been demonstrated by a recent Raman investigation.7 Entropy driven hydrophobic association is a sensitive function of temperature, not only due to the linear dependence with temperature of the –TΔS entropy change contribution to the Gibbs free energy, but also because the ΔS term itself is temperature dependent, since it primarily arises from water structuring, which diminishes with temperature as the individual solvent molecules become more labile. However, the mechanism of the enthalpy driven association of caffeine has not been explored at higher temperatures, although experimental studies have confirmed the increase in solubility of caffeine as the system is heated. We report here a coupled set of static- and dynamic light scattering (SLS, DLS) and small angle neutron scattering (SANS) experiments and molecular dynamics (MD) simulations of caffeine solutions to explore the effect of temperature and concentration on the solute aggregation in this system. The aim of this study was a coherent description, from the mesoscopic to the molecular level, of the size and shape of caffeine aggregates in aqueous solution at high temperature, surprisingly also revealing that vertical and linear caffeine stacks exhibit the supramolecular feature of branching chains.

Figure 1. The molecular structure of caffeine, illustrating the atom designations used in the present study.


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Experimental Materials Caffeine (purity by HPLC > 99.0 %) was purchased from Sigma-Aldrich. For light- and small angle neutron scattering experiments Millipore grade water and D2O (Sigma-Aldrich) were carefully filtered using a hydrophilic PVDF membrane (Merck-Millipore, US), 0.22 𝜇𝑚 pore size, to remove any dust particles present in the solution. Light Scattering Static- and dynamic light scattering experiments were performed with a setup consisting of an ALV 7004 Correlator, a CGS-3 Goniometer and a He-Ne Laser with a wavelength of 632.8 nm (available at the Partnership for Soft Condensed Matter, Grenoble). Cylindrical sample cells were placed in a toluene bath for index matching purposes.11 Intensity-autocorrelation functions, g(2)(t), were recorded under different scattering angles between 𝜃 = 25∘ and 𝜃 = 150∘ . Measuring time was adjusted according to the scattering power of the given sample. Equimolal caffeine solutions were prepared in both filtered D2O and H2O in order to detect any effect of the isotopic substitution in the solvent. Multi-angle Static Light Scattering (SLS) and Dynamic Light Scattering (DLS) measurements were performed for the 0.1 m, 0. 4m and 1.0 m samples at 25 ºC, 43 ºC and 85 ºC respectively. Additional DLS measurements were performed at 𝜃 = 90∘ on the 0.1 m sample in D2O at 43 ºC for comparison purposes. SLS Scattering intensity, 𝐼!"! 𝑄 was extracted from the SLS data after accounting for toluene and solvent scattering as described elsewhere.11 The scattering vector 𝑄 is related to the angle 𝜃 as 𝑄 = (4𝜋𝑛/𝜆)𝑠𝑖𝑛 𝜃/2 , with 𝑛 being the refractive index of the medium and 𝜆 the laser wavelength. Where possible, 𝐼!"! 𝑄 data were stitched to SANS data and analyzed as one as described in the next section. Guinier Analysis A model free analysis was performed by applying, where valid, the Guinier approximation.12 By this method it was possible to determine the radius of gyration, Rg, of the structures in solution from the slope of the ln 𝐼(𝑄) vs. 𝑄! . In particular the expression used was



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!

ln 𝐼(𝑄) = ln [𝐼(0)] − ! 𝑅! 𝑄

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!

.

(1)

It has to be noted that the Guinier approximation is only valid if the data show a linear trend given that the product 𝑅! 𝑄!"# ≤ 1, where 𝑄!"# is the higher 𝑄 value in the investigated linear regime.12 This implies that, given the Q-range investigated by SLS, only particles with Rg ≥ 400 Å could be characterized. DLS The analysis of DLS data was performed by a non-linear minimization of the intensity autocorrelation functions recorded at the same angles used for SLS measurements. Multi-angle DLS measurements allow the extrapolation of the real hydrodynamic radius of particles in solution defined for 𝜃 = 0∘ . At any angle, the chosen model was a sum of two stretched exponential functions given in Equation 1, 𝑔(!) (𝑡, 𝜃) − 1 = 𝑎! (𝜃)𝑒 !(!/!! (!))

!! (!)

+ 𝑎! (𝜃)𝑒 !(!/!! (!))

!! (!)

!

+ 𝑏𝑘𝑔(𝜃) .

(2)

Each stretched exponential function present in Eq. 1 was described by a weight (intensity fraction) 𝑎! , a decay constant 𝜏! and a stretching parameter 𝛽! . From these quantities the hydrodynamic size distributions were extracted as described in the Supporting Information Section. For comparison and illustration purposes, hydrodynamic size analysis was also performed by the builtin CONTIN algorithm13 present in the ALV acquisition software. The intensity fractions describe how much an individual population contributes to the total intensity. It has to be noted that, as a matter of fact, a single particle with size 10𝑟 gives a contribution equal to that of 1 million particles with size 𝑟. Small Angle Neutron Scattering Small Angle Neutron Scattering (SANS) experiments were performed at the Institut LaueLangevin (ILL, Grenoble) using the D11 and D22 small angle diffractometers. In order to cover a Qrange of about two decades from 3×10!! to 0.6 Å !! three sample-detector distances were used on both instruments, keeping the wavelength of the incident neutron beam fixed at 𝜆 = 6 Å. An additional configuration with a 12 Å neutron wavelength was used for the 0.4m sample on D22 in order to have a Q-range overlapping with the one covered by SLS. All the samples were prepared in D2O to improve the signal to noise ratio by reducing the incoherent background level. Sample holders were standard 6 ACS Paragon Plus Environment

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quartz flat cells 1 mm thick. Standard corrections, cell subtraction and normalization to absolute scattering units were performed using ILL software applications GRASP (for D22 data) and LAMP (for D11 data). Incoherent background was subtracted in a later stage. SANS analysis SANS data were characterized by the presence of a Guinier region in the high-Q regime. Given the Q-range values, only particles with Rg ≤ 50 Å contributed to the variation in scattering intensity. Details about the use of the Guinier approximation were given in the SLS section. In order to extract more detail about the local structure of the samples a model dependent analysis was adopted. Given that the samples were characterized by two distinct scattering signal arising from particles having completely different sizes, the scattering intensity was modeled as the sum of two distinct form factors as 𝐼(𝑄) = Δ𝜌!

!! !!

!

𝑃! (𝑄) + !! 𝑃! (𝑄) !

(3)

where 1 and 2 are the indices for the two different populations in solution. For each contribution 𝑓! is the volume fraction occupied by the scatterers (each one having volume V) and Δ𝜌 is the so called neutron contrast,14 i.e. the difference between the scattering length density (SLD) of the particle and that of the solvent. Molecular dynamics simulations indicated that the sample could be characterized by a coexistence of single caffeine molecules and other small clusters (caffeine dimers, trimers ...) in stacking geometry.5 Following this suggestion, the form factor 𝑃! (𝑄) was calculated from the atomic structure of stacked caffeine molecules using the GENFIT software application.15 For each aggregation (!)

number a partial form factor 𝑃!

(𝑄) was calculated using the all-atom form factor of a PDB structure

with a solvation shell of dummy atoms and a multipole expansion average algorithm.16 The total form factor was then expressed as 𝑃! (𝑄) =

!" !!!

(!)

𝛼! 𝑃!

(𝑄)

(4)

In this approach weight factors 𝛼! , representing the stacks distribution, were the only fit parameters. 7 ACS Paragon Plus Environment


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The form factor 𝑃! (𝑄) was ascribed to larger aggregates and modeled finite-size disordered aggregate as described by Mildner and Hall.17 The model was applied by the use of the SasView software. Molecular Dynamics Simulations MD simulations were performed using the CHARMM molecular mechanics program18,19 in the microcanonical ensemble (N,V,E) at a temperature of 80 ºC. The caffeine molecule was modeled using the force field previously developed for this molecule while water was described using the TIP4P model.20 The starting atomic coordinates for the caffeine molecules were taken from the reported monohydrate crystal structure. The procedure adopted to set up the simulation has already been reported.5,6 A periodic cubic box containing 64 caffeine molecules and 3552 water molecules was modeled, the system corresponding to a 1 m caffeine aqueous solution.6 The size of the cubic box was rescaled to 49.7 Å to achieve the density of 1.02 g cm-3 measured experimentally for a 1 m caffeine aqueous solution at 80 ºC. Trajectory data were collected for 30 ns. The lengths of the covalent bonds involving hydrogen atoms were kept fixed using the SHAKE algorithm.21 The Newtonian equations of motions were integrated using a time step of 1 fs. van der Waals interactions were smoothly truncated on an atom-by-atom basis using switching functions from 10.5 to 11.5. Electrostatic interactions were calculated using the particle-mesh Ewald method.22 Data for the lower concentration sample (0.1 m) were taken from a previous work.5 Cluster definition In the present manuscript we refer to a cluster as a stack of two or more caffeine molecules. Specifically, two caffeine molecules were considered to be in a cluster if the distance between the pairs of atoms C4-C4 and C5-C5 (see Figure 1) of adjacent caffeine molecules was less than the cutoff distance of 6.5 Å, and the cosine of the angle between the normal vectors of the paired molecules was lower than -0.7 or greater than 0.7. The cutoff distance represents the minimum of the pair distribution functions gC4C4(r) and gC5C5(r). Branched aggregate definition The definition of a branched aggregate follows that of a caffeine cluster. Two caffeine molecules were considered in a branched arrangement if they were within the cutoff distance and if the 8 ACS Paragon Plus Environment

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cosine of the angle between the normal vectors to the caffeine planes was lower than 0.7 and higher than -0.7. This definition involves the selection of caffeine molecules in a perpendicular arrangement, using the same cutoff distance as for the definition of clusters.

Results and discussion The experimental data, collected at the solubility limit of caffeine at the three different temperatures of 25 °C, 43 °C, and 80 °C, and the MD simulations performed at 80 °C indicate that caffeine molecules have a tendency to form two coexisting types of supramolecular structures: a smaller one, called here cluster, and a larger one, called a branched aggregate. The observation of two populations of caffeine structures of significantly different sizes is intriguing and has not been reported previously. The observation of this bimodal size distribution in the scattering experiments is not an artifact of the crowding of the clusters at a high concentration to distances closer than the mean cluster diameter, since the bimodal distribution is observed in all the investigated samples. The results will be presented first addressing the stacked clusters and then the branched structures. Small clusters In previous work5,23,24 it has been reported that the principal mode of association in caffeine clusters at room temperature is face-to-face stacking. The same tendency was confirmed by the MD simulation performed at 80 °C for the 1 m sample. Figure 2a displays contour surfaces of high density of caffeine ring atoms relative to a central molecule, as averaged over the trajectory, illustrating significant long-range correlations in extended aggregates. This stacking is further emphasized by Figure 2b, which displays the C4-C4 and C5-C5 autocorrelation radial distribution functions, illustrating the regular and nearly identical spacing for both pair types resulting from the ring stacking. Note from Figure 1 that the C4-C5 bond is the shared bond between the two rings and represents the approximate center of the hydrophobic faces. However, equal values of C4-C4’ and C5-C5’ distances do not imply that C4 stays over C4’ and not over C5’, as it is shown in Figure 3. The nearly identical curves for these two bonds also is indicative of face-to-face stacking; all other geometries would lead to the distributions for the two binding patterns being different.



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Because the caffeine molecules are not perfectly flat, due to the tetrahedral methyl groups attached to nitrogen atoms N1, N3, and N7, the rings cannot stack on top of one another in van der Waals contact and in perfect register; the rings must be rotated about their normal axes so as to avoid steric clashes between the methyl groups. Figure 3 displays calculated contours of atom density, colorcoded for atom type, for neighbors above and below a central caffeine molecule, illustrating how the orientations of the successive rings are staggered so as to avoid these steric clashes. In addition, since the caffeine molecules carry a permanent dipole moment of ~3.6-3.7 D, successive caffeine molecules tend to align in an antiparallel fashion, as is shown in Figure 4, which displays the dipole moment correlation function, expressed as the probability of observing the cosine of a particular angle, P(cos(θ)), plotted as a function of cos(θ). As can be seen, this function has a minimum at 0 and a maximum near ±1, showing a preference for an antiparallel alignment.

Figure 2. (a), left: caffeine ring atoms density map calculated relative to an arbitrary caffeine molecule as calculated from the MD simulations. The contours enclose regions with a caffeine atom density of 2.5 times bulk density or higher. (b), right: Comparison between the pair distribution function calculated for the caffeine atoms C4-C4 (black line) and C5-C5 (blue line) as extracted from the MD simulation.


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Figure 3. Comparison between contours of caffeine atom densities calculated relative to an arbitrary caffeine molecule from the MD simulation for individual groups of atoms: oxygen atoms are shown in red, carbon methyl atoms are shown in yellow, and the hydrogen-like bonding functionality C8-H8 is displayed in blue. The contours enclose regions with a caffeine atom density of 10 times bulk density or higher. The figure shows three red clouds of high oxygen atom density, because the two near-symmetric oxygen atoms on the left-side are representative of twisting of the caffeine molecules along the dipole axis (approximately C2→C8).

Figure 4. Cosine of the average angle θ between two consecutive stacked caffeine dipole vectors as calculated from the MD simulations.

The experimental evidence of caffeine self-aggregation was supported by all the scattering data collected. DLS (Figure 5) and SANS data (Figure 6) confirmed the presence of small particles in solution whose size was increasing as concentration and temperature increased. These particles were found to be coexistent with larger structures (slower decay in the intensity auto-correlation functions 11 ACS Paragon Plus Environment


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and intensity rise at low-Q in the SANS data) that is discussed below.

Figure 5. Intensity auto-correlation functions for the 0.1 m (blue), 0.4 m (cyan) and 1.0 m (red) caffeine samples measured at 𝜃 =90º in D2O at 25 °C, 43 °C and 80 °C, respectively. The solid lines are the fit according to Equation 1. Differences in the data quality were induced by differences in concentration. All the samples were characterized by the presence of two different non-overlapping decay processes.

Figure 6. Comparison of the SANS curves in absolute scale after subtraction of a flat incoherent background. The curves indicate a superposition of two well distinct contributions originated by small (high 𝑄) and large (low 𝑄) objects. Data were collected on D22 for the 0.1 m and 0.4 m samples and on D11 for the 1.0 m solution (blue, cyan and red symbols respectively). Differences in the quality of the data were induced by different instrumental configuration used and by the different scattering power of the solutions.

The size increase is visible in the hydrodynamic size distributions obtained by the CONTIN method (for illustration purposes) for 90° scattering angle data (Figure 7a), and from the values of average hydrodynamic radius extracted from the analysis of multiple-angle DLS data and reported in Figure 7b and Table 1. While the CONTIN algorithm was used for illustration purposes, the values 12 ACS Paragon Plus Environment

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obtained from the zero-scattering angle extrapolation are more precise and are less affected by apparent changes induced by polydispersity or by the presence of a second decay process in the 𝑔(!) (𝑡, 𝜃) functions.25 By comparing the average hydrodynamic radius for the samples at the maximum solubility point, a shift toward larger size is visible as the concentration increases. The size increase is clear between the 0.1 m and 0.4 m samples and is even larger for the 1.0 m one. Increasing temperature without a change in concentration (data for the 0.1 m sample only, green line and symbol in Figure 7a,b) does not affect the size of the caffeine stacks while making the size distribution narrower, indicating a decrease in polydispersity.

Figure 7. a) Comparison between the size distributions obtained from the fast decay process in the 𝑔 ! (𝑡, 90°) functions obtained from the CONTIN algorithm. b) Values of the average hydrodynamic radius obtained by the fit of the intensity auto-correlation functions according to Equation (2) and the subsequent analysis and extrapolation at zero-scattering angle performed by using Equations (SI.1 – SI.4). The same color code used in panel a) applies.

A signature of the increase in the size of small caffeine clusters was found as well in the behavior of the SANS intensity in the high-Q regime (𝑄~0.1 − 0.3 Å!! ). In fact, the intensity decay, due to the presence of structures characterized by a size smaller than 5 nm, took place at lower Qs as temperature and concentration increased. This portion of the SANS curve was analyzed by two independent methods, the Guinier (Equation 1) and the form-factor (Equations 3 and 4) analysis. Values of the radius of gyration 𝑅! (!) obtained by the Guinier analysis and related to small caffeine clusters are reported in Table 1. Their values and trend are in agreement with those of the hydrodynamic size 𝑅!(!) confirming the increase of the average cluster size from low to high concentration.



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Table 1. Hydrodynamic and gyration radii for small and large aggregates obtained from the analysis of DLS, SLS and SANS data. Errors were obtained as one standard deviation confidence interval. Indication of the techniques used is given for clarity. The ratio between gyration and hydrodynamic radius is reported. T (ºC)

(!) 𝑅!

(Å)

(!) 𝑅!

(Å)

(!) 𝑅!

(Å)

(!) 𝑅!

DLS

DLS

SANS

SLS

(Å)

(!)

𝑅!

(!)

𝑅!

𝑅! 𝑅!

(!)

(!)

0.1 m

25

3.8 ± 0.5

1100 ± 200

5.1 ± 0.1

1600 ± 100

1.3 ± 0.2

1.5 ± 0.4

0.4 m

43

6.3 ± 0.1

1380 ± 40

6.1 ± 0.1

1500 ± 100

0.97 ± 0.03

1.1 ± 0.1

1.0 m

80

9.6 ± 0.2

3700 ± 100

8.0 ± 0.1

2500 ± 200

0.83 ± 0.02

0.67 ± 0.07

The form factor analysis was performed using Equations 3 and 4 and under the condition 𝜙! = 0. This assumption was justified by the presence of a wide intermediate Q-range characterized by a constant intensity, indicating that the contribution of small and large objects in solution could be decoupled and analyzed separately. An example of such an analysis is given in Figure 8 for the 0.4 m sample (dashed line).

Figure 8. Scattering intensity for the 0.4 m caffeine sample measured in D2O at 43 ∘ C by SANS (blue circles) and by SLS (dark blue squares). The continuous line is the 𝐼(𝑄) calculated according to Equation 3. The contribution of the small caffeine clusters as reconstructed from the full-atoms PDB structure (Equation 4) is shown by the dashed line.

Using Equation 4 it was possible to determine not only the average size of the clusters but also their aggregation number expressed in terms of caffeine molecules per stack. Cluster distributions obtained in this way are reported in Figure 9. For the sake of clarity distributions were normalized to the number of non-associated caffeine molecules. The same information was derived by analysis of the MD simulations as described in the Experimental Section. It is worth remembering that only face-to14 ACS Paragon Plus Environment

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face stacked molecules were counted and that any other type of aggregation was excluded from the averaging. The agreement between the simulation and the experimental data is remarkable and can be used as a validation of the force field used.

Figure 9. Distributions of cluster sizes obtained from the analysis of the SANS data in terms of Equations 3 and 4 for 1.0 m (red circles), 0.4 m (blue circles) and 0.1 m (light blue circles) samples. The distribution of cluster sizes calculated from the MD simulations for the 1.0 m sample is shown with black circles.

As might be expected, the main component in any sample is represented by unassociated caffeine molecules (monomers). The distribution falls off monotonically for higher aggregation numbers. Nevertheless, the cluster distribution is very broad in the case of the 1.0 m sample where a non-negligible number of stacks formed by up to 12 caffeine molecules could be found in solution. Employing an isodesmic association energy, the osmotic coefficient obtained from the MD simulation is φ = 0.3, while from the SANS data it is φ = 0.9 ± 0.2, φ = 0.59 ± 0.03, φ = 0.30 ± 0.01 for the 0.1 m, 0.4 m, and 1.0 m sample, respectively. These values are obtained from the stack distribution only. The presence of larger clusters clearly explains the origin of the increase in the average sizes measured in terms of 𝑅!! and 𝑅!! . It has to be remembered that all the samples were very close to, but always below, the solubility limit of caffeine in water (or heavy water) for the given temperatures and concentrations. The persistence of stacked molecules even at high temperature can be explained by the values of the Gibbs free energy obtained from the MD simulations. In fact, by integrating the probability distribution of the cosine of the angle between the normal vectors of aggregated caffeine 15 ACS Paragon Plus Environment


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molecules and adopting the definition reported in the methods section, it was possible to estimate the probability of having caffeine molecules in a stacking configuration as Ps = 0.858. The associational free energy ΔGs corresponding to this probability is: ∆𝐺!!"°! = −𝑅𝑇𝑙𝑛

!! !!

= − 7.8 kJ mol!!

(5)

Where Pm = 0.060 is the probability for a caffeine molecule to be in the monomeric state. This free energy value is larger than the thermal energy (RT80°C = 2.9 kJ mol-1) at 80°C which explains the presence of stacks in solution even at high temperature. The ∆𝐺!!"°! value is also comparable to that reported in a previous work on the 0.1 m caffeine sample at room temperature. In that case the association free energy was evaluated as ∆𝐺!!"°! ≤ 8.4 kJ mol!! .5 A similar self-aggregation behavior was reported for other purine systems as nucleotides. In particular, self-diffusion measurements on mononucleotides showed that for samples below their solubility limit, clusters with an aggregation number up to 10 were formed.4 For mononucleotides it was reported that, as already reported for caffeine,5,23,24 vertical stacking of planar bases dominates over hydrogen bonding interaction.2 More interestingly, it was reported that for guanosine monophosphate (GMP) a more complex self-aggregation process was observed, involving the formation of clusters composed by up to 10 monomers, as well as the formation of larger aggregates composed by up to 72 GMP molecules.2 Branched aggregates The experimental data collected using several techniques suggests that all the caffeine samples measured are characterized by the coexistence of caffeine stacked clusters and larger aggregates. The intensity autocorrelation functions measured by DLS show two different non-overlapping decay processes (see Figure 5). The faster one has already been described as being associated to caffeine stacks. The slower one originates in aggregates having a much larger hydrodynamic size, i.e. in the (!)

1000-2000 Å range (see 𝑅!

values in Table 1 and size distributions in Figure 10). As already

mentioned, the size distributions obtained by DLS are intensity-weighted; this implies that larger particles dominate the signal even if they are few in number. The distributions reported in Figure 10 indicate that the majority of caffeine molecules are organized in small clusters and only few large aggregates are present in solution. 16 ACS Paragon Plus Environment

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Figure 10. Apparent size distributions obtained by the CONTIN algorithm from the intensity auto-correlation functions for the 0.1 m (light blue), 0.4 m (blue) and 1.0 m (red) caffeine samples measured at 90° scattering angle in D2O and at 25 °C, 43 °C and 80 °C respectively. The difference in average size and polydispersity of the aggregates populations is clearly visible.

The presence of large supramolecular structures was confirmed by SANS and SLS experiments (Figure 6 and Figure SI.2). Indeed, the intensity raise appearing at Q < 0.01 Å-1 in the SANS curves indicates the presence of very large structures in solution. A further confirmation of the presence of large structures was given by the Q dependence of the SLS scattering intensity (see Figure SI.2a). In these data the Guinier region is clearly visible in a Q-range lower than the one accessible by SANS. Therefore, the radius of gyration of the large aggregates 𝑅!! was determined by the analysis of SLS data only according to Equation 1. The values obtained are reported in Table 1. An important parameter, often used as a shape indicator for particles with complex geometry and morphology, is the ratio of the radius of gyration to the hydrodynamic radius. This ratio is reported in Table 1 for both stacked and large aggregates. They are in the range of 0.7 - 1.2, which is compatible with the presence of different supramolecular organizations such as star-like spherical aggregates26 and elongated objects27 such as ellipsoids. The same interval of values is compatible with the presence of random coil aggregates (ratio > 1) or of collapsed and spherical ones (ratio=0.77).28 Hence, given the non-constant values reported in Table 1 and the large uncertainty on some of them, it is rather difficult to deduce the correct morphology of the small and large aggregates. Nevertheless, MD simulation snapshots provide a clear indication of the shape of the aggregates. Stacks can be described as disks or cylinders according to their aggregation number while larger structures can be described as disordered aggregates i.e. with an internal organization similar to that of a random coil polymer. This evidence is also supported by the analysis of the full SLS+SANS available for the 0.4 m sample (see Figure 8). The full curve has been analyzed using Equation 3 where the P2(Q) term was modeled as a finite-size 17 ACS Paragon Plus Environment


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disordered aggregate and the agreement with the experimental data is significant. The physical interpretation of such a signal is coherent with the formation of branched aggregates where the single branch is composed by one or more stacked caffeine molecules. Although these aggregates are too large to be sampled as a whole with the MD simulation box size used, a clear signature of branching structures can be detected. Figure 11 illustrates a typical snapshot from the 80 °C simulation displaying extensive stacking as well as considerable branching-type interactions between stacks. While various geometries for such branches are possible, the majority occurred through an interaction of the anomalous H8 proton with the C4, C5, and C8 carbon atoms and the N7 and N9 nitrogen atoms of the five-membered ring, as can be seen in Figure 12. This figure displays contours of atomic density for individual atoms averaged only over central caffeine molecules involved in branching, either as a “donor” or “acceptor”, treating the H8 proton as if it were a hydrogen bond donor. This type of branching geometry results in a high probability that a branch will be essentially perpendicular to the main stack direction. Figure 13 displays the trajectory-averaged probability distribution of the angle between the vectors normal to the molecular planes of pairs of caffeine molecules in van der Waals contact. There is a strong peak at ±1, indicating parallel or antiparallel arrangements of stacked molecules. The inset in this figure at a significantly enlarged scale revels a weak, broad peak centered on 0, indicating a perpendicular arrangement. The dotted lines in the inset highlight the region considered to define a branch, -0.7 < cos(φ) < 0.7. As in the case of the stacks the association free energy, relative to non-interacting clusters and monomers, is: ∆𝐺!!"°! = −𝑅𝑇𝑙𝑛

!! !!

= − 6.3 kJ mol!! ,

(6)

where the probability of having a branch is Pb= 0.082 and the probability of having non interacting clusters and monomers is Pm= 0.69. By comparison with the previously obtained ∆𝐺!!"°! value it can be concluded that the formation of stacks is preferred.


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Figure 11. A snapshot showing a typical configuration of caffeine molecules in a 1.0 m aqueous solution as obtained from MD simulation.

Figure 12. Comparison between contours of caffeine atom densities calculated relative to an arbitrary caffeine molecule involved in the formation of a branch, seen from two different angles. The contours are displayed in different colors for specific groups of atoms: oxygen atoms O2 and O6 are shown in red; the H8 proton is shown in white; the carbon methyl atoms C1M, C3M and C7M are shown in cyan; the C4 and C5 carbon atoms are shown in yellow; the N1 C2 and N3 atoms are shown in violet; and the N7 C8 and N9 atoms are shown in green. Note that unlike the situation shown in Figure 2a, the density contours above and below the plane of the illustrated central caffeine molecule do not represent face-to-face stacking but rather “end-on” association.



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Figure 13. Probability distribution of the cosine of the angle φ between the normal vectors of two interacting caffeine molecules as calculated from the MD simulations.

It is still unclear what the origin of these branched aggregates is and what is their evolution as a function of thermodynamic parameters such as temperature and concentration. The experimental evidence is that these structures are not randomly generated. DLS and SLS measurements were repeated on fresh and aged samples and the contribution of the branched structures was always the same within the experimental accuracy of the techniques. The aggregates contributing to the smaller size distribution correspond to clusters of a few caffeine monomers, consistent with the results of molecular dynamics simulations, both previously reported at ambient temperature and in the present study at 80 °C, where the planar caffeine molecules were observed to associate by face-to-face stacking, driven by hydrophobic binding with a relatively small (~4.2 kJ/mol) binding energy. Under an isodesmic model, the energy of association of a monomer with an n-mer is the same as that of two monomers. As these stacks grow beyond about a dozen monomers, however, the probability of spontaneous disruption begins to outweigh the probability of addition of another monomer, and the overall probability of observing larger stacks asymptotically approaches nearly zero. The scission does not have to occur at the ends of the stack, since a stack could undergo disruption by separation in the middle, by collective breathing motions in opposite directions along the stack axis, which would not require large-scale diffusion, and thus could occur more rapidly than growth by random accretion, which would be diffusion-limited. Under this model, however, it is difficult to envision how a second population of very large clusters with dimensions greater than 500 Å could arise. 20 ACS Paragon Plus Environment

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The MD simulations reported here of concentrated caffeine at 80 °C suggest one possible explanation. Analysis of the SANS data suggests that these large clusters have a fractal or highly branched or dendritic character. This possibility is supported by the observation of shorter stacks clumping together in an end-to-side manner that suggests a branch. In such a higher “branched” stack, the stack that is backed up against another would have disruptive breathing expansions along the stack axis inhibited by steric clashes with the companion stack. A large cluster of caffeine stacks with a highly “branched” character would experience many more such stabilizing steric constraints, except at its outer edges, where monomers and short clusters could still escape. Such aggregates were observed in the MD simulations (Figure 11 displays a typical example). Perhaps some critical size of such dendritic or branched clusters is required to achieve significant stabilization, thus constituting a critical nucleation cluster size, and resulting in the near absence of clusters smaller than this size, until one gets down to the largest single-stack cluster size. At the upper end of the size distribution, the growth of the clusters might be limited when the steric crowding decreases enough, due to radial growth and decreasing density, to allow scission to balance monomer accretion, as for single stacks. This limitation is necessary to be consistent with the observation that a 1 molal solution of caffeine is below the solubility limit at 80 °C, so that the aggregates should not progress on to crystals. It should also be noted that this postulated branched organization is inconsistent with the known crystal structure of caffeine.

Conclusions In conclusion, we characterized the self-association mode of caffeine at the solubility limit, revealing that vertical and linear caffeine stacks exhibit supramolecular features of branching chains. The size distribution of vertical stacks can be changed by increasing both temperature and concentration in order to have the sample always very close to the solubility limit. This results in the formation of longer and longer stacks which results in an increase of the average sample size as measured by laboratory techniques such as light scattering. Details about the aggregation number were obtained by SANS experiments and MD simulations. Larger branched aggregates result from the interaction via T-shaped geometry of caffeine molecules, as demonstrated by MD simulations, of vertical stacks (identified as branches). Surprisingly 21 ACS Paragon Plus Environment


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these structures are present even at high temperature or at low concentration. As the samples investigated were always close to their solubility limit, their persistent presence could be interpreted as a signature of nucleation. This hypothesis will be further investigated for samples well below the solubility limits, were temperature and concentration can be changed independently. This work will also serve as a basis to enable the study of the interaction of caffeine with other relevant molecules, such as sugars or planar hydrophobic molecules, where interesting effects and supramolecular structures may be observed.

Acknowledgement YG and LT thank Dr. Ralf Schweins for his assistance during the D11 experiment and are grateful to Dr. Lionel Porcar and Dr. Anne Martel for their assistance during the experiment using the D22 instrument. The authors thank ILL for the awarded beam time and the PSCM for granting the access to the ALV instrument as well for the use of the laboratories. This work benefitted from SasView software developed by the DANSE project under NSF award DMR-0520547. Support for computational work by CINECA ISCRA grants (Bologna, Italy) is gratefully acknowledged.

Supporting Information Supporting Information contains a discussion of the Dynamic Light Scattering data analysis and their use to calculate diffusion constants; a discussion of the Guinier analysis of the Static Light Scattering data; and a discussion of isotopic effects in the experiments.

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Figure 1. The molecular structure of caffeine, illustrating the atom designations used in the present study. 84x59mm (300 x 300 DPI)


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Figure 2. (a), left: caffeine ring atoms density map calculated relative to an arbitrary caffeine molecule as calculated from the MD simulations. The contours enclose regions with a caffeine atom density of 2.5 times bulk density or higher. (b), right: Comparison between the pair distribution function calculated for the caffeine atoms C4-C4 (black line) and C5-C5 (blue line) as extracted from the MD simulation. 62x29mm (300 x 300 DPI)



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Figure 3. Comparison between contours of caffeine atom densities calculated relative to an arbitrary caffeine molecule from the MD simulation for individual groups of atoms: oxygen atoms are shown in red, carbon methyl atoms are shown in yellow, and the hydrogen-like bonding functionality C8-H8 is displayed in blue. The contours enclose regions with a caffeine atom density of 10 times bulk density or higher. The figure shows three red clouds of high oxygen atom density, because the two near-symmetric oxygen atoms on the left-side are representative of twisting of the caffeine molecules along the dipole axis (approx. C2→C8). 97x114mm (300 x 300 DPI)


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Figure 4. Cosine of the average angle θ between two consecutive stacked caffeine dipole vectors as calculated from the MD simulations. 61x44mm (300 x 300 DPI)



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Figure 5. Intensity auto-correlation functions for the 0.1 m (blue), 0.4 m (cyan) and 1.0 m (red) caffeine samples measured at θ=90º in D2O at 25°C, 43°C and 80°C respectively. The solid lines are the fit according to Equation 1. Differences in the data quality were induced by differences in concentration. All the samples were characterized by the presence of two different non-overlapping decay processes. 58x18mm (300 x 300 DPI)


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Figure 6. Comparison of the SANS curves in absolute scale after subtraction of a flat incoherent background. The curves indicate a superposition of two well distinct contributions originated by small (high Q) and large (low Q) objects. Data were collected on D22 for the 0.1 m and 0.4 m samples and on D11 for the 1.0 m solution (blue, cyan and red symbols respectively). Differences in the quality of the data were induced by different instrumental configuration used and by the different scattering power of the solutions. 63x48mm (300 x 300 DPI)



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Figure 7. a) Comparison between the size distributions obtained from the fast decay process in the g^((2) ) (t,90°) functions obtained from the CONTIN algorithm. b) Values of the average hydrodynamic radius obtained by the fit of the intensity auto-correlation functions according to Equation (2) and the subsequent analysis and extrapolation at zero-scattering angle performed by using Equations (SI.1 – SI.4). The same color code used in panel a) applies. 65x24mm (300 x 300 DPI)


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Figure 8. Scattering intensity for the 0.4 m caffeine sample measured in D2O at 43 ^∘ C by SANS (blue circles) and by SLS (dark blue squares). The continuous line is the I(Q) calculated according to Equation 3. The contribution of the small caffeine clusters as reconstructed from the full-atoms PDB structure (Equation 4) is shown by the dashed line. 64x50mm (300 x 300 DPI)



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Figure 9. Distributions of cluster sizes obtained from the analysis of the SANS data in terms of Equations 3 and 4 for 1.0 m (red circles), 0.4 m (blue circles) and 0.1 m (light blue circles) samples. The distribution of cluster sizes calculated from the MD simulations for the 1.0 m sample is shown with black circles. 63x48mm (300 x 300 DPI)


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Figure 10. Apparent size distributions obtained by the CONTIN algorithm from the intensity auto-correlation functions for the 0.1 m (light blue), 0.4 m (blue) and 1.0 m (red) caffeine samples measured at 90° scattering angle in D2O and at 25°C, 43°C and 80°C respectively. The difference in average size and polydispersity of the aggregates populations is clearly visible. 62x21mm (300 x 300 DPI)



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Figure 11. A snapshot showing a typical configuration of caffeine molecules in a 1.0 m aqueous solution as obtained from MD simulation. 80x77mm (300 x 300 DPI)


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Figure 12. Comparison between contours of caffeine atom densities calculated relative to an arbitrary caffeine molecule involved in the formation of a branch, seen from two different angles. The contours are displayed in different colors for specific groups of atoms: oxygen atoms O2 and O6 are shown in red; the H8 proton is shown in white; the carbon methyl atoms C1M, C3M and C7M are shown in cyan; the C4 and C5 carbon atoms are shown in yellow; the N1 C2 and N3 atoms are shown in violet; and the N7 C8 and N9 atoms are shown in green. 86x60mm (300 x 300 DPI)



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Figure 13. Probability distribution of the cosine of the angle φ between the normal vectors of two interacting caffeine molecules as calculated from the MD simulations. 65x51mm (300 x 300 DPI)


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Stacking and Branching in Self-Aggregation of Caffeine in Aqueous

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