Thermodynamic Analysis of the Molecular Interactions between

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Thermodynamic Analysis of the Molecular Interactions between Amyloid β-Peptide 42 and (-)-Epigallocatechin-3-gallate Shi-Hui Wang, Fu-Feng Liu, Xiao-Yan Dong, and Yan Sun* Department of Biochemical Engineering and Key Laboratory of Systems Bioengineering of the Ministry of Education, School of Chemical Engineering and Technology, Tianjin UniVersity, Tianjin 300072, China ReceiVed: January 6, 2010; ReVised Manuscript ReceiVed: July 21, 2010

One of the key factors of Alzheimer’s disease (AD) is the conversion of amyloid β-peptide (Aβ) from its soluble random coil form into various aggregated forms. (-)-Epigallocatechin-3-gallate (EGCG) has been proved effective in preventing the aggregation of Aβ, but the thermodynamic mechanisms are still unclear. In this work, isothermal titration calorimetry (ITC) was utilized to study the interactions between Aβ42 and EGCG at different temperatures, salt concentrations, pH values, and EGCG and Aβ42 concentrations. Molecular dynamics (MD) simulations were performed to study the hydrogen bonding between Aβ42 and EGCG. The results indicate that the binding stoichiometry N is linearly related to the EGCG/Aβ42 ratio. Hydrophobic interaction and hydrogen bonding are both substantial in the binding process, but the extent of their contributions changes with experimental conditions. Namely, the predominant interaction gradually shifts from a hydrogen bonding to a hydrophobic interaction with the increase of the EGCG/Aβ42 ratio, resulting in a transition of the binding from enthalpy-driven to entropy-driven. This experimental observation is validated by the MD simulations. The binding of EGCG to Aβ42 can be promoted by increasing temperature and salt concentration and changing pH away from Aβ42′s pI. The findings have provided new insight into the molecular interactions between Aβ42 and EGCG from a thermodynamic perspective and are expected to facilitate the research on the inhibition of Aβ42 aggregation. 1. Introduction Alzheimer’s disease (AD) is a prevalent neurodegenerative disease featured by extracellular senile plagues (SP) and intracellular neurofibrillary tangles (NFT).1 One of the key factors that provoke AD is the amyloid β-peptide (Aβ), a main component of SP, which converts from its soluble random coil form into various aggregated forms.2 Aβ is a series of peptides containing 39-43 amino acid residues which derived from proteolytic processing of amyloid precursor protein (APP). Of them, amyloid β-peptide 42 (Aβ42) is considered to be a vital factor to the onset of AD due to its strong hydrophobicity and aggregation capability.3-7 Thus, the prevention of Aβ aggregation has become the primary goal of a number of therapeutic strategies under development or in clinical trial.8 Up to now, numerous inhibitors have been reported, of which (-)-epigallocatechin-3-gallate (EGCG) (Figure S1, Supporting Information), a naturally occurring polyphenol derived from green tea, has gained more and more focus for its notable efficacy in the prevention of AD.9,10 It has been demonstrated that radioactively labeled EGCG can be detected in mouse brain after oral administration.11 Cell culture studies have demonstrated that EGCG can: (1) prevent neuronal cell death caused by the neurotoxins 6-hydroxydopamine (6-OHDA) and 1-methyl-4-phenylpyridinium (MPP+) in human neuroblastoma (NB) SH-SY5Y cells;12 (2) suppress Aβ-induced neurotoxicity through inhibiting c-Abl/FE65 nuclear translocation and GSK3 beta activation in the human neuronal cell line MC65;13 and (3) protect primary hippocampal neurons14 and rat pheochromocytoma (PC12) cells15,16 from Aβ-induced toxicity.17 Mice experiments have shown that EGCG can (1) improve age-related * Corresponding author. Phone: +86 22 27404981. Fax: +86 22 27406590. E-mail: [email protected].

cognitive decline and protect against cerebral ischemia/reperfusion injuries;18,19 (2) reduce the level of APP through suppressing the translation of a luciferase reporter gene fused to the APP mRNA 5′-untranslated region, encompassing the APP iron-responsive element;20 (3) reduce Aβ deposition21 and Aβ-mediated cognitive impairment, modulating tau protein pathology in Alzheimer transgenic mice;22,23 and (4) decrease the level of Aβ by stimulating activity of R-secretase22 and inhibiting activity of β-secretase and γ-secretase.24 In addition, EGCG was reported to redirect Aβ into unstructured, offpathway oligomers in vitro and finally reduce cellular toxicity.25 Despite some preliminary studies in kinetics and aggregation morphology, the mechanisms of the interactions between Aβ and EGCG are still unclear. It has been demonstrated that interacting forces involved in the binding of polyphenol to Aβ are mainly hydrophobic interaction and hydrogen bonding.25,26 Because EGCG is a kind of polyphenol, it is inferred that the interactions between EGCG and Aβ42 are hydrophobic interaction and hydrogen bonding, but the extent and relative importance of them are controversial. Herein, to explore the molecular interactions between Aβ42 and EGCG, isothermal titration calorimetry (ITC) was utilized to analyze the thermodynamic parameters involved in the interactions, i.e., binding constant (K), binding stoichiometry (N), enthalpy change (∆H), entropy change (∆S), and Gibbs free energy (∆G) at different experimental conditions. The effects of temperature, salt concentration, pH, and EGCG and Aβ42 concentrations were investigated in that the interactions are significantly influenced by these factors. Furthermore, molecular dynamics (MD) simulations were utilized to analyze the hydrogen bonding between EGCG and the peptide. The study is expected to provide new insight into

10.1021/jp1001435  2010 American Chemical Society Published on Web 08/18/2010

Molecular Interactions between Aβ42 and EGCG the molecular mechanisms for EGCG binding to Aβ42 and, to some extent, to facilitate the rational design of Aβ aggregation inhibitors. 2. Experimental Methods Materials. Aβ42, with an amino acid sequence of DAEFRHDSGYEVHHQKLVFFAEDVGSNKGAIIGLMVGGVVIA, was >95% pure and obtained as lyophilized powder from GL Biochem (Shanghai, China). EGCG and hexafluoroisopropanol (HFIP) were purchased from Sigma (St. Louis, MO). All other chemicals were the highest purity available from local sources. Pretreatment of Aβ42. Lyophilized Aβ42 was stored at -80 °C. The peptide was allowed to equilibrate to room temperature for 30 min before use to avoid condensation upon opening the vial.27 Aβ42 stock solution was prepared by dissolving the peptide at 1 mg/mL in HFIP for 30 min to eliminate all the secondary structures. After that, the solution was transferred to an Eppendorf tube, and the volatile solvent was removed by vacuum freeze-drying overnight. The peptide was then stored at -20 °C before use.28 Preparation of Sample Solutions. The buffers used were (1) sodium citrate/citric acid buffer (CB) of pH 4.0 and pH 5.0 and (2) sodium dihydrogen phosphate/disodium hydrogen phosphate buffer (PB) of pH 7.4 and pH 8.0. The concentrations of all the buffers were 10 mmol/L, and the required salt concentration was adjusted by adding a definite amount of sodium chloride (NaCl). The pretreated Aβ42 was dissolved in dimethylsulfoxide (DMSO) and then diluted to 10 or 20 µmol/L by various buffers with a final content of DMSO at 5% (v/v).8 EGCG was dissolved in various buffers mentioned above, and then an appropriate amount of DMSO was added to a final content of 5% (v/v). Isothermal Titration Calorimetry. Isothermal calorimetric titrations were performed using a VP isothermal titration calorimeter (MicroCal, Northampton, MA). Experiments were carried out in a titration mode with a 1.425 mL sample cell containing Aβ42 solution treated by 20 min degassing. A 10 µL EGCG solution in the same buffer as the Aβ42 solution was injected over 20 s 25 times at a constant interval of 600 s via a 416 rpm rotating stirrer syringe into the sample cell. In the control experiment, the titrant was injected into the buffer in the sample cell to obtain the heat of dilution. The value of the heat of dilution was subtracted from the experimental result in the final analysis. Each experiment was repeated three times, and the mean value with standard deviations was provided. Titration data were analyzed by the evaluation software, MicroCal Origin, Version 7.0, provided by the manufacturer. The binding curves were fitted by a single-site binding model. This leads to the calculations of K, N, ∆H, ∆S, and ∆G, as described in the Supporting Information (SI) (see Section S1, eqs S1-S5). Analysis of Thermodynamic Parameters. Detailed analysis of the driving forces and characteristic signs of the thermodynamic parameters involved in proteins and peptides are shown in the SI (see Section S2, Table S1, and eqs S6 and S7). As outlined in Section S2, ∆H and ∆S for the interactions involved in proteins and peptides can be expressed by eqs S6 and S7. As well-known, van der Waals force is a universal molecular interaction; its magnitude is relatively small compared to hydrogen bonding, and the characteristic signs of the thermodynamic parameters related to van der Waals force are identical to hydrogen bonding. Thus, van der Waals force will be integrated into hydrogen bonding in the following discussion on the results. As for iso-energetic conformation, it is mainly

J. Phys. Chem. B, Vol. 114, No. 35, 2010 11577 involved in the unfolding of proteins and can be neglected in this work. Therefore, for the binding of EGCG to Aβ42, eqs S6 and S7 (SI) are simplified to eqs 1 and 2, respectively. Equation 3 is derived from eqs 1 and 2.

∆H ) ∆HHI + ∆HHB

(1)

∆S ) ∆SHI + ∆SIV + ∆SEC

(2)

∆G ) ∆GHI + ∆GHB + ∆GIV + ∆GEC

(3)

where ∆H is the total enthalpy change in the process; ∆HHI is that introduced by hydrophobic interaction; ∆HHB is that introduced by hydrogen bonding; ∆S is the total entropy change in the process; ∆SHI is that introduced by hydrophobic interaction; ∆SIV is that introduced by intramolecular vibration; ∆SEC is that introduced by electrostatic charge; ∆G is the total Gibbs free energy change in the process; ∆GHI is that introduced by hydrophobic interaction; ∆GHB is that introduced by hydrogen bonding; ∆GIV is that introduced by intramolecular vibration; and ∆GEC is that introduced by electrostatic charge. MD Simulations. All MD simulations were performed using the GROMACS 4.0.5 package29 together with the GROMOS96 force field.30 The simple point charge (SPC) model was used to describe water.31 Newton equations of motion were integrated using the Verlet leapfrog algorithm32 with a 2 fs time step. The nonbond pair list cutoff was set to 0.9 nm with the pair list updated every four time steps. The long-range electrostatic interactions were treated with the particle mesh Ewald method.33 A nonbond pair list cutoff of 0.9 nm was used, and the pair list was updated every four steps. All bond lengths were constrained with the LINCS algorithm with a relative geometric tolerance of 10-4.34 Temperature (300 K) and pressure (1 atm) were controlled by the Berendsen thermostat and barostat with coupling constants of 0.1 and 1.0 ps, respectively. Initial velocities were assigned according to a Maxwell distribution. For all simulations, the atomic coordinates were saved every 0.5 ps for analysis. MD simulations were run on a 160-CPU Dawning TC2600 blade server (Dawning, Tianjin, China). Simulation Systems. The initial coordinates for the Aβ42 used in the simulations were taken from mode 10 of the PDB entry 1IYT,35 which is the closet to the average NMR structure and used as the initial structure in MD simulations previously.36 The topology of EGCG was generated using the Dundee PRODRG2.5 server (beta) (http://davapc1.bioch.dundee.ac.uk/ cgi-bin/prodrg_beta).37 Charges and force field parameters were developed based on the GROMOS96 force field parameters. An Aβ42 was first put in a cubic box with periodic boundary conditions.38 The size of the cubic box throughout the simulations was roughly 6 nm with negligible volume fluctuations. Then, EGCG molecules were located and oriented randomly around the peptide. And then, water molecules nonoverlapping with either the peptide or EGCG molecules were randomly added into the simulation box. Finally, three positive ions (Na+) were added by replacing the corresponding number of water molecules at the most negative electrical potential to achieve a neutral condition. After initial energy minimization of 1000 steps and 100 ps of MD equilibration, 100 ns of MD simulation was performed. Two MD simulations of 100 ns were conducted for each system under different initial conditions by assigning different initial velocities on each atom of the simulation systems. Table S2 (SI) summarizes the important data for the different simulated systems.

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Wang et al.

TABLE 1: Thermodynamic Parameters of the Interactions between Aβ42 and EGCG at Different Temperaturesa temperature (°C)

25

37

45

N K (L/mol) ∆H (kJ/mol) T∆S (kJ/mol) ∆G (kJ/mol)

4.87 ( 0.13 (1.50 ( 0.12) × 102 -16.55 ( 1.20 -4.13 ( 0.33 -12.42 ( 0.98

4.45 ( 0.10 (1.41 ( 0.10) × 104 -30.78 ( 2.42 -6.17 ( 0.31 -24.61 ( 1.59

3.45 ( 0.12 (4.47 ( 0.23) × 104 -66.88 ( 3.98 -38.57 ( 2.20 -28.31 ( 1.76

a The initial concentrations of EGCG and Aβ42 were 1.0 mmol/L and 20 µmol/L, and the final concentrations of them were 158.54 and 16.81 µmol/L, respectively. Experiments were carried out in 10 mmol/L PB containing 100 mmol/L NaCl (pH 7.4).

Hydrogen Bonding Analysis. Hydrogen bonding interactions between hydrogen donors and acceptors were analyzed using the program g_hbond provided with the GROMACS 4.0.5 package. Hydrogen bonds are considered to be intact if the donor-to-acceptor distance is less than 3.5 Å and the donorhydrogen-acceptor angle is within 30° of linearity. The last 50 ns simulation trajectories were used to compute the number of hydrogen bonds. The simulation data plotted in the figure are averaged over two simulation trajectories. 3. Results Self-Association of Aβ42. To examine the self-association of Aβ42 and evaluate its influence on the analysis of interactions between Aβ42 and EGCG, a buffer solution without EGCG was used for the titration of Aβ42 solution in the same buffer (Figure S2, SI). At the physiological conditions (pH 7.4, 100 mmol/L NaCl, 37 °C), the integrated enthalpy is only slightly negative. In a buffer of pH 5.0 (close to Aβ42′s pI, which is 5.0 to 5.52) and higher NaCl concentration at 45 °C, which is favorable for the self-association of the peptide,2,27 however, the integrated enthalpy is about -4 kJ/mol, lower than that at the physiological conditions. Extensive ITC experiments at different temperatures, pH values, and NaCl concentrations were performed to investigate the self-association of the peptide in more detail (data not shown). As a result, the integrated enthalpy values were mostly close to 0 and the magnitudes were all between the two conditions given in Figure S2 (SI). Different researchers have suggested that hydrophobic and electrostatic interactions are substantial factors to the selfassociation of Aβ42.2,27,39 After the study of the forces contributing to the stability of protein association, Ross and Subramanian40 suggested that the negative enthalpy and entropy changes involved in the protein association reactions primarily arise from hydrogen bonding and van der Waals force. Since the integrated enthalpy was mostly close to 0 or slightly negative, it is assumed that hydrogen bonding, hydrophobic interaction, and electrostatic interaction are all possibly involved in the self-association of the peptide, and the net effect of these forces on the integrated enthalpy of the self-association is relatively small. Consequently, it is considered that the selfassociation of Aβ42 would not significantly affect the studies on the binding of EGCG to Aβ42 by ITC, except for the condition of pH close to Aβ42′s pI in that the self-association of the peptide will become overwhelming over the binding of EGCG to Aβ42 at this condition, which will be mentioned in Section 3.4. Effect of Temperature. It is indicated that there are no specific interactions between Aβ42 and EGCG, and they mainly interact through hydrogen bonding and hydrophobic interaction.25,26 Thus, it is assumed in this work that the binding sites on the peptide are identical and independent, and therefore the binding energies of all possible binding sites (the binding sites on the peptide) are assumed the same. Consequently, the

integrated enthalpy was calculated from the single-site binding model (see Section S1 in the SI) by regressing to the ITC results. The ITC results for the titration of EGCG to Aβ42 at different temperatures are shown in Figure S3 (SI). Thermodynamic parameters derived from the model calculation are summarized in Table 1. In this group of experiments, the final EGCG/Aβ42 ratio ([EGCG]/[Aβ42]) was kept at 9.43. It can be seen from Table 1 that the N values are nearly half that of [EGCG]/[Aβ42] and slightly decrease with increasing temperature. However, the decrease of ∆G with increasing temperature indicates that the binding process is more favorable at higher temperature. Thus, theoretically the N value should not decrease with increasing temperature. This contradiction may be due to the fact that the hydroxyl groups in EGCG can be easily oxidized with increasing temperature, which leads to intermolecular polymerization.41 So, increasing temperature would result in the decrease of effective EGCG concentration and the N values. The ∆H values show an increase of exothermic effect with increasing temperature, suggesting that the binding of EGCG to Aβ42 is enthalpically favorable and temperature dependent. As illustrated in Section S2 (SI), hydrophobic interaction rises with temperature while hydrogen bonding takes the opposite trend.42 This phenomenon leads to the approaches of both ∆HHI (>0) and ∆HHB (0) and ∆SIV (47.15) were also performed to investigate the influence of m on the binding of EGCG to Aβ42 (data not shown). The integrated enthalpy values at those conditions were similar to that at m ) 47.15. It implies that Aβ42 is saturated by EGCG at m ≈ 47. When m > 47, the excess EGCG molecules could not bind to Aβ42 due to

(4 < m < 48)

(4)

where k is the proportionality coefficient. Herein, it should be noted that N is not the real number of EGCG molecules bound to each Aβ42 molecule. Instead, it is a thermodynamic statistic representing the overall binding effect of EGCG molecules to Aβ42. In other words, N is an apparent value. There are no specific interactions between Aβ42 and EGCG, and they mainly interact through hydrophobic interaction and hydrogen bonding. Thus, the binding sites, binding forces, and binding strengths can be affected by the conformations and relative positions of the two molecules. This results in a different contribution of each EGCG molecule to the binding onto Aβ42. So, if E0 is assumed to denote the binding energy of one Aβ42 molecule and one EGCG molecule whose every available functional group interacts with Aβ42 in an optimum way and Ei denotes the binding energy of one Aβ42 molecule and EGCG molecule i in reality, by setting εi ) Ei/E0, it can be deduced that n

N)

∑ εi (i ) 1, 2, 3,...,n) i)1

Molecular Interactions between Aβ42 and EGCG

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Figure 3. Linear relationships between ∆H and m as well as T∆S and m. Region I, m < 16. Both the ∆H and T∆S values are negative. Region II, 16 < m < 46. ∆H values are negative, and T∆S values are positive. Region III, m > 46. Both the ∆H and T∆S values are positive. The solid lines are calculated from eqs 6 (R2 ) 0.9981) and 7 (R2 ) 0.9908).

where n is the possible number of EGCG molecules that have interactions with every Aβ42 molecule. Therefore, we have N < n < m. Finally, it should be noted that eq 4 is derived at 4 < m < 48, so it holds only at limited range of the m values. At very high Aβ42 concentrations or at very small m values, self-association of the peptide would become overwhelming over the binding of EGCG to Aβ42; at very high m values, the excess EGCG would have no effect on Aβ42 due to steric hindrance effect. The equation would no longer be valid at those conditions. Interactions between Aβ42 and EGCG are Dependent on m. The ∆H and T∆S values in Tables 4 and 5 are also correlated with the m values, and good linear equations are achieved, as depicted in Figure 3 and eqs 6 and 7.

∆H ) 0.86m - 39.4 (4 < m < 48)

(6)

T∆S ) 1.10m - 18.2 (4 < m < 48)

(7)

Equation 6 indicates that the enthalpy-driven hydrogen bonding decreases, which is also validated by MD simulations (Figure 1), while eq 7 suggests that the entropy-driven hydrophobic interaction increases, with the increase of m. In other words, the magnitudes of the two kinds of interactions between Aβ42 and EGCG change with m. As can be seen from Figure 3, ∆H and T∆S values are both negative at m < 16, indicating that at the low m value range the binding of EGCG to Aβ42 is enthalpy-driven and thus hydrogen bonding plays a dominant role. The nature of hydrogen bonding, characterized by its saturation and directivity, is electrostatic attraction. Therefore, the formation of hydrogen bonds requires specific steric orientations of the donor and acceptor.43-45 When the m value is low enough, hydrogen bonding can be facilitated in that the steric hindrance between Aβ42 and EGCG is relatively small. Both EGCG and Aβ42 have hydrogen bonding acceptors and donors to form hydrogen bonds between the two molecules. For example, the hydroxyl groups in EGCG (Figure S1, SI), the backbone, and some of the side chains of Aβ42, such as the carboxyl groups in C-terminal, Asp, and Glu; the amino groups in N-terminal; the amides in Asn and Gln; the imidazole in His; and the hydroxyl groups in Arg and Tyr, can all act as hydrogen bonding donors and acceptors. Ehrnhoefer et al.25 studied the binding of EGCG to R-synuclein (RS) and Aβ at m ) 1 and 10 and suggested that EGCG binds directly

to the backbone of the peptides and redirects them into unstructured, off-pathway oligomers. This implies that the major force involved in the binding of EGCG to Aβ42 is hydrogen bonding in that the backbone of the peptide could mainly form hydrogen bonding with EGCG. In addition, Vianello et al.46 mimicked the hydrogen bonding between Ser and His residues through ethanol-ethanol and ethanol-N-methylimidazole (NMI) dimers by Fourier transform infrared spectroscopy (FTIR) and revealed that Ser and His should form weak hydrogen bonding, but stronger than that in the Ser-Ser system. Our system is composed by Aβ42 and EGCG. Aβ42 consists of Ser and Tyr, which can be mimicked by ethanol, and His, which can be mimicked by NMI. On the other hand, EGCG, a polyphenol consisting of eight hydroxyl groups, can be mimicked by ethanol. So it can be inferred that hydrogen bonds formed between Aβ42 and EGCG are mainly weak ones. Moreover, NMR is also useful in studying hydrogen bonding involved in proteins and peptides. For example, Fraser et al.47 studied how collective motions of human proline isomerase active centers directly contribute to the catalytic power of the enzyme by NMR. Stivers et al.48 studied backbone and side chain resonance assignments, solution secondary structure, and location of active site residues of 4-oxalocrotonate tautomerase by heteronuclear NMR spectroscopy. They both stated that hydrogen bonding is of paramount importance in protein structure and functions. The ITC results of our experiments also demonstrated that hydrogen bonding is essential in the binding of EGCG to Aβ42. However, Aβ42 is an unstructured peptide in solution and has no specific binding sites with EGCG. Furthermore, it possesses low solubility in phosphate buffer. Therefore, NMR is unsuitable for investigating hydrogen bonding formed between Aβ42 and EGCG. Figure 3 shows that at m > 46, ∆H and T∆S are both positive. This suggests that at the extremely high EGCG loading the binding of EGCG to Aβ42 is entropy-driven, or in other words, hydrophobic interaction becomes dominant as compared to hydrogen bonding. Hydrophobic association is a result of the tendency of water to form more ordered structure in the vicinity of nonpolar hydrocarbon groups.49 Hence, hydrophobic interaction has no specificity and directivity. When the m value is high enough, hydrophobic interaction would be more favorable than hydrogen bonding in that the steric hindrance between Aβ42 and EGCG is relatively large. As can be seen from Figure S1, EGCG consists of several aromatic rings that can induce hydrophobic association with other nonpolar groups. As for Aβ42, it contains 25 nonpolar amino acid residues (Ile, Leu, Met, Val and Phe) and they mainly locate in the β-sheet formation regions (the amino acid sequence of 17-27 and 31-42). Moreover, the phenyl rings in Phe and EGCG can even induce strong hydrophobic interaction through stacking interaction.50 Thus, it is suggested that hydrophobic interaction between Aβ42 andEGCG takes place mainly in the β-sheet formation regions, and the side chains of Aβ42 are therefore mainly involved in the binding. Finally, Figure 3 shows that ∆H values are negative and T∆S values are positive at 16 < m < 46. This indicates that the binding of EGCG to Aβ42 is facilitated by both enthalpy and entropy and thus hydrogen bonding and hydrophobic interaction are both significant. It is thus a transition region between the two extreme regions mentioned above. Summary. The results described in the above chapter indicate that hydrophobic interaction and hydrogen bonding are mainly involved in the binding of EGCG to Aβ42. Hydrophobic interaction can be promoted by increasing temperature and salt

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concentration, while hydrogen bonding is facilitated by decreasing temperature and salt concentration and changing pH away from Aβ42′s pI. In addition, the above discussion has suggested that hydrogen bonding is dominant at m < 16, while hydrophobic interaction is decisive at m > 46. When the binding of EGCG to Aβ42 is predominant, the interactions involved are m-dependent. (1) At m < 16, the process is always enthalpy-driven. This indicates that hydrogen bonding overwhelms over hydrophobic interaction at the low m value range. (2) At 16 < m < 46, the interactions are both favored by enthalpy and entropy, indicating that both hydrogen bonding and hydrophobic interaction play important roles. (3) At m > 46, the driving force is always entropy, suggesting that hydrophobic interaction contributes more to the binding than hydrogen bonding. The above summary indicates that with the increase of m the driving force gradually shifts from hydrogen bonding to hydrophobic interaction and the binding of EGCG to Aβ42 shifts from enthalpy-driven to entropy-driven correspondingly. However, the aforementioned rule would not be valid when pH approaches Aβ42′s pI because the selfassociation of Aβ42 becomes predominant due to the minimized electrostatic repulsion. Akaishi et al.51 studied the prevention of Aβ aggregation by flavonoid fisetin, and Riviere et al.52 and Shoval et al.53 studied the prevention of Aβ aggregation by polyphenols. Though the mechanisms were unclear, it was indicated that the inhibitory effects were greatly influenced by the different replacements of hydroxyl groups on the aromatic rings of the inhibitors. Therefore, the results imply that hydrogen bonding plays an important role in the inhibition. On the other hand, those inhibitors all consist of several aromatic rings, indicating that hydrophobic interaction is also substantial in the inhibition effect. These conclusions are all in agreement with the present findings that the binding of EGCG to Aβ42 is a delicate balance between hydrophobic interaction and hydrogen bonding. Besides, it is found that ∆H and ∆S are of the same sign in most cases, as observed in Tables 1-5. This indicates that there is enthalpy-entropy compensation in the binding of EGCG to Aβ42. This is a phenomenon that exists universally in the association of many substances.54 5. Conclusions Thermodynamic parameters (N, K, ∆H, ∆S, and ∆G) for the binding of EGCG to Aβ42 have been determined by ITC at different conditions to provide insight into the molecular interactions between the two molecules. The binding stoichiometry N is linearly related to EGCG/Aβ42 ratio. Both hydrophobic interaction and hydrogen bonding are substantial to the binding of EGCG to Aβ42, but the extent of their contributions changes with experimental conditions. Namely, the predominant interaction gradually shifts from hydrogen bonding to hydrophobic interaction with the increase of EGCG/ Aβ42 ratio, resulting in a transition of the binding from enthalpydriven to entropy-driven. The result was further validated by MD simulations. The binding of EGCG to Aβ42 can be promoted by increasing temperature and salt concentration and changing pH away from Aβ42′s pI. The findings have shed some light on the mechanisms of interactions between Aβ42 and EGCG and are expected to facilitate the research on the inhibition of Aβ42 aggregation. Acknowledgment. This work was supported by the Natural Science Foundation of China (No. 20636040, 20876111, and 20906068), the National Basic Research Program of China (973

Wang et al. Program, No. 2009CB724705), and the Natural Science Foundation of Tianjin from Tianjin Municipal Science and Technology Commission (Contract No. 08JCZDJC17100). Supporting Information Available: Additional information (Sections S1 and S2, Tables S1and S2, eqs S1 to S7, and Figures S1 to S7) is provided. Table S1 shows the characteristic signs of the thermodynamic parameters and driving forces of seven main sources involved in protein association; Table S2 shows the detailed information for the simulated systems; eqs S1-S5 indicate the calculation of thermodynamic parameters, i.e., K, N, ∆H, ∆S, and ∆G; eqs S6 and S7, respectively, describe the main factors contributing to ∆H and ∆S involved in the association of proteins; Figure S1 shows the chemical structure of EGCG; Figure S2 indicates the ITC results for the selfassociation of Aβ42; Figures S3-S7 show the ITC results of effects of temperature, salt concentration, pH, and EGCG and Aβ42 concentrations on the integrated enthalpy of the binding of EGCG to Aβ42. This material is available free of charge via the Internet at http://pubs.acs.org. References and Notes (1) Goate, A.; Chartier-Harlin, M. C.; Mullan, M.; Brown, J.; Crawford, F.; Fidani, L.; Giuffra, L.; Haynes, A.; Irving, N.; James, L.; et al. Nature 1991, 349, 704. (2) Lin, M. S.; Chen, L. Y.; Tsai, H. T.; Wang, S. S.; Chang, Y.; Higuchi, A.; Chen, W. Y. Langmuir 2008, 24, 5802. (3) Kitaguchi, N.; Takahashi, Y.; Tokushima, Y.; Shiojiri, S.; Ito, H. Nature 1988, 331, 530. (4) Green, J. D.; Kreplak, L.; Goldsbury, C.; Li Blatter, X.; Stolz, M.; Cooper, G. S.; Seelig, A.; Kistler, J.; Aebi, U. J. Mol. Biol. 2004, 342, 877. (5) Hardy, J.; Selkoe, D. J. Science 2002, 297, 353. (6) Selkoe, D. J. J. Neuropathol. Exp. Neurol. 1994, 53, 438. (7) Hardy, J. A.; Higgins, G. A. Science 1992, 256, 184. (8) Liu, R.; Barkhordarian, H.; Emadi, S.; Park, C. B.; Sierks, M. R. Neurobiol. Dis. 2005, 20, 74. (9) Avramovich-Tirosh, Y.; Reznichenko, L.; Mit, T.; Zheng, H.; Fridkin, M.; Weinreb, O.; Mandel, S.; Youdim, M. B. Curr. Alzheimer Res. 2007, 4, 403. (10) Youdim, M. B. H.; Geldenhuys, W. J.; Van der Schyf, C. J. Parkinsonism Relat. D 2007, 13, S281. (11) Suganuma, M.; Okabe, S.; Oniyama, M.; Tada, Y.; Ito, H.; Fujiki, H. Carcinogenesis 1998, 19, 1771. (12) Levites, Y.; Amit, T.; Youdim, M. B.; Mandel, S. J. Biol. Chem. 2002, 277, 30574. (13) Lin, C. L.; Chen, T. F.; Chiu, M. J.; Way, T. D.; Lin, J. K. Neurobiol. Aging 2009, 30, 81. (14) Choi, Y. T.; Jung, C. H.; Lee, S. R.; Bae, J. H.; Baek, W. K.; Suh, M. H.; Park, J.; Park, C. W.; Suh, S. I. Life Sci. 2001, 70, 603. (15) Levites, Y.; Amit, T.; Mandel, S.; Youdim, M. B. FASEB J. 2003, 17, 952. (16) Mandel, S.; Reznichenko, L.; Amit, T.; Youdim, M. B. Neurotoxic. Res. 2003, 5, 419. (17) Mandel, S. A.; Amit, T.; Kalfon, L.; Reznichenko, L.; Weinreb, O.; Youdim, M. B. H. J. Alzheimers Dis. 2008, 15, 211. (18) Lee, S.; Suh, S.; Kim, S. Neurosci. Lett. 2000, 287, 191. (19) Sutherland, B. A.; Shaw, O. M.; Clarkson, A. N.; Jackson, D. N.; Sammut, I. A.; Appleton, I. FASEB J. 2005, 19, 258. (20) Reznichenko, L.; Amit, T.; Zheng, H.; Avramovich-Tirosh, Y.; Youdim, M. B.; Weinreb, O.; Mandel, S. J. Neurochem. 2006, 97, 527. (21) Li, Q.; Gordon, M.; Tan, J.; Morgan, D. Exp. Neurol. 2006, 198, 576. (22) Rezai-Zadeh, K.; Arendash, G. W.; Hou, H. Y.; Fernandez, F.; Jensen, M.; Runfeldt, M.; Shytle, R. D.; Tan, J. Brain Res. 2008, 1214, 177. (23) Rezai-Zadeh, K.; Shytle, D.; Arendash, G.; Sun, N.; Hou, H.; Zeng, J.; Mori, T.; Morgan, D.; Tan, J. Cell Transplant 2007, 16, 342. (24) Lee, J. W.; Lee, Y. K.; Yuk, D. Y.; Choi, D. Y.; Ban, S. B.; Oh, K. W.; Hong, J. T. J. Neuroinflammation 2008, 5, 37. (25) Ehrnhoefer, D. E.; Bieschke, J.; Boeddrich, A.; Herbst, M.; Masino, L.; Lurz, R.; Engemann, S.; Pastore, A.; Wanker, E. E. Nat. Struct. Mol. Biol. 2008, 15, 558. (26) Lemkul, J. A.; Bevan, D. R. Biochemistry 2010, 49, 3935. (27) Stine, W. B., Jr.; Dahlgren, K. N.; Krafft, G. A.; LaDu, M. J. J. Biol. Chem. 2003, 278, 11612.

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