Replica Exchange Monte Carlo Simulation of Human Serum Albumin

Article pubs.acs.org/JPCB

Replica Exchange Monte Carlo Simulation of Human Serum Albumin−Catechin Complexes Yunqi Li,† Lijia An,‡ and Qingrong Huang*,§ †

Laboratory of Advanced Power Sources and ‡State Key Laboratory of Polymer Physics and Chemistry, Changchun Institute of Applied Chemistry (CIAC), Changchun 130022, P. R. China § Department of Food Science, Rutgers University, 65 Dudley Road, New Brunswick, New Jersey 08901, United States S Supporting Information *

ABSTRACT: Replica exchange Monte Carlo simulation equipped with an orientation-enhanced hydrophobic interaction was utilized to study the impacts of molar ratio and ionic strength on the complex formation of human serum albumin (HSA) and catechin. Only a small amount of catechins was found to act as bridges in the formation of HSA−catechin complexes. Selective binding behavior was observed at low catechin to HSA molar ratio (R). Increase of catechin amount can suppress HSA selfaggregation and diminish the selectivity of protein binding sites. Strong saturation binding with short-range interactions was found to level off at around 4.6 catechins per HSA on average, while this number slowly increased with R when long-range interactions were taken into account. Meanwhile, among the three rings of catechin, the 3,4-dihydroxyphenyl (B-ring) shows the strongest preference to bind HSA. Neither the aggregation nor the binding sites of the HSA−catechin complex was sensitive to ionic strength, suggesting that the electrostatic interaction is not a dominant force in such complexes. These results provide a further molecular level understanding of protein−polyphenol binding, and the strategy employed in this work shows a way to bridge phase behaviors at macroscale and the distribution of binding sites at residue level.

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Various experimental approaches3,5−7 have been utilized to understand protein−polyphenol complex formation. The bioactivities of polyphenols, the interactions and phase behaviors coupled with the complexes, and the identification of the binding sites in proteins as well as the chemical structure features in both proteins and polyphenols toward stronger associated complexes have been investigated. The two most prominent bioactivities of polyphenols may be the antioxidant capacity and the free radical scavenging activity. Zhou et al.8 reported that high concentration (e.g., 100−500 μM) of epigallocatechin-3-gallate (EGCG) has a strong antioxidant effect on food lipids. Other bioactivities, such as antiinflammatory and anticarcinogenic properties, have also been reported for tea polyphenols through skin experiments.9 Umeda et al.10 found that EGCG shows cancer chemo-

INTRODUCTION

Catechin, a major ingredient in tea polyphenols, has multiple positive health promoting properties, such as the chemoprevention of cancer, the suppression of Alzheimer’s and Parkinson’s diseases, and the control of obesity. However, the low plasma concentration (normally not greater than 1−7 μM/ L1,2) of catechin in the human body limits its bioactivities. Human serum albumin (HSA), which is around 60% of all plasma proteins, acts as a regulator of blood pressure and transporter for various nutriceuticals including catechin. In recent years, interactions between proteins and polyphenols have been intensively studied.3 Fundamental study on the interaction of catechin and HSA as well as their complex formation can improve our understanding on the physical mechanism behind versatile bioactivities of polyphenols when they interact with functional proteins and help develop better formulations with enhanced bioavailability of catechin4 toward health promotion. © 2014 American Chemical Society

Received: May 16, 2014 Revised: August 10, 2014 Published: August 11, 2014 10362

dx.doi.org/10.1021/jp5048503 | J. Phys. Chem. B 2014, 118, 10362−10372

The Journal of Physical Chemistry B

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polyphenols acting as bridges been identified, nor have the fractions of polyphenol bridges been quantified. Because of the significant increase in solved protein structures, bioactive molecule binding to proteins has become a cutting edge topic for drug discovery and new biomaterials development. Identification of binding sites in proteins is always a challenging and inevitable problem to understand protein complexes.30 Regarding the dominant role of hydrophobic interaction, Jőbstl et al.19 believed that “hydrophobic sticky patches” in protein are the most favorable binding sites. These hydrophobic patches may provide the packing of aromatic rings with polyphenols, resulting in remarkable enrichment of polyphenols at the vicinity of protein surfaces.31 In this case, there should be a saturation binding of polyphenol to protein. Frazier et al. reported that the polyphenol to protein molar ratio could reach 178:1, and hydrogen bonding dominates the complex formation with an affinity of 12−14 kJ/mol, as determined by isothermal titration microcalorimetry (ITC).32,33 Alternatively, Bae et al. believed that the sulfhydryl group on the protein surface had specific binding affinity to polyphenol, and the only isolated and exposed sulfhydryl group of 34CYS in HSA plays a critical role in binding to EGCG.17 Overall, research studies taking into account the distribution of binding sites, the binding affinity, and the saturation binding in protein−polyphenol complexes are still limited. Structural details such as the enrichment of polyphenol and the orientation of the galloyl group in polyphenol at the vicinity of binding site are still missing. In the present study, the interaction and the complex of catechin with HSA in the physicochemical environment mimicking plasma was studied using a replica exchange Monte Carlo (REMC) simulation. An orientation-enhanced hydrophobic interaction was introduced in an attempt to describe the π-stacking34 widely existing in the protein− polyphenol complex. The catechin to HSA molar ratio and the ionic strength were varied to investigate the phase behavior, the structure, and the distribution of binding sites of the HSA− catechin complex. Through this study, we intended to set up a method to investigate the complex systems with a balance of suitable spatial scale where aggregation of protein complexes can be investigated and sufficient details where the distribution of binding sites can be viewed.

prevention through interacting with two functional proteins. EGCG can inhibit the growth of prostate cancer cells through the regulation of androgen receptor-mediated signaling.11 At the molecular level, several mechanisms have been proposed to understand the health benefits of tea polyphenols. It was reported that polyphenols could regulate the Fenton reaction in the recycles of cellular reductants to protect DNA from damage.12 Polyphenol can also be conjugated to catechol-Omethyltransferase and regulate the metabolism of dopamine which is potentially related to the treatment of Parkinson’s disease.13,14 Although these mechanisms are specific for given systems, a general consensus is that the bioactivities of polyphenols are expressed through interactions with various functional proteins. Beyond the bioactivities, an important concern related to the usage of polyphenols is their low bioavailabilities, which are closely related to the interaction between polyphenol and plasma proteins.15,16 Therefore, the interaction between catechin and HSA can be employed as a model system to understand the bioavailability and the bioactivity of polyphenols upon binding with functional proteins. Phase behaviors and interactions in protein−polyphenol complexes are closely related to the bioactivities of both proteins and polyphenols. For example, the antioxidant activity of polyphenol is proportional to the binding affinity.6 Both the stability and the antioxidant activity can be improved through complex formation.16,17 Meanwhile, the phase behaviors of protein−polyphenol complexes have been widely investigated for food and beverage development. Typical phase behavior associated with protein−polyphenol complexes is the formation of haze or precipitate,18 which is critical for protein purification and beverage quality control. In the digestive system,3 polyphenol can interact with salivary protein to produce astringency19 and can regulate the bioactivities of trypsin.20,21 Besides, elementary interactions including hydrophobic interaction and hydrogen bonding are believed to dominate the formation and the aggregation of protein−polyphenol complexes.7 Accordingly, physicochemical conditions such as pH, ionic strength, the addition of cosolvent, and the presence of carbohydrates have strong impact on these interactions and can effectively change the phase behavior as well as the properties of protein−polyphenol complexes.22,23 Intrinsic factors such as the number and flexibility of galloyl groups in polyphenol and the structure and composition of protein all significantly affect the complex formation and aggregation. Conventionally, hydrophobic interaction can be calculated using the Lennard−Jones potential,24 which has a strength normally at several kBT (kB is the Boltzmann constant and one kBT equals 0.593 kcal/mol at room temperature) level.25 A single hydrophobic interaction pair is weaker than a hydrogen bond which is at tens of kBT level. But hydrophobic interaction pairs are far more than hydrogen bonds in protein−polyphenol complexes because the latter is restricted in the number of donors and acceptors. Therefore, quantification of the contributions from these two interactions still remains a challenge. Furthermore, a classical molecular mechanism associated with the protein−polyphenol complex considers that polyphenols can act as bridges to cross-link proteins and eventually lead to precipitation.26−28 This mechanism has been used to explain the adsorption of EGCG to the bovine serum albumin (BSA) surface using a quartz crystal microbalance with dissipation monitoring (Q-CMD).29 However, neither have the

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METHODS In this work, replica exchange Monte Carlo (REMC) simulation was carried out on coarse-grained (CG) models of protein and catechin. Specificity in the size, partial charge, and hydrophobicity of each coarse-grained particle was set up according to the structure and the ionizable group in the corresponding residues or rings. Then a force field including van der Waals potential, electrostatic interaction, and an orientation-enhanced hydrophobic interaction (methods newly developed in this work) was engaged in the REMC simulation. Coarse-Grained Modeling. The coarse-grained modeling of protein and catechin is illustrated in Figure 1. The CG model of catechin is based on an optimized conformation with energy minimization using MM3 force field.35 Following a popular nomination for the three rings in catechin,5,36−38 three particles A, B, and C were used to represent the structure of catechin. The location of the CG particle was at the geometric center of each ring, and the size of the particle was assigned to be the gyration radius of all atoms in the ring. In rings A and B, because the two hydroxyl groups on the phenol may have 10363



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Table 1. Parameters of Coarse-Grained Particles for Protein Residues and Polyphenol Rings at pH 7.4 CG particle

HSA Residues G 1.43−1.54 A 1.66−1.73 S 1.71−1.83 C 1.84−1.95 D 1.84−1.97 P 1.89−1.97 N 2.00−2.20 T 1.93−1.99 E 2.08−2.27 V 1.98−2.05 Q 2.21−2.49 H 2.38−2.47 M 2.32−2.60 I 2.14−2.28 L 2.10−2.31 K 2.44−2.88 R 2.72−3.16 F 2.62−2.72 Y 2.75−2.87 W 3.00−3.00 Catechin Rings A 1.63 B 1.74 C 1.81

Figure 1. Coarse-grained (CG) model for catechin and human serum albumin (HSA). The crystal structure of HSA was presented in cartoon and surface mode. The corresponding CG model at residue level is shown in sphere mode. The red, blue, or green color indicates the residue is positive charged, negative charged, or hydrophobic, respectively. Each CG particle (except the B-ring and GLY) has a vector which recorded the orientation of functional group related to the geometric center of each residue/ring.

strong interaction with proteins, a vector was defined to count the possible contribution from π-stacking and other hydrophobic interactions.27,29,36,39 The vector was from the geometric center of the CG particle to the geometric center of the hydroxyl groups on the ring. Similarly, the residue-level coarsegrain model of human serum albumin (HSA) was based on a full atomic structure with hydrogen addition using HAAD40 according to the crystal structure from the PDB entry 1E7I.41 Each residue was represented by a spherical particle located at the geometric center of all atoms in the residue, and the size of the particle equals the gyration radius of these atoms. A vector from the center of the CG particle to the geometric center of all atoms in the side chain was used for orientated hydrophobic interaction calculation. The partial charge brought by each CG particle was calculated according to the acid−alkali balance from ionizable groups in each ring or residue.42 The size, the partial charge, and the hydrophobic coefficients for each kind of CG particle are listed in Table 1. The correlation of the residue size with the structure-based calculation which included or excluded hydrogen atoms and theoretical values43 is presented in Figure S1 (Supporting Information). The size including hydrogen atoms provided higher correlation and thus was used throughout this work. The pKa values for protein residues were taken from recent reports by Pace et al.44,45 For catechin rings, the pKa values were adopted from the report by Perron et al.46 Additionally, for residues at the C-terminus and N-terminus, additional partial charge was accounted for with pKa of 3.3 and 7.7 respectively. Other parameters were kept unchanged. The hydrophobicity of each residue was taken from the coefficient associated with exposed hydrophobic area,47 while the hydrophobicity of catechin rings was determined according to the log P values of the molecules in the catechin group from the HMDB database.48 Energy Function. The energy function to guide simulation included three components: the van der Waals interaction, the Derjaguin−Landau−Verwey−Overbeekas (DLVO) interaction, and a newly defined orientation-enhanced hydrophobic interaction. It is written as E=

pKa and partial charge (e.u.)

khyd (a.u.)

− − − 6.8, −0.7999b 3.5, −0.9999 − − − 4.2, −0.9994 − − 6.6, 0.1368 − − − 10.5, 0.9992 12.3, 1.0000 − 10.3, −0.0013 −

0.00 0.31 −0.04 1.54 −0.77 0.72 −0.60 0.26 −0.64 1.22 −0.22 0.13 1.23 1.80 1.70 −0.99 −1.01 1.79 0.96 2.25

8.76, −0.0418 8.76, −0.0418 −

0.80 0.88 0.00

a Range was calculated based on all residues in the structure. bFor CYS with free thiol only, i.e., 34CYS in HSA.

The van der Waals interaction has a repulsive and an attractive term, defined as E VDW (i,j) = E VDW,rep + E VDW,att

⎛ σij ⎞12 ⎛ σij ⎞6 = ε⎜⎜ ⎟⎟ − 2ε⎜⎜ ⎟⎟ ⎝ rij ⎠ ⎝ rij ⎠ (2)

Here ε, the depth of the van der Waals well, was fixed at 0.12k B T according to the average strength used in CHARMM22 force field;49 σij equaled σi + σj with σi, the van der Waals radius, equal to the size of each CG particle (i.e., the gyration radius of all atoms in the chemical group represented by the CG particle) plus a constant size, 1.51 Å, an average size of a carbon atom. The DLVO interaction accounted for electrostatic interaction and long-range van der Waals attraction, which was calculated through UDLVO(i,j) = ρi ρj

e σi/λD e σj/λD e−rij/λD λB 1 + σi /λD 1 + σj/λD rij

(3)

Here ρ is the charge density of each particle at a given pH. λB and λD are the Bjerrum length and the Debye screening length, respectively. The magnitude of the orientation-enhanced hydrophobic interaction was calculated according to our recently developed method,50 where the hydrophobic interaction was proportional to the overlapped and solvent-accessible surface area. The contribution from orientation was counted using the method introduced in HINT.51 With the illustration of Figure 2, the hydrophobic interaction can be computed through

∑ {E VDW (i,j) + EDLVO(i,j) + EHYD(i,j)} i,j

size (Å)a

(1) 10364



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configuration was attempted to swap with one of the configurations from its neighboring replicas. Here each MCS represented the simulation time, and each molecule had one trial movement. The accepted ratio for the replica swap satisfied the Metropolis−Hastings rule,53 which was defined as P(swap_accept) = {1,exp[−1/kBT0(Ej − Ei)(1/T*j − 1/T*i )}. Here Ei and Ej were the energies of the configurations in replica i and j, respectively. We then iteratively ran the configuration update and the swap of replicas for 300 times until the energy against simulation time at low temperatures leveled off. Here it is worthy to note that the parameters employed in the REMC simulation were carefully optimized under the consideration of suitable temperature range to achieve reasonable sampling efficiency and energy convergence, rational selection of temperature increase to maximize the accepted ratio of replica swap, and long enough simulation time to achieve thermodynamic equilibrium. As a typical case, we plotted the accepted ratio of configuration update and the accepted ratio of replica swap versus reduced replica temperature, as well as the accepted ratio for six movements, and the results are shown in Figure S2 (Supporting Information). All the parameters shown here indicate the simulation settings in this work are reasonably good.54 To eliminate artificial impact on the initial simulation configurations, we first evenly dispersed all proteins and catechins in a simulation box and then carried out 5000 MCS × 200 swaps of REMC guided by the energy function EVDW,rep. The final configuration in the replica of the lowest temperature was selected as the initial configuration for all replicas in the simulation with all energy terms. In the simulation of 5000 MCS × 300 swaps, 300 configurations in each replica were evenly recorded, and the final 10 from each of the four low temperature replicas were retrieved. Meanwhile, to avoid possible bias from single simulation, we carried out four parallel REMC simulations with different initial configurations and random seeds. In this way, 160 configurations were accumulated for statistical analysis, and results are presented below. A cubic box with the size of 360 Å and a periodic boundary condition in all three directions was used in the simulation. It is much larger than the gyration radius of HSA, which is 29.5 Å, to avoid finite size impact. The number of HSA molecules in the box was fixed to be 20, which corresponds to a

Figure 2. Schematic plot for the orientation-enhanced hydrophobic interaction calculation.

π Uhyd(i,j) = k hyd(i,j)γ [h ihjrij − h i(rij − h i)2 − hj(rij − hj)2 ] rij (1 − cos θ ),

∀ σij < rij < h ij

(4)

Here khyd, the hydrophobicity coefficient of each CG particle, is listed in Table 1. γ is the surface tension coefficient which equaled −0.012kBT/Å2 (i.e., −0.0072 kcal/mol/Å2).52 hij equaled hi + hj with hi the hydration radius of a particle, which was the van der Waals radius plus the radius of a water molecule, 1.4 Å. The θ is the angle between two vectors associated with two interacting CG particles, and it equaled zero if either particle represented a glycine residue or a C ring. Replica Exchange Monte Carlo Simulation. The replica exchange Monte Carlo (REMC) simulation was explored over eight parallel replicas at a series of exponentially increased temperatures ranging from 2.0 to around 8.0. In each replica, the configuration update followed the Metropolis rule, i.e., the accepted ratio of an attempt P(accept) = min{1,exp[−(E1 − E0/kBT0Ti*]}. Here E0 and E1 are the energies for the current and the attempted configurations, T0 is room temperature (i.e., 293 K), and T*i is the reduced temperature of the replica i. Two elementary relaxation motions including random translation and random spin were applied on individual protein, catechin, and their complex. Therefore, six motions were used to update simulation configurations. During the simulation, a complex was recorded through the criteria where their intermolecular attraction was stronger than −2.0kBT0. When the configuration update reached 5000 Monte Carlo steps (MCS), the final

Figure 3. Distribution of residues in the HSA structure presented with internal spherical coordinates. The color map from blue to red is consistent with the structure from N-terminus to C-terminus. 10365



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concentration of 0.67 mM or 45 g/L, a normal level of serum albumin in blood plasma. Although the bioavailability of catechin in blood is low (at a level of 1−7 μM2), in vitro experiments may provide a higher concentration of catechin to study the interaction with HSA.55,56 Therefore, in a plasma medium with pH 7.4 and ionic strength of 0.15 M, the catechin to HSA molar ratio was changed from 0.5:1 to 100:1 to study the dosage impact. Furthermore, by fixing the molar ratio at 20:1 and pH at 7.4, we varied the ionic strength from 0.01 to 0.5 M to study the ionic strength impact on the HSA−catechin complex in a simulated body fluid condition. Complex Identification and Structure Clustering. A complex is defined as any aggregate that encloses at least two associated molecules, and one of them must be a protein. Two molecules are regarded as associated when at least one unit, i.e., residue or ring, from each molecule approaches each other closer than the cutoff distance Rmin. Rmin was set as 4.0, 6.0, 8.0, and 12.0 Å to distinguish short-range strong association (e.g., hydrogen bond, hydrophobic interaction), medium-range contact (e.g., side-chain packing), and long-range interaction (e.g., electrostatic interaction, salt bridge among extended side chains) which may dominate complex formation and aggregation. Once the complex was identified, the binding sites as well as the distribution of protein and polyphenol in the complex were investigated. Both of them can be clearly addressed through structure clustering. Any protein in a complex was selected as a central protein and could be superimposed onto the CG model structure of HSA. Then all the other molecules in this complex can be converted using the same rotation matrix and translation vector. The internal spherical coordinates (R, Θ, Φ) based on the structure of HSA (PDB: 1E7I), e.g., the original location at the geometric center, were directly converted from the Cartesian coordinates of the protein structure (Figure 3). Through the polar plots, it is intuitive to determine the most protuberant regions of HSA in (Θ, Φ) regions, such as (170− 220°, 35−65°) and (170−220°, 160−170°), etc. These regions are theoretically easy to be accessed by other molecules and may be the most favorable binding sites. On the basis of this spherical coordinate, the enrichment of molecules around the central protein and the most favorable binding sites can be identified by the highest distribution at a given (R, Θ, Φ). Therefore, through counting protein residues or catechin rings in a (Θ, Φ) region, clustered in a (5°, 5°) mosaic in Θ−Φ plots, the top-ranked enrichment regions around the central protein can then be identified as binding sites.

Figure 4. Simulation snapshots of the HSA−catechin complex (identified using Rmin of 8.0 Å) at catechin to HSA molar ratio of 1:1 (a), 20:1 (b), and 100:1 (c). Proteins are represented by spheres, the chains being distinguished by color. Catechins are represented by three CG beads. The catechins acting as bridges to connect proteins are highlighted in black in b, and the detailed structure is presented in b′, where two catechins in a bridge are shown in red.

On the basis of the simulation configurations under thermodynamic equilibrium, we counted the size of the complex with three Rmin cutoffs, 6.0, 8.0, and 12.0 Å using the summation of molecular weight enclosed in the complex. Here HSA and catechin have molecular weights of 67.18 kDa and 0.29 kDa, respectively. As shown in Figure 5, the size of complex was found to monotonously decrease with the increase in molar ratio. At a low molar ratio, the average size of the complex could be dimer or trimer, and a tetramer or pentamer of proteins when the long-range interaction was considered. As the molar ratio increased, catechins competitively bound to HSA. The energy gained from the association of catechins with HSA dominated the depletion interaction57 and the selfaggregation proneness of proteins50 and thus inhibited the aggregation of proteins and formed a complex with the protein monomer as a result. This agreed with the previous report that catechin−serum albumin complex could enhance the stability of both protein and polyphenol and inhibit the aggregation of proteins.17 It also suggested that a sufficient amount of catechins could disturb the aggregation pathway of proteins and may inhibit Alzheimer’s disease.58 For the average number of catechins per protein, we found that the saturation binding leveled off at 4.6 per HSA when only short-range interaction was counted. This number is close to the isothermal titration calorimetry (ITC) measurements by Frazier et al., where on average 3.3 to 4.0 catechins bound to a BSA protein.33 The number of catechins steeply increased until the molar ratio reached around 20:1 and then gradually increased when the intermediate and long-range interactions were counted. On the other hand, as indicated from the fraction of catechins in the complex, only around 10% of catechins were associated with HSA, with Rmin of 12.0 Å at high catechin to HSA molar ratio. Specially, when Rmin was set at 4.0 Å, the fraction of catechins participating in complex formation was less

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RESULTS AND DISCUSSION Complex Structures. Figure 4 presents typical simulation configurations under thermodynamic equilibrium at three molar ratios. At low catechin to HSA molar ratio (e.g., 1:1), almost all catechins were bound to proteins. As the molar ratio increases, hydrophobic interaction among catechins could dominate the nonspecific binding force with proteins, resulting in small molecular clusters of catechins evenly dispersed in the simulation box. Here a bridging catechin is defined as the catechin which binds to at least two proteins, and one representative complex with bridging catechins is shown using Rmin of 8.0 Å. When proteins approach close enough, multiple catechins may simultaneously act as bridges to cross-link two proteins. This complex structure intuitively verified the bridging effect of polyphenol.26−29 10366



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in protein−polyphenol complex-induced haze formation.59,60 At the three Rmin cutoffs, the fraction of catechins acting as bridges was always lower than 5%. This is the first direct proof regarding the bridging mechanism for polyphenol-induced protein precipitation.26 However, the contribution from the bridging effect of polyphenol may be lower than previous speculation because only a small amount of catechins acted as bridges. The pairwise correlation function (PCF) defined in our previous work50,61 was used to study the molar ratio impact on the aggregation and the distribution of proteins and catechins in the HSA−catechin complexes. As indicated from Figure 6, the molar ratio can remarkably change the PCF profiles. At low molar ratio of 1:1, catechins have large separation distance, HSA−catechin correlations show two prominent enrichment peaks, and the enrichment of proteins is significant in uniform regions. All this information suggested that a small amount of catechins led to specific binding and orientated association of proteins. With the increase in molar ratio, the separation among catechins became smaller and the correlation became stronger. The HSA−catechin correlation first gave a normal distributionlike enrichment and then vanished when catechins were largely excessive. The special enrichment of protein was also gradually weakened. The PCF of HSA−HSA gradually shifted to a large separation distance, suggesting that there should be large enthalpy gain during the formation of HSA−catechin complexes. Otherwise, the depletion force contributed from the entropy penalty of catechins may lead to stronger aggregation of proteins when more catechins are added. Here the translational entropy of catechin was significantly larger than that of HSA. As a result, HSA tended to aggregate to release more free volume for catechins. These results indicated that a small amount of catechins could lead to specific binding and orientation arrangement of proteins, while a large amount of catechins diminished such selectivity and suppressed the selfaggregation of proteins. Distribution of Catechins around a Central Protein. In the spherical coordinates, the possibilities to find a residue from another protein or a catechin at a given (Θ, Φ) space in a complex with different Rmin cutoffs are shown in Figure 7. The molar ratio is fixed at 20:1, and the salt concentration is 0.15 M. The distributions of protein residues and catechin rings around a central protein illustrated in d and h showed that there were clear differences not only in their number but also in their enriched regions. With the increase in Rmin, although proteins or catechins enclosed in a complex significantly increased when

Figure 5. The size of complex, the average number of catechins per protein, the fraction of catechins in complex, and the fraction of catechins acting as a bridge against all catechins in complex at different Rmin cutoffs as a function of the catechin to HSA molar ratio. The squares, circles, and triangles are the complexes identified using Rmin of 6.0, 8.0, and 12.0 Å, respectively.

than 0.4% at any molar ratio. Thus, we only discuss the complex with Rmin no less than 6.0 Å later on. The fraction of catechins acting as bridges in HSA−catechin dramatically decreased from 0.5:1 to 1:1 and then slightly decreased with a further increase in molar ratio. This suggests that at very low catechin concentration, a significantly higher fraction of catechins acted as bridges, which can efficiently promote protein aggregation. This agrees with previous studies

Figure 6. Molecular pairwise correlation function (PCF) as a function of the catechin to HSA molar ratio. 10367



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Figure 7. Distribution of protein residues (a, b, c, and d) and catechin rings (e, f, g, and h) around a central protein with Rmin of 6.0 Å (a, e), 8.0 Å (b, f, d, h), and 12.0 Å (c, g) presented in the Θ−Φ plot. The molar ratio and the ionic strength are 20:1 and 0.15 M, respectively. The distributions of residues and rings are also illustrated in d and h, where each residue/ring is denoted by a gray dot and the central protein is shown as spheres.

Figure 8. Distributions of protein residues (a, b, c) and catechin rings (d, e, f) with Rmin of 8.0 Å and catechin to HSA molar ratios at 1:1 (a, d), 10:1 (b, e), and 100:1 (c, f), respectively.

HSA−catechin complex. We found that the strong binding sites for protein residues and catechin rings were not overlapped. The top three enriched (Θ,Φ) regions for protein residues were located at (210°, 115°), (185−195°, 150−155°), and (5°, 90°), and at (200−215°, 65−70°), (210−225°, 155°), and (195°, 55−65°) for catechin rings. These most favorable binding regions for catechin rings were overlapped or at the vicinity of the two most protuberant regions of HSA at (170−220°, 35− 65°) and (170−220°, 160−170°). This suggests that the accessibility of protein residues is important for catechins to

the long-range interactions were taken into account, the most favored binding regions in the Θ−Φ plot were highly conservable for both protein residues and polyphenol rings. At Rmin of 12.0 Å, the possibility to find protein residue enrichment at a given region could reach the 4% level, while it could be higher than 40% to find catechin rings enriched in specific regions. It suggests that the binding affinity of catechin to HSA is remarkably stronger than the self-association of HSA. Furthermore, these specific enriched regions were around the strongest binding sites for protein self-association and the 10368



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distribution from low to high molar ratios, an indication for the disappearance of selective binding with proteins which agrees with the distribution change in the above Θ−Φ plot. Binding Affinity of Catechin Rings. Because the three catechin rings have different chemical structures, this may result in different binding affinities with proteins.5,62 To study such differences, we calculated the separation distances of these rings to the central protein in the complexes, which are presented in Figure 10. In the region from 10 to 60 Å, the average separation

achieve efficient binding. Through the combination with the spherical coordinates for protein residues, it is easy to identify the important binding sites, which will be discussed later. The distributions of protein residues and catechin rings at three molar ratios 1:1, 10:1, and 100:1 were presented in Figure 8, with Rmin being fixed at 8.0 Å. When the molar ratio increased, both the enrichment of protein residues and the selectivity of the enrichment of polyphenol rings were suppressed. Even though the number of residues participating in intermolecular interaction significantly decreased as more catechins were added into the mixture, the top-ranked enriched (Θ,Φ) regions of protein residues and catechin rings remained unchanged. Similar to the increase in catechins by taking into account the long-range interactions, when more catechins were enclosed in the complex through increasing the molar ratio, the top-ranked (Θ,Φ) regions were still conserved. When catechins were largely excessive, a small intermolecular separation distance among catechins allowed them to form small molecular clusters. They competed with the HSA−catechin complex to gain free energy and thus may weaken the binding affinity for proteins and lead to nonselective enrichment of catechins around a central protein. In addition to the (Θ,Φ) region, the distribution of protein residues and catechin rings along the distance axis in the spherical coordinates are also important to locate binding sites. The number of protein residues and catechin rings interacting with a protein within Rmin normalized against the number of proteins are presented in Figure 9. Consistent with the above

Figure 10. Distribution of catechin rings around a protein in complex. Black, red, and blue bars represent A, C, and B rings, respectively.

distance is in the order of A > C > B. The B-ring has a higher possibility to approach close to the central protein within 30 Å, while the A-ring has a higher chance to be found with separation distance larger than 40 Å. Because the C-ring has no specific binding affinity (i.e., no hydrophobic contribution) to protein, it has two enriched regions at around 30 and 40 Å due to chemical constraints from the B-ring and A-ring. Overall, catechins in the complex were oriented with the B-ring pointing toward the central protein. The orientation of catechins in the complex may be related to the specific role of different rings. Wang et al. reported that the A-ring was the major binding site for reactive carbonyl species, and the B-ring was the preferred site for antioxidation.63 The results presented here, which indicated that catechins were oriented rather than randomly distributed when binding to protein, can be a reasonable mechanism to understand why the protein−polyphenol complex can enhance the stability of both molecules.17 Salt Impact on the HSA−Catechin Complex. The impact of salt on the complex structure was also investigated at a molar ratio of 20:1. The PCF profiles (Figure S3 in Supporting Information) did not show any remarkable change, suggesting that the aggregation of the HSA−catechin complex was insensitive to the ionic strength change. While the distribution of catechins viewed from the Θ−Φ plot (Figure S4) showed that increasing salt concentration could suppress

Figure 9. Average number of residues/rings distributed around a central HSA against their separation distances from the center of the protein. The number was counted using Rmin 8 Å cutoff and normalized to each protein at different catechin to HSA molar ratios.

results, as the molar ratio increased, the average number of residues monotonously decreased, and the number of catechin rings increased simultaneously. At a low molar ratio, catechin rings were enriched in separation distances of around 28 and 44 Å, while at all ratios, protein residues tended to be enriched in 40 and 50 Å regions. Because catechin is much smaller than HSA, it can penetrate through the central protein and distribute at a small separation distance. Meanwhile, catechin rings gradually shifted from bimodal distribution to a normal-like 10369



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and interaction magnitude play important roles in the determination of the most favorable binding sites. Regarding the most important binding sites in serum albumin for the binding with catechins, Skrt et al. proposed that 213TRP was a critical binding site of bovine serum albumin to bind catechins,64 while Bae et al. believed that 34CYS had a specific binding affinity with EGCG.17 With Rmin as 8.0 Å cutoff, molar ratio of 20:1, and salt concentration of 0.15 M, the possibility to find catechins in the vicinity of 213TRP or 34CYS was found to be around 0.3% or 4.3%, which is significantly lower than that of the most favorable binding site at the C-terminus with a possibility of 44.3%. The obvious discrepancy may come from multiple aspects including the selection of different protein structures, such as bovine serum albumin vs human serum albumin, the different force field to guide protein−polyphenol complex formation, and the principle to find the most favorable binding sites, e.g., single molecular pair docking system vs multiple molecules system, single scoring function mode vs thermodynamic statistical approach, etc. Nevertheless, the identification of binding sites in the protein−polyphenol complex is so important that it requires more extensive molecular research.

both the self-aggregation of proteins and the formation of the HSA−catechin complex, such suppression was limited in 8.0 Å of intermolecular distance and brought indistinguishable change in PCF profiles. This suggested that although the HSA−catechin complex was insensitive to ionic strength change and the electrostatic interaction was not dominant, changing salt concentration could still be used to regulate protein−polyphenol complex formation. Instead, according to the simulation, both van der Waals and hydrophobic interactions cooperatively controlled the formation and aggregation of HSA−catechin complex. The contributions of the van der Waals, the electrostatic, and the hydrophobic interactions to the complex formation are shown in Figure S5 (see the Supporting Information). The contribution to the total energy in the simulation is EHYD ≥ EVDW > EDLVO. Electrostatic interaction has the least contribution to the complex formation. Top-Ranked Complex Structure Models. Based on the most favorable enriched (Θ,Φ) and R regions of catechins around a central protein, the top three ranked structure models are presented in Figure 11 using blue, green, and purple. These

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CONCLUSION In this work, the complex formed by human serum albumin and catechin was investigated by replica exchange Monte Carlo simulation. Newly developed orientation-enhanced hydrophobic interaction played an important role in the complex formation. Increasing the catechin or salt concentration was found to suppress the aggregation of proteins. The former can also inhibit large-scale aggregation of the complex and weaken the selectivity of protein binding sites. It was found that only a small amount, rather than the majority, of catechins acted as bridges in the complexes, suggesting the classic bridging mechanism associated with protein−polyphenol complexes. However, the contribution from the polyphenol bridging effect was smaller than that of previous expectations. The most favorable binding sites were determined by the binding affinity between protein residues and catechin rings and the accessibility of the residues. Meanwhile, our data also showed that the strong saturation binding leveled off at around 4.6 catechins per protein on average, and the catechins in the complex were oriented with the 3,4-dihydroxyphenyl group (Bring) pointing toward the central protein. The simulation protocols employed in this work can be used to study the aggregation behaviors of multiple molecules and to unveil detailed information on binding sites. It will be an extendable approach to bridge the research from macroscale phase behaviors to atomic level simulation, which can cover large spatial scales and provide significant amounts of molecular details.

Figure 11. Illustration of the top three most favorable complex structure models. HSA is shown in ribbons with rainbow colors, and the top 1, top 2, the top 3 most enriched regions of catechins are represented by spheres in blue, green, and purple, respectively.

top-ranked structure models are quite convergent at all ratios and salt concentrations and insensitive to the selection of Rmin values. Combined with the distribution of protein residues in the spherical coordinates, residues participating in the three binding sites can be identified. The top 1 and top 2 binding sites were merged into a large binding region. Catechins in the top 1 binding site were circularly distributed at the C-terminus 585LEU and in contact with 501GLU, which is also in the top 2 region. The top 2 enriched region was located in a pocket formed by 499PRO, 501GLU, and 538LYS, while the top 3 model was at the vicinity of 338HIS, 334TYR, 378LYS, and 304SER. Although the top 1 and top 2 binding sites have charged residues and the electrostatic interaction was not the dominant factor in the binding, these sites were protruding and most accessible to catechins. The top 3 binding site mostly consisted of aromatic resides, where strong π-stacking led to the enrichment of catechins. Therefore, both spatial accessibility

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ASSOCIATED CONTENT

S Supporting Information *

Figure S1 presents the correlation of residue size, Figure S2 provides the accept ratios of configuration sampling in simulation, Figure S3 shows ionic strength impact on pairwise correlation function, Figure S4 exhibits the distribution of residues and rings around a central protein as a function of ionic strength, and Figure S5 shows typical energy evolution along simulation time. This material is available free of charge via the Internet at http://pubs.acs.org. 10370



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AUTHOR INFORMATION

Corresponding Author

*Tel: 848-932-5514. Fax: 732-932-6776. E-mail: qhuang@ aesop.rutgers.edu. Notes

The authors declare no competing financial interest.

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ACKNOWLEDGMENTS This work was supported by the National Natural Science Foundation of China (21374117) and the United States Department of Agriculture National Research Initiative (#200935603-05071). We thank Dr. David A. Joiner at Kean University and the Computing Center of Jilin Province for providing computational resources.

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Replica Exchange Monte Carlo Simulation of Human Serum Albumin

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